Conserved structures of neural activity in sensorimotor cortex of freely moving rats allow cross-subject decoding

• Published in: Nature communications Vol. 13; no. 1; pp. 7420 - 14
• Main Authors: Melbaum, Svenja, Russo, Eleonora, Eriksson, David, Schneider, Artur, Durstewitz, Daniel, Brox, Thomas, Diester, Ilka
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 02.12.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/378/2632/1663; 631/378/2632/2635; Activity patterns; Animals; Behavior; Cardiac Electrophysiology; Decoding; Electrodes; Generalization, Psychological; Hemispheric laterality; Human-computer interface; Humanities and Social Sciences; Interfaces; multidisciplinary; Neurons; Population structure; Rats; Science; Science (multidisciplinary); Sensorimotor Cortex; Somatosensory cortex
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-35115-6

Our knowledge about neuronal activity in the sensorimotor cortex relies primarily on stereotyped movements that are strictly controlled in experimental settings. It remains unclear how results can be carried over to less constrained behavior like that of freely moving subjects. Toward this goal, we developed a self-paced behavioral paradigm that encouraged rats to engage in different movement types. We employed bilateral electrophysiological recordings across the entire sensorimotor cortex and simultaneous paw tracking. These techniques revealed behavioral coupling of neurons with lateralization and an anterior–posterior gradient from the premotor to the primary sensory cortex. The structure of population activity patterns was conserved across animals despite the severe under-sampling of the total number of neurons and variations in electrode positions across individuals. We demonstrated cross-subject and cross-session generalization in a decoding task through alignments of low-dimensional neural manifolds, providing evidence of a conserved neuronal code. Conservation of the neural code across subjects is crucial for training brain-computer interfaces. Through alignment of neural manifolds, the authors show cross-subject generalization in the decoding of unconstrained behavior from sensorimotor cortex.