Deep Learning-Based Feature Extraction with MRI Data in Neuroimaging Genetics for Alzheimer’s Disease

Chakraborty, Dipnil; Zhuang, Zhong; Xue, Haoran; Fiecas, Mark; Shen, Xiatong; Pan, Wei

doi:10.3390/genes14030626

Cited by 6 publications

(4 citation statements)

References 64 publications

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“…Given the tremendous successes of CNNs, especially for automatic feature extraction in image analysis, it is natural to apply CNNs to extract features from neuroimaging data, and use these features as the traits (ie, endophenotypes) to be associated with SNPs. 17 These extracted features can go beyond any given ROIs. Here we move one step forward: we’d like CNN-extract image features to be more likely to be causal to the outcome (ie, AD) by taking advantage of IV regression.…”

Section: Discussionmentioning

confidence: 99%

“…The sample size of the ADNI, around 800, is still small. Other techniques, such as transfer learning, 44 data augmentation 17 and self-supervised learning can be explored and incorporated in the future for better performance. Third, perhaps most importantly, it is still quite challenging to interpret CNN-extracted features.…”

Section: Discussionmentioning

confidence: 99%

“… 15 , 16 A recent study proposes to extract features by training 3D convolutional neural networks in an AD classification task, reporting an AUC score of 0.75 on test data. 17 They also perform GWAS scans to identify genetic variants associated with extracted features. However, due to the existence of hidden confounders that affect both brain images and the AD status (or other outcomes), the above methods are not designed for and cannot be interpreted as causal feature extraction, which may hinder biological understanding of AD mechanisms.…”

Section: Introductionmentioning

confidence: 99%

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Deep causal feature extraction and inference with neuroimaging genetic data

Yao

Charkraborty

Zhang

et al. 2023

Statistics in Medicine

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Alzheimer's disease (AD) is a severe public health issue in the world. Magnetic Resonance Imaging (MRI) offers a way to study brain differences between AD patients and healthy individuals through feature extraction and comparison. However, in most previous works, the extracted features were not aimed to be causal, hindering biological understanding and interpretation. In order to extract causal features, we propose using instrumental variable (IV) regression with genetic variants as IVs. Specifically, we propose Deep Feature Extraction via Instrumental Variable Regression (DeepFEIVR), which uses a nonlinear neural network to extract causal features from three‐dimensional neuroimages to predict an outcome (eg, AD status in our application) while maintaining a linear relationship between the extracted features and IVs. DeepFEIVR not only can handle high dimensional individual‐level data for model building, but also is applicable to GWAS summary data to test associations of the extracted features with the outcome in subsequent analysis. In addition, we propose an extension of DeepFEIVR, called DeepFEIVR‐CA, for covariate adjustment (CA). We apply DeepFEIVR and DeepFEIVR‐CA to the Alzheimer's Disease Neuroimaging Initiative (ADNI) individual‐level data as training data for model building, then apply to the UK Biobank neuroimaging and the International Genomics of Alzheimer's Project (IGAP) AD GWAS summary data, showcasing how the extracted causal features are related to AD and various brain endophenotypes.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep causal feature extraction and inference with neuroimaging genetic data

Yao

Charkraborty

Zhang

et al. 2023

Statistics in Medicine

Self Cite

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“…CARMA, a Bayesian model, not only explains the discrepancies between the summary statistics and the LD from the reference panels, but also integrates the data with the functional annotations, which improves the GWAS results' accuracy [41]. In recent years, the use of machine learning (ML) to detect and classify diseases has attracted a lot of attention, and more researchers are beginning to use ML to address complex diseases such as AD [42]. ML, especially deep learning, is considered a powerful tool for GWAS data analysis due to its ability to process large-scale data, automatically extract key features, and identify complex interaction effects between multiple SNPs [43].…”

Section: Plos Onementioning

confidence: 99%

Knowledge domains and emerging trends of Genome-wide association studies in Alzheimer’s disease: A bibliometric analysis and visualization study from 2002 to 2022

Kong,

Wu,

Dai

et al. 2024

PLoS ONE

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Objectives Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by a progressive decline in cognitive and behavioral function. Studies have shown that genetic factors are one of the main causes of AD risk. genome-wide association study (GWAS), as a novel and effective tool for studying the genetic risk of diseases, has attracted attention from researchers in recent years and a large number of studies have been conducted. This study aims to summarize the literature on GWAS in AD by bibliometric methods, analyze the current status, research hotspots and future trends in this field. Methods We retrieved articles on GWAS in AD published between 2002 and 2022 from Web of Science. CiteSpace and VOSviewer software were applied to analyze the articles for the number of articles published, countries/regions and institutions of publication, authors and cited authors, highly cited literature, and research hotspots. Results We retrieved a total of 2,751 articles. The United States had the highest number of publications in this field, and Columbia University was the institution with the most published articles. The identification of AD-related susceptibility genes and their effects on AD is one of the current research hotspots. Numerous risk genes have been identified, among which APOE, CLU, CD2AP, CD33, EPHA1, PICALM, CR1, ABCA7 and TREM2 are the current genes of interest. In addition, risk prediction for AD and research on other related diseases are also popular research directions in this field. Conclusion This study conducted a comprehensive analysis of GWAS in AD and identified the current research hotspots and research trends. In addition, we also pointed out the shortcomings of current research and suggested future research directions. This study can provide researchers with information about the knowledge structure and emerging trends in the field of GWAS in AD and provide guidance for future research.

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Efficient artificial intelligence approaches for medical image processing in healthcare: comprehensive review, taxonomy, and analysis

Alnaggar,

Jagadale,

Saif

et al. 2024

Artif Intell Rev

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In healthcare, medical practitioners employ various imaging techniques such as CT, X-ray, PET, and MRI to diagnose patients, emphasizing the crucial need for early disease detection to enhance survival rates. Medical Image Analysis (MIA) has undergone a transformative shift with the integration of Artificial Intelligence (AI) techniques such as Machine Learning (ML) and Deep Learning (DL), promising advanced diagnostics and improved healthcare outcomes. Despite these advancements, a comprehensive understanding of the efficiency metrics, computational complexities, interpretability, and scalability of AI based approaches in MIA is essential for practical feasibility in real-world healthcare environments. Existing studies exploring AI applications in MIA lack a consolidated review covering the major MIA stages and specifically focused on evaluating the efficiency of AI based approaches. The absence of a structured framework limits decision-making for researchers, practitioners, and policymakers in selecting and implementing optimal AI approaches in healthcare. Furthermore, the lack of standardized evaluation metrics complicates methodology comparison, hindering the development of efficient approaches. This article addresses these challenges through a comprehensive review, taxonomy, and analysis of existing AI-based MIA approaches in healthcare. The taxonomy covers major image processing stages, classifying AI approaches for each stage based on method and further analyzing them based on image origin, objective, method, dataset, and evaluation metrics to reveal their strengths and weaknesses. Additionally, comparative analysis conducted to evaluate the efficiency of AI based MIA approaches over five publically available datasets: ISIC 2018, CVC-Clinic, 2018 DSB, DRIVE, and EM in terms of accuracy, precision, Recall, F-measure, mIoU, and specificity. The popular public datasets and evaluation metrics are briefly described and analyzed. The resulting taxonomy provides a structured framework for understanding the AI landscape in healthcare, facilitating evidence-based decision-making and guiding future research efforts toward the development of efficient and scalable AI approaches to meet current healthcare needs.

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Deep Learning-Based Feature Extraction with MRI Data in Neuroimaging Genetics for Alzheimer’s Disease

Cited by 6 publications

References 64 publications

Deep causal feature extraction and inference with neuroimaging genetic data

Deep causal feature extraction and inference with neuroimaging genetic data

Knowledge domains and emerging trends of Genome-wide association studies in Alzheimer’s disease: A bibliometric analysis and visualization study from 2002 to 2022

Efficient artificial intelligence approaches for medical image processing in healthcare: comprehensive review, taxonomy, and analysis

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