Computational approaches to fMRI analysis

July 7, 2017

Thanks to fast-increasing computing power, it is becoming more available to perform computationally intensive analysis on high-dimensional fMRI data. Also, during last two decades, traditional analysis of localizing brain regions associated cognitive functions has been challenged by connectivity analysis among multiple brain regions and multi-voxel pattern analysis considering spatial patterns of activity over ensemble of voxels. Currently, numerous algorithms and analysis tools have been adapted from fields of network theory and machine learning and available for cognitive neuroscientists analyzing fMRI data. 

 

Our lab has experiences in both of connectivity analysis and multi-voxel pattern classification (MVPA) for fMRI data. We have investigated how large-scale network interaction could support item-context memory formation (submitted). We used a relatively novel application of probabilisitic connectivity that considers inter-subject variability in functional networks. This method was superior to a conventional method averaging connectivity across subjects for group analysis in finding underlying modular structures of the network (see figure below).  Additionally, we developed a novel algorithm comparing modular structures between two networks in a statistically rigorous way using normalized mutual information (NMI). We are currently investigating how motor skill learning can be represented as reconfiguration of network structures in a more efficient way using dynamic network analysis (see Basset et al.). 

 

For the technique of multi-voxel pattern classification, we have shown that spatial activity patterns in the cerebellum could gradually discriminate two motor tasks (see figure below and PLoS Biology, 2015).

 

 

This technique could be applied to discriminate activity patterns encoding different contexts in associative memory task and context-dependent learning task. More interestingly, both of connectivity analysis and MVPA could be applied to reveal how non-invasive stimulation such as transcranial magnetic stimulaton (TMS) changes large-scale brain network and spatial activity pattern in a way of enhancing cognitive functions. 

 

 

 

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