Enhancing Prediction of Forelimb Movement Trajectory through a Calibrating-Feedback Paradigm Incorporating RAT Primary Motor and Agranular Cortical Ensemble Activity in the Goal-Directed Reaching Task

Int J Neural Syst. 2023 Aug 24:2350051. doi: 10.1142/S012906572350051X. Online ahead of print.ABSTRACTComplete reaching movements involve target sensing, motor planning, and arm movement execution, and this process requires the integration and communication of various brain regions. Previously, reaching movements have been decoded successfully from the motor cortex (M1) and applied to prosthetic control. However, most studies attempted to decode neural activities from a single brain region, resulting in reduced decoding accuracy during visually guided reaching motions. To enhance the decoding accuracy of visually guided forelimb reaching movements, we propose a parallel computing neural network using both M1 and medial agranular cortex (AGm) neural activities of rats to predict forelimb-reaching movements. The proposed network decodes M1 neural activities into the primary components of the forelimb movement and decodes AGm neural activities into internal feedforward information to calibrate the forelimb movement in a goal-reaching movement. We demonstrate that using AGm neural activity to calibrate M1 predicted forelimb movement can improve decoding performance significantly compared to neural decoders without calibration. We also show that the M1 and AGm neural activities contribute to controlling forelimb movement during goal-reaching movements, and we report an increase in the power of the local field potential (LFP) in beta and gamma bands over AGm in response to a change...

Source: International Journal of Neural Systems - August 26, 2023 Category: Neurology Authors: Han-Lin Wang Yun-Ting Kuo Yu-Chun Lo Chao-Hung Kuo Bo-Wei Chen Ching-Fu Wang Zu-Yu Wu Chi-En Lee Shih-Hung Yang Sheng-Huang Lin Po-Chuan Chen You-Yin Chen Source Type: research