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Abstract BackgroundIn Alzheimer’s Diseases (AD) research, multimodal imaging analysis can unveil complementary information from multiple imaging modalities and further our understanding of the disease. One application is to discover disease subtypes using unsupervised clustering. However, existing clustering methods are often applied to input features directly, and could suffer from the curse of dimensionality with high-dimensional multimodal data. The purpose of our study is to identify multimodal imaging-driven subtypes in Mild Cognitive Impairment (MCI) participants using a multiview learning framework based on Deep Generalized Canonical Correlation Analysis (DGCCA), to learn shared latent representation with low dimensions from 3 neuroimaging modalities. ResultsDGCCA applies non-linear transformation to input views using neural networks and is able to learn correlated embeddings with low dimensions that capture more variance than its linear counterpart, generalized CCA (GCCA). We designed experiments to compare DGCCA embeddings with single modality features and GCCA embeddings by generating 2 subtypes from each feature set using unsupervised clustering. In our validation studies, we found that amyloid PET imaging has the most discriminative features compared with structural MRI and FDG PET which DGCCA learns from but not GCCA. DGCCA subtypes show differential measures in 5 cognitive assessments, 6 brain volume measures, and conversion to AD patterns. In addition, DGCCA MCI subtypes confirmed AD genetic markers with strong signals that existing late MCI group did not identify. ConclusionOverall, DGCCA is able to learn effective low dimensional embeddings from multimodal data by learning non-linear projections. MCI subtypes generated from DGCCA embeddings are different from existing early and late MCI groups and show most similarity with those identified by amyloid PET features. In our validation studies, DGCCA subtypes show distinct patterns in cognitive measures, brain volumes, and are able to identify AD genetic markers. These findings indicate the promise of the imaging-driven subtypes and their power in revealing disease structures beyond early and late stage MCI. 

Abstract BackgroundAlthough genome-wide association studies (GWAS) have successfully located various genetic variants susceptible to Alzheimer’s Disease (AD), it is still unclear how specific variants interact with genes and tissues to elucidate pathologies associated with AD. Summary-data-based Mendelian Randomization (SMR) addresses this problem through an instrumental variable approach that integrates data from independent GWAS and expression quantitative trait locus (eQTL) studies in order to infer a causal effect of gene expression on a trait. ResultsOur study employed the SMR approach to integrate a set of meta-analytic cis-eQTL information from the Genotype-Tissue Expression (GTEx), CommonMind Consortium (CMC), and Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) consortiums with three sets of meta-analysis AD GWAS results. ConclusionsOur analysis identified twelve total gene probes (associated with twelve distinct genes) with a significant association with AD. Four of these genes survived a test of pleiotropy from linkage (the HEIDI test).Three of these genes – RP11-385F7.1, PRSS36, and AC012146.7 – have not yet been reported differentially expressed in the brain in the context of AD, and thus are the novel findings warranting further investigation. 

Abstract BackgroundImaging, cognitive and fluid data have been widely studied to identify quantitative biomarkers that can help predict the status and progression of Alzheimer’s disease (AD). However, it is still an underexplored topic whether there exist subpopulations with different genetic profiles across which the biomarker‐based prediction models may vary. We propose to use the Chow test (Chow 1960 Econometrica 28(3)) to perform genetically stratified analyses for identifying SNP‐based subpopulations coupled with precision AD biomarkers with varying effects on future diagnosis in these subpopulations. The investigation of such SNPs and precision biomarkers may eventually pave the way for increased customization of AD care. MethodParticipants included 1,324 subjects from the ADNI cohort with both AD biomarker and genotyping data available (http://www.pi4cs.org/qt‐pad‐challenge). 30 significant (P < 1.5E‐278) AD SNPs were sourced from (Jansen 2019 NatGen). Chow tests were performed to determine whether each of baseline visit measures of 16 AD biomarkers predicted AD diagnosis at the three‐year visit with varying slopes when stratifying upon the allelic dosage of each of 30 chosen SNPs. Bonferroni correction (P < 1.04E‐4) was employed to correct for multiple comparisons. ResultMultiple SNP‐biomarker pairs showed significant genetically driven deviations in the regression coefficients when predicting diagnosis in three years using baseline biomarkers (Figure 1). Top SNP hits involved rs769449 (Chr 19,APOE) and rs7561528 (Chr 2,LOC105373605), and almost all 16 studied biomarkers demonstrated differential slopes in different genotype groups to predict diagnosis in three years. To examine the details of these top findings, the regression coefficients calculated for each of the five most significant biomarkers of both SNPs were bootstrapped and plotted in Figure 2. ConclusionGenetic analysis of AD candidate SNPs in conjunction with AD biomarker data via the Chow test identified several SNPs coupled with precision AD biomarkers with varying prognosis effects in the corresponding genotype groups. These findings provide valuable information to reveal disease heterogeneity and help facilitate precision medicine. 

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disease, yet its current treatments are limited to stopping disease progression. Moreover, the effectiveness of these treatments remains uncertain due to the heterogeneity of the disease. Therefore, it is essential to identify disease subtypes at a very early stage. Current data-driven approaches can be used to classify subtypes during later stages of AD or related disorders, but making predictions in the asymptomatic or prodromal stage is challenging. Furthermore, the classifications of most existing models lack explainability, and these models rely solely on a single modality for assessment, limiting the scope of their analysis. Thus, we propose a multimodal framework that utilizes early-stage indicators, including imaging, genetics, and clinical assessments, to classify AD patients into progression-specific subtypes at an early stage. In our framework, we introduce a tri-modal co-attention mechanism (Tri-COAT) to explicitly capture cross-modal feature associations. Data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (slow progressing = 177, intermediate = 302, and fast = 15) were used to train and evaluate Tri-COAT using a 10-fold stratified cross-testing approach. Our proposed model outperforms baseline models and sheds light on essential associations across multimodal features supported by known biological mechanisms. The multimodal design behind Tri-COAT allows it to achieve the highest classification area under the receiver operating characteristic curve while simultaneously providing interpretability to the model predictions through the co-attention mechanism. 

Human whole-brain functional connectivity networks have been shown to exhibit both local/quasilocal (e.g., a set of functional sub-circuits induced by node or edge attributes) and non-local (e.g., higher-order functional coordination patterns) properties. Nonetheless, the non-local properties of topological strata induced by local/quasilocal functional sub-circuits have yet to be addressed. To that end, we proposed a homological formalism that enables the quantification of higher-order characteristics of human brain functional sub-circuits. Our results indicate that each homological order uniquely unravels diverse, complementary properties of human brain functional sub-circuits. Noticeably, the H1 homological distance between rest and motor task was observed at both the whole-brain and sub-circuit consolidated levels, which suggested the self-similarity property of human brain functional connectivity unraveled by a homological kernel. Furthermore, at the whole-brain level, the rest–task differentiation was found to be most prominent between rest and different tasks at different homological orders: (i) Emotion task (H0), (ii) Motor task (H1), and (iii) Working memory task (H2). At the functional sub-circuit level, the rest–task functional dichotomy of the default mode network is found to be mostly prominent at the first and second homological scaffolds. Also at such scale, we found that the limbic network plays a significant role in homological reconfiguration across both the task and subject domains, which paves the way for subsequent investigations on the complex neuro-physiological role of such network. From a wider perspective, our formalism can be applied, beyond brain connectomics, to study the non-localized coordination patterns of localized structures stretching across complex network fibers.