Search bioRxiv⌕ Search

Biology subjects

Dogadov, A.

Publications and source records attributed to Dogadov, A..

2 recordsLinked to original sources

Operant conditioning of cortical waves through a brain-machine interface

At the surface of the cerebral cortex, the dynamics of brain activity at the mesoscopic scale are characterized by waves of synchronized neuronal activity. These waves have been shown to impact the processing of sensory information, but can they be actively shaped by the subject in a goal directed manner? To address this question, we designed a fast widefield optical brain-machine interface for mice that can detect and reinforce individual traveling waves, which follow a specific displacement at the surface of the somatosensory cortex. Trained mice learned to generate these Conditioned Waves, which became progressively more stereotyped. The Conditioned Waves resulted from a reshaping of the cortical activity associated with limb movements, which included a sharp pre-movement cortical suppression that emerged with learning. Our work demonstrates that traveling cortical waves of neuronal activity can be subject to operant control. It provides evidence for the plasticity and functional relevance of large-scale cortical dynamics, and establishes a new paradigm for manipulating mesoscale brain activity.

neuroscience↗

Study of Motor Unit Action Potential Conduction Velocity and Firing Rate in Low Force Contractions using Empirical Mode Decomposition

Arrays of surface electrodes may be used to record individual motor unit action potential (MUAP) trains from the skin surface. Processing of the later gives access to direct estimation of instantaneous conduction velocity and firing rate of individual MUAPs, which are informative physiological features. However, the estimation of conduction velocity and firing rate may be sensitive to additive noise, which is always present in surface EMG recordings. In this paper, we propose to use the Ensemble Empirical Mode Decomposition (EEMD) to represent individual MU instantaneous conduction velocity and firing rate time-series as a sum of components (intrinsic mode functions) and to omit the components with energy lower than the expected noise level. This approach enables to denoise the conduction velocity and firing rate time-sequencies, extracted from the surface EMG recordings, and to unmask a physiological relationship between them.

bioengineering↗