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| Principal Investigator | |
| Principal Investigator's Name: | Zhilin Zhang |
| Institution: | Kyoto University |
| Department: | The department of psychiatry |
| Country: | |
| Proposed Analysis: | Multimodal, imaging-genomics techniques offer a platform for understanding genetic influences on brain abnormalities in Alzheimer's disease. In this study, we propose a novel framework to combine the features for the functional and genetic modalities include dynamic (i.e., time-varying) functional network connectivity (dFNC) features and the SNP data, respectively. The dFNC features are estimated from component time-courses, obtained using group independent component analysis (ICA), by computing sliding-window functional network connectivity, and then estimating subject specific states from this dFNC data using a k-means clustering approach. For each subject, both the functional (dFNC states) and SNP data are selected as features for a parallel ICA based imaging-genomic framework. This analysis identified a significant association between a SNP component (defined by large clusters of functionally related SNPs statistically correlated with phenotype components) and time-varying or dFNC component (defined by clusters of related connectivity links among distant brain regions distributed across discrete dynamic states, and statistically correlated with genomic components) in Alzheimer's disease. Taken together, the current work provides preliminary evidence for a link between dFNC measures and genetic risk, suggesting the application of dFNC patterns as biomarkers in imaging genetic association study. Flowchart showing details on pre- and post-processing steps involved in fMRI data processing. 1. Preprocessing 1) Realignment 2) Slice-time correction 3) Despiked 4) Spatial normalization 5) Smoothing 6) Variance normalization 2. Group ICA 1) Single subject PCA 2) Group EM-PCA 3) Group ICA 4) ICASSO 5) Back-reconstruction 3. Feature Identification 1) ICN selection 2) Sliding window analysis 3) Estimate covariance 4) Promote sparsity 5) Cross-validation Flowchart showing details on pre- and post-processing steps involved in fMRI data processing 1) Gender consistency check 2) Sample relatedness 3) Genotyping call rate 4) Hardy-Weinberg equilibrium 5) Minor allele frequency 6) SHAPEIT for pre-phasing, IMPUTE2 for imputation, and 1000 Genome data as reference 7) Markers with high imputation qualities retained 8) Missing calls replaced using high linkage disequilibrium loci 9) Discrete numbers assigned to the categorical genotypes 10) Population structure corrected with PCA Our expectation is that the relationship between SNP patterns and time-varying functional connectivity is a more natural way to analyze the data which will improve our understanding of both genomic factors and functional connectivity measures. We estimate the functional features for neuroimaging data as subject-specific states that are revealed from the dynamic FNC data using a sliding window plus clustering approach. The SNP data were first pre-filtered using results from an independent GWAS study to locate risk variants based on a large cohort. These risk loci were then simultaneously analyzed for multivariate associations with the derived functional features from fMRI data using the pICA data fusion algorithm. Further, we explored the association between genetic risk and SNP and functional connectivity features. Polygenic risk score estimates the aggregate risk of a set of SNPs, by computing a linearly weighted sum of the genetic data where weights are derived from the effect sizes of the individual SNPs obtained from univariate analysis.This approach enables us to study genomic patterns grouped that co-vary across subjects with maximally independent dynamic FNC patterns. To guard against overfitting, the identified dFNC-SNP associations were evaluated with a permutation test. |
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