functional mri for clinical neuroscience
TRANSCRIPT
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Functional MRI for Clinical Neuroscience
Danny JJ Wang, PhD, MSCE Ahmanson-Lovelace Brain Mapping Center
Department of Neurology UCLA
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Outline 1. BOLD fMRI
• Functional hyperemia and common confounding factors
• Task activation BOLD fMRI – Memory encoding in epilepsy; Pre-surgical mapping for brain tumor
• Resting state BOLD fMRI - Functional connectivity, network analysis and applications
2. ASL perfusion MRI and clinical application 3. Calibrated fMRI and alternative fMRI
methods
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Neurovascular Coupling
With neural activity, increases in oxygen and glucose consumption are followed by an increase in CBF. CBF and glucose consumption are similar
in magnitude, oxygen consumption increases much less than CBF
Fox P et al Science 1988
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Mechanism of Neurovascular Coupling
Diagram of Neurovascular Coupling D'Esposito et al. Nat Neurosci Rev. 2003
• Diffusion of K+, H+, adenosine, NO from synapses
• Glutamate/glutamine cycle within astrocytes
• Direct innervation of smooth muscle cells by neurons/axons
• Linearity between CBF and neuronal activity is NOT always warranted
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Biophysics Mechanism of BOLD fMRI
OxyHb is diamagnetic and deoxyHb is paramagnetic and destroy local magnetic field
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Electrophysiological Correlates of BOLD
Local field potential (LFP) is the best correlate of BOLD signal Logothetis et al. Nature 2003
Structural MRI
BOLD fMRI
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Genetic Effects on Neurovascular Coupling
Diagram of Neurovascular Coupling Iadecola & Nedergaard, Nat Neurosci 2007
Hahn et al. Cerebral Cortex 2011
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Drug (Caffeine) Effects on Neurovascular Coupling
Chen & Parrish, Neuroimage 2009
20-30% CBF reduction by caffeine
Increased BOLD response by 30% at 2.5mg/kg dose
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Hormonal (Estrogen) Effects on Neurovascular Coupling
Dietrich et al, Neuroimage 2001
Word stem completion Mental rotation
Male Male
Female peak estrogen Female peak estrogen low estrogen low estrogen
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Pathophysiological Effects on Neurovascular Coupling
Pineiro et al, Stroke 2002 Roc et al, Stroke 2006
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Standard Procedures of BOLD fMRI Gradient-echo EPI
Motion correction
Detrending /
Temporal filtering
Spatial smoothing
General Linear Model (GLM) analysis of fMRI
Design matrix
β
“Activation map” G =
Data (X)
Voxels ->
Modeled time course
Software: SPM FSL AFNI Brainvoyager
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Seizure Localization using fMRI
WADA Test Rabin et al Brain 2004
Memory encoding fMRI
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Prediction of memory decline using fMRI
Rabin et al Brain 2004
Correlation between fMRI asymmetry and WADA test
Prediction of post-surgery memory change using fMRI
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Prediction of memory decline using fMRI
Dupont et al Radiology 2010
ROC curve using delayed recall fMRI as predictor of memory change post surgery
Binder et al Neurosurg Clin N Am 2011
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Pre-surgical mapping of brain tumor using fMRI
Sunaert S. JMRI 2006
Landmarks were used localize functional areas (Left: motor cortex; Right: Language areas including Broca’s and Wernicke’s area)
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Pre-surgical mapping of brain tumor using fMRI
Sunaert S. JMRI 2006
FMRI procedures for pre-surgical mapping of language, auditory and motor functions, as well as the combination with DTI
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Comparison of fMRI with direct cortical stimulation
Roux et al. Neurosurgery 2003
Imperfect correlation between fMRI and DCS was found for language areas (Sensitivity = ~30%, Specificity = 97%)
The agreement between fMRI and DCS for motor centers was found to be 84%; for sensory centers it was 83%.
Majos et al. Eur J Radiol 2005
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BOLD fMRI is based on neurovascular coupling and interplay of CBF, CBV and CMRO2.
Clinical BOLD fMRI is feasible and show promising results in epilepsy and pre-surgical mapping of brain tumor.
Caveats exist for interpreting BOLD signals.
BOLD signal may be limited by sensitivity and spatiotemporal resolution of fMRI methods.
Summary of Task Activation BOLD FMRI
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Resting State fMRI and Functional connectivity
Task Activation Resting State FC
Biswal et al MRM 1995
Resting Brain Function
Activation
Functional connectivity (FC) analysis based on cross-correlation revealed brain networks with synchronized activity
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Default Mode Network (DMN) and Functional Connectivity
Intrinsic correlations in spontaneous fluctuations in the fMRI BOLD signal between a seed region in the posterior cingulate cortex (PCC) and all other voxels in the brain. Functional and structural connectivity may not overlap.
Fox MD et al. PNAS 2005; Greicius MD et al. Cereb Cortex 2009
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Default Mode Network (DMN) in AD
Reduced DMN activity in PCC and hippocampus in AD vs. elderly control subjects
Greicius MD et al. PNAS 2004; Cereb Cortex 2011
Control AD
DMN (yellow) and PiB (blue) overlap (red)
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Independent Component Analysis (ICA) of Resting State FMRI
Calhoun & Adali IEEE Rev BME 2012
Control AD
Common software: GIFT, FSL (MELODIC)
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Independent Component Analysis (ICA) of Resting State FMRI
Smith S M et al. PNAS 2009
Ten well-matched pairs of networks from the 20-component analysis of the 29,671-subject BrainMap activation database and (a completely separate analysis of) the
36-subject resting FMRI dataset.
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Graph Theoretical Network Metrics
• Small world / biological networks have relatively good clustering (high local efficiency) with short cuts (low path length/high global efficiency), good fault tolerance, integrated processing, and information efficiency.
Graph Metric Regular/Lattice
Random Small World/ Biological Networks
Global Efficiency Low High High
Local Efficiency High Low High
Degree (# edges) Distribution
Unimodal Gaussian Power law, truncated power law
Betweenness Centrality
Unimodal Gaussian Nodes with really high BC (hubs)
Modularity Low Low High
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Graph Theory: Complex Brain Networks
Sporns & Bullmore, Nat Neurosci Rev 2009
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http://en.wikipedia.org
H(X) is Shannon entropy p(xi) is the probability mass function of outcome xi b is the base of the logarithm used
Information Theory and Entropy
H(X) is conditional entropy p(xi,yj) is the probability that X=xi and Y=yj. This quantity should be understood as the amount of randomness in the random variable X given that you know the value of Y
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Liu et al. JMRI (2013); Smith et al BIB (2014)
Multi-Scale Entropy (MSE) of RS-FMRI in Aging
Mean GM MSE difference between young and elderly subjects
Regional MSE difference between 25 MCI (CDR=0.5) and 25 control (CDR=0)
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Resting state fMRI is synchronized between brain regions belonging to networks
RS-fMRI can be analyzed by cross-correlation, ICA, graph theory and information theory
Clinical applications in aging and dementia are promising
Need derive reliable clinical markers.
Summary of Resting State BOLD FMRI
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Imaging Slice
Arterial Tagging Plane
Continuous Adiabatic Inversion Geometry
Control Inversion Plane
B F
ield
Gra
dien
t
Single Slice Perfusion Image about 1% effect
Control - Label
Detre et al. MRM 1992, Williams et al. PNAS 1992
Arterial Spin Labeled (ASL) Perfusion MRI
CBF in “classical” units of ml/100g/min
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ASL Strategies
pCASL EPI pCASL GRASE PET CBF
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Comparison of ASL and FDG-PET
CBF (pCASL) CMRglc (FDG-PET)
Cha et al. JCBFM (2013)
20 healthy volunteers (23-59yrs) participated both ASL MRI and FDG-PET scans
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Perfusion MRI in Alzeimer’s Dementia
AD vs. CONTROL
Alsop et al Ann Neuroloy (2000)
AD vs. CONTROL
MCI vs. CONTROL
Johnson et al Radiology (2005) Alsop et al Neuroimage (2008)
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Representative AIS cases showing hypo-perfusion lesions
Wang et al Stroke (2012)
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Multi-delay Multi-parametric ASL in Acute Ischemic Stroke
Wang et al NI: Clinical (2013)
PCASL 3D GRASE with 4 delays (1.5, 2, 2.5, 3s) allows
estimation of ATT, CBF and arterial CBV (aCBV)
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3T CASL in Brain Tumor
Tumor grading and biopsy guiding Wolf et al. JMRI (2005)
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Delineation of Shunted flow in AVM
Wolf et al, AJNR 2008
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Seizure Localization using ASL
Oishi et al, Neurorad 2012
Ictal Perfusion
Interictal Perfusion
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Vessel Encoded pCASL (VE-PCASL)
Apply Gx or Gy in pCASL to encode L/R ICA and VA using Hadamard scheme
Wong EC MRM (2007)
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Vessel-encoded pCASL in AVM
VE-PCASL DSA Yu et al AJNR (Pre-accepted)
AUC = 0.95
Standard and custom labeling efficiencies are used to estimate supply fractions of feeding arteries.
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behavior or drug neural
function
metabolism
blood flow
biophysics***
***site/scan effects
Physiological Basis of fMRI/phMRI
disease
blood volume
BOLD fMRI ASL fMRI
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Cortical responses to amphetamine exposure studied by pCASL MRI
Nortin et al, Neuroimage, 2013
subject 1
0 2 4 6 8 10
subject 2
3050
70
subject 3
3050
70
subject 4 subject 5 subject 6
subject 7 subject 8
3050
70
subject 9
0 2 4 6 8 10
3050
70
subject 10 subject 11
0 2 4 6 8 10
subject 12
Cer
ebra
l Blo
od F
low
(m
L/10
0g/m
in)
Time after dose (h)
• 12 healthy subjects double blinded design • 6 20mg d-amphetamine; 6 placebo • ASL and blood samples were collected 10 time points during 10hr after dose
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ASL Vascular Reactivity in Hypertension
Hajjar et al Hypertension 2010
NC HT
Baseline 5% CO2 100% O2
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Calibrated fMRI with concurrent ASL/BOLD
Hoge et al PNAS 1999
Concurrent ASL/BOLD with CO2 and visual stimulation
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Concurrent ASL/BOLD fMRI
Gauthier et al Neurobio Aging 2013
Stroop Task CO2
BOLD
CBF
BOLD CBF
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ASL/BOLD Vascular Reactivity in AD
Yezhuvath et al Neurobio Aging 2012
BOLD CO2 reactivity ASL resting Perfusion
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CBV based fMRI - VASO
Lu et al MRM 2003
Inversion-recovery null the blood signal, VASO fMRI shows negative signal changes in response to brain activation
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ASL perfusion fMRI is an important quantitative tool complementing BOLD fMRI ASL perfusion fMRI has unique value in characterizing baseline brain function and pharmacological manipulations Concurrent ASL/BOLD is a promising tool for clinical fMRI Alternative fMRI methods exist such as VASO
Summary of ASL Perfusion MRI
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BOLD fMRI remains the main tool for clinical fMRI, and resting state fMRI is particularly promising.
ASL perfusion MRI is an important quantitative imaging method complementing BOLD fMRI. Combinations of BOLD/ASL and other imaging modalities (DTI, structural MRI) offer comprehensive evaluation of brain function.
Conclusion
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Acknowledgement
UCLA UPenn Jeff Alger, PhD John Detre, MD Lirong Yan, PhD Hee Kwon Song, PhD Emily Kilroy, MS Yiqun Xue, PhD David Liebeskind, MD Robert Smith, PhD International collaborators Liana Apostolova, MD Yan Zhuo, MS Noriko Salamon, MD PhD Matthias Guenther, PhD John Ringman, MD Songlin Yu, MD Collin Liu, MD Zengtao Zuo, BS NIH grants R01-MH080892, R01-NS081077 and R01-EB014922 Siemens Healthcare, Biogen IDEC