Resting-state network dysfunction in Alzheimer's disease: A systematic review and meta-analysis.
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ABSTRACT:
SUBMITTER: Badhwar A
PROVIDER: S-EPMC5436069 | biostudies-literature | 2017
REPOSITORIES: biostudies-literature
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Resting-state network dysfunction in Alzheimer's disease: A systematic review and meta-analysis.
Badhwar AmanPreet A Tam Angela A Dansereau Christian C Orban Pierre P Hoffstaedter Felix F Bellec Pierre P
Alzheimer's & dementia (Amsterdam, Netherlands) 20170418
<h4>Introduction</h4>We performed a systematic review and meta-analysis of the Alzheimer's disease (AD) literature to examine consistency of functional connectivity alterations in AD dementia and mild cognitive impairment, using resting-state functional magnetic resonance imaging.<h4>Methods</h4>Studies were screened using a standardized procedure. Multiresolution statistics were performed to assess the spatial consistency of findings across studies.<h4>Results</h4>Thirty-four studies were inclu ...[more]
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Project description:Alzheimer's disease (AD) is a neurodegenerative disorder that accounts for nearly 70% of the more than 46 million dementia cases estimated worldwide. Although there is no cure for AD, early diagnosis and an accurate characterization of the disease progression can improve the quality of life of AD patients and their caregivers. Currently, AD diagnosis is carried out using standardized mental status examinations, which are commonly assisted by expensive neuroimaging scans and invasive laboratory tests, thus rendering the diagnosis time consuming and costly. Notwithstanding, over the last decade, electroencephalography (EEG) has emerged as a noninvasive alternative technique for the study of AD, competing with more expensive neuroimaging tools, such as MRI and PET. This paper reports on the results of a systematic review on the utilization of resting-state EEG signals for AD diagnosis and progression assessment. Recent journal articles obtained from four major bibliographic databases were analyzed. A total of 112 journal articles published from January 2010 to February 2018 were meticulously reviewed, and relevant aspects of these papers were compared across articles to provide a general overview of the research on this noninvasive AD diagnosis technique. Finally, recommendations for future studies with resting-state EEG were presented to improve and facilitate the knowledge transfer among research groups.
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Project description:Background: The dopaminergic system has been associated with the progression of Alzheimer's disease. But previous studies found inconsistent results regarding the relationship between Alzheimer's disease and dopamine when looking at dopamine receptor concentrations. Objective: The aim of this review was to synthesize, using a random-effects model of meta-analysis, the link between the dopaminergic system and Alzheimer's disease. Methods: A detailed analysis protocol was registered at the PROSPERO database prior to data extraction (CRD42018110798). Electronic databases of PubMed, Embase, Web of Science, and Psyc-ARTICLES were searched up to December 2018 for studies that examined dopamine and dopamine receptors in relation to Alzheimer's disease. Standardized mean differences (SMD) were calculated to assess group differences in the levels of dopaminergic neurometabolites. Results: Seventeen studies met the eligibility criteria. Collectively, they included 512 patients and 500 healthy controls. There were significantly lower levels of dopamine in patients with Alzheimer's disease compared with controls (SMD = -1.56, 95% CI: -2.64 to -0.49). In addition, dopamine 1 receptor (SMD = -5.05, 95% CI: -6.14 to -3.97) and dopamine 2 receptor (SMD = -1.13, 95% CI: -1.52 to -0.74) levels were decreased in patients with Alzheimer's disease compared with controls. The results of network meta-analysis indicated that the rank of correlation with Alzheimer's disease from highest to lowest was dopamine (0.74), dopamine 2 receptor (0.49), dopamine 3 receptor (0.46), dopamine 4 receptor (0.33), dopamine 5 receptor (0.31), and dopamine 1 receptor (0.64). Conclusions: Overall, decreased levels of dopaminergic neurotransmitters were linked with the pathophysiology of Alzheimer's disease. Nonetheless, there is a clear need for more prospective studies to validate these hypotheses.
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Project description:Resting-state fMRI (rs-fMRI) detects functional connectivity (FC) abnormalities that occur in the brains of patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). FC of the default mode network (DMN) is commonly impaired in AD and MCI. We conducted a systematic review aimed at determining the diagnostic power of rs-fMRI to identify FC abnormalities in the DMN of patients with AD or MCI compared with healthy controls (HCs) using machine learning (ML) methods. Multimodal support vector machine (SVM) algorithm was the commonest form of ML method utilized. Multiple kernel approach can be utilized to aid in the classification by incorporating various discriminating features, such as FC graphs based on "nodes" and "edges" together with structural MRI-based regional cortical thickness and gray matter volume. Other multimodal features include neuropsychiatric testing scores, DTI features, and regional cerebral blood flow. Among AD patients, the posterior cingulate cortex (PCC)/Precuneus was noted to be a highly affected hub of the DMN that demonstrated overall reduced FC. Whereas reduced DMN FC between the PCC and anterior cingulate cortex (ACC) was observed in MCI patients. Evidence indicates that the nodes of the DMN can offer moderate to high diagnostic power to distinguish AD and MCI patients. Nevertheless, various concerns over the homogeneity of data based on patient selection, scanner effects, and the variable usage of classifiers and algorithms pose a challenge for ML-based image interpretation of rs-fMRI datasets to become a mainstream option for diagnosing AD and predicting the conversion of HC/MCI to AD.
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Project description:ImportanceMajor depressive disorder (MDD) has been linked to imbalanced communication among large-scale brain networks, as reflected by abnormal resting-state functional connectivity (rsFC). However, given variable methods and results across studies, identifying consistent patterns of network dysfunction in MDD has been elusive.ObjectiveTo investigate network dysfunction in MDD through a meta-analysis of rsFC studies.Data sourcesSeed-based voxelwise rsFC studies comparing individuals with MDD with healthy controls (published before June 30, 2014) were retrieved from electronic databases (PubMed, Web of Science, and EMBASE) and authors contacted for additional data.Study selectionTwenty-seven seed-based voxel-wise rsFC data sets from 25 publications (556 individuals with MDD and 518 healthy controls) were included in the meta-analysis.Data extraction and synthesisCoordinates of seed regions of interest and between-group effects were extracted. Seeds were categorized into seed-networks by their location within a priori functional networks. Multilevel kernel density analysis of between-group effects identified brain systems in which MDD was associated with hyperconnectivity (increased positive or reduced negative connectivity) or hypoconnectivity (increased negative or reduced positive connectivity) with each seed-network.ResultsMajor depressive disorder was characterized by hypoconnectivity within the frontoparietal network, a set of regions involved in cognitive control of attention and emotion regulation, and hypoconnectivity between frontoparietal systems and parietal regions of the dorsal attention network involved in attending to the external environment. Major depressive disorder was also associated with hyperconnectivity within the default network, a network believed to support internally oriented and self-referential thought, and hyperconnectivity between frontoparietal control systems and regions of the default network. Finally, the MDD groups exhibited hypoconnectivity between neural systems involved in processing emotion or salience and midline cortical regions that may mediate top-down regulation of such functions.Conclusions and relevanceReduced connectivity within frontoparietal control systems and imbalanced connectivity between control systems and networks involved in internal or external attention may reflect depressive biases toward internal thoughts at the cost of engaging with the external world. Meanwhile, altered connectivity between neural systems involved in cognitive control and those that support salience or emotion processing may relate to deficits regulating mood. These findings provide an empirical foundation for a neurocognitive model in which network dysfunction underlies core cognitive and affective abnormalities in depression.
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Project description:BackgroundApproximately 35 million people world-wide have Alzheimer's disease and this is projected to nearly double by 2030. Cognitive enhancers, including cholinesterase inhibitors (for example, donepezil, galantamine and rivastigmine) and memantine (N-methyl-D-aspartic acid (NMDA) receptor antagonist) have been approved for the treatment of Alzheimer's disease in many countries. Our objective is to evaluate the comparative effectiveness, safety, and cost of cognitive enhancers for Alzheimer's disease through a systematic review.Methods/designStudies examining the efficacy, safety, and cost of cognitive enhancers compared to placebo, supportive care, and other cognitive enhancers for Alzheimer's patients will be included. The primary outcome is cognition and secondary outcomes include function, behavior, quality of life, safety, and cost. Experimental studies (randomized controlled trials, quasi-randomized controlled trials, controlled clinical trials), quasi-experimental studies (controlled before-after, interrupted time series), and observational studies (cohort, case-control studies) will be eligible for inclusion. Inclusion will not be limited by publication status, time period or language of dissemination.We will search electronic databases (for example, MEDLINE, Cochrane Central Register of Controlled Trials, EMBASE, CINAHL, Ageline) from inception onwards. The electronic database search will be supplemented by searching for grey literature (for example, conference proceedings, searches in Google and relevant organization websites). Two reviewers will independently screen the studies for inclusion using the eligibility criteria established a priori and independently extract data. Risk of bias will be assessed using the Cochrane Risk of Bias tool for experimental and quasi-experimental studies and the Newcastle Ottawa Scale for observational studies. If deemed appropriate, meta-analysis and network (that is, indirect comparisons) meta-analysis will be conducted.DiscussionOur systematic review will inform the decision of healthcare providers, policy-makers, Alzheimer's patients and family members about the use of cognitive enhancers, by improving their understanding of the costs, benefits and harms that are associated with these agents. PROSPERO REGISTRY NUMBER: CRD42012001948.
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Project description:Background and Purpose: Previous studies found inconsistent results regarding the relationship between Alzheimer's disease (AD) and catecholamines, such as dopamine (DA), norepinephrine (NE), and epinephrine (EPI). Therefore, the purpose of this study was to perform a systematic review and meta-analysis to evaluate the results of previous studies on this relationship. Method: Literature retrieval of eligible studies was performed in four databases (Web of Science, PubMed, Embase, and PsycARTICLES). Standardized mean differences (SMDs) were calculated to assess differences in catecholamine concentrations between the AD groups and controls. Results: Thirteen studies met the eligibility criteria. Compared with the controls, significant lower concentrations of NE (SMD = -1.10, 95% CI: -2.01 to -0.18, p = 0.019) and DA (SMD = -1.12, 95% CI: -1.88 to -0.37, p = 0.003) were observed in patients with AD. No difference was found in the concentrations of EPI between the two groups (SMD = -0.74, 95% CI: -1.85 to 0.37, p = 0.189). Conclusion: Overall, these findings are in line with the hypothesis that reduced NE and DA may be an important indicator for AD (Registration number CRD42018112816).
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