Transcript Functional connectivity

Functional connectivity:
Diseases of connectivity
Gwenaëlle Douaud
FMRIB, University of Oxford
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Diseases of connectivity
or disconnection?
• Lesion/degeneration/synaptic malfunction  structural connectivity  functional
connectivity (e.g., Cabral et al., 2012):
Abnormal functional connectivity in depression
chronic pain
Parkinson’s
Alzheimer’s
schizophrenia
• Functional connectivity impairment  disconnection syndrome, where “damage
to the connection results in deficit that is dinstinct both from damage to the target and
source regions” (Kleinschmidt & Vuilleumier, 2013)
Gerstmann syndrome:
acalculia
+finger agnosia
+left-right disorientation
+agraphia
Rusconi et al., 2009
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Resting-state fMRI:
advantages
• Increased signal-to-noise ratio (Fox & Greicius, Review 2010):
- at best, task-related modulation explains 20% of BOLD variance
- spontaneous ongoing activity explains 50-80% of BOLD variance
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Resting-state fMRI:
advantages
• Covers the entire repertoire of functional networks used by the brain in “action”
(Smith et al., 2009)
RSN: 36 healthy subjects
fMRI: ~7,300 maps, ~30,000 subjects
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Resting-state fMRI:
advantages
• Allows for a broader sampling of patient populations
 asleep, sedated, too impaired for task-based fMRI scanning, etc.
Greicius et al., 2008
• Is not confounded by task performance, effort, practice effects, etc.
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Resting-state fMRI:
inconvenients
• “Rest” is a task state in itself, with potential performance differences, rather than
differences in the underlying, stable brain organisation (Buckner et al., 2008, 2013)
 Might still reveal some meaningful differences, just need careful interpretation
• More susceptible to movement confounds:
 add motion parameters as covariate
 use ICA+FIX (automatic denoising using FSL tools: Salimi-Khorshidi et al., 2014,
Griffanti et al., 2014)
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Resting-state fMRI:
inconvenients
• Interpretation:
- no causality information (yet)  effective functional connectivity
- no easy interpretation what (a change in) + and – correlations mean
Smith et al., 2013
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Resting-state fMRI in disease:
reviews
• Mild cognitive impairment/Alzheimer’s disease:
- Dennis & Thompson, 2014
- Sheline & Raichle, 2013
• Movement disorders (esp. Parkinson’s disease):
- Poston & Eidelberg, 2012
• Psychiatric disorders (e.g., schizophrenia, ADHD, autism):
- Greicius, 2008
- Posner et al., 2014
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Resting-state fMRI analysis:
seed-based approach in Parkinson’s disease
• Seed-based approach - a priori knowledge/hypothesis
Parkinson’s disease: Helmich et al., 2010
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Resting-state fMRI analysis:
seed-based approach in Parkinson’s disease
• Seed-based approach - a priori knowledge/hypothesis
Parkinson’s disease: Helmich et al., 2010
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Resting-state fMRI analysis:
seed-based approach in Parkinson’s disease
• Seed-based approach - a priori knowledge/hypothesis
Parkinson’s disease: Helmich et al., 2010
 Functional compensation with anterior putamen “taking over” connections to IPC:
increased connectivity between IPC and anterior putamen in Parkinson’s was larger
for the least-affected side
• Very careful study:
- negative control with DMN
- corrected for motion (higher in patients)
- checked for the effect of tremor: no tremor versus tremor spatial map, regressing
out muscle activity (electromyography)
- checked effect of medication
- checked for grey matter volume differences of seeds and whole-brain VBM
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Resting-state fMRI analysis:
ICA-based approach in Alzheimer’s disease
• ICA-based approach – more exploratory (though can also be hypothesis-driven)
Alzheimer’s disease: Zamboni et al., 2013
Dual regression for
group comparisons
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Resting-state fMRI analysis:
ICA-based approach in Alzheimer’s disease
• ICA-based approach – more exploratory (though can also be hypothesis-driven)
Alzheimer’s disease: Zamboni et al., 2013
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Resting-state fMRI analysis:
ICA-based approach in Alzheimer’s disease
• ICA-based approach – more exploratory (though can also be hypothesis-driven)
Alzheimer’s disease: Zamboni et al., 2013
 Resting-state fMRI less confounds, task fMRI more interpretable:
“Increased frontal activity during a memory task overlaps with increased frontal
connectivity during rest in AD patients, suggesting that residual cognitive ability can be
assessed using resting fMRI.”
• Very careful study:
- same number of healthy and AD participants for ICA
- negative control with auditory RSN
- corrected for GM volume
- checked for the effect of physiological fluctuations (respiratory + cardiac activity)
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Resting-state fMRI analysis:
Graph-based approach in schizophrenia
• Graph theory – exploratory (though mostly no basal ganglia or cerebellum)
Schizophrenia: van den Heuvel et al., 2013
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Resting-state fMRI analysis:
Graph-based approach in schizophrenia
• Graph theory – exploratory (though mostly no basal ganglia or cerebellum)
Schizophrenia: van den Heuvel et al., 2013
 “Reduced level of rich club interconnectivity in patients with schizophrenia (…),
potentially resulting in decreased global communication capacity and altered functional
brain dynamics”
• Careful study:
- includes basal ganglia
- used Freesurfer parcellation for ROIs (as opposed to AAL)
- replication dataset  effects not specific to Rich Club
- but: “This study did not reveal a clear association between
clinical metrics of patients and rich club organization”
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Resting-state fMRI analysis:
Multi-modal approach in motor neuron disease
• Combining information – diffusion tensor and tractography
Amyotrophic lateral sclerosis: Douaud, Filippini et al., 2011
Increase FC in ALS
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Resting-state fMRI analysis:
Multi-modal approach in motor neuron disease
• Combining information – diffusion tensor and tractography
Amyotrophic lateral sclerosis: Douaud, Filippini et al., 2011
• Careful registration (BBR + VBM)
Disease duration
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Resting-state fMRI analysis:
Multi-modal approach in motor neuron disease
• Combining information – diffusion tensor and tractography
Amyotrophic lateral sclerosis: Douaud, Filippini et al., 2011
 Higher functional connectivity not necessarily better
• Reconciling lower structural connectivity (SC) with higher functional connectivity?
corpus
callosum
GABAergic
interneurons
Innocenti, 2009
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Resting-state fMRI analysis:
Multi-modal approach in motor neuron disease
• Combining information – diffusion tensor and tractography
Amyotrophic lateral sclerosis: Douaud, Filippini et al., 2011
 Low SC + high FC in ALS
= loss of GABA interneurons
+ FC
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- GABA
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Resting-state fMRI analysis:
Multi-modal approach in neurodegenerative diseases
• Combining information – grey matter volume/structural covariance
Array of neurodegenerative disorders: Seeley et al., 2009
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Resting-state fMRI analysis:
Multi-modal approach in neurodegenerative diseases
• Combining information – grey matter volume/structural covariance
Array of neurodegenerative disorders: Seeley et al., 2009
 Dissociable networks for each disease
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Variability of results in fcMRI
Fox & Greicius, 2010
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Variability of results in fcMRI:
some guidelines
Parkinson’s:
Seeds in the striatum
DMN as negative control
Alzheimer’s:
RSN (ICA) involving frontal areas
auditory RSN as negative control
Fox & Greicius, 2010
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Variability of results in fcMRI:
some guidelines
+ careful registration
Fox & Greicius, 2010
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Variability of results in fcMRI:
some guidelines
+ careful registration
Fox & Greicius, 2010
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Variability of results in fcMRI:
movement
 “Scrub” the data, add motion parameters, or use ICA+FIX
Power et al., 2012
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Variability of results in fcMRI:
movement
 “Scrub” the data, add motion parameters, or use ICA+FIX
Salimi-Khorshidi et al., 2014
Griffanti et al., 2014
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Variability of results in fcMRI:
some guidelines
 Global signal regression, # of ICs etc.
Fox & Greicius, 2010
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Variability of results in fcMRI:
some guidelines
Fox & Greicius, 2010
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Variability of results in fcMRI:
stability of networks
• Inter-subject variability is higher in higher-order regions (Mueller et al., 2013)
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Interpretation
of functional connectivity results
• Some RSN are more stable than others
• Higher not necessarily better
• Always check for each contrast what happens in each cluster
 Absolute values of correlations matter
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Interpretation
of functional connectivity results
• Some RSN are more stable than others
• Higher not necessarily better
• Always check for each contrast what happens in each cluster
 It’s the absolute values of correlations that matter
• Bear in mind that change in correlations can be observed even in the absence of a
change in coupling (Friston, 2011)
 Changes in correlation between A and B could be caused by a change in correlation
elsewhere
 Changes in correlation could be caused by a change in SNR (e.g., heart rate
variability differs between two populations)
 Changes in correlation could be caused by a change in the amplitude of the
fluctuations
• Bear in mind that “resting” is to some extent also a task
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Special thanks to:
FMRIB, University of Oxford
- Steve Smith
- Eugene Duff
- Christian Beckmann
- Reza Salimi-Khorshidi
- Martin Turner
- Giovanna Zamboni
- Nicola Filippini
- Marina Charquero Ballester
THANK YOU
FOR YOUR ATTENTION
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