Aperiodic activity underlies the multiple-demand system in the human brain: A MEG-fMRI fusion study
Poster No:
725
Submission Type:
Abstract Submission
Authors:
Runhao Lu1, Moataz Assem1, John Duncan1, Alexandra Woolgar1
Institutions:
1MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridge
First Author:
Co-Author(s):
Alexandra Woolgar
MRC Cognition and Brain Sciences Unit, University of Cambridge
Cambridge, Cambridge
MRC Cognition and Brain Sciences Unit, University of Cambridge
Cambridge, Cambridge
Introduction:
The multiple-demand (MD) network, spanning the frontal and parietal areas, is essential for the human brain's capacity to perform a variety of cognitively demanding tasks (Assem, 2020; Duncan, 2010). This network robustly responds to different task demand and adaptively represents various task-relevant features (Woolgar, 2016). Our recent findings (Lu, 2024) suggested that aperiodic broadband power (BB), rather than oscillatory activity, may be a key underpinning of MD activity, due to its strong cross-task generalizability in coding task demand and broad frontoparietal distribution. However, MEG's limited spatial resolution raises questions about the direct relationship between BB changes and MD activations. To address this, here we integrate new fMRI data with existing MEG data to investigate how aperiodic and oscillatory responses correspond with MD activations across tasks.
Methods:
36 participants (21–49 years, 20 females) participated in the MRI study, and another 43 (18-39 years, 31 females) in the MEG study. Both studies used three cognitive tasks (working memory, switching, and multi-source interference) with two levels of demand (hard vs. easy) and content (alphanumeric vs. colour) (Fig.1A), yielding 12 conditions. We used 3T Siemens Prisma to collect structural (0.8 mm T1 and T2 scans) and functional MRI data, following the Human Connectome Project (HCP) protocols with a multi-band EPI sequence (2.4 mm isotropic, TR = 1.13 s, TE = 37 ms, multi-band 4). We preprocessed the data using HCP minimal preprocessing pipelines, plus spatial ICA+FIX and MSMAll alignment. The cerebral cortex was parcelled into 360 regions using HCP-MMP1.0 Parcellation. For univariate fMRI analysis, we used a general linear model in FSL to derive activation estimates for each task, focusing on hard vs. easy contrasts, and computed into beta maps. MEG data acquisition and preprocessing were reported in (Lu, 2024). In short, following preprocessing, we used irregular resampling auto-spectral analysis (IRASA) (Wen, 2016) to extract oscillatory (theta, alpha, beta power) and aperiodic activities (3-30 Hz BB, slope, and intercept) for further analysis. For the model-based MEG-fMRI fusion analysis, we constructed representational dissimilarity matrices (RDMs) for each MEG signal and fMRI ROI (MD and V1) using 1-Spearman's correlation. We included 3 model RDMs to capture task demand, content, and behavioural accuracy. Using commonality analysis (Hebart, 2018), we estimated shared variance between fMRI and MEG uniquely explained by each model, with statistical significance determined through permutation tests and FDR correction.
Results:
Univariate fMRI analysis (Fig.1) of the hard > easy contrast showed robust activations in the MD network across tasks, identifying 9 patches that closely resemble predefined MD areas with slight shifts due to task specificity. We redefined empirical MD patches for further analysis based on the methods in Assem (2020). Model-based fusion analysis (Fig.2) showed that all MEG signals exhibited positive commonality with task demand in the MD ROI, with BB indicating the highest commonality and significantly outperforming theta, alpha, and aperiodic slope. In V1, both BB and alpha power showed higher commonality levels. For task content, while all MEG signals showed significant commonality in both MD and V1 ROIs, no significant differences were found among the signals within the MD ROI. However, alpha power stood out with the highest commonality in V1, significantly surpassing others.
Conclusions:
Combining multimodal MEG-fMRI data, this study reveals that broadband aperiodic activity likely underpins human MD network in coordinating flexible, domain-general cognitive control across various tasks. In contrast, sensory regions such as V1 may be more associated with oscillatory activities like alpha power. Our findings provide new insights into the meso-level electrophysiological basis of domain-general brain network.
·Experimental paradigm and univariate activation results
·Model-based fusion of MEG and fMRI data
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making 1
Higher Cognitive Functions Other
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
EEG/MEG Modeling and Analysis
Multivariate Approaches 2
Keywords:
Cognition
ELECTROPHYSIOLOGY
FUNCTIONAL MRI
MEG
Multivariate
NORMAL HUMAN
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
Duncan, J. (2010). The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour. Trends in Cognitive Sciences, 14(4), 172-179.
Hebart, M. N., Bankson, B. B., Harel, A., Baker, C. I., & Cichy, R. M. (2018). The representational dynamics of task and object processing in humans. Elife, 7.
Lu, R., Dermody, N., Duncan, J., & Woolgar, A. (2024). Aperiodic and oscillatory systems underpinning human domaingeneral cognition. BioRXiv.
Wen, H., & Liu, Z. (2016). Separating Fractal and Oscillatory Components in the Power Spectrum of Neurophysiological Signal. Brain Topography, 29(1), 13-26.
Woolgar, A., Jackson, J., & Duncan, J. (2016). Coding of Visual, Auditory, Rule, and Response Information in the Brain: 10 Years of Multivoxel Pattern Analysis. Journal of Cognitive Neuroscience, 28(10), 1433-1454.