Abnormal whole-brain functional connection in amnestic mild cognitive impairment patients

Bai, Feng; Liao, Wei; Watson, David; Shi, Yongmei; Wang, Yi; Yue, Chunxian; Teng, Yuhuan; Wu, Di; Yuan, Yonggui; Jia, Jianping

doi:10.1016/j.bbr.2010.09.010

Cited by 72 publications

(61 citation statements)

References 43 publications

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“…Bai and colleagues further divided the bilateral hippocampus into six subregions as seeds and have indicated that decreased FC of these subregions is related to episodic memory declines in aMCI patients. The sensitivity is 83.3% and the specificity 91.7% when differentiating aMCI patients from controls, and the sensitivity is 83.3% and the specificity 83.3% when differentiating progressing from stable aMCI patients [52] . In a recent fMRI study, Xie and colleagues selected the whole hippocampus as the ROI and have also shown diminished hippocampal FC with the right frontal, left temporal, and insular regions.…”

Section: Resting-state Functional Mrimentioning

confidence: 91%

“…Our studies have routinely divided the hippocampus into more precise subregions, established the association of FC with neuropsychological performance, and increased the statistical power by using large samples [52,53] . A longitudinal aMCI study from another research group has also indicated that the left hippocampus has significantly reduced connectivity with the right parahippocampal gyrus and bilateral hippocampus.…”

Section: Resting-state Functional Mrimentioning

confidence: 99%

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Can multi-modal neuroimaging evidence from hippocampus provide biomarkers for the progression of amnestic mild cognitive impairment?

Chen

Zhang

2015

Neurosci. Bull.

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Impaired structure and function of the hippocampus is a valuable predictor of progression from amnestic mild cognitive impairment (aMCI) to Alzheimer's disease (AD). As a part of the medial temporal lobe memory system, the hippocampus is one of the brain regions affected earliest by AD neuropathology, and shows progressive degeneration as aMCI progresses to AD. Currently, no validated biomarkers can precisely predict the conversion from aMCI to AD. Therefore, there is a great need of sensitive tools for the early detection of AD progression. In this review, we summarize the specifi c structural and functional changes in the hippocampus from recent aMCI studies using neurophysiological and neuroimaging data. We suggest that a combination of advanced multi-modal neuroimaging measures in discovering biomarkers will provide more precise and sensitive measures of hippocampal changes than using only one of them. These will potentially affect early diagnosis and disease-modifying treatments. We propose a new sequential and progressive framework in which the impairment spreads from the integrity of fibers to volume and then to function in hippocampal subregions. Meanwhile, this is likely to be accompanied by progressive impairment of behavioral and neuropsychological performance in the progression of aMCI to AD.

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Section: Resting-state Functional Mrimentioning

confidence: 91%

Section: Resting-state Functional Mrimentioning

confidence: 99%

Can multi-modal neuroimaging evidence from hippocampus provide biomarkers for the progression of amnestic mild cognitive impairment?

Chen

Zhang

2015

Neurosci. Bull.

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“…This approach to hippocampal seeding has been employed previously in resting-state studies of neurodegenerative disease. 47,48 Functional magnetic resonance imaging data analyses Preparation and extraction of seed time series for functional connectivity analyses were performed using the CONN Toolbox. This toolbox employs the aCompCor technique for noise correction, 49 a method that avoids the spurious introduction or magnification of negative correlations between voxels associated with global mean signal regression.…”

Section: Region Of Interest (''Seed'') Determinationmentioning

confidence: 99%

“…For example, it has been found that restingstate antiphase synchrony between regions of different networks is impaired in AD, 48,[83][84][85] and the loss of antiphase synchrony between posterior regions and frontal cortex in AD-risk populations may be especially relevant to the development of AD. 47 Hippocampal hypersynchrony with proximal regions and/or other regions of the DMN has been noted in risk groups for AD, including individuals positive for the apolipoprotein Ee4 allele [86][87][88] and mild cognitive impairment (MCI) patients 89,90 as well as individuals with early AD itself. 48 Similarly, increased resting-state connectivity between the PCC and other DMN regions has been noted in elderly individuals with elevated regional brain atrophy and memory complaints, 91 in MCI, 47,92,93 and in the very early stages of AD.…”

Section: Potential Markers Of Long-term Outcomementioning

confidence: 99%

“…47 Hippocampal hypersynchrony with proximal regions and/or other regions of the DMN has been noted in risk groups for AD, including individuals positive for the apolipoprotein Ee4 allele [86][87][88] and mild cognitive impairment (MCI) patients 89,90 as well as individuals with early AD itself. 48 Similarly, increased resting-state connectivity between the PCC and other DMN regions has been noted in elderly individuals with elevated regional brain atrophy and memory complaints, 91 in MCI, 47,92,93 and in the very early stages of AD. 85,94,95 The concordance between the current results and those in neurodegenerative disease orients us to the potential consequences of TBIinduced network changes on long-term neurological status.…”

Section: Potential Markers Of Long-term Outcomementioning

confidence: 99%

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Chronology and Chronicity of Altered Resting-State Functional Connectivity after Traumatic Brain Injury

Venkatesan

Dennis

Hillary

2015

Journal of Neurotrauma

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Whereas traumatic brain injury (TBI) results in widespread disruption of neural networks, changes in regional resting-state functional connectivity patterns after insult remain unclear. Specifically, little is known about the chronology of emergent connectivity alterations and whether they persist after a critical recovery window. We used resting-state functional magnetic resonance imaging and seed-voxel correlational analyses in both cross-sectional and longitudinal designs to probe intrinsic connectivity patterns involving the posterior cingulate cortex (PCC) and hippocampi, regions shown to be important in the default mode network (DMN) and vulnerable to neuropathology. A total of 22 participants in the chronic stage of moderateto-severe TBI and 18 healthy controls were included for cross-sectional study. Longitudinal analyses included 13 individuals in the TBI group for whom data approximately 3 months after injury (subacute) were available. Overall, results indicated dissociable connectivity trajectories of the PCC and hippocampi during recovery from TBI, with PCC alterations characterized by early hypersynchrony with the anterior DMN that is gradually reduced, and hippocampal changes marked by increasing synchrony with proximal cortex and subcortex. The PCC also showed increasing antiphase synchrony with posterior attentional regions, and the hippocampi showed decreasing antiphase synchrony with frontal attentional regions. Antiphase synchrony of the hippocampus and dorsolateral prefrontal cortex at the subacute stage of TBI was positively associated with attentional performance on neuropsychological tests at both the subacute and chronic stages. Our findings highlight the heterogeneity of regional whole-brain connectivity changes after TBI, and suggest that residual connectivity alterations exist in the clinically stable phase of TBI. Parallels between the chronicity of the observed effects and findings in neurodegenerative disease are discussed in the context of potential long-term outcomes of TBI.

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Topological graph kernel on multiple thresholded functional connectivity networks for mild cognitive impairment classification

et al. 2013

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Recently, brain connectivity networks have been used for classification of Alzheimer’s disease and mild cognitive impairment (MCI) from normal controls (NC). In typical connectivity-networks-based classification approaches, local measures of connectivity networks are first extracted from each region-of-interest as network features, which are then concatenated into a vector for subsequent feature selection and classification. However, some useful structural information of network, especially global topological information, may be lost in this type of approaches. To address this issue, in this article, we propose a connectivity-networks-based classification framework to identify accurately the MCI patients from NC. The core of the proposed method involves the use of a new graph-kernel-based approach to measure directly the topological similarity between connectivity networks. We evaluate our method on functional connectivity networks of 12 MCI and 25 NC subjects. The experimental results show that our proposed method achieves a classification accuracy of 91.9%, a sensitivity of 100.0%, a balanced accuracy of 94.0%, and an area under receiver operating characteristic curve of 0.94, demonstrating a great potential in MCI classification, based on connectivity networks. Further connectivity analysis indicates that the connectivity of the selected brain regions is different between MCI patients and NC, that is, MCI patients show reduced functional connectivity compared with NC, in line with the findings reported in the existing studies.

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Abnormal whole-brain functional connection in amnestic mild cognitive impairment patients

Cited by 72 publications

References 43 publications

Can multi-modal neuroimaging evidence from hippocampus provide biomarkers for the progression of amnestic mild cognitive impairment?

Can multi-modal neuroimaging evidence from hippocampus provide biomarkers for the progression of amnestic mild cognitive impairment?

Chronology and Chronicity of Altered Resting-State Functional Connectivity after Traumatic Brain Injury

Topological graph kernel on multiple thresholded functional connectivity networks for mild cognitive impairment classification

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