Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

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

Validation-Aware Retrospective EEG Treatment-Response Modelling Using Chaotic Pattern of Prime Numbers Features: Segment-Level Separability and Subject-Wise Generalisation.

Bioengineering (Basel, Switzerland)·2026
Same author

Towards the Development of a Deep Learning Framework Using Adaptive and Non-Adaptive Time-Frequency Features for EEG-Based Depression Therapy Prediction.

Brain sciences·2026
Same author

Comparing Theta Beta Ratio in Children With Attention-deficit/hyperactive Disorder and Specific Learning Disorder During Active EEG.

Basic and clinical neuroscience·2025
Same author

Simultaneous Multi-Treatment Strategy for Brain Tumor Reduction via Nonlinear Control.

Brain sciences·2025
Same author

Noradrenergic and Dopaminergic modulation of meta-cognition and meta-control.

PLoS computational biology·2025
Same author

Explainable AI for Bipolar Disorder Diagnosis Using Hjorth Parameters.

Diagnostics (Basel, Switzerland)·2025

Related Experiment Video

Updated: Jan 16, 2026

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment
05:19

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

3.2K

Predicting Depression Therapy Outcomes Using EEG-Derived Amplitude Polar Maps.

Hesam Akbari1, Wael Korani2, Sadiq Muhammad3

  • 1Department of Information Science, University of North Texas, Denton, TX 76205, USA.

Brain Sciences
|September 27, 2025
PubMed
Summary
This summary is machine-generated.

A new computer-aided decision (CAD) system accurately predicts patient response to depression treatments like SSRIs and rTMS. This AI tool helps psychiatrists select the most effective therapies, improving patient outcomes for mental health conditions.

Keywords:
EEGSSRIamplitude-polar mapdepression therapyfeature engineeringrTMS

More Related Videos

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping
09:55

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

2.5K
Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method
07:12

Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method

Published on: August 2, 2021

4.1K

Related Experiment Videos

Last Updated: Jan 16, 2026

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment
05:19

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

3.2K
Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping
09:55

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

2.5K
Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method
07:12

Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method

Published on: August 2, 2021

4.1K

Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Psychiatry

Background:

  • Depression treatment response rates for SSRIs and rTMS are around 50%, necessitating improved therapeutic selection.
  • Identifying effective depression treatments is challenging for psychiatrists, impacting patient outcomes.

Purpose of the Study:

  • To develop and validate a computer-aided decision (CAD) system for predicting patient response to selective serotonin reuptake inhibitors (SSRIs) and repetitive transcranial magnetic stimulation (rTMS).
  • To enhance clinical decision-making for depression treatment selection using EEG-based AI.

Main Methods:

  • Utilized amplitude polar maps (APMs) to extract features from EEG signals across different channels.
  • Employed neighborhood component analysis for feature selection and a feed-forward neural network for classification.
  • Implemented a 10-fold cross-validation strategy for robust performance evaluation.

Main Results:

  • The CAD system achieved high prediction accuracy: 98.06% for SSRI response and 97.19% for rTMS response.
  • Identified key EEG channels predictive of SSRI response (prefrontal/parietal) and rTMS response (frontal/temporal/occipital).

Conclusions:

  • The proposed CAD framework shows significant potential as a clinical decision-support tool for personalized depression therapy.
  • This AI-driven approach can assist mental health professionals in optimizing treatment selection for depressed patients.