P243 Hierarchical fluctuations scales in whole-brain resting activity
Adrián Ponce-Alvarez1,2,3*
1Departament de Matemàtiques, Universitat Politècnica de Catalunya, Barcelona, Spain.
2Institut de Matemàtiques de la UPC - Barcelona Tech (IMTech), Barcelona, Spain.
3Centre de Recerca Matemàtica, Barcelona, Spain.
*Email :adrian.ponce@upc.edu
Introduction
Brain activity fluctuates at different timescales across regions, with higher-order areas exhibiting slower dynamics than sensory regions [1]. Connectivity and local properties shape this hierarchy: spine density and synaptic gene expression gradients correlate with timescales [2–4], while strongly connected regions exhibit slower dynamics [5].
Beyond temporal features, signal variability has been linked to aging [6], brain states [7], disorders [8], and tasks [9]. However, whether spontaneous activity variance is hierarchically organized remains unknown.
This work analyses the relation between timescales, variances, and connectivity using human f/dMRI data, while exploring the mechanisms through connectome-based whole-brain models.
Methods
Publicly available data from the Human Connectome Project was used, consisting of connectome matrices and resting-state (rs) fMRI signals from 100 subjects across 3 parcellations. For each ROI, the average variance of the rs-fMRI signal, the node’s strength of the connectome, and the autocorrelation function (ACF) were calculated.
To model the variance and temporal scales of resting-state fluctuations, two commonly used whole-brain models were studied here, namely the Hopf and the Wilson-Cowan models. These models use the brain’s connectome to coupled local nodes displaying noise-driven oscillations, with intrinsic dynamics either homogeneous or constrained by the T1w/T2w macroscopic gradient.
Results
Results show that while more connected brain regions have longer timescales, their activity fluctuations exhibit lower variance. Using the Hopf and Wilson-Cowan models, we found that variance and timescales can oppositely relate to connectivity within specific model’s parameter regions, even when all nodes have the same intrinsic dynamics —but also when intrinsic dynamics are constrained by the myelinization-related macroscopic gradient. These findings suggest that connectivity and network state alone can explain regional differences in fluctuation scales. Ultimately, timescale and variance hierarchies reflect a balance between stability and responsivity, with faster, greater responsiveness at the periphery and robustness at the core.
Discussion
This study shows that the variance of fluctuations is hierarchically organized but, in contrast to timescales, it decreases with structural connectivity. Whole-brain models show that the hierarchies of timescales and variances jointly emerge within specific parameter regions, indicating a state-dependence that could serve as a biomarker for different behavioral, vigilance, or conscious states, and neuropsychiatric disorders Finally, in line with previous works on principles of core-periphery network structures [10–12], these hierarchies link to the responsivity of different network parts, with greater and faster responsiveness at the network periphery and more stable dynamics at the core, achieving a balance between stability and responsiveness.
Acknowledgements
A.P-A. is supported by the Ramón y Cajal Grant RYC2020-029117-I funded by MICIU/AEI/10.13039/501100011033 and "ESF Investing in your future". This work is supported by the Spanish State Research Agency, through the Severo Ochoa and María de Maeztu Program for Centers and Units of Excellence in R&D (CEX2020-001084-M).
References
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