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.

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

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.

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 High-Density EEG Signal Processing Based on Active-Source Reconstruction for Brain Network Analysis in Alzheimer’s Disease

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1031 ◽  
Author(s):  
Fabio La Foresta ◽  
Francesco Carlo Morabito ◽  
Silvia Marino ◽  
Serena Dattola
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Small World ◽  
Brain Network ◽  
High Density ◽  
Clustering Coefficient ◽  
Characteristic Path Length ◽  
Brain Functions ◽  
Network Parameters ◽  
Brain Functional Connectivity

Alzheimer’s Disease (AD) is a neurological disorder characterized by a progressive deterioration of brain functions that affects, above all, older adults. It can be difficult to make an early diagnosis because its first symptoms are often associated with normal aging. Electroencephalography (EEG) can be used for evaluating the loss of brain functional connectivity in AD patients. The purpose of this paper is to study the brain network parameters through the estimation of Lagged Linear Connectivity (LLC), computed by eLORETA software, applied to High-Density EEG (HD-EEG) for 84 regions of interest (ROIs). The analysis involved three groups of subjects: 10 controls (CNT), 21 Mild Cognitive Impairment patients (MCI) and 9 AD patients. In particular, the purpose is to compare the results obtained using a 256-channel EEG, the corresponding 10–10 system 64-channel EEG and the corresponding 10–20 system 18-channel EEG, both of which are extracted from the 256-electrode configuration. The computation of the Characteristic Path Length, the Clustering Coefficient, and the Connection Density from HD-EEG configuration reveals a weakening of small-world properties of MCI and AD patients in comparison to healthy subjects. On the contrary, the variation of the network parameters was not detected correctly when we employed the standard 10–20 configuration. Only the results from HD-EEG are consistent with the expected behavior of the AD brain network.

scholarly journals Miswiring the brain: Human prenatal Δ9-tetrahydrocannabinol use associated with altered fetal hippocampal brain network connectivity

2021 ◽  
pp. 101000
Author(s):  
Moriah E. Thomason ◽  
Ava C. Palopoli ◽  
Nicki N. Jariwala ◽  
Denise M. Werchan ◽  
Alan Chen ◽  
...  
Keyword(s):  
Network Connectivity ◽  
Brain Network ◽  
Brain Network Connectivity ◽  
The Brain

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 Distinct network topology in Alzheimer’s disease and behavioral variant frontotemporal dementia

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Adeline Su Lyn Ng ◽  
Juan Wang ◽  
Kwun Kei Ng ◽  
Joanna Su Xian Chong ◽  
Xing Qian ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Frontotemporal Dementia ◽  
Network Topology ◽  
Neuropsychiatric Symptoms ◽  
Brain Network ◽  
Salience Network ◽  
Behavioral Variant Frontotemporal Dementia ◽  
Control Networks ◽  
And Control

Abstract Background Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD) cause distinct atrophy and functional disruptions within two major intrinsic brain networks, namely the default network and the salience network, respectively. It remains unclear if inter-network relationships and whole-brain network topology are also altered and underpin cognitive and social–emotional functional deficits. Methods In total, 111 participants (50 AD, 14 bvFTD, and 47 age- and gender-matched healthy controls) underwent resting-state functional magnetic resonance imaging (fMRI) and neuropsychological assessments. Functional connectivity was derived among 144 brain regions of interest. Graph theoretical analysis was applied to characterize network integration, segregation, and module distinctiveness (degree centrality, nodal efficiency, within-module degree, and participation coefficient) in AD, bvFTD, and healthy participants. Group differences in graph theoretical measures and empirically derived network community structures, as well as the associations between these indices and cognitive performance and neuropsychiatric symptoms, were subject to general linear models, with age, gender, education, motion, and scanner type controlled. Results Our results suggested that AD had lower integration in the default and control networks, while bvFTD exhibited disrupted integration in the salience network. Interestingly, AD and bvFTD had the highest and lowest degree of integration in the thalamus, respectively. Such divergence in topological aberration was recapitulated in network segregation and module distinctiveness loss, with AD showing poorer modular structure between the default and control networks, and bvFTD having more fragmented modules in the salience network and subcortical regions. Importantly, aberrations in network topology were related to worse attention deficits and greater severity in neuropsychiatric symptoms across syndromes. Conclusions Our findings underscore the reciprocal relationships between the default, control, and salience networks that may account for the cognitive decline and neuropsychiatric symptoms in dementia.

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

A high frequency of apolipoprotein E4 isoprotein in Japanese patients with late-onset nonfamilial Alzheimer's disease

1993 ◽  
Vol 163 (2) ◽  
pp. 166-168 ◽  
Author(s):  
Akira Ueki ◽  
Mikihiko Kawano ◽  
Yoshio Namba ◽  
Masanobu Kawakami ◽  
Kazuhiko Ikeda
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
High Frequency ◽  
Late Onset ◽  
Japanese Patients ◽  
Apolipoprotein E4
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