Lifespan Trajectories of Continual Learning in the Human Brain
Poster No:
980
Submission Type:
Abstract Submission
Authors:
Jungmin Lee1, Yebin Park2, Seok-Jun Hong3
Institutions:
1CNIR, Suwon, Gyeonggi-do, 2Sungkyunkwan University, Suwon, Gyeonggi-do, 3IBS Center for Neuroscience Imaging Research, Sungkyunkwan University, Suwon, Korea, Republic of
First Author:
Co-Author(s):
Seok-Jun Hong
IBS Center for Neuroscience Imaging Research, Sungkyunkwan University
Suwon, Korea, Republic of
IBS Center for Neuroscience Imaging Research, Sungkyunkwan University
Suwon, Korea, Republic of
Introduction:
Continual learning is fundamental to general intelligence, which grants for significant adaptability allowing us to acquire generalizable knowledge from limited experiences and apply it to novel environments. Such cognitive ability emerges from a unique learning course during life, that is related to how knowledge is acquired, accumulated, and transformed across different ages. Despite its importance, the neural basis of this lifelong learning remains largely unexplored. This study aims to examine three key brain substrates supporting continual learning[1] - modularity, functional variability, and cortical hierarchy-across the human lifespan. The dynamics of these features, measured by resting-state fMRIs and anatomical MRI, could reveal unprecedented details of age-dependent trajectories showing how the brain sustains general intelligence throughout the whole life.
Methods:
A total of 3,141 subjects, aged 8 to 85, from the two large-cohort databases, HCP[2] and Cam-CAN[3], were analyzed (Fig 1A). We constructed brain charts using generalized additive models for location, scale, and shape (GAMLSS)[4], a robust framework for capturing non-linear growth trajectories.
I. Functional segregation. This mechanism, related to functional compositionality, refers to how distinct networks, each with specialized functions, can be flexibly reconfigured to form larger, task-dependent networks. To quantify this, we have leveraged the Yeo-12 functional components characterized by distinct task-compositional profiles[5] and calculated the degree of their functional segregation (Fig 1B).
II. Functional Flexibility. This feature, derived from dynamic functional connectivity[6], captures how dynamical the interactions between the functional networks are along the time. We assessed the temporal variability across those 12 components, creating a 12×12×N matrix, where N is the number of temporal sliding windows with a 30-second (Fig 1C).
III. Cortical Hierarchy. We metricized the whole-brain functional hierarchy using the recently established gradient approach[7]. Notably, Gradient 1 represented the transition of functional connectome from unimodal to multimodal regions, while Gradient 2 captured the transitions within the unimodal regions. To assess their developmental changes, we systematically estimated the height of unimodal and transmodal regions and the distances between visual and somatomotor regions, and tracked their age-dependent profiles (Fig 1D).
I. Functional segregation. This mechanism, related to functional compositionality, refers to how distinct networks, each with specialized functions, can be flexibly reconfigured to form larger, task-dependent networks. To quantify this, we have leveraged the Yeo-12 functional components characterized by distinct task-compositional profiles[5] and calculated the degree of their functional segregation (Fig 1B).
II. Functional Flexibility. This feature, derived from dynamic functional connectivity[6], captures how dynamical the interactions between the functional networks are along the time. We assessed the temporal variability across those 12 components, creating a 12×12×N matrix, where N is the number of temporal sliding windows with a 30-second (Fig 1C).
III. Cortical Hierarchy. We metricized the whole-brain functional hierarchy using the recently established gradient approach[7]. Notably, Gradient 1 represented the transition of functional connectome from unimodal to multimodal regions, while Gradient 2 captured the transitions within the unimodal regions. To assess their developmental changes, we systematically estimated the height of unimodal and transmodal regions and the distances between visual and somatomotor regions, and tracked their age-dependent profiles (Fig 1D).
Results:
According to the recent study mapping the lifespan change of the anatomical brain, the brain structure shows a peak of development during late childhood and adolescence[8]. In contrast, our study demonstrates that functional substrates supporting continual learning follow a delayed trajectory, reaching their peak in young adulthood (20-25 years) before gradually declining with age. During this period, the brain exhibits a heightened capacity for specialization (Fig. 2A). As individuals move into their late 20s and beyond, however, functional segregation diminishes, transitioning toward more integration and flexible reconfiguration across different brain states (Fig. 2B). Such a shift in brain dynamic structures arises from distinct functional gradient embeddings, with lower-order unimodal regions (e.g., visual and somatomotor) and higher-order transmodal regions (e.g., DMN) following divergent developmental trajectories (Fig. 2C).
Conclusions:
Understanding how the brain develops and matures its ability in continual learning is essential for uncovering the mechanisms underlying human intelligence. Beginning in young adulthood, the brain prioritizes specialization, gradually transitioning to peak integrative abilities with age. These adaptive functions then subtly decline, reflecting a dynamic balance between specialization and integration across the lifespan.
Lifespan Development:
Early life, Adolescence, Aging 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Task-Independent and Resting-State Analysis 2
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Aging
Cognition
Development
FUNCTIONAL MRI
Learning
1|2Indicates the priority used for review
Abstract Information
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