scholarly journals Altered functional connectivity in early Alzheimer's disease: A resting-state fMRI study

2007 ◽  
Vol 28 (10) ◽  
pp. 967-978 ◽  
Author(s):  
Kun Wang ◽  
Meng Liang ◽  
Liang Wang ◽  
Lixia Tian ◽  
Xinqing Zhang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Resting State ◽  
Resting State Fmri ◽  
Fmri Study ◽  
Early Alzheimer’S Disease

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.

scholarly journals Hippocampal functional connectivity in Alzheimer’s disease: a resting state 7T fMRI study

2020 ◽  
pp. 1-2 ◽  
Author(s):  
Latha Velayudhan ◽  
Susan Francis ◽  
Richard Dury ◽  
Subhadip Paul ◽  
Sana Bestwn ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Resting State ◽  
Fmri Study ◽  
7T Fmri

scholarly journals Disrupted Small-World Brain Networks in Moderate Alzheimer's Disease: A Resting-State fMRI Study

PLoS ONE ◽  
2012 ◽  
Vol 7 (3) ◽  
pp. e33540 ◽  
Author(s):  
Xiaohu Zhao ◽  
Yong Liu ◽  
Xiangbin Wang ◽  
Bing Liu ◽  
Qian Xi ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Resting State ◽  
Brain Networks ◽  
Small World ◽  
Resting State Fmri ◽  
Fmri Study ◽  
World Brain
Sign in / Sign up

Export Citation Format

Share Document