scholarly journals A Winding Road: Alzheimer’s Disease Increases Circuitous Functional Connectivity Pathways

2015 ◽  
Vol 9 ◽  
Author(s):  
John Suckling ◽  
Tiago Simas ◽  
Shayanti Chattopadhyay ◽  
Roger Tait ◽  
Li Su ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity

Altered Functional Connectivity of the Marginal Division in Alzheimer’s Disease

2014 ◽  
Vol 11 (2) ◽  
pp. 145-155 ◽  
Author(s):  
Zengqiang Zhang ◽  
Yong Liu ◽  
Bo Zhou ◽  
Jinlong Zheng ◽  
Hongxiang Yao ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Marginal Division

scholarly journals Functional connectivity of the nucleus basalis of Meynert in Lewy body dementia and Alzheimer’s disease

2021 ◽  
pp. 1-6
Author(s):  
Julia Schumacher ◽  
Alan J. Thomas ◽  
Luis R. Peraza ◽  
Michael Firbank ◽  
John T. O’Brien ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Lewy Body ◽  
Cholinergic System ◽  
Structural Changes ◽  
Lewy Body Dementia ◽  
Occipital Cortex ◽  
Nucleus Basalis ◽  
Nucleus Basalis Of Meynert

ABSTRACT Cholinergic deficits are a hallmark of Alzheimer’s disease (AD) and Lewy body dementia (LBD). The nucleus basalis of Meynert (NBM) provides the major source of cortical cholinergic input; studying its functional connectivity might, therefore, provide a tool for probing the cholinergic system and its degeneration in neurodegenerative diseases. Forty-six LBD patients, 29 AD patients, and 31 healthy age-matched controls underwent resting-state functional magnetic resonance imaging (fMRI). A seed-based analysis was applied with seeds in the left and right NBM to assess functional connectivity between the NBM and the rest of the brain. We found a shift from anticorrelation in controls to positive correlations in LBD between the right/left NBM and clusters in right/left occipital cortex. Our results indicate that there is an imbalance in functional connectivity between the NBM and primary visual areas in LBD, which provides new insights into alterations within a part of the corticopetal cholinergic system that go beyond structural changes.

scholarly journals P2-228: Detection of changes in functional connectivity following Aricept® treatment in Mild Alzheimer's disease subjects

2009 ◽  
Vol 5 (4S_Part_11) ◽  
pp. P327-P327 ◽  
Author(s):  
Shi-Jiang Li ◽  
Douglas B. Ward ◽  
Zhilin Wu ◽  
Jennifer Jones ◽  
Thomas McRae ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity

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

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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