Literature DB >> 30953441

Changes in Functional Connectivity Predict Outcome of Repetitive Transcranial Magnetic Stimulation Treatment of Major Depressive Disorder.

Juliana Corlier1,2, Andrew Wilson1,2, Aimee M Hunter1,2, Nikita Vince-Cruz1,2, David Krantz1,2, Jennifer Levitt1,2, Michael J Minzenberg1,2, Nathaniel Ginder1,2, Ian A Cook1,2,3, Andrew F Leuchter1,2.   

Abstract

Repetitive transcranial magnetic stimulation (rTMS) treatment of major depressive disorder (MDD) is associated with changes in brain functional connectivity (FC). These changes may be related to the mechanism of action of rTMS and explain the variability in clinical outcome. We examined changes in electroencephalographic FC during the first rTMS treatment in 109 subjects treated with 10 Hz stimulation to left dorsolateral prefrontal cortex. All subjects subsequently received 30 treatments and clinical response was defined as ≥40% improvement in the inventory of depressive symptomatology-30 SR score at treatment 30. Connectivity change was assessed with coherence, envelope correlation, and a novel measure, alpha spectral correlation (αSC). Machine learning was used to develop predictive models of outcome for each connectivity measure, which were compared with prediction based upon early clinical improvement. Significant connectivity changes were associated with clinical outcome (P < 0.001). Machine learning models based on αSC yielded the most accurate prediction (area under the curve, AUC = 0.83), and performance improved when combined with early clinical improvement measures (AUC = 0.91). The initial rTMS treatment session produced robust changes in FC, which were significant predictors of clinical outcome of a full course of treatment for MDD.
© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  depression; electroencephalogram (EEG); functional connectivity; machine learning; repetitive transcranial magnetic stimulation (rTMS)

Mesh:

Year:  2019        PMID: 30953441      PMCID: PMC7305800          DOI: 10.1093/cercor/bhz035

Source DB:  PubMed          Journal:  Cereb Cortex        ISSN: 1047-3211            Impact factor:   5.357


  52 in total

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2.  Responders to rTMS for depression show increased fronto-midline theta and theta connectivity compared to non-responders.

Authors:  N W Bailey; K E Hoy; N C Rogasch; R H Thomson; S McQueen; D Elliot; C M Sullivan; B D Fulcher; Z J Daskalakis; P B Fitzgerald
Journal:  Brain Stimul       Date:  2017-10-27       Impact factor: 8.955

3.  A precision medicine approach to repetitive Transcranial Magnetic Stimulation (rTMS).

Authors:  Andrew F Leuchter; Juliana Corlier
Journal:  Brain Stimul       Date:  2018-02-16       Impact factor: 8.955

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5.  Natural frequencies of human corticothalamic circuits.

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Review 6.  A review of combined TMS-EEG studies to characterize lasting effects of repetitive TMS and assess their usefulness in cognitive and clinical neuroscience.

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7.  Baseline and treatment-emergent EEG biomarkers of antidepressant medication response do not predict response to repetitive transcranial magnetic stimulation.

Authors:  Alik S Widge; David H Avery; Paul Zarkowski
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8.  Resting-state quantitative electroencephalography reveals increased neurophysiologic connectivity in depression.

Authors:  Andrew F Leuchter; Ian A Cook; Aimee M Hunter; Chaochao Cai; Steve Horvath
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Review 9.  Connectomics: a new paradigm for understanding brain disease.

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10.  Longitudinal Changes in Depressive Circuitry in Response to Neuromodulation Therapy.

Authors:  Yagna Pathak; Oludamilola Salami; Sylvain Baillet; Zhimin Li; Christopher R Butson
Journal:  Front Neural Circuits       Date:  2016-07-29       Impact factor: 3.492

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  11 in total

1.  Identification of Clinical Features and Biomarkers that may inform a Personalized Approach to rTMS for Depression.

Authors:  Sarah L Garnaat; Andrew M Fukuda; Shiwen Yuan; Linda L Carpenter
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2.  A systematic data-driven approach to analyze sensor-level EEG connectivity: Identifying robust phase-synchronized network components using surface Laplacian with spectral-spatial PCA.

Authors:  Ezra E Smith; Tarik S Bel-Bahar; Jürgen Kayser
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3.  Predicting the Effects of Repetitive Transcranial Magnetic Stimulation on Cognitive Functions in Patients With Alzheimer's Disease by Automated EEG Analysis.

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4.  Large-scale EEG neural network changes in response to therapeutic TMS.

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Journal:  Brain Stimul       Date:  2022-01-17       Impact factor: 8.955

Review 5.  Precise Modulation Strategies for Transcranial Magnetic Stimulation: Advances and Future Directions.

Authors:  Gangliang Zhong; Zhengyi Yang; Tianzi Jiang
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6.  Transcranial Direct Current Stimulation Over Prefrontal Areas Improves Psychomotor Inhibition State in Patients With Traumatic Brain Injury: A Pilot Study.

Authors:  Xu Zhang; Baohu Liu; Nan Li; Yuanyuan Li; Jun Hou; Guoping Duan; Dongyu Wu
Journal:  Front Neurosci       Date:  2020-05-20       Impact factor: 4.677

7.  Intersubject correlation analysis reveals the plasticity of cerebral functional connectivity in the long-term use of social media.

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Review 8.  Predicting treatment response using EEG in major depressive disorder: A machine-learning meta-analysis.

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9.  Effects of HF-rTMS over the left and right DLPFC on proactive and reactive cognitive control.

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10.  Effectiveness of Repetitive Transcranial Magnetic Stimulation in the Treatment of Bipolar Disorder in Comparison to the Treatment of Unipolar Depression in a Naturalistic Setting.

Authors:  Abdullah Alhelali; Eisa Almheiri; Mohamed Abdelnaim; Franziska C Weber; Berthold Langguth; Martin Schecklmann; Tobias Hebel
Journal:  Brain Sci       Date:  2022-02-23
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