Introduction to functional neuroimaging Didem Gkay
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Imaging modalities Lesion maps - ~5 mm -
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Where do we stand historically Brain Mapping: The systems (Toga
& Mazziotta, Chap.2)
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Introduction to functional MRI
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Outline of fMRI topics 1. The basis of the fMRI signal:
hemodynamic response 2. Imaging the function: fMRI experimental
setup fMRI paradigms fMRI problems 3. Data analysis techniques fMRI
Preprocessing fMRI Block design data analysis fMRI Event related
data analysis 4. Aggregation of activity maps from multiple people
Individual ROIs Blurring
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1. Basis of the fMRI signal: hemodynamic response
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Changes in the active brain As long as we eat and breathe we
can continue to think
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The working brain requires a continuous supply of glucose and
oxygen This is delivered through cerebral blood flow (cbf) Human
brain accounts for 2% of body weight but 15% of cardiac output (700
ml/min) Arteries Veins Arteries contain oxygenated blood
(oxyhemoglobin) Veins contain deoxygenated blood
(deoxyhemoglobin)
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Local blood flow varies 18-fold between different brain regions
(the number of capillaries in the tissue is dissimilar) The ratio
of capillary density in GM:WM is 2-3:1 The CBF ratio of GM:WM is
4:1, The CBV ratio of GM:WM is 2 Neuronal activity is associated
with an increase in metabolic activity and hence, blood flow
The change in diameter of arterioles following sciatic
stimulation. after activity
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BEFORE ACTIVITY AFTER ACTIVITY venous flow
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Obtaining the fMRI signal (intensity) T2*: The transverse
relaxation time actually decays faster than T2, due to field
inhomogeneity (the spinning tops gets out of phase, so we observe a
rapid destruction of the alignment with the field)
deoxyhaemoglobin: is contained in blood and paramagnetic, so
introduces field inhomogeneity fMRI process: mainly measures the
field inhomogeneity - upon stimulus, the capillary and venous blood
are more oxygenated, so there is less deoxyhemoglobin - the
capillaries susceptibility is reflected on the surrounding tissue,
so the surrounding field gradients are reduced. - T2* becomes
longer so the signal measured via the T2*-weighted pulse sequence
increases by a few percent
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animal study human HRF (HemRespFunc) BOLD: Blood oxygenated
level dependent (hemodynamic response)
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SUMMARY
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Krimer, Muly, Williams, Goldman-Rakic, Nature Neuroscience,
1998 Pial Arteries 10 m NoradrenergicDopamine Sub-cortical
CONFOUNDS Not only neuronal activity but noradrenergic or dopamine
activity affects BOLD !!
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Features of hemodynamic activity
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Percent Signal Change Peak / mean(baseline) Often used as a
basic measure of amount of processing Amplitude variable across
subjects, age groups, etc. Amplitude increases with increasing
field strength: 1.5T < 3T 500 505 200 205 1%
Correlation of Electrical and BOLD activities in monkey
(Logothetis)
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Dale & Buckner, 1997 Responses to consecutive presentations
of a stimulus add in a roughly linear fashion Subtle departures
from linearity are evident
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Linear Systems Scaling The ratio of inputs determines the ratio
of outputs Example: if Input 1 is twice as large as Input 2, Output
1 will be twice as large as Output 2 Superposition The response to
a sum of inputs is equivalent to the sum of the response to
individual inputs Example: Output 1+2+3 = Output 1 +Output 2
+Output 3
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Scaling (A) and Superposition (B) B A
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Linear additivity AB CD
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Refractory Periods Definition: a change in the responsiveness
to an event based upon the presence or absence of a similar
preceding event Neuronal refractory period Vascular refractory
period
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Refractory Effects in the fMRI Hemodynamic Response Huettel
& McCarthy, 2000 Time since onset of second stimulus (sec)
Signal Change over Baseline(%) Stimulus latency after initial
stimulus
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fMRI measurements are of amount of deoxyhemoglobin per voxel We
assume that amount of deoxygenated hemoglobin is predictive of
neuronal activity SUMMARY Variability in the Hemodynamic Response
Across Subjects Across Sessions in a Single Subject Across Brain
Regions Across Stimuli Relative measures fMRI provides relative
change over time Signal measured in arbitrary MR units Percent
signal change over baseline
2. Imaging the function: experimental setup Subject lies in the
scanner awaiting for commands from the scanner operator: - a 3d
high-resolution MRI is collected for high precision localization -
multiple runs of an experimental protocol is performed next. At
this phase, the subject is presented with auditory, visual or
tactile stimulation. Stimulus presentation is achieved through
headphones, goggles/screen, air pumps As the subject performs the
experiment behavioral/physiological data is collected through voice
recording, push-buttons, electrodes on the head/feet (either for
eeg or for heart rate, skin conductance) Stimulus presentation and
recording of subject response is done via a pc synchronized to the
rf pulses of the scanner 3 msec 100 msec
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t 2 5 8 11 14 I : Change of intensity of an active voxel in
time I t 2 5 8 11 14 I : Change of intensity of a passive voxel in
time I t (sec) 0 2 5 8 11 14.......... 300.......... responses and
images slice j fMR experiment impulse fMRI experiments Data
acquisition
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How large are anatomical voxels? .9375mm 5.0mm .9375mm =
~.004cm 3 Within a typical brain (~1300cm 3 ), there may be about
300,000+ anatomical voxels.
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How large are functional voxels? 3.75mm 5.0mm 3.75mm = ~.08cm 3
Within a typical brain (~1300cm 3 ), there may be about 20,000
functional voxels.
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sample 6 slice T2* functional acquisition
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Partial Volume Effects A single voxel may contain multiple
tissue components Many gray matter voxels will contain other tissue
types Large vessels are often present The signal recorded from a
voxel is a combination of all components
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fMRI experimental paradigms
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Trial Averaging: Does it work? Static signal, variable noise
Assumes that the MR data recorded on each trial are composed of a
signal + (random) noise Effects of averaging Signal is present on
every trial, so it remains constant when averaged Noise randomly
varies across trials, so it decreases with averaging Thus, SNR
increases with averaging
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Caveats Signal averaging is based on assumptions Data = signal
+ temporally invariant noise Noise is uncorrelated over time If
assumptions are violated, then averaging ignores potentially
valuable information Amount of noise varies over time Some noise is
temporally correlated (physiology) Response latency may vary This
is why averaging methods are useless in fMRI
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fMRI Paradigms
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fMRI paradigms There are 2 major paradigms for acquisition of
fMRI: - block design - event related design
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fMRI block design Task waveform t 5-6 samples Measures
cumulative activity in the ON block Signal amplitude is about
1.5-3% in 1.5T scanner signal amplitude
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fMRI event-related design OVERALL Task Impulse rapid
designstandard design t Measures single event activity Signal
amplitude is about 1% in 3T Task Impulse Signal Amplitude
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What temporal resolution do we want? 10,000-30,000ms: Arousal
or emotional state 1000-10,000ms: Decisions, recall from memory
500-1000ms: Response time 250ms: Reaction time 10-100ms: Difference
between response times Initial visual processing 10ms: Neuronal
activity in one area fMRI
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Basic Sampling Theory Nyquist Sampling Theorem To be able to
identify changes at frequency X, one must sample the data at
(least) 2X. For example, if your task causes brain changes at 1 Hz
(every second), you must take two images per second.
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Aliasing Mismapping of high frequencies (above the Nyquist
limit) to lower frequencies Results from insufficient sampling
Potential problem for long TRs and/or fast stimulus changes Also
problem when physiological variability is present
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Sampling Rate in Event-related fMRI
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Costs of Increased Temporal Resolution Reduced signal amplitude
Shorter flip angles must be used (to allow reaching of steady
state), reducing signal Fewer slices acquired Usually, throughput
expressed as slices per unit time
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fMRI problems
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experimental problems Some important problems that get in the
way for better data acquisition in fMRI: - venous flow artifacts
Any signal larger than 5% change is probably due to venous activity
so it should be discarded - head motion Could be correlated with
the task. May be avoided with bite bars or head-stabilization
devices - scanner noise Creates problems with the auditory tasks
during the rest period. Also distracts the subject - small SNR The
fMRI signal is on the range of 1-3%
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fMRI data analysis techniques
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The fMRI Linear Transform Schematic of the data obtained
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fMRI Preprocessing
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preprocessing
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What is preprocessing? Correcting for non-task-related
variability in experimental data Usually done without consideration
of experimental design; thus, pre-analysis Occasionally called
post-processing, in reference to being after acquisition Attempts
to remove, rather than model, data variability
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Quality assurance
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Preprocessing Alignment of slice timings It takes about 2 sec
to finish one functional 3d acquisition. During this time, there
will be a time difference between the hemodynomic responses sampled
from slice 1 versus the last slice, slice n. This needs to be
corrected for, by shifting the individual intensity data in each
slice t (sec) t=0 t=1.6 sec
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Preprocessing Head Motion correction All 3d functional images
(samples) should be aligned with the single anatomic image
collected at the beginning or end of the session t (sec)
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Head Motion: Good, Bad,
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Why does head motion introduce problems? ABC When you look at
the time course of a single voxel, this is a specific voxel in the
data matrix, not a specific voxel in the brain. When head moves,
the data matrix stays same but the voxel assignment in the brain
changes. You are no longer looking at the same voxel
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Correcting Head Motion Rigid body transformation 6 parameters:
3 translation, 3 rotation Minimization of some cost function E.g.,
sum of squared differences Mutual information 3dVolreg in AFNI
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Prevention of head motion !!!
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fMRI Block design data analysis
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What are Blocked Designs? Blocked designs segregate different
cognitive processes into distinct time periods Task ATask BTask
ATask BTask ATask BTask ATask B Task ATask BREST Task ATask
BREST
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What baseline should you choose? Task A vs. Task B Example:
Squeezing Right Hand vs. Left Hand Allows you to distinguish
differential activation between conditions Does not allow
identification of activity common to both tasks Can control for
uninteresting activity Task A vs. No-task Example: Squeezing Right
Hand vs. Rest Shows you activity associated with task May introduce
unwanted results
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Choosing Length of Blocks Longer block lengths allow for
stability of extended responses Hemodynamic response saturates
following extended stimulation After about 10s, activation reaches
max Many tasks require extended intervals Processing may differ
throughout the task period Shorter block lengths allow for more
transitions Task-related variability increases (relative to
non-task) with increasing numbers of transitions Periodic blocks
may result in aliasing of other variance in the data Example: if
the person breathes at a regular rate of 1 breath/5sec, and the
blocks occur every 10s
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Non-Task Processing In many experiments, activation is greater
in baseline conditions than in task conditions! Requires
interpretations of significant activation Suggests the idea of
baseline/resting mental processes Emotional processes
Gathering/evaluation about the world around you Awareness (of self)
Online monitoring of sensory information Daydreaming
Data analysis techniques: block design - subtraction intensity
samples X1X1 X2X2 X3X3 XiXi XjXj XkXk yiyi yjyj ykyk active if :
Threshold (average(Y i ) - average(X i )) > a y1y1 y2y2 y3y3
This method is outdated color code
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The Hemodynamic Response Lags Neural Activity Experimental
Design Convolving HDR Time-shifted Epochs Introduction of Gaps
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Data analysis techniques: block design - correlation Sinusoidal
waves: X i, Y i, Z i Square wave (ideal fmri signal): T i (in
reality, we observe t) Find: sum( (X i -avg(X)) (t i -avg(t))) /
stdev(X)*stdev(t)*(N-1) sum( (Y i -avg(Y)) (t i -avg(t))) /
stdev(Y)*stdev(t)*(N-1) sum( (Z i -avg(Z)) (t i -avg(t))) /
stdev(Z)*stdev(t)*(N-1) choose MAX
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Data analysis techniques: block design - t_test Samples: X i, Y
i (N samples each) Find: (X i -avg(X)) (Y i -avg(Y))) /
SQRT(stdev(X) 2 *stdev(Y) 2 ) Look-up table for probability value
wrt degrees of freedom: (number of points -1 which is 2N-2 here) if
prob
