Polarization of brain dynamics in mania and depression
Poster No:
450
Submission Type:
Abstract Submission
Authors:
Paola Magioncalda1, Matteo Martino1, Benedetta Conio2, Mario Amore2, Zirui Huang3
Institutions:
1Taipei Medical University, Taipei, Taiwan, 2University of Genoa, Genoa, Italy, 3University of Michigan, Ann Arbor, MI, US
First Author:
Co-Author(s):
Introduction:
Bipolar disorder (BD) is a major psychiatric disorder clinically characterized by episodes of mania and depression. The neurobiology underlying this psychopathological condition is still largely unknown. In this work, we investigated brain dynamics in the manic and depressive states of BD.
Methods:
Clinical information and resting-state functional magnetic resonance imaging (fMRI) data were collected from 69 BD patients during the manic state (n=34) or depressed state (n=35), along with 73 healthy controls (HC). The analysis of dynamic brain activity was performed, modeling intrinsic brain activity as a dynamic sequence of discrete (quasi-stationary) brain states, i.e., transient, momentary co-activation patterns (CAPs) in fMRI signal. After standard preprocessing of fMRI data, CAPs were identified using a k-means clustering algorithm, and their occurrence rate was calculated. Finally, CAP occurrence rates were compared between HC, mania, and depression (using an ANOVA with age and sex as covariates, followed by post-hoc comparisons) and related to clinical symptomatology (performing Spearman's correlation analyses).
Results:
Brain dynamics in HC showed a balanced distribution of the occurrence rates among the CAPs. In contrast, brain dynamics in mania was dominated by a CAP involving the sensorimotor and insular areas (primarily belonging to the somatomotor network (SMN) and visual network (VN)), which showed a significant increase in the occurrence rates in manic patients compared to HC (uncorrected p<0.05) and depressed patients (Bonferroni-corrected p<0.05). Conversely, brain dynamics in depression was dominated by a CAP involving the associative areas (primarily belonging to the default-mode network (DMN)), which showed a significant increase in the occurrence rates in depressed patients compared to HC and manic patients (Bonferroni-corrected p<0.05). See Figure 1. Finally, the occurrence rate of the SMN/VN-related CAP positively correlated with manic symptomatology, in particular psychomotor hyperactivity and mood elevation, and negatively correlated with depressive symptomatology, in particular apathy and psychomotor retardation (Bonferroni-corrected p<0.05). Conversely, the occurrence rate of the DMN-related CAP negatively correlated with manic symptomatology, in particular psychomotor hyperactivity and mood elevation (Bonferroni-corrected p<0.05).
·Figure 1
Conclusions:
These data may suggest that polarization of the repertoire of brain dynamics toward a brain state involving the low-order sensorimotor and insular areas (subserving the perception and modulation of the outer and inner/body environments, respectively) might over-tune brain activity and phenomenal-behavioral patterns onto the current environment, manifesting in mania; conversely, polarization of the repertoire toward a brain state involving the high-order associative areas (subserving the associative processing unrelated to environmental changes) might de-tune brain activity and phenomenal-behavioral patterns from the current environment, manifesting in depression. See Figure 2. This construct might show how changes in the architecture of intrinsic brain activity may alter the structure of phenomenal experience and behavior in physiology and psychopathology.
·Figure 2
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Task-Independent and Resting-State Analysis 2
Keywords:
Affective Disorders
FUNCTIONAL MRI
Psychiatric Disorders
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
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.
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.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
2. Martino M and Magioncalda P. (2023). A three-dimensional model of neural activity and phenomenal-behavioral patterns. Molecular Psychiatry, 29(3):639-652.
3. Magioncalda P and Martino M. (2021). A unified model of the pathophysiology of bipolar disorder. Molecular Psychiatry, 27(1):202-211.
4. Martino M, Magioncalda P, Huang Z, et al. (2016). Contrasting variability patterns in the default mode and sensorimotor networks balance in bipolar depression and mania. Proc Natl Acad Sci U S A, 113(17):4824-9.
5. Liu X, et al. (2028). Co-activation patterns in resting-state fMRI signals. Neuroimage, 180(Pt B): 485-494.
6. Huang Z, et al. (2020). Temporal circuit of macroscale dynamic brain activity supports human consciousness. Sci Adv, 6(11): eaaz0087.