Anderson, A., Han, D., Douglas, P., Bramen, J., Cohen, M.

2011

“Real-time functional MRI Classification of BrainStates using Markov-SVM Hybrid Models: Peering

inside the rt-fMRI black box.”

rt-fMRI

• fMRI signal analyzed immediately• Opens the door for biofeedback and BCIs

– modulate insular cortex activity– navigate through mazes, communicate desired motor movements– Manage chronic pain

• Models that can train/predict online become very interesting• rt ML poses many challenges

– Choice of features– Nonstationarity– Computational expense– Interpretability

Paradigm

• Smoking cessation study• 51 subjects (pre and post)• Conditions– Rest– Neutral videos– Passive smoking provocation videos– ‘Resist craving’ smoking provocation videos– Auditory stimulus

Autocorrelation

• Hemodynamic Response Curve• Noise• Time course of cognitive states• Common classification algorithms don’t take

this into account• Are we losing valuable information?

Markov Models

• Models state as dependent upon previous state• Commonly used in fMRI analysis offline:– Markov Random Field (MRF) theory has been

typically used to process and analyze fMRI data– Hidden Markov Model (HMM) analyses are also

employed for fMRI activation detection, including voxel based modeling

– Markov Chain Monte Carlo (MCMC) sampling technique to extend modeling to group analysis

Markov Transition Matrices

Feature Selection

ICA

• 279 single session ICs were aligned• Dimensionality reduced by averaging within

ROIs (Harvard Oxford atlas)• 20,000 ICs -> 20 ICs via k-means clustering• 20 exemplars of underlying functional networks • Any given cognitive state can now be modeled

as a point in a 20 dimensional feature space defined by these “dictionary ICs”.

Model Selection

Model Types

A: Online SVM B: Online SVM Markov

C: SVM test trained offline, tested on a new scan from the same subjectD: SVM Markov test – hyperplane and transition matrix both learned from the training data

Model parameters• 64 total models were tested / contrasted• Online vs. Offline• SVM vs. SVM + Markov• ROI vs. IC – 110D ROI features– 20D IC features–

• Accuracy was averaged across 4 encoding paradigms– Video / audio– Task (video + audio) / rest– Video / audio / rest– Audio / rest / video crave / video resist / video neutral

Results

• Including the temporal information using a Markov transition matrix increased the predictive accuracy by roughly 23% (roughly equal across all other parameters).

• ROI-based models were impacted by demeaning and training (online or offline), but IC dictionary models did not have substantial changes in accuracy based on these changes.

• Using ROI features, demeaning increased SVM-offline accuracy (24%), but decreased SVM-online accuracy.

• With IC features, demeaning had little effect.

Discussion IC vs. ROI

• Individual ROIs cant be interpreted easily• Cognitive states are due to multiple regions acting

as a network• ICs potentially capture actual functional networks– calcarine sulcus and cuneus (vision)– ventral striatum and medial orbito-frontal gyrus

(craving)– Observed overlap between IC dictionary and GLM

results• ICs are more stable across model parameters

DiscussionMarkov methods

• Cognitive state changes are unlikely to be random events

• Removes certain unlikely state transitions• Essentially acts as a high pass filter

Limitations

• May be atlas-sensitive• Size of dictionary remains uninvestigated

parameter• This IC dictionary might not generalize well to non-

craving paradigms• Linear SVM – even with Markov transition matrices

– does not capture HDR or temporal dependencies of the features, only the cognitive states

• No task break-down presented