Reardon, T. | Search bioRxiv

bioRxiv2024

Distinct anatomical and functional corticospinal inputs innervate different spinal neuron types

The corticospinal tract exerts its influence on movement through spinal neurons, which can be divided into types that exhibit distinct functions. However, it remains unknown whether these functional distinctions are reflected in the corticospinal inputs that different types of spinal neurons receive. Using rabies monosynaptic tracing from individual neuron types in the cervical cord and 3D histological reconstruction in mice, we discovered that different types receive inputs distinctly distributed across cortex, and aligned with cell type function. This included a distinct, sparse distribution of direct inputs from cortex onto motor neurons. Coupling rabies tracing with activity measurement during motor behavior revealed different interneuron types receive different input activity patterns, primarily due to the topographical distribution of the corticospinal neurons contacting them. Our results establish that different spinal neuron types get distinct anatomical and functional inputs from the cortex, and reveal functionally relevant homology to primate corticospinal organization.

neuroscience↗

bioRxiv2024

A generic noninvasive neuromotor interface for human-computer interaction

Since the advent of computing, humans have sought computer input technologies that are expressive, intuitive, and universal. While diverse modalities have been developed, including keyboards, mice, and touchscreens, they require interaction with an intermediary device that can be limiting, especially in mobile scenarios. Gesture-based systems utilize cameras or inertial sensors to avoid an intermediary device, but they tend to perform well only for unobscured or overt movements. Brain computer interfaces (BCIs) have been imagined for decades to solve the interface problem by allowing for input to computers via thought alone. However high-bandwidth communication has only been demonstrated using invasive BCIs with decoders designed for single individuals, and so cannot scale to the general public. In contrast, neuromotor signals found at the muscle offer access to subtle gestures and force information. Here we describe the development of a noninvasive neuromotor interface that allows for computer input using surface electromyography (sEMG). We developed a highly-sensitive and robust hardware platform that is easily donned/doffed to sense myoelectric activity at the wrist and transform intentional neuromotor commands into computer input. We paired this device with an infrastructure optimized to collect training data from thousands of consenting participants, which allowed us to develop generic sEMG neural network decoding models that work across many people without the need for per-person calibration. Test users not included in the training set demonstrate closed-loop median performance of gesture decoding at 0.5 target acquisitions per second in a continuous navigation task, 0.9 gesture detections per second in a discrete gesture task, and handwriting at 17.0 adjusted words per minute. We demonstrate that input bandwidth can be further improved up to 30% by personalizing sEMG decoding models to the individual, anticipating a future in which humans and machines co-adapt to provide seamless translation of human intent. To our knowledge this is the first high-bandwidth neuromotor interface that directly leverages biosignals with performant out-of-the-box generalization across people.

neuroscience↗