Novel Multimodal Neural, Physiological, and Behavioral Sensing and Machine Learning for Mental States

NCT ID: NCT07110688

Last Updated: 2025-10-01

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Clinical Phase

NA

Total Enrollment

90 participants

Study Classification

INTERVENTIONAL

Study Start Date

2024-08-01

Study Completion Date

2028-11-30

Brief Summary

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In this program, the investigators will develop novel multimodal neural-behavioral-physiological monitoring tools (software and hardware), and machine learning models for mental states within social processes and beyond. The tools consist of a multimodal skin-like wearable sensor for physiological and biochemical sensing; a conversational virtual human platform to evoke naturalistic social processes; audiovisual affect recognition software; synchronization tools; and machine learning methods to model the multimodal data. The investigators will demonstrate the tools in healthy subjects without neural recordings and in patients with drug-resistant epilepsy who already have intracranial EEG (iEEG) electrodes implanted based on clinical criteria for standard monitoring to localize seizures, which is unrelated to our study.

Detailed Description

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Conditions

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No Condition Study Mental State in Healthy Populations and Drug-resistant Epilepsy Patients With Existing iEEG

Study Design

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Allocation Method

NA

Intervention Model

SINGLE_GROUP

Primary Study Purpose

BASIC_SCIENCE

Blinding Strategy

NONE

Study Groups

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Behavioral tasks to assess mental states

Group Type EXPERIMENTAL

Virtual human social interaction

Intervention Type BEHAVIORAL

The participants will perform a social interaction with the virtual human agent platform to assess mental states.

Cold pressor test

Intervention Type BEHAVIORAL

Healthy participants will immerse one hand in cold water for less than 3 minutes, while physiological data will be collected by the new wearable sensor from the other hand.

Interventions

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Virtual human social interaction

The participants will perform a social interaction with the virtual human agent platform to assess mental states.

Intervention Type BEHAVIORAL

Cold pressor test

Healthy participants will immerse one hand in cold water for less than 3 minutes, while physiological data will be collected by the new wearable sensor from the other hand.

Intervention Type BEHAVIORAL

Eligibility Criteria

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Inclusion Criteria

* Patients who suffer from drug-resistant epilepsy and already have intracranial EEG (iEEG) electrodes implanted based on clinical criteria for their standard seizure localization (unrelated to our study) will be eligible. Most patients are healthy adults outside of their epilepsy.
* Subjects \>= 18 are only included in this study.
* All patients with the above conditions and with already-implanted electrodes who are willing to participate and able to cooperate and follow research instructions will be recruited.
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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National Institute of Mental Health (NIMH)

NIH

Sponsor Role collaborator

University of Southern California

OTHER

Sponsor Role lead

Responsible Party

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Maryam Shanechi

Professor

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Maryam Shanechi, PhD

Role: PRINCIPAL_INVESTIGATOR

University of Southern California

Locations

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Rancho Los Amigos National Rehabilitation Center

Downey, California, United States

Site Status RECRUITING

University of Southern California

Los Angeles, California, United States

Site Status RECRUITING

Countries

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United States

Facility Contacts

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Charles Liu, MD, PhD

Role: primary

323-226-7421

Maryam M Shanechi, PhD

Role: primary

213-740-1377

HyeongChan Jo, PhD

Role: backup

Other Identifiers

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R61MH135407

Identifier Type: NIH

Identifier Source: secondary_id

View Link

R61MH135407

Identifier Type: NIH

Identifier Source: org_study_id

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