Individual-Specific fMRI-Subspaces Improve Functional Connectivity Prediction of Behavior

Abstract

There is significant interest in using resting-state functional connectivity (RSFC) to predict human behavior. Good behavioral prediction should in theory require RSFC to be sufficiently distinct across participants; if RSFC were the same across participants, then behavioral prediction would obviously be poor. Therefore, we hypothesize that removing common resting-state functional magnetic resonance imaging (rs-fMRI) signals that are shared across participants would improve behavioral prediction. Here, we considered 803 participants from the human connectome project (HCP) with four rs-fMRI runs. We applied the common and orthogonal basis extraction (COBE) technique to decompose each HCP run into two subspaces: a common (group-level) subspace shared across all participants and a subject-specific subspace. We found that the first common COBE component of the first HCP run was localized to the visual cortex and was unique to the run. On the other hand, the second common COBE component of the first HCP run and the first common COBE component of the remaining HCP runs were highly similar and localized to regions within the default network, including the posterior cingulate cortex and precuneus. Overall, this suggests the presence of run-specific (state-specific) effects that were shared across participants. By removing the first and second common COBE components from the first HCP run, and the first common COBE component from the remaining HCP runs, the resulting RSFC improves behavioral prediction by an average of 11.7% across 58 behavioral measures spanning cognition, emotion and personality.

Focality-Oriented Selection of Current Dose for Transcranial Direct Current Stimulation

“…We have shown how DTDI can be used for a target ROI. However, a user may be interested in stimulating multiple target ROIs, or a network of ROIs specific to a particular functionality [ 82 , 83 , 84 , 85 , 86 , 87 , 88 ]. This may be because studies have shown that more than one brain area can be involved in neuropsychiatric disorders like depression [ 89 , 90 ].…”

Discovering the Computational Relevance of Brain Network Organization

“…For instance, there have been significant advances in characterizing how the synchronization of neural time series during tasks -task-state functional connectivity -relates to ongoing cognitive processes. While links between resting-state functional connectivity and cognitive ability (estimated via individual difference correlations) have been widely reported [21,22,[81][82][83], changes to the underlying functional network organization from resting state to task state can shed light on how those network changes contribute to task-related cognitive processes [30,34].…”

Group Linear non-Gaussian Component Analysis with Applications to Neuroimaging

“…Our framework is related to Joint and Individual Variation Explained (Lock et al, 2013) and Common Orthogonal Basis Extraction (COBE) (Zhou et al, 2015). Individual subspaces in rsfMRI improved the prediction of behavior (Kashyap et al, 2019). This is also a promising direction for future research in group LNGCA.…”