Refining Brain Stimulation Therapies: An Active Learning Approach to Personalization.

Brain stimulation holds promise for treating brain disorders, but personalizing therapy remains challenging. Effective treatment requires establishing a functional link between stimulation parameters and brain response, yet traditional methods like random sampling (RS) are inefficient and costly. To overcome this, we developed an active learning (AL) framework that identifies optimal relationships between stimulation parameters and brain response with fewer experiments.

Refining Brain Stimulation Therapies: An Active Learning Approach to Personalization.

Brain stimulation holds promise for treating brain disorders, but personalizing therapy remains challenging. Effective treatment requires establishing a functional link between stimulation parameters and brain response, yet traditional methods like random sampling (RS) are inefficient and costly. To overcome this, we developed an active learning (AL) framework that identifies optimal relationships between stimulation parameters and brain response with fewer experiments.

SAFE-OPT: a Bayesian optimization algorithm for learning optimal deep brain stimulation parameters with safety constraints.

To treat neurological and psychiatric diseases with deep brain stimulation (DBS), a trained clinician must select parameters for each patient by monitoring their symptoms and side-effects in a months-long trial-and-error process, delaying optimal clinical outcomes. Bayesian optimization has been proposed as an efficient method to quickly and automatically search for optimal parameters. However, conventional Bayesian optimization does not account for patient safety and could trigger unwanted or dangerous side-effects.

Irregular optogenetic stimulation waveforms can induce naturalistic patterns of hippocampal spectral activity.

. Therapeutic brain stimulation is conventionally delivered using constant-frequency stimulation pulses. Several recent clinical studies have explored how unconventional and irregular temporal stimulation patterns could enable better therapy.

Selective chronic recording in small nerve fascicles of sciatic nerve with carbon nanotube yarns in rats.

. The primary challenge faced in the field of neural rehabilitation engineering is the limited advancement in nerve interface technology, which currently fails to match the mechanical properties of small-diameter nerve fascicles. Novel developments are necessary to enable long-term, chronic recording from a multitude of small fascicles, allowing for the recovery of motor intent and sensory signals.

Measurement of block thresholds in kiloHertz frequency alternating current peripheral nerve block.

Background: Kilohertz frequency alternating currents (KHFAC) produce rapid nerve conduction block of mammalian peripheral nerve and have potential clinical applications in reducing peripheral nerve hyperactivity. The experimental investigation of KHFAC nerve block requires a robust output measure and this has proven to be the block threshold (BT), the lowest current or voltage at which the axons of interest are completely blocked. All significant literature in KHFAC nerve block, both simulations and experimental, were reviewed to determine the block threshold method that was used.

Recovering Motor Activation with Chronic Peripheral Nerve Computer Interface.

Interfaces with the peripheral nerve provide the ability to extract motor activation and restore sensation to amputee patients. The ability to chronically extract motor activations from the peripheral nervous system remains an unsolved problem. In this study, chronic recordings with the Flat Interface Nerve Electrode (FINE) are employed to recover the activation levels of innervated muscles.

Chronic interfacing with the autonomic nervous system using carbon nanotube (CNT) yarn electrodes.

The ability to reliably and safely communicate chronically with small diameter (100-300 µm) autonomic nerves could have a significant impact in fundamental biomedical research and clinical applications. However, this ability has remained elusive with existing neural interface technologies. Here we show a new chronic nerve interface using highly flexible materials with axon-like dimensions.

Model-based Bayesian signal extraction algorithm for peripheral nerves.

Objective: Multi-channel cuff electrodes have recently been investigated for extracting fascicular-level motor commands from mixed neural recordings. Such signals could provide volitional, intuitive control over a robotic prosthesis for amputee patients. Recent work has demonstrated success in extracting these signals in acute and chronic preparations using spatial filtering techniques.