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Detection of Cognitive States from fMRI data using Machine Learning Techniques
Over the past decade functional Magnetic Resonance
Imaging (fMRI) has emerged as a powerful
technique to locate activity of human brain while
engaged in a particular task or cognitive state. We
consider the inverse problem of detecting the cognitive
state of a human subject based on the fMRI
data. We have explored classification techniques
such as Gaussian Naive Bayes, k-Nearest
Neighbour and Support Vector Machines. In order
to reduce the very high dimensional fMRI data, we
have used three feature selection strategies. Discriminating
features and activity based features
were used to select features for the problem of
identifying the instantaneous cognitive state given
a single fMRI scan and correlation based features
were used when fMRI data from a single time interval
was given. A case study of visuo-motor sequence
learning is presented. The set of cognitive
states we are interested in detecting are whether the
subject has learnt a sequence, and if the subject is
paying attention only towards the position or towards
both the color and position of the visual
stimuli. We have successfully used correlation
based features to detect position-color related cognitive
states with 80% accuracy and the cognitive
states related to learning with 62.5% accuracy
A supervised clustering approach for fMRI-based inference of brain states
We propose a method that combines signals from many brain regions observed in
functional Magnetic Resonance Imaging (fMRI) to predict the subject's behavior
during a scanning session. Such predictions suffer from the huge number of
brain regions sampled on the voxel grid of standard fMRI data sets: the curse
of dimensionality. Dimensionality reduction is thus needed, but it is often
performed using a univariate feature selection procedure, that handles neither
the spatial structure of the images, nor the multivariate nature of the signal.
By introducing a hierarchical clustering of the brain volume that incorporates
connectivity constraints, we reduce the span of the possible spatial
configurations to a single tree of nested regions tailored to the signal. We
then prune the tree in a supervised setting, hence the name supervised
clustering, in order to extract a parcellation (division of the volume) such
that parcel-based signal averages best predict the target information.
Dimensionality reduction is thus achieved by feature agglomeration, and the
constructed features now provide a multi-scale representation of the signal.
Comparisons with reference methods on both simulated and real data show that
our approach yields higher prediction accuracy than standard voxel-based
approaches. Moreover, the method infers an explicit weighting of the regions
involved in the regression or classification task
Pattern classification of valence in depression
Copyright @ The authors, 2013. This is an open access article available under Creative Commons Licence, CC-BY-NC-ND 3.0.Neuroimaging biomarkers of depression have potential to aid diagnosis, identify individuals at risk and predict treatment response or course of illness. Nevertheless none have been identified so far, potentially because no single brain parameter captures the complexity of the pathophysiology of depression. Multi-voxel pattern analysis (MVPA) may overcome this issue as it can identify patterns of voxels that are spatially distributed across the brain. Here we present the results of an MVPA to investigate the neuronal patterns underlying passive viewing of positive, negative and neutral pictures in depressed patients. A linear support vector machine (SVM) was trained to discriminate different valence conditions based on the functional magnetic resonance imaging (fMRI) data of nine unipolar depressed patients. A similar dataset obtained in nine healthy individuals was included to conduct a group classification analysis via linear discriminant analysis (LDA). Accuracy scores of 86% or higher were obtained for each valence contrast via patterns that included limbic areas such as the amygdala and frontal areas such as the ventrolateral prefrontal cortex. The LDA identified two areas (the dorsomedial prefrontal cortex and caudate nucleus) that allowed group classification with 72.2% accuracy. Our preliminary findings suggest that MVPA can identify stable valence patterns, with more sensitivity than univariate analysis, in depressed participants and that it may be possible to discriminate between healthy and depressed individuals based on differences in the brain's response to emotional cues.This work was supported by a PhD studentship to I.H. from the National Institute for Social Care and Health Research (NISCHR) HS/10/25 and MRC grant G 1100629
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