The transdiagnostic relevance of brain networks identified through network mapping in psychiatry
Poster No:
1379
Submission Type:
Abstract Submission
Authors:
Marius Gruber1, Tilo Kircher2, Udo Dannlowski3, Andrew Zalesky4, Robin Cash5, Jonathan Repple1
Institutions:
1Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt, Germany, 2Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany, 3Institute for Translational Psychiatry, University of Münster, Münster, Germany, 4Systems Lab, Department of Psychiatry, The University of Melbourne, Melbourne, Australia, 5University of Melbourne, Melbourne, Australia
First Author:
Marius Gruber
Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt
Frankfurt, Germany
Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt
Frankfurt, Germany
Co-Author(s):
Andrew Zalesky
Systems Lab, Department of Psychiatry, The University of Melbourne
Melbourne, Australia
Systems Lab, Department of Psychiatry, The University of Melbourne
Melbourne, Australia
Jonathan Repple
Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt
Frankfurt, Germany
Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt
Frankfurt, Germany
Introduction:
Despite extensive research, reliable neural biomarkers for Major Depressive Disorder (MDD) remain elusive (Winter et al., 2022, 2024), at least in part due the spatial variability of depression-related brain alterations. For instance, gray matter alterations and deviations in functional activity during cognitive or emotional tasks lack regional convergence (Müller et al., 2017; Segal et al., 2023). These findings suggest a widespread, individually variable neurobiological signature of depression, rendering single-region alterations unlikely as universal biomarkers. Network mapping analysis offers a promising solution by mapping the locations of individual brain alterations onto the underlying brain networks. Previous studies used normative connectome data to demonstrate that depression-related alterations and treatment targets converge on common networks (Cash et al., 2023; Siddiqi et al., 2021); however, evaluations of these networks in psychiatric cohorts are lacking.
Methods:
Our study will map depression networks identified in previous network mapping analyses onto diffusion-weighted and resting-state MRI data from a sample of healthy controls (HC) and individuals diagnosed with MDD, bipolar disorder (BD), or schizophrenia (SZ) from the Marburg-Münster Affective Disorders Cohort Study (Kircher et al., 2019). Connectivity in these networks will be related to psychiatric symptoms, cognition, and environmental and genetic risk factors.
Results:
Data has been acquired from a total of N=2592 individuals (n=1157 HCs and n=1115 MDD, n=195 BD, and n=125 SZ patients). Linear regression models will test whether depression network connectivity is associated with psychiatric symptoms, cognitive deficits, polygenic risk for depression, and childhood maltreatment. Given the symptomatic overlap across mental disorders, we anticipate these associations to be detectable across MDD, BD, and SZ. Machine learning analyses will evaluate their potential as reliable biomarkers for individual diagnostic decisions.
Conclusions:
Our study aims to provide the first transdiagnostic, multimodal phenotyping of depression networks identified in network mapping analyses in a large psychiatric cohort. By integrating diverse data modalities, our findings could advance our understanding of the neurobiological underpinnings of depression and potentially facilitate the development of personalized treatment strategies.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 1
Keywords:
Affective Disorders
Psychiatric Disorders
Other - Network mapping
1|2Indicates the priority used for review
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Provide references using APA citation style.
Kircher, T., et al. (2019). Neurobiology of the major psychoses: A translational perspective on brain structure and function—The FOR2107 consortium. European Archives of Psychiatry and Clinical Neuroscience, 269(8), 949–962. https://doi.org/10.1007/s00406-018-0943-x
Müller, V. I., et al. (2017). Altered brain activity in unipolar depression revisited: Meta-analyses of neuroimaging studies. JAMA Psychiatry, 74(1), 47–55. https://doi.org/10.1001/jamapsychiatry.2016.2783
Segal, A., et al. (2023). Regional, circuit and network heterogeneity of brain abnormalities in psychiatric disorders. Nature Neuroscience, 26(9), 1613–1629. https://doi.org/10.1038/s41593-023-01404-6
Siddiqi, S. H., et al. (2021). Brain stimulation and brain lesions converge on common causal circuits in neuropsychiatric disease. Nature Human Behaviour, 5(12), 1707–1716. https://doi.org/10.1038/s41562-021-01161-1
Winter, N. R., et al. (2024). A Systematic Evaluation of Machine Learning-Based Biomarkers for Major Depressive Disorder. JAMA Psychiatry, 81(4), 386–395. https://doi.org/10.1001/jamapsychiatry.2023.5083
Winter, N. R., et al. (2022). Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder Across Neuroimaging Modalities. JAMA Psychiatry, 79(9), 879–888. https://doi.org/10.1001/jamapsychiatry.2022.1780