Multiple Stages Classification of Alzheimer’s Disease Based on Structural Brain Networks Using Generalized Low Rank Approximations (GLRAM)

2014 ◽  
pp. 35-44 ◽  
Author(s):  
L. Zhan ◽  
Z. Nie ◽  
J. Ye ◽  
Y. Wang ◽  
Y. Jin ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Networks ◽  
Low Rank ◽  
Low Rank Approximations ◽  
Multiple Stages ◽  
Structural Brain

scholarly journals Stability of graph theoretical measures in structural brain networks in Alzheimer’s disease

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Gustav Mårtensson ◽  
Joana B. Pereira ◽  
Patrizia Mecocci ◽  
Bruno Vellas ◽  
Magda Tsolaki ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Networks ◽  
Structural Brain

scholarly journals Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer’s disease

2015 ◽  
Vol 7 ◽  
Author(s):  
Liang Zhan ◽  
Jiayu Zhou ◽  
Yalin Wang ◽  
Yan Jin ◽  
Neda Jahanshad ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Networks ◽  
Structural Brain

scholarly journals Graph analysis of structural brain networks in Alzheimer’s disease

2016 ◽  
Author(s):  
Majnu John ◽  
Toshikazu Ikuta ◽  
Janina Ferbinteanu
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clustering Algorithm ◽  
Brain Networks ◽  
Small World ◽  
Small Data ◽  
Graph Analysis ◽  
Brain Areas ◽  
Graph Properties ◽  
Structural Brain

ABSTRACTBackgroundChanges in brain connectivity in patients with early Alzheimer’s disease (AD) have been investigated using graph analysis. However, these studies were based on small data sets, explored a limited range of network parameters, and did not focus on more restricted sub-networks, where neurodegenerative processes may introduce more prominent alterations.MethodsIn this study, we constructed structural brain networks out of 87 regions by using data from 135 healthy elders and 100 early AD patients selected from the Open Access Series of Imaging Studies (OASIS) database. We evaluated the graph properties of these networks by investigating metrics of network efficiency, small world properties, segregation, product measures of complexity, and entropy. Because degenerative processes take place at different rates in different brain areas, analysis restricted to sub-networks may reveal changes otherwise undetected. Therefore, we first analyzed the graph properties of a network encompassing all brain areas considered together, and then repeated the analysis after dividing the brain areas into two sub-networks constructed by applying a clustering algorithm.ResultsAt the level of large scale network, the analysis did not reveal differences between AD patients and controls. In contrast, the same analysis performed on the two sub-networks revealed modifications accompanying AD. Changes in small world properties suggested that the ability to engage concomitantly in integration and segregation of information diminished with AD in the sub-network containing the areas of medial temporal lobe known to be heaviest and earliest affected. In contrast, we found that the second network showed an increase in small world propensity, a novel metric that unbiasedly quantifies small world structure. Complexity and entropy measures indicated that the intricacy of connection patterns and structural diversity decreased in both sub-networks.ConclusionsThese results show that neurodegenerative processes impact volumetric networks in a non-global fashion. Our findings provide new quantitative insights into topological principles of structural brain networks and their modifications during early stages of Alzheimer’s disease.

Voxel-MARS: a method for early detection of Alzheimer’s disease by classification of structural brain MRI

2017 ◽  
Vol 258 (1) ◽  
pp. 31-57 ◽  
Author(s):  
Alper Çevik ◽  
◽  
Gerhard-Wilhelm Weber ◽  
B. Murat Eyüboğlu ◽  
Kader Karlı Oğuz
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Detection ◽  
Brain Mri ◽  
Structural Brain

Topic coherence analysis for the classification of Alzheimer's disease

2018 ◽  
Author(s):  
Anna Pompili ◽  
Alberto Abad ◽  
David Martins de Matos ◽  
Isabel Pavão Martins
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Coherence Analysis
Sign in / Sign up

Export Citation Format

Share Document