Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

It is now appreciated that condition-relevant information can be present within distributed patterns of functional magnetic resonance imaging (fMRI) brain activity, even for conditions with similar levels of univariate activation. Multi-voxel pattern (MVP) analysis has been used to decode this information with great success. FMRI investigators also often seek to understand how brain regions interact in interconnected networks, and use functional connectivity (FC) to identify regions that have correlated responses over time. Just as univariate analyses can be insensitive to information in MVPs, FC may not fully characterize the brain networks that process conditions with characteristic MVP signatures. The method described here, informational connectivity (IC), can identify regions with correlated changes in MVP-discriminability across time, revealing connectivity that is not accessible to FC. The method can be exploratory, using searchlights to identify seed-connected areas, or planned, between pre-selected regions-of-interest. The results can elucidate networks of regions that process MVP-related conditions, can breakdown MVPA searchlight maps into separate networks, or can be compared across tasks and patient groups.

[1]  Matthew F. S. Rushworth,et al.  Frontal and Parietal Cortical Interactions with Distributed Visual Representations during Selective Attention and Action Selection , 2013, The Journal of Neuroscience.

[2]  A. Ishai,et al.  Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex , 2001, Science.

[3]  Marc N. Coutanche,et al.  Distinguishing Multi-voxel Patterns and Mean Activation: Why, How, and What Does It Tell Us? a Question of Spatial Frequency , 2022 .

[4]  Sean M. Polyn,et al.  Beyond mind-reading: multi-voxel pattern analysis of fMRI data , 2006, Trends in Cognitive Sciences.

[5]  Sharon L. Thompson-Schill,et al.  The advantage of brief fMRI acquisition runs for multi-voxel pattern detection across runs , 2012, NeuroImage.

[6]  Jarrod A. Lewis-Peacock,et al.  Multi-Voxel Pattern Analysis of fMRI Data , 2014 .

[7]  Robert T. Schultz,et al.  Multi-voxel pattern analysis of fMRI data predicts clinical symptom severity , 2011, NeuroImage.

[8]  Karl J. Friston,et al.  Psychophysiological and Modulatory Interactions in Neuroimaging , 1997, NeuroImage.

[9]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[10]  Michael S. Pratte,et al.  Decoding patterns of human brain activity. , 2012, Annual review of psychology.

[11]  Rainer Goebel,et al.  Information-based functional brain mapping. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[12]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

[13]  Sharon L. Thompson-Schill,et al.  Informational connectivity: identifying synchronized discriminability of multi-voxel patterns across the brain , 2013, Front. Hum. Neurosci..

[14]  Russell A. Poldrack,et al.  Analyses of regional-average activation and multivoxel pattern information tell complementary stories , 2012, Neuropsychologia.

[15]  A. Dale,et al.  Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System , 1999, NeuroImage.

[16]  Yu-Chin Chiu,et al.  Tracking cognitive fluctuations with multivoxel pattern time course (MVPTC) analysis , 2012, Neuropsychologia.

[17]  R W Cox,et al.  AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. , 1996, Computers and biomedical research, an international journal.