Multiscale structural mapping of Alzheimer’s disease neurodegeneration

Jang, Ikbeom; Li, Binyin; Riphagen, Joost M.; Dickerson, Bradford C.; Salat, David H.

doi:10.1016/j.nicl.2022.102948

Cited by 10 publications

(4 citation statements)

References 66 publications

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“… 42 In a recent report, they combined these microstructural measures based on tissue contrast with macrostructural metrics in a machine-learning framework to demonstrate sensitivity to disease stage. 40 …”

Section: Use Of Traditional Radiomics In Adrd Neuroimagingmentioning

confidence: 99%

Beyond Macrostructure: Is There a Role for Radiomics Analysis in Neuroimaging ?

Das,

Ilesanmi,

Wolk

et al. 2024

magn reson med sci

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Section: Use Of Traditional Radiomics In Adrd Neuroimagingmentioning

confidence: 99%

Beyond Macrostructure: Is There a Role for Radiomics Analysis in Neuroimaging ?

Das,

Ilesanmi,

Wolk

et al. 2024

magn reson med sci

View full text Add to dashboard Cite

“…Neurodegeneration biomarkers, such as hippocampal atrophy and cortical thickness, provide clinicians and researchers with a non‐invasive, feasible, and less expensive option to detect AD. Recent advancements in high‐resolution MRI, as demonstrated by studies conducted by Jang et al ( 2022 ) and Westlye et al ( 2009 ), enable the detection of altered tissue signal properties of brain microstructure even at earlier phases of pathogenesis. These advances in MRI technology can provide additional information on neurodegeneration beyond traditional neurodegeneration biomarkers.…”

Section: Introductionmentioning

confidence: 99%

“…In our recent study, we employed a strategy called multi‐scale structural mapping (MSSM) to combined GWRs obtained from multiple layers of the cortex and subcortical white matter with cortical morphometry maps (i.e., MSSM). This approach allowed us to accurately detect patients with AD dementia as well as individuals with MCI who subsequently progressed to AD dementia within the next 3 years (Jang et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Cortical gray to white matter signal intensity ratio as a sign of neurodegeneration and cognition independent of β‐amyloid in dementia

Xu,

Jang,

Zhang

et al. 2023

Human Brain Mapping

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Cortical gray to white matter signal intensity ratio (GWR) measured from T1‐weighted magnetic resonance (MR) images was associated with neurodegeneration and dementia. We characterized topological patterns of GWR during AD pathogenesis and investigated its association with cognitive decline. The study included a cross‐sectional dataset and a longitudinal dataset. The cross‐sectional dataset included 60 cognitively healthy controls, 61 mild cognitive impairment (MCI), and 63 patients with dementia. The longitudinal dataset included 26 participants who progressed from cognitively normal to dementia and 26 controls that remained cognitively normal. GWR was compared across the cross‐sectional groups, adjusted for amyloid PET. The correlation between GWR and cognition performance was also evaluated. The longitudinal dataset was used to investigate GWR alteration during the AD pathogenesis. Dementia with β‐amyloid deposition group exhibited the largest area of increased GWR, followed by MCI with β‐amyloid deposition, MCI without β‐amyloid deposition, and controls. The spatial pattern of GWR‐increased regions was not influenced by β‐amyloid deposits. Correlation between regional GWR alteration and cognitive decline was only detected among individuals with β‐amyloid deposition. GWR showed positive correlation with tau PET in the left supramarginal, lateral occipital gyrus, and right middle frontal cortex. The longitudinal study showed that GWR increased around the fusiform, inferior/superior temporal lobe, and entorhinal cortex in MCI and progressed to larger cortical regions after progression to AD. The spatial pattern of GWR‐increased regions was independent of β‐amyloid deposits but overlapped with tauopathy. The GWR can serve as a promising biomarker of neurodegeneration in AD.

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“…This often emerges as alterations in memory, analytical thinking, perception, language, mood, and emotions, and eventually impairs physical control over the body. Several studies have been conducted in recent years in an attempt to identify early biomarkers that may be used to assess Alzheimer’s disease risk prior to the onset of symptoms in a speedy and thorough manner 5 – 7 .…”

Section: Introductionmentioning

confidence: 99%

Multimodal multitask learning for predicting MCI to AD conversion using stacked polynomial attention network and adaptive exponential decay

Jeong

Kim

2023

Sci Rep

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Early identification and treatment of moderate cognitive impairment (MCI) can halt or postpone Alzheimer’s disease (AD) and preserve brain function. For prompt diagnosis and AD reversal, precise prediction in the early and late phases of MCI is essential. This research investigates multimodal framework-based multitask learning in the following situations: (1) Differentiating early mild cognitive impairment (eMCI) from late MCI and (2) predicting when an MCI patient would acquire AD. Clinical data and two radiomics features on three brain areas deduced from magnetic resonance imaging were investigated (MRI). We proposed an attention-based module, Stack Polynomial Attention Network (SPAN), to firmly encode clinical and radiomics data input characteristics for successful representation from a small dataset. To improve multimodal data learning, we computed a potent factor using adaptive exponential decay (AED). We used experiments from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort study, which included 249 eMCI and 427 lMCI participants at baseline visits. The proposed multimodal strategy yielded the best c-index score in time prediction of MCI to AD conversion (0.85) and the best accuracy in MCI-stage categorization ($$83.19\%$$ 83.19 % ). Moreover, our performance was equivalent to that of contemporary research.

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Multiscale structural mapping of Alzheimer’s disease neurodegeneration

Abstract: Graphical abstract

Cited by 10 publications

References 66 publications

Beyond Macrostructure: Is There a Role for Radiomics Analysis in Neuroimaging ?

Beyond Macrostructure: Is There a Role for Radiomics Analysis in Neuroimaging ?

Cortical gray to white matter signal intensity ratio as a sign of neurodegeneration and cognition independent of β‐amyloid in dementia

Multimodal multitask learning for predicting MCI to AD conversion using stacked polynomial attention network and adaptive exponential decay

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