Prediction of Anxiety and Memory State
NCT Number: NCT06551090
Acronym: CAMERA
Status: RECRUITING
Study Type: OBSERVATIONAL
Conditions: Anxiety|Memory|Epilepsy
Interventions: OTHER: CAMERA (Context-Aware Multimodal Ecological Research and Assessment)
Sponsor: Columbia University
Collaborators: National Institute of Mental Health (NIMH)|Rutgers University|University of Minnesota
Sex: ALL
Age: ADULT
Enrollment: 40
Study Design: Observational Model: |Time Perspective: p
Start Date: 2024-07-23
Completion Date: 2027-07-22
Locations: Columbia University, New York, New York, 10032, United States

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.

Primary Outcome Measures: 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
Secondary Outcome Measures: 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., 1-30 days
Other Outcome Measures: 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., 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