Poster No:
1942
Submission Type:
Abstract Submission
Authors:
Bonnie Ng1, Vasily Vakorin1, Hayyan Liaqat1, Sam Doesburg1, Sylvain Moreno1
Institutions:
1Simon Fraser University, Vancouver, British Columbia
First Author:
Bonnie Ng
Simon Fraser University
Vancouver, British Columbia
Co-Author(s):
Hayyan Liaqat
Simon Fraser University
Vancouver, British Columbia
Sam Doesburg
Simon Fraser University
Vancouver, British Columbia
Introduction:
Although most neuroimaging studies assume bell-shaped patterns, brain activity often follows skewed, heavy-tailed distributions (Buzsáki & Mizuseki, 2014). These distributions are present across levels of brain organization, from synaptic weights and neuronal firing rates observed in vitro to neural population synchrony measured via MEG and EEG (Buzsáki & Mizuseki, 2014). In graph theory, such distributions suggest the presence of hub nodes (Opsahl et al., 2017), balancing between local and global communication, which may support cognitive performance. However, skewed distributions in brain activity are still rarely quantified or investigated.
By contrast, brain networks, particularly resting-state functional and structural connectivity, are widely studied and linked to cognitive abilities (Litwińczuk et al., 2022; Thomas et al., 2023). The alignment between structural and functional mapping (Meier et al., 2016) highlights the mutual supporting role between brain structure and its functional activity. For example, cortical thickness, a widely used indication of brain structure, has shown to be associated with functional connectivity and cognitive performance (Hou et al., 2021; Park et al., 2017).
This study investigates skewed distributions as a novel metric, akin to network connectivity, that reflects cognitive abilities or brain structure. Using resting-state MEG, structural MRI, and cognitive assessments from the Cambridge Centre for Aging and Neuroscience (Cam-CAN) dataset (Taylor et al., 2017), we tested the hypothesis that resting-state neurodynamics predict cognitive performance or structural properties of the brain.
Methods:
We analyzed Cam-CAN data for 214 participants, including resting-state MEG (306-channel Elekta Neuromag), T1-weighted structural MRI (3T Siemens TIM Trio), and cognitive assessments.
To quantify extreme events in neurodynamics, we computed kurtosis of temporal variability in MEG signal power at five frequencies each from canonical frequency bands (2 Hz, 6 Hz, 12 Hz, 24 Hz, 48 Hz). For each gradiometer, median kurtosis for the signal power distribution across all 30-second segments of the MEG recording was extracted. Age effects from kurtosis was removed using a quadratic model before correlating to cognition or cortical thickness (CTh) since we are interested in the relationship between the variables independent of age.
Cortical thickness (CTh) was estimated from T1 MRI using the Destrieux atlas (70 ROIs) via FreeSurfer. 14 cognitive variables, representing abilities like short-term memory and executive function were considered.
To equalize CTh and cognition in terms of number of variables, we applied a dimensionality reduction technique Uniform Manifold Approximation & Projection (UMAP) (McInnes et al., 2018). CTh and cognition are reduced to three UMAP components each. We then applied a multivariate analysis, behavioural partial least squares (PLS) (McIntosh & Lobaugh, 2004), to correlate MEG kurtosis with CTh and cognition separately. The first latent variable (LV) decomposed from the PLS analysis explaining the largest variance in the data was considered.
Results:
Behavioural PLS analysis revealed statistically significant correlation of kurtosis of temporal variability in MEG power at 12 Hz alpha oscillations with cognitive performance, but not with cortical thickness. Results were consistent when males and females were analyzed separately.
Conclusions:
MEG dynamics cannot be statistically explained by CTh when controlling for age, but it can be explained by cognitive performance. Our findings suggest that extreme value events are not a by-product of modifications in underlying brain structures but are an inherent component of healthy neurodynamics. Extreme events in the brain, potentially reflective of small-world organization, might be integral to cognitive performance independent of age.
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis
Methods Development
Multivariate Approaches
Task-Independent and Resting-State Analysis 2
Novel Imaging Acquisition Methods:
MEG 1
Keywords:
Cognition
Computational Neuroscience
Data analysis
MEG
Open Data
STRUCTURAL MRI
Other - extreme events
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.
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:
MEG
Behavior
Structural MRI
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
Other, Please list
-
mne-python
Provide references using APA citation style.
Buzsáki, G. (2014). The log-dynamic brain: How skewed distributions affect network operations. Nature Reviews Neuroscience, 15(4), 264–278. https://doi.org/10.1038/nrn3687
Hou, M. (2021). Specific and general relationships between cortical thickness and cognition in older adults: A longitudinal study. Neurobiology of Aging, 102, 89–101. https://doi.org/10.1016/j.neurobiolaging.2020.11.004
Litwińczuk, M. C. (2022). Combination of structural and functional connectivity explains unique variation in specific domains of cognitive function. NeuroImage, 262, 119531. https://doi.org/10.1016/j.neuroimage.2022.119531
McInnes, L. (2018). UMAP: Uniform Manifold Approximation and Projection. Journal of Open Source Software, 3(29), 861. https://doi.org/10.21105/joss.00861
McIntosh, A. R. (2004). Partial least squares analysis of neuroimaging data: Applications and advances. NeuroImage, 23, S250–S263. https://doi.org/10.1016/j.neuroimage.2004.07.020
Meier, J. (2016). A Mapping Between Structural and Functional Brain Networks. Brain Connectivity, 6(4), 298–311. https://doi.org/10.1089/brain.2015.0408
Opsahl, T. (2017). Revisiting the Small-World Phenomenon: Efficiency Variation and Classification of Small-World Networks. Organizational Research Methods, 20(1), 149–173. https://doi.org/10.1177/1094428116675032
Park, H. (2017). Agreement between functional connectivity and cortical thickness-driven correlation maps of the medial frontal cortex. PloS One, 12(3), e0171803. https://doi.org/10.1371/journal.pone.0171803
Taylor, J. R. (2017). The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample. NeuroImage, 144, 262–269. https://doi.org/10.1016/j.neuroimage.2015.09.018
Thomas, S. A.(2023). Resting state network connectivity is associated with cognitive flexibility performance in youth in the Adolescent Brain Cognitive Development Study. Neuropsychologia, 191, 108708. https://doi.org/10.1016/j.neuropsychologia.2023.108708
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