scholarly journals Altered Functional Network Affects Amyloid and Structural Covariance in Alzheimer’s Disease

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Ya-Ting Chang ◽  
Chi-Wei Huang ◽  
Wen-Neng Chang ◽  
Jun-Jun Lee ◽  
Chiung-Chih Chang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Grey Matter ◽  
Structural Covariance ◽  
Functional Failure ◽  
Intrinsic Connectivity ◽  
Intrinsic Connectivity Networks ◽  
Temporal Trajectory ◽  
Normal Controls

Background. We aimed to investigate how altered intrinsic connectivity networks (ICNs) affect pathologic changes of Alzheimer’s disease (AD) at a network-based level. Methods. Thirty normal controls (NCs), 23 patients with AD-mild cognitive impairment (MCI), and 20 patients with AD-dementia were enrolled. We compared the organization of grey matter structural covariance and functional connectivity in ICNs between NCs and all AD patients who were amyloid β (Aβ)-positive. We further used seed-based interregional covariance analysis to compare structural and Aβ plaque covariance in default mode network (DMN) between AD-MCI and AD-dementia groups. Results. The patients with AD had increased functional interregional covariance among the regions of the ICN anchored to dorsal caudate (DC) seeds compared to the NCs. The increased connectivity was associated with extended patterns of reduced Aβ plaque covariance in the AD-dementia group compared to the AD-MCI group within the striatal network anchored to DC seeds. Patterns of lower Aβ plaque covariance in the AD-dementia group compared to the AD-MCI group were more extended within the network anchored to DC seeds than within the DMN, which was undergoing functional failure in the patients with AD. Significant decreased structural covariance in the AD-dementia group compared to the AD-MCI group was more extended in the DMN during functional failure. Conclusions. Functional connectivity in ICNs affects the topographic spread of molecular pathologies. The temporal trajectory of pathologic alterations can be well demonstrated by pathologic covariance comparisons between different clinical stages. Pathologic covariance can provide critical support to pathologic interactions at network and molecular levels.

O2-04-07: Functional connectivity changes during visual processing associated with grey matter atrophy in Alzheimer's disease

2010 ◽  
Vol 6 ◽  
pp. S105-S106
Author(s):  
Michael Ewers ◽  
Arun L.W. Bokde ◽  
Jane Park ◽  
Srinivas Rachakonda ◽  
Stefan J. Teipel ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Visual Processing ◽  
Grey Matter ◽  
Grey Matter Atrophy

scholarly journals Tau covariance patterns in Alzheimer's disease patients match intrinsic connectivity networks in the healthy brain

2019 ◽  
Vol 23 ◽  
pp. 101848 ◽  
Author(s):  
Rik Ossenkoppele ◽  
Leonardo Iaccarino ◽  
Daniel R. Schonhaut ◽  
Jesse A. Brown ◽  
Renaud La Joie ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Intrinsic Connectivity ◽  
Intrinsic Connectivity Networks

O3-08-04: Tau Covariance Patterns in Alzheimer's Disease Patients Resemble Intrinsic Connectivity Networks in Young Adults

2016 ◽  
Vol 12 ◽  
pp. P305-P306
Author(s):  
Rik Ossenkoppele ◽  
Daniel R. Schonhaut ◽  
Jesse Brown ◽  
James P. O'Neil ◽  
Mustafa Janabi ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Young Adults ◽  
Intrinsic Connectivity ◽  
Intrinsic Connectivity Networks

scholarly journals A Tensor-Based Framework for rs-fMRI Classification and Functional Connectivity Construction

2020 ◽  
Vol 14 ◽  
Author(s):  
Ali Noroozi ◽  
Mansoor Rezghi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Extraction Methods ◽  
Classification Performance ◽  
Brain Diseases ◽  
Brain Disorder ◽  
Brain Images ◽  
The Brain ◽  
Normal Controls

Recently, machine learning methods have gained lots of attention from researchers seeking to analyze brain images such as Resting-State Functional Magnetic Resonance Imaging (rs-fMRI) to obtain a deeper understanding of the brain and such related diseases, for example, Alzheimer's disease. Finding the common patterns caused by a brain disorder through analysis of the functional connectivity (FC) network along with discriminating brain diseases from normal controls have long been the two principal goals in studying rs-fMRI data. The majority of FC extraction methods calculate the FC matrix for each subject and then use simple techniques to combine them and obtain a general FC matrix. In addition, the state-of-the-art classification techniques for finding subjects with brain disorders also rely on calculating an FC for each subject, vectorizing, and feeding them to the classifier. Considering these problems and based on multi-dimensional nature of the data, we have come up with a novel tensor framework in which a general FC matrix is obtained without the need to construct an FC matrix for each sample. This framework also allows us to reduce the dimensionality and create a novel discriminant function that rather than using FCs works directly with each sample, avoids vectorization in any step, and uses the test data in the training process without forcing any prior knowledge of its label into the classifier. Extensive experiments using the ADNI dataset demonstrate that our proposed framework effectively boosts the fMRI classification performance and reveals novel connectivity patterns in Alzheimer's disease at its early stages.

Altered Functional Connectivity of the Marginal Division in Alzheimer’s Disease

2014 ◽  
Vol 11 (2) ◽  
pp. 145-155 ◽  
Author(s):  
Zengqiang Zhang ◽  
Yong Liu ◽  
Bo Zhou ◽  
Jinlong Zheng ◽  
Hongxiang Yao ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Marginal Division

Subjective Spatial Navigation Complaints - A Frequent Symptom Reported by Patients with Subjective Cognitive Decline, Mild Cognitive Impairment and Alzheimer's Disease

2018 ◽  
Vol 15 (3) ◽  
pp. 219-228 ◽  
Author(s):  
Jiri Cerman ◽  
Ross Andel ◽  
Jan Laczo ◽  
Martin Vyhnalek ◽  
Zuzana Nedelska ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cognitive Decline ◽  
Spatial Navigation ◽  
Subjective Cognitive Decline ◽  
Great Effort ◽  
Frequent Symptom ◽  
Normal Controls

Background: Great effort has been put into developing simple and feasible tools capable to detect Alzheimer's disease (AD) in its early clinical stage. Spatial navigation impairment occurs very early in AD and is detectable even in the stage of mild cognitive impairment (MCI). Objective: The aim was to describe the frequency of self-reported spatial navigation complaints in patients with subjective cognitive decline (SCD), amnestic and non-amnestic MCI (aMCI, naMCI) and AD dementia and to assess whether a simple questionnaire based on these complaints may be used to detect early AD. Method: In total 184 subjects: patients with aMCI (n=61), naMCI (n=27), SCD (n=63), dementia due to AD (n=20) and normal controls (n=13) were recruited. The subjects underwent neuropsychological examination and were administered a questionnaire addressing spatial navigation complaints. Responses to the 15 items questionnaire were scaled into four categories (no, minor, moderate and major complaints). Results: 55% of patients with aMCI, 64% with naMCI, 68% with SCD and 72% with AD complained about their spatial navigation. 38-61% of these complaints were moderate or major. Only 33% normal controls expressed complaints and none was ranked as moderate or major. The SCD, aMCI and AD dementia patients were more likely to express complaints than normal controls (p's<0.050) after adjusting for age, education, sex, depressive symptoms (OR for SCD=4.00, aMCI=3.90, AD dementia=7.02) or anxiety (OR for SCD=3.59, aMCI=3.64, AD dementia=6.41). Conclusion: Spatial navigation complaints are a frequent symptom not only in AD, but also in SCD and aMCI and can potentially be detected by a simple and inexpensive questionnaire.

Allocentric to Egocentric Spatial Switching: Impairment in aMCI and Alzheimer's Disease Patients?

2018 ◽  
Vol 15 (3) ◽  
pp. 229-236 ◽  
Author(s):  
Gennaro Ruggiero ◽  
Alessandro Iavarone ◽  
Tina Iachini
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Reference Frame ◽  
Reference Frames ◽  
Memory Task ◽  
Spatial Representations ◽  
The Novel ◽  
Spatial Memory Task ◽  
Switching Condition ◽  
Normal Controls

Objective: Deficits in egocentric (subject-to-object) and allocentric (object-to-object) spatial representations, with a mainly allocentric impairment, characterize the first stages of the Alzheimer's disease (AD). Methods: To identify early cognitive signs of AD conversion, some studies focused on amnestic-Mild Cognitive Impairment (aMCI) by reporting alterations in both reference frames, especially the allocentric ones. However, spatial environments in which we move need the cooperation of both reference frames. Such cooperating processes imply that we constantly switch from allocentric to egocentric frames and vice versa. This raises the question of whether alterations of switching abilities might also characterize an early cognitive marker of AD, potentially suitable to detect the conversion from aMCI to dementia. Here, we compared AD and aMCI patients with Normal Controls (NC) on the Ego-Allo- Switching spatial memory task. The task assessed the capacity to use switching (Ego-Allo, Allo-Ego) and non-switching (Ego-Ego, Allo-Allo) verbal judgments about relative distances between memorized stimuli. Results: The novel finding of this study is the neat impairment shown by aMCI and AD in switching from allocentric to egocentric reference frames. Interestingly, in aMCI when the first reference frame was egocentric, the allocentric deficit appeared attenuated. Conclusion: This led us to conclude that allocentric deficits are not always clinically detectable in aMCI since the impairments could be masked when the first reference frame was body-centred. Alongside, AD and aMCI also revealed allocentric deficits in the non-switching condition. These findings suggest that switching alterations would emerge from impairments in hippocampal and posteromedial areas and from concurrent dysregulations in the locus coeruleus-noradrenaline system or pre-frontal cortex.

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