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1. Introduction

Parkinson's disease (PD) is a progressive disorder of the central nervous

system. It occurs predominantly in the elderly and has a substantial impact

on quality of life. Although the etiology of PD is unclear, it is likely to result

from interplay of genetic and environmental factors (1). A Small percentage

of familial PD has often been found to coincide with dominantly inherited

mutations in the gene for alpha-synuclein, or with the recessive gene

mutation for parkin (2). In recent years, attempts to define the disease

genetically have become possible with the discovery of monogenic forms of

the disease. However, such families account for a very small proportion of

cases (3). PD is involving primarily a degeneration of certain nerve cells in

deep parts of the brain called the basal ganglia, and in particular a loss of

neurons in a part of the midbrain called substantia nigra (SN) (4). This

neuronal loss can lead to reduction of the volume of the midbrain and

atrophy of the brainstem as a whole (5). The structural and extent of changes

of the brainstem and total brain as the result of PD is still poorly understood (6).

The SN contains dopaminergic cells that project to the corpus striatum and

are affected by the neurodegenerative process that appears in PD (7).

Dopamine depletion due to the death of dopaminergic neurons in the

striatum and substantia nigra pars compacta (SNc) is among the hallmark of

PD pathology. Selected SN neuronal populations are affected in PD.

However, the neurodegeneration may extend to dopaminergic neurons

outside the SNc, as well as non-dopaminergic neurons (2). PD is traditionally

defined, pathologically, by the finding of Lewy bodies and degeneration of

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catecholaminergic neurons at post-mortem specimens. Mitochondrial

dysfunction is also found in a large percentage of PD patients (8).

The pathophysiology of the parkinsonian symptoms, especially that of

parkinsonian tremor is under debate (8). The loss of neuronal populations

within the basal ganglia-frontal circuits can have a profound effect upon the

motor and neurobehavioral symptoms in PD. L-3,4-dihydroxyphenylalanine

(L-DOPA) remains the most effective pharmacologic therapy for PD,

however as the disease progresses, the drug lose its efficacy and troublesome

side effects often occur. For better understanding of the symptoms and signs

that accompany PD, the interrelationship of deep brain structures and

cortical areas involved with this neurodegenerative disease must be

investigated (2).

1.1 Statement of the problem

PD is characterized by motor and non-motor deficits. The motor signs of PD

include resting tremor, rigidity, bradykinesia and postural instability (9). In

clinical practice, diagnosis of PD typically depends upon the presence of a

combination of cardinal motor signs, hence there is no definitive test for

diagnosis (10). Confirmation of the diagnosis is often brought by disease

progression and the positive responsiveness of patients to medication

(Levodopa –L-DOPA and other dopamine replacement therapy).

Accordingly PD diagnosis is particularly prone to errors, as this array of

motor symptoms is also present in a wide range of other parkinsonian

conditions such as multiple system atrophy (MSA), progressive supranuclear

palsy (PSP), and dementia with Lewy bodies (11).

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Neuropathological studies demonstrate that about 10–25% of patients

clinically diagnosed in life as having PD were proved to have another

neurodegenerative disorder at postmortem examinations. So, differential

diagnosis of PD and MSA is often difficult, especially at onset of the disease (12, 13). Inaccuracy in PD diagnosis and the desire to identify presymptomatic

patients have prompted the search for sensitive and specific biomarkers that

include imaging techniques and application of quantitative analysis of brain

atrophy on these images. This will help clinician to differentiate PD from

other movement disorders with overlapping clinical symptoms (13, 14).

So far, neither radiological findings nor laboratory investigations were found

to be specific to confirm the diagnosis of PD. Accordingly, pre-

symptomatic detection of PD seems impossible on clinical basis. Recently,

Jubault et al. (11), conducted a study using anatomical Magnetic resonance

imaging (MRI) and provided in vivo evidence that brainstem damage may

be the first identifiable stage of PD neuropathology, and that the

identification of this consistent damage along with other factors could help

with an earlier diagnosis in the future. This damage could also explain some

of the non-motor symptoms in PD that often precede diagnosis, such as

autonomic dysfunction and sleep disorders.

In recent years, neuroimaging abnormalities, genetic mutations, and

biochemical markers have been increasingly focused as an objective method

for improving PD diagnosis and allowing the identification of persons at risk (10). Most previous studies were based on invasive imaging modalities (15, 14).

A non-invasive biomarker that is able to diagnose PD before the onset of

motor symptoms would provide a better chance to develop interventions

capable of monitoring disease progression and better prognosis (14). Magnetic

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resonance imaging is considered helpful to facilitate the in vivo diagnosis of

patients with PD, MSA or PSP. It reveals either signal changes or atrophy of

specific brain regions (16). MRI is far more widely available, than other

imaging techniques like positron emission tomography (PET) and single-

photon emission computed tomography (SPECT), and is most commonly

used in clinical practice to differentiate PD from secondary causes of

Parkinsonism (17).

Imaging studies that use MRI volumetry could improve the differential

diagnosis of Parkinsonism, but cost-effectiveness remains to be established.

Complete separation of idiopathic PD from MSA and PSP has been achieved

with sophisticated techniques of MRI volumetry, but this approach is not

widely applicable (3). Two studies (18, 19), addressed the diagnostic

effectiveness of magnetic resonance volumetry against retrospective clinical

diagnosis in determining an accurate diagnosis in patients with parkinsonian

syndrome.

Alterations of the brain volume have been reported in a series of studies,

such as spino-cerebellar ataxia, autism, schizophrenia, epilepsy and

Alzheimer's disease (20-23). However the presence and extent of structural

changes in the brain as a consequence of PD is still poorly understood (6). To

our knowledge very limited amount of studies have been done denoting the

brainstem and/or its subdivisions volume changes in PD (12, 11, 16, 24, 25).

Cordato et al. (18), reported in their study that all MRI measures, including

hippocampal volume, were preserved in PD.

Anatomical measurement of brainstem volume is important in estimating the

pathological changes that affect the brainstem. The brainstem atrophy is a

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significant finding in many neurological diseases such as cerebral palsy,

spino-cerebellar ataxia, amyotrophic lateral sclerosis, syringobulbia,

craniocervical junction abnormalities, central pontine myelinosis and

multiple sclerosis. For this reason the foundation of data with an objective

and efficient volumetric measurement of the brainstem using modern

imaging techniques is crucially important for the early diagnosis, as well as

to determine the rate of progression and prognosis of these neurological

disorders (5).

1.2 Aim of the study

The aim of this case-control study is to determine the volume of brainstem,

midbrain, pons and medulla oblongata and their volume fraction to total

brainstem in PD patients. The relation between the aforementioned

volumetric changes and age and gender of patients will also be investigated

and compared to normal healthy controls.

1.3 Research objectives

1.3.1 General

• To study the volumetric alteration of midbrain and total

brainstem in PD patients and to study the effect of age, sex, and

onset and duration of the disease on their volume alteration.

1.3.2 Specific

• To measure the volume of the midbrain, pons, medulla

oblongata and brainstem as a whole in patients with PD &

compare the findings with that of healthy controls.

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• To correlate the variations in the volume of midbrain, pons,

medulla oblongata and brainstem as a whole with sex, age, and

onset and duration of the disease.

1.4 Hypotheses

• The volume of midbrain and brainstem as a whole is reduced in

patients with PD.

• The degree of alteration in the volume of midbrain and total

brainstem is influenced by the age and gender.

• The degree of alteration in the volume of midbrain and total

brainstem volume is correlated to age at onset and duration of

PD.

• The alteration in the volume of the midbrain and brainstem can

be applied in the diagnosis of PD.

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2.1 Anatomy of the brain

The brain is one of the most complex and magnificent organs in the human

body. The brain is the part of the central nervous system which lies wholly

inside the cranium. The average adult brain weighs between 1300 – 1400

grams and that of new born is 350 – 400 grams (26). The brain gives

awareness of internal and external environment through processing a

constant stream of sensory data that helps in maintaining homeostasis of the

body. Besides that it is responsible for our perceptions, behaviors, and

memories. It also initiates all voluntary movements.

The brain is composed of cerebrum, cerebellum and brainstem. The

cerebrum lies in the anterior and middle cranial fossae constituting the

largest part of the brain, and composed of right and left cerebral

hemispheres. The cerebrum consists of an outer grey matter, inner white

matter and deep structures e.g. basal ganglia, thalamus and hypothalamus.

The cerebellum lies in the posterior cranial fossa under the cerebrum and is

connected to the cerebrum through the brainstem. The brainstem consists of

three parts; midbrain, pons and medulla oblongata, and it connects the spinal

cord to the cerebrum and cerebellum (Figure 2.1) (27).

The brain contains cavities called ventricles filled by cerebrospinal fluid

(CSF); two lateral, a third and a fourth ventricles. The two lateral ventricles

connect with the third ventricle through the interventricular foramina

(foramina of Monro). The third ventricle connects to the fourth ventricle

through a narrow canal called the cerebral aqueduct of Sylvius that traverses

the midbrain (Figure 2.2).

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Histologically the brain is made up of two type of cells; nerve cells

(neurons) and glial cells. All neurons consist of a cell body, dendrites and an

axon. The neurons convey information through electrical and chemical

signals (neurotransmitter). The neurotransmitter is conveyed through a tiny

gap between neurons or between neuron and receptor called a synaptic cleft

(Figure 2.3).

Figure 2.1: Median sagittal section of the brainstem (28)

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Figure 2.2: Ventricular system of the brain (28)

Figure 2.3: A neuron showing cell body, dendrites, axon and a synapse (29)

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2.2 Anatomy of the brainstem

The brainstem is situated in the posterior cranial fossa, and its ventral

surface lies on the clivus (30). It can be divided into three parts; from caudal

to cranial, these are the medulla oblongata, pons and midbrain. It is

connected inferiorly with the spinal cord, superiorly with the cerebrum and

posteriorly with the cerebellum (27). The brainstem is one of the least

understood parts of the human brain despite its prime importance for the

maintenance of basic vital functions (31). The brainstem is the primary relay

center for afferent and efferent connections between the cerebral cortex, the

cerebellum and the spinal cord (32).

Histologically the human brainstem is composed of a multitude of axonal

nerve fibers as well as cranial and non-cranial nerve nuclei (32). It also

contains cholinergic, dopaminergic, noradrenergic, and serotonergic nuclei

whose cortical and subcortical projections are essential to the regulation of

consciousness, sleep, behavior, cognition, muscle tone, posture; and cardiac

and respiratory functions (31, 33). Due to the spatial concentration of important

neural structures in this relatively small brain region, pathological injury of

the brainstem is often life-threatening and results in severe neurological

effect (30, 32). It is involved also in a number of neurodegenerative diseases,

such as PD, Alzheimer's disease, and Huntington's disease (32).

Despite the crucial importance of the brainstem, most of the knowledge

about structure and organization of white and grey matter within the

brainstem is derived from ex vivo dissection and histological studies (34). One

reason for rare in vivo data of the brainstem is due to the anatomical

characteristics of the brainstem, specially its close vicinity to large arteries

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and ventricles. The small size of the brainstem anatomical substructures

presents inherent challenges to neuroimaging analysis. These properties

make the brainstem a difficult structure for study with non-invasive methods

like MRI, as they place high demands on image acquisition as well as data

analysis methods (31). Nevertheless, in more recent years neuroimaging

studies with different MRI methods provides many valuable insights to

brainstem architecture (31, 32, 34-37).

The cranial limit of the brainstem is identified as the midbrain-diencephalic

junction (or diencephalic-mesencephalic junction) where the brainstem

meets the thalamus and hypothalamus at the level of the tentorium cerebelli.

The cranial border of the brainstem in a sagittal section was defined as the

axial plane passing through posterior commissure (posteriorly) and

mammillary bodies (anteriorly) (38, 21, 39-41). Planes parallel to the mammillary

body-posterior commissure line passing through the cranial and caudal notch

of the pons were used to further separate the brainstem into midbrain, pons

and medulla oblongata (41). Accordingly, the midbrain joins the pons at the

pontomesencephalic junction, and the pons meets the medulla oblongata at

the pontomedullary junction. The caudal border of the brainstem is the

cervicomedullary junction at the level of the foramen magnum and

pyramidal decussation (Figure 2.4) (33, 42). Eichler et al. and Schulz et al. (21,

41), determined the caudal border of the brainstem by a parallel line to

mammillary body-posterior commissure plane at the posterior rim of the

foramen magnum. Brainstem and cerebellum are separated from each other

by a plane through the obex and posterior commissure shifted posteriorly to

include the inferior colliculus.

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Figure 2.4: Parts of brainstem: cranial and caudal border and planes dividing it to midbrain, pons & medulla oblongata (28)

2.3. Anatomy of the medulla oblongata

The medulla oblongata connects the pons superiorly with the spinal cord

inferiorly. The junction of the medulla oblongata and spinal cord is at the

origin of the anterior and posterior roots of the first cervical spinal nerves,

which corresponds approximately to the level of the foramen magnum. The

medulla oblongata is conical in shape, its broad extremity being directed

superiorly (27). It is approximately 3 cm in length and 2 cm in diameter. The

ventral surface of the medulla oblongata is separated from the basilar part of

the occipital bone and apex of the dens by the meninges and occipito-axial

ligaments. Caudally, the dorsal surface of the medulla oblongata occupies

the midline notch between the cerebellar hemispheres (30).

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On the anterior surface of the medulla oblongata is the anterior median

fissure, which is continuous inferiorly with the anterior median fissure of the

spinal cord. On each side of the median fissure, there is a swelling called the

pyramid. The pyramids taper inferiorly, and it is here that the majority of the

descending fibers cross over to opposite side, forming the pyramidal

decussation. Posterolateral to the pyramids are the olives, which are oval

elevation produced by the underlying inferior olivary nuclei. In the groove

between the pyramid and the olive emerge the rootlets of the hypoglossal

nerve. Posterior to the olives are the inferior cerebellar peduncles, which

connect the medulla oblongata to the cerebellum. In the groove between the

olive and the inferior cerebellar peduncle emerge the roots of the

glossopharyngeal and vagus nerves and the cranial roots of the accessory

nerve (27).

The posterior surface of the superior half of the medulla oblongata forms the

lower part of the floor of the fourth ventricle. The posterior surface of the

inferior half of the medulla oblongata is continuous with the posterior aspect

of the spinal cord and possesses a posterior median sulcus. On each side of

the median sulcus, there is an elongated swelling, the gracile tubercle,

produced by the underlying gracile nucleus. Lateral to the gracile tubercle is

a similar swelling, the cuneate tubercle, produced by the underlying cuneate

nucleus. The central canal of the spinal cord continues upward into the lower

half of the medulla oblongata; in the upper half of the medulla oblongata, it

expands as the cavity of the fourth ventricle (27).

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2.4 Anatomy of the pons

The pons is anterior to the cerebellum and connects the medulla oblongata to

the midbrain. It is about 2.5 cm long and owes its name to the appearance

presented on the anterior surface, which is that of a bridge connecting the

right and left cerebellar hemispheres (27). On the ventral surface of the

brainstem, the transition between medulla oblongata and pons is clearly

demarcated by a transverse sulcus. Laterally, in a region known as the

cerebellopontine angle, the facial, vestibulocochlear and glossopharyngeal

nerves emerge. The ventral surface of the pons is separated from the clivus

(basisphenoid and dorsum sellae) by the cisterna pontis. It is markedly

convex transversely, less so vertically, and grooves the petrous part of the

temporal bone laterally up to the internal acoustic meatus (30). The anterior

surface shows many transverse fibers that converge on each side to form the

middle cerebellar peduncle. There is a shallow groove in the midline, the

basilar groove, which lodges the basilar artery. On the anterolateral surface

of the pons, the trigeminal nerve emerges on each side. The posterior surface

of the pons is hidden from view by the cerebellum. It forms the upper half of

the floor of the fourth ventricle and is triangular in shape. The posterior

surface is limited laterally by the superior cerebellar peduncles and is

divided into symmetrical halves by a median sulcus (27).

2.5 Anatomy of the midbrain

The midbrain traverses the hiatus in the tentorium cerebelli, and connects the

pons and cerebellum with the forebrain. It is the shortest brainstem segment,

not more than 2 cm in length and most of it lies in the posterior cranial fossa.

Lateral to it are the parahippocampal gyri, which hide its sides when the

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inferior surface of the brain is examined (30). On the posterior surface are two

colliculi (corpora quadrigemina), which are partly overlain by the pulvinar

of the thalamus. These are rounded eminences that are divided into superior

and inferior pairs by a vertical and a transverse grooves. The superior

colliculi are centers for visual reflexes, and the inferior colliculi are lower

auditory centers. In the midline below the inferior colliculi, the trochlear

nerves emerge. On the lateral aspect of the midbrain, the superior and

inferior brachia ascend in an anterolateral direction. The superior brachium

passes from the superior colliculus to the lateral geniculate body and optic

tract. The inferior brachium connects the inferior colliculus to the medial

geniculate body (27).

On the anterior aspect of the midbrain, there is a deep depression in the

midline, the interpeduncular fossa, which is bounded on either side by the

crus cerebri (27). The crura cerebri are superficially corrugated and emerge

from the cerebral hemispheres. They converge as they descend and meet as

they enter the pons (30). Many small blood vessels perforate the floor of the

interpeduncular fossa, and this region is termed the posterior perforated

substance. The oculomotor nerve emerges from a groove on the medial side

of the crus cerebri (27).

2.5.1 Internal structure of midbrain

The midbrain comprises two lateral halves, called the cerebral peduncles;

each of these is divided into an anterior part, the crus cerebri, and a posterior

part, the tegmentum, by a pigmented band of grey matter called the SN(27),

(Figure 2.5). The tegmenti are continuous across the midline but the crura

are separated by the interpeduncular fossa (30). The narrow cavity of the

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midbrain is the cerebral aqueduct, which connects the third and fourth

ventricles and contains cerebrospinal fluid. The tectum is the part of the

midbrain posterior to the cerebral aqueduct; it has the superior and inferior

colliculi. The cerebral aqueduct is lined by ependyma and is surrounded by

central grey matter (27). The red nucleus is a rounded mass of grey matter

situated between the cerebral aqueduct and the SN. Its reddish hue, seen in

fresh specimens, is due to its vascularity and the presence of an iron-

containing pigment in the cytoplasm of many of its neurons. The red nucleus

is connected to the SN through afferent and efferent fibers (27).

2.5.2 Substantia nigra

The SN is a large motor nucleus situated between the tegmentum, and the

crus cerebri throughout the midbrain. Its medial part is traversed by

oculomotor axons passing ventrally to their point of exit in the

interpeduncular fossa. Histologically, the SN is composed of medium-size

multipolar neurons that possess inclusion granules of melanin pigment

within their cytoplasm. The SN is divided into a dorsal pars compacta and a

ventral pars reticulata; the neurons in each part have quite different

connections (30).

2.5.2.1 Pars compacta

The pars compacta of the substantia nigra (SNc) is dorsal and composed of

closely packed dopaminergic neurons that synthesize dopamine as their

neurotransmitter. The dopaminergic neurons contain neuromelanin granules

that appear dark in cut sections. This dark pigment, a polymer derived from

dopamine, gives the SN its name (Latin, black substance). The pigmentation

increases with age and is most abundant in primates, maximal in man, and

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present even in albinos (43, 44). The medial lemniscus constitutes its dorsal

border and pars reticulata ventral border (44). The dopaminergic neurons of

the SN are located in three cell groups referred to as A8 (or retrorubral cell

group), A9 (or nigral cell group), and A10 (or ventral tegmental area, which

includes the parabrachial pigmentosus and paranigral nuclei). Because these

cell groups are directly continuous with one another, the outlines of the SNc

where A9 cells are located, are difficult to assess (44). Neuromelanin can

interact with many heavy metal ions such as zinc, copper, manganese,

chromium, cobalt, mercury, lead, and cadmium, and it strongly binds with

iron. The iron content of the SN increases with age. Iron is also located in

neuromelanin-containing neurons and its accumulation limits synthesis of

dopamine (44).

In PD, the levels of dopamine in the SN and striatum decrease dramatically

as a result of the degeneration of pars compacta neurons (30). Several

attempts were made to improve visualization of the SN using multiple

magnetic resonance (MR) contrasts, but the correspondence between MR

images and the actual anatomy of the SN was unclear (44). The inputs to the

SNc are predominantly from striatal gamma-aminobutyric acid (GABA)

neurons, but also receive inputs from the frontal lobe. The output from SNc

projects to the caudate nucleus and putamen in a topographically organized

fashion (nigrostriatal fibres) (30).

2.5.2.2 The pars reticulata

The pars reticulata of the substantia nigra (SNr) is the pale ventral zone and

extends cranially as far as the subthalamic region. It is considered to be a

homologue of the medial (internal) segment of the globus pallidus and

resembles it in terms of cell type and connectivity (30). Because of the

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striking similarities in cytology, connectivity and function of the internal

segments of the globus pallidus and SN pars reticulata, these two nuclei can

be considered as a single structure arbitrarily divided by the internal capsule (43). It consists of clusters of neurons, many of which intermingle with fibers

of the crus cerebri. The cells in the pars reticulata are composed mainly of

GABA neurons (44).

The pars reticulata has afferent (input) and efferent (output) connections.

The main input to the pars reticulata is derived from the striatum

(striatonigral fibres) mainly from the caudate nucleus and putamen. They

come by two routes, known as the direct and indirect pathways. The direct

pathway consists of axons of cells in the striatum that project directly to the

pars reticulata. This direct pathway exerts an inhibitory effect on pars

reticulata neurons. The indirect pathway has three connections before it

reaches the pars reticulata; a) a projection from the striatal cells to the

external part of the globus pallidus, b) a GABAergic projection from

external part of globus pallidus to the subthalamic nucleus and c) a

glutamatergic projection from the subthalamic nucleus to the pars reticulata

(subthalamonigral projection). The subthalamonigral projections are

important in the pathophysiology of movement disorders such as PD and

dyskinesias. The indirect pathway is an excitatory to the pars reticulata.

Some corticonigral fibers appear to exist, passing from precentral and

postcentral gyri to neurons in the pars reticulata (30).

The output from the pars reticulata projects significantly to the thalamus

(nigrothalamic pathway) and superior colliculus. These pathways use GABA

as their neurotransmitter. In addition, the pars reticulata also inhibits

dopaminergic activity in the pars compacta via axon collaterals (30).

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Figure 2.5: A cross sectional diagram through the superior colliculus of the midbrain showing the SN (45)

2.6 Age and sex-related effects on brain volume

Examination of the structural brain alterations that occur with age may assist

in understanding age-related functional changes and susceptibility to

neurodegeneration (46). The whole brain volume changes throughout the life

span and it includes reduction in the brain volume and increase in brain

ventricles and cerebrospinal fluid volume (47, 48). Brain volume increases

during childhood and adolescence up to the age of ~13 years after which the

whole brain starts to decrease. There is a suggestive evidence of a second

period of growth for brain or stability in brain volume during adolescent up

to age 35 years. After age 35 years a steady volume loss of 0.2% per year is

found, which increases gradually to 0.5% per year at age 60. Over 60 years

there is a steady annual brain volume loss of more than 0.5% (47, 49). It was

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also reported in the literature that, the brain gains volume until age 40-50

before the shrinkage of advanced age begins (50). Beyond the age of 50 years

the average person will lose 5% of brain volume per decade (46).

Although brain volumes are affected globally by age, the pattern of brain

atrophy is not consistent across different brain regions (51, 52). There is an

increasing consensus on the overall pattern of grey matter development over

the course of childhood and adolescence. In childhood a global increase of

cortical grey matter volume takes place, which is then followed by a gradual

decrease in adolescence and early adulthood. Total brain volume loss is

attributed primarily to loss of grey matter (51, 53-58). The grey matter loss is

greater in cortex than in subcortical structures (52). The total white matter

volume increases until approximately the fifth decade of life and declines

thereafter and no significant decline was shown with age(51, 53-58), whereas

Mortamet et al. found a surprising result that white matter increases

significantly with age (57). On the other hand, another study reported that

total white matter volume is negatively related to age (52).

Concerning sex many studies showed that the total brain volume and total

grey and white matter volumes were larger in males compared to females (48,

55, 59, 56, 60, 61). Erbagci et al. (5), reported a statistically significant gender-

related difference in the total brain volume (1,074.06 ± 111.75 cm³ for male

and 966.81 ± 77.44 cm³ for female). Ekinci et al. (62), also confirmed the

above result and found that the mean total brain volume in males was greater

than in females (1,202.05 and 1,143.65 cm³ respectively). Although the total

brain volume of males is bigger than that of females, there is a significant

more prominent decrease in grey matter in male than in female during

childhood and adolescence (53). The cerebellum and pons are also larger in

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men than women (63). In general males are more affected than females

through their life-span by age-related changes concerning brain volume (48, 53,

59). Inconsistently it was also reported in another study that annual rates of

brain tissue loss were similar in male and in female and in older and younger

adults. These rates were estimated as 5.4±0.3cm³ per year for total brain

volume loss and 2.4±0.4 cm³ and 3.1±0.4 cm³ per year for that of grey and

white matter respectively (64).

The aging brain undergoes biochemical, molecular, structural and functional

changes, rendering it vulnerable to a range of neuropsychiatric disorders (46).

Many factors affect structural age changes in the brain, among these factors

are; (1) degeneration of myelinated axons which reduces the speed of axonal

conduction, (2) structural changes in the cerebral vessels leading to

generalized decrease of cerebral blood flow and hence resulting in

disturbance of glucose and oxygen supply to the tissues and neuronal death.

The middle cerebral artery seems to be the most affected while arteries to the

cerebellum and brainstem are secured, (3) hypertension, (4) neurotransmitter

changes which cause damage to critical lipid, protein and DNA components

of cells, leading to neuronal dysfunction and eventually cell death, (5)

changes in calcium homeostasis, (6) alteration in genetic expression

resulting in vulnerability to oxidative stress, inflammatory responses and

regulation of DNA repair, and (7) decline in mitochondrial function (47, 46).

2.7 Age and sex-related effects on brainstem volume Although the human brain exhibits a global complex pattern of differential

aging, it is still unclear whether differential aging is observed in the posterior

fossa structures; brainstem and cerebellum. With constantly improving MRI

technology, greater progress has been made over the past 20 years in

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mapping how the human brain matures and changes across the life span (65).

Despite this, the reported brainstem age-related changes remain sparse due

to the technical and methodological limitations of segmentations and

quantitative assessment of data from this region (35).

Moderate age-related shrinkage of the cerebellum has been noted. There was

a clear agreement in the literature that pons and medulla oblongata do not

show volumetric loss along with age (35, 66, 63, 67, 52).

Many studies(5, 35, 66, 50, 46, 52), have approved that in normal adults, changes in

brainstem volume was unaffected by age. They have explained the reason

for the brainstem volume being constant with age as the presence of

important nuclei in this region, which regulates vital centers of respiratory

and cardiovascular functions. In addition Lambert et al. and Lee et al. (35, 66),

attributed the consistent volume of brainstem with age due to the minimal

effect of decreased blood flow to the brainstem compared to the other

cerebral regions. On the contrary to the above studies Kruggel (68), has

reported that brainstem volume decreases with age in both sexes.

Luft et al. (50), have found an age-related shrinkage in the midbrain. A cross-

sectional study (66), also proved the age-related changes in the midbrain and

no change in the medulla oblongata. Midbrain structures such as SN showed

marked sensitivity to age related volume losses (r = -0.42) (46). Aging is

associated with a linear decline of pigmented neurons in the SN and with

decreased levels of striatal dopamine (50).

In the majority of studies, it is reported that the volumes of brainstem are

significantly bigger in males than in females (62, 5, 68, 66). The inconsistencies

between studies concerning the measurements of brainstem volume obtained

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may be due to the different methods used for measurement, environmental

factors and age. One of the major reasons that influence the brainstem

volume by gender is that males have proportionately bigger mass index than

females, in addition to the sexual hormonal effects on the brain in males. It

has been reported by Xu et al. (69), that testosterone level has a relation with

brain mass.

2.8 Imaging techniques of the brain

In 1895 Wilhelm Röntgen used the X-rays from a cathode ray tube to expose

a photographic plate and produced the first radiographic exposure of his

wife's hand. Over the past 30 years there has been a revolution in medical

imaging, which has been paralleled by developments in computer

technology (70).

Brain imaging techniques allow doctors and researchers to view structures or

problems within the human brain, without invasive neurosurgery. There are

a number of accepted and safe imaging techniques used in hospitals and

researches like; computed tomography (CT) scanning that builds up a

picture of the brain based on the differential absorption of X-rays. CT scans

reveal the gross features of the brain but do not resolve its structure well.

Positron emission tomography (PET) uses trace amounts of radioactive

materials that are injected into bloodstream to map functional processes in

the brain. Single-photon emission computed tomography (SPECT) is similar

to PET and uses gamma rays and cameras to construct two or three

dimensional images of active brain regions. Magnetic resonance imaging

(MRI) uses strong magnetic fields and radio waves to produce high quality

two or three dimensional images of brain structures without use of

24

radioactive tracers (Figure 2.6). Functional magnetic resonance imaging

(fMRI) relies on the paramagnetic properties of oxygenated and

deoxygenated hemoglobin to see images of changing blood flow in the

brain, so it can show which part of the brain is active or functioning, in

response to a certain task performed by the patient through the recording of

movement of blood flow. Diffusion tensor imaging (DTI) is a type of

diffusion MRI used to observe functions of the brain as they occur (in vivo)

and it is often used to image white matter. Diffuse optical tomography

(DOT) is a non-invasive imaging technique that uses near-infrared lights to

produce brain images for recording oxygenation and other physiological

changes which may occur after a stroke, seizures or hemorrhage. A

resolution obtained from DOT is limited compared to MRI, but its advantage

is that the machine is simple and portable and can therefore easily be taken

to the bedside for constant monitoring of brain activity (71-73).

Figure 2.6: Philips MRI scanner (73).

25

2.9 Magnetic Resonance Imaging (MRI)

2.9.1 Introduction

MRI is a method of imaging which has gained worldwide acceptance and, in

addition to many new indications, has replaced other diagnostic imaging

techniques. MR images are no longer the exclusive domain of radiologist,

but also practiced and/or interpreted by a large number of clinicians and

surgeons. With each examination, one is confronted with a number of MR

image findings that require interpretation in order to reach a general

diagnostic impression and a reasonable differential diagnosis. MR images

are still developing rapidly and new imaging sequences are published almost

daily (74). MR images can provide in vivo anatomic images of portions of the

human body with high contrast resolution and can detect many abnormalities

or tumors. Major advantages of MR images include excellent soft-tissue

contrast resolution, multiplanar imaging capabilities, dynamic rapid data

acquisition, and various available contrast agents (75).

MRI uses the magnetic properties of hydrogen atom to produce images. The

nucleus of the hydrogen atom is a single proton. The hydrogen nuclei

(protons) behave like small, spinning bar magnets and align with the

magnetic force when placed in a strong magnetic field. These nuclei in water

molecules and lipids are responsible for producing the anatomical images in

MRI (76, 77).

The first step in MRI, a patient is placed within a large, powerful magnet.

The hydrogen atoms within the patient align in a direction either parallel or

antiparallel to the strong external field. A greater proportion aligns in the

parallel direction so that the net vector of their alignment, and therefore the

26

net magnetic vector, will be in the direction of the external field. This is

known as the longitudinal magnetization. Although aligned in a strong

magnetic field, the hydrogen nuclei do not lie motionless. Each nucleus

spins around the axis of the magnetic field in a motion known as precession.

The frequency of precession is an inherent property of the hydrogen atom in

a given magnetic field and is known as the Larmor frequency. A second

magnetic field is then applied at right angles to the original external field.

This second magnetic field is applied at the same angle as the Larmor

frequency and is known as the radiofrequency pulse (RF pulse) (78, 79).

A magnetic coil known as the RF coil, placed around the patient, applies the

RF pulse. The RF pulse causes the net magnetization vector of the hydrogen

atoms to turn towards the transverse plane, i.e. a plane at right angles to the

direction of the original, strong external field. Depending on the strength and

duration of the RF pulse the magnetization vector will rotate away from the

longitudinal direction to a varying degree. A 90° pulse rotates the vector into

the transverse plane. This is known as the transverse magnetization. A 180°

pulse rotates the vector to the opposite longitudinal direction. Smaller angles

of rotation, known as 'flip angles', of the order of 15 -30° are used for

gradient- recalled-echo sequences (77).

The component of the net magnetization vector in transverse plane induces

an electrical current in the RF coil. This current is known as the MR signal

and is the basis for formation of an image. Computer analysis of the

complex MR signal from the RF receiver coils is used to produce a magnetic

resonance image. In viewing MR images, white or light grey areas are

referred to as 'high signal' dark grey or black areas referred to as 'low signal'.

27

On certain sequences flowing blood is seen as a black area referred to as a

'flow void' (77, 79).

Following the application of a 90° RF pulse, the net magnetization vector

lies in the transverse plane. Also, all of the hydrogen protons are processing

at the same rate – they are said to be 'in phase'. Upon cessation of the RF

pulse two things begin to happen. The net magnetization vector will rotate

back to the longitudinal direction. This is known as longitudinal relaxation

or T1 relaxation (80, 81). At the same time, the spinning hydrogen atoms will

start to process at slightly varying rates. This dephasing process is known as

transverse relaxation or T2 relaxation (decay).

The T1 is defined as the time taken for the longitudinal magnetization to

resume 63 per cent of its final value. The T2 is defined as the amount of time

for the transverse magnetization to decay to 37 per cent of its original value.

The rates at which T1 and T2 relaxation occur are inherent properties of the

various tissues. Sequences that primarily use differences in T1 relaxation

rates produce T1-weighted images. Tissues with long T1 values are shown

as low signal ' dark grey' while those with shorter T1 values are displayed as

higher signal 'light grey'.

T2-weighted images reflect differences in T2 relaxation rates. Tissues whose

protons dephase slowly have a long T2 and are displayed as high signal on

images. Tissues with shorter T2 valves are shown as lower signals. Both T1

and T2 –weighted sequences are performed very commonly in most parts of

the body (77). Most pathological processes show increased T1 and T2

relaxations times and appear lowered in signal (blacker) on a T1-weighted

scan and higher in signal (whiter) on a T2-weighted scan than the normal

28

surrounding tissues. The T1 & T2 weighting of an image can be selected by

appropriately altering the timing and sequence of radiofrequency pulses (76).

To generate contrast between the various soft tissue structures examined,

MRI signals depend on many varied properties e.g.: the number of hydrogen

atoms present in tissue (proton density), the chemical environment of

hydrogen atom, and T1 & T2 relaxation times. By altering the duration and

amplitude of the RF pulse various imaging sequences use these properties to

produce image contrast. Terms used to describe the different types of MRI

sequences include spin echo, inversion recovery, and gradient recalled echo

(gradient echo) (77, 81).

The standard MRI unit consists of a number of magnetic coils systems. First

is the large magnet itself. This is usually a superconducting magnet that uses

liquid helium. Secondly, a series of gradient coils is used to produce

variations to the magnetic field that allow image formation. It is the rapid

switching of these gradients that causes the loud noises associated with MRI

scanning. Lastly the RF coils which are applied around the area of interest,

they are used to transmit the RF pulse and to receive the RF signals. The

coils come in varying shapes and size depending on the part of the body to

be examined (77).

In some cases contrast agent or medium is used to enhance the contrast of

structures or visibility of blood vessels. The contrast media in MRI depends

on agents that have magnetic and paramagnetic properties, the most widely

used agent is a combination of gadolinium and diethylene triamine

pentaacetic acid (DTPA) which dramatically decreases the T1 relaxation

time (76, 72).

29

The processional frequency of hydrogen nuclei at 1.5 T (Tesla) is 64 MHz.

Soft tissue contrast results from: 1) the density of protons (hydrogen nuclei)

within different tissue; 2) the different rates at which the protons in various

tissues realign themselves with the magnetic field of the magnet (also

referred to as T1 relaxation, longitudinal or spin-lattice relaxation); 3) rates

of signal decay or dephasing (also referred to as T2 relaxation, transverse or

spin-spin relaxation). Using these biophysical properties of different normal

and abnormal tissues allows MRI to have greater soft-tissue contrast than CT (74). The main components of a typical MRI scanner includes: 1) a large-bore

magnet with high field strength (0.3 to 1.5 T); 2) RF coils within the magnet

which can transmit and receive properly tuned RF pulse, as well as set

spatially-dependent magnetic fields (gradients) that allow localization of

specific regions of anatomic interest; 3) a computer that operates the device

and processes the RF signal data received from the patient to form an

anatomic image (74, 79).

To generate an MR image, a person is placed onto a table that can be

specifically located within the bore of the magnet. Once in the magnet the

operator selects programs that include the RF pulse sequences necessary to

generate images with the desired contrast parameters based on the proton

densities, T1 and T2 values of the various tissues. The data received from

the subject or patient is processed by the computer using computer

algorithms (2D or 3D Fourier transformation). The images are displayed on

the monitor console and transferred to film or other computers (79). Many

systems store the image data on digital tape or optical discs for easy

retrieval. Not all patients can have MRI examinations. Intracranial aneurysm

clips, cardiac pacemakers, and metallic foreign bodies in the eyes are

30

absolute contraindications for MRI. In addition, the presence of surgical

clips, metallic rods, wires, and other orthopedic hardware can produce

artifacts obscuring visualization of the anatomic structures in the region of

interest and can harm the patient or person near the magnet (74, 82).

2.9.2 Magnetic resonance imaging of the brain

Over the last two decades, there have been dramatic improvements in the

capabilities of the techniques used for imaging the brain. Methods providing

only indirect evidence of an abnormality, e.g. skull radiography, have been

replaced by those that result in direct visualization of the anatomy, i.e. CT

and MRI, and function, i.e. PET of the brain itself. As a result of these

advances, it is now possible to examine the brain routinely with little risk in

a manner superior to that possible by gross anatomic inspection. CT and MR

images are now the principal techniques used for the evaluation of patients

with neurologic diseases. Foreseeable advances in shortening the time

necessary for an MRI examination, improving the environment of MRI

devices to accommodate critically ill patients, and developing the

availability to obtain accurate anatomic as well as physiologic information

about the vasculature of the brain make it likely that MRI will soon become

the principal method for diagnostic imaging of patients with central nervous

system diseases (83).

MRI has a superior contrast resolution facilities discrimination of the grey

and white matter. MRI based volume quantification is now being

increasingly used to investigate neuro-anatomic structures in neurological

and psychiatric disorders, e.g. schizophrenia, Alzheimer's disease and

epilepsy (84-86). MRI volumetry is also useful to examine structures that

31

require assessment of changes in volume over time as an indicator of

therapeutic effectiveness (87).

MRI has proven to be a powerful imaging modality in the evaluation of: 1)

congenital anomalies of the brain; 2) disorders of histogenesis; 3) neoplasms

of central nervous system, cranial nerves, pituitary gland, meninges, and

skull base; 4) traumatic lesions; 5) intracranial hemorrhage; 6) ischemia &

infarction; 7) infectious and noninfectious diseases; 8) metabolic disorders;

and 9) dysmyelinating and demyelinating diseases. MR data can also be

used to generate images of arteries and veins (MR angiography [MRA]) in

displays similar to conventional angiography. Another option with clinical

MRI scanners is the acquisition of spectral data to characterize the

biochemical properties of selected region of interest in the brain (MR

spectroscopy [MRS]) (74). MRI is also considered helpful to facilitate the

diagnosis of neurodegenerative diseases such as PD, multiple system

atrophy (MSA) or progressive supranuclear palsy (PSP), revealing either

signal changes or atrophy of specific brain regions (16).

The routine techniques used for MRI vary from centre to centre. Axial,

coronal and sagittal projections are all considered standard and two of these

projections are usually chosen for a routine examination. A variety of signal

sequences are used to create the image: usually T1-weighted, T2-weighted

and balanced (proton density) images. No signal is produced from bone, so

there is no bone artifact, which means the posterior fossa structures are more

clearly demonstrated. The characteristics of grey and white matter are

different, and both are clearly different from the CSF in the ventricular

system and subarachnoid space. Therefore, the anatomy of the brain can be

exquisitely displayed (76).

32

The appearance of the brain tissue depends on the MRI pulse sequence used

as well as the age of the patient imaged. Myelination of the brain begins in

the fifth fetal month and progresses rapidly during the first two years of life.

The degree of myelination affects the appearance of the brain parenchyma

on MRI (74).

Limitations to brainstem imaging with MRI are the induced field

inhomogeneities caused by nearby air-tissue or bone tissue interfaces which

lead to distortion of the images. Recently, a novel quantitative MR method

called quantitative susceptibility mapping (QSM) is introduced to the

imaging field. This technique is unique in its sensitivity to tissue

constituents, thus rendering it an excellent method for anatomical

delineation of cortical and deep grey matter structures especially for

brainstem. It is also reported that multiple image contrasts provides a

distinctly improved picture of the morphology of the brainstem (32).

Morphological changes in SN can be detected better by the use of 7 tesla

MRI, which provides an increase in both spatial resolution and contrast of

the image (44).

2.9.3 Normal brainstem appearance in MRI

2.9.3.1 Midbrain

The main identifiable structure of the midbrain with MRI is the cerebral

aqueduct of Sylvius. The cerebral aqueduct is a tubular channel about 2mm

in diameter that is surrounded by grey matter. The cerebral peduncles are

symmetrical rounded protuberance on the anterior surface of the midbrain (83). In axial sections the SN can be recognized as thin bands (slightly

hypodense in MR) just behind the peduncles (44). The red nuclei lie in the

33

rostral part of the midbrain. At the caudal end of the midbrain, the

decussation of the superior cerebellar peduncles appears as a central, oval-

shaped region. On the posterior surface of the midbrain, the superior and

inferior colliculi, which together form the quadrigeminal plate, can be

recognized (83).

2.9.3.2 Pons

The pons which connects the midbrain and medulla oblongata is the largest

segment of the brain stem. Its ventral surface is convex anteriorly, and its

nearly flat posterior surface forms part of the floor of the fourth ventricle.

The pontine cistern is between the clivus and the anterior surface of the pons (88). It contains the basilar and proximal anterior inferior cerebellar arteries,

the anterior pontomesencephalic vein, and cranial nerves V and VI. The

cerebellopontine angle cistern, which lies laterally between the middle

cerebellar peduncle and temporal bone, contains cranial nerves VII and VIII,

petrosal veins, and distal portions of the anterior inferior cerebellar artery.

The flocculus, a part of the flocculonodular lobule of the cerebellum,

projects into the posterior aspect of the cerebellopontine angle cistern (83).

2.9.3.3 Medulla oblongata

The medulla oblongata connects the pons with the spinal cord. Its landmarks

are the pyramids, olives, and inferior cerebellar peduncles that protrude from

the anterior, anterolateral, and lateral margins, respectively, of the medulla

oblongata. Depending on the level studied, axial scans show different

contour of the medulla oblongata. In sections through the upper medulla

oblongata, it has an angular configuration due to the pyramids projecting

from the anterior surface (83). The inferior cerebellar peduncles projecting

34

from the lateral surface; at a slightly lower level, has a different contour due

to the protuberance of the olives, from the anterolateral aspects. Near its

caudal ends, the medulla oblongata becomes circular, and where it joins the

cervical cord, elliptical. In coronal images, the pyramids and olives

projecting from the inferolateral margins of the medulla oblongata can be

identified. The medulla oblongata is related anteriorly to a small anterior

medullary cistern, laterally to the narrow lateral medullary cisterns, and

posteriorly to the cisterna magna. The cisterna magna contains portions of

the vertebral and posterior inferior cerebellar arteries (88, 83).

2.10 Design-based (unbiased) stereology

2.10.1 Definition

Stereology is a set of methods used to make unbiased estimates of biological

features of 3D objects through interpretation of 2D images. In stereology

irregular-shaped 3D structures are sampled using geometric test probes

(slabs, sections, lines and points) for quantification in 2D profiles based on a

sound statistical and stochastic background (89, 90). Stereology can be applied

to a variety of physical and optical imaging techniques. It can measure a

wide range of quantitative parameters including number, length, size, shape,

volume and density. The stereological quantification methods are unbiased,

less time consuming and precise. For these reasons, stereological application

on imaging approaches has become gold standard in quantitative structural

analysis of different organs. According to the sampling strategy this type of

stereology was first known as unbiased stereology. In order to avoid the

controversy involving 'biased vs. unbiased' data, many bio-stereologists

35

now prefer the term design-based stereology on behalf of unbiased

stereology (91, 92).

The objective of stereology is to estimate geometrical parameters that

characterize the composition of the structures using a few samples from the

whole. Typical global parameters are 3D (volume or size), 2D (surface area),

1D (length or thickness), or 0D (number) (89).

2.10.2 History

The term “Stereo” is derived from the Greek word for a “geometric object”.

The stereo set at home or stereo images are not called "stereo" because there

are two speakers or two pictures. They are called "stereo", because they try

to recreate sounds or objects in 3-dimentional (3-D) space. The 3-D analysis

of objects dates to ancient Egypt and the development of Euclidean

geometry (92).

In 1635, Bonaventura Cavalieri showed that the mean volume of solids

could be measured from the sum of their profile areas in cut sections of 2D

"Cavalieri principle". In 1777, Georges-Louis Leclerc Comte de Buffon

showed that a needle tossed onto a grid intersects the lines with a probability

proportional to the length of the needle and the spacing of the grid lines;

"Buffon's needle" led to estimation of total length and surface area of

irregular objects in sections. In 1847, the French mining engineer and

geologist, Auguste Delesse provided the basis for accurate and efficient

estimation of objects and region volumes by point counting "Delesse

principle". This principle enables volume estimation of irregular objects

based on their profile areas on random. These and other stereological

methods followed subsequently minimized the potential bias introduced by

36

measuring 3D objects in 2D profiles sections (89, 93, 92). Stereology was then

introduced into the scientific fields in the early 1960s. In 1961, Hans Elias

suggested stereology as a useful method to be used in different scientific

disciplines like geology, biology, engineering and material sciences (92). In

the 1970s biologists began to develop unbiased sampling strategies for

analysis of anatomically defined reference space in biological tissue (94, 92).

The believe in the use of stereological methods for biological researches

developed gradually until the year 2000, many journal editors and reviewer

and funding organizations began to state preference for modern stereology

approaches, which they regard as the state-of-the-art methodology for

morphological quantification of biological tissue. During the 21st century,

several major developments undergone for modern stereology. One of these

achievements is the development of computerized hard-software systems for

unbiased sampling and probes connected to microscopy. These

computerized systems are now affordable for interested biologists in support

of accurate, precise, and efficient approaches for testing a wide variety of

biological hypotheses (92).

2.10.3 Practical applications

Design-based stereology provides the first step towards accurate, efficient,

and more reliable results in morphometric analysis of biological tissue (92).

Certain terms are commonly used in designed-based stereology; these are

accuracy, bias and precision. Accuracy refers to the validity of the data (i.e.

without bias). Bias refers to methodological errors that cause measurement

to be inaccurate or deviation of the result from the expected value. Precision

refers to the reproducibility of measurements, which depend on data

variance, sampling design, sample size and distribution (89). With the

37

growing improvement in the stereological techniques, volume estimation of

irregular shape of various human organs becomes easily achievable.

Stereology has been recommended as the method of choice for

quantification of structures in kidney, lungs and brain researches (90).

Recently stereology has been increasingly applied by neuroscientists for

diagnosing neurological diseases (95).

2.10.4 Sampling

Stereology depends upon careful sampling which utilizes a systematic

uniform random sampling (SURS) paradigm. This means that every part

within the tissue or image have the same chance of being selected for

analysis, and are not being subjected to human or methodological bias. This

system of sampling combines both the unbiasedness of random sampling

and the efficiency of a systematic sampling. SURS is based on selecting the

final sample with a predetermining interval (systemically) while selecting

the first sample of the set randomly within the first sample interval (96). In

stereology the ratio of systematic random selection of a number of sections

from all the set of sections of the structure is referred to as the section

sampling fraction (SSF). For example, if 25 sections are selected using

SURS out of 250 sections from the whole structure; then the sampling

fraction will be 25/250, or 1/10 (96, 97).

In determining sample size for stereological quantification the paradigm "do

more, less well" is applied. Accordingly the simple guideline suffices for

sample size within a structure of interest is as follows: 100 – 200 counting

events (e.g., point hitting the structure of interest) distributed on 50 – 100

38

fields of view from 6 – 9 blocks are sufficient to obtain an appropriate

precision of stereological parameters (89, 92, 90).

2.10.5 Volumetry

Volumetry is a method of quantitative analysis in which the amount of a

substance is determined by measuring the volume that it occupies. Two

approaches are widely used by stereologists. The first approach is fluid

displacement based on Archimedes’ principle. The second approach is the

Cavalieri method which is a stereological approach. Both methods can easily

be implemented in the experimental protocol without making demands on

equipment or work expenditure (90). Nisari et al. (98), compared the two

methods for volumetric measurement of brain and brain components and

found no difference between fluid displacement and Cavalieri principle.

In vivo morphometric analysis of structural changes of many different

human organs is now possible through imaging techniques such as CT, MRI

and PET. Since the images obtained by these techniques are a series of

sections through organs, stereology can be applied for them to obtain

accurate measurements.

2.10.6 Cavalieri principle

The Cavalieri principle is named in honour of Bonaventura Cavalieri (1598 -

1647) who, as a student of Galileo in the seventeenth century, made

significant advances in the mathematics of numerical integrations and was

the first to consider measurement of volume via the analysis of sections

through three dimensional solid objects. Cavalieri pointed out that the

39

volume of any object could be estimated from a set of slices through the

object, provided that they are parallel, separated by a known distance. It is

known that the volume of regular-shaped objects (i.e., prism, cube, cylinder)

can be estimated by the following formula:

V= t × a,

Where (t) is the height and (a) is the base area of the object (99).

Similar to this approach, the Cavalieri principle is a modern stereological

method that can be applied on either microscopic or CT or magnetic

resonance (MR) images for estimation of volumes. This method allows an

estimation to be made of the volumes of irregular objects in an efficient and

unbiased manner (5).

In the Cavalieri principle the sectioning begins at a random starting point of

the structure of interest and cuts it from end to end with a series of parallel

probes at a constant known distance apart (100). The surface areas of all cut

sections are estimated and multiplied by the known section thickness "cross-

distance between the two cutting edges of a section" to provide the volume

of the examined object (Figure 2.7).

Planimetry and point-counting are two methods for estimating volume based

on the Cavalieri principle. Planimetry is a widely used approach in

volumetry and it depends on manual or automatic delineation of the

boundaries of the object of interest on image sections. The sectional area

delineated is estimated by use of software that read pixels inside the

boundary(96), (Figure 2.8). Examples of the software analyzing systems used

in planimetry method are ImageJ, DicomWorks …etc.). The sum of the

40

measured areas of sections obtained by planimetry is then multiplied by the

section thickness and the volume of structure is estimated according to the

following equation:

V= t ×Σa cm3

• Σa denotes the sum of section areas in cm2 and

• (t) is the sectioning thickness in cm

Figure 2.7: Sectioning for volume estimation by Cavalieri principle (101)

In point-counting method, a set of points at specific densities on a

transparent sheet (point-counting grid), is randomly superimposed on

sections, and the number of points that hit the region of interest are counted.

Finally the volume of the structure (V) is estimated by multiplying section

41

thickness (t), total number of points hitting the structure (ΣP) and the

representing area per point in the grid (a/p) as the following equation:

V= t × (a/p) × ΣP cm3

Figure 2.8: Planimetry manual tracing method of Cavalieri principle (102)

2.10.7 Coefficient of error (CE)

Coefficient of error (CE) or relative standard error represents the total

amount of error arising from sampling estimation procedures in a

stereological study using the Cavalieri principle (96). CE allows the

calculation of the optimum number of sections required to attain a given

precision for a particular scanning direction. It also evaluates the reliability

of the point density of the grid and sectioning intervals (87). A preliminary

estimate of CE can be obtained by dividing the standard deviation by the

42

sample average, showing the difference between the calculated average of

the population and the average variability. Usually, a total CE of less than

5% is considered to be adequate for most of the studies to provide accurate

quantitative data (96). Thus a researcher can see the potential variability in

any given volume measurement. When the CE of the measurements is high,

it can generate obvious problem in accuracy and hence interpretation. A CE

of less than 10% is an acceptable range (62).

CE is calculable before the work has been done i.e. amount of error in the

stereological study can be known before conducting the final measurements.

CE estimation has two different equations used according to whether the

study uses point counting or planimetry methods of Cavalieri volume

estimation. In planimetry methods CE is measured using the following

formula;

Where, i = 1,2,3, …, m is the number of sections. A is the measured area of

sections using planimetry method and the others are constant.

2.10.8 Coefficient of variation (CV)

It is a statistical method for standardizing measurement of dispersion and is

expressed in percentage. CV is obtained by dividing the standard deviation

by the mean of the population. It is an important stereological application

that shows extent of variability in relation to the mean of the population (96).

43

2.10.9 Volume fraction (VF)

The maximum size of brain of an individual is affected by factors related to

the brain growth, like gender and physical size. So, the human brain varies

widely from person to another (62). The volume fraction is a stereological

approach denoting the ratio of body components to each other independently

of the body size of individual (5). The volume fraction is used to express the

proportion of a component within a reference volume (62).

The volume fraction of a phase Y within a reference volume is simply the

proportion of each unit volume of the reference space taken up by Y and it is

measured by the following formula:

Vv(Y,ref) =

Volume of phase Y in reference space

Volume of reference space

Where the notation Vv(Y,ref) indicates volume fraction. The brackets are

used to which phase of interest (e.g. Y) and which volume reference the

volume fraction refers to. Example includes Vv (midbrain, brainstem).

Volume fraction ranges from 0 to 1 and is often expressed as percentage (91).

2.11 Parkinson's Disease (PD)

2.11.1 Introduction

Parkinson's disease (PD) was first described by Dr. James Parkinson in a

little book entitled "An Essay on the Shaking Palsy", published in 1817 (103).

For the next century, the condition was known as the shaking palsy and in

the medical community by its Latin equivalent, paralysis agitans. It is

44

sometimes called idiopathic parkinsonism (the term idiopathic means that

the cause is unknown), but more commonly today it is simply called

Parkinson’s disease, to honor the physician who first described it (4).

PD is the most common movement disorder and the second most common

neurodegenerative disease after Alzheimer disease (15). The primary

pathological feature of PD is death of SN cells and degeneration of

nigrostriatal pathway as well as presence of Lewy bodies (intracytoplasmic

inclusion bodies) in residual dopaminergic neurons (104). The SN neurons

make the neurochemical messenger dopamine, which is partly responsible

for starting a circuit of messages that coordinate normal movement (4). The

death of SN cells may affect to a lesser extent the basal ganglia; globus

pallidus, putamen and caudate nucleus (105).

2.11.2 Etiology

Although the main etiology of PD is unknown, genetic predisposing factors

in combination with environmental factors are thought to be responsible for

the death of dopaminergic and non-dopaminergic cells in the brains of PD

patients (106, 10). In addition also occupational status of the individual (e.g:

farmers exposed to pesticides) may play a role in affection with PD (107).

There is increasing evidence that PD may be inherited. Around 15–16% of

individuals with PD have a first-degree relative who has the disease (108, 109).

Both autosomal dominant and recessive forms of inherited PD are described

in Arabic families, associated with four genes mutations (Parkin, PINK1,

LRRK2, and PARK9) (110). Mutation of PINK1 gene analysis should be

considered in early-onset recessive PD patients, particularly those from Arab

origin (111).

45

Some of the environmental factors that may play a role in the development

of PD are certain toxins, oxidative stress, mitochondrial dysfunction,

inflammation and other pathological mechanisms (10, 112). The toxins

including, cyanide, manganese, carbon monoxide, carbon disulfide, N-

methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), methanol, organic

solvents and some other pesticides (113, 109). The prevalence of PD was found

to be twice as high among the residents of rural areas compared to urban

communities. This could be explained by environmental factors like

exposure to toxins and difference in dietary habits, in addition to greater

numbers of consanguineous marriages among rural communities (114, 107).

Other causes of PD beyond genetic and environmental factors may include

head injury(109), other traumas, and vascular compression of cerebral

peduncles by the posterior cerebral artery. This last factor raised a question

regarding etiology of PD which may be answered over time (115).

2.11.3 Prevalence

Data on prevalence and incidence of PD are of particular interest because it

can provide insight into suspected risk factors, protective factors and

primary causes of the disease. But still there are few reliable data of global

prevalence or morbidity of PD. The prevalence of PD varies among ethnic

and geographic groups around the world. The PD worldwide average

prevalence rate was estimated to be 1% in people 60 years and older (104).

But in some studies it was below this rate (0.5%) and the highest reported

rate was 4 % (116, 104). The incidence rate is very low in China (15/100,000)

and high in Argentina (657/100,000) (107). In Europe & United states, the

overall prevalence of the disease is estimated to be 1.6% among persons

46

older than 65 years (103, 104). The annual incidence of PD in the Unites states

was approximated by 446 cases /100,000 populations (104).

The prevalence of PD among the Arab populations is low compared to

worldwide distribution (107). The incidence of PD in Arabs is reported at

4.5/100,000 person/year and reported prevalence at 27 to 43/100,000

persons (110). A prevalence rate of 27/100,000 and 31.4/100,000 have been

found in Thugban – Saudi Arabia and in Benghazi – Libya respectively.

While in Kelibia – Tunisia, Attia Romdhane et al. found a similar prevalence

rate of PD to the above mentioned studies in Arab countries (117-119). The

overall crude prevalence of PD in populations in Israel region was as low as

43.24/100,000 (107). Recently El-Tallawy et al. (114), reported a high

prevalence rate of PD (213.15/100,000) in Al Kharga district – Egypt,

compared to similar studies conducted in Arab countries. This prevalence of

PD in Arabs is higher than black Africans (Nigeria 10/100,000 and Ethiopia

7/100,000) (1). To our knowledge no study has been conducted reporting

prevalence rate of PD in Sudan.

2.11.4 Onset & duration

PD occurs commonly between the age of 45 -55 and roughly in the same age

range in men and women, (114, 4, 120) although it is slightly more common in

men (107, 105). Solla et al. (121), reported that prevalence of PD is more in male

than female by a ratio of 2:1, suggesting a biological diversity. After age of

fifties, the incidence increases sharply with age, ultimately affecting 1.5% of

population over 65 and 2.5% over 85(122), then declines after 85 years of age (114). The onset of PD is rare before the age of 40 years. If PD onset occurs in

patients before 40 years, it is referred to as early-onset PD, although some

47

authors reported early onset to be up to age 50 years. Late onset PD includes

patients whose onset is after 55 years. Early-onset PD has been further

subdivided into; juvenile PD and young-onset PD (YOPD). The juvenile PD

includes cases with onset before 21 years. It is rare and seems to be more

common in Japanese. YOPD includes cases with onset at or above age

21years (123). It was also reported by Willis et al. (124), and Rana et al. (120), that

the YOPD is commonly defined as PD occurring in those aged

approximately between 20 to 49 years and Willis et al. showed that YOPD is

most common among white males in the United States.

The mean duration of PD from onset of symptoms up to death is

approximately 15 years. The natural progression of PD is variable but it is

usually more rapid in patients with late-onset (10). Non-motor symptoms

depend more on age at onset rather than disease duration. Ageing accelerates

disease prognosis after age of 70 years, regardless of previous disease

duration or age at onset of the disease (125). The mortality ratio of PD has

been reported to range 1 – 3.4 (10).

2.11.5 Signs & Symptoms

PD is characterized by motor and non- motor deficits. The motor symptoms

of PD are likely to appear when the pathology of the disease has caused

significant loss of 50–70% of the nigrostriatal dopaminergic neurons in the

SN (112, 126). The motor cardinal signs of PD include resting tremor,

bradykinesia (slowness to initiate movement), rigidity (cogwheel rigidity)

and postural instability (8, 104, 10). Other secondary motor symptoms may

occur (e.g, dysphagia, shuffling gait & micrographia) (10). Rate of annual

decline in these motor signs depends strongly on age at onset, late-onset

48

having higher rate (3.8%) than those of early-onset (2.4%) (104). Progression

of symptoms in PD may occur over 10 – 30 years but the rate of this

progression varies from person to person (17).

The non-motor symptoms of PD remain the most under-appreciated and

under-researched when taken as a whole. These symptoms occur in over

90% of patients across all stages of the disease and include a range of

symptoms from cognitive and autonomic dysfunction, sleep disturbance,

dementia to sensory abnormality and pain (127, 104, 10). Four distinct non-motor

symptoms of PD such as olfactory problems, rapid eye movement behavior

disorder, constipation and depression may be recognized as pre-motor

features. However these symptoms can be unrecognized as clinic

consultation focus largely on the motor aspects of PD which are easier to

manage with a reasonable evidence base for treatment (127). The spectrum

and severity of non-motor symptoms may be presented in different ways for

male and female PD patients, suggesting possible sex-related effects (121).

2.11.6 Pathophysiology

The pathophysiology of PD occurs at multiple levels: at the beginning,

molecular pathogenesis occurs at the targeted areas which in turn lead to

cellular/tissue abnormalities. This tissue abnormality results in

neurochemical changes which then cause site and circuit dysfunction. The

circuit dysfunction will lead to global network activity dysfunction finally

ending by abnormal behavior of PD patients (128).

The early clue to the pathology of the disease came from Brissaud, who

speculated that damage in the SN might lead to PD (13). Therefore the SN

was the focus of attention for decades. In late 1950s, Arvid Carlsson

49

observed that 80% of the dopamine in the brain is localized in the basal

ganglia. Soon afterwards, Oleh Hornykiewicz, studied brains obtained at

postmortem examinations and found that the content of dopamine,

norepinephrine and serotonin in patients with PD was low. He next observed

that, out of the three biogenic amines, dopamine was most drastically

reduced. Therefore PD became the first example of diseases of the brain

associated with a deficiency in a specific neurotransmitter (43).

The SN is now known to be central to the pathology of PD, because the SNc

contains many of the dopaminergic nerve cell bodies (30, 44, 27). Degeneration

of the neurons of the SN results in reduction of the release of the

neurotransmitter dopamine that controls muscle action leading to loss of the

body movements' control. Reduction or loss of more than 80% of the

normal level of dopamine and 50% of pars compacta cells leads to an

inhibitory output activity from basal ganglia to the ventral thalamus and

frontal cortex and subsequent impairment of movement with tremor,

slowness, stiffness, or balance problems, among other symptoms of

parkinsonism (17).

This severity of changes in the SN parallels the reduction of dopamine in the

striatum, this observation suggested that the dopaminergic pathway from the

SN to the striatum is disturbed in PD (43). The nigrostriatal dopaminergic

system originates in the SN and projects primarily to the putamen and

caudate nucleus. So, partial degeneration of this system contributes to the

symptoms of PD (129). With greater understanding of basal ganglia

physiology, the concept of the "circuit abnormalities" caused by the SN

lesions provided a simplistic but workable model for the symptoms that

reduce the normal functions of PD patient (128). The available data strongly

50

support the hypothesis that parkinsonian symptoms are related to abnormal

activity within the basal ganglia. The cerebello-thalamo-cortical loop plays a

role for the frequency of PD tremors (8). But the most famous circuit

involved in PD is the frontostriate circuit dysfunction. This comes from the

converging evidence that striatal dopamine depletion alters the basal ganglia

output to the frontal cortex through the thalamus. Frontal cortical areas are

commonly affected by Lewy pathology (128).

The cardinal pathological feature of the disease consists of the formation of

proteinaceous intraneuronal inclusion called Lewy body and progressive

neuronal loss particularly targeting the SN (17). Because of this, the disorder

is sometimes called Lewy body PD, Lewy body parkinsonism, or simply

Lewy body disease (4).

Braak et al. (130), proposed six point staging procedure for the pathological

process in PD; stage 1 – 2 are presymptomatic and the inclusion bodies are

confined to medulla/pontine tegmentum and olfactory bulb and nucleus,

stage 3 – 4 where the pathological changes are focused on the SN and other

nuclei of midbrain and forebrain, stage 5 – 6 are also called the end or late

stage where the disease can be clinically assessed. The pathological findings

in the late stage extend into the mature neocortex.

Non-motor symptoms correlate with advancing of the disease due to

involvement of lower brainstem nuclei. These nuclei are thought to be key

areas mediating non-motor symptoms such as olfaction, sleep homeostasis,

depression and cognition, pain, constipation and central autonomic vagal

control (127).

51

2.11.7 Diagnosis

Even with recent advances in our understanding of the disease mechanisms,

there is no definitive test for the diagnosis of PD. In clinical practice, the

diagnosis is essentially based on the presence of a combination of the motor

cardinal features and responsiveness to levodopa, which provides only

accuracy of 75 – 90% (10, 131, 13). The percentage rate for PD misdiagnosis is

approximately 10 – 25%. This percentage accounts for those diagnosed as

PD, but exhibited similar disorders such as PSP, MSA, Alzheimer disease,

or cerebrovascular pathology (132, 15, 13). The diagnosis of PD in Arabic

countries is based on two or more cardinal signs: resting tremor,

bradykinesia, rigidity and changes in postural reflexes (110).

A number of rating scales are used for evaluation of the motor signs of PD,

but most of these scales are not fully evaluated for validity and reliability.

The Hoehn and Yahr (H&Y) scale is commonly used to compare group of

patients and ranges from scale 0 (no signs) to stage 5 (wheel-chaired). The

Unified Parkinson's Disease Rating Scale (UPDRS) is the most well

established scale (10).

Over the past two decades neuroimaging techniques such as MRI, SPECT,

PET, and TCUS has increasingly been employed in diagnosis of PD patients

to study their morphological and functional characteristics (17, 25). So many

studies tried neuroimaging markers like, MRI and PET, to help improve

accuracy of the diagnosis of PD.

In the past functional brain imaging with PET had been proved to be of

some value for the differential diagnosis of parkinsonism. However PET

52

imaging was largely restricted to measurement of straital function. Because

of the recent development of high resolution PET and three dimensional

magnetic resonance imaging (3D MRI) based on methods of PET data

analysis, extrastriatal cerebral regions can now also be investigated (12).

TCUS has emerged as a viable tool in differential diagnosis of PD and

recently has been shown to have promising potential as a screening

technique for early detection of PD, even before onset of motor symptoms.

TCUS yields promising results with a sensitivity and specificity of around

83% (131). Another example was conducted by Menke et al. (7), who used

driven equilibrium single pulse observation of T1 (DESPOT1) method

combined with diffusion tensor imaging (DTI). Their study showed that this

method provides a useful set of markers that can be used to differentiate PD

patients from controls. Images synthesized by DESPOT1 provide clear

views of SN allowing this technique to be useful tool for accurate

segmentation of the SN.

In addition, 3D MRI Volumetry has become available and has been recently

applied in patients with PD, MSA, and PSP (12). MRI findings of PD patients

can reveal reduced T2-weighted putamen signal, and midbrain and cortical

atrophy (17).

New application of other techniques has been done by Morgen et al. (126), to

investigate structural damage occurring in PD patients before measurable

atrophy. They used voxel-based magnetic transfer imaging technique and

found that it constitutes a potentially effective method to track early PD-

related pathology. The wealth of methods and applications covered by the

authors indicates that functional and structural brainstem-MRI methods have

53

developed to a point where they can be applied to study a wide range of

neuroscientific problems. It is the hope of the editors that the brainstem will

soon lose its label of a terra incognita and become a region of major interest

in the neuroscience community (31).

2. 11. 8 MRI appearance and volumetric brain changes in PD

The neurodegenerative process of neuronal atrophy and death seen in normal

aging is accelerated and premature in PD; therefore, focal or diffuse atrophy

with sulcal enlargement is a common finding in imaging of these disorders.

Acceleration or abnormal deposition of brain iron, hyperintense foci in the

grey or white matter, and lack of interruption of the blood-brain barrier are

also seen. In general, these disorders clinically and radiologically show a

progressive decline that can continue for years or decades and occasionally

shows relative stability (44).

Pathologically, in PD, there is loss of pigmented cells in the SN, which leads

to malfunction of the major efferent tracts of the SN, the nigrostriatal tract.

Remaining cells may contain eosinophilic cytoplasmic inclusions named

Lewy bodies. A deficiency in striatal dopamine is identified in PD. MRI

findings in PD are relatively nonspecific. The thickness of the SNc has been

described to be diminished in some patients with PD, possibly reflecting

increased iron deposition. MRI may show evidence of atrophy of the

midbrain (74, 133).

Previous studies examining volumetric age-related decline in brainstem

volume in normal aging have reported that there is no volume alteration in

total brainstem, pons and medulla oblongata volumes. However some

studies have supported the finding that the most significantly atrophied part

54

of the brainstem is the midbrain due to shrinkage of its nuclei and a marked

increase in iron concentration (35, 66). A 30-35% increase of iron (total and

ferric iron) content in the SNc of patients with PD has been reported (44).

The primary loss occurs in the neurons of SN, although additional

degeneration occurs in several brain areas including neocortex (134). Nair et

al. (135), reported the mean volumes of putamen, pons and cerebellum in PD

to be 3.3cm³, 11.9cm³ and 121cm³ respectively

Schulz et al. (25), also reported that brainstem volume were normal in PD

patient compared to age-matched controls subjects. In addition they reported

that idiopathic Parkinson's disease (IPD) patients normally do not exhibit

neuronal loss or atrophy in posterior fossa structures. Messina et al. (16),

supported the idea of Schluz in that patients with PD did not show evidence

of volumetric changes in the brainstem with respect to healthy control

(19.87±2.4cm³ v 19.12±2.0 cm³ respectively).

2.11.9 Treatment

Treatments used in PD, unfortunately only provide temporary relief from

early symptoms and do not halt disease progression. In addition,

pathological changes outside of the motor symptoms leading to cognitive,

autonomic, and psychiatric symptoms are not sufficiently treated by current

therapies (13).

Ehringer and Hornykiewicz in 1960 discovered that PD patients have a

marked decrease in dopamine concentration in the striatum and hence

carried out the first trials of dopamine precursor levodopa as a PD therapy (10). Rational therapies of PD aim at correcting the deficiency of dopamine

55

through the use of levodopa which is proved to be a powerful PD treatment

drug. With subsequent advances in therapy, combination of levodopa with

carbidopa or benseraide which are peripheral decarboxylase inhibitor, is a

gold standard treatment for PD (104). This combination reduces the side-

effects associated with levodopa such as nausea and vomiting. The focus of

therapeutic design was also on limiting the breakdown of endogenous

dopamine, so they used selegiline (monoamine oxidase type B inhibitor)

which provides symptomatic benefit (13). Recent discoveries concerning the

role of specific genes in PD pathology will lead to the next revolution in the

disease treatment (13). Drugs used in the treatment of PD are often associated

with side effects in elderly patients. In particular domaminergic drugs can

impair cognitive function and cause postural hypotension (136).

Motor complications in PD result from the short half-life and irregular

plasma fluctuations of oral levodopa. When strategies of providing more

continuous dopaminergic stimulation by adjusting oral medication fail,

patients may be candidates for one of three device-aided therapies: deep

brain stimulation (DBS), continuous subcutaneous apomorphine infusion, or

continuous duodenal/jejunal levodopa/carbidopa pump infusion (DLI) (137).

In DBS, an electrode is surgically implanted in the subthalamic neucleus,

globus pallidus, or ventral intermediate nucleus, introducing continuous

high-frequency stimulation (104).

Despite these advances in symptomatic PD therapy, the ability of these

treatments to facilitate an acceptable quality of life for the patients wanes

with advancing age. This is due to development of motor complications

including decreased levodopa responsiveness, more severe gait and postural

impairment, and cognitive decline with development of dementia (138, 13).

56

The isolation of human embryonic stem cells has provided a potential source

for transplantation materials. Over the past few years, a good effort was

done to develop a protocol that induces the proper dopaminergic

characteristics in these undifferentiated cells to make them suitable

candidates for transplant. But transplant in animal models led to

disappointing results due to poor cell survival (13).

57

3.1 Introduction

The aim of this study is to assess the relation between midbrain, pons and

medulla oblongata volumes to total brainstem volume in Parkinson’s disease

(PD) patients, and in age and sex matched control group using magnetic

resonance imaging (MRI). The volume and volume fraction approaches of

modern stereological techniques is applied on MR images. In this chapter,

the study design, area, duration and groups, the methods used for data

collection (MRI), protocol used for the volumetric analysis of the obtained

images and the statistics used to analyze the measures obtained will be

included.

3.2 Study design

This is an observational case-control study. The cases are patients diagnosed

clinically as having idiopathic Parkinson’s disease and the controls are age

and sex matched normal, neurological disease free volunteers.

The study was ethically approved from the research ethical committee,

Faculty of Graduate Studies and Scientific Research, the National Ribat

University - Sudan. Official permission was also obtained from the

administration of Alamal National Hospital (North Khartoum – Sudan)

where the MR imaging was conducted.

3.3 Study area and duration

The study was conducted in Khartoum city in Sudan during the period from

2012 -2015.

58

3.4 Study groups

The original data set was for 82 subjects; 40 patients with PD and 42 healthy

controls. The total number of cases included in the study was 78 individuals.

The study excluded 4 subjects with extreme young ages; 1 patient and 3

controls. Therefore a total of 78 subjects were enrolled in this study; 39

patients with PD & 39 healthy controls.

Patients with PD (n = 39, 28 males & 11 females), age ranges from 35 – 80

years were included in the study. The mean age of the patients was 59.49 ±

13.02 years. The diagnosis of PD was made using clinical criteria (10) by

expert consultant neurologists, at Neurosciences Specialist Center and

Neurology Outpatient Clinic in Ibrahim Malik and Soba Teaching Hospitals

in Khartoum state - Sudan. Patients with stroke, head trauma, tumor,

multiple system atrophy (MSA), progressive supranuclear palsy (PSP) or

drug induced Parkinsonism were excluded from the study.

Age and sex matched healthy volunteers (n = 39, 25 males & 14 females),

age ranges from 36 – 80 were selected to participate in the study. The mean

age of controls was 56.79 ± 12.39 years. All volunteers were subjected to

MRI scanning and those with normal MRI findings, diagnosed by the

consultant radiologist at the MRI unit in the Diagnostic Center at Alamal

National Hospital (Khartoum - Sudan), were included in the control group.

All patients and controls were subjected to T1-weighed 3D brain MRI at

Alamal National Hospital – MRI unit. Before conducting the MRI scanning

all subjects were informed of the study and written informed consents were

taken. They were also requested to fill constructed questionnaires under

supervision. The questionnaires included socio-demographic data and

59

clinical history for all subjects and clinical data concerning PD for the

patients only.

3.5 Magnetic resonance imaging

The following protocol was used for the accumulated MR imaging data. T1-

weighted coronal images were obtained using three-dimensional acquisition

by Magnetization Prepared Rapid Acquisition Gradient Echo (MP-RAGE).

It provides good grey/white matter contrast in a very short acquisition time.

A 1.5 Tesla Philips Intera MR system (Release 2.6 Level 3 2010 – 11- 24,

The Netherlands) was used. The following scanning parameters were used

for the imaging process: slice distance was zero, the field of view was 230

mm feet to head, 184 mm right to left phase & 184 mm antroposterior. TE=

shortest 4ms, TR=25 ms, bandwidth 189.7 Hz/pixel, flip angle 30º, ECHO

spacing= 1 ÷ 1, phase resolution=100%, slice resolution=50%, and

acquisition time = 3 minutes and 38 seconds

The images of both patients and controls were transferred from the

computer linked with the MRI scanner and saved in CDs. Then the data

from CDs were downloaded to the personal PC and backed up in an external

hard disk.

3.6 Volumetric image analysis

Cavalieri principle was applied to MRI sections for calculation of the

volume and volume fraction of the brainstem and the three subdivisions of

the brainstem.

60

3.6.1 MRI image processing

This was carried out in the Department of Anatomy, Medical Faculty,

Ondokuz Mayis University, Samsun, Turkey. The T1 sequences were

transferred to the PC and further morphometric measurements were

conducted blind to the clinical data, using Image Processing and Analysis in

Java (ImageJ) software. The author of ImageJ, Wayne Rasband

(wayne@codon.nih.gov), is at the Research Services Branch, National

Institute of Mental Health (NIH), Bethesda, Maryland, USA. ImageJ

software is found on the public domain and freely downloadable from

http://imagej.nih.gov.ij//. It runs on any computer with Java 1.4 or later

virtual machine.

3.6.2 Planimetry method of Cavalieri principle

This is a widely used approach in morphometry, which relies on manual or

automatic boundary delineation of an object in order to estimate its cross-

sectional surface area. Planimetry is generally conducted using software that

automatically counts pixel dimensions enclosed within the traced area (96).

This method gives more accurate results than point counting method despite

that it is more time consuming (139). In this study planimetry method was

applied using ImageJ software by manual tracing of the boundaries of the

region of interest (ROI) (139, 140). The midbrain, pons and medulla oblongata

boundaries were traced manually on each MRI section using the computer

mouse. The surface areas (mm²) were measured automatically on ImageJ.

Since the surface area and the slice thickness were known the volume of

ROI could be calculated according to Cavalieri principle of volume

estimation.

61

3.6.3 Preparation of images to use on ImageJ software

The MRI images were generated and saved on the PC in digital imaging and

communications in medicine (DICOM) format. DICOM viewer software

was installed on the PC to view these images. The software used in this

study was RadiAnt DICOM viewer software, installed freely from

http://www.radiantviewer.com/. After installation the MRI images were

opened in RadiAnt then exported and saved on the PC in two formats;

DICOM and JPEG. This was done to magnify the area of interest without

any resolution loss.

3.6.4 Protocol of MRI image processing for volume estimation of the

brainstem and its subdivisions

The ImageJ software was installed and the measurement was set on the

software to read area in stack position with decimal spaces zero. The

DICOM and JPEG saved images were open using the ImageJ. First the

JPEG image was used to set the scale and then the DICOM image was

reoriented to adjust the head tilting and other deviation in the position of the

head of patients during imaging. The reorient stack(s) window of the ImageJ

opens sections in three views; coronal, axial and sagittal views. The three

views were reoriented and the coronal image was saved to a directory in the

PC for further analysis.

The reoriented coronal image was then transferred to the ImageJ software

and converted into stacks for re-slicing. The output space of re-slice was

adjusted to be one millimeter. The stack of sagittal image series was then

obtained. The mid-sagittal image of the brainstem, where the cerebral

aqueduct was most visible, was selected of the series. This image was

62

rotated to make the brainstem at right angle orientation, and then the rotation

automatically occurs in all of the stacks.

From the midsagittal view of the stack the lower and upper boundaries of the

brainstem were marked on the image using the arrow drawing tool from the

ImageJ software. For the cranial boundary of the brainstem a straight line

was drawn from the posterior commissure crossing anteriorly. To mark the

caudal boundary of the brainstem a straight line was drawn from the upper

border of the arch of the atlas crossing anteriorly.

Because the borders of the foramen magnum are not seen well in MR images

and the atlas was the most fixed clear point in all MR images. The upper

border of the arch of the atlas was considered as the caudal border of the

brainstem and was standardized for all subjects. This landmark which was

referenced in books that the medulla oblongata ends caudally by joining the

spinal cord at the level of the superior border of the posterior arch of the

atlas was chosen as the reference point (141-143).

For segmentation of the three parts of the brainstem also two lines were

drawn selecting the arrow drawing tool from the software, one between

medulla oblongata and pons and the other between the pons and midbrain.

To divide the medulla oblongata from the pons the anterior notch between

them was used as a landmark and a straight line was drawn going posteriorly

from the notch. For dividing the midbrain from the pons a straight line was

drawn from the notch between them anteriorly and to the back. A transparent

sheet was mounted on the PC screen, and then these four lines were drawn

on it to maintain the boundaries landmarks when shifting on the series of

stacks laterally (Figure 3.1).

63

Figure 3.1: Separation of brainstem from the cerebrum and segmentation of

the subdivisions

3.6.4.1 Image processing protocol for volume estimation of the midbrain

Considering the aforementioned procedure as a base for measuring the

different parts of the brainstem in each subject midbrain was scrolled

laterally over the series of slices until last part of it disappears then the

previous slice was selected as a starting point of sampling. Systematic

random sampling was done and the sampling fraction used for midbrain was

64

1/2 "skip one slice and delineate the next". The outer boundary of the

midbrain between the two drawn lines was delineated using the polygon

selection tool from the ImageJ software (Figure 3.2). The sectional cut

surface area of interest was measured by the software automatically. This

was repeated for all selected cuts of midbrain until all samples were finished.

Figure 3.2: Delineation of the midbrain

3.6.4.2 Image processing protocol for volume estimation of the pons

The researcher scrolled laterally over the series of slices of the pons until last

part disappears then the previous slice was selected as a starting point of

sampling. Systematic random sampling was done and the sampling fraction

65

used for pons was 1/2 "skip one slice and delineate the next". The posterior

boundary of the pons was determined to split it from the cerebellum. A

straight line was drawn, by selecting the line drawing tool from the software,

at the back of the pons where the most lateral part of the fourth ventricle

appears to cut the middle cerebellar peduncle (Figure 3.3). This line was

then drawn over the transparency sheet. The outer boundary of the pons was

delineated using the polygon selection tool from the ImageJ software (Figure

3.4). The sectional cut surface area of interest was measured by the software

automatically. This was repeated for all selected cuts of pons until all

samples were finished.

Figure 3.3: Marking the posterior boundary of the pons

66

Figure 3.4: Delineation of the pons

3.6.4.3 Image processing protocol for volume estimation of the medulla

oblongata

For the measurement of the medulla oblongata volume the researcher

scrolled laterally over the series of slices until the last part of medulla

oblongata disappears then the previous slice was selected as a starting point

of sampling. All the slices of medulla oblongata were measured as the

medulla oblongata gives very limited number of sections over the series of

images. This increase in examined sections was done to increase precision of

stereological parameters (90) after tracing the coefficient of error which was

high when the sampling fraction was 1/2. The outer boundary of the medulla

67

oblongata was delineated using the polygon selection tool from the ImageJ

software (Figure 3.5). The sectional cut surface area of interest was

measured by the software automatically. This was repeated for all selected

cuts of medulla oblongata until all samples were finished.

Figure 3.5: Delineation of the medulla oblongata

3.6.5 Estimation of volume by Cavalieri Principle

Estimation of volume was obtained according to Cavalieri principle (144),

which estimates the volume of three-dimensional structures based on two

dimensional slices of objects.

68

The surface area measured from all slices of midbrain, pons and medulla

oblongata on ImageJ were transferred to a prepared spreadsheet on

Microsoft Excel program for measurement of their volume according to

Cavalieri principle. The volume was estimated by multiplication of the total

of sectional area measured of each region by the section thickness which

was 1 mm, as shown in the formula below:

V = t × ∑a,

Where "V" is the volume of midbrain, pons or medulla oblongata, "t" is the

section thickness and "∑a" is the total sectional area of the consecutive

sections millimeter square (m²) of the midbrain, pons or medulla oblongata

3.6.6 Estimation of volume fraction

The volume fraction of stereological methods provides information about

volumetric relations of the components of structure. It ranges from 0 to 1

and is often expressed as percentage. In present study the volume fraction of

midbrain, pons and medulla oblongata was calculated. For the measurement

of the volume fraction of the midbrain the following formula was used:

Vv(midbrain, brainstem) = ∑s midbrain ∑s total brainstem

Where ∑s midbrain is the total surface area of the midbrain and ∑s total

brainstem is the total surface area of the brainstem. The value obtained is the

volume fraction of the midbrain to total brainstem expressed as percentage.

For the measurement of volume fractions of the pons and medulla oblongata

the same formula was used with the change of total surface area of midbrain

by total surface area of pons or medulla oblongata.

69

3.6.7 Reliability of the measurement

All MRI datasets were measured by one investigator blinded to the clinical

data of the subject. Reliability of the measurement was expressed by intra-

observer reliability to indicate how stable were the results obtained. This

was done by repeating the measurement twenty-times at different times for

same MRI sample for a single patient and the coefficient of error (CE) was

measured according to the following formula:

Where, i=1,2,…, m is the number of sections. A is the measured area of

sections using planimetry and the others are constant.

3.7 Statistical analysis

The data was collected and verified by hand then coded before computerized

data entry. The data in the questionnaires were coded and entered in a sheet

prepared in Microsoft Excel. The surface areas measured by the ImageJ

software were transferred to a spreadsheet prepared in Microsoft Excel. The

statistical Package for Social Sciences (SPSS) software version 16.0 was

used for all data entry and analysis. Descriptive statistics (e.g. number,

percentage, mean, range, standard deviation) and analytical tests like t-test

and ANOVA were used to compare variables between the two groups. Also

correlation tests were used and a p-value <0.05 was considered as

statistically significant.

70

4.1 Introduction

The original data set was for 82 subjects; 40 patients with PD and 42 healthy

controls. The total number of cases included in the study was 78 individuals.

The study excluded 4 subjects with extreme young ages; 1 patient and 3

controls. Therefore a total of 78 subjects were enrolled in this study; 39

patients with PD & 39 healthy controls.

In this chapter first the main demographic data and age of the subjects were

compared between and across the groups of patients and controls. Then

analysis of the main clinical characteristics of the PD patients (n=39) was

performed. Next volumetric analysis of the region of interest (ROI) of both

patients and controls was done and their results were compared together.

After that volume of the ROI was correlated with age and main clinical

characteristics of the disease. Finally estimation of the coefficient of error

(CE) for the measurement of the volumes of the data was calculated.

4.2 Demographic data

Patients’ group was consisting of 39 patients with PD; 28 male and 11

female. Their age ranged from 35 to 80 years with a mean age of 59.49 ±

13.02 years.

Controls’ group was consisting of 39 healthy controls; 25 male and 14

female. Their age ranged from 36 to 80 years with a mean age of 56.79 ±

12.39 years.

No significant differences between patients and controls were found with

respect to age, gender and body mass index. Table 4.1 summarizes patients

and control profiles. Concerning the residence of the subjects most of the

71

patients and controls were from urban area. The percentage of patients living

in urban was higher when compared to the controls. Almost half of the

subjects among the patients and controls were having secondary and higher

education. Comparing the occupation among the patients and controls it was

observed that equal percentage of controls and patients were involved in

labour work. About half or more of the patients and controls were

considered as poor according to their financial status (Table 4.1).

Table 4.1: Demographic Characteristics of patients & controls (Cont...)

Variables Patients Controls N % N %

Sex:

Male

Female

28

11

71.8

28.2

25

14

64.1

35.9

Residence:

Rural

Urban

04

35

10.3

89.7

13

26

33.3

66.7

Education:

Illiterate

Primary

Secondary

University & above

06

14

03

16

15.4

35.9

07.7

41.0

05

12

12

10

12.8

30.8

30.8

25.6

72

Table 4.1: Demographic Characteristics of patients & controls

Variables Patients Controls

N % N %

Occupation:

Professional

Govt. Employee

Military

Labour

Housewife

13

04

01

13

08

33.3

10.3

02.5

33.3

20.5

04

04

06

15

10

10.3

10.3

15.4

38.4

25.6

Marital status:

Single

Married

Widow

00

37

02

00.0

94.9

05.1

03

36

00

07.7

92.3

00.0

Monthly income (SDG*)

≤500

600 – 2000

>2000

22

16

01

56.4

41.0

02.6

25

12

02

64.1

30.8

05.1

*SDG = Sudanese Pounds

73

4.3 Age of all subjects

The mean age for all subjects was 58.13±12.70 years and the median was 61

years as shown in (Table 4.2). The age distribution was almost normally

distributed with slight left skewness (- 0.188), (Figure 4.1).

The age range for PD patients was 35 – 80 years, while that of the controls

was 36 – 80 years. The mean age of PD patient (59.46 years) was slightly

higher than that of controls (56.79 years). However, the difference between

them did not rise to a significant level (p>0.05). When comparing the mean

age of the two groups sex wise, there was no statistical difference (p>0.05)

between the mean age of male patients (59.29 years) and male controls

(57.76 years). The same finding exists between the female patients (59.91

years) and female controls (55.07 years). Accordingly there was no

statistical difference of the mean age of males and females between and

across the groups (Table 4.3).

Both patients and controls were divided according to their age into three

groups; first group of age ranges 35-49 years, second group 50-64 and third

group equals or above 65 years (Table 4.4).

Table 4.2: Age (year) of the patient and control groups and their details

Subject N Age range Mean ± SD

Patient 39 35 – 80 59.46 ± 13.02

Control 39 36 – 80 56.79 ± 12.39

Total 78 35 – 80 58.13 ± 12.70

74

Figure 4.1: Age distribution for all study groups

Table 4.3: Age (years) of the patient and control groups depending on sex and their details

Subject N Age Range Mean ± SD

Male Patient 28 35 – 80 59.29 ± 12.67

Female Patient 11 35 – 78 59.91 ± 14.51

Male Control 25 39 – 80 57.76 ± 12.66

Female Control 14 36 – 71 55.07 ± 12.16

Total 78 35 – 80 58.13 ± 12.70

75

Table 4.4: Age groups distribution of all the subjects

Age range No. of Control No. of Patients

35-49 years 13 10

50-64 years 15 10

≥ 65 years 11 19

Total 39 39

4.4 Clinical characteristics of the PD patients

4.4.1 Onset of the disease

The age range at onset of the disease among PD patients was 25 – 76 years.

This range was divided into small groups of 10 years interval to see effect of

age at onset of the disease. The mean age at onset of PD was 54.00 ± 12.22

years and the median age at onset of PD was 55.0 years. The onset of PD

among patients was highest in the age group from 55–64 years (Figure 4.2).

The possibility of onset of PD may increase with age up to 64 years and then

decreases after that (Figure 4.3).

76

Figure 4.2: Percentages of age distribution at onset of Parkinson’s disease

Figure 4.3: Age distribution (years) at onset of Parkinson’s disease

77

4.4.2 Duration of the disease

Concerning the duration of the PD after the patients have first been

diagnosed; the mean duration of the disease was 5.68 ± 4.21 years and the

median was 5 years (Figure 4.4). Regarding the distribution of the duration,

the patients were divided into three groups; the first group from less than 4

years, the second group more than 4years up to 8 years and the third group

more than 8 years. Slightly more than a half of the PD patients were

suffering of the disease for a period less than four years from diagnosis

(Table 4.5). The mean duration interval was 1.74 ± 0.85 years and the

median was 1 year.

Figure 4.4: Duration (years) of Parkinson’s disease

78

Table 4.5: Distribution of duration of Parkinson’s disease

Years interval N Percent (%) Mean ± SD ≤1 – 4 20 51.3

1.74 ± 0.85 > 4 – 8 09 23.1 > 8 10 25.6 Total 39 100

4.4.3 Duration of the symptoms before medical diagnosis of PD

Most of the patients (84.6 %) seek medical diagnosis after more than one

year from the start of symptoms (Table 4.6). The mean duration was 1.85 ±

0.37 years and the median was 2.0 years.

Table 4.6: Duration of symptoms before medical diagnosis of Parkinson’s disease

Duration N Percent (%) Mean ± SD ≤ one year 06 15.4

1.85 ± 0.37 >one year 33 84.6 Total 39 100

4.4.4 Medical characteristics of the Parkinson’s disease patients

Most of the PD diagnosed patients (76.9%) had no family history of PD

while only the remaining percent (23.08%) were having PD in their families.

Regarding the other medical family histories, slightly more than a half

(51.28%) of the PD patient were not having any of the medical histories, a

quarter (25.64%) of them were either having diabetes or hypertension and

79

the remaining were having combination or other medical histories (Figure

4.5). More than half of the patients were under a combination of medical

treatment e.g. Levodopa and other dopamine agonist (Figure 4.6).

Concerning the compliance with medication; most of the patients (74.36%)

were using their medications regularly, 20.51% of them were using

medication only during acute phase of symptoms and 5.13% of the patients

were not using their medication (Figure 4.7).

Figure 4.5: Medical history of Parkinson’s patients

80

Figure 4.6: Medications used by Parkinson patients

Figure 4.7: Compliance of Parkinson patients with medication used

81

4.5 Volumetric analysis

4.5.1 Volumetric analysis of the midbrain for patients and controls

The overall comparison of the mean volumes of midbrain between the

patients and controls reveals high significance difference (p<0.001); the

details of this is shown in (Table 4.7 and Figure 4.8).

Multiple comparisons test shows the details of interaction between and

across the groups (Tables 4.7) as follows:

• The mean volume of midbrain in the male controls was (8.62±1.34

cm³). It was slightly larger compared to the mean volume of midbrain

in female controls (8.45±1.44 cm³). However, this difference did not

reach a statistical significant value (p>0.05).

• While the mean volume of midbrain in the male patients (7.72±1.16

cm³) was larger compared to the female patients’ midbrain volume

(6.53±1.14 cm³). However, the difference of mean volume of

midbrain within the PD patients was also not significant (p>0.05).

• The mean volume of midbrain in the male patients (7.72±1.16 cm³)

was smaller compared to the male controls’ midbrain volume

(8.62±1.34 cm³). However, the difference of mean volume of

midbrain between males of the two groups was not significant

(p>0.05).

• While the mean volume of midbrain in the female patients (6.53±1.14

cm³) was much smaller compared to the female controls’ midbrain

volume (8.45±1.44 cm³). This difference of mean volume of midbrain

between females of patients and controls was statistically significant

(p<0.05).

82

Table 4.7: Volume (cm³) of midbrain for all groups and overall

Groups N Mean ± SD Range Total

Male Patient 28 7.72 ± 1.16 5.76 – 10.24 7.38 ± 1.26¥

Female Patient 11 6.53 ± 1.14* 4.93 – 8.87

Male Control 25 8.62 ± 1.34 6.44 – 11.08 8.56 ± 1.36¥

Female Control 14 8.45 ± 1.44* 5.56 – 11.46

Total 78 7.97 ± 1.43 4.93 – 11.46

* and ¥= p<0.05

Figure 4.8: Boxplot of the mean volume (cm³) of the midbrain for the

patients and controls by gender

83

4.5.2 Volumetric analysis of the pons for patients and controls

The overall comparison of the mean volumes of pons between the patients

and controls reveals significant difference (p=0.015), the details of this is

shown in (Table 4.8 and Figure 4.9).

Multiple comparisons test shows the details of interaction between and

across the groups (Tables 4.8) as follows:

• The mean volume of pons in the male controls (15.50±2.14 cm³) was

larger than that of female controls (14.37±2.34 cm³). This difference

was not significant (p>0.05).

• The mean volume of pons in the male patients (14.11±1.81 cm³) was

larger compared to the female patients’ (13.09±2.61 cm³). However,

the difference of mean volume of pons in this group was not

significant (p>0.05).

• The mean volume of pons in the male patients (14.11±1.81 cm³) was

smaller compared to the male controls’ (15.50±2.14 cm³). However,

the difference of mean volume of pons between males of the two

groups was not significant (p>0.05).

• The mean volume of pons in the female patients (13.09±2.61 cm³)

was smaller compared to the female controls’ (14.37±2.34 cm³).

However this difference of mean volume of pons between females of

patients and controls was not significant (p>0.05).

Multiple comparison test indicates that the significance difference of the

volume of the pons between the patients and controls is due to the effect of

female patients over male controls. Female patients mean volume of pons

was 2.41 cm³ less than that of male controls (p=0.02).

84

Table 4.8: Volume (cm³) of pons for all groups and overall

Groups N Mean ± SD Range Total

Male Patient 28 14.11 ± 1.81 10.28 – 17.29 13.82 ± 2.25¥

Female Patient 11 13.09 ± 2.61* 9.27 – 17.19

Male Control 25 15.50 ± 2.14* 11.80 – 21.03 15.11 ± 2.25¥

Female Control 14 14.37 ± 2.34 11.35 – 18.22

Total 78 14.46 ± 2.25 9.27 – 21.03

* and ¥= p<0.05

Figure 4.9: Boxplot of the mean volume (cm³) of the pons for the patients and controls by gender

85

4.5.3 Volumetric analysis of the medulla oblongata for patients and

controls

The overall comparison of the mean volumes of medulla between the

patients and controls reveals significant difference (p=0.007), the details of

this is shown in (Table 4.9 and Figure 4.10).

Multiple comparisons test shows the details of interaction between and

across the groups (Tables 4.9) as follows:

• The mean volume of medulla in the male controls (4.76±0.78 cm³)

was slightly larger compared to the female controls’ medulla volume

(4.67±0.64 cm³). But, the difference of mean volume of medulla

within the controls did not rise to a significant level (p>0.05).

• The mean volume of medulla in the male patients (4.28±0.49 cm³)

was slightly larger compared to the female patients’ medulla volume

(4.09±0.60 cm³). However, the difference of mean volume of medulla

oblongata within the PD patients was not significant (p>0.05).

• The mean volume of medulla oblongata in the male patients

(4.28±0.49 cm³) was smaller compared to the male controls’ medulla

volume (4.76±0.78 cm³). This difference of mean volume of medulla

between males of the two groups was statistically significant (p<0.05).

• The mean volume of medulla in the female patients (4.09±0.60 cm³)

was smaller compared to the female controls’ medulla volume

(4.67±0.64 cm³). However this difference of mean volume of medulla

between females of both groups was not significant (p>0.05).

Multiple comparison test indicates that the significant difference of the

volume of the medulla between the patients and controls is due to the effect

86

of male patients over the male controls and female patients over male

controls (p=0.03).

Table 4.9: Volume (cm³) of medulla oblongata for all groups and overall

Groups N Mean ± SD Range Total

Male Patient 28 4.28 ± 0.49* 3.46 – 5.49 4.22 ± 0.52¥

Female Patient 11 4.09 ± 0.60 3.10 – 5.18

Male Control 25 4.76 ± 0.78* 3.41 – 7.23 4.73 ± 0.73¥

Female Control 14 4.67 ± 0.64 3.67 – 6.20

Total 78 4.48 ± 0.68 3.10 – 7.23

* and ¥= p<0.05

Figure 4.10: Boxplot of the mean volume (cm³) of the medulla oblongata for the patients and controls by gender

87

4.5.4 Volumetric analysis of the brainstem for patients and controls

The overall comparison of the mean volumes of brainstem between the

patients and controls reveals high significant difference (p=0.001), the

details of this is shown in (Table 4.10 and Figure 4.11).

Multiple comparisons test shows the details of interaction between and

across the groups (Tables 4.10) as follows:

• The mean volume of the brainstem in the male controls (28.89±3.68

cm³) was larger compared to the female controls’ volume (27.48±3.79

cm³). But, the difference of mean volume of the brainstem within the

controls did not rise to a significant level (p>0.05).

• The mean volume of brainstem in the male patients (26.10±2.80 cm³)

was larger compared to the female patients’ volume (23.71±4.11 cm³).

However, the difference of mean volume of brainstem within the PD

patients was not significant (p>0.05).

• The mean volume of the brainstem in the male patients (26.10±2.80

cm³) was smaller compared to the male controls’ volume (28.89±3.68

cm³). This difference of mean volume of the brainstem between

males of the two groups was statistically significant (p<0.05).

• The mean volume of the brainstem in the female patients (23.71±4.11

cm³) was much smaller compared to the female controls’ volume

(27.48±3.79 cm³). This difference of mean volume of the brainstem

between females of the two groups shows a statistical significance

(p<0.05).

88

Multiple comparison test indicates that the significance difference of the

volume of brainstem between the patients and controls is due to the effect

of patients over controls

Table 4.10: Volume (cm³) of brainstem for all groups and overall

Groups N Mean ± SD Range Total

Male Patient 28 26.10 ± 2.80* 21.81 – 31.61 25.43 ± 3.35¥

Female Patient 11 23.71 ± 4.11# 17.87 – 29.66

Male Control 25 28.89 ± 3.68* 23.37 – 37.54 28.38 ± 3.73¥

Female Control 14 27.48 ± 3.79# 22.63 – 34.65

Total 78 26.90 ± 3.82 17.87 – 37.54

*, # and ¥= p<0.05

Figure 4.11: Boxplot of the mean volume (cm³) of the brainstem for the patients and controls by gender

89

4.5.5 Volume fraction analysis of midbrain

The mean volume fraction of midbrain in males was higher than in females

in patients group (29.55±2.88 % vs 27.62±1.10 %). While the mean volume

fraction of midbrain in males was lower than in females in controls group

(29.82±2.54 % vs 30.73±3.36 %).

The mean volume fraction of midbrain in male patients was slightly lower

than male controls (29.55±2.88 % vs 29.82±2.54 %). The mean volume

fraction of midbrain in female patients was lower than female controls

(27.62±1.10 % vs 30.73±3.36 %).

Over all there was a statistical difference in the volume fractions of midbrain

between the patients and controls (p≤0.05), this is shown in (Table 4.11 &

Figure 4.12).

Table 4.11: Volume fractions (%) of midbrain among the groups

Groups N Mean ± SD Range

Male Patient 28 29.55 ± 2.88 24.77 – 36.44

Female Patient 11 27.62 ± 1.10 24.58 – 31.39

Male Control 25 29.82 ± 2.54 25.37 – 34.86

Female Control 14 30.73 ± 3.36 24.57 – 35.54

Total 78 29.58 ± 2.85 24.57 – 36.44

p=0.051

90

Figure 4.12: Boxplot of the mean volume fraction (%) of the midbrain for

the patients and controls by gender

4.5.6 Volume fraction analysis of pons

The mean volume fraction of pons in male was slightly lower than female in

patients group (54.01±3.08 % vs 54.98±2.22 %). While the mean volume

fraction of pons in male was higher than female in controls group

(53.67±3.01 % vs 52.21±3.55 %).

The mean volume fraction of pons in male patients was higher than male

controls (54.01±3.08 % vs 53.67±3.01 %). The mean volume fraction of

pons in female patients was higher than female controls (54.98±2.22 % vs

52.21±3.55 %).

91

Over all there was no statistical difference in the volume fractions of pons

between the patients and controls (p>0.05), this is shown in (Table 4.12 &

Figure 4.13).

Table 4.12: Volume fractions (%) of pons among groups

Groups N Mean ± SD Range

Male patient 28 54.01 ± 3.08 47.14 - 59.04

Female patient 11 54.98 ± 2.22 51.90 - 57.95

Male control 25 53.67 ± 3.01 47.86 - 59.14

Female control 14 52.21 ± 3.55 47.41 - 56.59

Total 78 53.72 ± 3.09 47.14 - 59.14

p= 0.15

Figure 4.13: Boxplot of the mean volume fraction (%) of the pons for the

patients and controls by gender

92

4.5.7 Volume fraction analysis of medulla oblongata

The mean volume fraction of medulla oblongata in male was lower than

female in patients group (16.45±1.65 % vs 17.40±1.79 %). The mean

volume fraction of medulla oblongata in male was also lower than female in

controls group (16.51±1.76 % vs 17.05±1.59 %). The mean volume fraction

of medulla oblongata in male patients was slightly lower than male controls

(16.45±1.65 % vs 16.51±1.76 %). While the mean volume fraction of

medulla oblongata in female patients was slightly higher than female

controls (17.40±1.79 % vs 17.05±1.59 %).

Over all there was no statistical difference in the volume fractions of

medulla oblongata between the patients and controls (p>0.05), this is shown

in (Table 4.13 & Figure 4.14).

Table 4.13: Volume fraction (%) of medulla oblongata among groups

Groups N Mean ± SD Range

Male patient 28 16.45 ± 1.65 13.20 - 19.72

Female patient 11 17.40 ± 1.79 14.86 - 20.49

Male control 25 16.51 ± 1.76 14.17 - 20.59

Female control 14 17.05 ± 1.59 13.88 - 19.83

Total 78 16.71 ± 1.70 13.20 - 20.59

p-value = 0.34

93

Figure 4.14: Boxplot of the mean volume fraction (%) of the medulla oblongata for the patients and controls by gender

4.6 Correlation of volumes and volume fractions of midbrain,

pons, medulla oblongata & total brainstem with age

Pearson’s correlation test was computed to assess the relationship between

the volumes and volume fractions of the ROI and age.

4.6.1 Correlation of age with volume of midbrain

Overall, there was a significant negative correlation between the age and

volume of midbrain (r = - 0.514 & p ≤0.001) (Figure 4.15). This explains

that when the subjects become older, the volume of midbrain become

94

smaller in size. When analyzed separately with patients, there was a

significant negative correlation between the age of PD patients and volume

of midbrain (r = - 0.452 & p = 0.004) (Figure 4.16). In controls there was

also a significant negative correlation between the age and volume of

midbrain (r = - 0.587 & p ≤0.001) (Figure 4.17). A significant negative

correlation was found when analyzing age of male patients and male

controls with volume of midbrain (r = - 0.532 & p ≤0.001) (Figure 4.18). A

significant negative correlation was found when analyzing age of female

patients and female controls with volume of midbrain (r = - 0.537 & p =

0.006) (Figure 4.19).

Figure 4.15: Correlation of age with volume of midbrain for all groups

95

Figure 4.16: Correlation of age with volume of midbrain for PD patients

Figure 4.17: Correlation of age with volume of midbrain for controls

96

Figure 4.18: Correlation of age with volume of midbrain among male patient and male controls

Figure 4.19: Correlation of age with volume of midbrain among female patients and female controls

97

4.6.2 Correlation of age with volume of pons

Overall, there was a significant negative correlation between the age and

volume of pons (r = - 0.305 & p = 0.007) (Figure 4.20). Along with increase

of age, volume of pons was decreased. When analyzed separately with

patients, there was a significant negative correlation between the age of PD

patients and volume of pons (r = - 0.352 & p = 0.025) (Figure 4.21). In

controls there was a non significant negative correlation between the age and

volume of midbrain (r = - 0.223 & p = 0.172) (Figure 4.22). A significant

negative correlation was found when analyzing age of male patients and

male controls with volume of pons (r = - 0.310 & p = 0.024) (Figure 4.23).

A non significant negative correlation was found when analyzing age of

female patients and female controls with volume of pons (r = - 0.346 & p =

0.090) (Figure 4.24).

Figure 4.20: Correlation of age with volume of pons for all groups

98

Figure 4.21: Correlation of age with volume of pons for PD patients

Figure 4.22: Correlation of age with volume of pons for controls

99

Figure 4.23: Correlation of age with volume of pons for male patients and male controls

Figure 4.24: Correlation of age with volume of pons for female patients and female controls

100

4.6.3 Correlation of age with volume of medulla oblongata

Overall, there was a significant negative correlation between the age and

volume of medulla oblongata (r = - 0.304 & p = 0.007) (Figure 4.25). Along

with increase of the age, volume of medulla oblongata was decreased. When

analyzed separately with patients, there was a non significant negative

correlation between the age of PD patients and volume of medulla oblongata

(r = - 0.160 & p = 0.332) (Figure 4.26). In controls there was a significant

negative correlation between the age and volume of medulla oblongata (r = -

0.392 & p = 0.014) (Figure 4.27). A significant negative correlation was

found when analyzing age of male patients and male controls with volume

of medulla oblongata (r = - 0.272 & p = 0.049) (Figure 4.28). A non

significant negative correlation was found when analyzing age of female

patients and female controls with volume of medulla oblongata (r = - 0.385

& p = 0.058) (Figure 4.29).

Figure 4.25: Correlation of age with volume of medulla oblongata for all groups

101

Figure 4.26: Correlation of age with volume of medulla oblongata for PD patients

Figure 4.27: Correlation of age with volume of medulla oblongata for controls

102

Figure 4.28: Correlation of age with volume of medulla oblongata for male patients & male controls

Figure 4.29: Correlation of age with volume of medulla oblongata for female patients & female controls

103

4.6.4 Correlation of age with volume of brainstem

Overall, there was a significant negative correlation between the age and

volume of brainstem (r = - 0.426 & p ≤0.001) (Figure 4.30). Along with

increase of age, the volume of brainstem was decreased. When analyzed

separately with patients, there was a significant negative correlation between

the age of PD patients and volume of brainstem (r = - 0.418 & p = 0.008)

(Figure 4.31). In controls there was also a significant negative correlation

between the age and volume of brainstem (r = - 0.426 & p = 0.007) (Figure

4.32). A significant negative correlation was found when analyzing age of

male patients and male controls with volume of brainstem (r = - 0.437 & p =

0.001) (Figure 4.33). A significant negative correlation was found when

analyzing age of female patients and female controls with volume of

brainstem (r = - 0.463 & p = 0.020) (Figure 4.34).

Figure 4.30: Correlation of age with volume of brainstem for all groups

104

Figure 4.31: Correlation of age with volume of brainstem for PD patients

Figure 4.32: Correlation of age with volume of brainstem for Controls

105

Figure 4.33: Correlation of age with volume of brainstem for male patients & male controls

Figure 4.34: Correlation of age with volume of brainstem for female patients & female controls

106

4.6.5 Correlation of age groups with total volume of the brainstem and

each of its subdivisions

The subjects have been divided into three groups according to age range;

first group from 35-49 years, the second group from 50-64 years and the

third group equal or above 65 years. This has been performed to investigate

the extent of reduction in the volume of the brainstem and its subdivisions in

relation to different age ranges between PD patients and control group. A

more reduction was observed in the volume of midbrain in age group 35-49

years than in the other groups, however this did not reach a significant value

(Table 4.14).

Table 4.14: The volume of brainstem and its subdivisions in controls and PD patients in relation to different age groups

Brainstem

(cm³ ± SD)

Medulla oblongata

(cm³ ± SD)

Pons

(cm³ ± SD)

Midbrain

(cm³ ± SD) Subject

Age range

(years)

30.60±3.40 5.14±0.88 15.88±1.96 9.58±1.24 * Control(n=13)

35 -49 27.15±2.86 4.23±0.46 14.85±1.9 8.07±1.16 * PD (n=10)

0.40 0.13 0.91 0.71 p-value

27.34±3.53 4.51±0.60 14.62±2.36 8.20±1.20 Control(n=15)

50 - 64 26.47±2.57 4.44±0.47 13.98±1.40 8.04±1.24 PD(n=10)

0.68 0.35 0.53 0.91 p-value

27.17±3.45 4.54±0.49 14.81±2.38 7.82±1.02 Control(n=11)

≥ 65 23.10±3.44 4.10±0.57 13.20±2.32 6.68±0.94 PD (n=19)

0.68 0.38 0.79 0.78 p-value

* Reduction in midbrain volume between PD and control was 1.51 cm³

107

4.6.6 Correlation of age with volume fraction of midbrain

Overall, there was a significant negative correlation between the age and

volume fraction of midbrain (r = - 0.318 & p = 0.005) (Figure 4.35). Along

with increase of age, volume fraction of midbrain was decreased. When

analyzed separately with patients, there was a non-significant negative

correlation between the age of PD patients and volume fraction of midbrain

(r = - 0.218 & p = 0.182) (Figure 4.36). In controls there was a significant

negative correlation between the age and volume fraction of midbrain (r = -

0.393 & p = 0.013) (Figure 4.37). A significant negative correlation was

found when analyzing age of male patients and male controls with volume

fraction of midbrain (r = - 0.310 & p = 0.024) (Figure 4.38). A non-

significant negative correlation was found when analyzing age of female

patients and female controls with volume fraction of midbrain (r = - 0.343 &

p = 0.093) (Figure 4.39).

Figure 4.35: Correlation of age with volume fraction of midbrain for all groups

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Figure 4.36: Correlation of age with volume fraction of midbrain for PD patients

Figure 4.37: Correlation of age with volume fraction of midbrain for controls

109

Figure 4.38: Correlation of age with volume fraction of midbrain for male patients & male controls

Figure 4.39: Correlation of age with volume fraction of midbrain for female patients & female controls

110

4.6.7 Correlation of age with volume fraction of pons

Overall, there was a non-significant positive correlation between the age and

volume fraction of pons (r = 0.189 & p = 0.097) (Figure 4.40). Along with

increase of age, the volume fraction of pons is not affected. When analyzed

separately with patients, there was a non-significant negative correlation

between the age of PD patients and volume fraction of pons (r = - 0.004 & p

= 0.982) (Figure 4.41). In controls there was a significant positive

correlation between the age and volume fraction of pons (r = 0.339 & p =

0.035) (Figure 4.42). A non-significant positive correlation was found when

analyzing age of male patients and male controls with volume fraction of

pons (r = 0.183 & p = 0.190) (Figure 4.43). A non-significant positive

correlation was found when analyzing age of female patients and female

controls with volume fraction of pons (r = 0.194 & p = 0.354) (Figure 4.44).

Figure 4.40: Correlation of age with volume fraction of pons all groups

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Figure 4.41: Correlation of age with volume fraction of pons PD patients

Figure 4.42: Correlation of age with volume fraction of pons controls

112

Figure 4.43: Correlation of age with volume fraction of pons for male patients & male controls

Figure 4.44: Correlation of age with volume fraction of pons for female patients & female controls

113

4.6.8 Correlation of age with volume fraction of medulla oblongata

Overall, there was a non-significant positive correlation between the age and

volume fraction of medulla (r = 0.189 & p = 0.097) (Figure 4.45). This

explains that there was no correlation between the age and volume fraction

of medulla. When analyzed separately with patients, there was a significant

positive correlation between the age of PD patients and volume fraction of

medulla (r = 0.357 & p = 0.026) (Figure 4.46). In controls there was a non-

significant positive correlation between the age and volume fraction of

medulla (r = 0.012 & p = 0.941) (Figure 4.47). A non-significant positive

correlation was found when analyzing age of male patients and male

controls with volume fraction of medulla (r = 0.169 & p = 0.227) (Figure

4.48). A non-significant positive correlation was found when analyzing age

of female patients and female controls with volume fraction of medulla (r =

0.278 & p = 0.178) (Figure 4.49).

Figure 4.45: Correlation of age with volume fraction of medulla oblongata for all groups

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Figure 4.46: Correlation of age with volume fraction of medulla oblongata for PD patients

Figure 4.47: Correlation of age with volume fraction of medulla oblongata for controls

115

Figure 4.48: Correlation of age with volume fraction of medulla oblongata for male patients & male controls

Figure 4.49: Correlation of age with volume fraction of medulla oblongata for female patients & female controls

116

4.7 Correlation of volumes and volume fractions of midbrain, pons,

medulla oblongata & total brainstem with onset of Parkinson's disease

Pearson’s correlation test was computed to access the relationship between

the volumes and volume fractions of the ROI and onset of the disease.

4.7.1 Correlation of onset of Parkinson’s disease with volume of

midbrain

Overall, there was a significant negative correlation between the onset of PD

and volume of midbrain (r = - 0.412 & p = 0.009). A significant negative

correlation was found between onset of PD in male patient and volume of

midbrain (r = - 0.405, n = 28 & p = 0.032). There was no correlation

between the onset of PD in female patients and volume of midbrain (r = -

0.556, n = 11 & p = 0.076). This explains that along with increase of age of

onset of PD, the volume of midbrain was decreased. A scatter plot

summarizes the results (Figure 4.50).

Figure 4.50: Correlation of onset of PD with volume of midbrain

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4.7.2 Correlation of onset of Parkinson’s disease with volume of pons

Overall there was a significant negative correlation between the onset of PD

and volume of pons (r = - 0.352 & p = 0.028). When the onset of PD was

correlated separately with male patients there was no correlation (r= - 0.235,

n = 28 & p = 0.228). There was no correlation between the onset of PD in

female patients and volume of pons (r=- 0.338, n=11 & p=0.088). This

explains that; although there was no relationship with gender, in overall

patient group when the age of onset of the disease was increased, the volume

of pons was decreased. A scatter plot summarizes the results (Figure 4.51).

Figure 4.51: Correlation of onset of PD with volume of pons

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4.7.3 Correlation of onset of Parkinson’s disease with volume of medulla

oblongata

Overall there was no correlation between the onset of PD and volume of

medulla oblongata (r = - 0.183 & p = 0.264). When the onset of PD was

correlated separately with male patients and female patients there was no

correlation (r = - 0.173, n = 28 & p = 0.380) and (r = - 0.202, n = 11 & p =

0.551) respectively. This explains that there is no relationship between age

of onset of the disease and volume of medulla oblongata. A scatter plot

summarizes the results (Figure 4.52).

Figure 4.52: Correlation of onset of PD with volume of medulla oblongata

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4.7.4 Correlation of onset of Parkinson’s disease with volume of

brainstem

Overall there was a significant negative correlation between the onset of PD

and volume of brainstem (r = - 0.403 & p = 0.011). When the onset of PD

was correlated with volume of brainstem separately with male and female

patients there was no correlation (r = - 0.350, n = 28 & p = 0.068) and (r = -

0.526, n = 11 & p = 0.097) respectively. This explains that; although there

was no relationship with gender, when the overall age of onset of the disease

was increased, the volume of brainstem was decreased. A scatter plot

summarizes the results (Figure 4.53).

Figure 4.53: Correlation of onset of PD with volume of brainstem

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4.7.5 Correlation of onset of Parkinson’s disease with volume fraction of

midbrain

Overall there was no correlation between the onset of PD and volume

fraction of midbrain (r = - 0.173 & p = 0.293). When the onset of PD was

correlated with volume fraction of midbrain separately among the male and

female patients there was no correlation (r = - 0.214, n=28 & p=0.274) & (r

= - 0.092, n = 11 & p = 0.789) respectively. This explains that there is no

relationship between age of onset of the disease and volume fraction of

midbrain. A scatter plot summarizes the results (Figure 4.54).

Figure 4.54: Correlation of onset of PD with volume fraction of midbrain

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4.7.6 Correlation of onset of Parkinson’s disease with volume fraction of

pons

Overall there was no correlation between the onset of PD and volume

fraction of pons (r = - 0.023 & p = 0.891). When the onset of PD was

correlated with volume fraction of pons separately among the male and

female patients there was no correlation (r = 0.121, n=28 & p = 0.541) and (r

= - 0.0453, n = 11 & p = 0.162) respectively. This explains that there is no

relationship between age of onset of the disease and volume fraction of pons.

A scatter plot summarizes the results (Figure 4.55).

Figure 4.55: Correlation of onset of PD with volume fraction of pons

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4.7.7 Correlation of onset of Parkinson’s disease and volume fraction of

medulla oblongata

Overall there was a non significant positive correlation between the onset of

PD and volume fraction of medulla oblongata (r = 0.315 & p = 0.051).

When the onset of PD was correlated with volume fraction of medulla

oblongata separately for male patients there was a non significant positive

correlation (r = 0.148, n = 28 & p = 0.451). A significant positive correlation

was found between the onset of PD in female patients and volume fraction

of medulla oblongata (r = 0.663, n = 11 & p = 0.026). This explains that

there is no overall relationship between age of onset and volume fraction of

medulla oblongata. Although in female patients when age of onset of the

disease was increased, the volume of brainstem was also increased. A scatter

plot summarizes the results (Figure 4.56).

Figure 4.56: Correlation of onset of PD in with volume fraction of medulla oblongata

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4.8 Correlation of volumes and volume fractions of midbrain,

pons, medulla oblongata & total brainstem with duration of

Parkinson's disease

Pearson’s correlation test was computed to assess the relationship between

the volumes and volume fractions of the ROI and duration of the disease.

Overall there was no correlation between the duration of PD and the volume

and volume fraction of midbrain, pons, medulla oblongata and brainstem.

This correlation was also performed between male and females separately

and the same results exist (P>0.05). This results is shown in table (4.15)

4.9 Estimations of coefficient of error for the measurements of

the volumes of the data

The coefficient of error (CE) for measurement of volumes of the midbrain,

pons and medulla oblongata were estimated. The mean of CE of midbrain

and medulla oblongata were almost the same (0.21 %). The mean of CE of

pons (0.16 %) was estimated as the minimum of the three regions of the

brainstem, details are shown in (Table 4.16).

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Table 4.15: Correlation of duration of PD with volume and volume fraction of midbrain, pons, medulla oblongata and brainstem

Variable Patients

(n=39)

Male

(n=28)

Female

(n=11)

Volume of midbrain (cm³) r-value - 0.15 - 0.25 0.27

p-value 0.36 0.20 043

Volume of pons (cm³) r-value - 0.06 - 0.12 0.12

p-value 0.71 0.54 0.72

Volume of medulla oblongata (cm³) r-value 0.05 0.15 - 0.12

p-value 0.74 0.45 0.73

Volume of brainstem (cm³) r-value - 0.09 - 0.15 0.13

p-value 0.60 0.43 0.70

Volume fraction of midbrain (%) r-value - 0.14 - 0.21 0.34

p-value 0.41 0.28 0.31

Volume fraction of pons (%) r-value 0.03 0.01 0.05

p-value 0.84 0.95 0.90

Volume fraction of medulla oblongata

(%)

r-value 0.16 0.35 - 0.43

p-value 0.32 0.07 0.19

* Correlation is significant when the p-value is ≤ 0.05

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Table 4.16: Results of the coefficient of error (CE in %) for volume of data

Structure Mean SD Range

Midbrain volume 0.21 0.04 0.15 – 0.37

Pons volume 0.16 0.03 0.11 – 0.23

Medulla oblongata volume 0.21 0.05 0.13 – 0.35

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5.1 Introduction

Parkinson's disease is a neurodegenerative disorder characterized by

progressive loss of dopaminergic neurons of the substantia nigra pars

compacta (SNc). It is clinically diagnosed on the basis of its motor

symptoms (11). In this case-control study, the researcher is trying to make a

contribution to explore the in vivo volume changes of the brainstem and its

subdivisions in patients with PD. This is a novel study to quantify in vivo

the regional brainstem and its subdivisions atrophy in brains of patients with

PD using T1-weighted 3D brain MRI and manual planimetry tracing method

of the region of interest (ROI), and applying the volumetric method of

Cavalieri on the data of patients (n=39) and healthy controls (n=39). The

volume fraction of brainstem regions, which is a stereological approach

denoting the proportions of the body components independent of body mass

index was also calculated (5).

The motor cardinal signs of PD include resting tremor, rigidity, bradykinesia

and postural instability (9). In clinical practice, diagnosis of PD typically

depends upon the presence of a combination of motor cardinal signs and

responsiveness to levodopa, so its diagnosis is subjected to a rate of

misdiagnosis that varies from 10% to 50 % (14). This is mainly due to the fact

that there is no sensitive and specific biomarker valid for clinicians to use in

the differential diagnosis of PD from other neurological disorder with

overlapping motor cardinal signs such as multiple system atrophy (MSA)

and progressive supranuclear palsy (PSP) (10, 11, 14).

With the advancing development in imaging techniques of the brain

researchers have started employing different anatomical methods on MRI, to

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evaluate the structural changes in brain regions critical to PD (145). Most

researches have focused on neuroimaging and biochemical markers of PD to

improve diagnosis of the disease as well as monitoring its progression (14).

The use of multi-modality approach methods such as a combination of

structural and iron sensitive imaging increased diagnostic sensitivity to the

disease status and progression (145). In the study of Nair et al. (135), a

combination of conventional MRI and diffusion tensor imaging techniques

gave a reliable data to differentiate PD from MSA.

Many studies have tried to contribute in providing a neuroimaging

biomarker as a better diagnostic tool for PD, and several of them

concentrated in quantifying structural and functional changes in the

substantia nigra (SN) and basal ganglia components mainly putamen and

caudate nucleus (146, 147, 7, 6). Other studies were directed towards evaluation

of atrophy in cerebral regions mainly frontal lobe and cerebellum (148-150).

Few studies have aimed at measuring atrophy of brainstem of PD patients (12,

11, 16, 24, 25).

Magnetic resonance imaging has advantages over other imaging modalities

such as CT which have been used previously to diagnose disorders of the

brain. CT requires ionizing radiation while MRI is a noninvasive imaging

method. In addition MRI has a superior contrast resolution that facilities

discrimination of the grey and white matter of the brain tissue. MRI affords

a multiplanar imaging capabilities and dynamic rapid data acquisition (74). In

addition MRI is far more widely available than other imaging techniques

like PET and SPECT and is most commonly used in clinical practice (17).

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However, the spatial resolution of MRI scanners was insufficient to detail

the anatomy of the SN. Several attempts were made to improve visualization

of the SN using multiple MR contrasts, but the correspondence between MR

images and the actual anatomy of the SN was unclear (44). Due to the

diversity of imaging findings of SN and the inaccurate delineation of the SN

on MR images(151), The researcher used T1-weighted 3D images with rapid

acquisition gradient-echo MR sequences, and applied Cavalieri stereological

method on these images for volumetric measurements of the brainstem as a

whole and each of its subdivisions of PD patients and healthy controls. This

imaging sequences allow the acquisition of T1-weighted 3D data sets of the

brain that can be reformatted to provide images in different orientations, a

process often called multiplanar reconstruction (81).

To date, the most widely used T1-weighted 3D GRE sequence is a

magnetization-prepared rapid acquisition gradient-echo (MP RAGE)

sequence. Images obtained by T1-weighted MP RAGE sequence provide

appropriate timing parameters, and give excellent contrast between grey and

white matter which facilitates perfect anatomical quantification of brain

structures (81). In addition T1-weighted 3D MR sequence does not produce

signals from bones and therefore there is no effect of bone artifact on the

images. This facilitates better visualization of the posterior cranial fossa

structures and hence better delineation of brainstem boundaries (76). Still, the

full potential of MRI has not yet been reached; there is continuing

refinement of the equipment, contrast agent and software in progress every

day. Therefore, this study is designed to work on the volumetric evaluation

of the brainstem. The whole structure and its components are clearly

recognized in the MR images.

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Cavalieri principle is a modern stereological method that is applied on MR

images for estimation of volumes of irregular objects based on their profile

areas on random sections (89, 93, 92). Stereology estimates biological features

of 3D objects through interpretation of 2D images and can be applied to a

variety of imaging techniques (89, 90). These stereological quantification

methods are unbiased, less time consuming and precise. For these reasons,

stereological application on imaging approaches has become a gold standard

in quantitative structural analysis of different human organs. MRI based

volume quantification is now being increasingly used to investigate

neuroanatomic structures in neurological disorders (5, 95). In the present study,

the researcher applied the volume and volume fraction approach of the

stereological methods over the Cavalieri principle to reveal the size changes

in the PD.

5.2 Main findings

The main finding of the present study was that patients with PD showed

significant atrophy in total brainstem volume and in all of its subdivisions;

midbrain, pons and medulla oblongata compared to healthy controls. The

greatest effect of the disease was observed in the midbrain volume

suggesting that the SN neuronal cell death may play a role in this atrophy.

This study is in accordance with a study conducted by Jubault et al. (11), who

found that brainstem damage may be the first exclusive point of PD

neuropathology. They evaluated the reduction of white matter in the

brainstem region and found that cranial part of the medulla oblongata and

caudal part of the pons showed reduction in white matter. On contrary, a few

case-control studies, have reported that the volume of brainstem was not

affected in PD (12, 16, 25). This could be explained by variation in segmentation

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methods, software used for measurements and sample size, age and type of

patients in the present study compared to the aforementioned studies. They

used fully-automated segmentation method whereas manual tracing method

was used in this study. They used FreeSurfer software while this study used

ImageJ software. Their sample included patients with PD, PSP and MSA

and the present study was confined to patients with PD only. It is worth

mentioning here that the mean age of their patients was also older than that

of the present study group.

Brainstem studies have been neglected until recently because it was hard to

approach experimentally due to its location in a relatively inaccessible

region of the nervous system. But these difficulties have been solved by

technical imaging advances and computational methods (152). However, the

reported brainstem volume changes remain sparse due to the technical and

methodological limitations of segmentations and quantitative assessment of

data from this region (35).

5.3 The volumes of ROI by Age

In this study, the effects of age and gender on brainstem volume and its

subdivisions as well as comparing these effects between controls and PD

patients was investigated. During aging, both morphological and

neurochemical changes occur in the human central nervous system, which

increase vulnerability toward physiological disturbances and

neurodegenerative disorders such as PD (46). Therefore aging is the strongest

risk factor for the development of PD. Although a marked age-related

decline in the dopaminergic neuronal levels suggest that nigrostriatal system

dysfunction occurs, this also happens during normal aging (153). Application

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of the modern imaging techniques adds a lot in understanding the pattern of

volume loss in normal ageing and diseases related to ageing (47, 46).

Concerning age effects on brain volume in general, studies have shown that

the total cerebral brain volume is reduced and the ventricles size is increased

with age (47-49, 52). The brain atrophy has variable patterns & degree of

changes along different brain regions (52). Several factors affect the regional

vulnerability of brain atrophy, such as decreased blood flow, demyelination

of nerve fibers, and regional gene expression (47, 46). The grey matter declines

with age in a linear constant function throughout adult life, whereas that of

the white matter seems to be delayed or not affected until middle adult life

and then decline after that (54, 55).

Although total cerebral brain volume declines with age, most studies have

reported that the total brainstem volume remains stable with advancing of

age. Mainly the volume of pons and medulla oblongata seemed not to be

affected along with age (5, 35, 50, 46, 52). Their justification to this stability of

brainstem volume with age was based on the minimal effect of decreased

blood flow to the brainstem in comparison to other cerebral regions and

presence of the vital cardiovascular and respiratory centers in the region of

the brainstem (35, 66). In healthy control group studied in the current study a

significant negative correlation in the volumes of brainstem and each of its

subdivision with age was noticed except in the pons this reduction was not

significant. This finding is in discordance with the aforementioned studies.

A possible source of variation in the findings across previous studies and

that of this study may be due to the different imaging parameters, scanning

sequence, slice thickness and technique used for segmentation of ROI. The

present study has the advantage to use 1mm slice thickness images with no

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inter slice gap, enabling the evaluation of the full extent of the ROI. In

addition, T1-weighted MP RAGE sequence applied to imaging in this study

provides an excellent visualization of brainstem boundaries.

Although previous studies examining volumetric age-related decline in

brainstem volume in normal aging have reported that there is no volume

alteration in total brainstem, pons and medulla oblongata volumes. However

some studies have supported the present findings that the most significantly

atrophied part of the brainstem is the midbrain due to shrinkage of its nuclei

and a marked increase in iron concentration (35, 66). The dopaminergic

neurons of the SN are characterized by their pigmentation which is a

resultant of the intraneuronal accumulation of neuromelanin. Neuromelanin

has strong ability to bind with iron. This explains the high concentration of

iron seen in stained histological sections of SN. The SN shows loss of

melanized neurons and deposition of iron with increasing age which appears

as hyperintense areas in T1-weighted images (44). Our results were in

accordance with the previous studies regarding age-related volumetric

alterations in midbrain.

The discrepancy in the age-related reduction and volume of medulla

oblongata between this and the others studies could be due to the different

methodologies used. Most of the literature based their delineation of the

caudal border of the medulla oblongata along a plane parallel to the

mammillary body-posterior commissure plane at the posterior rim of the

foramen magnum (21, 41). However borders of the foramen magnum are not

seen well in MR image and the atlas was the most fixed clear point in all

MR images. Accordingly during the present assessment of the volume of the

medulla oblongata in this study and to standardize the lower border of the

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medulla oblongata for all subjects, the upper border of the posterior arch of

the atlas was considered as the landmark. This landmark was recommended

on what have been referenced in books that the medulla oblongata ends

caudally by joining the spinal cord at the level of the superior border of the

posterior arch of the atlas (141-143). In addition to the present knowledge, in

judicial hangings that breaking of the odontoid process is held to be the

actual cause of death and not only strangulation because it makes pressure

on the medulla oblongata. This may also explain that medulla oblongata

extends to the level of the arch of the atlas.

In PD patients, a highly significant reduction in the volumes of brainstem as

well as that of the midbrain with age was found. The reduction of these

volumes was significant when compared to healthy control. There is also a

significant reduction in the volume of pons with age however the reduction

of the volume of the medulla oblongata was insignificant in PD patients. It

can be concluded that the reduced brainstem volume in PD patients was

mainly due to the reduction in the volume of their midbrains reflecting a

progressive neurodegenerative process in the neurons of SNc, which is a

hallmark of PD (153, 44). Also it is reported that the iron concentration in SN is

30-35% more in PD patients than in healthy aging people, and hence more

loss in melanized neurons (44).

5.4 The volume of ROI by gender

There is a generalized agreement in the literature that the total volume of the

brain is bigger in males than in females (54, 55). According to Erbagci et al. (5),

the average brainstem volume of healthy individuals, in the age group 20-40

years, was found to be 22.05 ± 4.01 cm³ in males and 18.99 ± 2.36 cm³ in

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females. This difference was found to be statistically significant when

compared gender wise. Ekinci et al. (62), conducted a study on healthy

volunteers and measured volume of the brainstem, their results showed that

males were having greater brainstem volumes than females (24.3 and 22.9

cm³ respectively). However, this difference did not rise to a statistically

significant level (p>0.05). Kruggel (68) and Lee et al. (66), have found the

brainstem volume to be 30.6 ± 6.9 cm³ in males, 27.9 ± 5.3 cm³ in females,

and 27.81 ± 2.95 cm³ in males and 25.15 ± 2.23 cm³ in females respectively.

One of the major reasons that influence the brain volume by gender is that

males have proportionately bigger body size than females, in addition to the

sex hormonal effects on the brain in males (69). Although the total brain

volume of males is bigger than that of females, it is reported that generally

males are more affected than females through their life-span by age-related

changes concerning total brain volume (48, 53, 59). De Bellis et al. (53), reported

that there is a significant decrease in grey matter in males than in females

during childhood and adolescence.

With regard to the brainstem volume, almost all the studies have agreed that

brainstem volume is greater in males than in females (62, 5, 66, 63). The current

study supports the above studies as it was found that the volumetric

measurements of total brainstem and all its subdivisions were bigger in

males than in females. Despite this, it was reported that brainstem showed

more age-related atrophy in males, whereas that of females almost remains

constant (154). In the current study with regards to the total brainstem

volumes and its subdivisions in both healthy controls and PD patients groups

there was no gender discrepancy in the reduction of the volumes in ROI, this

was in accordance with Lee et al. (66), and Kurggel et al. (68), found also no

135

gender discrepancy in the volume of total brainstem, midbrain, pons and

medulla oblongata.

When overall males and females in the study were compared in regard to

their ROI volume reduction there was more decline in the volume of total

brainstem and its subdivisions particularly that of the midbrain in males

more than in females, which is in accordance with what has been reported

that males are more affected by age related changes (48, 53, 59).

Also at the level of the clinical outcome of non-motor symptoms of PD, the

spectrum and severity was presented in different ways for male and female

patients, suggesting a possible sex-related effect (121). One possible source of

male-female differences in the clinical and cognitive characteristics of PD is

the effect of estrogen on dopaminergic neurons and pathways in the brain.

This effect is not yet understood, as insight into how the fluctuation of

estrogen over the lifetime affects the brain is currently limited (155).

5.5 Comparison of brainstem volumetric changes between PD

and other neurological disorders

Camargos et al. (156), have reported that the brainstem volume was 19.79 ±

2.67 cm³ in healthy individuals and 14.15 ± 2.35 cm³ in spinocerebellar

ataxia patients. This difference was statistically significant and the most

affected brainstem structure was the pons. In another case-control study

conducted by Eichler et al. (21), the mean volume of the brainstem in normal

adult was 28.8 ml. They reported a marked loss of brainstem volume in

patients with spinocerebellar ataxia. In multiple sclerosis, a case-control

study conducted by Alper et al. (157), showed that the brainstem volume of the

control and patients was 4,517 ± 553 mm³ and 3,515 ± 512 mm³

136

respectively. Fearing et al. (158), have reported in their study that the

brainstem volume of children with traumatic brain injury 21.77 ± 1.8 cm³,

while the average of the healthy children was 22.78 ± 2.54 cm³

Nair et al. (135), found that the mean volumes of putamen, pons and

cerebellum in MSA patients lower than in PD patient and this difference was

statistically significant.

5.6 The volumes of ROI by Age at onset & duration

The average age at onset of PD was estimated to be 54.00 ± 12.22 years in

this study which was around what estimated previously (125). In the current

study, the volumes of midbrain, pons and total brainstem showed correlation

with age at onset of PD except the medulla oblongata which did not show

correlation with age at onset of PD. Only one study commented on age at

onset and disease progression(125), and reported that age at onset is

independent determinant of the disease clinical features. In this study,

gender wise, males' midbrain volume decreased significantly with early

onset of the disease in comparison to females’ midbrain volume, this may be

due to the small sample size of female PD patients. Apart from the midbrain

volume, all other parts of the ROI; pons, medulla oblongata and total

brainstem volume did not show differences in male and female in relation to

age at onset of PD. On clinical basis the average rate of annual decline in

the motor signs of PD depends strongly on age at onset, late-onset having

higher rate (3.8%) than those of early-onset (2.4%) (104).

Motor symptoms of PD are likely to appear when the pathology of the

disease has caused significant damage to the SN (126). Progression of these

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motor symptoms in PD may occur over 10 – 30 years but the rate of this

progression varies from person to person (17). In the current study, when

duration of the disease was correlated with the volume findings of the

brainstem and its subdivisions, the extent of brainstem, midbrain, pons and

medulla oblongata neurodegeneration did not show correlation with the

duration of the disease. This was in contrast to what have been reported that

the volume loss became marked in parallel with disease severity and

duration (146).

To avoid confounding variables, in the current study, the volume fraction of

the midbrain, pons and medulla oblongata in relation to total brainstem

volume were calculated. The volume fraction calculates the absolute volume

and reduces variability of the data and corrects effect of factors affecting

brain size, other than age and disease, like body mass index (5). In the current

study regarding the volume fraction of the midbrain, pons and medulla

oblongata, the only part which showed significant difference between PD

patients and controls was in the midbrain.

The volume fraction of midbrain was lower in male controls than in female

controls. This is in accordance with the above mentioned studies that males

have more decline in their brain volumes with age. The volume fraction of

midbrain in male PD patients was higher than that in females, indicating that

the volumetric fractional changes of midbrain were more severe in females.

This may be due the variation of the severity and duration of the disease.

Concerning the volume fraction of both pons and medulla oblongata, there

was no significant difference between male and females in both control and

patients group. The males and females were almost similarly affected by the

disease.

138

6. 1 Conclusions

• This quantitative volumetric study demonstrated a global atrophy of

midbrain, pons, medulla oblongata and the total brainstem volumes in

PD patients compared to healthy control. It was also obvious that the

progressive reduction in the midbrain region leads to the reduction in

the total brainstem volume. Apart from the midbrain, both pons and

medulla oblongata showed variation in atrophy when analyzed with

other variables like, age, gender, age at onset and duration of the

disease.

• When the volumes of midbrain, pons, medulla oblongata and total

brainstem volumes were correlated to age of both healthy controls and

PD patients an obvious age related effects was observed. In the

controls group there was a reduction in the volumes of the brainstem

and its subdivision except the pons seemed to be not affected. When

this normal age-related reduction in volumes of the brainstem and its

subdivisions in healthy controls were compared to the reduction in PD

patients with age there was a significant reduction of these volumes in

PD patients. This means that the disease-related effects have caused

more progressive reduction in the volume of the region of interest in

PD patients beside the normal age-related changes.

• Gender wise, there was no correlation found in this study between

gender and volume reduction in both healthy control and PD patients

this may be due to the small sample size of female group in this study

compared to the number of males.

139

• The volumes of midbrain, pons and total brainstem showed

correlation with age at onset of PD except the medulla oblongata

which did not show correlation with age at onset of PD. Gender wise,

only the males’ midbrain volume showed significant correlation with

age at onset of PD, i.e. early diseased more reduction. The duration of

PD did not show correlation with volumetric changes of total

brainstem and its subdivisions.

• The volume fraction of the PD patients’ midbrain showed significant

difference when compared to healthy controls. Gender wise the

volume fraction of females was higher than that of males in the

control group. In the patient group this was the opposite, males were

having higher volume fraction of midbrain than females. The pons

and medulla oblongata did not show significant difference of volume

fraction when comparing patients with PD with the controls group.

6.2 Recommendations

• Although this volumetric neuroimaging study is promising, future

refinement in resolution of images and improvement in techniques

sensitivity of methods are needed before their diagnostic potential in

PD are fully realized.

• It is believed that the volumetric measurement of midbrain and

brainstem can help in early diagnosis of PD patients. In addition this

will provide an opportunity to initiate neuroprotective therapy which

can assist prognosis of the disease and valued outcomes of its

treatment.

140

• Further longitudinal studies of physiological ageing with large number

of subjects and wide range of age groups will be necessary to verify

the results of normal aging on the brainstem and to compare this with

PD patients.

• Volumetric measurement of the SN in PD needs further investigation.

• As PD has significant negative impact on the quality of life of patients

and their families, establishment of specialized center for PD in

Khartoum-Sudan will be valuable resource for patients, physician and

researchers by providing referral services, education materials and

programs.

6. 3 Limitations

• Limitation in structural imaging techniques and sequences for

visualization of SN.

• The relationship between brainstem volumes changes and clinical

dysfunction based on different stages of the disease was not tested in

this study. This will require diagnosis of PD patients according to

Hoehn and Yahr scale (H&Y) and /or Unified Parkinson's Disease

Rating Scale (UPDRS). It will also require greater number of patient

at different age groups to participate in the analysis.

• Recruitment difficulties

141

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