Altered Basal Forebrain Regulation of Intrinsic Brain Networks in Depressive and Anxiety Disorders
Poster No:
422
Submission Type:
Abstract Submission
Authors:
Alec Jamieson1, Trevor Steward1, Christopher Davey1, Sevil Ince1, James Agathos1, Bradford Moffat1, Rebecca Glarin1, Kim Felmingham1, Ben Harrison1
Institutions:
1University of Melbourne, Melbourne, VIC
First Author:
Co-Author(s):
Introduction:
Altered connectivity both within and between the default mode and salience networks have been observed across depressive and anxiety disorders (Tse et al., 2024; Zhang et al., 2023). Recent work has highlighted the importance of subcortical regions, including subdivisions of the basal forebrain, in coordinating the activity of these networks (Aguilar & McNally, 2022). The basal forebrain's influence over the default has been hypothesised to reflect a mechanism for switching between internally and externally directed attention states, a process which is known to be altered across these mental health disorders (Harrison et al., 2022; Nair et al., 2018). However, the precise influence of basal forebrain subregions on intrinsic networks across these disorders remains largely unknown, due to the technical limitations of conventional 3T neuroimaging for investigating subcortical regions (Keuken et al., 2018).
Methods:
Using ultra-high field (7-Tesla) functional magnetic resonance imaging, we examined the resting-state effective connectivity of three basal forebrain subregions (Fig. 1) in a transdiagnostic sample of 70 individuals with predominantly depressive and anxiety disorders, compared to 77 healthy controls. Using spectral dynamic causal modelling (Friston et al., 2014), we explored connectivity between these subregions and regions of the salience network (anterior insula and dorsal anterior cingulate) and default mode network (ventromedial prefrontal cortex, posterior cingulate cortex, and inferior parietal lobule).
·Fig. 1. Basal forebrain masks used in the effective connectivity analysis. A) Render and B) axial slices of the Ch1-3 (green), Ch4 (blue), and VP (red) masks on the “Synthesized_FLASH25” (500 μm)
Results:
Clinical participants showed very strong evidence (posterior probability > .99) for increased inhibitory connectivity from the nucleus basalis of Meynert to the ventromedial prefrontal cortex (-0.30 Hz), inferior parietal lobules (R: -0.27; L: -0.29 Hz), posterior cingulate cortex (-0.24 Hz), and dorsal anterior cingulate (-0.20 Hz). Increased inhibitory connectivity was also observed from the ventral pallidum to regions of the posterior default mode network. Leave-one-out cross validation in the parametric empirical Bayes framework revealed that alterations between the nucleus basalis of Meynert, ventral pallidum and cortical midline regions were predictive of overall negative emotional state (r = 0.31, p < 0.05 ) as assessed through the Depression, Anxiety and Stress Scale short-form.
·Fig. 2. Differences in effective connectivity from the A) nucleus basalis of Meynert (Ch4) and B) ventral pallidum to cortical brain regions in clinical participants compared with healthy controls
Conclusions:
These findings suggest that changes in the connectivity between the basal forebrain and the default mode network may be driving wider changes to default mode network connectivity which have been observed in previous research. Moreover, the fact that these alterations were predominately localised to the nucleus basalis of Meynert, a key cholinergic hub, may suggest novel mechanistic avenues for pharmacological treatments targeting the cholinergic system.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Subcortical Structures
Keywords:
Affective Disorders
Anxiety
Computational Neuroscience
FUNCTIONAL MRI
HIGH FIELD MR
Sub-Cortical
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
Friston, K. J., Kahan, J., Biswal, B., & Razi, A. (2014). A DCM for resting state fMRI. Neuroimage, 94(100), 396-407. https://doi.org/10.1016/j.neuroimage.2013.12.009
Harrison, B. J., Davey, C. G., Savage, H. S., Jamieson, A. J., Leonards, C. A., Moffat, B. A., Glarin, R. K., & Steward, T. (2022). Dynamic subcortical modulators of human default mode network function. Cerebral Cortex, 32(19), 4345-4355. https://doi.org/10.1093/cercor/bhab487
Keuken, M. C., Isaacs, B. R., Trampel, R., van der Zwaag, W., & Forstmann, B. U. (2018). Visualizing the Human Subcortex Using Ultra-high Field Magnetic Resonance Imaging. Brain Topogr, 31(4), 513-545. https://doi.org/10.1007/s10548-018-0638-7
Nair, J., Klaassen, A. L., Arato, J., Vyssotski, A. L., Harvey, M., & Rainer, G. (2018). Basal forebrain contributes to default mode network regulation. Proc Natl Acad Sci U S A, 115(6), 1352-1357. https://doi.org/10.1073/pnas.1712431115
Tse, N. Y., Ratheesh, A., Tian, Y. E., Connolly, C. G., Davey, C. G., Ganesan, S., Gotlib, I. H., Harrison, B. J., Han, L. K. M., Ho, T. C., Jamieson, A. J., Kirshenbaum, J. S., Liu, Y., Ma, X., Ojha, A., Qiu, J., Sacchet, M. D., Schmaal, L., Simmons, A. N.,…Zalesky, A. (2024). A mega-analysis of functional connectivity and network abnormalities in youth depression. Nature Mental Health, 2(10), 1169-1182. https://doi.org/10.1038/s44220-024-00309-y
Zhang, X., Yang, X., Wu, B., Pan, N., He, M., Wang, S., Kemp, G. J., & Gong, Q. (2023). Large-scale brain functional network abnormalities in social anxiety disorder. Psychol Med, 53(13), 6194-6204. https://doi.org/10.1017/S0033291722003439