Short- and Long-Term Functional Connectivity Differences Associated with Alzheimer’s Disease Progression

Introduction: Progression 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. Methods: Seventy-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. Results: All 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. Discussion: sMCI 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.

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Evolutionary optimization in classification of early-MCI patients from healthy controls using graph measures of resting-state fMRI

10.1101/2021.03.04.433989 ◽

2021 ◽

Author(s):

Jafar Zamani ◽

Ali Sadr ◽

Amir-Homayoun Javadi

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Functional Connectivity ◽

Evolutionary Algorithms ◽

Resting State ◽

Optimization Methods ◽

Resting State Fmri ◽

Optimization Method ◽

Effective Parameters

AbstractsIdentifying individuals with early mild cognitive impairment (EMCI) can be an effective strategy for early diagnosis and delay the progression of Alzheimer’s disease (AD). Many approaches have been devised to discriminate those with EMCI from healthy control (HC) individuals. Selection of the most effective parameters has been one of the challenging aspects of these approaches. In this study we suggest an optimization method based on five evolutionary algorithms that can be used in optimization of neuroimaging data with a large number of parameters. Resting-state functional magnetic resonance imaging (rs-fMRI) measures, which measure functional connectivity, have been shown to be useful in prediction of cognitive decline. Analysis of functional connectivity data using graph measures is a common practice that results in a great number of parameters. Using graph measures we calculated 1155 parameters from the functional connectivity data of HC (n=36) and EMCI (n=34) extracted from the publicly available database of the Alzheimer’s disease neuroimaging initiative database (ADNI). These parameters were fed into the evolutionary algorithms to select a subset of parameters for classification of the data into two categories of EMCI and HC using a two-layer artificial neural network. All algorithms achieved classification accuracy of 94.55%, which is extremely high considering single-modality input and low number of data participants. These results highlight potential application of rs-fMRI and efficiency of such optimization methods in classification of images into HC and EMCI. This is of particular importance considering that MRI images of EMCI individuals cannot be easily identified by experts.

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Ozone, Particulate Matter, and Newly Diagnosed Alzheimer’s Disease: A Population-Based Cohort Study in Taiwan

Advances in Alzheimer’s Disease - Alzheimer’s Disease and Air Pollution ◽

10.3233/aiad210002 ◽

2021 ◽

Author(s):

Chau-Ren Jung ◽

Yu-Ting Lin ◽

Bing-Fang Hwang

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Air Pollution ◽

Cohort Study ◽

Particulate Matter ◽

Proportional Hazards ◽

Newly Diagnosed ◽

Hazards Model

Several studies with animal research associate air pollution in Alzheimer’s disease (AD) neuropathology, but the actual impact of air pollution on the risk of AD is unknown. Here, this study investigates the association between long-term exposure to ozone (O3) and particulate matter (PM) with an aerodynamic diameter equal to or less than 2.5 μm (PM2.5), and newly diagnosed AD in Taiwan. We conducted a cohort study of 95,690 individuals’ age ≥ 65 during 2001–2010. We obtained PM10 and O3 data from Taiwan Environmental Protection Agency during 2000–2010. Since PM2.5 data is only accessible entirely after 2006, we used the mean ratio between PM2.5 and PM10 during 2006–2010 (0.57) to estimate the PM2.5 concentrations from 2000 to 2005. A Cox proportional hazards model was used to evaluate the associations between O3 and PM2.5 at baseline and changes of O3 and PM2.5 during the follow-up period and AD. The adjusted HR for AD was weakly associated with a raised concentration in O3 at baseline per increase of 9.63 ppb (adjusted HR 1.06, 95% confidence interval (CI) 1.00–1.12). Further, we estimated a 211% risk of increase of AD per increase of 10.91 ppb in O3 over the follow-up period (95% CI 2.92–3.33). We found a 138% risk of increase of AD per increase of 4.34 μg/m3 in PM2.5 over the follow-up period (95% CI 2.21–2.56). These findings suggest long-term exposure to O3 and PM2.5 above the current US EPA standards are associated with increased the risk of AD.

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The Identification of Alzheimer’s Disease Using Functional Connectivity Between Activity Voxels in Resting-State fMRI Data

IEEE Journal of Translational Engineering in Health and Medicine ◽

10.1109/jtehm.2020.2985022 ◽

2020 ◽

Vol 8 ◽

pp. 1-11

Author(s):

Yuhu Shi ◽

Weiming Zeng ◽

Jin Deng ◽

Weifang Nie ◽

Yifei Zhang

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Functional Connectivity ◽

Resting State ◽

Resting State Fmri ◽

Fmri Data

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The Impact of a Long-Term Rivastigmine and Donepezil Treatment on All-Cause Mortality in Patients With Alzheimer’s Disease

American Journal of Alzheimer s Disease & Other Dementias® ◽

10.1177/1533317518775044 ◽

2018 ◽

Vol 33 (6) ◽

pp. 385-393 ◽

Cited By ~ 3

Author(s):

Jakub Kazmierski ◽

Chaido Messini-Zachou ◽

Mara Gkioka ◽

Magda Tsolaki

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cox Regression ◽

Symptomatic Treatment ◽

Risk Of Death ◽

Increased Risk ◽

Functional Assessments ◽

The Impact

Cholinesterase inhibitors (ChEIs) are the mainstays of symptomatic treatment of Alzheimer’s disease (AD); however, their efficacy is limited, and their use was associated with deaths in some groups of patients. The aim of the current study was to assess the impact of the long-term use of ChEIs on mortality in patients with AD. This observational, longitudinal study included 1171 adult patients with a diagnosis of AD treated with donepezil or rivastigmine. Each patient was observed for 24 months or until death. The cognitive and functional assessments, the use of ChEIs, memantine, antipsychotics, antidepressants, and anxiolytics were recorded. The total number of deaths at the end of the observational period was 99 (8.45%). The patients who had received rivastigmine treatment were at an increased risk of death in the follow-up period. The higher risk of death in the rivastigmine group remained significant in multivariate Cox regression models.

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