Anxiety, Epilepsy, Memory
Conditions
Brief summary
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.
Detailed description
CAMERA will record neural, physiological, behavioral, and environmental signals, as well as measurements from ecological momentary assessments (EMAs), to develop a continuous high-resolution prediction of a person's level of anxiety and cognitive performance. CAMERA will provide a significant advance over current methods for human behavioral measurement because it leverages the complementary features of multimodal data sources and combines them with interpretable machine learning to predict human behavior. A further distinctive aspect of CAMERA is that it incorporates context-aware, adaptive EMA, where the timing of assessments depends on the subject's physiology and behavior to improve response rates and model learning. In this study, CAMERA focuses on predicting anxiety state and concurrent memory performance, but the platform is flexible for use in various domains. Currently, it is challenging to study complex, longitudinal relationships between the brain, body, and environment in humans. Most existent tools do not allow the investigator to measure transient internal states or cognitive functions comprehensively or continuously. Instead the investigators typically rely on sparsely collected and constrained self-reports or experimental constructs, including EMA.
Interventions
OTHERCAMERA (Context-Aware Multimodal Ecological Research and Assessment)
The 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).
Sponsors
Columbia University
National Institute of Mental Health (NIMH)
Rutgers University
University of Minnesota
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
SCREENING
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to 55 Years
Healthy volunteers
No
Inclusion criteria
* 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)
Exclusion criteria
* 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) * Psychotic disorder (lifetime) * Current Anxiety disorder, Major Depressive Disorder, or Bipolar Disorder * Neurodegenerative diseases, presence of widespread brain lesions, language problems (other than naming difficulty) * Medical conditions that could potentially affect cognitive performance (e.g., human immunodeficiency virus (HIV) infection, cancer with metastatic potential). * Acute renal failure or end-stage renal disease
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Mean absolute error between predicted and actual ecological momentary assessment (EMA) scores | 1-30 days | 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. | 1-30 days | 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
| Measure | Time frame | Description |
|---|---|---|
| Mean absolute error between predicted and actual absolute error on a daily basis | 1-30 days | 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. |
Countries
United States
Contacts
CONTACTBrett E Youngerman, MD
CONTACTAngela Velazquez
STUDY_DIRECTORJoshua Jacobs, PhD
University of Chicago
PRINCIPAL_INVESTIGATORBrett E Youngerman, MD
Columbia University
Outcome results
None listed