Poster No:
1148
Submission Type:
Abstract Submission
Authors:
Yue Gu1, Tatia Lee2
Institutions:
1the University of Hong Kong, Hong Kong, select a state, 2the University of Hong Kong, Hong Kong, -- SELECT --
First Author:
Yue Gu
the University of Hong Kong
Hong Kong, select a state
Co-Author:
Tatia Lee
the University of Hong Kong
Hong Kong, -- SELECT --
Introduction:
Ageing is a complex biological process (López-Otín et al., 2013). This deviation between the predicted-brain age and chronological age, referred to as the 'brain-age gap'(Franke, Ziegler, et al., 2010). There are association between individual brain-age gap and adverse outcomes such as mortality (Cole et al., 2018), neurodegenerative disorders (Franke et al. 2010; Gaser et al. 2013; Gonneaud et al. 2021). Employing brain-age gap assessments in elderly could enrich our comprehension of resilience to structural and functional insults that accumulate with advancing age, and the repercussions of diseases on the aging brain. For ageing, resilience is particularly important. Because ageing may face a variety of challenges, such as chronic illness and disability (Madsen et al., 2019; Rentz et al., 2017). We aimed to develop a deep learning-based brain age prediction model using multimodal imaging data, including resting-state fMRI, MRI and DTI scans from 124 participants aged 53-76 years old.
Methods:
Demographic variables were assessed at the time of the scan. The data were obtained from a cohort of 124 right-handed older adults who underwent a comprehensive neuropsychological assessment. The final sample consisted of 93 participants, with a mean age of 61.06 years (SD = 4.90 years, range = 53-76 years), including 33 females. This study received approval from the institutional review board of the Affiliated Rehabilitation Hospital, Fujian University of Traditional Chinese Medicine, and all participants provided written informed consent. All participants underwent scanning on a 3T Scanner. The Brainnetome Atlas, which contains 246 subregions of the bilateral hemispheres, was selected for this study due to its multimodal characterization of the human brain. The brain age prediction model employed in this study integrates a multimodal set of neuroimaging features to estimate an individual's brain age. To comprehend the intricate interplay between the brain-age gap and network metrics, as well as cognitive and emotional assessments, supplementary partial correlation analyses were conducted.
Results:
The model exhibited strong predictive performance, with a MSE of 19.218, indicating the model's ability to minimize prediction errors. The R² value of 0.968 signifies the high proportion of variance in the data that could be explained by the model, underscoring its robust predictive capacity. The selection of 159 features with the highest contribution to the prediction model, specifically in SC. Using the partial correlation, our analysis revealed a significant negative correlation between resilience and the brain-age gap.
Conclusions:
In summary, this study highlights the brain-age gap as a potential biomarker for aging, underscores the role of resilience as a protective factor.
Lifespan Development:
Aging 1
Novel Imaging Acquisition Methods:
Multi-Modal Imaging 2
Keywords:
ADULTS
Aging
Modeling
MRI
NORMAL HUMAN
1|2Indicates the priority used for review
Provide references using author date format
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