Abnormal Resting-State Functional Connectivity Strength in Mild Cognitive Impairment and Its Conversion to Alzheimer’s Disease

Li, Yuxia; Wang, Xiaoni; Li, Yongqiu; Sun, Yu; Sheng, Can; Li, Hongyan; Li, Xuanyu; Yu, Yang; Chen, Guanqun; Hu, Xiaochen; Jing, Bin; Wang, Defeng; Li, Kuncheng; Jessen, Frank; Xia, Mingrui

doi:10.1155/2016/4680972

Cited by 70 publications

(52 citation statements)

References 61 publications

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“…At the global brain level, we found that MCI patients had weaker functional connections in the brain network, which was consistent with previous functional network studies of AD (Li et al, 2016) and MCI (Wang et al, 2013;Lee et al, 2016). Some results demonstrated that these abnormal functional connections were directly related to the global topological attributes of brain networks (Wang et al, 2013).…”

Section: Altered Pattern Of the Brain Network Connectome In MCIsupporting

confidence: 90%

Feature Selection and Combination of Information in the Functional Brain Connectome for Discrimination of Mild Cognitive Impairment and Analyses of Altered Brain Patterns

Mei

et al. 2020

Front. Aging Neurosci.

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Mild cognitive impairment (MCI) is often considered a critical time window for predicting early conversion to Alzheimer's disease (AD). Brain functional connectome data (i.e., functional connections, global and nodal graph metrics) based on resting-state functional magnetic resonance imaging (rs-fMRI) provides numerous information about brain networks and has been used to discriminate normal controls (NCs) from subjects with MCI. In this paper, Student's t-tests and group-least absolute shrinkage and selection operator (group-LASSO) were used to extract functional connections with significant differences and the most discriminative network nodes, respectively. Based on group-LASSO, the middle temporal, inferior temporal, lingual, posterior cingulate, and middle frontal gyri were the most predominant brain regions for nodal observation in MCI patients. Nodal graph metrics (within-module degree, participation coefficient, and degree centrality) showed the maximum discriminative ability. To effectively combine the multipattern information, we employed the multiple kernel learning support vector machine (MKL-SVM). Combined with functional connectome information, the MKL-SVM achieved a good classification performance (area under the receiving operating characteristic curve = 0.9728). Additionally, the altered brain connectome pattern revealed that functional connectivity was generally decreased in the whole-brain network, whereas graph theory topological attributes of some special nodes in the brain network were increased in MCI patients. Our findings demonstrate that optimal feature selection and combination of all connectome features (i.e., functional connections, global and nodal graph metrics) can achieve good performance in discriminating NCs from MCI subjects. Thus, the combination of functional connections and global and nodal graph metrics of brain networks can predict the occurrence of MCI and contribute to the early clinical diagnosis of AD.

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Section: Altered Pattern Of the Brain Network Connectome In MCIsupporting

confidence: 90%

Feature Selection and Combination of Information in the Functional Brain Connectome for Discrimination of Mild Cognitive Impairment and Analyses of Altered Brain Patterns

Mei

et al. 2020

Front. Aging Neurosci.

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“…The T2DM-specific pattern of reduced CBF (in posterior and anterior regions) is very similar to the pattern observed in patients with MCI and Alzheimer’s disease (AD) (Li et al 2016; Mevel et al 2011). Moreover, in the current study the T2DM-specific pattern of reduction in CBF was associated with insulin resistance, mobility function, and executive function.…”

Section: Discussionsupporting

confidence: 67%

The resting perfusion pattern associates with functional decline in type 2 diabetes

Dai

Duan

Alfaro

et al. 2017

Neurobiology of Aging

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We investigated the relationships between cerebral blood flow (CBF), cognitive and mobility decline in type 2 diabetes mellitus (T2DM) over a two-year period. Seventy-three participants (41 T2DM and 32 controls) were evaluated using volumetric CBF with arterial spin labeling (ASL) perfusion magnetic resonance imaging (pMRI) at baseline and at the two-year follow-up. Regions with significant CBF differences between T2DM participants and controls at baseline were detected using voxel-wise analysis. Correlation analysis was performed to investigate the association between regional CBF and cognitive or mobility performance over the two-year span. Compared to controls, participants with T2DM had decreased CBF in the resting state default mode, visual, and cerebellum networks. Greater decrease in longitudinal CBF values at these regions over a two-year span was associated with worse gait, memory and executive functions, and higher baseline insulin resistance and worse baseline cognitive performance. In T2DM, impairment of resting regional perfusion is closely related to worse cognitive and mobility performance. Insulin resistance may further contribute to regional perfusion deficit in T2DM.

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“…The present study confirms prior findings of reduced functional connectivity in disparate regions of the DMN network in MCI relative to cognitively healthy controls (Bai et al, ; Chhatwal & Sperling, ; De Vogelaere, Santens, Achten, Boon, & Vingerhoets, ; Jin, Pelak, & Cordes, ; Li et al, ; Sorg et al, ; Weiler et al, ), while extending this line of work to demonstrate that regional connectivity patterns differed both within and between groups as a function of WMSA volume and cortical thickness. Both structural measures differentially influenced group differences in functional connectivity between the precuneus‐seed region and the LTC, IPL, and pCC, with WMSA exhibiting the strongest effect on DMN connectivity within mPFC regions.…”

Section: Discussionsupporting

confidence: 91%

The effect of white matter signal abnormalities on default mode network connectivity in mild cognitive impairment

Wang

Williams

Stephens

et al. 2019

Human Brain Mapping

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Regions within the default mode network (DMN) are particularly vulnerable to Alzheimer's disease pathology and mechanisms of DMN disruption in mild cognitive impairment (MCI) are still unclear. White matter lesions are presumed to be mechanistically linked to vascular dysfunction whereas cortical atrophy may be related to neurodegeneration. We examined associations between DMN seed‐based connectivity, white matter lesion load, and cortical atrophy in MCI and cognitively healthy controls. MCI showed decreased functional connectivity (FC) between the precuneus‐seed and bilateral lateral temporal cortex (LTC), medial prefrontal cortex (mPFC), posterior cingulate cortex, and inferior parietal lobe compared to those with controls. When controlling for white matter lesion volume, DMN connectivity differences between groups were diminished within bilateral LTC, although were significantly increased in the mPFC explained by significant regional associations between white matter lesion volume and DMN connectivity only in the MCI group. When controlling for cortical thickness, DMN FC was similarly decreased across both groups. These findings suggest that white matter lesions and cortical atrophy are differentially associated with alterations in FC patterns in MCI. Associations between white matter lesions and DMN connectivity in MCI further support at least a partial but important vascular contribution to age‐associated neural and cognitive impairment.

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Abnormal Resting-State Functional Connectivity Strength in Mild Cognitive Impairment and Its Conversion to Alzheimer’s Disease

Cited by 70 publications

References 61 publications

Feature Selection and Combination of Information in the Functional Brain Connectome for Discrimination of Mild Cognitive Impairment and Analyses of Altered Brain Patterns

Feature Selection and Combination of Information in the Functional Brain Connectome for Discrimination of Mild Cognitive Impairment and Analyses of Altered Brain Patterns

The resting perfusion pattern associates with functional decline in type 2 diabetes

The effect of white matter signal abnormalities on default mode network connectivity in mild cognitive impairment

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