Poster No:
1450
Submission Type:
Abstract Submission
Authors:
Lorenzo Pini1, Sadaf Moaveninejad1, Maurizio Corbetta1, Camillo Porcaro1
Institutions:
1Department of Neuroscience, University of Padova, Padova, Italy
First Author:
Lorenzo Pini
Department of Neuroscience, University of Padova
Padova, Italy
Co-Author(s):
Introduction:
Physiological systems exhibit complex, dynamic signals that are non-stationary and nonlinear, challenging traditional analysis methods. In the brain, this complexity reflects a balance between inhibition and excitation (I-E), maintaining criticality - the "edge of chaos" where stability and flexibility coexist. This balance may support efficient information transfer, self-organization, and cognitive processing. The fractal nature of neural activity captures these critical dynamics and can be analyzed through fractal dimension (FD), offering insights into brain function and connectivity. However, the relationship between FD, brain connectivity organization, and measures of intelligence remains an open question. Here, we use resting-state fMRI data from the Human Connectome Project (HCP) to investigate this tripartite relationship. We hypothesize that (i) FD shows a hierarchical organization along the unimodal-to-multimodal axis, (ii) FD relates to brain connectivity organization, and (iii) FD correlates with intelligence measures.
Methods:
Resting-state fMRI data from 173 HCP individuals scanned at 7T were preprocessed using the HCP minimal preprocessing pipeline. Blood-oxygen-level-dependent (BOLD) signals were parcellated using the Schaefer atlas (100 parcels) and Tian's subcortical atlas (16 regions). Fractal dimension (FD) was computed using Higuchi's method to quantify the complexity of BOLD signaling. Univariate analyses identified parcels and networks with the highest and lowest FD values, followed by ANOVA to compare FD across networks. Multivariate patterns were extracted via principal component analysis to assess BOLD-FD patterns. Further, partial least squares (PLS) analysis explored associations between FD and graph theory-based connectivity measures (centrality vs. local measures). Finally, we assessed the relationship between network-level FD and cognitive performance in fluid and crystallized intelligence using factor analysis and non-parametric testing corrected for multiple comparisons.
Results:
Multivariate factor analysis revealed three main BOLD-FD components: one associated with frontoparietal regions, a second with motor and lateral occipital regions, and a third with subcortical regions. Among these patterns, subcortical regions (mainly the globus pallidus) exhibited the highest FD, while frontoparietal cortical regions showed the lowest values. At the network level, ANOVA confirmed that subcortical regions had the highest FD, followed by the limbic network. When focusing on cortical networks, a pattern emerged from sensorial to high-cognitive networks (F=635, p<0.0001). PLS analysis identified two significant components linking FD with functional connectivity for both centrality measures (eigenvector centrality and participation coefficient). Notably, all these latent components showed negative correlations between cortical connectivity and FD (r < -0.65, p<0.0001 for all components). In contrast, local connectivity measures showed no significant latent components with FD outcomes (p>0.05). Finally, a significant negative correlation between FD in frontoparietal networks and fluid intelligence scores was observed.
Conclusions:
This study advances our understanding of the brain's critical organization, showing a specific subcortical-unimodal-transmodal axis of FD, where cognitive fronto-parietal networks exhibit lower FD values. Regions optimized for local complexity (high FD) may exhibit lower centrality, and vice versa, suggesting that regions with high FD serve as flexible, adaptive units, while regions with high connectivity centrality act as stabilizing hubs, consistent with the integration-segregation framework of brain organization. The correlation between fluid intelligence and lower FD in frontoparietal networks confirms that these networks operate with more predictable and organized dynamics, which are essential for higher-order cognitive functions such as processing speed, working memory, and cognitive flexibility.
Higher Cognitive Functions:
Reasoning and Problem Solving
Higher Cognitive Functions Other 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 1
Other Methods
Keywords:
Cognition
Computational Neuroscience
FUNCTIONAL MRI
Modeling
Sub-Cortical
Other - Criticality
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?
Yes
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Yes
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Not applicable
Please indicate which methods were used in your research:
Functional MRI
Computational modeling
For human MRI, what field strength scanner do you use?
7T
Which processing packages did you use for your study?
FSL
Free Surfer
Provide references using APA citation style.
not applicable
No