Dynamics of mesoscale brain network during decision-making learning revealed by chronic, large-scale single-unit recording

Associating unfamiliar stimuli with appropriate behavior through experience is crucial for survival. While task-relevant information has been found to be distributed across multiple brain regions, how regional nodes in this distributed network reorganize their functional interactions throughout learning remains to be elucidated. Here, we performed chronic, large-scale single-unit recording across 10 cortical and subcortical regions using ultra-flexible microelectrode arrays in mice performing a visual decision-making task and tracked mesoscale functional network dynamics throughout learning. Task learning reshaped interregional functional connectivity, leading to the emergence of a subnetwork involving visual and frontal regions during the acquisition of correct No-Go responses. This reorganization was accompanied by a more widespread representation of visual stimulus across regions, and a region’s network rank strongly predicted its peak timing of visual information encoding. Methods Behavioral experiments were controlled using custom Matlab (MathWorks) scripts together with digital I/O devices (Arduino Uno R3, Arduino) for trial events and triggers. Neural signals were amplified and filtered with the SpikeGadgets 1024-channel recording system (SpikeGadgets). Task-related behavioral events were converted to TTL pulses and simultaneously recorded by the SpikeGadgets system to ensure synchronization with neural data. Data analyses were conducted using custom Matlab scripts.