Energy inefficiency underpinning brain state dysregulation in Major Depressive Disorder
Poster No:
462
Submission Type:
Abstract Submission
Authors:
Qianhui Liu1,2, Hui Xiong2, Weiyang Shi2, Shiqi Di1,2, Yu Zhang3, Tianzi Jiang1,2,4
Institutions:
1School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China, 2Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 3Zhejiang Lab, Hangzhou, China, Hangzhou, Zhejiang, 4Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital, Yongzhou, China
First Author:
Qianhui Liu
School of Artificial Intelligence, University of Chinese Academy of Sciences|Brainnetome Center, Institute of Automation, Chinese Academy of Sciences
Beijing, China|Beijing, China
School of Artificial Intelligence, University of Chinese Academy of Sciences|Brainnetome Center, Institute of Automation, Chinese Academy of Sciences
Beijing, China|Beijing, China
Co-Author(s):
Shiqi Di
School of Artificial Intelligence, University of Chinese Academy of Sciences|Brainnetome Center, Institute of Automation, Chinese Academy of Sciences
Beijing, China|Beijing, China
School of Artificial Intelligence, University of Chinese Academy of Sciences|Brainnetome Center, Institute of Automation, Chinese Academy of Sciences
Beijing, China|Beijing, China
Tianzi Jiang
School of Artificial Intelligence, University of Chinese Academy of Sciences|Brainnetome Center, Institute of Automation, Chinese Academy of Sciences|Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital
Beijing, China|Beijing, China|Yongzhou, China
School of Artificial Intelligence, University of Chinese Academy of Sciences|Brainnetome Center, Institute of Automation, Chinese Academy of Sciences|Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital
Beijing, China|Beijing, China|Yongzhou, China
Introduction:
A key aspect in understanding major depressive disorder (MDD) lies in how brain dynamics are altered in this disorder (Braun et al., 2018). Individuals with MDD have been thought to have difficulty regulating brain states effectively (Holtzheimer & Mayberg, 2011), and empirical studies have also indicated an impaired ability to maintain stable activity patterns, switch between states, and transition through them appropriately (Javaheripour et al., 2023). However, the underlying dynamic mechanisms that give rise to brain state dysregulations in MDD and which specific brain regions contribute to these dysregulations remains unclear. Neurotransmitter and genetic changes have long been recognized as key contributors to the biological underpinnings of MDD (Cui et al., 2024; Kendall et al., 2021). We postulate that the neurotransmitters and genetic profiles might contribute mechanistic insights into control energy, and thereby the neurobiological underpinnings of MDD. Yet, this remain to be determined.
Methods:
Structural, diffusion and resting-state functional MRI images of 890 MDD and 890 age, sex-matched healthy controls from the UK biobank were preprocessed to extract brain functional activity and structural connectivity. We begin by identifying recurrent patterns of whole brain activity and examining how they differ between patients with MDD and healthy controls. Then, we apply network control theory (Lynn & Bassett, 2019) to simulate state transitions and estimate the associated energy costs, as well as regional energy-regulation capacity, providing energetic evidence for the brain state disruptions observed in MDD. Moreover, we explore whether these energy metrics are related with depressive symptoms. Finally, we investigate the relationship between these energy alterations and neurotransmitter engagement as well as gene expression, aiming to elucidate the biological mechanisms that contribute to the observed energy inefficiencies in MDD.
Results:
Our results demonstrate that MDD is characterized by significantly altered brain state dynamics, evidenced by decreased state stability and increased transition frequency among brain states. Such brain state dysregulation is underpinned by the reduced system stability and elevated energy demands of state transitions (Fig. 1d), suggesting a more challenging dynamic landscape for MDD patients. Analysis of regional energy-regulation capacities (Fig. 2a) highlighted ten brain regions (Fig. 2b) might contribute to the energy inefficiency in MDD, particularly implicating the left dorsolateral prefrontal cortex and several areas within the limbic system as potential targets for therapeutic interventions. Furthermore, we explored the relationship between energy alterations and neurotransmitter engagement, finding most significant correlations with the density of the vesicular acetylcholine transporter (VAChT), 5-HT2a and histamine H3 receptors. Moreover, the genes highly correlated with changes in energy regulation were mainly enriched in Gene ontology (GO) biological processes (BPs) related to synaptic signaling and neurodevelopment. Besides significantly higher expression in excitatory neurons and inhibitory neurons were found in cell type deconvolution analysis.
Conclusions:
Our work provides a comprehensive investigation into the energetic and biological mechanisms underlying altered brain state dynamics in MDD, highlighting the potential of brain energy dynamics as biomarkers and as a framework for exploring targeted interventions. These findings advance our understanding of the neurobiological underpinnings of MDD and our ability to control brain dynamics, paving the way for developing targeted interventions for depression.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Task-Independent and Resting-State Analysis
Keywords:
Affective Disorders
MRI
Other - Major Depressive Disorder; Brain state transition; Brain dynamics; Network control theory; Energy landscape; Transcriptomic-neuroimaging; Neurobiological mechanism
1|2Indicates the priority used for review
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2. Cui, L., Li, S., Wang, S., et al. (2024). Major depressive disorder: hypothesis, mechanism, prevention and treatment. Signal Transduct Target Ther, 9(1), 30.
3. Holtzheimer, P. E., & Mayberg, H. S. (2011). Stuck in a rut: rethinking depression and its treatment. Trends Neurosci, 34(1), 1-9.
4. Javaheripour, N., Colic, L., Opel, N., et al. (2023). Altered brain dynamic in major depressive disorder: state and trait features. Transl Psychiatry, 13(1), 261.
5. Kendall, K. M., Van Assche, E., Andlauer, T. F. M., et al. (2021). The genetic basis of major depression. Psychol Med, 51(13), 2217-2230.
6. Lynn, C. W., & Bassett, D. S. (2019). The physics of brain network structure, function and control. Nature Reviews Physics, 1(5), 318-332.