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The Disrupted Balance of Dynamic Functional Networks Plasticity in Brain Aging: A UK Biobank Study

Poster No:

876

Submission Type:

Abstract Submission

Authors:

Xinran Wu¹, Jie Zhang¹, Zhengxu Lian¹, Songjun Peng²

Institutions:

¹Fudan University, Shanghai, Shanghai, ²Hong Kong Baptist University, Hong Kong, Hong Kong

First Author:

Xinran Wu
Fudan University
Shanghai, Shanghai

Co-Author(s):

Jie Zhang
Fudan University
Shanghai, Shanghai

Zhengxu Lian
Fudan University
Shanghai, Shanghai

Songjun Peng
Hong Kong Baptist University
Hong Kong, Hong Kong

Introduction:

The brain's ability to flexibly adapt its connectivity patterns to changing environments is critical to survival and support higher cognitive abilities, but the mechanisms by which it changes during successful and non-successful aging and how it leads to the decline and maintenance of cognition have not been fully explored.

Methods:

The study integrated resting state functional MRI scans of 38,681 people aged 40-80 years from the UK Biobank to capture the aging trajectory of flexible variability in brain connection patterns using time-varying functional connection variability analysis, which measures variability across brain regions by calculating the dissimilarity of connection patterns in each brain region across different time windows (Fig 1A). The association with cognitive and behavioral phenotypes, brain structural measures, genetic factors, immune, metabolic and other biomarkers was examined to understand the functional significance and potential physiological mechanism of aging-related variability changing, and to investigate which environmental factors and lifestyles can bring cognitive and health-level changes through regulating connectivity variability based on mediation models.

Results:

The study identified two patterns of connectivity variability associated with aging: "increased sensorimotor fluctuations" (positive correlated with age, Vpos) and "decreased frontoparietal flexibility" (negative correlated with age, Vneg) (Fig 1B). The increasing Vpos with age is associated with more cortical atrophy, depression, fatigue, more physical illness and body inflammation, and genetic risk associated with AD, and can predict an elevated future risk of depression. In contrast, keeping higher Vneg might be a hallmark of successful aging and is associated with higher levels of myelination in the frontoparietal cortex, better cognitive abilities, and a younger body state, possibly reflecting the remained ability of the frontoparietal cortex to switch connectivity patterns flexibily (Fig 1C-E). Further mediating analysis revealed that healthy lifestyle habits such as more physical activity may reduce the risk of future mental health problems by decreasing Vpos (Fig 1F).

·Fig 1. Main results of the study.

Conclusions:

This study reveals different aging trajectories for connectivity variability in low-order sensemotor and high-order frontoparietal control networks, and suggests that a cross-hierarchical imbalance in variability of brain networks may be an effective neuromarker for age-related cognitive decline, mental health problems, and neurodegenerative diseases.

Higher Cognitive Functions:

Higher Cognitive Functions Other

Lifespan Development:

Aging ¹

Modeling and Analysis Methods:

fMRI Connectivity and Network Modeling ²

Novel Imaging Acquisition Methods:

BOLD fMRI

Keywords:

Aging

Cognition

Cortex

Data analysis

MRI

^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):

Patients

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.

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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.

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Please indicate which methods were used in your research:

Functional MRI

Neuropsychological testing

Computational modeling

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

3.0T

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