Evidence map›Paper›PMID 40441660›Full record

ArticleJournal of affective disorders2025

Multiband EEG signatures decoded using machine learning for predicting rTMS treatment response in MDD.

Alexander Arteaga, Xiaoyu Tong, Kanhao Zhao, Nancy B Carlisle, Desmond J Oathes, Gregory A Fonzo, Corey J Keller, Yu Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of affective disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Alexander ArteagaDepartment of Bioengineering, Lehigh University, Bethlehem, PA, USA.
Xiaoyu TongDepartment of Bioengineering, Lehigh University, Bethlehem, PA, USA.
Kanhao ZhaoDepartment of Bioengineering, Lehigh University, Bethlehem, PA, USA.
Nancy B CarlisleDepartment of Psychology, Lehigh University, Bethlehem, PA, USA.
Desmond J OathesCenter for Brain Imaging and Stimulation, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Penn Brain Science, Translation, Innovation, and Modulation Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Departments of Neurology, Neurosurgery, Bioengineering and Neuroscience, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Gregory A FonzoCenter for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, Austin, TX, USA.
Corey J KellerDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, USA.
Yu ZhangDepartment of Bioengineering, Lehigh University, Bethlehem, PA, USA; Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA. Electronic address: yuzi20@lehigh.edu.

Funding

Establishing Multimodal Brain Biomarkers Using Data-driven Analyticsfor Treatment Selection in DepressionR01MH129694 · NIMH · STANFORD UNIVERSITY · PI Yu Zhang · 2023 to 2026
$2.8M
Assess Neural Circuits and Subtypes Underlying Dimensions of Neuropsychiatric Symptoms in Alzheimer's DiseaseR21AG080425 · NIA · LEHIGH UNIVERSITY · PI ZHANG, YU · 2023 to 2024
$426k
Identifying Transdiagnostic Functional Connectivity Biomarkers for Cognitive Health and PsychopathologyR21MH130956 · NIMH · LEHIGH UNIVERSITY · PI BERDICHEVSKY, YEVGENY · 2023 to 2024
$409k
NIA NIH HHS R21 AG080425NIMH NIH HHS R01 MH129694NIMH NIH HHS R21 MH130956
6 · The paper itself

Abstract

backgroundRepetitive transcranial magnetic stimulation (rTMS) is a promising treatment for major depression disorder (MDD), particularly for treatment-resistant cases. However, identifying translatable biomarkers predictive of treatment outcomes remains underexplored.

methodsParticipants with treatment resistant depression from the TDBRAIN dataset underwent either high frequency rTMS (10 Hz) at the left dorsolateral prefrontal cortex (DLPFC) (Protocol 1, n = 44) or low frequency rTMS (1 Hz) at the right DLPFC (Protocol 2, n = 73). Pre-treatment electroencephalograms (EEG) was collected, and changes in Beck Depression Inventory were measured post-treatment. EEG oscillations were decomposed into multiband intrinsic mode functions (IMF) and integrated under a latent space predictive modeling framework to identify signatures for predicting treatment outcomes.

resultsMultiband signatures significantly predicted rTMS outcomes (Protocol 1: r = 0.40, p < 0.01; Protocol 2: r = 0.26, p < 0.05). Key spatial patterns linked to treatment outcomes were identified, revealing three main oscillations: IMF-Alpha, IMF-Beta, and the residual signal. In Protocol 1, critical regions included the left frontal and parietal regions for IMF-Alpha, left frontal-central and right parietal regions for IMF-Beta, and bi-hemispheric central and left parietal-occipital regions for residual signals. In Protocol 2, critical regions involved the left frontal and parietal regions for IMF-Alpha, left frontal-central region IMF-Beta, and right frontal, left frontal-central, midline central, and left parietal-occipital regions for residual signals. These oscillatory features also showed correlations with specific personality measures, suggesting their potential clinical relevance.

conclusionOur findings demonstrate the promise of machine learning-driven multiband EEG signatures for personalized MDD treatment prediction, offering a translatable pathway for improved patient outcomes.

Indexed as

Depressive Disorder, Treatment-ResistantElectroencephalographyMachine LearningMajor Depressive DisorderTranscranial Magnetic StimulationAdultDorsolateral Prefrontal CortexFemaleHumansMaleMiddle AgedTreatment Outcome

Identifiers

PMID40441660
PMCPMC12981298

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.