A benchmark for prediction of psychiatric multimorbidity from resting EEG data in a large pediatric sample
Autor(in); Institution
Haufe, Stefan; 8.4, Mathematische Modellierung und Datenanalyse, PTB-Berlin; Berlin Center for Advanced Neuroimaging, Charité Universitätsmedizin Berlin, Berlin, GERMANY; Uncertainty, Inverse Modeling and Machine Learning Group, Technische Universität Berlin, Berlin, GERMANY
Langer, Nicolas; Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, SWITZERLAND; University Research Priority Program (URPP) Dynamics of Healthy Aging, Zurich, SWITZERLAND; Neuroscience Center Zurich (ZNZ), Zurich, SWITZERLAND
Plomecka, Martyna Beata; Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, SWITZERLAND; University Research Priority Program (URPP) Dynamics of Healthy Aging, Zurich, SWITZERLAND; Neuroscience Center Zurich (ZNZ), Zurich, SWITZERLAND
Tröndle, Marius; Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, SWITZERLAND; University Research Priority Program (URPP) Dynamics of Healthy Aging, Zurich, SWITZERLAND; Neuroscience Center Zurich (ZNZ), Zurich, SWITZERLAND
Negi, Anuja; Berlin Center for Advanced Neuroimaging, Charité–Universitätsmedizin Berlin, Berlin, GERMANY; Bernstein Center for Computational Neuroscience Berlin, Berlin, GERMANY
Popov, Tzvetan; Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, SWITZERLAND; University Research Priority Program (URPP) Dynamics of Healthy Aging, Zurich, SWITZERLAND
Milham, Michael; Center for the Developing Brain, Child Mind Institute, New York, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
benchmark for prediction of psychiatric multimorbidity ; EEG ; Five-dimensional neuroimaging ; Electromagnetic brain imaging ; DSM-V
Zusammenfassung
Psychiatric disorders are among the most common and debilitating illnesses across the lifespan and begin usually during childhood and adolescence, which emphasizes the importance of studying the developing brain. Most of the previous pediatric neuroimaging studies employed traditional univariate statistics on relatively small samples. Multivariate machine learning approaches have a great potential to overcome the limitations of these approaches. On the other hand, the vast majority of existing multivariate machine learning studies have focused on differentiating between children with an isolated psychiatric disorder and typically developing children. However, this line of research does not reflect the real-life situation as the majority of children with a clinical diagnosis have multiple psychiatric disorders (multimorbidity), and consequently, a clinician has the task to choose between different diagnoses and/or the combination of multiple diagnoses. Thus, the goal of the present benchmark is to predict psychiatric multimorbidity in children and adolescents. For this purpose, we implemented two kinds of machine learning benchmark challenges: The first challenge targets the prediction of the seven most prevalent DSM-V psychiatric diagnoses for the available data set, of which each individual can exhibit multiple ones concurrently (i.e. multi-task multi-label classification). Based on behavioral and cognitive measures, a second challenge focuses on predicting psychiatric symptom severity on a dimensional level (i.e. multiple regression task). For the present benchmark challenges, we will leverage existing and future data from the biobank of the Healthy Brain Network (HBN) initiative, which offers a unique large-sample dataset ( N = 2042) that provides a wide array of different psychiatric developmental disorders and true hidden data sets. Due to limited real-world practicability and economic viability of MRI measurements, the present challenge will permit only resting state EEG data and demographic information to derive predictive models. We believe that a community driven effort to derive predictive markers from these data using advanced machine learning algorithms can help to improve the diagnosis of psychiatric developmental disorders.
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Open Access Hybrid
Rechteinformation
CC BY 4.0 ; Creative Commons Attribution 4.0 License
Themenbereich der Metrologie
Metrologie in der Medizin
Zitierung
Haufe, S., Langer, N., Plomecka, M. B., Tröndle, M., Negi, A., Popov, T., & Milham, M. (2022). A benchmark for prediction of psychiatric multimorbidity from resting EEG data in a large pediatric sample. NeuroImage, 258, 16 S. https://doi.org/10.1016/j.neuroimage.2022.119348
