About this trial
The purpose of this study is to look at how signals in the brain, body, and behavior relate to anxiety and memory function. This project seeks to develop the CAMERA (Context-Aware Multimodal Ecological Research and Assessment) platform, a state-of-the-art open multimodal hardware/software system for measuring human brain-behavior relationships.
The R61 portion of the project is designed to develop the CAMERA platform, which will use multimodal, passive sensor data to predict anxiety-memory state in patients undergoing inpatient monitoring with intracranial electrodes for clinical epilepsy, as well as to build CAMERA's passive data framework and active data framework.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Patients must have known or suspected Temporal Lobe Epilepsy.
Native or proficient in speaking English or Spanish.
Stereoelectroencephalography (sEEG) cases: The implant plan must include hippocampal head, body, and tail electrodes either unilaterally or bilaterally.
7th grade reading level (minimum level considered literate for adults)
Disqualifiers
Hearing impaired (i.e., not corrected with a hearing aid)
Unable to read the newspaper at arm's length with corrective lenses.
Objective intellectual impairment (estimated IQ < 70)
Any history of Electroconvulsive Therapy or psychosis (except postictal psychosis for patients)
Trial design
Single group
Treatments tested in this trial
CAMERA (Context-Aware Multimodal Ecological Research and Assessment)
Other interventionThe CAMERA platform is a multimodal, hardware-software framework for measuring brain-behavior interactions in an unstructured environment and predict ecological states. CAMERA will use multimodal, passive sensor data to predict anxiety-memory state in patients undergoing inpatient monitoring with intracranial electrodes for clinical epilepsy. CAMERA consists of: Wristband sensors of autonomic physiologic signals, emphasizing heart rate metrics and electrodermal activity; Smartphone usage, emphasizing natural language processing of text input for linguistic features; Subject-tracking audiovisual array, emphasizing subject vocal activity; Intracranial neural recordings, emphasizing hippocampal theta power and high-frequency activity (\~70-200 Hz).
Treatment groups
Trial outcomes
Primary outcomes
Mean absolute error between predicted and actual ecological momentary assessment (EMA) scores
Use a multimodal machine learning model (EMANet ) to predict ≥1 EMA anxiety-memory state outcome (target) in held-out data at the population level. Mean absolute error will be the mean difference in absolute value of predicted EMA and actual EMA scores. A higher mean error represents a less accurate prediction. Prediction must use ≥2 different passive modalities, showing significantly better prediction accuracy than either of the modalities alone.
Percent of subjects demonstrating improvement in the EMANet prediction over time.
Use EMANet to predict ≥1 ecological momentary assessment (EMA) anxiety-memory state outcome (target) demonstrating improvement over time as measured with a linear regression applied to the mean absolute error between predicted and actual EMA values measured over days. Prediction must use ≥2 different passive modalities, showing significantly better prediction accuracy than either of the modalities alone.
Secondary outcomes
Mean absolute error between predicted and actual absolute error on a daily basis
Use a multimodal machine learning model of prediction uncertainty (UncertaintyNet) to predict the mean absolute prediction error of ecological momentary assessment (EMA) predictions in held-out data, at single-subject level on each day. Mean absolute error will measure the difference between the predicted error (based on all available data) and the actual error.
Other outcomes
Jitter of neural data (milliseconds)
Precise synchronization of neural data with jitter \<30 milliseconds.
Latency of audiovisual data (seconds)
Precise synchronization of audiovisual data with latency \<10 seconds.
Latency of wrist sensor data (minutes)
Precise synchronization of wrist sensor data with latency \<2 minutes.
Latency of smartphone data (minutes)
Precise synchronization of smartphone data with latency \<20 minutes.
Sponsors and contacts
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Columbia University
Lead sponsor
National Institute of Mental Health (NIMH)
Collaborator
Rutgers University
Collaborator
University of Minnesota
Collaborator