Poster No:
1161
Submission Type:
Abstract Submission
Authors:
Caroline Dartora1, Anna Marseglia1, Johan Skoog2, Sebastian Muehlboeck1, Silke Kern2, Anna Zettergren2, Ingmar Skoog2, Eric Westman1
Institutions:
1Karolinska Institutet, Stockholm, Sweden, 2University of Gothenburg, Gothenburg, Sweden
First Author:
Co-Author(s):
Johan Skoog
University of Gothenburg
Gothenburg, Sweden
Silke Kern
University of Gothenburg
Gothenburg, Sweden
Introduction:
Exposure to dementia risk factors throughout life can lead to brain atrophy and older-appearing brains on neuroimaging. Resilience mechanisms can help sustain the brain structure (brain maintenance, BM) and/or compensate for the neuropathological damage (cognitive reserve, CR), preserving cognition. Traditional proxy-based approaches (e.g., education) face challenges in measuring these mechanisms as they cannot capture the core biological dimension. However, with deep learning is possible to develop algorithms predicting the biological age of the brain from raw brain images. This study investigated whether differences between predicted brain age and chronological age (PBA-CA) can be used as a marker of BM and/or CR following the NIH-funded Collaboratory on Reserve and Resilience framework.
Methods:
The study population included 719 dementia-free septuagenarians from the Gothenburg H70-1944 MRI cohort. We applied the deep learning model-developed in-house using minimally processed T1-w MRI from ~17000 neurologically intact individuals from UK Biobank, ADNI, AIBL, and GENIC and validated via a cross-validation approach to H70 participants' MRI to predict their brain age, and computed PBA-CA. MRI markers of brain pathology included cortical thickness (overall and Alzheimer's disease-related), cerebral small vessel disease (SVD; individual markers and score), and white-matter microstructural alterations (DTI's fractional anisotropy). Global and domain-specific cognitive function was based on composite scores from ten tests. Data analysis included regression models and stratification by sex.
Results:
In the brain, decreasing differences between PBA and CA (reflecting younger-appearing brains) were associated with a thicker brain (overall and AD signature areas), lower SVD score-particularly lower white matter hyperintensities volume, lacunes, large infarcts-, and higher fractional anisotropy (more integrity). Decreasing differences were also related to better cognitive performance, globally and in attention/speed, executive function, and visuospatial abilities. In stratified analysis by sex, such associations were evident in men but not in women, except fractional anisotropy [robust regressions' β-coefficients for women -4.89 (95%CI -8.87,-0.90) and men -23.7 (95%CI -32.9,-14.4)].
Conclusions:
Negative differences PBA<CA are related to less atrophy, less cerebrovascular alterations, and better cognition, suggesting more preserved brain structure and cognition, thus BM. However, differences between the sexes suggest that women and men may have different pathways to resilience.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s)
Lifespan Development:
Aging 1
Modeling and Analysis Methods:
Classification and Predictive Modeling 2
Novel Imaging Acquisition Methods:
Anatomical MRI
Keywords:
Aging
Data analysis
Machine Learning
MRI
STRUCTURAL MRI
Other - Brain age
1|2Indicates the priority used for review
Provide references using author date format
Dartora, C. et al., 2023. https://doi.org/10.1101/2022.09.06.22279594