Slide 1
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Functional Imaging: A review of
fMRI, DTI and Non-invasive
Perfusion Imaging
Kristine Mosier DMD, Ph.D.
Neuroradiology & Imaging Science
Department of Radiology
Clinical fMRI, Chief Head & Neck Imaging
Associate Professor of Radiology, Neuroscience and
Biomedical Engineering
Indiana University School of Medicine & IUPUI
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Slide 2
Overview

Functional Brain Mapping
Neurophysiology and hemodynamic basis of BOLD
/ CBF.
 fMRI paradigms, data acquistion and processing.
 Clinical case examples.

Diffusion Tensor Imaging (DTI) & Fibertracking.
 Non-invasive Perfusion Imaging (Arterial Spin
Labeling).
 Cases

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Slide 3
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Slide 4
Functional Imaging: fMRI
Brain activity can be mapped using either
BOLD technique (Blood Oxygen Level
Dependent) or • rCBF.
 Both BOLD and CBF changes dependent on
neurovascular coupling.
 BOLD signal most closely correlated with LFP
(local field potentials).
 fMRI performed in the neurosurgical setting to
map eloquent cortex.

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Slide 5
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Slide 6
fMRI
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Slide 7
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BOLD mechanism: summary
Neuronal activity  focal net increase in
blood flow and oxygenation.
 Increase in focal oxygenated blood 
decrease in deoxyhemoglobin less T2*
effect  increase in signal intensity.

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Slide 8
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BOLD fMRI: contrast mechanism
Relative mismatch between O2
delivery and O2 extraction during
activation period
 Blood flow is increased to activated
regions of brain
 O2 extraction also increased, but less
than increase in O2 delivery
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Slide 11
BOLD fMRI: contrast mechanism
Thus increased oxyHb at post-capillary
level decreased deoxyHb
 DeoxyHb is paramagnetic
 decreases T2* (decreases signal)
 Decrease in local deoxyHb results in
increased signal intensity on T2*-wtd
images (≈ 1-5%)

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Slide 12
Basis of BOLD fMRI
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From: Moseley ME & Glover GH. NeuroImaging Clinics of North America; Functional
Neuroimaging 5(2): 161-191, 1995
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Slide 13
Basis of BOLD fMRI
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From: Moseley ME & Glover GH. NeuroImaging Clinics of North America; Functional
Neuroimaging 5(2): 161-191, 1995
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Slide 14
Raw Image Time Series
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visual stim
no stim
visual stim
no stim
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Slide 15
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Difference Image Time Series
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visual stim
no stim
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visual stim
no stim
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Slide 16
• Peak BOLD signal arises
at the level of the postcapillary venule .
•Problem: contribution to
signal from draining veins
• spatial, temporal artifact.
• Animal expt. at high
field (e.g. 7-9T) within
200 µm of LFP.
• Humans: several
studies BOLD accurate
to w/in 1cm of
electrode.
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Slide 17
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Slide 18
Echo Planar Imaging
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FFT
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k-space
image space
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Slide 19
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fMRI Data acquistion
Acquire time-series of fast images while
subject performs sensorimotor, language or
cognitive task
 Process time-series data using statistical
methods & compare signal change during
task performance with signal during rest /
baseline.
 Accurate processing: need to remove drift,
motion, physiological noise.

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Slide 20
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fMRI : Study Overview
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Patient Prepar ation
Wor kstation
Image Reconstr uction
and Processing
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Par adigm Design
Data Tr ansfer
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Statistical Maps Computation
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Visualization of Maps and Analysis
Data Acquisition
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Slide 21
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Slide 22
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Slide 23
fMRI Data: Statistical Analysis
Most commercially available and custom
algorithms use GLM (General Linear
Model).
 Y = M*a+e; Y = data vector, M = model of
amplitude response, a = response
amplitude, e = noise.
 Current scanner software (GE, Siemens)
has “real-time” processing using t-test:
BEWARE; not all noise & artifact
removed!

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Slide 24
fMRI Post-Procesing: FT paradigm
Raw EPI
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Slide 25
fMRI Post-Procesing: FT paradigm
Bas_MoCo
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Slide 26
Post-Processing: AFNI
#!/bin/bash
# set HOST = `hostname`
echo "subj=080821_HPA"
read subj
echo $subj
stadir=/data6/${subj}
refpath=/home/yang/fmriref
#TR=3s
#nslices=32
#frame=144
##tasks=(FT RB LB VS RM WG NA)
tasks=(BWchbd CLchbd)
#frames=(144 144 144 144 144)
##datadir=(8-fMRI-EPI-FT 22-fMRI-EPI-RB 15-fMRI-EPI-LB 29-fMRI-EPI-WG 36-fMRI-EPI-RM 43-fMRI-EPI-NA)
datadir=(10-ep2d_pace_stamp_BA 14-ep2d_pace_stamp_WG 16-ep2d_pace_stamp_NA 18-ep2d_pace_stamp_RM 20-ep2d_pace_stamp_PL 26-ep2d_pace_stamp_CS)
cd ${stadir}/afni
## segmentation mprage in SPM5 and generate brain only mask on segemented brain
## coregister T2 or T1 to mprage in SPM5 and then coregister EPI-MC.nii to rT2.nii or rT1.nii
## coregister *MC.nii in SPM5 --- estimate ONLY
##first convert these spm_output_files back to afni
#files=`ls r*MC*.nii`
#for file in ${files}
#do
#echo run for ${file}
#
origfile=${file#r}
#
\rm tmp*
#
3dresample -dxyz 2.5 2.5 3.5 -prefix tmp-rs -inset ${file}
#
3dresample -master tmp-rs+orig -inset ${origfile} -prefix tmp-orig-rs
#
3dWarpDrive -affine_general -base tmp-rs+orig -prefix ${origfile%.nii}-fix tmp-orig-rs+orig
#done
#files=`ls rMask*.nii`
#for file in ${files}
#do
#echo run for ${file}
#
origfile=${file#r}
#
\rm tmp*
#
3dresample -dxyz 2.5 2.5 3.5 -prefix tmp-rs -inset ${file}
#
3dresample -master tmp-rs+orig -inset ${origfile} -prefix tmp-orig-rs
#
3dWarpDrive -affine_general -base tmp-rs+orig -prefix ${origfile%.nii}-fix tmp-orig-rs+orig
#
###3dresample -master ${origfile} -inset ${file} -prefix tmp-rs
#
###3dWarpDrive -affine_general -base tmp-rs+orig -prefix ${origfile%.nii}-fix -twopass ${origfile}
#done
## now analyze each task
#echo all tasks are ${tasks[*]}
n=0
for task in ${tasks[*]}
do
echo run for $task
3dvolreg -verbose -Fourier -prefix ${subj}-${task}-MC.nii -base 0 -zpad 4 -tshift 0 -1Dfile ${subj}-${task}mc ${task}.nii
1dplot -ps -volreg -one -nopush -xlabel ${subj}-${task} ${subj}-${task}mc > ${subj}-${task}mc.ps
3dNotes -HH " " ${subj}-${task}-MC.nii.gz
3dAutomask -prefix Mask-${task} ${subj}-${task}-MC.nii.gz
3dDespike -prefix ${subj}-${task}-MCds ${subj}-${task}-MC.nii.gz
3dmerge -1blur_fwhm 5 -doall -prefix ${subj}-${task}-MCdssm ${subj}-${task}-MCds+orig
file=${subj}-${task}-MCdssm+orig
cp ${subj}-${task}mc mc
3dDeconvolve -input ${file} \
-progress 10000 \
-mask Mask-${task}+orig \
-polort A -num_stimts 8 \
-stim_file 1 ${refpath}/test_on.wav.1D -stim_label 1 'On' \
-stim_file 2 ${refpath}/test_off.wav.1D -stim_label 2 'Off' \
-stim_file 3 'mc[0]' -stim_base 2 -stim_label 3 Roll \
-stim_file 4 'mc[1]' -stim_base 3 -stim_label 4 Pitch \
-stim_file 5 'mc[2]' -stim_base 4 -stim_label 5 Yaw \
-stim_file 6 'mc[3]' -stim_base 5 -stim_label 6 dS \
-stim_file 7 'mc[4]' -stim_base 6 -stim_label 7 dL \
-stim_file 8 'mc[5]' -stim_base 7 -stim_label 8 dP \
-num_glt 4 \
-glt_label 1 'On' -gltsym 'SYM: +On' \
-glt_label 2 'Off' -gltsym 'SYM: +Off' \
-glt_label 3 'On-Off' -gltsym 'SYM: +On -Off' \
-glt_label 4 'Off-On' -gltsym 'SYM: +Off -On' \
-bucket ${subj}-${task}+mlr -nocout -tout -fout -rout -vout \
-fitts ${subj}-${task}+fit -errts ${subj}-${task}+ert -xsave \
-cbucket ${subj}-${task}+cbk -x1D ${task}
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3drefit -addFDR ${subj}-${task}+mlr+orig
#3dFDR -input ${subj}-${task}+mlr+orig -mask_file Mask-${task}-fix+orig -prefix ${subj}-${task}+mlrFDR
#3dNotes -HH " " ${subj}-${task}+mlrFDR+orig
((n=n+1))
done
Slide 27
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fMRI Post-Procesing: FT paradigm
Motion Parameters
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Slide 28
fMRI Post-Procesing: FT paradigm
<matrix
# ni_type = "11*double"
# ni_dimen = "144"
# ColumnLabels = "Run#1Pol#0 ; Run#1Pol#1 ; Run#1Pol#2 ; On#0 ; Off#0 ; Roll#0 ; Pitch#0 ; Yaw#0 ;
dS#0 ; dL#0 ; dP#0"
# ColumnGroups = "3@-1,1,6@0,8"
# RowTR = "2"
# GoodList = "0..143"
# NRowFull = "144"
# RunStart = "0"
# Nstim = "2"
# StimBots = "3,10"
# StimTops = "3,10"
# StimLabels = "On ; dP"
# Nglt = "4"
# GltLabels = "On ; Off ; On-Off ; Off-On"
# GltMatrix_000000 = "1,11,3@0,1,7@0"
# GltMatrix_000001 = "1,11,4@0,1,6@0"
# GltMatrix_000002 = "1,11,3@0,1,-1,6@0"
# GltMatrix_000003 = "1,11,3@0,-1,1,6@0"
# CommandLine = "3dDeconvolve -input 100712OOS-FT-dsMCewsm+orig -progress 30000 -automask -float polort A -num_stimts 8 -stim_file 1 /home/yang/doc/fmriref/p4on5off_on_wav.1D -stim_label 1 On -stim_file 2
/home/yang/doc/fmriref/p4on5off_off_wav.1D -stim_label 2 Off -stim_file 3 &apos;mc[0]&apos; -stim_base 2 stim_label 3 Roll -stim_file 4 &apos;mc[1]&apos; -stim_base 3 -stim_label 4 Pitch -stim_file 5 &apos;mc[2]&apos;
-stim_base 4 -stim_label 5 Yaw -stim_file 6 &apos;mc[3]&apos; -stim_base 5 -stim_label 6 dS -stim_file 7
&apos;mc[4]&apos; -stim_base 6 -stim_label 7 dL -stim_file 8 &apos;mc[5]&apos; -stim_base 7 -stim_label 8 dP num_glt 4 -glt_label 1 On -gltsym &apos;SYM: +On&apos; -glt_label 2 Off -gltsym &apos;SYM: +Off&apos; glt_label 3 On-Off -gltsym &apos;SYM: +On -Off&apos; -glt_label 4 Off-On -gltsym &apos;SYM: +Off -On&apos; bucket 100712OOS-FT+mlr -nocout -tout -fout -rout -vout -fitts 100712OOS-FT+fit -errts 100712OOS-FT+ert xsave -cbucket 100712OOS-FT+cbk -x1D FT"
#>
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Slide 29
fMRI Post-Procesing: FT paradigm
Intermediate T-map
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fMRI Post-Procesing: FT paradigm
Design
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fMRI Post-Procesing: FT paradigm
Mean + T-map
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fMRI Post-Procesing: FT paradigm
GLM
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Slide 33
fMRI: Typical Tasks

Sensorimotor
Gross motor: Finger-tapping, tongue tapping
Fine motor: object manipulation (Mosier)
 Sensory: Visual field / retinotopic mapping
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Language: expressive & receptive speech
Expressive speech: Word generation, Object naming,
Rhyming
 Receptive speech: Passive Listening, Rhyming
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Memory: Working memory
 Other: Swallowing / articulated speech (Mosier)
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Not yet standardized: ASFNR working on that!
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Slide 34
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fMRI: Choice of Tasks
Tasks
Location
Gross
Motor
Fine
Motor
Language
Frontal
+
+ /-
+
Parietal
+
+
+
Temporal
+/-
Occipital
Insular
Visual
Field
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+ /-
Object
Naming
+
Other
+
+
+
Working
Memory
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+
+
+
+
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Slide 35
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fMRI: Patient Selection
Intracranial lesion requiring eloquent cortex
mapping.
 Patient able to undergo MR imaging at 1.5T or
3T (only 3T at IUPUI).

AVM patients: specific clips not safe @ 3T.
 Stents; not all safe @ 3T
 Body habitus
 Claustrophobia

Able to speak & understand English
 Able to read @ 6th grade level.
 Peds + /
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Slide 36
Clinical fMRI: Neurosurgical Mapping
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A
fMRI
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ESC
(Language rhyming task )
IU Radiology fMRI
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Slide 37
Case 1: Oligodendroglioma; Bilateral Finger
tapping
Pre-central gyrus
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Central sulcus
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Slide 38
Case 1 Oligodendroglioma; Object manipulation –right
hand: fine motor, sensory – tactile, proprioception
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Slide 39
Case 1: Oligodendroglioma Language: Word
Generation: speech execution
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Activation
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Slide 40
Case 1: Oligodendroglioma Language: Naming;
semantic language: speech reception and
execution
Noise
Activation
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Noise
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Slide 41
Case 1: Oligodendroglioma Language: Rhyming;
semantic language
Noise
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Slide 42
Case 2: Finger-tapping
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Slide 43
Case 2: Object Manipulation
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Slide 44
Case 2: Working Memory
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fMRI Brain Mapping
Advantages:
 Non-invasive mapping of eloquent cortex w/
maps co-registered to anatomical images.

In many institutions, this has completely replaced
WADA testing.
Disadvantages:
 Not all subjects are candidates: MRI safety,
patient must be awake & cooperative, peds.
 Requires a team with expertise: neuroradiology,
neurosurgery, neuropsych., MR physicists, image processing
specialists, etc.

Indirect measure of neuronal activity.
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Slide 46
Case 3:WHO Grade II oligoastrocytoma
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Slide 47
Case 3:WHO Grade II oligoastrocytoma
FT
TT
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Naming
Rhyming
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Slide 48
Case 3:WHO Grade II oligoastrocytoma
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Slide 49
Case 4: Visual Cortex, Awake
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Slide 50
Case 4: Visual Cortex, Awake
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Note: patient has granted permission for his photos to be used for publication
and teaching
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Slide 51
Case 4: Visual Cortex, Awake
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Note: patient has granted permission for his photos to be used for publication
and teaching
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Slide 52
Case 4:Oligoastrocytoma < Gr III
Left Chkbd
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Face
Matching
Right Chkbd
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Slide 53
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Fig. 9.3
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Slide 57
MR Diffusion Tensor Imaging
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In liquids
In tissues
Anisotropy
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Slide 58
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Slide 59
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Slide 60
DTI
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Slide 62
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Case 5 - DTI/FA
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Slide 64
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Slide 65
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Slide 66
DTI: Fiber Tracking
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Adapted from:
Descoteaux et al. 2007
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Slide 67
DTI at 3T
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Slide 68
Case 1 DTI
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Slide 69
Case 2: DTI
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Slide 70
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A
B
C
D
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Slide 71
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Slide 72
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Slide 73
Case 6: 54 y.o. w/partial complex seizures & speech
arrest
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Slide 74
Case 6: Bilateral Finger-tapping
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Slide 75
Case 6: Word Generation
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Slide 76
Case 6: Naming
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Slide 77
Case 6: DTI
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Slide 78
Case 6: Perfusion (ASL)
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Slide 79
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