IC-P-064: Changes of brain network connectivity in early Alzheimer's disease: Preliminary findings applying a data-driven approach

Aim: 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. Methods: 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. Results: 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. Conclusions: 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.

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

Journal of Alzheimer s Disease ◽

10.3233/jad-181097 ◽

2019 ◽

Vol 69 (1) ◽

pp. 237-252 ◽

Cited By ~ 4

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

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

Neural Plasticity ◽

10.1155/2021/8868447 ◽

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.

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Spatial distribution pattern of tau depositions in Alzheimer’s disease using data-driven approach of flortaucipir PET

10.1055/s-0041-1726790 ◽

2021 ◽

Author(s):

J Hong ◽

K Shi ◽

A Rominger ◽

H Choi

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Spatial Distribution ◽

Distribution Pattern ◽

Spatial Distribution Pattern ◽

Data Driven ◽

Data Driven Approach ◽

Using Data

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

International Journal of Image and Graphics ◽

10.1142/s021946782240006x ◽

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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The link between static and dynamic brain functional network connectivity and genetic risk of Alzheimer’s disease

10.1101/2021.03.27.437354 ◽

2021 ◽

Author(s):

Mohammad S. E. Sendi ◽

Elaheh Zendehrouh ◽

Charles A. Ellis ◽

Jiayu Chen ◽

Robyn L. Miller ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Genetic Risk ◽

Pearson Correlation ◽

Network Connectivity ◽

Risk Groups ◽

Functional Network ◽

Functional Network Connectivity ◽

Normal Individuals ◽

Data Driven Approach

AbstractApolipoprotein E (APOE) polymorphic alleles are genetic factors associated with Alzheimer’s disease (AD) risk. Although previous studies have explored the link between AD genetic risk and static functional network connectivity (sFNC), to the best of our knowledge, no previous studies have evaluated the association between dynamic FNC (dFNC) and AD genetic risk. Here, we examined the link between sFNC, dFNC, and AD genetic risk with a reproducible, data-driven approach. We used rs-fMRI, demographic, and APOE data from cognitively normal individuals (N=894) between 42 to 95 years of age (mean = 70 years). We divided individuals into low, moderate, and high-risk groups. Using Pearson correlation, we calculated sFNC across seven brain networks. We also calculated dFNC with a sliding window and Pearson correlation. The dFNC windows were partitioned into three distinct states with k-means clustering. Next, we calculated the amount of time each subject spent in each state, called occupancy rate or OCR. We compared both sFNC and OCR, estimated from dFNC, across individuals with different genetic risk and found that both sFNC and dFNC are related to AD genetic risk. We found that higher AD risk reduces within-visual sensory network (VSN) sFNC and that individuals with higher AD risk spend more time in a state with lower within-VSN dFNC. Additionally, we found that AD genetic risk affects whole-brain sFNC and dFNC in women but not in men. In conclusion, we presented novel insights into the links between sFNC, dFNC, and AD genetic risk.

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Brain functional network integrity sustains cognitive function despite atrophy in presymptomatic genetic frontotemporal dementia

10.1101/19012203 ◽

2019 ◽

Cited By ~ 1

Author(s):

Kamen A. Tsvetanov ◽

Stefano Gazzina ◽

Simon P. Jones ◽

John van Swieten ◽

Barbara Borroni ◽

...

Keyword(s):

Cognitive Function ◽

Frontotemporal Dementia ◽

Cognitive Performance ◽

Network Connectivity ◽

Brain Network ◽

Functional Network ◽

Brain Structure And Function ◽

Brain Network Connectivity ◽

Data Driven Approach ◽

And Function

AbstractINTRODUCTIONThe presymptomatic phase of neurodegenerative disease can last many years, with sustained cognitive function despite progressive atrophy. We investigate this phenomenon in familial Frontotemporal dementia (FTD).METHODSWe studied 121 presymptomatic FTD mutation carriers and 134 family members without mutations, using multivariate data-driven approach to link cognitive performance with both structural and functional magnetic resonance imaging. Atrophy and brain network connectivity were compared between groups, in relation to the time from expected symptom onset.RESULTSThere were group differences in brain structure and function, in the absence of differences in cognitive performance. Specifically, we identified behaviourally-relevant structural and functional network differences. Structure-function relationships were similar in both groups, but coupling between functional connectivity and cognition was stronger for carriers than for non-carriers, and increased with proximity to the expected onset of disease.DISCUSSIONOur findings suggest that maintenance of functional network connectivity enables carriers to maintain cognitive performance.

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Mild cognitive impairment understanding: an empirical study by data-driven approach

BMC Bioinformatics ◽

10.1186/s12859-019-3057-1 ◽

2019 ◽

Vol 20 (S15) ◽

Author(s):

Liyuan Liu ◽

Bingchen Yu ◽

Meng Han ◽

Shanshan Yuan ◽

Na Wang

Keyword(s):

Risk Factors ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Decline ◽

Sleep Time ◽

Data Driven ◽

Control And Prevention ◽

Data Driven Approach ◽

Personal Welfare ◽

First Time

Abstract Background Cognitive decline has emerged as a significant threat to both public health and personal welfare, and mild cognitive decline/impairment (MCI) can further develop into Dementia/Alzheimer’s disease. While treatment of Dementia/Alzheimer’s disease can be expensive and ineffective sometimes, the prevention of MCI by identifying modifiable risk factors is a complementary and effective strategy. Results In this study, based on the data collected by Centers for Disease Control and Prevention (CDC) through the nationwide telephone survey, we apply a data-driven approach to re-exam the previously founded risk factors and discover new risk factors. We found that depression, physical health, cigarette usage, education level, and sleep time play an important role in cognitive decline, which is consistent with the previous discovery. Besides that, the first time, we point out that other factors such as arthritis, pulmonary disease, stroke, asthma, marital status also contribute to MCI risk, which is less exploited previously. We also incorporate some machine learning and deep learning algorithms to weigh the importance of various factors contributed to MCI and predicted cognitive declined. Conclusion By incorporating the data-driven approach, we can determine that risk factors significantly correlated with diseases. These correlations could also be expanded to another medical diagnosis besides MCI.

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DATA-DRIVEN PARTICIPANT RECRUITMENT: FINDINGS FROM THE ALZHEIMER’S DISEASE NEUROIMAGING INITIATIVE 3

Journal of Prevention of Alzheimer's Disease ◽

10.14283/jpad.2020.5 ◽

2020 ◽

pp. 1-6

Author(s):

C. Barger ◽

J. Fockler ◽

W. Kwang ◽

S. Moore ◽

D. Flenniken ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Clinical Studies ◽

Newspaper Coverage ◽

Data Driven ◽

Participant Recruitment ◽

Recruitment Methods ◽

Data Driven Approach ◽

Recruitment Activities ◽

Survey Responses

Background: Effective and measurable participant recruitment methods are urgently needed for clinical studies in Alzheimer’s disease. Objectives: To develop methods for measuring recruitment tactics and evaluating effectiveness. Methods: Recruitment tactics for the Alzheimer’s Disease Neuroimaging Initiative (ADNI3) were measured using web and phone analytics, campaign metrics and survey responses. Results: A total of 462 new participants were enrolled into ADNI3 through recruitment efforts. We collected metrics on recruitment activities including 82,003 unique visitors to the recruitment website and 3,335 calls to study phone numbers. The recruitment sources that produced the most screening and enrollment included online advertisements, local radio and newspaper coverage and emails and referrals from registries. Conclusions: Analysis of recruitment activity obtained through tracking methods provided some insight for effective recruitment. ADNI3 can serve as an example of how a data-driven approach to centralized participant recruitment can be utilized to facilitate clinical research.

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