Dysfunctional whole brain networks in mild cognitive impairment patients: an fMRI study

2012 ◽  
Author(s):  
Zhenyu Liu ◽  
Lijun Bai ◽  
Ruwei Dai ◽  
Chongguang Zhong ◽  
Ting Xue ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Brain Networks ◽  
Whole Brain ◽  
Fmri Study

scholarly journals Altered Weibull Degree Distribution in Resting-State Functional Brain Networks Is Associated With Cognitive Decline in Mild Cognitive Impairment

2021 ◽  
Vol 12 ◽  
Author(s):  
Yifei Zhang ◽  
Xiaodan Chen ◽  
Xinyuan Liang ◽  
Zhijiang Wang ◽  
Teng Xie ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Human Brain ◽  
Cognitive Performance ◽  
Power Law ◽  
Resting State ◽  
Brain Networks ◽  
Functional Networks ◽  
Whole Brain ◽  
Connectivity Degree

The topological organization of human brain networks can be mathematically characterized by the connectivity degree distribution of network nodes. However, there is no clear consensus on whether the topological structure of brain networks follows a power law or other probability distributions, and whether it is altered in Alzheimer's disease (AD). Here we employed resting-state functional MRI and graph theory approaches to investigate the fitting of degree distributions of the whole-brain functional networks and seven subnetworks in healthy subjects and individuals with amnestic mild cognitive impairment (aMCI), i.e., the prodromal stage of AD, and whether they are altered and correlated with cognitive performance in patients. Forty-one elderly cognitively healthy controls and 30 aMCI subjects were included. We constructed functional connectivity matrices among brain voxels and examined nodal degree distributions that were fitted by maximum likelihood estimation. In the whole-brain networks and all functional subnetworks, the connectivity degree distributions were fitted better by the Weibull distribution [f(x)~x(β−1)e(−λxβ)] than power law or power law with exponential cutoff. Compared with the healthy control group, the aMCI group showed lower Weibull β parameters (shape factor) in both the whole-brain networks and all seven subnetworks (false-discovery rate-corrected, p < 0.05). These decreases of the Weibull β parameters in the whole-brain networks and all subnetworks except for ventral attention were associated with reduced cognitive performance in individuals with aMCI. Thus, we provided a short-tailed model to capture intrinsic connectivity structure of the human brain functional networks in health and disease.

The Anterior-posterior Functional Connectivity Disconnection in the Elderly with Subjective Memory Impairment and Amnestic Mild Cognitive Impairment

2020 ◽  
Vol 17 (4) ◽  
pp. 373-381
Author(s):  
Wuhai Tao ◽  
Jinping Sun ◽  
Xin Li ◽  
Wen Shao ◽  
Jing Pei ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Functional Connectivity ◽  
Memory Impairment ◽  
Amnestic Mild Cognitive Impairment ◽  
Magnetic Resonance Imaging Scan ◽  
Subjective Memory ◽  
Long Distance ◽  
Whole Brain ◽  
Subjective Memory Impairment

Background: Subjective Memory Impairment (SMI) may tremendously increase the risk of Alzheimer’s Disease (AD). The full understanding of the neuromechanism of SMI will shed light on the early intervention of AD. Methods: In the current study, 23 Healthy Controls (HC), 22 SMI subjects and 24 amnestic Mild Cognitive Impairment (aMCI) subjects underwent the comprehensive neuropsychological assessment and the resting-state functional magnetic resonance imaging scan. The difference in the connectivity of the Default Mode Network (DMN) and Functional Connectivity (FC) from the Region of Interest (ROI) to the whole brain were compared, respectively. Results: The results showed that HC and SMI subjects had significantly higher connectivity in the region of the precuneus area compared to aMCI subjects. However, from this region to the whole brain, SMI and aMCI subjects had significant FC decrease in the right anterior cingulum, left superior frontal and left medial superior frontal gyrus compared to HC. In addition, this FC change was significantly correlated with the cognitive function decline in participants. Conclusion: Our study indicated that SMI subjects had relatively intact DMN connectivity but impaired FC between the anterior and posterior brain. The findings suggest that long-distance FC is more vulnerable than the short ones in the people with SMI.

scholarly journals Using Regional Homogeneity to Reveal Altered Spontaneous Activity in Patients with Mild Cognitive Impairment

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yumei Wang ◽  
Xiaochuan Zhao ◽  
Shunjiang Xu ◽  
Lulu Yu ◽  
Lan Wang ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Spontaneous Activity ◽  
Healthy Aging ◽  
Early Stage ◽  
Brain Activity ◽  
Regional Homogeneity ◽  
Whole Brain ◽  
Spontaneous Brain Activity ◽  
Regional Brain Activity

Most patients with mild cognitive impairment (MCI) are thought to be in an early stage of Alzheimer’s disease (AD). Resting-state functional magnetic resonance imaging reflects spontaneous brain activity and/or the endogenous/background neurophysiological process of the human brain. Regional homogeneity (ReHo) rapidly maps regional brain activity across the whole brain. In the present study, we used the ReHo index to explore whole brain spontaneous activity pattern in MCI. Our results showed that MCI subjects displayed an increased ReHo index in the paracentral lobe, precuneus, and postcentral and a decreased ReHo index in the medial temporal gyrus and hippocampus. Impairments in the medial temporal gyrus and hippocampus may serve as important markers distinguishing MCI from healthy aging. Moreover, the increased ReHo index observed in the postcentral and paracentral lobes might indicate compensation for the cognitive function losses in individuals with MCI.

Hippocampus segmentation and classification for dementia analysis using pre-trained neural network models

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ahana Priyanka ◽  
Kavitha Ganesan
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Network Models ◽  
Curve Evolution ◽  
Neural Network Models ◽  
Brain Images ◽  
Whole Brain ◽  
Shape Constraint ◽  
Mr Brain Images ◽  
Trained Neural Network

Abstract The diagnostic and clinical overlap of early mild cognitive impairment (EMCI), mild cognitive impairment (MCI), late mild cognitive impairment (LMCI) and Alzheimer disease (AD) is a vital oncological issue in dementia disorder. This study is designed to examine Whole brain (WB), grey matter (GM) and Hippocampus (HC) morphological variation and identify the prominent biomarkers in MR brain images of demented subjects to understand the severity progression. Curve evolution based on shape constraint is carried out to segment the complex brain structure such as HC and GM. Pre-trained models are used to observe the severity variation in these regions. This work is evaluated on ADNI database. The outcome of the proposed work shows that curve evolution method could segment HC and GM regions with better correlation. Pre-trained models are able to show significant severity difference among WB, GM and HC regions for the considered classes. Further, prominent variation is observed between AD vs. EMCI, AD vs. MCI and AD vs. LMCI in the whole brain, GM and HC. It is concluded that AlexNet model for HC region result in better classification for AD vs. EMCI, AD vs. MCI and AD vs. LMCI with an accuracy of 93, 78.3 and 91% respectively.

scholarly journals Group-level progressive alterations in brain connectivity patterns revealed by diffusion-tensor brain networks across severity stages in Alzheimer’s disease

2017 ◽  
Author(s):  
J. Rasero ◽  
C. Alonso-Montes ◽  
I. Diez ◽  
L. Olabarrieta-Landa ◽  
L. Remaki ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Disease Severity ◽  
Diffusion Tensor ◽  
Brain Networks ◽  
Stage Ii ◽  
Group Level ◽  
The Brain

AbstractAlzheimer’s disease (AD) is a chronically progressive neurodegenerative disease highly correlated to aging. Whether AD originates by targeting a localized brain area and propagates to the rest of the brain across disease-severity progression is a question with an unknown answer. Here, we aim to provide an answer to this question at the group-level by looking at differences in diffusion-tensor brain networks. In particular, making use of data from Alzheimer's Disease Neuroimaging Initiative (ADNI), four different groups were defined (all of them matched by age, sex and education level): G1 (N1=36, healthy control subjects, Control), G2 (N2=36, early mild cognitive impairment, EMCI), G3 (N3=36, late mild cognitive impairment, LMCI) and G4 (N4=36, AD). Diffusion-tensor brain networks were compared across three disease stages: stage I 3(Control vs EMCI), stage II (Control vs LMCI) and stage III (Control vs AD). The group comparison was performed using the multivariate distance matrix regression analysis, a technique that was born in genomics and was recently proposed to handle brain functional networks, but here applied to diffusion-tensor data. The results were three-fold: First, no significant differences were found in stage I. Second, significant differences were found in stage II in the connectivity pattern of a subnetwork strongly associated to memory function (including part of the hippocampus, amygdala, entorhinal cortex, fusiform gyrus, inferior and middle temporal gyrus, parahippocampal gyrus and temporal pole). Third, a widespread disconnection across the entire AD brain was found in stage III, affecting more strongly the same memory subnetwork appearing in stage II, plus the other new subnetworks,including the default mode network, medial visual network, frontoparietal regions and striatum. Our results are consistent with a scenario where progressive alterations of connectivity arise as the disease severity increases and provide the brain areas possibly involved in such a degenerative process. Further studies applying the same strategy to longitudinal data are needed to fully confirm this scenario.

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