scholarly journals Shared effects of the clusterin gene on the default mode network among individuals at risk for Alzheimer's disease

2017 ◽  
Vol 23 (5) ◽  
pp. 395-404 ◽  
Author(s):  
Qing Ye ◽  
Fan Su ◽  
Hao Shu ◽  
Liang Gong ◽  
Chun-Ming Xie ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
At Risk ◽  
Default Mode Network ◽  
Default Mode

scholarly journals Functional MRI Assessment of Task-Induced Deactivation of the Default Mode Network in Alzheimer’s Disease and At-Risk Older Individuals

2009 ◽  
Vol 21 (1-2) ◽  
pp. 77-91 ◽  
Author(s):  
Maija Pihlajamäki ◽  
Reisa A. Sperling
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
At Risk ◽  
Functional Imaging ◽  
Default Mode Network ◽  
Cognitive Task ◽  
Memory Function ◽  
Brain Regions ◽  
Older Individuals ◽  
Default Mode

Alzheimer’s disease (AD) is the most common form of dementia in old age, and is characterized by prominent impairment of episodic memory. Recent functional imaging studies in AD have demonstrated alterations in a distributed network of brain regions supporting memory function, including regions of the default mode network. Previous positron emission tomography studies of older individuals at risk for AD have revealed hypometabolism of association cortical regions similar to the metabolic abnormalities seen in AD patients. In recent functional magnetic resonance imaging (fMRI) studies of AD, corresponding brain default mode regions have also been found to demonstrate an abnormal fMRI task-induced deactivation response pattern. That is, the relative decreases in fMRI signal normally observed in the default mode regions in healthy subjects performing a cognitive task are not seen in AD patients, or may even be reversed to a paradoxical activation response. Our recent studies have revealed alterations in the pattern of deactivation also in elderly individuals at risk for AD by virtue of their APOE e4 genotype, or evidence of mild cognitive impairment (MCI). In agreement with recent reports from other groups, these studies demonstrate that the pattern of fMRI task-induced deactivation is progressively disrupted along the continuum from normal aging to MCI and to clinical AD and more impaired in e4 carriers compared to non-carriers. These findings will be discussed in the context of current literature regarding functional imaging of the default network in AD and at-risk populations.

scholarly journals Differential default mode network trajectories in asymptomatic individuals at risk for Alzheimer's disease

2019 ◽  
Vol 15 (7) ◽  
pp. 940-950 ◽  
Author(s):  
Patrizia A. Chiesa ◽  
Enrica Cavedo ◽  
Andrea Vergallo ◽  
Simone Lista ◽  
Marie-Claude Potier ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
At Risk ◽  
Default Mode Network ◽  
Default Mode ◽  
Asymptomatic Individuals

scholarly journals Fasting plasma insulin and the default mode network in women at risk for Alzheimer's disease

2013 ◽  
Vol 34 (3) ◽  
pp. 641-649 ◽  
Author(s):  
Heather Kenna ◽  
Fumiko Hoeft ◽  
Ryan Kelley ◽  
Tonita Wroolie ◽  
Bevin DeMuth ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
At Risk ◽  
Default Mode Network ◽  
Plasma Insulin ◽  
Default Mode ◽  
Fasting Plasma ◽  
Fasting Plasma Insulin

Spectral dynamic causal modelling of resting-state fMRI: an exploratory study relating effective brain connectivity in the default mode network to genetics

2020 ◽  
Vol 19 (3) ◽  
Author(s):  
Yunlong Nie ◽  
Eugene Opoku ◽  
Laila Yasmin ◽  
Yin Song ◽  
Jie Wang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Default Mode Network ◽  
Resting State ◽  
Brain Connectivity ◽  
Parametric Bootstrap ◽  
Imaging Genetics ◽  
Nucleotide Polymorphisms ◽  
Mixed Effect ◽  
Default Mode

AbstractWe conduct an imaging genetics study to explore how effective brain connectivity in the default mode network (DMN) may be related to genetics within the context of Alzheimer’s disease and mild cognitive impairment. We develop an analysis of longitudinal resting-state functional magnetic resonance imaging (rs-fMRI) and genetic data obtained from a sample of 111 subjects with a total of 319 rs-fMRI scans from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. A Dynamic Causal Model (DCM) is fit to the rs-fMRI scans to estimate effective brain connectivity within the DMN and related to a set of single nucleotide polymorphisms (SNPs) contained in an empirical disease-constrained set which is obtained out-of-sample from 663 ADNI subjects having only genome-wide data. We relate longitudinal effective brain connectivity estimated using spectral DCM to SNPs using both linear mixed effect (LME) models as well as function-on-scalar regression (FSR). In both cases we implement a parametric bootstrap for testing SNP coefficients and make comparisons with p-values obtained from asymptotic null distributions. In both networks at an initial q-value threshold of 0.1 no effects are found. We report on exploratory patterns of associations with relatively high ranks that exhibit stability to the differing assumptions made by both FSR and LME.

IC-P-133: Music as a probe of default mode network function in young-onset Alzheimer's disease

2015 ◽  
Vol 11 (7S_Part_2) ◽  
pp. P91-P91
Author(s):  
Catherine F. Slattery ◽  
Jennifer L. Agustus ◽  
Ross W. Paterson ◽  
Mark J. White ◽  
Alexander J.M. Foulkes ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Default Mode Network ◽  
Default Mode ◽  
Young Onset ◽  
Network Function

The Effects of Gene Mutations on Default Mode Network in Familial Alzheimer’s Disease

2017 ◽  
Vol 56 (1) ◽  
pp. 327-334 ◽  
Author(s):  
Xiaozhen Li ◽  
Eric Westman ◽  
Steinunn Thordardottir ◽  
Anne Kinhult Ståhlbom ◽  
Ove Almkvist ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Default Mode Network ◽  
Gene Mutations ◽  
Familial Alzheimer’S Disease ◽  
Default Mode ◽  
Familial Alzheimer's Disease

scholarly journals Cognitive Improvement is Correlated with Increased Dynamic Functional Connectivity of Default Mode Network by Personalized rTMS in Alzheimer's Disease

2021 ◽  
Author(s):  
Lili Wei ◽  
Jintao Wang ◽  
Yingchun Zhang ◽  
Luoyi Xu ◽  
Kehua Yang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Default Mode Network ◽  
Present Report ◽  
Verbal Learning ◽  
Dynamic Functional Connectivity ◽  
Default Mode ◽  
Cognitive Improvement ◽  
Promising Therapeutic Approach

Abstract Background Repetitive transcranial magnetic stimulation (rTMS) is thought to be a promising therapeutic approach for Alzheimer's disease patients. Methods In the present report, a double-blind, randomized, sham-controlled rTMS trial was conducted in mild-to-moderate Alzheimer's disease patients. High-frequency rTMS was delivered to a subject-specific left lateral parietal region that demonstrated highest functional connectivity with the hippocampus using resting-state fMRI. The Mini Mental State Examination (MMSE) and Philadelphia Verbal Learning Test (PVLT) were used to evaluate patients’ cognitive functions. Results Patients receiving active rTMS treatment (n = 31) showed a significant increase in the MMSE, PVLT-Immediate recall, and PVLT-Short Delay recall scores after two weeks of rTMS treatment, whereas patients who received sham rTMS (n = 27) did not show significant changes in these measures. Dynamic functional connectivity (dFC) magnitude of the default mode network (DMN) in the active-rTMS group showed a significant increase after two weeks of rTMS treatment, and no significant changes were found in the sham-rTMS group. There was a significantly positive correlation between changes of the MMSE and changes of the dFC magnitude of DMN in the active-rTMS group, but not the sham-rTMS group. Conclusions Our findings are novel in demonstrating the feasibility and effectiveness of the fMRI-guided rTMS treatment in Alzheimer's disease patients, and DMN might play a vital role in therapeutic effectiveness of rTMS in Alzheimer’s disease. Trial registration: China National Medical Research Platform (http://114.255.48.20/login, No:MR-33-20-004217), retrospectively registered 2020-12-23.

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