Poster No:
1413
Submission Type:
Abstract Submission
Authors:
Nooshin Javaheripour1, Antonia Schulz1, Lisa Sindermann1, Teutenberg Lea2, Sarah Alizadeh3, Elina Stocker4, Madleen Stenger5, Frederike Stein2, Florian Thomas-Odenthal2, Paul Usemann2, Nils Winter6, Janik Goltermann7, Carlotta Barkhau6, Daniel Emden8, Jan Ernsting6, Maximilian Konowski9, Ramona Leenings6, Lukas Fisch10, Tiana Borgers11, Kira Flinkenflügel11, Dominik Grotegerd12, Anna Kraus7, Susanne Meinert12, Andreas Jansen2, Andreas J. Forstner13, Igor Nenadić14, Benjamin Straube2, Udo Dannlowski11, Tim Hahn6, Tilo Kircher14, Hamidreza Jamalabadi2
Institutions:
1Philipps University of Marburg, Marburg, Hessen, 2Department of Psychiatry and Psychotherapy, University of Marburg, Germany, Marburg, Hesse, 3Philips University of Marburg, Marburg, Marburg, 4Philips University of Marburg, Marburg, Hessen, 5Philips Univercity of Marburg, Marburg, Hessen, 6University of Münster, Münster, Germany, 7University of Münster, Münster, North Rhine-Westphalia, 8University of Münster, Münster, North Rhine–Westphalia, 9University of Münster, Münster, Nordrhein-Westfalen, 10Institute for Translational Psychiatry, Münster, North Rhine Westphalia, 11Institute for Translational Psychiatry, Münster, North Rhine-Westphalia, 12Institute for Translational Psychiatry, University of Münster, Münster, North Rhine-Westphalia, 13Bonn, Bonn, North Rhine-Westphalia, 14Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Hesse
First Author:
Co-Author(s):
Teutenberg Lea
Department of Psychiatry and Psychotherapy, University of Marburg, Germany
Marburg, Hesse
Frederike Stein
Department of Psychiatry and Psychotherapy, University of Marburg, Germany
Marburg, Hesse
Paul Usemann
Department of Psychiatry and Psychotherapy, University of Marburg, Germany
Marburg, Hesse
Daniel Emden
University of Münster
Münster, North Rhine–Westphalia
Lukas Fisch
Institute for Translational Psychiatry
Münster, North Rhine Westphalia
Tiana Borgers
Institute for Translational Psychiatry
Münster, North Rhine-Westphalia
Dominik Grotegerd
Institute for Translational Psychiatry, University of Münster
Münster, North Rhine-Westphalia
Anna Kraus
University of Münster
Münster, North Rhine-Westphalia
Susanne Meinert
Institute for Translational Psychiatry, University of Münster
Münster, North Rhine-Westphalia
Andreas Jansen
Department of Psychiatry and Psychotherapy, University of Marburg, Germany
Marburg, Hesse
Igor Nenadić
Department of Psychiatry and Psychotherapy, University of Marburg
Marburg, Hesse
Benjamin Straube
Department of Psychiatry and Psychotherapy, University of Marburg, Germany
Marburg, Hesse
Udo Dannlowski
Institute for Translational Psychiatry
Münster, North Rhine-Westphalia
Tim Hahn
University of Münster
Münster, Germany
Tilo Kircher
Department of Psychiatry and Psychotherapy, University of Marburg
Marburg, Hesse
Hamidreza Jamalabadi
Department of Psychiatry and Psychotherapy, University of Marburg, Germany
Marburg, Hesse
Introduction:
While no single brain region or network reliably explains the aetiology and progression of Major Depressive Disorder (MDD) [Winter et al., 2022], subtle, distributed disturbances in functional connectivity between large-scale neural systems appear to underlie depressive symptomatology [Braun et al., 2018]. The Default Mode Network (DMN) and Central Executive Network (CEN) are frequently implicated in MDD, alongside alterations in Sensorimotor (SMN), Visual (VIS), Salience (SAL), Dorsal Attention (DAN), and Language (LAN) networks [Javaheripour et al., 2021]. However, a quantitative and mechanistic framework capable of systematically linking symptom severity to altered network dynamics remains elusive.
Methods:
Extending dynamic network control theory [Gu et al., 2015; Li et al., 2022], we conceptualized MDD-related neurofunctional abnormalities as changes in the "controllability" of large-scale brain networks. Specifically, we estimated energy controllability (EC) metrics from the resting-state functional MRI data of n = 522 healthy controls (HC) and n = 452 MDD patients [Kircher et al., 2019]. EC quantifies how combinations of 7 large-scale networks (DMN, CEN, SMN, VIS, SAL, DAN, LAN) can influence others over time, generalizing the notion of average controllability [Gu et al., 2015]. We considered all j∈{1,…,127} possible network subsets (Kj) to determine their ability to steer brain dynamics (A), where A7×7 is defined by partial correlations of the seven-time series. We then used Canonical Correlation Analysis (CCA) to examine associations between EC metrics and symptom severity indices, particularly focusing on Hamilton Depression Rating Scale (HAMD) thresholds and differences across MDD remission states (fully, partially, and acute).
Results:
Our analyses revealed robust, strong correlations between EC metrics and depressive symptoms (CCA r ~ [0.3 - 0.9]), supporting a direct linkage between network controllability and symptom severity. We identified two distinct clusters of network subsets: one set whose EC values increased with symptom severity, and another that decreased, each cluster being comprised multiple networks with each network being involved to multiple networks. Notably, the pattern of controllability changes exhibited a strong shift around a HAMD score of 10, suggesting a critical threshold in the clinical course of MDD. When examining subsets of subjects to assess the stability of these findings, we found that more than 50% of the network subsets showed consistent patterns of EC-symptom relationships across different samples. Furthermore, comparing CCA-derived covariates of brain-behavior associations revealed high cosine similarity (r ~ [0.1- 0.4, p <0.01]) across various subsamples, indicating robust and reproducible patterns. Evaluating subjects with different levels of remission (fully, partially, acute MDD), we observed a gradual shift toward HC-like controllability dynamics, albeit with occasional overshoots in their return to normative patterns.
Conclusions:
Our results demonstrate that analyzing brain network controllability provides a clear and robust correlation with depressive symptomatology, identifying distinct and reproducible network clusters that scale with symptom severity. The observed threshold effect around a HAMD score of 10, along with stable cluster overlap and consistent covariate similarity across subsamples, underscores the reliability of the EC-based approach. While these findings are promising, replication in other and larger datasets is required. If corroborated, this perspective on network controllability may offer a novel lens to understand the neural dynamics of MDD, shifting the focus from static activation patterns to the energetic and controllable properties of large-scale brain networks.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 1
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Computational Neuroscience
Machine Learning
Psychiatric
Psychiatric Disorders
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Patients
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Not applicable
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
Functional MRI
Structural MRI
Computational modeling
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
FSL
Free Surfer
Provide references using APA citation style.
Braun U, Schaefer A, Betzel RF, Tost H, Meyer-Lindenberg A, Bassett DS (2018): From Maps to Multi-dimensional Network Mechanisms of Mental Disorders. Neuron 97:14–31.
Gu S, Pasqualetti F, Cieslak M, Telesford QK, Yu AB, Kahn AE, Medaglia JD, Vettel JM, Miller MB, Grafton ST, Bassett DS (2015): Controllability of structural brain networks. Nat Commun 6:8414.
Javaheripour N, Li M, Chand T, Krug A, Kircher T, Dannlowski U, Nenadić I, Hamilton JP, Sacchet MD, Gotlib IH (2021): Altered resting-state functional connectome in major depressive disorder: a mega-analysis from the PsyMRI consortium. Translational psychiatry 11:1–9.
Kircher T, Wöhr M, Nenadic I, Schwarting R, Schratt G, Alferink J, Culmsee C, Garn H, Hahn T, Müller-Myhsok B, Dempfle A, Hahmann M, Jansen A, Pfefferle P, Renz H, Rietschel M, Witt SH, Nöthen M, Krug A, Dannlowski U (2019): Neurobiology of the major psychoses: a translational perspective on brain structure and function-the FOR2107 consortium. Eur Arch Psychiatry Clin Neurosci 269:949–962.
Li Q, Yao L, You W, Liu J, Deng S, Li B, Luo L, Zhao Y, Wang Y, Wang Y (2022): Controllability of Functional Brain Networks and Its Clinical Significance in First-Episode Schizophrenia. Schizophrenia Bulletin.
Winter NR, Leenings R, Ernsting J, Sarink K, Fisch L, Emden D, Blanke J, Goltermann J, Opel N, Barkhau C, Meinert S, Dohm K, Repple J, Mauritz M, Gruber M, Leehr EJ, Grotegerd D, Redlich R, Jansen A, Nenadic I, Nöthen MM, Forstner A, Rietschel M, Groß J, Bauer J, Heindel W, Andlauer T, Eickhoff SB, Kircher T, Dannlowski U, Hahn T (2022): Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder Across Neuroimaging Modalities. JAMA Psychiatry 79:879–888.
No