Predicting trail-making test performance using 7-Tesla structural cerebral MRI
Translational Psychiatry, vol. 16
Abstract
Psychopharmaceutical therapy has seen a lack of improvement in efficacy, highlighting the need for a more individualized treatment approach that optimally leverages pharmaceutical effects. To achieve this, it is necessary to identify factors that can predict psychological attributes and treatment outcomes. We hypothesize that brain morphology may serve as a predictor of performance in psychological tests. A 7-Tesla-MRI system was used to measure 74 transdiagnostic patients and 41 healthy controls. Values for grey matter volume (GMV), cortical thickness and gyrification based on the Schaefer 100 Atlas for parcellation were determined. Using this morphological data, performance in a Trail-Making Test (TMT) - A and B was predicted with the help of a deep learning model. A SHAP analysis was conducted to identify the key variables important for the model. The accuracy in predicting TMT performance was very good at 0.795. The most decisive feature influencing predictions was gyrification. Areas indicating inter-group differences were focused in the frontal cortex, as well as in temporal and parietal regions. Morphological characteristics can predict test performance with high accuracy. These results support the implementation of a transdiagnostic approach in clinical practice to better address the underlying pathophysiology. Symptom prediction based on neuroimaging data appears to be an achievable goal.
Authors 9
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Affiliation as printed
Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
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Forschungszentrum Jülich · Universität Ulm
Affiliation as printed
Department of Psychiatry II, Ulm University and BKH Günzburg, Günzburg, Germany
Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Jülich, Germany
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Forschungszentrum Jülich · LVR-Klinik Köln · RWTH Aachen University
Affiliation as printed
Department of Psychiatry and Psychotherapy II, LVR-Klinik Köln, Köln, Germany
Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Jülich, Germany
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Forschungszentrum Jülich · RWTH Aachen University
Affiliation as printed
Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Jülich, Germany
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Affiliation as printed
Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
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Affiliation as printed
Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
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Forschungszentrum Jülich · RWTH Aachen University
Affiliation as printed
Department of Neurology, RWTH Aachen University, Aachen, Germany
Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Jülich, Germany
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Forschungszentrum Jülich · University of Southern Denmark · Odense University Hospital · RWTH Aachen University
Affiliation as printed
Department of Psychiatry II, Ulm University and BKH Günzburg, Günzburg, Germany. Irene.Neuner@bkh-guenzburg.de
Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany. Irene.Neuner@bkh-guenzburg.de
Department of Psychiatry, University of Southern Denmark, Odense University Hospital, Odense C, Denmark. Irene.Neuner@bkh-guenzburg.de
Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Jülich, Germany. Irene.Neuner@bkh-guenzburg.de
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Forschungszentrum Jülich · RWTH Aachen University
Affiliation as printed
Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Jülich, Germany