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Neurotech Development

The field of neurotechnology is entering a new era, driven by breakthroughs in neural interfaces, brain-computer interfaces, and targeted neuromodulation. These advances are creating new opportunities to study brain function and develop therapies for neurological and psychiatric disorders. From decoding neural activity to restoring healthy brain networks, these emerging technologies are reshaping both neuroscience research and clinical care. The Sheth Lab contributes to this growing ecosystem through research, clinical translation, and partnerships with pioneering neurotechnology companies such as Motif Neurotech, which is developing therapeutic brain-computer interfaces designed to help regulate brain networks involved in mental health

Motif Neurotech

Motif Neurotech is a Houston-based therapeutic brain-computer interface company co-founded by Drs. Jacob Robinson (Rice University), Sunil Sheth (UT Health), and Sameer Sheth. The company is focused on developing innovative neuromodulation technologies to improve the treatment of mental health disorders, beginning with major depressive disorder.

Motif's lead technology, the Digitally Programmable Over-brain Therapeutic (DOT), is a minimally invasive neural interface designed to stimulate brain networks implicated in depression. DOT targets the Central Executive Network (CEN), a brain network involved in cognitive control and emotion regulation that is often underactive in individuals with depression. By delivering specific patterns of stimulation, the device aims to restore healthy network function and alleviate symptoms.

A key innovation of DOT is its wireless design. Powered through magnetoelectric technology developed by Robinson Lab, the implant is approximately the size of a pea and can be placed during a brief outpatient procedure without exposing or contacting the brain. Patients receive therapy through a wearable external device that wirelessly powers and communicates with the implant, enabling treatment to be delivered conveniently outside of a traditional clinical setting.

The development of DOT builds upon years of research in neural circuitry, brain stimulation, and translational neuroscience. Preclinical studies have demonstrated the feasibility of the technology and its ability to modulate targeted brain networks, supporting its potential as a next-generation approach to treating neuropsychiatric disorders.

Publications and News Articles

Woods JE, et al. (2024). “Miniature Battery-Free Epidural Cortical Stimulators.” Science Advances. https://pubmed.ncbi.nlm.nih.gov/38608028/

Robinson JT, et al. (2024). “An Application-Based Taxonomy for Brain-Computer Interfaces.” Nature Biomedical Engineering. https://www.nature.com/articles/s41551-024-01326-z

Business Wire, April 2026

Motif Neurotech Receives FDA Approval to Begin First Clinical Trial of its Therapeutic BCI for Treatment-Resistant Depression

https://www.businesswire.com/news/home/20260427361988/en/Motif-Neurotech-Receives-FDA-Approval-to-Begin-First-Clinical-Trial-of-its-Therapeutic-BCI-for-Treatment-Resistant-Depression

Rice University News, April 2026

Brain-computer interface based on Rice research wins FDA approval for first clinical trial

https://news.rice.edu/news/2026/brain-computer-interface-based-rice-research-wins-fda-approval-first-clinical-trial

Wired, April 2026

A Brain Implant for Depression Is About to Be Tested in Humans

https://www.wired.com/story/a-brain-implant-for-depression-is-about-to-be-tested-in-humans/

Deep Brain Stimulation for Treatment-Resistant Depression

The success of deep brain stimulation (DBS) for movement disorders has encouraged many groups around the world to apply the DBS platform to treat severe and refractory psychiatric disorders. We are running an NIH BRAIN Initiative funded (UH3 NS103549) trial of DBS for treatment-resistant depression (TRD). This trial focuses on depression patients who have tried and failed conventional treatments, including anti-depressant medications, behavioral therapy, and electroconvulsive therapy. Sameer Sheth is the contact principle investigator and co-primary investigators are Wayne Goodman, M.D., professor and chair of psychiatry at Baylor College of Medicine, and Nader Pouratian, M.D., Ph.D., professor and chair of neurological surgery at UT Southwestern Medical Center

Early open-label trials of DBS for TRD demonstrated promising results, but two large industry-sponsored pivotal trials were halted at interim analyses and were not able to substantiate efficacy. With this history as context, we planned our current trial with a different approach in mind. Patients with the same diagnosis of TRD may yet have very different constellations of symptoms, different “phenotypes.” We reason that these different phenotypes are manifestations of different brain network activity patterns across individuals. Thus, to optimize delivery of DBS therapy to a particular individual, we have to derive a detailed understand of his/her depression-relevant brain networks, such as those regulating mood, affect, anxiety, attention, cognition, etc. The better we understand these pathological networks within the individual, the better we will be able to engage them and restore balanced, healthy function.

To this end, we borrow an approach common to epilepsy surgery, but not previously applied to most other neurological or psychiatric conditions. We employ semi-chronically implanted stereo-EEG (sEEG) electrodes to perform intracranial recordings and stimulation to understand network activity at the individual patient level. TRD patients in this trial are implanted with DBS electrodes targeting two previously studied targets, the ventral capsule/ventral striatum (VCVS) and subgenual cingulate (SGC). We also temporarily implant sEEG electrodes in depression-relevant regions (prefrontal, mesial temporal, etc.) and keep the patient in the epilepsy monitoring unit (EMU) for 1.5 weeks. During this time, we perform a number of recording and stimulation studies to understand the patient’s network activity and how it responds to stimulation. Patients then transition to the outpatient phase, during which we use the EMU-derived information to optimize programming of the DBS system. We expect that this trial will provide an unparalleled understanding of symptomatic networks in TRD and how they can be engaged and manipulated back to a healthy state with individualized DBS. More broadly, we hope that this approach of using the intracranial EEG platform can be applied to many other neurological and psychiatric disorders whose circuit-level dysfunction remains incompletely understood.

Deep Brain Stimulation for Obsessive-Compulsive Disorder

Deep brain stimulation (DBS) of the ventral capsule/ventral striatum (VC/VS) improves symptoms in ~66% of patients with treatment-resistant obsessive-compulsive disorder (OCD). Conventional stimulation paradigms for OCD treatment are open-loop, continuous DBS (cDBS). Stimulation parameter adjustments require the patient to visit their psychiatrist in the clinic. Programming visits are burdensome and time intensive for both patients and clinicians. Our study aims (Grant: UH3NS100549) to develop closed-loop, adaptive DBS (aDBS), in which stimulation would be automatically titrated in response to specific symptom/side effect states. An aDBS system would decrease programming burden and increase efficacy of DBS for OCD.

Neural biomarkers of OCD symptom severity and side effects of VC/VS must be identified in OCD to develop aDBS algorithms that improve on the traditional cDBS paradigm. The development of bidirectional DBS devices has equipped us to stream neural data chronically while delivering therapeutic stimulation. We collect these data in the clinic and at home in naturalistic settings. Integrating in-clinic and at-home data will inform our understanding of patient-specific pathology and allow us to investigate how OCD affects brain networks across patients.

Deep Brain Stimulation for Treatment-Resistant Bipolar Depression

Bipolar depression (BD) is characterized by depressive episodes with occasional occurrences of mania or hypomania. Standard treatments of bipolar depression are limited due to the possibility of inducing mania. Of those with BD, approximately 25% are considered to have treatment-resistant bipolar depression (TRBD). Patients with BD are at a higher risk of suicide (20-30x the general population), live on average 15 years below the average life expectancy, and make up around 2% of the general population. Given the gravity of the disease and the paucity of available treatments, we are conducting an early feasibility study for the treatment of TRBD with deep brain stimulation (DBS) of the ventral capsule/ventral striatum (VC/VS). DBS was first deployed to treat movement disorders and DBS of the VC/VS was shown to improve symptoms in 66% of patients with treatment-resistant obsessive-compulsive disorder. Improvement of comorbid depression in these patients suggests the validity of the VC/VS target for treating TRBD.

This study (Grant: UH3NS136631) aims to evaluate the safety and efficacy of DBS targeting the VC/VS for TRBD, build a model to detect neural features of mania and mixed states in real time, and identify neural markers as classifiers for state changes. We collect data from patients at home, in the clinic, and in the neurobehavioral unit (NBU), a naturalistic environment which allows for clinic-level data collection. From the data collected at the clinic and at the NBU, behavioral states can be time-synced with neural signals, allowing us to identify important neural markers associated with both acute state changes and long term symptom improvement. Additionally, we have implemented a clinician-facing dashboard of daily biometrics summaries in order to detect and notify the clinician of any signs of mania or other complications should they arise. Establishing the safety of DBS for TRBD, discerning the neural changes associated with state changes, and identifying the neural markers of improvement will allow for better assessment, prevention, and treatment of bipolar depression through both surgical and non-surgical means

Neural Representations of Language

Consciousness is a fundamental component of cognition, but the extent to which higher-order learning and language processing depend on conscious awareness remains disputed. In this line of work, we investigate how the human hippocampus supports sensory learning and language processing across altered states of consciousness, using intracranial microelectrode recordings collected both intraoperatively and in the epilepsy monitoring unit (EMU).

Using high-density Neuropixels probes, we recorded single-unit and local field potential activity from the human hippocampus during neurosurgical procedures in patients undergoing anterior temporal lobectomy for drug-resistant epilepsy, as well as from implanted microelectrodes in the EMU. During general anesthesia, we presented structured auditory stimuli and found that hippocampal neurons and local oscillations retained sensitivity to auditory oddball structure, reflecting preserved pattern detection and online prediction despite loss of consciousness.

Extending these findings to language, we showed that hippocampal single units and local field potentials encode semantic and grammatical features of natural speech, and in some cases predict semantic information about upcoming words. Together, these results indicate that the hippocampus—despite being anatomically and functionally distant from primary sensory cortices—supports complex learning and language-related computations even in unconscious or minimally conscious states. Understanding how sensory and linguistic processing persists under anesthesia and during clinical monitoring may inform new approaches to intraoperative and bedside assessment of brain function, while also deepening insight into preserved cognition in altered states of consciousness.

Investigating the Neural Mechanisms Underlying Ad-hoc Self-Programmability

Human beings possess the remarkable ability to hear a series of instructions for the first time and implement them without practice and with a high degree of accuracy. How do our brains afford us this tremendous flexibility? This ability to ‘re-program’ ourselves has so far eluded artificial intelligence systems, and its underlying neural implementation in biological brains remains unknown.

We aim to explore this question using a novel behavioral task, neurosurgical recordings of neural activity, and advanced neural network modeling. Patients are asked to perform a complex rule-following task where the rule changes dynamically from one trial to the next, and each rule contains more than one sub-rule that determines the mapping between incoming stimuli and participants’ responses. While patients perform this task, we record neural activity from medio-temporal and prefrontal regions putatively involved in higher-level reasoning and procedural learning.

We are interested in whether individual neurons in these regions keep track of where we are in ‘cognitive’ space. We are also interested in whether some neurons represent rule structures more generally, independent of the specific stimuli that instantiate them. How do these individual neurons then form circuits that can dynamically interpret sensory stimuli in light of the ongoing rule, and dynamically reconfigure their activity to represent new information? How does the network switch back and forth between different sub-rules without interference? What neural signatures are associated with errors that would indicate a failure of this process?

In collaboration with researchers at Google DeepMind, Columbia University, UCLA, University of Utah, SKKU and Oxford University we hope to further understand this process by simulating it in silico using deep learning models. Recent artificial neural networks models of brain circuits have shed light on the circuit mechanisms underlying a myriad of perceptual and cognitive processes - all the way from basic processing in the retina to complex, context-dependent perceptual decision-making. Using the same models that have led to remarkable advances in AI - deep reinforcement learning models - we hope to better understand the brain mechanisms underlying this remarkable human ability.

Investigating the Physiology of Controlled Decision-Making in the Human Prefrontal Cortex

Every day we are faced with decisions that require fast but accurate actions. ‘Cognitive control’ refers to our ability to optimize our behavior to meet the demands of our ever-changing world. For example, when driving in traffic, we must pay attention to relevant factors such as speed, distance to destination and the presence of other drivers. We must also prevent ourselves from being too distracted by signals that are irrelevant to driving, such as music or children arguing with each other in the back seat. All of these dynamic processes occur while keeping our long-term goals in mind. The human prefrontal cortex (PFC) is the most evolutionarily advanced region of our brains. Across thousands of years, the PFC has developed specialized neuronal networks for making quick and accurate decisions. The PFC also coordinates with brain regions such as the hippocampus to encode spatial and memory representations. In our lab, we have designed complex cognitive tasks that evoke distinct components of cognitive control and decision-making. These tasks have rules that change after a certain number of iterations. We seek to answer questions such as, 'How does the brain quickly adjust to new scenarios and stimulus-response patterns?' We use a combination of single-unit and local field potential (LFP) recordings from neurosurgical patients to answer these questions.