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February, 2012
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2012 International Neuropsychological Society Montreal, Canada
Price, Lamar, Giovannetti, Libon
Understand the role of different imaging modalities typically requested for dementia
Increase literature awareness – Education of imaging methodologies (be an educated consumer)
Understand basic biomarker-‐cognitive associations/dissociations for dementia subtypes
Future directions: applying research techniques to clinical scans and diagnoses
Improved communication between colleagues – i.e., dementia case conference
Improved understanding of disease processes and relationship to brain-‐behavior
Improved diagnostic efficiency – and potential treatment efficiency
Educated Consumer for Imaging Research
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CT MRI “Biological imaging”
Most commonly used imaging modality Advantages:
Widely availability Very short examination time Low costs
Disadvantages: Radiation exposure Administration of iodinated contrast agents
without iv contrast with iv contrast
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Second most common imaging modality Advantages:
Allergies to contrast extremely rare Superior soft tissue detail
Disadvantages: More contraindications Expensive Long acquisition time => ↑motion degradation
Requires 20-‐60 minutes to be performed T1 T2 FLAIR weighted images Diffusion weighted images (DWI)
= minimum
⇒ More complex study type sequences imaging thickness imaging planes
Is superior to CT in detection of Acute superimposing chronic ischemic changes Acute to early subacute small to large infarctions Posterior fossa lesions Infections Small lesions
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“Normal subject”
T1 T2 FLAIR
Patient with multiple sclerosis
T1 T2 FLAIR
Patient with dementia and leukoaraiosis
T1 T2 FLAIR
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Patient with colloid cyst
T1 T2 FLAIR
Acute to early subacute infarctions
CT FLAIR
Diffusion imaging Perfusion imaging FDG -‐ PET imaging
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Diffusion weighted imaging – primary topic for today
Perfusion imaging FDG -‐ PET imaging
Based on random motion of water molecules through a tissue compartment
Normal motion water molecules => dark on DWI
Stroke => Interruption of cerebral blood flow Rapid breakdown of energy metabolism and ion exchange pumps Massive shift of water from extra-‐ to intra-‐cellular = cytotoxic edema
=> NO motion water molecules = “restricted diffusion”
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“restricted diffusion” => bright on DWI
Restricted diffusion visible within minutes post stroke
Detection of “dead” tissue Determination of the age of an ischemic lesion Differentiates chronic white matter disease from new
small infarctions Detection of tiny infarctions Detection of “diffuse ischemia”
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DWI is the most sensitive sequence for detection of early changes
Extent on DWI predicts prognosis
Arbelaez: DW MR imaging of global cerebral anoxia. AJNR 1999.
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Cortical Atrophy Subcortical Atrophy Lacunes versus Perivascular Spaces Leukoaraiosis
Example: “Classic” Alzheimer’s Disease General features: Diffuse atrophy with superimposed Parietal and temporal cortical atrophy ▪ ↑ CSF spaces surrounding the temporal and parietal lobes
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Specific features: MRI – volumetric acquisitions ▪ hippocampal volume bilaterally ▪ temporal horns
Normal Alzheimer’s disease
de Leon et al., 2004. J Int Med, 256 (3)
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de Leon et al., 2004. J Int Med, 256 (3)
http://www.itksnap.org/pmwiki/pmwiki.php
http://surfer.nmr.mgh.harvard.edu/
http://rsbweb.nih.gov/
http://www.fmrib.ox.ac.uk/fsl/
Brain volumes were attained using a semi-automated ROI method
I. Freesurfer auto-segmentation
II. ITK-SNAP Visualization
III. Manual Rater Clean-up
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Brain volumes were attained using a semi-automated ROI method
I. Freesurfer auto-segmentation
II. ITK-SNAP Visualization
III. Manual Rater Clean-up
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Proportions of twenty-two different MR FLAIR voxel volumes observed in one retrospective sample (n=143)
Relevant for “subcortical disorders” For example: Parkinson’s disease Huntington’s disease Small vessel stroke related atrophy
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*z-score relative to GL’s peer group (L-onset PD); Volumes corrected for Total Brain Volume (TBVc, Bigler, Neeley et al., 2004).
N=4 N=16 N=18
Leukoaraiosis/ White matter abnormalities – how much is too much?
Lacunes versus enlarged perivascular spaces
Hippocampal “cysts”
Normal appearing white matter – is it really normal?
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ACA MCA
Superior Division
MCA Inferior Division
MCA Deep Branches PCA
Anterior Choroidal Artery
PCA Deep Branches
Temporal Lobe
Lateral Ventricle Caudate
Thalamus
Putamen
Globus Pallidus
Hippocampal Formation
Debatable
Depends on patient sample, imaging approach, imaging methodology
Important area of study
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Cognitively “Normal” Participant.
Movie
68 year old
68 year old
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moving from highest MMSE scores (a) to lowest MMSE scores (d)
Yoshita, M. et al. Neurology 2006;67:2192-2198
Yoshita, M. et al. Neurology 2006;67:2192-2198
Price, Mitchell, Brumback, Tanner, et al., in revision
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http://www.itksnap.org/pmwiki/pmwiki.php
http://rsbweb.nih.gov/
1) Many Visual Rating Scales
2) Semi-automated Approaches
3) Automated Approaches: Validation is an issue
84 mm3 of diseased white matter
615 mm3 of diseased white
matter
…but their REAL TISSUE volumes differ:
(to scale)
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= # of LA Pixels * In-‐Plane Area * Slice Thickness
arcuate fasciculus
inferior fronto-occipital fasciculus
internal capsule, cerebellar and thalamic projections
a
c d
e
a frontal centrum semiovale b parietal centrum semiovale
d body of the lateral ventricle; corona radiata
e posterior horn Regions of interest
b
c anterior frontal horn
Figure from Lamar et al., 2008. The impact of region specific leukoaraiosis on working memory deficits. Neuropsychologia, 46(10), 2597-2601.
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Axial FLAIR images Scion Image
Freeware rsbweb.nih.gov
Trained rater uses pixel counts to quantify by region
Intra-‐rater reliability: r ≥.99 Inter-‐rater reliability: r ≥92%
Hachinski VC, Potter P, Merskey H. Leuko-‐araiosis. (1987). Arch Neurology;44:21-‐23.
In dementia, 3% threshold for executive dysfunction (Price, Tanner, et al.).
Leukoaraiosis/ White matter abnormalities – how much is too much?
Lacunes versus enlarged perivascular spaces
Hippocampal “cysts”
Normal appearing white matter – is it really normal?
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http://pn.bmj.com/content/8/4/222.full
Pract Neurol 2008;8:222-228 doi:10.1136/jnnp.2008.153601 Pract Neurol 2008;8:222-228 doi:10.1136/jnnp.2008.153601
Pract Neurol 2008;8:222-228 doi:10.1136/jnnp.2008.153601
Lacunes:
lacunar infarcts are thought to account for about one quarter of all ischaemic strokes
1) Measurement obtained on slice with largest lacunae diameter 2) Calculated sphere for each lacunae ( 4/3 πr3) 3) All spheres summed for estimated total volume
Most common approach:
Less reliable But theoretically an ideal method
Semi-automated approach:
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Tsutsumi et al., 2011.
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Movie
Leukoaraiosis/ White matter abnormalities – how much is too much?
Lacunes versus enlarged perivascular spaces
Hippocampal “cysts”
Normal appearing white matter – is it really normal?
February, 2012
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Leukoaraiosis/ White matter abnormalities – how much is too much?
Lacunes versus enlarged perivascular spaces
Hippocampal “cysts”
Normal appearing white matter – is it really normal?
Diffusion tensor imaging Using eigenvalues for parsing white matter Regional specificity in white matter integrity Using other methods besides tensor analyses – high angular methods.
PET imaging with non-‐conventional radiotracers
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Diffusion tensor imaging PET imaging with non-‐conventional radiotracers
Based on DWI Is performed in many more directions => many more images => generation of maps
Captures Diffusibility of water Direction of water diffusion
Isotropic diffusion Anisotropic diffusion
X
Z
Y
X
Y
Z
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Isotropic & anisotropic
X
Z Y
X
Y Z
Diffusion properties Mean diffusivity (MD): quantifies the magnitude of water diffusion & reflects integrity of tissue ultrastructure. MD is increased if damaged.
Fractional anisotropy (FA): Reflects principle direction of water diffusion & can be used to identify white matter paths. FA is reduced if damaged.
FLAIR image DTI (FA) image
Difference between MRI approaches for normal appearing white matter
Mean diffusivity Fractional Anisotropy
O’Sullivan et al. Neurology, 2001; slide
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Lebel et al., 2011
Lebel et al., 2011
Lebel et al., 2011
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Wide range of applications e.g. Multiple sclerosis Schizophrenia Trauma Dementia
A lot we DO NOT know, however, regarding mathematics/ validity of the metrics
DTI-‐derived variables of interest
Fractional Anisotrophy (FA)
Axial Diffusivity λ1
Radial Diffusivity (l2 + l3)/2 denoted as l
NOTE: λ1, λ2, and λ3, represent the eigenvalues from the diffusion tensor and λ represents MD
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* Kantarci K: MCI and Alzheimer Disease: Regional Diffusivity of Water. Radiology 2001.
ADC x 10-6 mm2/sec
* Seppi K: DWI discriminates PSP from PD but not from the parkinson variant of MSA. Neurology 2003.
ADC x 10-6 mm2/sec
Plays are more and more important role in treatment Pre Op for brain tumor resection Neuroplasticity ▪ Brain ▪ Spinal cord
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Figure courtesy of Sean Deoni
The left side shows cerebral regions with coherent default network activity under resting conditions in young adults. These resemble areas of both increased amyloid plaque deposition assessed by molecular imaging modalities such as PET (middle) and of cortical atrophy measured by morphological MR-imaging (right). The similarity might be explained by steady increased baseline activity in default networks leading to an increased pathology with subsequent neurodegeneration (figure: Buckner et al. 2005; text: Wermke et al., 2008)
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Rubinov & Sporns. NeuroImage, 2010
Yao et al., 2010
Areas in blue show significant decreases in nodal centrality and areas in red show significant increases in nodal centrality within a hub region of three cortical networks identified in normal aging. A - differences for both MCI and AD; B - differences only in the AD group.
Yao et al., 2010
Lines in blue show significant decreases in interregional correlations between corresponding regions and lines in red show significant increases in interregional correlations between corresponding regions. A – NC compared to AD; B – NC compared to MCI; C – MCI compared to AD.
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Wire image from: http://www.cirris.com/testing/resistance/broken_strands.html
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UF UIC Temple Drexel-‐Hahneman
Ilona Schmalfuss
Rebecca Charlton
Greg Seidel Joel Eppig
Thomas Mareci Olu Ajilore Al Pennisi
Jared Tanner
Sandra Mitchell
Sean Deoni at Brown University
Peter Nguyen
Nicole Coronado
Nadine Schwab
**NINDS K23NS60660 (Price); Alzheimer’s Association IIRG0627542 (DL)
