Latent feature representation learning for Alzheimer’s disease classification

Dong, Aisheng; Zhang, Guodong; Liu, Jian; Wei, Zhonghe

doi:10.1016/j.compbiomed.2022.106116

Cited by 8 publications

(4 citation statements)

References 42 publications

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“…AD NC vs. AD Train/Test split 70% to 30% No 100 93.3 100 87.5 90.0 100 75.0 90.0 87.5 100 100 80.0 91.3 92.0 94.4 94.4 95.0 92.0 Pan and wang ( 2022 ) 2022 fMRI, DTI Attention mechanism Vision Transformer AD vs. NC LMCI vs. NC EMCI vs. NC Random split No 93.3 90.0 90.4 95.2 95.2 95.0 94.4 93.5 92.6 Dong et al ( 2022 ) 2022 sMRI, PET Latent feature representation SVM AD vs. NC MCI vs. NC AD vs. MCI tenfold No 83.6 71.3 70.3 80.5 66.1 70.3 81.8 71.1 70.8 …”

Section: Resultsmentioning

confidence: 99%

“…This technique could capture both location and frequency information, and it could extract spatial structures over a range of sizes, thereby being able to separate high frequencies from low frequencies. Most of the studies utilized feature-level methods which operate on features extracted from the images, and the extracted information is achieved using some intelligent computing techniques such as machine learning based methods (Zuo et al 2021 ; Xu et al 2022 ), region-based algorithms (Pan and Wang 2022 ), and similarity-matching to content (Dong et al 2022 ). Machine learning-based methods (CNN) of multimodality fusion is an effective medical image analysis method (Mathotaarachchi et al 2017 ; Huang et al 2019 ; Jiang et al 2021 ; Liu et al 2018 ) for multi-class classification (Goenka and Tiwari 2022b , c ).…”

Section: Discussionmentioning

confidence: 99%

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Machine learning with multimodal neuroimaging data to classify stages of Alzheimer’s disease: a systematic review and meta-analysis

Odusami,

Maskeliūnas,

Damaševičius

et al. 2023

Cogn Neurodyn

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In recent years, Alzheimer’s disease (AD) has been a serious threat to human health. Researchers and clinicians alike encounter a significant obstacle when trying to accurately identify and classify AD stages. Several studies have shown that multimodal neuroimaging input can assist in providing valuable insights into the structural and functional changes in the brain related to AD. Machine learning (ML) algorithms can accurately categorize AD phases by identifying patterns and linkages in multimodal neuroimaging data using powerful computational methods. This study aims to assess the contribution of ML methods to the accurate classification of the stages of AD using multimodal neuroimaging data. A systematic search is carried out in IEEE Xplore, Science Direct/Elsevier, ACM DigitalLibrary, and PubMed databases with forward snowballing performed on Google Scholar. The quantitative analysis used 47 studies. The explainable analysis was performed on the classification algorithm and fusion methods used in the selected studies. The pooled sensitivity and specificity, including diagnostic efficiency, were evaluated by conducting a meta-analysis based on a bivariate model with the hierarchical summary receiver operating characteristics (ROC) curve of multimodal neuroimaging data and ML methods in the classification of AD stages. Wilcoxon signed-rank test is further used to statistically compare the accuracy scores of the existing models. With a 95% confidence interval of 78.87–87.71%, the combined sensitivity for separating participants with mild cognitive impairment (MCI) from healthy control (NC) participants was 83.77%; for separating participants with AD from NC, it was 94.60% (90.76%, 96.89%); for separating participants with progressive MCI (pMCI) from stable MCI (sMCI), it was 80.41% (74.73%, 85.06%). With a 95% confidence interval (78.87%, 87.71%), the Pooled sensitivity for distinguishing mild cognitive impairment (MCI) from healthy control (NC) participants was 83.77%, with a 95% confidence interval (90.76%, 96.89%), the Pooled sensitivity for distinguishing AD from NC was 94.60%, likewise (MCI) from healthy control (NC) participants was 83.77% progressive MCI (pMCI) from stable MCI (sMCI) was 80.41% (74.73%, 85.06%), and early MCI (EMCI) from NC was 86.63% (82.43%, 89.95%). Pooled specificity for differentiating MCI from NC was 79.16% (70.97%, 87.71%), AD from NC was 93.49% (91.60%, 94.90%), pMCI from sMCI was 81.44% (76.32%, 85.66%), and EMCI from NC was 85.68% (81.62%, 88.96%). The Wilcoxon signed rank test showed a low P-value across all the classification tasks. Multimodal neuroimaging data with ML is a promising future in classifying the stages of AD but more research is required to increase the validity of its application in clinical practice.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Machine learning with multimodal neuroimaging data to classify stages of Alzheimer’s disease: a systematic review and meta-analysis

Odusami,

Maskeliūnas,

Damaševičius

et al. 2023

Cogn Neurodyn

View full text Add to dashboard Cite

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“… 140 Genetic susceptibility to AD progression and epigenetic age acceleration may be shared. 141 Systemic factors beyond AD pathology and AD genetic risk alleles may contribute to the observed accelerated pathology in AD patients. A range of neurological and systemic biological processes were implicated in a study that identified modules of coexpressed genes associated with accelerated biological aging.…”

Section: Adni's Contributions To Understanding Ad Disease Progressionmentioning

confidence: 99%

“…This clock is based on methylation patterns related to the rate of physiological change that characterizes the aging process rather than deviation from chronological age, and so reflects a range of age-related physiological processes from multiple organs 140. Genetic susceptibility to AD progression and epigenetic age acceleration may be shared 141. Systemic factors beyond AD pathology and AD genetic risk alleles may contribute to the observed accelerated pathology in and these were enriched for immune response, signal transduction, and cellular responses to external stimuli.…”

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confidence: 99%

The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022

Veitch,

Weiner,

Miller

et al. 2023

Alzheimer's & Dementia

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The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to improve Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging, and cognitive data, and biofluid samples. We used conventional search methods to identify 1459 publications from 2021 to 2022 using ADNI data/samples and reviewed 291 impactful studies. This review details how ADNI studies improved disease progression understanding and clinical trial efficiency. Advances in subject selection, detection of treatment effects, harmonization, and modeling improved clinical trials and plasma biomarkers like phosphorylated tau showed promise for clinical use. Biomarkers of amyloid beta, tau, neurodegeneration, inflammation, and others were prognostic with individualized prediction algorithms available online. Studies supported the amyloid cascade, emphasized the importance of neuroinflammation, and detailed widespread heterogeneity in disease, linked to genetic and vascular risk, co‐pathologies, sex, and resilience. Biological subtypes were consistently observed. Generalizability of ADNI results is limited by lack of cohort diversity, an issue ADNI‐4 aims to address by enrolling a diverse cohort.

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Assessing cognitive impairment and disability in older adults through the lens of whole brain white matter patterns

Roh,

Chauhan,

Seo

et al. 2024

Alzheimer's & Dementia

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INTRODUCTIONThis study aimed to explore the potential of whole brain white matter patterns as novel neuroimaging biomarkers for assessing cognitive impairment and disability in older adults.METHODSWe conducted an in‐depth analysis of magnetic resonance imaging (MRI) and amyloid positron emission tomography (PET) scans in 454 participants, focusing on white matter patterns and white matter inter‐subject variability (WM‐ISV).RESULTSThe white matter pattern ensemble model, combining MRI and amyloid PET, demonstrated a significantly higher classification performance for cognitive impairment and disability. Participants with Alzheimer's disease (AD) exhibited higher WM‐ISV than participants with subjective cognitive decline, mild cognitive impairment, and vascular dementia. Furthermore, WM‐ISV correlated significantly with blood‐based biomarkers (such as glial fibrillary acidic protein and phosphorylated tau‐217 [p‐tau217]), and cognitive function and disability scores.DISCUSSIONOur results suggest that white matter pattern analysis has significant potential as an adjunct neuroimaging biomarker for clinical decision‐making and determining cognitive impairment and disability.Highlights The ensemble model combined both magnetic resonance imaging (MRI) and amyloid positron emission tomography (PET) and demonstrated a significantly higher classification performance for cognitive impairment and disability. Alzheimer's disease (AD) revealed a notably higher heterogeneity compared to that in subjective cognitive decline, mild cognitive impairment, or vascular dementia. White matter inter‐subject variability (WM‐ISV) was significantly correlated with blood‐based biomarkers (glial fibrillary acidic protein and phosphorylated tau‐217 [p‐tau217]) and with the polygenic risk score for AD. White matter pattern analysis has significant potential as an adjunct neuroimaging biomarker for clinical decision‐making processes and determining cognitive impairment and disability.

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Latent feature representation learning for Alzheimer’s disease classification

Cited by 8 publications

References 42 publications

Machine learning with multimodal neuroimaging data to classify stages of Alzheimer’s disease: a systematic review and meta-analysis

Machine learning with multimodal neuroimaging data to classify stages of Alzheimer’s disease: a systematic review and meta-analysis

The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022

Assessing cognitive impairment and disability in older adults through the lens of whole brain white matter patterns

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