Poster No:
1079
Submission Type:
Abstract Submission
Authors:
Sarah Wallis1, Adeel Razi2, Ian Harding3
Institutions:
1Monash University, Melbourne, Victoria, 2Monash University, Melbourne, VIC, 3QIMR Berghofer Medical Research Institute, Brisbane, Queensland
First Author:
Co-Author(s):
Introduction:
Recent research has revealed that the cerebellum and striatum directly communicate, at the subcortical level, and that their contribution to cognitive processing may be better understood when these regions are recognised as nodes of an interconnected network rather than targets of isolated cortical loops (Bostan et al, 2018; Caligiore et al, 2017; Milardi et al, 2019). Direct communication between the cerebellum and striatum has the potential not only to facilitate the independent collaboration of these structures but also provide a modulatory influence over their respective cortical connections. We used stochastic dynamic causal modelling (Li et al, 2011; Razi et al, 2015) to investigate effective connectivity in the coritco-striato-cerebellar system and delineate the role of direct cerebellar-striatal connectivity in this network.
Methods:
This study used data from the S1200 release of the Human Connectome Project. Participants with both a T1w structural image and resting-state fMRI (rsfMRI) were included in analyses (N = 1,084). All anatomical and functional data were collected on a 3T Seimens Skyra scanner. Structural and functional images were preprocessed according to the HCP Minimal Preprocessing Pipelines detailed here: Glasser et al, 2013. Three models of the cortico-striato-cerebellar system were created to investigate the system across behavioural domains of motor, cognition and mood using functionally derived parcellations of the motor, frontoparietal and default mode networks. Functional atlases of the cortex (Yeo et al, 2011), striatum (Choi et al, 2012), and cerebellum (Buckner et al, 2011) were used to create regions of interest for each node, cortex, striatum and cerebellum, in each network of interest. Each ROI timeseries was calculated as the first principal component of the voxels' activity within a given parcel. Stochastic dynamic causal modelling was used to estimate subject-level connectivity within the cortico-striato-cerebellar network, with nonlinear modulatory influences between the cortex and cerebellar-striatal connections, striatal and cerebellar-cortical connections, and cerebellum and striatal-cortical connections. Parametric empirical bayes was employed to create group-level average models.
Results:
Model estimation was excellent with an average variance-explained of 93.57% and 86.69% for the default mode and frontoparietal network models and motor network model respectively. No modulatory influences of any node on the connection between two other nodes survived model reduction. Group average effective connectivity revealed positive bidirectional connections between the cerebellum and striatum in all three network models (motor, frontoparietal control, default mode), the strength of these connections was also consistent across all networks. Bidirectional connections between the cerebellum and cortex as well as striatum and cortex were consistently positive, however the strength of these connections differed across networks.
Conclusions:
These findings suggest that direct communication between the cerebellum and striatum may serve to support or be additive to cortical loops rather than work to modulate them. This supportive role may be consistent across functional domains irrespective of behavioural outcome, operating in a similar fashion to other cerebellar functions as proposed by the 'universal cerebellar transform' and 'cerebellar scaffolding' theories.
Modeling and Analysis Methods:
Bayesian Modeling 1
fMRI Connectivity and Network Modeling
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping
Subcortical Structures 2
Keywords:
Basal Ganglia
Cerebellum
Computational Neuroscience
Cortex
Modeling
Systems
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):
Healthy subjects
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
Behavior
Computational modeling
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
SPM
FSL
Free Surfer
Provide references using APA citation style.
Bostan, A. C., & Strick, P. L. (2018). The basal ganglia and the cerebellum: nodes in an integrated network. Nature Reviews Neuroscience, 19(6), 338–350. https://doi.org/10.1038/s41583-018-0002-7
Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C., & Yeo, B. T. T. (2011). The organization of the human cerebellum estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(5), 2322–2345. https://doi.org/10.1152/jn.00339.2011
Caligiore, D., Pezzulo, G., Baldassarre, G., Bostan, A. C., Strick, P. L., Doya, K., Helmich, R. C., Dirkx, M., Houk, J., Jörntell, H., Lago-Rodriguez, A., Galea, J. M., Miall, R. C., Popa, T., Kishore, A., Verschure, P. F. M. J., Zucca, R., & Herreros, I. (2017). Consensus Paper: Towards a Systems-Level View of Cerebellar Function: the Interplay Between Cerebellum, Basal Ganglia, and Cortex. The Cerebellum, 16(1), 203–229. https://doi.org/10.1007/s12311-016-0763-3
Choi, E. Y., Yeo, B. T. T., & Buckner, R. L. (2012). The organization of the human striatum estimated by intrinsic functional connectivity. Journal of Neurophysiology, 108(8), 2242–2263. https://doi.org/10.1152/jn.00270.2012
Li, B., Daunizeau, J., Stephan, K. E., Penny, W., Hu, D., & Friston, K. (2011). Generalised filtering and stochastic DCM for fMRI. NeuroImage, 58(2), 442–457. https://doi.org/10.1016/j.neuroimage.2011.01.085
Milardi, D., Quartarone, A., Bramanti, A., Anastasi, G., Bertino, S., Basile, G. A., Buonasera, P., Pilone, G., Celeste, G., Rizzo, G., Bruschetta, D., & Cacciola, A. (2019). The Cortico-Basal Ganglia-Cerebellar Network: Past, Present and Future Perspectives. Frontiers in Systems Neuroscience, 13, 61. https://doi.org/10.3389/fnsys.2019.00061
Razi, A., Kahan, J., Rees, G., & Friston, K. J. (2015). Construct validation of a DCM for resting state fMRI. NeuroImage, 106, 1–14. https://doi.org/10.1016/j.neuroimage.2014.11.027
Yeo, B. T. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., Fischl, B., Liu, H., & Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125–1165. https://doi.org/10.1152/jn.00338.2011
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