scholarly journals Longitudinal changes in hippocampal network connectivity in Alzheimer's disease

2021 ◽  
Author(s):  
Sophie Dautricourt ◽  
Robin Flores ◽  
Brigitte Landeau ◽  
Géraldine Poisnel ◽  
Matthieu Vanhoutte ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Connectivity ◽  
Longitudinal Changes ◽  
Hippocampal Network

scholarly journals Changes in anterior temporal and posterior medial hippocampal network connectivity in Alzheimer’s disease

2020 ◽  
Vol 16 (S5) ◽  
Author(s):  
Sophie Dautricourt ◽  
Robin de Flores ◽  
Brigitte Landeau ◽  
Géraldine Poisnel ◽  
Matthieu Vanhoutte ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Connectivity ◽  
Hippocampal Network

Longitudinal Changes in Individual BrainAGE in Healthy Aging, Mild Cognitive Impairment, and Alzheimer’s Disease

GeroPsych ◽  
2012 ◽  
Vol 25 (4) ◽  
pp. 235-245 ◽  
Author(s):  
Katja Franke ◽  
Christian Gaser
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Healthy Aging ◽  
Brain Aging ◽  
Elderly Subjects ◽  
Additional Increase ◽  
Longitudinal Changes ◽  
Novel Method ◽  
The Brain

We recently proposed a novel method that aggregates the multidimensional aging pattern across the brain to a single value. This method proved to provide stable and reliable estimates of brain aging – even across different scanners. While investigating longitudinal changes in BrainAGE in about 400 elderly subjects, we discovered that patients with Alzheimer’s disease and subjects who had converted to AD within 3 years showed accelerated brain atrophy by +6 years at baseline. An additional increase in BrainAGE accumulated to a score of about +9 years during follow-up. Accelerated brain aging was related to prospective cognitive decline and disease severity. In conclusion, the BrainAGE framework indicates discrepancies in brain aging and could thus serve as an indicator for cognitive functioning in the future.

scholarly journals Longitudinal changes of tau PET imaging in relation to hypometabolism in prodromal and Alzheimer’s disease dementia

2017 ◽  
Vol 23 (7) ◽  
pp. 1666-1673 ◽  
Author(s):  
K Chiotis ◽  
L Saint-Aubert ◽  
E Rodriguez-Vieitez ◽  
A Leuzy ◽  
O Almkvist ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Pet Imaging ◽  
Longitudinal Changes ◽  
Tau Pet ◽  
Alzheimer’S Disease Dementia ◽  
Tau Pet Imaging

scholarly journals Flexible modulation of network connectivity related to cognition in Alzheimer's disease

NeuroImage ◽  
2014 ◽  
Vol 100 ◽  
pp. 544-557 ◽  
Author(s):  
Donald G. McLaren ◽  
Reisa A. Sperling ◽  
Alireza Atri
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Connectivity

Graph Theory-Based Brain Network Connectivity Analysis and Classification of Alzheimer’s Disease

2021 ◽  
Author(s):  
A. Thushara ◽  
C. Ushadevi Amma ◽  
Ansamma John
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Neurodegenerative Disorder ◽  
Brain Networks ◽  
Network Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Fiber Tracts ◽  
The Brain

Alzheimer’s Disease (AD) is basically a progressive neurodegenerative disorder associated with abnormal brain networks that affect millions of elderly people and degrades their quality of life. The abnormalities in brain networks are due to the disruption of White Matter (WM) fiber tracts that connect the brain regions. Diffusion-Weighted Imaging (DWI) captures the brain’s WM integrity. Here, the correlation betwixt the WM degeneration and also AD is investigated by utilizing graph theory as well as Machine Learning (ML) algorithms. By using the DW image obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, the brain graph of each subject is constructed. The features extracted from the brain graph form the basis to differentiate between Mild Cognitive Impairment (MCI), Control Normal (CN) and AD subjects. Performance evaluation is done using binary and multiclass classification algorithms and obtained an accuracy that outperforms the current top-notch DWI-based studies.

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