The role of multimodal MRI in mild cognitive impairment and Alzheimer's disease

Knudsen, Laust V; Gazerani, Parisa; Duan, Yunyun; Sheldrick-Michel, T.; Vafaee, Manouchehr Seyedi

doi:10.1111/jon.12940

Cited by 7 publications

(4 citation statements)

References 56 publications

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“…Beyond the macrostructural differences detected using computed tomography (CT), or more accurately using MRI, DTI can additionally reveal microstructural differences in white matter integrity. HIV infection commonly causes a widespread pattern of white matter abnormalities with a frontal predominance; in contrast, AD is associated with changes that are more evident in the posterior white matter and fornix [126,127]. Another powerful tool used to differentiate between HIV-associated NCI and AD is PET.…”

Section: Role Of Neuroimaging In Differentiating Hiv-associated Nci F...mentioning

confidence: 99%

Multimodal Approach to Neurocognitive Function in People Living with HIV in the cART Era: A Comprehensive Review

Moschopoulos,

Stanitsa,

Protopapas

et al. 2024

Life

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Combination antiretroviral treatment (cART) has revolutionized the management of human immunodeficiency virus (HIV) and has markedly improved the disease burden and life expectancy of people living with HIV. HIV enters the central nervous system (CNS) early in the course of infection, establishes latency, and produces a pro-inflammatory milieu that may affect cognitive functions, even in the cART era. Whereas severe forms of neurocognitive impairment (NCI) such as HIV-associated dementia have declined over the last decades, milder forms have become more prevalent, are commonly multifactorial, and are associated with comorbidity burdens, mental health, cART neurotoxicity, and ageing. Since 2007, the Frascati criteria have been used to characterize and classify HIV-associated neurocognitive disorders (HAND) into three stages, namely asymptomatic neurocognitive impairment (ANI), mild neurocognitive disorder (MND), and HIV-associated dementia (HAD). These criteria are based on a comprehensive neuropsychological assessment that presupposes the availability of validated, demographically adjusted, and normative population data. Novel neuroimaging modalities and biomarkers have been proposed in order to complement NCI assessments, elucidate neuropathogenic mechanisms, and support HIV-associated NCI diagnosis, monitoring, and prognosis. By integrating neuropsychological assessments with biomarkers and neuroimaging into a holistic care approach, clinicians can enhance diagnostic accuracy, prognosis, and patient outcomes. This review interrogates the value of these modes of assessment and proposes a unified approach to NCI diagnosis.

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Section: Role Of Neuroimaging In Differentiating Hiv-associated Nci F...mentioning

confidence: 99%

Multimodal Approach to Neurocognitive Function in People Living with HIV in the cART Era: A Comprehensive Review

Moschopoulos,

Stanitsa,

Protopapas

et al. 2024

Life

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“…They noted two limitations in the field: (1) data leakage leading to inflated accuracy estimates, and (2) insufficient testing of models on diverse cohorts and data from different scanners. With a few recent exceptions [4,5], most CNNs developed for detecting rely on T1-weighted brain MRI, the most commonly collected type of brain scan. Nevertheless, others [6,7] have found initial evidence that dMRI, which is more sensitive than T1-weighted MRI to subtle alterations in the brain's white matter microstructure, can provide metrics strongly correlated with age, dementia severity, and even levels of brain amyloid-a primary contributor to AD pathology not detectable on standard MRI.…”

Section: Introductionmentioning

confidence: 99%

Brain Age Analysis and Dementia Classification using Convolutional Neural Networks trained on Diffusion MRI: Tests in Indian and North American Cohorts

Chattopadhyay,

Joshy,

Ozarkar

et al. 2024

Preprint

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Deep learning models based on convolutional neural networks (CNNs) have been used to classify Alzheimer’s disease or infer dementia severity from T1-weighted brain MRI scans. Here, we examine the value of adding diffusion-weighted MRI (dMRI) as an input to these models. Much research in this area focuses on specific datasets such as the Alzheimer’s Disease Neuroimaging Initiative (ADNI), which assesses people of North American, largely European ancestry, so we examine how models trained on ADNI, generalize to a new population dataset from India (the NIMHANS cohort). We first benchmark our models by predicting “brain age” - the task of predicting a person’s chronological age from their MRI scan and proceed to AD classification. We also evaluate the benefit of using a 3D CycleGAN approach to harmonize the imaging datasets before training the CNN models. Our experiments show that classification performance improves after harmonization in most cases, as well as better performance for dMRI as input.

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“…With some recent exceptions [34,35], most CNNs for predicting AD severity have only used T1-weighted brain MRI -the most commonly collected type of brain MRI scan. Even so, we and others have recently shown [14,15] that diffusion-weighted brain MRI (dMRI), which is sensitive to subtle alterations in the brain's microstructure, can yield metrics that are well-correlated with age, dementia severity, and even the levels of brain amyloid, a key cause of Alzheimer's pathology which is not directly measurable on MRI.…”

Section: Introduction 11 Deep Learning In Alzheimer's Disease Researchmentioning

confidence: 99%

Predicting dementia severity by merging anatomical and diffusion MRI with deep 3D convolutional neural networks

Chattopadhyay

Singh²,

Joshy³

et al. 2023

18th International Symposium on Medical Information Processing and Analysis

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Machine learning methods have been used for over a decade for staging and subtyping a variety of brain diseases, offering fast and objective methods to classify neurodegenerative diseases such as Alzheimer's disease (AD). Deep learning models based on convolutional neural networks (CNNs) have also been used to infer dementia severity and predict future clinical decline. Most CNN-based deep learning models use T1-weighted brain MRI scans to identify predictive features for these tasks. In contrast, we examine the added value of diffusion-weighted MRI (dMRI) -a variant of MRI, sensitive to microstructural tissue properties -as an additional input in CNN-based models of dementia severity. dMRI is sensitive to microstructural brain abnormalities not evident on standard anatomical MRI. By training CNNs on combined anatomical and diffusion MRI, we hypothesize that we could boost performance when predicting widely-used clinical assessments of dementia severity, such as individuals' scores on the ADAS11, ADAS13, and MMSE (mini-mental state exam) clinical scales. For benchmarking, we evaluate CNNs that use T1-weighted MRI and dMRI to estimate "brain age" -the task of predicting a person's chronological age from their neuroimaging data. To assess which dMRI-derived maps were most beneficial, we computed DWI-derived diffusion tensor imaging (DTI) maps of mean and radial diffusivity (MD/RD), axial diffusivity (AD) and fractional anisotropy (FA) for 1198 elderly subjects (age: 74.35 +/-7.74 yrs.; 600 F/598 M, with a distribution of 636 CN/421 MCI/141 AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We tested both 2D Slice CNN and 3D CNN neural network models for the above predictive tasks. Our results suggest that for at least some deep learning architectures, diffusion-weighted MRI may enhance performance for several AD-relevant deep learning tasks relative to using T1-weighted images alone.

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The role of multimodal MRI in mild cognitive impairment and Alzheimer's disease

Cited by 7 publications

References 56 publications

Multimodal Approach to Neurocognitive Function in People Living with HIV in the cART Era: A Comprehensive Review

Multimodal Approach to Neurocognitive Function in People Living with HIV in the cART Era: A Comprehensive Review

Brain Age Analysis and Dementia Classification using Convolutional Neural Networks trained on Diffusion MRI: Tests in Indian and North American Cohorts

Predicting dementia severity by merging anatomical and diffusion MRI with deep 3D convolutional neural networks

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