Poster No:
1165
Submission Type:
Abstract Submission
Authors:
Sevil Ince1, Ben Harrison2, Kim Felmingham1, Christopher Davey2, Alec Jamieson2, Bradford Moffat3, Rebecca Glarin3, Trevor Steward1,2
Institutions:
1Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, Victoria, 3010, Australia, 2Department of Psychiatry, The University of Melbourne, Parkville, Victoria, 3010, Australia, 3The Melbourne Brain Centre Imaging Unit, Department of Radiology, The University of Melbourne, Parkville, Victoria, 3010, Australia
First Author:
Sevil Ince
Melbourne School of Psychological Sciences, The University of Melbourne
Parkville, Victoria, 3010, Australia
Co-Author(s):
Ben Harrison
Department of Psychiatry, The University of Melbourne
Parkville, Victoria, 3010, Australia
Kim Felmingham
Melbourne School of Psychological Sciences, The University of Melbourne
Parkville, Victoria, 3010, Australia
Christopher Davey
Department of Psychiatry, The University of Melbourne
Parkville, Victoria, 3010, Australia
Alec Jamieson, PhD
Department of Psychiatry, The University of Melbourne
Parkville, Victoria, 3010, Australia
Bradford Moffat
The Melbourne Brain Centre Imaging Unit, Department of Radiology, The University of Melbourne
Parkville, Victoria, 3010, Australia
Rebecca Glarin
The Melbourne Brain Centre Imaging Unit, Department of Radiology, The University of Melbourne
Parkville, Victoria, 3010, Australia
Trevor Steward
Melbourne School of Psychological Sciences, The University of Melbourne|Department of Psychiatry, The University of Melbourne
Parkville, Victoria, 3010, Australia|Parkville, Victoria, 3010, Australia
Introduction:
Emerging evidence suggests the mediodorsal thalamus (MD) plays an integral role in modulation and information integration in large brain networks that show functional alterations in mood and anxiety disorders, including the salience (SN), default-mode (DMN) and fronto-parietal networks (FPN). However, the precise causal interactions between the MD and these large networks remain largely unexplored. This study aimed to investigate alterations in intrinsic effective connectivity between the MD, and the SN, DMN and FPN in mood and anxiety disorders.
Methods:
Forty-three participants with mood and anxiety disorders (n=33 females) and 43 sex and age-matched healthy controls underwent resting-state scanning during 7-Tesla ultra-high field magnetic resonance imaging. Spectral dynamic causal modelling and parametric empirical bayes were used to examine effective connectivity. Additionally, leave-one-out cross validation was used to assess whether the group differences in connectivity were predictive of diagnostic status and individual levels of psychopathology.
Results:
Compared to healthy controls, participants with mood and anxiety disorders exhibited greater MD inhibition of the left dorsolateral prefrontal cortex (dlPFC; -0.07 Hz), lower MD inhibition of the left angular gyrus (0.09 Hz), and lower excitation of the MD from the SN (from the dorsal anterior cingulate: -0.04 Hz & from right anterior insula: -0.03 Hz). Leave-one-out cross validation results revealed that MD to left dlPFC connectivity was predictive of participants' perseverative thinking tendency (r =0.25, p = 0.011), whereas MD to left angular gyrus connectivity specifically predicted anxiety (r =0.28, p = 0.004) and stress levels (r =0.28, p = 0.004). In contrast, top-down connectivity from the SN to MD predicted depression severity (r=0.25, p = 0.011). Collectively, these bottom-up and top-down connectivity alterations of the MD predicted diagnostic status of participants (r=0.20, p = 0.034).
Conclusions:
Our findings indicate that aberrant MD interactions with core brain networks are linked to multiple dimensions of psychopathology in mood and anxiety disorders, likely contributing to preservative cognition, anxiety, stress and depressive symptomatology.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Modeling and Analysis Methods:
Bayesian Modeling
Connectivity (eg. functional, effective, structural) 1
fMRI Connectivity and Network Modeling
Task-Independent and Resting-State Analysis
Keywords:
Affective Disorders
Anxiety
Computational Neuroscience
FUNCTIONAL MRI
Sub-Cortical
Thalamus
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.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
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Not applicable
Please indicate which methods were used in your research:
Functional MRI
Computational modeling
For human MRI, what field strength scanner do you use?
7T
Which processing packages did you use for your study?
SPM
Provide references using APA citation style.
Harrison, B. J., et al. (2022). Dynamic subcortical modulators of human default mode network function. Cerebral Cortex, 32(19), 4345–4355. https://doi.org/10.1093/cercor/bhab487
Hwang, K., et al. (2017). The human thalamus is an integrative hub for functional brain networks. Journal of Neuroscience, 37(23), 5594–5607. https://doi.org/10.1523/JNEUROSCI.0067-17.2017
Li, F., et al. (2024). Mediodorsal thalamus projection to medial prefrontal cortical mediates social defeat stress-induced depression-like behaviors. Neuropsychopharmacology, 49(8), 1318–1329. https://doi.org/10.1038/s41386-024-01829-y
Menon, V. (2011). Large-scale brain networks and psychopathology: A unifying triple network model. Trends in Cognitive Sciences, 15(10), 483–506. https://doi.org/10.1016/j.tics.2011.08.003
Mukherjee, A., et al. (2021). Thalamic circuits for independent control of prefrontal signal and noise. Nature, 600(7887), 100–104. https://doi.org/10.1038/s41586-021-04056-3
Shine, J. M., et al. (2019). The Low-Dimensional Neural Architecture of Cognitive Complexity Is Related to Activity in Medial Thalamic Nuclei. Neuron, 104(5), 849-855.e3. https://doi.org/10.1016/j.neuron.2019.09.002
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