Diagnosis of Alzheimer’s disease using resting-state fMRI and graph theory

2018 ◽  
Vol 26 (6) ◽  
pp. 921-931 ◽  
Author(s):  
Mahtab Mohammadpoor Faskhodi ◽  
Zahra Einalou ◽  
Mehrdad Dadgostar
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Resting State ◽  
Resting State Fmri

Identifying patients with Alzheimer’s disease using resting-state fMRI and graph theory

2015 ◽  
Vol 126 (11) ◽  
pp. 2132-2141 ◽  
Author(s):  
Ali Khazaee ◽  
Ata Ebrahimzadeh ◽  
Abbas Babajani-Feremi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Resting State ◽  
Resting State Fmri

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 A comprehensive analysis of resting state fMRI measures to classify individual patients with Alzheimer's disease

NeuroImage ◽  
2018 ◽  
Vol 167 ◽  
pp. 62-72 ◽  
Author(s):  
Frank de Vos ◽  
Marisa Koini ◽  
Tijn M. Schouten ◽  
Stephan Seiler ◽  
Jeroen van der Grond ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Resting State ◽  
Resting State Fmri ◽  
Comprehensive Analysis

scholarly journals Functional network alterations in early‐onset Alzheimer’s disease studied with resting‐state fMRI

2020 ◽  
Vol 16 (S4) ◽  
Author(s):  
Roser Sala‐Llonch ◽  
José Contador ◽  
Agnés Pérez‐Millan ◽  
Neus Falgàs ◽  
Mariona Ruiz‐Peris ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Resting State ◽  
Early Onset ◽  
Resting State Fmri ◽  
Functional Network ◽  
Early Onset Alzheimer’S Disease
Sign in / Sign up

Export Citation Format

Share Document