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 22 publications

(2 citation statements)

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

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“…The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an extensive and longitudinal study with the primary objective of exploring the amalgamation of serial MR imaging, positron emission tomography, other biomarkers, as well as clinical and neuropsychological assessments to gauge the risk of and progression to MCI and early AD. 20 In this study, we used the ADNI dataset to explore the optimal therapeutic window in MCI patients with DM and the effect of DM on cognitive‐related brain structures. Moreover, understanding the neurobiological characteristics and their correlation with cognitive performance may provide insights for early clinical interventions and shared target therapies.…”

Section: Introductionmentioning

confidence: 99%

Diabetes accelerates Alzheimer's disease progression in the first year post mild cognitive impairment diagnosis

Ding,

Yin,

Zhang

et al. 2024

Alzheimer's & Dementia

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BACKGROUNDMild cognitive impairment (MCI) heightens Alzheimer's disease (AD) risk, with diabetes mellitus (DM) potentially exacerbating this vulnerability. This study identifies the optimal intervention period and neurobiological targets in MCI to AD progression using the Alzheimer's Disease Neuroimaging Initiative dataset.METHODSAnalysis of 980 MCI patients, categorized by DM status, used propensity score matching and inverse probability treatment weighting to assess rate of conversion from MCI to AD, neuroimaging, and cognitive changes.RESULTSDM significantly correlates with cognitive decline and an increased risk of progressing to AD, especially within the first year of MCI follow‐up. It adversely affects specific brain structures, notably accelerating nucleus accumbens atrophy, decreasing gray matter volume and sulcal depth.DISCUSSIONFindings suggest the first year after MCI diagnosis as the critical window for intervention. DM accelerates MCI‐to‐AD progression, targeting specific brain areas, underscoring the need for early therapeutic intervention.Highlights Diabetes mellitus (DM) accelerates mild cognitive impairment (MCI)‐to‐Alzheimer's disease (AD) progression within the first year after MCI diagnosis. DM leads to sharper cognitive decline within 12 months of follow‐up. There is notable nucleus accumbens atrophy observed in MCI patients with DM. DM causes significant reductions in gray matter volume and sulcal depth. There are stronger correlations between cognitive decline and brain changes due to DM.

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The plasma miRNAome in ADNI: Signatures to aid the detection of at‐risk individuals

Krüger,

Pena‐Centeno,

Liu

et al. 2024

Alzheimer's & Dementia

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INTRODUCTIONMicroRNAs are short non‐coding RNAs that control proteostasis at the systems level and are emerging as potential prognostic and diagnostic biomarkers for Alzheimer's disease (AD).METHODSWe performed small RNA sequencing on plasma samples from 847 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants.RESULTSWe identified microRNA signatures that correlate with AD diagnoses and help predict the conversion from mild cognitive impairment (MCI) to AD.DISCUSSIONOur data demonstrate that plasma microRNA signatures can be used to not only diagnose MCI, but also, critically, predict the conversion from MCI to AD. Moreover, combined with neuropsychological testing, plasma microRNAome evaluation helps predict MCI to AD conversion. These findings are of considerable public interest because they provide a path toward reducing indiscriminate utilization of costly and invasive testing by defining the at‐risk segment of the aging population.Highlights We provide the first analysis of the plasma microRNAome for the ADNI study. The levels of several microRNAs can be used as biomarkers for the prediction of conversion from MCI to AD. Adding the evaluation of plasma microRNA levels to neuropsychological testing in a clinical setting increases the accuracy of MCI to AD conversion prediction.

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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 22 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

Diabetes accelerates Alzheimer's disease progression in the first year post mild cognitive impairment diagnosis

The plasma miRNAome in ADNI: Signatures to aid the detection of at‐risk individuals

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