Dataset Information

Long Longitudinal Tract Lesion Contributes to the Progression of Alzheimer's Disease.

ABSTRACT: Background: The degenerative pattern of white matter (WM) microstructures during Alzheimer's disease (AD) and its relationship with cognitive function have not yet been clarified. The present research aimed to explore the alterations of the WM microstructure and its impact on amnestic mild cognitive (aMCI) and AD patients. Mechanical learning methods were used to explore the validity of WM microstructure lesions on the classification in AD spectrum disease. Methods: Neuropsychological data and diffusion tensor imaging (DTI) images were collected from 28 AD subjects, 31 aMCI subjects, and 27 normal controls (NC). Tract-based spatial statistics (TBSS) were used to extract diffusion parameters in WM tracts. We performed ANOVA analysis to compare diffusion parameters and clinical features among the three groups. Partial correlation analysis was used to explore the relationship between diffusion metrics and cognitive functions controlling for age, gender, and years of education. Additionally, we performed the support vector machine (SVM) classification to determine the discriminative ability of DTI metrics in the differentiation of aMCI and AD patients from controls. Results: As compared to controls or aMCI patients, AD patients displayed widespread WM lesions, including in the inferior longitudinal fasciculus, inferior fronto-occipital fasciculi, and superior longitudinal fasciculus. Significant correlations between fractional anisotropy (FA), mean diffusivity (MD), and radial diffusion (RD) of the long longitudinal tract and memory deficits were found in aMCI and AD groups, respectively. Furthermore, through SVM classification, we found DTI indicators generated by FA and MD parameters can effectively distinguish AD patients from the control group with accuracy rates of up to 89 and 85%, respectively. Conclusion: The WM microstructure is extensively disrupted in AD patients, and the WM integrity of the long longitudinal tract is closely related to memory, which would hold potential value for monitoring the progression of AD. The method of classification based on SVM and WM damage features may be objectively helpful to the classification of AD diseases.

SUBMITTER: Luo C

PROVIDER: S-EPMC7597387 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Long Longitudinal Tract Lesion Contributes to the Progression of Alzheimer's Disease.

Luo Caimei C Li Mengchun M Qin Ruomeng R Chen Haifeng H Huang Lili L Yang Dan D Ye Qing Q Liu Renyuan R Xu Yun Y Zhao Hui H Bai Feng F

Frontiers in neurology 20201016

<b>Background:</b> The degenerative pattern of white matter (WM) microstructures during Alzheimer's disease (AD) and its relationship with cognitive function have not yet been clarified. The present research aimed to explore the alterations of the WM microstructure and its impact on amnestic mild cognitive (aMCI) and AD patients. Mechanical learning methods were used to explore the validity of WM microstructure lesions on the classification in AD spectrum disease. <b>Methods:</b> Neuropsychologi ...[more]

PMID: 33178095

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Project description:IntroductionProgression of amnestic mild cognitive impairment (aMCI) to Alzheimer's disease (AD) is a clinical event with highly variable progression rates varying from 10-15% up to 30-34%. Functional connectivity (FC), the temporal similarity between spatially remote neurophysiological events, has previously been reported to differ between aMCI patients who progress to AD (pMCI) and those who do not (i.e., remain stable; sMCI). However, these reports had a short-term follow-up and do not provide insight into long-term AD progression.MethodsSeventy-nine participants with a baseline and 78 with a 12-month, 51 with a 24-month, and 22 with a +48-month follow-up resting-state fMRI with aMCI diagnosis from the Alzheimer's Disease Neuroimaging Initiative database were included. FC was assessed using the CONN toolbox. Local correlation and group independent component analysis were utilized to compare regional functional coupling and between-network FC, respectively, between sMCI and pMCI groups. Two-sample t tests were used to test for statistically significant differences between groups, and paired t-tests were used to assess cognitive changes over time.ResultsAll participants (i.e., 66 sMCI and 19 pMCI) had a baseline and a year follow-up fMRI scan. Progression from aMCI to AD occurred in 19 patients (10 at 12 months, 5 at 24 months, and 4 at >48 months), while 73 MCI patients remained cognitively stable (sMCI). The pMCI and sMCI cognitive profiles were different. More between-network FC than regional functional coupling differences were present between sMCI and pMCI patients. Activation in the salience network (SN) and the default mode network (DMN) was consistently different between sMCI and pMCI patients across time.DiscussionsMCI and pMCI patients have different cognitive and FC profiles. Only pMCI patients showed cognitive differences across time. The DMN and SN showed local correlation and between-network FC differences between the sMCI and pMCI patient groups at multiple moments in time.

Project description:BackgroundAlzheimer's disease (AD) is the most common neurodegenerative disease ultimately manifesting as clinical dementia. Despite considerable effort and ample experimental data, the role of neuroinflammation related to systemic inflammation is still unsettled. While the implication of microglia is well recognized, the exact contribution of peripheral monocytes/macrophages is still largely unknown, especially concerning their role in the various stages of AD.ObjectivesAD develops over decades and its clinical manifestation is preceded by subjective memory complaints (SMC) and mild cognitive impairment (MCI); thus, the question arises how the peripheral innate immune response changes with the progression of the disease. Therefore, to further investigate the roles of monocytes/macrophages in the progression of AD we assessed their phenotypes and functions in patients at SMC, MCI and AD stages and compared them with cognitively healthy controls. We also conceptualised an idealised mathematical model to explain the functionality of monocytes/macrophages along the progression of the disease.ResultsWe show that there are distinct phenotypic and functional changes in monocyte and macrophage populations as the disease progresses. Higher free radical production upon stimulation could already be observed for the monocytes of SMC patients. The most striking results show that activation of peripheral monocytes (hyperactivation) is the strongest in the MCI group, at the prodromal stage of the disease. Monocytes exhibit significantly increased chemotaxis, free radical production, and cytokine production in response to TLR2 and TLR4 stimulation.ConclusionOur data suggest that the peripheral innate immune system is activated during the progression from SMC through MCI to AD, with the highest levels of activation being in MCI subjects and the lowest in AD patients. Some of these parameters may be used as biomarkers, but more holistic immune studies are needed to find the best period of the disease for clinical intervention.

Project description:BackgroundAlzheimer's disease and related dementia (ADRD) are characterized by multiple and progressive anatomo-clinical changes including accumulation of abnormal proteins in the brain, brain atrophy and severe cognitive impairment. Understanding the sequence and timing of these changes is of primary importance to gain insight into the disease natural history and ultimately allow earlier diagnosis. Yet, modeling changes over disease course from cohort data is challenging as the usual timescales (time since inclusion, chronological age) are inappropriate and time-to-clinical diagnosis is available on small subsamples of participants with short follow-up durations prior to diagnosis. One solution to circumvent this challenge is to define the disease time as a latent variable.MethodsWe developed a multivariate mixed model approach that realigns individual trajectories into the latent disease time to describe disease progression. In contrast with the existing literature, our methodology exploits the clinical diagnosis information as a partially observed and approximate reference to guide the estimation of the latent disease time. The model estimation was carried out in the Bayesian Framework using Stan. We applied the methodology to the MEMENTO study, a French multicentric clinic-based cohort of 2186 participants with 5-year intensive follow-up. Repeated measures of 12 ADRD markers stemmed from cerebrospinal fluid (CSF), brain imaging and cognitive tests were analyzed.ResultsThe estimated latent disease time spanned over twenty years before the clinical diagnosis. Considering the profile of a woman aged 70 with a high level of education and APOE4 carrier (the main genetic risk factor for ADRD), CSF markers of tau proteins accumulation preceded markers of brain atrophy by 5 years and cognitive decline by 10 years. However we observed that individual characteristics could substantially modify the sequence and timing of these changes, in particular for CSF level of A[Formula: see text].ConclusionBy leveraging the available clinical diagnosis timing information, our disease progression model does not only realign trajectories into the most homogeneous way. It accounts for the inherent residual inter-individual variability in dementia progression to describe the long-term anatomo-clinical degradations according to the years preceding clinical diagnosis, and to provide clinically meaningful information on the sequence of events.Trial registrationclinicaltrials.gov, NCT01926249. Registered on 16 August 2013.

Dataset Information

Long Longitudinal Tract Lesion Contributes to the Progression of Alzheimer's Disease.

Publications

Long Longitudinal Tract Lesion Contributes to the Progression of Alzheimer's Disease.

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