scholarly journals Differences and the Relationship in Default Mode Network Intrinsic Activity and Functional Connectivity in Mild Alzheimer's Disease and Amnestic Mild Cognitive Impairment

2014 ◽  
Vol 4 (8) ◽  
pp. 567-574 ◽  
Author(s):  
Marina Weiler ◽  
Camila Vieira Ligo Teixeira ◽  
Mateus Henrique Nogueira ◽  
Brunno Machado de Campos ◽  
Benito Pereira Damasceno ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Functional Connectivity ◽  
Default Mode Network ◽  
Amnestic Mild Cognitive Impairment ◽  
Intrinsic Activity ◽  
Default Mode ◽  
The Relationship

scholarly journals Differential medial temporal lobe and default-mode network functional connectivity and morphometric changes in Alzheimer’s Disease

2017 ◽  
Author(s):  
Kamil A. Grajski ◽  
Steven L. Bressler ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Functional Connectivity ◽  
Temporal Lobe ◽  
Default Mode Network ◽  
Medial Temporal Lobe ◽  
Resting State Functional Connectivity ◽  
Default Mode

AbstractWe report group level differential detection of medial temporal lobe resting-state functional connectivity disruption and morphometric changes in the transition from cognitively normal to early mild cognitive impairment in an age-, education- and gender-matched 105 subjects Alzheimer’s Disease Neuroimaging Initiative dataset. In mild Alzheimer’s Disease, but not early mild cognitive impairment, characteristic brain atrophy was detected in FreeSurfer estimates of cortical thickness and subcortical and hippocampal subfield volumes. By contrast, functional connectivity analysis detected earlier significant changes. In early mild cognitive impairment these changes involved medial temporal lobe regions of transentorhinal, perirhinal and entorhinal cortices (associated with the earliest stages of neurofibrillary changes in Alzheimer’s Disease), hippocampus, parahippocampal gyrus and temporal pole, and cortical regions comprising or co-activated with the default-mode network, including rostral and medial prefrontal cortex, anterior cingulate cortex, precuneus and inferior temporal cortex. Key findings include: a) focal and bilaterally symmetric spatial organization of affected medial temporal lobe regions; b) mutual hyperconnectivity bilaterally involving ventral medial temporal lobe structures (temporal pole, uncus); and c) dorsal medial temporal lobe hypoconnectivity with anterior and posterior midline default-mode network nodes. These findings position medial temporal lobe resting state functional connectivity as a candidate biomarker of an Alzheimer’s Disease pathophysiological cascade, potentially in advance of clinical biomarkers, and coincident with biomarkers of the earliest stages of Alzheimer’s neuropathology. Our results indicate that medial temporal lobe resting-state functional connectivity should be further investigated as a potential biomarker in the diagnosis of Alzheimer’s Disease.HighlightsFunctional connectivity change seen before structural change in Alzheimer’s DiseaseMedial temporal lobes mutually hyper-connect in mild cognitive impairmentMedial temporal lobe and default mode network decouple in mild cognitive impairmentLoci of functional change in hippocampi are focal with bilaterally symmetric featuresNonmonotonic functional connectivity changes in Alzheimer’s Disease progression

IC-P-031: EFFECTIVE CONNECTIVITY WITHIN THE DEFAULT MODE NETWORK IN MILD COGNITIVE IMPAIRMENT AND ALZHEIMER'S DISEASE

2006 ◽  
Vol 14 (7S_Part_1) ◽  
pp. P35-P36
Author(s):  
Cole John Cook ◽  
Gyujoon Hwang ◽  
Veena A. Nair ◽  
Andrew L. Alexander ◽  
Piero G. Antuono ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Default Mode Network ◽  
Effective Connectivity ◽  
Default Mode

scholarly journals Eigenvector alignment: assessing functional network changes in amnestic mild cognitive impairment and Alzheimer’s disease

2020 ◽  
Author(s):  
Ruaridh Clark ◽  
Niia Nikolova ◽  
William J. McGeown ◽  
Malcolm Macdonald
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Functional Connectivity ◽  
Structural Changes ◽  
Amnestic Mild Cognitive Impairment ◽  
Functional Networks ◽  
Limited Information ◽  
The Impact

AbstractEigenvector alignment, introduced herein to investigate human brain functional networks, is adapted from methods developed to detect influential nodes and communities in networked systems. It is used to identify differences in the brain networks of subjects with Alzheimer’s disease (AD), amnestic Mild Cognitive Impairment (aMCI) and healthy controls (HC). Well-established methods exist for analysing connectivity networks composed of brain regions, including the widespread use of centrality metrics such as eigenvector centrality. However, these metrics provide only limited information on the relationship between regions, with this understanding often sought by comparing the strength of pairwise functional connectivity. Our holistic approach, eigenvector alignment, considers the impact of all functional connectivity changes before assessing the strength of the functional relationship, i.e. alignment, between any two regions. This is achieved by comparing the placement of regions in a Euclidean space defined by the network’s dominant eigenvectors. Eigenvector alignment recognises the strength of bilateral connectivity in cortical areas of healthy control subjects, but also reveals degradation of this commissural system in those with AD. Surprisingly little structural change is detected for key regions in the Default Mode Network, despite significant declines in the functional connectivity of these regions. In contrast, regions in the auditory cortex display significant alignment changes that begin in aMCI and are the most prominent structural changes for those with AD. Alignment differences between aMCI and AD subjects are detected, including notable changes to the hippocampal regions. These findings suggest eigenvector alignment can play a complementary role, alongside established network analytic approaches, to capture how the brain’s functional networks develop and adapt when challenged by disease processes such as AD.

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