Voxelwise Meta-Analysis of Gray Matter Abnormalities in Mild Cognitive Impairment and Subjective Cognitive Decline Using Activation Likelihood Estimation

2020 ◽  
Vol 77 (4) ◽  
pp. 1495-1512
Author(s):  
Shanshan Chen ◽  
Wenwen Xu ◽  
Chen Xue ◽  
Guanjie Hu ◽  
Wenying Ma ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cognitive Decline ◽  
Gray Matter ◽  
Meta Analysis ◽  
Likelihood Estimation ◽  
Subjective Cognitive Decline ◽  
Activation Likelihood Estimation ◽  
Amnestic Mci ◽  
The Right

Background: Voxel-based morphometry studies have not yielded consistent results among patients with mild cognitive impairment (MCI) and subjective cognitive decline (SCD). Objective: Therefore, we aimed to conduct a meta-analysis of gray matter (GM) abnormalities acquired from these studies to determine their respective neuroanatomical changes. Methods: We systematically searched for voxel-based whole-brain morphometry studies that compared MCI or SCD subjects with healthy controls in PubMed, Web of Science, and EMBASE databases. We used the coordinate-based method of activation likelihood estimation to determine GM changes in SCD, MCI, and MCI sub-groups (amnestic MCI and non-amnestic MCI). Results: A total of 45 studies were included in our meta-analysis. In the MCI group, we found structural atrophy of the bilateral hippocampus, parahippocampal gyrus (PHG), amygdala, right lateral globus pallidus, right insula, and left middle temporal gyrus. The aMCI group exhibited GM atrophy in the bilateral hippocampus, PHG, and amygdala. The naMCI group presented with structural atrophy in the right putamen, right insula, right precentral gyrus, left medial/superior frontal gyrus, and left anterior cingulate. The right lingual gyrus, right cuneus, and left medial frontal gyrus were atrophic GM regions in the SCD group. Conclusion: Our meta-analysis identified unique patterns of neuroanatomical alternations in both the MCI and SCD group. Structural changes in SCD patients provide new evidence for the notion that subtle impairment of visual function, perception, and cognition may be related to early signs of cognitive impairment. In addition, our findings provide a foundation for future targeted interventions at different stages of preclinical Alzheimer’s disease.

scholarly journals Neuroimaging in Parkinson’s disease dementia: connecting the dots

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Rimona S Weil ◽  
Joey K Hsu ◽  
Ryan R Darby ◽  
Louis Soussand ◽  
Michael D Fox
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Meta Analysis ◽  
Likelihood Estimation ◽  
Brain Network ◽  
Activation Likelihood Estimation ◽  
Stage Specificity ◽  
Network Mapping

Abstract Dementia is a common and devastating symptom of Parkinson’s disease but the anatomical substrate remains unclear. Some evidence points towards hippocampal involvement but neuroimaging abnormalities have been reported throughout the brain and are largely inconsistent across studies. Here, we test whether these disparate neuroimaging findings for Parkinson’s disease dementia localize to a common brain network. We used a literature search to identify studies reporting neuroimaging correlates of Parkinson’s dementia (11 studies, 385 patients). We restricted our search to studies of brain atrophy and hypometabolism that compared Parkinson’s patients with dementia to those without cognitive involvement. We used a standard coordinate-based activation likelihood estimation meta-analysis to assess for consistency in the neuroimaging findings. We then used a new approach, coordinate-based network mapping, to test whether neuroimaging findings localized to a common brain network. This approach uses resting-state functional connectivity from a large cohort of normative subjects (n = 1000) to identify the network of regions connected to a reported neuroimaging coordinate. Activation likelihood estimation meta-analysis failed to identify any brain regions consistently associated with Parkinson’s dementia, showing major heterogeneity across studies. In contrast, coordinate-based network mapping found that these heterogeneous neuroimaging findings localized to a specific brain network centred on the hippocampus. Next, we tested whether this network showed symptom specificity and stage specificity by performing two further analyses. We tested symptom specificity by examining studies of Parkinson’s hallucinations (9 studies, 402 patients) that are frequently co-morbid with Parkinson’s dementia. We tested for stage specificity by using studies of mild cognitive impairment in Parkinson’s disease (15 studies, 844 patients). Coordinate-based network mapping revealed that correlates of visual hallucinations fell within a network centred on bilateral lateral geniculate nucleus and correlates of mild cognitive impairment in Parkinson’s disease fell within a network centred on posterior default mode network. In both cases, the identified networks were distinct from the hippocampal network of Parkinson’s dementia. Our results link heterogeneous neuroimaging findings in Parkinson’s dementia to a common network centred on the hippocampus. This finding was symptom and stage-specific, with implications for understanding Parkinson’s dementia and heterogeneity of neuroimaging findings in general.

scholarly journals Divergent Connectivity Changes in Gray Matter Structural Covariance Networks in Subjective Cognitive Decline, Amnestic Mild Cognitive Impairment, and Alzheimer’s Disease

2021 ◽  
Vol 13 ◽  
Author(s):  
Zhenrong Fu ◽  
Mingyan Zhao ◽  
Yirong He ◽  
Xuetong Wang ◽  
Jiadong Lu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cognitive Decline ◽  
Gray Matter ◽  
Amnestic Mild Cognitive Impairment ◽  
Subjective Cognitive Decline ◽  
Structural Covariance ◽  
Cholinergic Basal Forebrain

Alzheimer’s disease (AD) has a long preclinical stage that can last for decades prior to progressing toward amnestic mild cognitive impairment (aMCI) and/or dementia. Subjective cognitive decline (SCD) is characterized by self-experienced memory decline without any evidence of objective cognitive decline and is regarded as the later stage of preclinical AD. It has been reported that the changes in structural covariance patterns are affected by AD pathology in the patients with AD and aMCI within the specific large-scale brain networks. However, the changes in structural covariance patterns including normal control (NC), SCD, aMCI, and AD are still poorly understood. In this study, we recruited 42 NCs, 35 individuals with SCD, 43 patients with aMCI, and 41 patients with AD. Gray matter (GM) volumes were extracted from 10 readily identifiable regions of interest involved in high-order cognitive function and AD-related dysfunctional structures. The volume values were used to predict the regional densities in the whole brain by using voxel-based statistical and multiple linear regression models. Decreased structural covariance and weakened connectivity strength were observed in individuals with SCD compared with NCs. Structural covariance networks (SCNs) seeding from the default mode network (DMN), salience network, subfields of the hippocampus, and cholinergic basal forebrain showed increased structural covariance at the early stage of AD (referring to aMCI) and decreased structural covariance at the dementia stage (referring to AD). Moreover, the SCN seeding from the executive control network (ECN) showed a linearly increased extent of the structural covariance during the early and dementia stages. The results suggest that changes in structural covariance patterns as the order of NC-SCD-aMCI-AD are divergent and dynamic, and support the structural disconnection hypothesis in individuals with SCD.

scholarly journals Convergent Functional Changes of Default Mode Network in Mild Cognitive Impairment Using Activation Likelihood Estimation

2021 ◽  
Vol 13 ◽  
Author(s):  
Qianqian Yuan ◽  
Wenzhang Qi ◽  
Chen Xue ◽  
Honglin Ge ◽  
Guanjie Hu ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Frontal Lobe ◽  
Default Mode Network ◽  
Meta Analysis ◽  
Low Frequency ◽  
Likelihood Estimation ◽  
Frequency Fluctuation ◽  
Activation Likelihood Estimation ◽  
Default Mode

Background: Mild cognitive impairment (MCI) represents a transitional state between normal aging and dementia disorders, especially Alzheimer's disease (AD). The disruption of the default mode network (DMN) is often considered to be a potential biomarker for the progression from MCI to AD. The purpose of this study was to assess MRI-specific changes of DMN in MCI patients by elucidating the convergence of brain regions with abnormal DMN function.Methods: We systematically searched PubMed, Ovid, and Web of science for relevant articles. We identified neuroimaging studies by using amplitude of low frequency fluctuation /fractional amplitude of low frequency fluctuation (ALFF/fALFF), regional homogeneity (ReHo), and functional connectivity (FC) in MCI patients. Based on the activation likelihood estimation (ALE) algorithm, we carried out connectivity modeling of coordination-based meta-analysis and functional meta-analysis.Results: In total, this meta-analysis includes 39 articles on functional neuroimaging studies. Using computer software analysis, we discovered that DMN changes in patients with MCI mainly occur in bilateral inferior frontal lobe, right medial frontal lobe, left inferior parietal lobe, bilateral precuneus, bilateral temporal lobe, and parahippocampal gyrus (PHG).Conclusions: Herein, we confirmed the presence of DMN-specific damage in MCI, which is helpful in revealing pathology of MCI and further explore mechanisms of conversion from MCI to AD. Therefore, we provide a new specific target and direction for delaying conversion from MCI to AD.

Identification of odors, faces, cities and naming of objects in patients with subjective cognitive decline, mild cognitive impairment and Alzheimer´s disease: a longitudinal study

2018 ◽  
Vol 31 (04) ◽  
pp. 537-549 ◽  
Author(s):  
R. Tahmasebi ◽  
S. Zehetmayer ◽  
G. Pusswald ◽  
G. Kovacs ◽  
E. Stögmann ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cognitive Decline ◽  
Semantic Memory ◽  
Predictive Accuracy ◽  
Olfactory Dysfunction ◽  
Subjective Cognitive Decline ◽  
Amnestic Mci ◽  
Olfactory Identification

ABSTRACTObjective:Recent studies have tried to find a reliable way of predicting the development of Alzheimer´s Disease (AD) among patients with mild cognitive impairment (MCI), often focusing on olfactory dysfunction or semantic memory. Our study aimed to validate these findings while also comparing the predictive accuracy of olfactory and semantic assessments for this purpose.Method:Six hundred fifty patients (median age 68, 58% females) including controls, SCD (subjective cognitive decline), non-amnestic MCI (naMCI), amnestic MCI (aMCI), and AD patients were tested for olfactory dysfunction by means of odor identification testing and semantic memory. Of those 650 patients, 120 participants with SCD, naMCI, or aMCI at baseline underwent a follow-up examination after two years on average. Of these 120 patients, 12% had developed AD at follow-up (converters), while 88% did not develop AD at follow-up (non-converters).Results:Analysis showed a significant difference only for initial olfactory identification between converters and non-converters. Sensitivity of impairment of olfactory identification for AD prediction was low at 46.2%, although specificity was high at 81.9%. Semantic memory impairment at baseline was not significantly related to AD conversion, although, when naming objects, significant differences were found between AD patients and all other groups and between naMCI and aMCI patients compared to controls and SCD patients.Conclusions:Objective olfactory assessments are promising instruments for predicting the conversion to AD among MCI patients. However, due to their low sensitivity and high specificity, a combination with other neuropsychological tests might lead to an improved predictive accuracy. Further longitudinal studies with more participants are required to investigate the usefulness of semantic memory tests in this case.

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