Abnormal Brain Network Connectivity in a Triple-Network Model of Alzheimer’s Disease

2019 ◽  
Vol 69 (1) ◽  
pp. 237-252 ◽  
Author(s):  
Chenxi Li ◽  
Youjun Li ◽  
Liang Zheng ◽  
Xiaoqi Zhu ◽  
Bixin Shao ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Model ◽  
Network Connectivity ◽  
Brain Network ◽  
Model Of Alzheimer’S Disease ◽  
Brain Network Connectivity ◽  
Abnormal Brain

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 The Role of Acupuncture Improving Cognitive Deficits due to Alzheimer’s Disease or Vascular Diseases through Regulating Neuroplasticity

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Shaozhen Ji ◽  
Jiayu Duan ◽  
Xiaobing Hou ◽  
Li Zhou ◽  
Weilan Qin ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Deficits ◽  
Environmental Changes ◽  
Vascular Diseases ◽  
Network Connectivity ◽  
Complementary Therapy ◽  
Vascular Cognitive Impairment ◽  
Brain Network ◽  
Brain Network Connectivity

Dementia affects millions of elderly worldwide causing remarkable costs to society, but effective treatment is still lacking. Acupuncture is one of the complementary therapies that has been applied to cognitive deficits such as Alzheimer’s disease (AD) and vascular cognitive impairment (VCI), while the underlying mechanisms of its therapeutic efficiency remain elusive. Neuroplasticity is defined as the ability of the nervous system to adapt to internal and external environmental changes, which may support some data to clarify mechanisms how acupuncture improves cognitive impairments. This review summarizes the up-to-date and comprehensive information on the effectiveness of acupuncture treatment on neurogenesis and gliogenesis, synaptic plasticity, related regulatory factors, and signaling pathways, as well as brain network connectivity, to lay ground for fully elucidating the potential mechanism of acupuncture on the regulation of neuroplasticity and promoting its clinical application as a complementary therapy for AD and VCI.

ID 388 – Dynamic alterations of brain network oscillations in a Tau seeding mouse model of Alzheimer’s disease

2016 ◽  
Vol 127 (3) ◽  
pp. e74
Author(s):  
A. Ahnaou ◽  
L. Raeymaekers ◽  
R. Biermans ◽  
D. Moechars ◽  
E. Peeraer ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Mouse Model ◽  
Brain Network ◽  
Network Oscillations ◽  
Model Of Alzheimer’S Disease

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 Dampened Slow Oscillation Connectivity Anticipates Amyloid Deposition in the PS2APP Mouse Model of Alzheimer’s Disease

Cells ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 54 ◽  
Author(s):  
Alessandro Leparulo ◽  
Mufti Mahmud ◽  
Elena Scremin ◽  
Tullio Pozzan ◽  
Stefano Vassanelli ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Posterior Parietal Cortex ◽  
Relevant Information ◽  
Brain Network ◽  
Plaque Deposition ◽  
Model Of Alzheimer’S Disease ◽  
Linear Probe ◽  
Before And After ◽  
Phase Amplitude

To fight Alzheimer’s disease (AD), we should know when, where, and how brain network dysfunctions initiate. In AD mouse models, relevant information can be derived from brain electrical activity. With a multi-site linear probe, we recorded local field potentials simultaneously at the posterior-parietal cortex and hippocampus of wild-type and double transgenic AD mice, under anesthesia. We focused on PS2APP (B6.152H) mice carrying both presenilin-2 (PS2) and amyloid precursor protein (APP) mutations, at three and six months of age, before and after plaque deposition respectively. To highlight defects linked to either the PS2 or APP mutation, we included in the analysis age-matched PS2.30H and APP-Swedish mice, carrying each of the mutations individually. Our study also included PSEN2−/− mice. At three months, only predeposition B6.152H mice show a reduction in the functional connectivity of slow oscillations (SO) and in the power ratio between SO and delta waves. At six months, plaque-seeding B6.152H mice undergo a worsening of the low/high frequency power imbalance and show a massive loss of cortico-hippocampal phase-amplitude coupling (PAC) between SO and higher frequencies, a feature shared with amyloid-free PS2.30H mice. We conclude that the PS2 mutation is sufficient to impair SO PAC and accelerate network dysfunctions in amyloid-accumulating mice.

Applying brain network activation tool on working memory ERPs in a scopolamine model of Alzheimer's disease

2011 ◽  
Vol 500 ◽  
pp. e44-e45
Author(s):  
Revital Shani-Hershkovitch ◽  
Amit Reches ◽  
Dani Kerem ◽  
Noga Pinchuk ◽  
Naama Levy-Cooperman ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Working Memory ◽  
Brain Network ◽  
Network Activation ◽  
Model Of Alzheimer’S Disease

scholarly journals Prion-like spreading of Alzheimer’s disease within the brain’s connectome

2019 ◽  
Vol 16 (159) ◽  
pp. 20190356 ◽  
Author(s):  
Sveva Fornari ◽  
Amelie Schäfer ◽  
Mathias Jucker ◽  
Alain Goriely ◽  
Ellen Kuhl
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Human Brain ◽  
Network Model ◽  
Kinetic Models ◽  
Aggregate Size ◽  
Brain Network ◽  
Misfolded Protein ◽  
Size Distributions ◽  
The Mean

The prion hypothesis states that misfolded proteins can act as infectious agents that template the misfolding and aggregation of healthy proteins to transmit a disease. Increasing evidence suggests that pathological proteins in neurodegenerative diseases adopt prion-like mechanisms and spread across the brain along anatomically connected networks. Local kinetic models of protein misfolding and global network models of protein spreading provide valuable insight into several aspects of prion-like diseases. Yet, to date, these models have not been combined to simulate how pathological proteins multiply and spread across the human brain. Here, we create an efficient and robust tool to simulate the spreading of misfolded protein using three classes of kinetic models, the Fisher–Kolmogorov model, the Heterodimer model and the Smoluchowski model. We discretize their governing equations using a human brain network model, which we represent as a weighted Laplacian graph generated from 418 brains from the Human Connectome Project. Its nodes represent the anatomic regions of interest and its edges are weighted by the mean fibre number divided by the mean fibre length between any two regions. We demonstrate that our brain network model can predict the histopathological patterns of Alzheimer’s disease and capture the key characteristic features of finite-element brain models at a fraction of their computational cost: simulating the spatio-temporal evolution of aggregate size distributions across the human brain throughout a period of 40 years takes less than 7 s on a standard laptop computer. Our model has the potential to predict biomarker curves, aggregate size distributions, infection times, and the effects of therapeutic strategies including reduced production and increased clearance of misfolded protein.

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