Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Radiomics to understand pre-treatment tumor biology for resectable non-small cell lung cancer.

Frontiers in oncology·2026

Same author

Comparative outcomes based on pre-Bronchoscopic lung volume reduction (BLVR) Six-minute walk distance (6MWD).

Respiratory medicine·2026

Same author

The evolving role of the immune microenvironment of tumor draining lymph nodes in the development of biomarkers of non-small cell lung cancer.

Frontiers in oncology·2026

Same author

Identification of Stress Location During Low-Speed Mobility Travel Using Environmental Data.

Sensors (Basel, Switzerland)·2026

Same author

Vascular invasion-associated gene expression is detectable in pre-surgical biopsies of stage I lung adenocarcinoma.

Nature communications·2026

Same author

Multimodal single-cell and spatial profiling reveals altered T cell-mediated immunity and B-cell follicular architecture in non-metastatic lymph nodes of patients with aggressive non-small cell lung cancer.

medRxiv : the preprint server for health sciences·2026

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 18, 2026

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

Published on: July 1, 2014

Machine Learning-Based Alexithymia Assessment Using Resting-State Default Mode Network Functional Connectivity.

Kei Suzuki¹, Midori Sugaya¹

¹Functional Control Systems, Graduate School of Engineering and Science, TOYOSU Campus, Shibaura Institute of Technology, Research Building #14A32, 3-7-5 Toyosu, Koto-ku, Tokyo 135-8548, Japan.

Sensors (Basel, Switzerland)

|September 13, 2025

Summary

This summary is machine-generated.

Machine learning models using electroencephalogram (EEG) signals can effectively assess alexithymia severity. The models identified functional connectivity in specific brain regions and frequency bands as key indicators for this mental health condition.

Keywords:

alexithymia default mode network electroencephalogram machine learning source localization

More Related Videos

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

Resting-State Connectivity and Neuroimaging of Prefrontal Cortex Activity During a Block-Design Yoga Asana Practice Using fNIRS

Resting-State Connectivity and Neuroimaging of Prefrontal Cortex Activity During a Block-Design Yoga Asana Practice Using fNIRS

Published on: June 24, 2025

Related Experiment Videos

Last Updated: Jan 18, 2026

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

Published on: July 1, 2014

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

Resting-State Connectivity and Neuroimaging of Prefrontal Cortex Activity During a Block-Design Yoga Asana Practice Using fNIRS

Resting-State Connectivity and Neuroimaging of Prefrontal Cortex Activity During a Block-Design Yoga Asana Practice Using fNIRS

Published on: June 24, 2025

Area of Science:

Neuroscience
Psychiatry
Computational Biology

Background:

Alexithymia is a risk factor for numerous mental health disorders.
There's a need for objective and accessible methods to measure alexithymia.
Current assessment methods may lack convenience or objectivity.

Purpose of the Study:

To develop machine learning models for alexithymia assessment using electroencephalogram (EEG) signals.
To identify key neurophysiological markers of alexithymia.
To explore the utility of explainable artificial intelligence (XAI) in this context.

Main Methods:

Resting-state EEG data was collected.
Functional connectivity within the default mode network (DMN) was calculated for different frequency bands.
Source localization estimated brain regions.
Machine learning models classified participants into low or high alexithymia groups.
Explainable AI (XAI) analyzed model feature importance.

Main Results:

The classification model achieved a maximum ROC-AUC score of 0.70.
Effective alexithymia assessment was demonstrated, dependent on the chosen classification threshold.
Functional connectivity in theta and gamma bands, particularly in the Left Hippocampus, was identified as a significant feature.
XAI highlighted the importance of specific DMN connectivity patterns.

Conclusions:

EEG signals combined with machine learning offer a promising approach for objective alexithymia assessment.
Specific patterns of brain functional connectivity, especially within the Left Hippocampus across theta and gamma bands, are relevant for alexithymia.
This methodology has potential clinical applications for identifying and managing alexithymia.