Molly McVoy, Serhiy Chumachenko, Maia Gersten, Benjamin Wade, Oscar Corcelles, Joy Yala, Mikaila Gray, Alla Morris, Asif Jamil, Paolo Cassono, Farhad Kaffashi, Kenneth Loparo, Farren Briggs, Martha Sajatovic
{"title":"评估定量脑电图一致性在青少年重度抑郁症中的预测效用:一种机器学习方法。","authors":"Molly McVoy, Serhiy Chumachenko, Maia Gersten, Benjamin Wade, Oscar Corcelles, Joy Yala, Mikaila Gray, Alla Morris, Asif Jamil, Paolo Cassono, Farhad Kaffashi, Kenneth Loparo, Farren Briggs, Martha Sajatovic","doi":"10.1177/10445463251358742","DOIUrl":null,"url":null,"abstract":"<p><p><b><i>Background:</i></b> Improving early recognition and accurate diagnosis of major depressive disorder (MDD) in childhood is a pressing concern. Quantitative electroencephalogram (qEEG) may be an effective, noninvasive diagnostic biomarker for MDD. Prior work by our team demonstrated decreased resting connectivity, as measured by qEEG coherence, in a heterogeneous group of adolescents with MDD compared with age and gender-matched healthy controls (HCs). This study explored qEEG coherence as a predictor of MDD diagnosis in a prospective, longitudinal sample of medication-free, adolescents with MDD versus HCs. <b><i>Methods:</i></b> Twenty-eight adolescents with MDD (Children's Depression Rating Scale score ≥40) and 27 age and gender-matched HCs (age 14-17, 78% female) received a baseline resting 32-channel EEG. Brain-wide coherence between channel pairs was calculated for the frequency bands (alpha, beta, theta, and delta) and compared between MDD youth and HC. Random forest classifiers were used to predict individual MDD status using baseline qEEG coherence. Models were trained and tested using 10-repeated, 10-fold cross-validation, and performance was evaluated with the area under the receiver operating characteristic curve (AUC-ROC). The contribution of individual predictors was assessed using permutation importance. Model significance was assessed using permutation testing (B = 1000 resamples). <b><i>Results:</i></b> Random forest models predicted depression status with a trend-level of significance (mean AUC-ROC = 0.65, <i>p</i> = 0.08). Among the most predictive channel pairs, adolescent MDD was characterized by lower coherence in T7-P7 (<i>p</i> < 0.05), Fz-Cz, and Fp2-F8 as well as higher coherence in P4-O2 and Cz-Pz. <b><i>Conclusions:</i></b> This study provides preliminary evidence that multivariate patterns of qEEG may inform the diagnosis of adolescent MDD. Specific aberrant patterns of coherence within the default mode network and cognitive control network were characteristic of adolescent MDD. Ongoing work will seek to replicate these findings in a larger cohort.</p>","PeriodicalId":15277,"journal":{"name":"Journal of child and adolescent psychopharmacology","volume":" ","pages":""},"PeriodicalIF":2.2000,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessing the Predictive Utility of Quantitative Electroencephalography Coherence in Adolescent Major Depressive Disorder: A Machine Learning Approach.\",\"authors\":\"Molly McVoy, Serhiy Chumachenko, Maia Gersten, Benjamin Wade, Oscar Corcelles, Joy Yala, Mikaila Gray, Alla Morris, Asif Jamil, Paolo Cassono, Farhad Kaffashi, Kenneth Loparo, Farren Briggs, Martha Sajatovic\",\"doi\":\"10.1177/10445463251358742\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><b><i>Background:</i></b> Improving early recognition and accurate diagnosis of major depressive disorder (MDD) in childhood is a pressing concern. Quantitative electroencephalogram (qEEG) may be an effective, noninvasive diagnostic biomarker for MDD. Prior work by our team demonstrated decreased resting connectivity, as measured by qEEG coherence, in a heterogeneous group of adolescents with MDD compared with age and gender-matched healthy controls (HCs). This study explored qEEG coherence as a predictor of MDD diagnosis in a prospective, longitudinal sample of medication-free, adolescents with MDD versus HCs. <b><i>Methods:</i></b> Twenty-eight adolescents with MDD (Children's Depression Rating Scale score ≥40) and 27 age and gender-matched HCs (age 14-17, 78% female) received a baseline resting 32-channel EEG. Brain-wide coherence between channel pairs was calculated for the frequency bands (alpha, beta, theta, and delta) and compared between MDD youth and HC. Random forest classifiers were used to predict individual MDD status using baseline qEEG coherence. Models were trained and tested using 10-repeated, 10-fold cross-validation, and performance was evaluated with the area under the receiver operating characteristic curve (AUC-ROC). The contribution of individual predictors was assessed using permutation importance. Model significance was assessed using permutation testing (B = 1000 resamples). <b><i>Results:</i></b> Random forest models predicted depression status with a trend-level of significance (mean AUC-ROC = 0.65, <i>p</i> = 0.08). Among the most predictive channel pairs, adolescent MDD was characterized by lower coherence in T7-P7 (<i>p</i> < 0.05), Fz-Cz, and Fp2-F8 as well as higher coherence in P4-O2 and Cz-Pz. <b><i>Conclusions:</i></b> This study provides preliminary evidence that multivariate patterns of qEEG may inform the diagnosis of adolescent MDD. Specific aberrant patterns of coherence within the default mode network and cognitive control network were characteristic of adolescent MDD. 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Assessing the Predictive Utility of Quantitative Electroencephalography Coherence in Adolescent Major Depressive Disorder: A Machine Learning Approach.
Background: Improving early recognition and accurate diagnosis of major depressive disorder (MDD) in childhood is a pressing concern. Quantitative electroencephalogram (qEEG) may be an effective, noninvasive diagnostic biomarker for MDD. Prior work by our team demonstrated decreased resting connectivity, as measured by qEEG coherence, in a heterogeneous group of adolescents with MDD compared with age and gender-matched healthy controls (HCs). This study explored qEEG coherence as a predictor of MDD diagnosis in a prospective, longitudinal sample of medication-free, adolescents with MDD versus HCs. Methods: Twenty-eight adolescents with MDD (Children's Depression Rating Scale score ≥40) and 27 age and gender-matched HCs (age 14-17, 78% female) received a baseline resting 32-channel EEG. Brain-wide coherence between channel pairs was calculated for the frequency bands (alpha, beta, theta, and delta) and compared between MDD youth and HC. Random forest classifiers were used to predict individual MDD status using baseline qEEG coherence. Models were trained and tested using 10-repeated, 10-fold cross-validation, and performance was evaluated with the area under the receiver operating characteristic curve (AUC-ROC). The contribution of individual predictors was assessed using permutation importance. Model significance was assessed using permutation testing (B = 1000 resamples). Results: Random forest models predicted depression status with a trend-level of significance (mean AUC-ROC = 0.65, p = 0.08). Among the most predictive channel pairs, adolescent MDD was characterized by lower coherence in T7-P7 (p < 0.05), Fz-Cz, and Fp2-F8 as well as higher coherence in P4-O2 and Cz-Pz. Conclusions: This study provides preliminary evidence that multivariate patterns of qEEG may inform the diagnosis of adolescent MDD. Specific aberrant patterns of coherence within the default mode network and cognitive control network were characteristic of adolescent MDD. Ongoing work will seek to replicate these findings in a larger cohort.
期刊介绍:
Journal of Child and Adolescent Psychopharmacology (JCAP) is the premier peer-reviewed journal covering the clinical aspects of treating this patient population with psychotropic medications including side effects and interactions, standard doses, and research on new and existing medications. The Journal includes information on related areas of medical sciences such as advances in developmental pharmacokinetics, developmental neuroscience, metabolism, nutrition, molecular genetics, and more.
Journal of Child and Adolescent Psychopharmacology coverage includes:
New drugs and treatment strategies including the use of psycho-stimulants, selective serotonin reuptake inhibitors, mood stabilizers, and atypical antipsychotics
New developments in the diagnosis and treatment of ADHD, anxiety disorders, schizophrenia, autism spectrum disorders, bipolar disorder, eating disorders, along with other disorders
Reports of common and rare Treatment Emergent Adverse Events (TEAEs) including: hyperprolactinemia, galactorrhea, weight gain/loss, metabolic syndrome, dyslipidemia, switching phenomena, sudden death, and the potential increase of suicide. Outcomes research.