Grey-matter volume as a potential feature for the classification of Alzheimer’s disease and mild cognitive impairment: an exploratory study

Guo, Yane; Zhang, Zengqiang; Zhou, Bo; Wang, Pan; Yao, Hongxiang; Min-shao, Yuan; An, Ning; Dai, Haitao; Wang, Luning; Zhang, Xi; Liu, Yong

doi:10.1007/s12264-013-1432-x

Cited by 43 publications

(32 citation statements)

References 61 publications

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“…There are a number of factors that can hinder MCI classification. Firstly, the pathological changes that occur in dementia can emerge up to 20 years before any symptoms appear; thus, it is possible that some participants included as HCs were in a pre-clinical stage of dementia (Guo et al, 2014). Secondly, the heterogeneity of MCI can lead to considerable diagnostic variability among clinicians (Duara et al, 2010).…”

Section: Discussionmentioning

confidence: 99%

A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment

Farina

Emek‐Savaş

Rueda‐Delgado

et al. 2019

Preprint

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AbstractAlzheimer’s disease (AD) is a neurodegenerative disorder characterised by severe cognitive decline and loss of autonomy. AD is the leading cause of dementia. AD is preceded by mild cognitive impairment (MCI). By 2050, 68% of new dementia cases will occur in low- and middle-income countries. In the absence of objective biomarkers, psychological assessments are typically used to diagnose MCI and AD. However, these require specialist training and rely on subjective judgements. The need for low-cost, accessible and objective tools to aid AD and MCI diagnosis is therefore crucial. Electroencephalography (EEG) has potential as one such tool: it is relatively inexpensive (cf. magnetic resonance imaging; MRI) and is portable. In this study, we collected resting state EEG, structural MRI and rich neuropsychological data from older adults (55+ years) with AD, with MCI and from healthy controls (n~60 per group). Our goal was to evaluate the utility of EEG, relative to MRI, for the classification of MCI and AD. We also assessed the performance of combined EEG and behavioural (Mini-Mental State Examination; MMSE) and structural MRI classification models. Resting state EEG classified AD and HC participants with moderate accuracy (AROC=0.76), with lower accuracy when distinguishing MCI from HC participants (AROC=0.67). The addition of EEG data to MMSE scores had no additional value compared to MMSE alone. Structural MRI out-performed EEG (AD vs HC, AD vs MCI: AROCs=1.00; HC vs MCI: AROC=0.73). Resting state EEG does not appear to be a suitable tool for classifying AD. However, EEG classification accuracy was comparable to structural MRI when distinguishing MCI from healthy aging, although neither were sufficiently accurate to have clinical utility. This is the first direct comparison of EEG and MRI as classification tools in AD and MCI participants.

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Section: Discussionmentioning

confidence: 99%

A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment

Farina

Emek‐Savaş

Rueda‐Delgado

et al. 2019

Preprint

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“…Analysis based on region-of-interest (ROI) measurements and voxel-based morphometry (VBM) is widely used to evaluate morphometric changes [19]. In particular, VBM is sensitive and hypothesis-free in terms of localizing small-scale regional differences in gray matter [20], so it has been commonly applied to the study of gray-matter alterations in AD [21–23]. We also used the combination of VBM analysis and texture analysis to study AD in this paper.…”

Section: Introductionmentioning

confidence: 99%

Brain MR Image Classification for Alzheimer’s Disease Diagnosis Based on Multifeature Fusion

Xiao

Ding

Lan

et al. 2017

Computational and Mathematical Methods in Medicine

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We propose a novel classification framework to precisely identify individuals with Alzheimer's disease (AD) or mild cognitive impairment (MCI) from normal controls (NC). The proposed method combines three different features from structural MR images: gray-matter volume, gray-level cooccurrence matrix, and Gabor feature. These features can obtain both the 2D and 3D information of brains, and the experimental results show that a better performance can be achieved through the multifeature fusion. We also analyze the multifeatures combination correlation technologies and improve the SVM-RFE algorithm through the covariance method. The results of comparison experiments on public Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrate the effectiveness of the proposed method. Besides, it also indicates that multifeatures combination is better than the single-feature method. The proposed features selection algorithm could effectively extract the optimal features subset in order to improve the classification performance.

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“…Alzheimer's disease [58] but rarely in mood disorders. Our results suggest that the amygdala and hippocampus may not be key brain regions associated with UD or BP1.…”

mentioning

confidence: 99%

Grey matter volume abnormalities in patients with bipolar I depressive disorder and unipolar depressive disorder: a voxel-based morphometry study

Cai

Zhang

et al. 2014

Neurosci. Bull.

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Bipolar disorder and unipolar depressive disorder (UD) may be different in brain structure. In the present study, we performed voxel-based morphometry (VBM) to quantify the grey matter volumes in 23 patients with bipolar I depressive disorder (BP1) and 23 patients with UD, and 23 age-, gender-, and education-matched healthy controls (HCs) using magnetic resonance imaging. We found that compared with the HC and UD groups, the BP1 group showed reduced grey matter volumes in the right inferior frontal gyrus and middle cingulate gyrus, while the UD group showed reduced volume in the right inferior frontal gyrus compared to HCs. In addition, correlation analyses revealed that the grey matter volumes of these regions were negatively correlated with the Hamilton depression rating scores. Taken together, the results of our study suggest that decreased grey matter volume of the right inferior frontal gyrus is a common abnormality in BP1 and UD, and decreased grey matter volume in the right middle cingulate gyrus may be specific to BP1.

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Grey-matter volume as a potential feature for the classification of Alzheimer’s disease and mild cognitive impairment: an exploratory study

Cited by 43 publications

References 61 publications

A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment

A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment

Brain MR Image Classification for Alzheimer’s Disease Diagnosis Based on Multifeature Fusion

Grey matter volume abnormalities in patients with bipolar I depressive disorder and unipolar depressive disorder: a voxel-based morphometry study

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