Functional co-fluctuation patterns dynamically reconfigure along SA Axis in human cortex
Poster No:
1474
Submission Type:
Abstract Submission
Authors:
De-Zhi Jin1, Xi-Nian Zuo2, Ye He1
Institutions:
1School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China, 2Beijing Normal University, Beijing, Beijing
First Author:
De-Zhi Jin
School of Artificial Intelligence, Beijing University of Posts and Telecommunications
Beijing, China
School of Artificial Intelligence, Beijing University of Posts and Telecommunications
Beijing, China
Co-Author(s):
Ye He
School of Artificial Intelligence, Beijing University of Posts and Telecommunications
Beijing, China
School of Artificial Intelligence, Beijing University of Posts and Telecommunications
Beijing, China
Introduction:
Spontaneous fluctuations in brain activity can be measured by functional magnetic resonance imaging (fMRI) as blood-oxygen-level-dependent (BOLD) signals. A recent method, which focuses on co-fluctuation timeseries, enables investigation of dynamic interactions between regions on a single-frame timescale [1-2]. Brain activity exhibits time-varying co-fluctuations that reflect the dynamic reorganization of functional networks across different amplitude levels. While previous research has emphasized high-amplitude co-fluctuations [3], the spatial reconfiguration of regional co-fluctuation contributions across varying amplitudes remains underexplored [4-6]. In this study, we decomposed dynamic co-fluctuation patterns both spatially and temporally to explore the dynamic functional organization of the human cerebral cortex under varying global co-fluctuation amplitudes.
Methods:
We preprocessed resting-state fMRI datasets from Human Connectome Project, including 3T adults as well as 3T development dataset. Edge timeseries was defined as the element-wise product between the z-scored BOLD timeseries. At each timepoint, we calculated RSS (root sum of squares) over all edges and called it RSS GLOBAL, meanwhile we also calculated RSS over edges related with a given brain region and called it RSS REGION (Fig. 1a). The timepoints were ranked based on RSS GLOBAL and divided into 20 bins (Fig. 1b). Then, we proposed a measure--co-fluctuation score (Fig. 1b), which can be considered as the contribution of regional co-fluctuation to the global co-fluctuation.
For each region, we employed generalized additive models to flexibly model linear or non-linear relationship between regional co-fluctuation scores and global co-fluctuation amplitudes. For each amplitude bin, we averaged co-fluctuation score map across subject as group-level measures. Then we correlated sensorimotor-association (SA) rank [7] (z-scored) map with group-level cofluctuation score map of each amplitude bin (Fig. 1c).
For each region, we employed generalized additive models to flexibly model linear or non-linear relationship between regional co-fluctuation scores and global co-fluctuation amplitudes. For each amplitude bin, we averaged co-fluctuation score map across subject as group-level measures. Then we correlated sensorimotor-association (SA) rank [7] (z-scored) map with group-level cofluctuation score map of each amplitude bin (Fig. 1c).
Results:
The changes in co-fluctuation scores varied heterogeneously across the cerebral cortex (Fig. 1d). Three distinct trends emerged: upward, inverted U-shape, and downward trend as global co-fluctuation amplitudes increased. Notably, the trajectory patterns can be largely separated as primary sensory networks and high-order functional networks (Fig. 1e).
Under high-amplitude conditions, sensorimotor regions predominated in global co-fluctuation, whereas association regions exhibited dominance during middle-to-low amplitude conditions (Fig. 1f). The spatial distribution of co-fluctuation scores exhibited two distinct similarity clusters (Fig. 1g). Meta-analysis further demonstrated functional distinctions between these clusters (Fig. 1h). The similarity between the co-fluctuation score maps and the SA axis varied systematically across global amplitudes (Fig. 1i). This dynamic alignment underscores the hierarchical and context-dependent organization of brain functional networks.
The distinction between the two patterns became more pronounced with age (Fig. 2a-b). These spatial patterns evolve throughout childhood and adolescence, gradually becoming more aligned with adult-like functional organization (Fig. 2c-d). Furthermore, the development of these patterns unfolds along the SA axis in opposite directions (Fig. 2e-f), ultimately contributing to the hierarchical organization of functional networks.
Under high-amplitude conditions, sensorimotor regions predominated in global co-fluctuation, whereas association regions exhibited dominance during middle-to-low amplitude conditions (Fig. 1f). The spatial distribution of co-fluctuation scores exhibited two distinct similarity clusters (Fig. 1g). Meta-analysis further demonstrated functional distinctions between these clusters (Fig. 1h). The similarity between the co-fluctuation score maps and the SA axis varied systematically across global amplitudes (Fig. 1i). This dynamic alignment underscores the hierarchical and context-dependent organization of brain functional networks.
The distinction between the two patterns became more pronounced with age (Fig. 2a-b). These spatial patterns evolve throughout childhood and adolescence, gradually becoming more aligned with adult-like functional organization (Fig. 2c-d). Furthermore, the development of these patterns unfolds along the SA axis in opposite directions (Fig. 2e-f), ultimately contributing to the hierarchical organization of functional networks.
·Figure. 1 Heterogeneous trajectories of co-fluctuation scores as a function of whole-brain co-fluctuation amplitude and the spatial pattern of co-fluctuation score at different amplitudes.
·Figure. 2 The spatial patterns of co-fluctuation score gradually mature across childhood and adolescence.
Conclusions:
Our study suggests a hierarchical basis for the dynamic reconfiguration of co-fluctuation patterns, which are associated with whole-brain co-fluctuation amplitudes in the human cerebral cortex. These functional co-fluctuation patterns exhibit two distant, opposing patterns along the sensorimotor-association axis across varying co-fluctuation amplitudes. These opposing patterns gradually emerge during development. This study highlights the differential contributions of brain regions to dynamic functional connectivity at various moments, shaped by fluctuating amplitudes.
Lifespan Development:
Early life, Adolescence, Aging
Normal Brain Development: Fetus to Adolescence 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Normal Development
Keywords:
Cognition
Consciousness
Development
FUNCTIONAL MRI
Meta-Cognition
Motor
Perception
Systems
Other - Edge Timeseries; functional co-fluctuation; SA axis; rs-fMRI
1|2Indicates the priority used for review
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