Biology subjects

De Schrijver, S.

Publications and source records attributed to De Schrijver, S..

3 recordsLinked to original sources

bioRxiv2025

An intracortical brain-machine interface based on macaque ventral premotor activity

The majority of brain-machine interface (BMI) studies have focused on decoding intended movements based on neural activity of primary motor (M1) and dorsal premotor cortex (PMd). The ventral premotor cortex (PMv), and more specifically area F5c, has been implicated in object grasping and action observation, and may represent an alternative for motor BMI control due to its phasic modulation during action observation. Using chronically implanted Utah arrays in F5c, PMd, and M1 in two male macaques, we compared the efficacy of controlling a motor BMI based on neural activity of each area. PMv decoding reached similar or even higher success rates than M1 and PMd in a 2D cursor control task, especially when controlling for the number of motion selective channels that were used by the decoder. We found similar results during a 2D robot avatar control task in a simulated 3D environment. At both the multi-unit and the population level, neural responses were highly similar during the training phase (passive observation of cursor movements) and the online decoding phase, and only a small subset of neurons modulated its selectivity for the direction of motion. Thus, ventral premotor area F5c may represent an alternative for online motor BMI control. Significance statementWe present the first study on online cursor and robot avatar control using neural activity of ventral premotor cortical area F5c. Known for decades for the presence of mirror neurons, which are active during both action execution and action observation, area F5c can support online BMI control with performance comparable to that of dorsal premotor and primary motor cortex. The population dynamics in all three areas were highly similar between the training phase and the online decoding phase.

neuroscience↗

bioRxiv2025

Action observation responses in macaque frontal cortex

Neurons that are active during action execution and action observation (i.e. Action Observation/Execution Neurons, AOENs) are distributed across the brain in a network of parietal, motor, and prefrontal areas. In a previous study, we showed that most AOENs in ventral premotor area F5c, where they were discovered three decades ago, responded in a highly phasic way during the observation of a grasping action, did not require the perception of causality or a meaningful action, and even responded to static frames of the action videos. To assess whether these characteristics are shared with AOENs in other areas of the AOE network, we performed the first large-scale neural recordings during action execution and action observation in multiple frontal areas including dorsal premotor (PMd) area F2, primary motor (M1) cortex, ventral premotor area F5p, frontal eye field (FEF) and 45B. In all areas, AOENs displayed highly phasic responses during specific epochs of the action video and strong responses to simple movements of an object, similar to F5c. In addition, the population dynamics in PMv, PMd and M1 showed a shared representation between action execution and action observation, with an overlap that was as large as the overlap between action execution and passive viewing of simple translation movements. These results pose important constraints on the interpretation of action observation responses in frontal cortical areas.

neuroscience↗

bioRxiv2025

An intracortical brain-computer interface for navigation in virtual reality in macaque monkeys

We present an innovative intracortical Brain-Computer Interface (BCI) to bridge the gap between laboratory settings and real-world applications. This BCI approach introduces three key advancements. First, we utilized neural signals from three macaque brain regions - primary motor, dorsal and ventral premotor cortex - enabling precise and flexible decoding of real-time three-dimensional (3D) sphere/avatar velocities. Second, we developed a realistic, immersive 3D virtual reality setup with dynamic camera tracking, allowing continuous navigation and obstacle avoidance that closely mimic real-world scenarios. Finally, our BCI approach is very well suited for use by paralyzed patients, featuring a brief passive fixation without overt movements and closed-loop operation without retraining of the decoder during online decoding, relying on the users neural plasticity and the decoders robust generalization across tasks. Our BCI adapted to different environments, targets, and obstacles, illustrating its potential to substantially enhance the quality of life for paralyzed patients by enabling natural, reliable and flexible control in complex settings.

neuroscience↗