SeeMe: Using Automated Facial Tracking to Detect Voluntary Behavior in Brain Injury

Overview

Objective: This prospective interventional study introduces "SeeMe," an automated, high-resolution computer vision platform designed to objectively quantify microscopic, auditory command-evoked movements in patients with Traumatic Brain Injury (TBI). Current clinical assessments, such as the Glasgow Coma Scale (GCS) and Coma Recovery Scale-Revised (CRS-R), rely on subjective human observation and often fail to detect low-amplitude motor responses, potentially misclassifying up to 25% of patients as unresponsive. Methodology: SeeMe utilizes vector analysis, cross-correlation, and deep neural networks (DNNs) to track individual facial pores and hand movements with sub-millimeter precision (0.5 mm) and high temporal resolution (0.03s). The study will enroll a cohort of 60-80 TBI patients, alongside healthy controls and pharmacologically paralyzed subjects, to validate SeeMe's sensitivity and specificity. Primary Goals: 1. Validation: Compare SeeMe's detection of voluntary motor recovery against gold-standard clinical examinations (CRS-R). 2. Synchronization: Simultaneously record and time-lock electroencephalography (EEG) and electrocorticography (ECoG) with SeeMe-detected movements. 3. Biomarker Identification: Characterize neural signatures (specifically Beta-band oscillations) associated with the return of voluntary behavior. Impact: By providing a real-time, objective measure of motor intention and execution, SeeMe aims to identify "Cognitive-Motor Dissociation" (CMD) earlier than current methods, facilitating more accurate prognostications and laying the framework for future closed-loop neuromodulation (e.g., Vagus Nerve Stimulation) to accelerate TBI recovery.

1. Study Rationale and Scientific Gap Standard clinical assessments for Traumatic Brain Injury (TBI), such as the Glasgow Coma Scale (GCS) and Coma Recovery Scale-Revised (CRS-R), are limited by human subjectivity, examiner bias, and low spatiotemporal resolution. Recent multicenter studies indicate that up to 25% of patients who appear "unresponsive" at the bedside may exhibit "Cognitive-Motor Dissociation" (CMD)-a state of covert awareness where the brain intends to move, but motor output is too microscopic for the naked eye to detect. Misclassifying these patients as unresponsive negatively impacts clinical outcomes due to the potential withholding of rehabilitative resources. This study validates SeeMe, a multimodal platform designed to bridge this diagnostic gap by objectively quantifying microscopic behavior in real-time. 2. The SeeMe Technological Framework SeeMe is an innovative computer-vision sensing platform that utilizes vector analysis, cross-correlation, and deep neural networks (DNNs) to track individual facial pores and hand landmarks. Sub-millimeter Precision: The system offers high spatial precision (0.5 mm) and high temporal resolution (0.03s), allowing it to capture "micro-expressions" and low-amplitude motor responses (e.g., vertical eye movements or subtle mouth twitches) that escape clinical detection. Advanced Classification: The investigators utilize a bidirectional long short-term memory (LSTM) network to classify heatmaps of facial movement. This ensures that detected responses are specific to the auditory command provided (e.g., "Show me a smile" vs. "Open your eyes") rather than generalized arousal or non-specific reflexive movements. 3. Multimodal Brain-Behavior Synchronization The primary objective of the R61 phase is the seamless integration of SeeMe with electrophysiological activity (EEG/ECoG). By time-locking microscopic motor initiation with neural oscillations, the investigators aim to identify objective biomarkers of recovery. Beta-Band Signatures: The investigators focus on Beta-band Event-Related Desynchronization (ERD) and Synchronization (ERS) as signatures of motor planning and execution. One-to-One Mapping: This multimodal approach provides a robust computational framework to synchronize brain activity with behavior, allowing us to map the "bigger picture" of recovery during the critical transition from coma to consciousness. 4. Study Design and Methodology This prospective validation study will utilize three distinct cohorts (Total N = 80-100): Cohort 1: Healthy Controls (n=10): Establishes the "ground truth" for robust, voluntary motor movements and baseline algorithm performance. Cohort 2: Sedated/Anesthetized Controls (n=10): Patients undergoing spine surgery under general anesthesia/pharmacological paralysis provide a "zero-movement" baseline to quantify the algorithm's noise floor and establish specificity. Cohort 3: Recovering TBI Patients (n=60-80): The primary clinical cohort. Procedures: Participants listen to simple auditory commands (e.g., "Stick out your tongue," "Open your eyes," "Close your hands") alongside a control command ("Today is a sunny day") to distinguish auditory-evoked movements from true command following. Comparison: SeeMe data will be collected daily and compared against blinded CRS-R assessments to determine the "detection lead time"-the number of days SeeMe identifies a response before the clinical team notes command following. 5. Clinical and Neuroethical Oversight Acknowledging the vulnerability of the TBI population, the study incorporates two novel neuroethical safeguards: Patient Advocate: Modeled on pioneering work in organ donation, a dedicated liaison will be appointed to act as a bridge between the research team and families, ensuring participant safety and well-being remain the central focus. TBI-Affected Family Council: A consultative council of family members who have experienced TBI will review findings and provide guidance on the ethical communication of results to families. 6. Future Directions The data gathered during this R61 phase-specifically the synchronization of SeeMe and EEG-will define the physiological triggers and milestones for Phase R33. In the subsequent phase, the SeeMe-EEG platform will be integrated with Vagus Nerve Stimulation (VNS). This closed-loop system will test the hypothesis that precisely-timed VNS pulses, paired with SeeMe-detected motor intentions, can facilitate the reorganization of motor circuits and accelerate the recovery of goal-directed behavior after TBI.