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| ID | Type | Description | Link |
|---|---|---|---|
| 1R61MH135405-01 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
|---|---|
| National Institute of Mental Health (NIMH) | NIH |
| Rutgers University | OTHER |
| University of Minnesota | OTHER |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| CAMERA | Other | Adult subjects with epilepsy will undergo noninvasive video Electroencephalography (EEG) and intracranial electrodes sampling the amygdala and hippocampus (unilateral or bilateral). A subset of subjects (n=10) will use the Context-Aware Multimodal Ecological Research and Assessment (CAMERA) platform for 2 weeks after discharge with a subset of modalities: physiologic wristband, smartphone phenotyping, ecological momentary assessment (EMA) surveys, and memory task. At unpredictable intervals, CAMERA will interrupt subjects with: (a) an audible alarm to elicit an acoustic startle response; (b) a self-reported anxiety state scale; and (c) a visuospatial memory task with threat interference. For example, participants will fill out a brief survey and play a video game several times each day and wear a wristband with sensors. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| CAMERA (Context-Aware Multimodal Ecological Research and Assessment) | Other | 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). |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | 1-30 days |
| 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. | 1-30 days |
| Measure | Description | Time Frame |
|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Jitter of neural data (milliseconds) | Precise synchronization of neural data with jitter <30 milliseconds. | 1-30 days |
| Latency of audiovisual data (seconds) | Precise synchronization of audiovisual data with latency <10 seconds. |
Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Brett E Youngerman, MD | Contact | 516-946-2145 | bey2103@cumc.columbia.edu | |
| Angela Velazquez | Contact | 646-515-1909 | agv2113@cumc.columbia.edu |
| Name | Affiliation | Role |
|---|---|---|
| Joshua Jacobs, PhD | University of Chicago | Study Director |
| Brett E Youngerman, MD | Columbia University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Columbia University Irving Medical Center | Recruiting | New York | New York | 10032 | United States |
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| ID | Term |
|---|---|
| D001008 | Anxiety Disorders |
| D004827 | Epilepsy |
| ID | Term |
|---|---|
| D001523 | Mental Disorders |
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
| D009422 | Nervous System Diseases |
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| ID | Term |
|---|---|
| D012149 | Restraint, Physical |
| ID | Term |
|---|---|
| D032763 | Behavior Control |
| D013812 | Therapeutics |
| D007103 | Immobilization |
| D008919 | Investigative Techniques |
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| 1-30 days |
| 1-30 days |
| Latency of wrist sensor data (minutes) | Precise synchronization of wrist sensor data with latency <2 minutes. | 1-30 days |
| Latency of smartphone data (minutes) | Precise synchronization of smartphone data with latency <20 minutes. | 1-30 days |
| Jitter of ecological momentary assessment (EMA) delivery. (milliseconds) | Successful delivery of EMA assessments with precise synchronization of ecological momentary assessment delivery with jitter <50 milliseconds. | 1-30 days |
| Percentage improvement in normalized response rate to ecological momentary assessment (EMA) delivery. | Successful delivery of EMA assessments with ≥10% statistically significant (p<0.05) improvement in normalized response rate over time (across subjects) with implementation of context-aware EMA delivery using ResponseNet. | 1-30 days |
| Number of subjects demonstrating feasibility of translation to the ambulatory setting. | Collection of (synchronized) non-neural physiological, smartphone and EMA (survey and task) data from 10 outpatients in the ambulatory setting consecutively for 2 weeks with <10% passive data loss. | 14 days |