Stuck in a rut: low-dimensional brain state reconfigurations govern response inhibition
Poster No:
747
Submission Type:
Abstract Submission
Authors:
Natasha Taylor1, Christopher Whyte1, Brandon Munn1, Joshua Tan1, Sunjae Shim2, Patrick Bissett3, James Shine1
Institutions:
1The University of Sydney, Sydney, Australia, 2University of California, Berkeley, Berkeley, U.S.A, 3Stanford University, Stanford, U.S.A
First Author:
Co-Author(s):
Introduction:
The ability to flexibly adapt one's behaviour requires executive cognitive control and appropriate response inhibition (Bissett and Poldrack 2022). As this behaviour is flexibly adaptive across temporal scales we can propose that response inhibition is dependent upon the brain's ability to flexibly shift between states. We hypothesised that an inability to inhibit a response would relate to being 'stuck' in a particular brain state.
Methods:
110 participants completed a stop-signal task in a functional MRI scanner (TR=0.68sec) (for details Bissett et al. 2024). All task-based fMRI scans were pre-processed (fMRIPrep 20.2.1) and standard denoising performed with regression of six head motions (their derivatives), combined white-matter and cerebrospinal fluid confounds, z-scored and high-pass filter (0.01Hz) applied to the extracted BOLD signal. We extracted time-series from 400 cortical 17-network parcellation (Schaefer et al. 2018), subcortex (Tian et al. 2020), cerebellum (Diedrichsen et al. 2009), and several brainstem nuclei. 90 participants passed behavioural quality controls; and we separated the data into a test and re-test group (n=45) for statistical validation. Using a design matrix of task time-points for go, successful-stops and failed-stops trials; we grouped the data for each participant into the trial groups to perform the attractor landscape analysis for these trial windows (see Fig. 1). The attractor landscape frames the likelihood of the brain shifting into a new brain state as dependent upon the energy required to shift into this new brain state, depicted upon a landscape manifold (Munn et al. 2021). We analysed differences in the brain landscape trajectories between go trials, successful stop trials, and failed stop trials. After categorising brain state trajectories for cortex, subcortex and cerebellar regions we calculated an adjacency matrix by correlating the brain landscape trajectories with the associated brain connectivity patterns related to each trial condition (defines which regions in the brain are more related to a specific brain state trajectory). Then, we used a consensus partition with a Louvain algorithm (iterated over gamma range of 0.5-2.5) which resulted in two clusters that defined the characteristic brain energy landscape and corresponding regions of interest that are most related to the given energy landscape for the two conditions (go prior to successful stop and go prior to failed stop).
·Methodological illustration of the stop-signal task performed in fMRI.
Results:
We found that there were specific brain networks that were reconfiguring as a function of successful inhibition compared to failed inhibition. Higher-order cognitive regions (dorsal attentional networks) required 'less energy' to shift into a new state in go trials prior to successful stops suggesting that these regions are critical in flexibly shifting behaviour, as the energy barrier is lower proceeding the successful stops (see Fig. 2A). Interestingly, upon running low-dimensional analysis of brain network reconfigurations, we established unique patterns of neural network activity associated with the go trials prior to a successful stop and the go trials prior to failed stops. We established that the visual, dorsal attentional networks and parts of the fronto-ventral temporal areas have a unique pattern during the go trials prior to successful stops (see Fig. 2B i). Whereas, the frontal cortices, pre-supplementary motor areas, motor and inferior-frontal gyrus were more unique during the go trials prior to failed stops (see Fig. 2B ii).
·Low-dimensional Brain State Attractor Landscapes define differences in successful stops and failed stops in stop-signal task in fMRI.
Conclusions:
In conclusion, we established that there are specific clusters of networks that are important for response inhibition that adaptively shift between states to facilitate response inhibition. We could extend this approach to mental disorders, in which the brains are 'stuck in a rut', meaning they are less flexible to dynamically shift into a different brain state that may be critical for facilitating response inhibition and cognitive control.
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making 1
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Other Methods
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Subcortical Structures
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Basal Ganglia
Cognition
FUNCTIONAL MRI
Modeling
Statistical Methods
Thalamus
Other - dynamic functional connectivity
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
Bissett, Patrick G., and Russell A. Poldrack. 2022. “Estimating the Time to Do Nothing: Toward Next-Generation Models of Response Inhibition.” Current Directions in Psychological Science 31 (6): 556–63. https://doi.org/10.1177/09637214221121753.
Diedrichsen, Jörn, Joshua H. Balsters, Jonathan Flavell, Emma Cussans, and Narender Ramnani. 2009. “A Probabilistic MR Atlas of the Human Cerebellum.” NeuroImage 46 (1): 39–46. https://doi.org/10.1016/j.neuroimage.2009.01.045.
Munn, Brandon R., Eli J. Müller, Gabriel Wainstein, and James M. Shine. 2021. “The Ascending Arousal System Shapes Neural Dynamics to Mediate Awareness of Cognitive States.” Nature Communications 12 (1): 6016. https://doi.org/10.1038/s41467-021-26268-x.
Schaefer, Alexander, Ru Kong, Evan M Gordon, Timothy O Laumann, Xi-Nian Zuo, Avram J Holmes, Simon B Eickhoff, and B T Thomas Yeo. 2018. “Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI.” Cerebral Cortex 28 (9): 3095–3114. https://doi.org/10.1093/cercor/bhx179.
Tian, Ye, Daniel S. Margulies, Michael Breakspear, and Andrew Zalesky. 2020. “Topographic Organization of the Human Subcortex Unveiled with Functional Connectivity Gradients.” Nature Neuroscience 23 (11): 1421–32. https://doi.org/10.1038/s41593-020-00711-6.