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

被引:36
|
作者
Corlier, Juliana [1 ,2 ]
Wilson, Andrew [1 ,2 ]
Hunter, Aimee M. [1 ,2 ]
Vince-Cruz, Nikita [1 ,2 ]
Krantz, David [1 ,2 ]
Levitt, Jennifer [1 ,2 ]
Minzenberg, Michael J. [1 ,2 ]
Ginder, Nathaniel [1 ,2 ]
Cook, Ian A. [1 ,2 ,3 ]
Leuchter, Andrew F. [1 ,2 ]
机构
[1] Univ Calif Los Angeles, TMS Clin & Res Program, Neuromodulat Div, Semel Inst Neurosci & Human Behav, Los Angeles, CA 90024 USA
[2] Univ Calif Los Angeles, Dept Psychiat & Biobehav Sci, David Geffen Sch Med, Los Angeles, CA 90024 USA
[3] Univ Calif Los Angeles, Dept Bioengn, Henry Samueli Sch Engn & Appl Sci, Los Angeles, CA 90024 USA
关键词
repetitive transcranial magnetic stimulation (rTMS); depression; functional connectivity; machine learning; electroencephalogram (EEG); PREFRONTAL CORTEX; RTMS TREATMENT; BASE-LINE; NONRESPONSE; BIOMARKERS; THERAPY; EXCITABILITY; MODULATION; MECHANISM; THETA;
D O I
10.1093/cercor/bhz035
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
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 (alpha SC). 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 alpha SC 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.
引用
收藏
页码:4958 / 4967
页数:10
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