Poster No:
749
Submission Type:
Abstract Submission
Authors:
Giulia Baracchini1, Joshua Tan1, Jungwoo Kim2, Eli Müller3, Brandon Munn3, James Shine4
Institutions:
1The University of Sydney, Sydney, New South Wales, 2SKKU, Suwon, Gyeonggi-do, 3University of Sydney, Sydney, NSW, 4The University of Sydney, Sydney, NSW
First Author:
Co-Author(s):
Joshua Tan
The University of Sydney
Sydney, New South Wales
Introduction:
Human cognitive neuroscience has long called for better links between neural activity and cognition. Yet, we still lack cognitive models able to match the theoretical and methodological complexity of neuroimaging data. To achieve this, we need a unifying theoretical framework able to synthesize across common elements of brain function and cognition – their multiscale dynamic nature. Here, we introduce such a framework, and we apply it to examine the neural underpinnings of cognitive control as a multi- vs single-domain construct[1].
Methods:
Inspired by multiscale design theory[2], the framework we propose builds on three key principles: multiply (micro to macroscale integration), map (multimodal spatial associations), shift perspective (temporal dynamics estimation). While each of these principles has been individually explored in resting-state neuroimaging, we argue that innovation in cognitive neuroimaging will require their synthesis within the same experiment.
To this end, we leveraged openly available 3T task fMRI data (TR = 2sec, TE = 27msec, flip angle = 70°, FOV = 192 mm, acquisition matrix = 64 × 64 × 40, 3mm3 isotropic voxels)[3] collected while healthy younger adults (n=66) completed three tasks tapping into core domains of cognitive control (Go-NoGo, Task Switch, and N-back; for more information on study design and preprocessing[4]). For each individual, we used SPM12 to model the BOLD response associated with each experimental condition and obtained beta values. We then generated one spatial map per task per individual (NoGo vs Go, Switch vs Repeat, 2back vs 0back). Spatial maps were parcellated into 1024 regions (1000 cortical[5]+ 24 subcortical and cerebellum[6-7]). Regional spatial and temporal values were used to test our framework.
Multiplication: we applied an iterative coarse graining method from statistical physics[8] to probe cognitive control across spatial scales (from 2 regions to 1024 increasing by a factor of 2). Map: we used Neuromaps[9] to quantify spatial relationships between cognitive control and neuromodulatory systems. Shift perspective: at each iterative coarse graining step, we employed laminar flow analysis, an approach from fluid dynamics[10], to extract linear and non-linear temporal dynamics from each region. Although these three methods were applied on each task separately, their results were then combined via conjunction and correlation analyses to examine cross-task generalisation. Non-parametric statistical testing was carried out to assess significance of results.
Results:
Our framework revealed that cognitive control is both a multi- and single-domain process depending on sampling scale (multiply and shift perspective): the finer the spatial and temporal scale of analysis, the more domain-specific and less cortico-centric the functional activation; the coarser the spatiotemporal resolution, the more domain-general and fronto-parietal-dominant the activity. At the whole brain level, our framework additionally demonstrated a strong association between each cognitive control domain and the distribution of dopaminergic and noradrenergic activity in the brain (map). Together, these insights expand on popular single-scale static neural accounts describing cognitive control as preferentially arising from higher-order cortical activity.
Conclusions:
Via a novel theoretical framework that synthesizes across three core principles of multiscale systems (multiply, map, shift perspective), this study offers an enriched conceptualisation and formalisation of human cognitive control as a multiscale dynamic process. Beyond cognitive control, our framework promotes the development of multiscale dynamic models of cognition more broadly, ultimately paving the path for the next frontier of brain-behaviour investigations, wherein cognition and brain function are finally matched in their theoretical and methodological complexity.
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making 1
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 2
Univariate Modeling
Neuroinformatics and Data Sharing:
Workflows
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Cognition
Computational Neuroscience
Statistical Methods
Systems
Other - theoretical framework; executive function
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.
Task-activation
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.
Yes
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
Behavior
Computational modeling
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
AFNI
SPM
FSL
Free Surfer
Provide references using APA citation style.
[1] Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D. (2000). The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cognitive psychology, 41(1), 49-100.
[2] Lupfer, Nic, Hannah Fowler, Alyssa Valdez, Andrew Webb, Jeremy Merrill, Galen Newman, and Andruid Kerne. "Multiscale design strategies in a landscape architecture classroom." In Proceedings of the 2018 ACM Designing Interactive Systems Conference, pp. 1081-1093. 2018.
[3] Rieck, J. R., Baracchini, G., Nichol, D., Abdi, H., & Grady, C. L. (2021). Dataset of functional connectivity during cognitive control for an adult lifespan sample. Data in Brief, 39, 107573.
[4] Rieck, J. R., Baracchini, G., & Grady, C. L. (2021). Contributions of brain function and structure to three different domains of cognitive control in normal aging. Journal of Cognitive Neuroscience, 33(9), 1811-1832.
[5] Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X. N., Holmes, A. J., ... & Yeo, B. T. (2018). Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cerebral cortex, 28(9), 3095-3114.
[6] Tian, Y., Margulies, D. S., Breakspear, M., & Zalesky, A. (2020). Topographic organization of the human subcortex unveiled with functional connectivity gradients. Nature neuroscience, 23(11), 1421-1432.
[7] Diedrichsen, J. (2006). A spatially unbiased atlas template of the human cerebellum. Neuroimage, 33(1), 127-138.
[8] Munn, B. R., Müller, E. J., Favre-Bulle, I., Scott, E., Lizier, J. T., Breakspear, M., & Shine, J. M. (2024). Multiscale organization of neuronal activity unifies scale-dependent theories of brain function. Cell.
[9] Markello, R. D., Hansen, J. Y., Liu, Z. Q., Bazinet, V., Shafiei, G., Suárez, L. E., ... & Misic, B. (2022). Neuromaps: structural and functional interpretation of brain maps. Nature Methods, 19(11), 1472-1479.
[10] Muller, E. J., Munn, B., Baracchini, G., Fulcher, B. D., Medel, V., Redinbaugh, M. J., ... & Shine, J. (2024). Thalamic Control Over Laminar Cortical Dynamics Across Conscious States. bioRxiv, 2024-07.
No