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Updated: Jun 18, 2026

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder

Published on: July 7, 2023

Explainable EEG-based prediction of depression therapy outcomes using local Fibonacci pattern analysis.

Hesam Akbari¹, Zhenni Liang¹, Narges Nasehi Najafabadi²

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

Psychiatry Research. Neuroimaging

|June 16, 2026

Summary

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This summary is machine-generated.

This study introduces a novel EEG-based prediction model using local Fibonacci patterns (LFP) to identify depression treatment responders before therapy. The model shows high accuracy in initial tests but requires larger cohorts for clinical use.

Area of Science:

Neuroscience
Computational psychiatry
Biomedical engineering

Background:

Predicting depression treatment outcomes for selective serotonin reuptake inhibitors (SSRI) and repetitive transcranial magnetic stimulation (rTMS) is challenging.
Current trial-and-error approaches delay effective clinical benefit for patients with depression.

Purpose of the Study:

To develop and validate an electroencephalogram (EEG)-based predictive model for depression treatment response.
To utilize local Fibonacci pattern (LFP) features extracted from pre-treatment EEG signals to classify responders to SSRI and rTMS therapies.

Main Methods:

Pre-treatment EEG data from SSRI and rTMS treatment groups were analyzed.
Local Fibonacci Pattern (LFP) features were extracted from 19-channel EEG recordings.

Keywords:

Depression EEG Local Fibonacci pattern Machine learning SSRI Treatment prediction rTMS

Related Experiment Videos

Last Updated: Jun 18, 2026

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder

Published on: July 7, 2023

A feedforward neural network was employed for classification, with validation using 10-fold cross-validation and leave-one-subject-out (LOSO) methods.

Main Results:

Segment-level 10-fold cross-validation yielded high accuracies (up to 100%) for predicting treatment responders.
Subject-wise LOSO validation demonstrated more modest accuracies (61.83% for SSRI, 77.93% for rTMS), indicating potential overfitting.
Discriminative EEG channels were identified in frontal, temporal, and parietal regions crucial for emotional and cognitive processing.

Conclusions:

Local Fibonacci Patterns (LFP) offer a computationally efficient method for EEG-based depression treatment prediction.
The model's performance in LOSO validation suggests a need for larger, independent datasets before clinical deployment.
Further research with diverse cohorts is essential to validate the generalizability and clinical utility of this predictive approach.