Open AccessNonlinear manifold learning in functional magnetic resonance imaging uncovers a low‐dimensional space of brain dynamics
Author(s) -
Gao Siyuan,
Mishne Gal,
Scheinost Dustin
Publication year - 2021
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.25561
Subject(s) - functional magnetic resonance imaging , embedding , resting state fmri , nonlinear dimensionality reduction , nonlinear system , space (punctuation) , task (project management) , computer science , artificial intelligence , dynamics (music) , cognition , brain activity and meditation , state space , brain mapping , neuroscience , psychology , pattern recognition (psychology) , physics , mathematics , dimensionality reduction , electroencephalography , quantum mechanics , operating system , pedagogy , statistics , management , economics
Abstract Large‐scale brain dynamics are believed to lie in a latent, low‐dimensional space. Typically, the embeddings of brain scans are derived independently from different cognitive tasks or resting‐state data, ignoring a potentially large—and shared—portion of this space. Here, we establish that a shared, robust, and interpretable low‐dimensional space of brain dynamics can be recovered from a rich repertoire of task‐based functional magnetic resonance imaging (fMRI) data. This occurs when relying on nonlinear approaches as opposed to traditional linear methods. The embedding maintains proper temporal progression of the tasks, revealing brain states and the dynamics of network integration. We demonstrate that resting‐state data embeds fully onto the same task embedding, indicating similar brain states are present in both task and resting‐state data. Our findings suggest analysis of fMRI data from multiple cognitive tasks in a low‐dimensional space is possible and desirable.