A novel MRI contrast weighted ratio method for measuring myelin in older adults at risk for Alzheimer’s disease

Price, Lauren R.; Deardorff, Rachael; Wu, Yu‐Chien; West, John D.; McDonald, Brenna C.; Risacher, Shannon L.; Saykin, Andrew J.

doi:10.1002/alz.046297

Cited by 3 publications

(3 citation statements)

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“…First, we selected participants with images containing three structural MRI (sMRI) modalities (T1, T2‐star, and T2‐flair) and matched PET AV45 within 3 months. The three sMRI modalities were chosen for the following reasons: they were the most widely used sequences in clinical AD examinations; Previous literature suggested that they are potential biomarkers for the diagnosis of AD (Fleisher et al, 2011; Price et al, 2020; van Rooden et al, 2015; Xie et al, 2019); Prior evidence also indicates they were significantly associated with cerebral Aβ deposition (Fleisher et al, 2011; Grimmer et al, 2012; van Rooden et al, 2014; Yasuno et al, 2017). Then, a visual inspection was carried out to ensure acceptable image quality.…”

Section: Methodsmentioning

confidence: 99%

MRAβ: A multimodal MRI‐derived amyloid‐β biomarker for Alzheimer's disease

Ding

et al. 2023

Human Brain Mapping

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Florbetapir 18F (AV45), a highly sensitive and specific positron emission tomographic (PET) molecular biomarker binding to the amyloid‐β of Alzheimer's disease (AD), is constrained by radiation and cost. We sought to combat it by combining multimodal magnetic resonance imaging (MRI) images and a collaborative generative adversarial networks model (CollaGAN) to develop a multimodal MRI‐derived Amyloid‐β (MRAβ) biomarker. We collected multimodal MRI and PET AV45 data of 380 qualified participants from the ADNI dataset and 64 subjects from OASIS3 dataset. A five‐fold cross‐validation CollaGAN were applied to generate MRAβ. In the ADNI dataset, we found MRAβ could characterize the subject‐level AV45 spatial variations in both AD and mild cognitive impairment (MCI). Voxel‐wise two‐sample t‐tests demonstrated amyloid‐β depositions identified by MRAβ in AD and MCI were significantly higher than healthy controls (HCs) in widespread cortices (p < .05, corrected) and were much similar to those by AV45 (r > .92, p < .001). Moreover, a 3D ResNet classifier demonstrated that MRAβ was comparable to AV45 in discriminating AD from HC in both the ADNI and OASIS3 datasets, and in discriminate MCI from HC in ADNI. Finally, we found MRAβ could mimic cortical hyper‐AV45 in HCs who later converted to MCI (r = .79, p < .001) and was comparable to AV45 in discriminating them from stable HC (p > .05). In summary, our work illustrates that MRAβ synthesized by multimodal MRI could mimic the cerebral amyloid‐β depositions like AV45 and lends credence to the feasibility of advancing MRI toward molecular‐explainable biomarkers.

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Section: Methodsmentioning

confidence: 99%

MRAβ: A multimodal MRI‐derived amyloid‐β biomarker for Alzheimer's disease

Ding

et al. 2023

Human Brain Mapping

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“…In the human brain, the SFO can either be an isolated fasciculus or a branch of the superior longitudinal fasciculus (Bao et al., 2017). It plays a major role in speech and language, as well as the top‐down modulation of visual processing and spatial aspects of cognitive processing (Bar et al., 2006; Schmahmann et al., 2007), and was found to be associated with cognitive decline in the aging population (Price et al., 2020). Negative direct/total effects are mainly observed in the lateral ventricles.…”

Section: The Alzheimer's Disease Neuroimaging Initiative Studymentioning

confidence: 99%

Identifying Brain Hierarchical Structures Associated with Alzheimer's Disease Using a Regularized Regression Method with Tree Predictors

et al. 2022

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Brain segmentation at different levels is generally represented as hierarchical trees. Brain regional atrophy at specific levels was found to be marginally associated with Alzheimer's disease outcomes. In this study, we propose an ℓ1‐type regularization for predictors that follow a hierarchical tree structure. Considering a tree as a directed acyclic graph, we interpret the model parameters from a path analysis perspective. Under this concept, the proposed penalty regulates the total effect of each predictor on the outcome. With regularity conditions, it is shown that under the proposed regularization, the estimator of the model coefficient is consistent in ℓ2‐norm and the model selection is also consistent. When applied to a brain sMRI dataset acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the proposed approach identifies brain regions where atrophy in these regions demonstrates the declination in memory. With regularization on the total effects, the findings suggest that the impact of atrophy on memory deficits is localized from small brain regions, but at various levels of brain segmentation. Data used in preparation of this paper were obtained from the ADNI database.

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“…In the human brain, the SFO can either be an isolated fasciculus or a branch of the superior longitudinal fasciculus (Bao et al, 2017). It plays a major role in speech and language, as well as the top-down modulation of visual processing and spatial aspects of cognitive processing (Bar et al, 2006;Schmahmann et al, 2007), and was found to be associated with cognitive decline in the aging population (Price et al, 2020). Negative direct/total effects are mainly observed in the lateral ventricles.…”

Section: The Alzheimer's Disease Neuroimaging Initiative Studymentioning

confidence: 99%

Identifying brain hierarchical structures associated with Alzheimer's disease using a regularized regression method with tree predictors

Zhao¹,

Wang²,

Liu³

et al. 2021

Preprint

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Brain segmentation at different levels is generally represented as hierarchical trees. Brain regional atrophy at specific levels was found to be marginally associated with Alzheimer's disease outcomes. In this study, we propose an 1 -type regularization for predictors that follow a hierarchical tree structure. Considering a tree as a directed acyclic graph, we interpret the model parameters from a path analysis perspective. Under this concept, the proposed penalty regulates the total effect of each predictor on the outcome. With regularity conditions, it is shown that under the proposed regularization, the estimator of the model coefficient is consistent in 2 -norm and the model selection is also consistent. By applying to a brain structural magnetic resonance imaging dataset acquired from the Alzheimer's Disease Neuroimaging Initiative, the proposed approach identifies brain regions where atrophy in these regions demonstrates the declination in memory. With regularization on the total effects, the findings suggest that the impact of atrophy on memory deficits is localized from small brain regions but at various levels of brain segmentation.

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A novel MRI contrast weighted ratio method for measuring myelin in older adults at risk for Alzheimer’s disease

Cited by 3 publications

References 0 publications

MRAβ: A multimodal MRI‐derived amyloid‐β biomarker for Alzheimer's disease

MRAβ: A multimodal MRI‐derived amyloid‐β biomarker for Alzheimer's disease

Identifying Brain Hierarchical Structures Associated with Alzheimer's Disease Using a Regularized Regression Method with Tree Predictors

Identifying brain hierarchical structures associated with Alzheimer's disease using a regularized regression method with tree predictors

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