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Spontaneous Brain Regional Dynamics Contribute to Generalizable Brain-Behavior Associations

Presented During:

Lifespan Development

Thursday, June 26, 2025: 11:30 AM - 12:45 PM

Brisbane Convention & Exhibition Centre

Room: Great Hall

Poster No:

1682

Submission Type:

Abstract Submission

Authors:

Xiaohan Tian¹, Yingjie Peng², Shu Liu³, Golia Shafiei⁴, Meng Wang¹, Yuqing Sun¹, Jing Lou¹, Junxing Xian¹, Ke Hu⁵, Yini He¹, Qi Wang⁵, Chaoyue Ding⁵, Tian Gao², Shangzheng Huang², Kaixin Li², Qi Wang⁶, Zhanjun Zhang¹, Ang Li², Bing Liu¹

Institutions:

¹State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, Beijing, ²State Key Lab of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, Beijing, ³Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming,Yunnan, ⁴University of Pennsylvania, Philadelphia, PA, ⁵Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese, Beijing, Beijing, ⁶College of Science, China Agricultural University, Beijing, Beijing

First Author:

Xiaohan Tian
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, Beijing

Co-Author(s):

Yingjie Peng
State Key Lab of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences
Beijing, Beijing

Shu Liu
Kunming Institute of Zoology, Chinese Academy of Sciences
Kunming,Yunnan

Golia Shafiei, PhD
University of Pennsylvania
Philadelphia, PA

Meng Wang
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, Beijing

Yuqing Sun
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, Beijing

Jing Lou
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, Beijing

Junxing Xian
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, Beijing

Ke Hu
Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese
Beijing, Beijing

Yini He
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, Beijing

Qi Wang
Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese
Beijing, Beijing

Chaoyue Ding
Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese
Beijing, Beijing

Tian Gao
State Key Lab of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences
Beijing, Beijing

Shangzheng Huang
State Key Lab of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences
Beijing, Beijing

Kaixin Li
State Key Lab of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences
Beijing, Beijing

Qi Wang
College of Science, China Agricultural University
Beijing, Beijing

Zhanjun Zhang
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, Beijing

Ang Li
State Key Lab of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences
Beijing, Beijing

Bing Liu
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, Beijing

Introduction:

Large-scale MRI datasets have established brain-wide association studies (BWAS) as crucial for mapping individual variability in brain function and behavior. While BWAS has primarily focused on inter-regional connectivity via resting-state functional connectivity (RSFC), intra-regional neural dynamics remain underexplored. Neural variability within regions provides critical insights into brain-behavior relationships, but existing metrics of resting-state regional dynamics (RSRD), such as variability, fluctuation, and correlation, offer fragmented perspectives, risking oversimplification. Data-driven approaches integrating diverse temporal features are essential to fully realize RSRD's potential. Incorporating dynamic elements creates nuanced, individual-specific profiles of brain activity, offering deeper insights into behavior across spatial and temporal scales (Petersen et al., 2024). Using the hctsa toolbox (Fulcher & Jones, 2017), we analyzed three independent lifespan datasets (N=30,138; ages 8–82) and developed RSRD profiles capturing multifaceted temporal patterns of regional brain activity. Our goals (Fig. 1A) were to construct robust profiles, identify behavior-specific features, and evaluate their generalizability across populations and life stages.

Methods:

We analyzed 3T rs-fMRI and behavioral data from HCP-YA, HCP-D, and UKB (N=30,138). Individual-specific RSRD profiles, developed in HCP-YA and validated in HCP-D and UKB, were computed using the hctsa toolbox across 271 brain regions. From 100 unrelated HCP-YA participants, 44 high-reliability features (refined RSRD; Fig1B) were identified and categorized into four dimensions: distribution, non-linearity, non-stationarity, and stochasticity. In an independent HCP-YA cohort, a correlation-based "fingerprinting" method evaluated RSRD's ability to capture individual differences, compared to RSFC features using the same feature selection strategy. CCA modeling identified two significant CCA modes: Externalizing Problems (Fig2A; r=0.81, Pperm <0.001) and Cognition (Fig2C; r=0.72, Pperm<0.001), from 159 behavioral phenotypes in independent 774 HCP-YA participants, adjusting for confounds (age, blood A1C/pressure, mean FD, scan version), with sex-specific impacts analyzed separately. To generalize, we applied CCA models from HCP-YA to refined RSRD in HCP-D and UKB, generating mode-specific Polydynamic Brain Scores (PBS; Fig2E) summarizing regional dynamics for Externalizing Problems and Cognition Modes and correlating them with behavioral phenotypes, controlling for covariates through partial correlation.

Results:

RSRD profiles outperformed RSFC features in identification tasks, achieving 96% accuracy under challenging conditions (Fig1C) and demonstrating strong specificity. CCA revealed that refined RSRD link temporal and spatial brain dynamics to behavior: nonlinear autocorrelations in unimodal and subcortical regions correlate with externalizing problems (Fig. 2B), while random walk dynamics in higher-order networks and the cerebellum predict cognitive abilities (Fig2C). These associations generalize across the lifespan, with externalizing patterns showing developmental shifts tied to stage-specific behaviors (Fig2F), while cognitive patterns remain stable, reflecting their alignment with fluid intelligence across life stages (Fig2G).

Conclusions:

We refined thousands of rs-fMRI features to create individualized RSRD profiles, uncovering two brain-behavior associations characterized by distinct spatiotemporal brain activity patterns: one linked to externalizing behaviors and another to cognitive ability. These patterns, summarized by PBS, exhibit lifespan generalizability, with externalizing patterns showing developmental shifts and cognitive patterns remaining stable. These findings underscore the individual specificity, behavioral relevance, and lifespan generalizability of RSRD profiles, advancing BWAS.

Lifespan Development:

Early life, Adolescence, Aging

Lifespan Development Other

Modeling and Analysis Methods:

Multivariate Approaches ²

Univariate Modeling ¹

Keywords:

Data analysis

Development

FUNCTIONAL MRI

Modeling

MRI

Multivariate

Psychiatric

Univariate

Other - Brain-wide association studies (BWAS)

^1|2Indicates the priority used for review

Abstract Information

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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?

Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

Yes

Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Please indicate which methods were used in your research:

Functional MRI

Behavior

For human MRI, what field strength scanner do you use?

3.0T

Which processing packages did you use for your study?

Analyze

Provide references using APA citation style.

Petersen SE (2024), Principles of cortical areas and their implications for neuroimaging. Neuron.
Fulcher, B. D., & Jones, N. S. (2017). hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature Extraction. Cell Syst
Fulcher, B. D (2013). Highly comparative time-series analysis: the empirical structure of time series and their methods. J R Soc Interface,

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