Biology subjects

Koh, N.

Publications and source records attributed to Koh, N..

3 recordsLinked to original sources

bioRxiv2025

Interactions between motor cortical forelimb regions and their influence on muscles reorganize across behaviors

It remains unclear how classical models of motor cortical hierarchy align with emerging evidence of behavioral organization in motor cortex. To address this, we combined optogenetic inactivation, Neuropixels recording, and electromyography to quantify the pattern and influence of activity in the mouse analogs of forelimb premotor and primary motor cortex (RFA and CFA) during reaching and climbing. Results revealed that RFAs dominant influence on forelimb muscles and on CFA during reaching is replaced by a dominant influence of CFA on muscles and on RFA during climbing, even when forelimb muscle activity during climbing resembles that during reaching. Short-latency influence between regions on excitatory and inhibitory populations in different cortical laminae also showed behavioral specificity. Simultaneous recordings in both areas during climbing revealed a loss of activity timing differences seen during reaching previously interpreted as reflective of hierarchy. These findings demonstrate that hierarchical interactions between forelimb motor cortical regions are behavior-specific.

neuroscience↗

bioRxiv2023

A distinct neural activity subspace for direct motor cortical influence on muscles

It remains poorly resolved when and how motor cortical output directly influences limb muscle activity through descending projections, which impedes mechanistic understanding of motor control. Here we addressed this in mice performing an ethologically inspired climbing behavior. We quantified the direct influence of forelimb primary motor cortex (caudal forelimb area, CFA) on muscles across the muscle activity states expressed during climbing. We found that CFA instructs muscle activity pattern by selectively activating certain muscles, while less frequently activating or suppressing their antagonists. From Neuropixels recordings, we identified linear combinations (components) of motor cortical activity that covary with these effects. These components differ partially from those that covary with muscle activity and differ almost completely from those that covary with kinematics. Collectively, our results reveal an instructive direct motor cortical influence on limb muscles that is selective within a motor behavior and reliant on a distinct neural activity subspace.

neuroscience↗

bioRxiv2019

A Common Architecture for Human and Artificial Cognition Explains Brain Activity Across Domains

The Common Model of Cognition (CMC) is a recently proposed, consensus architecture intended to capture decades of progress in cognitive science on modeling human and human-like intelligence. Because of the broad agreement around it and preliminary mappings of its components to specific brain areas, we hypothesized that the CMC could be a candidate model of the large-scale functional architecture of the human brain. To test this hypothesis, we analyzed functional MRI data from 200 participants and seven different tasks that cover a broad range of cognitive domains. The CMC components were identified with functionally homologous brain regions through canonical fMRI analysis, and their communication pathways were translated into predicted patterns of effective connectivity between regions. The resulting dynamic linear model was implemented and fitted using Dynamic Causal Modeling, and compared against six alternative brain architectures that had been previously proposed in the field of neuroscience (three hierarchical architectures and three hub-and-spoke architectures) using a Bayesian approach. The results show that, in all cases, the CMC vastly outperforms all other architectures, both within each domain and across all tasks. These findings suggest that a common set of architectural principles that could be used for artificial intelligence also underpins human brain function across multiple cognitive domains.

neuroscience↗