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

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 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.

scholarly journals Circuitry and Synaptic Dysfunction in Alzheimer’s Disease: A New Tau Hypothesis

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Siddhartha Mondragón-Rodríguez ◽  
Humberto Salgado-Burgos ◽  
Fernando Peña-Ortega
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Connectivity ◽  
Aspartate Receptor ◽  
Potential Biomarker ◽  
Prodromal Ad ◽  
Brain Circuit ◽  
New Evidence ◽  
Ad Mouse Model

For more than five decades, the field of Alzheimer’s disease (AD) has focused on two main hypotheses positing amyloid-beta (Aβ) and Tau phosphorylation (pTau) as key pathogenic mediators. In line with these canonical hypotheses, several groups around the world have shown that the synaptotoxicity in AD depends mainly on the increase in pTau levels. Confronting this leading hypothesis, a few years ago, we reported that the increase in phosphorylation levels of dendritic Tau, at its microtubule domain (MD), acts as a neuroprotective mechanism that prevents N-methyl-D-aspartate receptor (NMDAr) overexcitation, which allowed us to propose that Tau protein phosphorylated near MD sites is involved in neuroprotection, rather than in neurodegeneration. Further supporting this alternative role of pTau, we have recently shown that early increases in pTau close to MD sites prevent hippocampal circuit overexcitation in a transgenic AD mouse model. Here, we will synthesize this new evidence that confronts the leading Tau-based AD hypothesis and discuss the role of pTau modulating neural circuits and network connectivity. Additionally, we will briefly address the role of brain circuit alterations as a potential biomarker for detecting the prodromal AD stage.

Structural Brain Network Correlated with Reading Impairment in Alzheimer’s Disease

2020 ◽  
Vol 49 (3) ◽  
pp. 264-269
Author(s):  
Miho Ota ◽  
Yuko Koshibe ◽  
Shinji Higashi ◽  
Kiyotaka Nemoto ◽  
Eriko Tsukada ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Negative Correlation ◽  
Network Connectivity ◽  
Japanese Patients ◽  
Significant Negative Correlation ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Reading Impairment ◽  
Brain Network Connectivity

<b><i>Aim:</i></b> Alzheimer’s disease (AD) is the most common age-related neurodegenerative disease and leads to dementia. AD is characterized by progressive declines in memory and, as the disease progresses, language dysfunction. Although it has been reported that AD patients show progressive aphasia, no study has examined the relationship between language functions estimated by the Standard Language Test for Aphasia (SLTA) and brain network connectivity in Japanese AD patients. If present, such a relationship would be of particular interest because Japanese speakers are accustomed to mingling ideography and phonography. <b><i>Methods:</i></b> 22 Japanese patients with AD who underwent 1.5-tesla MRI scan and SLTA, the scale for speech and reading impairment, participated in this study. We computed brain network connectivity metrics such as degree, betweenness centrality, and clustering coefficient, and estimated their relationships with the subscores of SLTA. <b><i>Results</i></b>: There was a significant negative correlation between the score for “reading aloud Kanji words” and the clustering coefficient in the left inferior temporal region, bilateral hippocampal regions, and right parietotemporal region. We also found a significant negative correlation between the score for “auditory comprehension of words” and the clustering coefficient in the left prefrontal region. No significant relationship was found between the other SLTA scores and the network metrics. <b><i>Conclusions:</i></b> Our data suggest relationships between reading impairments and regional brain network connectivity in Japanese patients with AD. The brain connectome may provide adjunct biological information that could improve our understanding of reading impairment.

scholarly journals Divergent network connectivity changes in behavioural variant frontotemporal dementia and Alzheimer’s disease

Brain ◽  
2010 ◽  
Vol 133 (5) ◽  
pp. 1352-1367 ◽  
Author(s):  
Juan Zhou ◽  
Michael D. Greicius ◽  
Efstathios D. Gennatas ◽  
Matthew E. Growdon ◽  
Jung Y. Jang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Frontotemporal Dementia ◽  
Network Connectivity
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