PurposeFunctional magnetic resonance imaging (fMRI) and functional connectivity (FC) have been used to follow aging in both children and older adults. Robust changes have been observed in children, in which high connectivity among all brain regions changes to a more modular structure with maturation. We examine FC changes in older adults after 2 years of aging in the UK Biobank (UKB) longitudinal cohort.ApproachWe process fMRI connectivity data using the Power264 atlas and then test whether the average internetwork FC changes in the 2722-subject longitudinal cohort are statistically significant using a Bonferroni-corrected t-test. We also compare the ability of Power264 and UKB-provided, independent component analysis (ICA)-based FC to determine which of a longitudinal scan pair is older. Finally, we investigate cross-sectional FC changes as well as differences due to differing scanner tasks in the UKB, Philadelphia Neurodevelopmental Cohort, and Alzheimer’s Disease Neuroimaging Initiative datasets.ResultsWe find a 6.8% average increase in somatomotor network (SMT)–visual network (VIS) connectivity from younger to older scans (corrected p<10−15) that occurs in male, female, older subject (>65 years old), and younger subject (<55 years old) groups. Among all internetwork connections, the average SMT–VIS connectivity is the best predictor of relative scan age. Using the full FC and a training set of 2000 subjects, one is able to predict which scan is older 82.5% of the time using either the full Power264 FC or the UKB-provided ICA-based FC.ConclusionsWe conclude that SMT–VIS connectivity increases with age in the UKB longitudinal cohort and that resting state FC increases with age in the UKB cross-sectional cohort.
KEYWORDS: Functional magnetic resonance imaging, Feature selection, Data modeling, Principal component analysis, Visualization, Genomics, Open source software, Software, Software development
It can be difficult to identify trends and perform quality control in large, high-dimensional fMRI or omics datasets. To remedy this, we develop ImageNomer, a data visualization and analysis tool that allows inspection of both subject-level and cohort-level features. The tool allows visualization of phenotype correlation with functional connectivity (FC), partial connectivity (PC), dictionary components (PCA and our own method), and genomic data (single-nucleotide polymorphisms, SNPs). In addition, it allows visualization of weights from arbitrary ML models. ImageNomer is built with a Python backend and a Vue frontend. We validate ImageNomer using the Philadelphia Neurodevelopmental Cohort (PNC) dataset, which contains multitask fMRI and SNP data of healthy adolescents. Using correlation, greedy selection, or model weights, we find that a set of 10 FC features can explain 15% of variation in age, compared to 35% for the full 34,716 feature model. The four most significant FCs are either between bilateral default mode network (DMN) regions or spatially proximal subcortical areas. Additionally, we show that whereas both FC (fMRI) and SNPs (genomic) features can account for 10-15% of intelligence variation, this predictive ability disappears when controlling for race. We find that FC features can be used to predict race with 85% accuracy, compared to 78% accuracy for sex prediction. Using ImageNomer, this work casts doubt on the possibility of finding unbiased intelligence-related features in fMRI and SNPs of healthy adolescents.
A fundamental understanding of sex differences that exist in healthy individuals is critical for the study of neurological illnesses that exhibit phenotypic differences between both genders. Functional magnetic resonance imaging(fMRI) is a useful way to study this problem since it provides a non-invasive and high-resolution tool for observing the fluctuation in blood oxygenation level dependent (BOLD) signals to characterize the metabolism of the human brain. In the meantime, graph neural networks (GNNs) can be applied to fMRI data to effectively discover novel biomarkers underlying brain development. We propose a multi-modal graph isomorphism network (MGIN) to analyze the sex differences based on fMRI task data. Our method is able to integrate all the available connectivity data into graphs for deep learning, and it can be applied to multigraphs with different nodes to learn local graph information without binding to the entire graph. MGIN model can identify important subnetworks between and within multi-task data. In addition, it is interpretable by using GNNExplainer to provide important domain insights to identify graph structures and node features that contribute significantly to the classification results. Our MGIN model can achieve better classification accuracy compared to competing models. We applied the model to a cohort of brain development study to classify sex during different stages of adolescence and experimental results showed that our model can improve classification accuracy and help in our understanding of neurodevelopment during adolescence.
A novel phenotype guided interpretable graph convolutional network (PGI-GCN) for the analysis of fMRI data is proposed. We utilize PGI-GCN to predict the ages of children and young adults based on multi-paradigm fMRI data of the Philadelphia Neurodevelopmental Cohort (PNC) dataset. We show PGI-GCN to have superior predictive capability compared to a simpler deep model that uses functional connectivity plus gender without the population-level graph. A learnable mask identifies 3 important intra-network (Memory Retrieval, Dorsal Attention, and Subcortical) and 3 important inter-network (Visual-Cerebellar, Visual-Dorsal Attention, and Subcortical-Cerebellar) connectivity differences between children and young adults.
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