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

Veitch, Dallas P.; Weiner, Michael W.; Miller, Melanie; Aisen, Paul S.; Ashford, Miriam A.; Beckett, Laurel A.; Green, Robert C.; Harvey, Danielle; Jack, Clifford R.; Jagust, William; Landau, Susan M.; Morris, John C.; Nho, Kwangsik T.; Nosheny, Rachel; Okonkwo, Ozioma; Perrin, Richard J.; Petersen, Ronald C.; Rivera Mindt, Monica; Saykin, Andrew; Shaw, Leslie M.; Toga, Arthur W.; Tosun, Duygu; ,

doi:10.1002/alz.13449

Cited by 3 publications

(1 citation statement)

References 329 publications

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“…We used three widely used publicly available brain MRI datasets in our analyses, the UK Biobank [11], ADNI [12] and OASIS [13] (Table 1). As in [14], all 3D T1-w brain MRI scans were pre-processed using standard steps for neuroimaging analyses, including: nonparametric intensity normalization (N4 bias field correction) [15], 'skull-stripping', linear registration to a template with 9 degrees of freedom, and isotropic resampling of voxels to 2-mm resolution.…”

Section: Datamentioning

confidence: 99%

Counterfactual MRI Generation with Denoising Diffusion Models for Interpretable Alzheimer’s Disease Effect Detection

Dhinagar,

Thomopoulos,

Laltoo

et al. 2024

Preprint

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Generative AI models have recently achieved mainstream attention with the advent of powerful approaches such as stable diffusion, DALL-E and MidJourney. The underlying breakthrough generative mechanism of denoising diffusion modeling can generate high quality synthetic images and can learn the underlying distribution of complex, high-dimensional data. Recent research has begun to extend these models to medical and specifically neuroimaging data. Typical neuroimaging tasks such as diagnostic classification and predictive modeling often rely on deep learning approaches based on convolutional neural networks (CNNs) and vision transformers (ViTs), with additional steps to help in interpreting the results. In our paper, we train conditional latent diffusion models (LDM) and denoising diffusion probabilistic models (DDPM) to provide insight into Alzheimer's disease (AD) effects on the brain's anatomy at the individual level. We first created diffusion models that could generate synthetic MRIs, by training them on real 3D T1-weighted MRI scans, and conditioning the generative process on the clinical diagnosis as a context variable. We conducted experiments to overcome limitations in training dataset size, compute time and memory resources, testing different model sizes, effects of pretraining, training duration, and latent diffusion models. We tested the sampling quality of the disease-conditioned diffusion using metrics to assess realism and diversity of the generated synthetic MRIs. We also evaluated the ability of diffusion models to conditionally sample MRI brains using a 3D CNN-based disease classifier relative to real MRIs. In our experiments, the diffusion models generated synthetic data that helped to train an AD classifier (using only 500 real training scans) - and boosted its performance by over 3% when tested on real MRI scans. Further, we used implicit classifier-free guidance to alter the conditioning of an encoded individual scan to its counterfactual (representing a healthy subject of the same age and sex) while preserving subject-specific image details. From this counterfactual image (where the same person appears healthy), a personalized disease map was generated to identify possible disease effects on the brain. Our approach efficiently generates realistic and diverse synthetic data, and may create interpretable AI-based maps for neuroscience research and clinical diagnostic applications.

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Section: Datamentioning

confidence: 99%

Counterfactual MRI Generation with Denoising Diffusion Models for Interpretable Alzheimer’s Disease Effect Detection

Dhinagar,

Thomopoulos,

Laltoo

et al. 2024

Preprint

View full text Add to dashboard Cite

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Cost-effectiveness comparison between blood biomarkers and conventional tests in Alzheimer's disease diagnosis

Noda,

Lim,

Goto

et al. 2024

Drug Discovery Today

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Alzheimer’s Disease Detection Using Deep Learning on Neuroimaging: A Systematic Review

Alsubaie,

Luo,

Shaukat

2024

MAKE

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Alzheimer’s disease (AD) is a pressing global issue, demanding effective diagnostic approaches. This systematic review surveys the recent literature (2018 onwards) to illuminate the current landscape of AD detection via deep learning. Focusing on neuroimaging, this study explores single- and multi-modality investigations, delving into biomarkers, features, and preprocessing techniques. Various deep models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative models, are evaluated for their AD detection performance. Challenges such as limited datasets and training procedures persist. Emphasis is placed on the need to differentiate AD from similar brain patterns, necessitating discriminative feature representations. This review highlights deep learning’s potential and limitations in AD detection, underscoring dataset importance. Future directions involve benchmark platform development for streamlined comparisons. In conclusion, while deep learning holds promise for accurate AD detection, refining models and methods is crucial to tackle challenges and enhance diagnostic precision.

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

Cited by 3 publications

References 329 publications

Counterfactual MRI Generation with Denoising Diffusion Models for Interpretable Alzheimer’s Disease Effect Detection

Counterfactual MRI Generation with Denoising Diffusion Models for Interpretable Alzheimer’s Disease Effect Detection

Cost-effectiveness comparison between blood biomarkers and conventional tests in Alzheimer's disease diagnosis

Alzheimer’s Disease Detection Using Deep Learning on Neuroimaging: A Systematic Review

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