Imaging Genetics Study Based on a Temporal Group Sparse Regression and Additive Model for Biomarker Detection of Alzheimer’s Disease

AbstractWe conduct an imaging genetics study to explore how effective brain connectivity in the default mode network (DMN) may be related to genetics within the context of Alzheimer’s disease and mild cognitive impairment. We develop an analysis of longitudinal resting-state functional magnetic resonance imaging (rs-fMRI) and genetic data obtained from a sample of 111 subjects with a total of 319 rs-fMRI scans from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. A Dynamic Causal Model (DCM) is fit to the rs-fMRI scans to estimate effective brain connectivity within the DMN and related to a set of single nucleotide polymorphisms (SNPs) contained in an empirical disease-constrained set which is obtained out-of-sample from 663 ADNI subjects having only genome-wide data. We relate longitudinal effective brain connectivity estimated using spectral DCM to SNPs using both linear mixed effect (LME) models as well as function-on-scalar regression (FSR). In both cases we implement a parametric bootstrap for testing SNP coefficients and make comparisons with p-values obtained from asymptotic null distributions. In both networks at an initial q-value threshold of 0.1 no effects are found. We report on exploratory patterns of associations with relatively high ranks that exhibit stability to the differing assumptions made by both FSR and LME.

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Deep Ensemble Sparse Regression Network for Alzheimer’s Disease Diagnosis

Machine Learning in Medical Imaging - Lecture Notes in Computer Science ◽

10.1007/978-3-319-47157-0_14 ◽

2016 ◽

pp. 113-121 ◽

Cited By ~ 8

Author(s):

Heung-Il Suk ◽

Dinggang Shen

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Disease Diagnosis ◽

Sparse Regression ◽

Alzheimer’S Disease Diagnosis

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Integrating Transcriptomics, Genomics, and Imaging in Alzheimer's Disease: A Federated Model

10.1101/2021.09.14.460367 ◽

2021 ◽

Author(s):

Jianfeng Wu ◽

Yanxi Chen ◽

Panwen Wang ◽

Richard J Caselli ◽

Paul M Thompson ◽

...

Keyword(s):

Gene Expression ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Imaging Modality ◽

Association Studies ◽

Imaging Genetics ◽

Health Concern ◽

Genome Wide Association Studies ◽

Imaging Data ◽

Stable Performance

Alzheimer's disease (AD) affects more than 1 in 9 people age 65 and older and becomes an urgent public health concern as the global population ages. In clinical practice, structural magnetic resonance imaging (sMRI) is the most accessible and widely used diagnostic imaging modality. Additionally, genome-wide association studies (GWAS) and transcriptomic, the study of gene expression, also play an important role in understanding AD etiology and progression. Sophisticated imaging genetics systems have been developed to discover genetic factors that consistently affect brain function and structure. However, most studies to date focused on the relationships between brain sMRI and GWAS or brain sMRI and transcriptomics. To our knowledge, few methods have been developed to discover and infer multimodal relationships among sMRI, GWAS, and transcriptomics. To address this, we propose a novel federated model, Genotype-Expression-Imaging Data Integration (GEIDI), to identify genetic and transcriptomic influences on brain sMRI measures. The relationships between brain imaging measures and gene expression are allowed to depend on a person's genotype at the single-nucleotide polymorphism (SNP) level, making the inferences adaptive and personalized. We performed extensive experiments on publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Experimental results demonstrated our proposed method outperformed state-of-the-art expression quantitative trait loci (eQTL) methods for detecting genetic and transcriptomic factors related to AD and has stable performance when data are integrated from multiple sites. Our GEIDI approach may offer novel insights into the relationship among image biomarkers, genotypes, and gene expression and help discover novel genetic targets for potential AD drug treatments.

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Longitudinal clinical score prediction in Alzheimer's disease with soft-split sparse regression based random forest

Neurobiology of Aging ◽

10.1016/j.neurobiolaging.2016.07.005 ◽

2016 ◽

Vol 46 ◽

pp. 180-191 ◽

Cited By ~ 52

Author(s):

Lei Huang ◽

Yan Jin ◽

Yaozong Gao ◽

Kim-Han Thung ◽

Dinggang Shen

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Random Forest ◽

Clinical Score ◽

Sparse Regression

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Direction of Information Flow in Alzheimer's Disease and MCI Patients

International Journal of Alzheimer s Disease ◽

10.4061/2011/214580 ◽

2011 ◽

Vol 2011 ◽

pp. 1-7 ◽

Cited By ~ 8

Author(s):

Fabrizio Vecchio ◽

Claudio Babiloni

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Information Flow ◽

Additive Model ◽

Functional Coupling ◽

Directed Transfer Function ◽

Directional Flow ◽

Eeg Data ◽

Rest Condition ◽

Amnesic Mild Cognitive Impairment

Is directionality of electroencephalographic (EEG) synchronization abnormal in amnesic mild cognitive impairment (MCI) and Alzheimer's disease (AD)? And, do cerebrovascular and AD lesions represent additive factors in the development of MCI as a putative preclinical stage of AD? Here we reported two studies that tested these hypotheses. EEG data were recorded in normal elderly (Nold), amnesic MCI, and mild AD subjects at rest condition (closed eyes). Direction of information flow within EEG electrode pairs was performed by directed transfer function (DTF) atδ(2–4 Hz),θ(4–8 Hz),α1 (8–10 Hz),α2 (10–12 Hz),β1 (13–20 Hz),β2 (20–30 Hz), andγ(30–40 Hz). Parieto-to-frontal direction was stronger in Nold than in MCI and/or AD subjects forαandβrhythms. In contrast, the directional flow within interhemispheric EEG functional coupling did not discriminate among the groups. More interestingly, this coupling was higher atθ,α1,α2, andβ1 in MCI with higher than in MCI with lower vascular load. These results suggest that directionality of parieto-to-frontal EEG synchronization is abnormal not only in AD but also in amnesic MCI, supporting the additive model according to which MCI state would result from the combination of cerebrovascular and neurodegenerative lesions.

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Network Regularization in Imaging Genetics Improves Prediction Performances and Model Interpretability on Alzheimer’s Disease

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) ◽

10.1109/isbi.2019.8759593 ◽

2019 ◽

Author(s):

N. Guigui ◽

C. Philippe ◽

A. Gloaguen ◽

S. Karkar ◽

V. Guillemot ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Imaging Genetics

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Incorporating spatial–anatomical similarity into the VGWAS framework for AD biomarker detection

Bioinformatics ◽

10.1093/bioinformatics/btz401 ◽

2019 ◽

Vol 35 (24) ◽

pp. 5271-5280

Author(s):

Meiyan Huang ◽

Yuwei Yu ◽

Wei Yang ◽

Qianjin Feng ◽

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Normal Control ◽

Genome Wide Association Study ◽

Computational Cost ◽

Genetic Data ◽

Imaging Genetics ◽

Imaging Data ◽

Spatial Correlations ◽

Novel Method

Abstract Motivation The detection of potential biomarkers of Alzheimer’s disease (AD) is crucial for its early prediction, diagnosis and treatment. Voxel-wise genome-wide association study (VGWAS) is a commonly used method in imaging genomics and usually applied to detect AD biomarkers in imaging and genetic data. However, existing VGWAS methods entail large computational cost and disregard spatial correlations within imaging data. A novel method is proposed to solve these issues. Results We introduce a novel method to incorporate spatial correlations into a VGWAS framework for the detection of potential AD biomarkers. To consider the characteristics of AD, we first present a modification of a simple linear iterative clustering method for spatial grouping in an anatomically meaningful manner. Second, we propose a spatial–anatomical similarity matrix to incorporate correlations among voxels. Finally, we detect the potential AD biomarkers from imaging and genetic data by using a fast VGWAS method and test our method on 708 subjects obtained from an Alzheimer’s Disease Neuroimaging Initiative dataset. Results show that our method can successfully detect some new risk genes and clusters of AD. The detected imaging and genetic biomarkers are used as predictors to classify AD/normal control subjects, and a high accuracy of AD/normal control classification is achieved. To the best of our knowledge, the association between imaging and genetic data has yet to be systematically investigated while building statistical models for classifying AD subjects to create a link between imaging genetics and AD. Therefore, our method may provide a new way to gain insights into the underlying pathological mechanism of AD. Availability and implementation https://github.com/Meiyan88/SASM-VGWAS.

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