Abstract
Multivariate Pattern Analysis (MVPA) has grown in importance due to its capacity to use both coarse and fine scale patterns of brain activity. However, a major limitation of multivariate analysis is the difficulty of aligning features across brains, which makes MVPA a subject specific analysis. Recent work by Haxby et al. (2011) introduced a method called Hyperalignment that explored neural activity in ventral temporal cortex during object recognition and demonstrated the ability to align individual patterns of brain activity into a common high dimensional space to facilitate Between Subject Classification (BSC). Here we examined BSC based on Hyperalignment of motor cortex during a task of motor imagery of three natural actions (lift, knock and throw). To achieve this we collected brain activity during the combined tasks of action observation and motor imagery to a parametric action space containing 25 stick-figure blends of the three natural actions. From these responses we derived Hyperalignment transformation parameters that were used to map subjects’ representational spaces of the motor imagery task in the motor cortex into a common model representational space. Results showed that BSC of the neural response patterns based on Hyperalignment exceeded both BSC based on anatomical alignment as well as a standard Within Subject Classification (WSC) approach. We also found that results were sensitive to the order in which participants entered the Hyperalignment algorithm. These results demonstrate the effectiveness of Hyperalignment to align neural responses across subject in motor cortex to enable BSC of motor imagery.
Highlights
Multivariate Pattern Analysis (MVPA) has grown in importance due to its capacity to use both coarse and fine scale patterns of brain activity
A 1-way mixed effect analysis of variance (ANOVA) was performed to compare the classification accuracies between Within Subject Classification (WSC), Between Subject Classification (BSC) based on anatomical alignment and BSC based on Hyperalignment
Support Vector Machine (SVM) classifiers were used to decode the motor imagery (MI) conditions of each individual subject using that subject’s anatomically aligned data and hyperaligned data
Summary
Multivariate Pattern Analysis (MVPA) has grown in importance due to its capacity to use both coarse and fine scale patterns of brain activity. Recent work by Haxby et al (2011) introduced a method called Hyperalignment that explored neural activity in ventral temporal cortex during object recognition and demonstrated the ability to align individual patterns of brain activity into a common high dimensional space to facilitate Between Subject Classification (BSC). Hyperalignment has been extended to regularized hyperalignment with a connection to Canonical Correlation Analysis[22] and to joint Singular Value Decomposition hyperalignment with an improvement in computational speed[23] Following on from this original work, two new algorithms, searchlight Hyperalignment and connectivity Hyperalignment have been developed to produce a common high-dimensional model of the whole cortex using either complex, dynamic audiovisual stimuli or resting state functional connectivity[24,25]. WSC of individual actions has already been demonstrated for both observed and imagined actions[4,5,6,7,8]
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