Assistant Professor of Psychology Vikranth Bejjanki was the lead author of “Noise correlations in the human brain and their impact on pattern classification,” recently published by PLOS Computational Biology.
According to Bejjanki, “A central challenge in cognitive neuroscience is decoding mental representations from patterns of brain activity. With functional magnetic resonance imaging (fMRI), currently the dominant technique for imaging brain activation, multivariate decoding methods like multivoxel pattern analysis have produced numerous discoveries about the brain.
“However, what information these methods draw upon remains the subject of debate. Typically, each voxel (volumetric pixel) is thought to contribute information through its selectivity (i.e., how differently it responds to the classes being decoded).”
Bejjanki and his co-authors, Rava Azeredo da Silveira of Ecole Normale Supérieure in Paris, Jonathan D. Cohen of Princeton University, and Nicholas B. Turk-Browne of Yale University, show that “this interpretation downplays an important factor: multivoxel pattern analysis is also highly attuned to noise correlations (i.e., the extent to which noise in the activity of a voxel is correlated across voxels) between voxels with opposite selectivity.”
Bejjanki says “the findings in this article help elucidate the computational underpinnings of multivariate decoding in cognitive neuroscience and provide insight into the nature of neural representations in the human brain.”
PLOS Computational Biology is an official journal of the International Society for Computational Biology. According to its website, the journal “features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods.”
