Structural MRI-based detection of Alzheimer's disease using feature ranking and classification error

Beheshti, Iman; Demirel, Hasan; Farokhian, Farnaz; Yang, Chunlan; Matsuda, Hiroshi

doi:10.1016/j.cmpb.2016.09.019

Cited by 66 publications

(44 citation statements)

References 68 publications

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“…The purpose of any feature‐ranking strategy is to sort the features based on their information and then select an optimal informative subset in order to speed up the learning process and promote the performance of models (Zhou & Wang, 2007). The details of the feature‐ranking strategy were as described (Beheshti & Demirel, 2016; Beheshti, Demirel, Farokhian, Yang, & Matsuda, 2016). The proposed feature selection approach is applied only on training data.…”

Section: Methodsmentioning

confidence: 99%

The association between “Brain‐Age Score” (BAS) and traditional neuropsychological screening tools in Alzheimer's disease

2018

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IntroductionWe present the Brain‐Age Score (BAS) as a magnetic resonance imaging (MRI)‐based index for Alzheimer's disease (AD). We developed a fully automated framework for estimating the BAS in healthy controls (HCs) and individuals with mild cognitive impairment (MCI) or AD, using MRI scans.MethodsWe trained the proposed framework using 385 HCs from the IXI and OASIS datasets and evaluated 146 HCs, 102 stable‐MCI (sMCI), 112 progressive‐MCI (pMCI), and 147 AD patients from the J‐ADNI dataset. We used a correlation test to determine the association between the BAS and four traditional screening tools of AD: the Mini‐Mental State Examination (MMSE), Clinical Dementia Ratio (CDR), Alzheimer's Disease Assessment Score (ADAS), and Functional Assessment Questionnaire (FAQ). Furthermore, we assessed the association between BAS and anatomical MRI measurements: the normalized gray matter (nGM), normalized white matter (nWM), normalized cerebrospinal fluid (nCSF), mean cortical thickness as well as hippocampus volume.ResultsThe correlation results demonstrated that the BAS is in line with traditional screening tools of AD (i.e., the MMSE, CDR, ADAS, and FAQ scores) as well as anatomical MRI measurements (i.e., nGM, nCSF, mean cortical thickness, and hippocampus volume).DiscussionThe BAS may be useful for diagnosing the brain atrophy level and can be a reliable automated index for clinical applications and neuropsychological screening tools.

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Section: Methodsmentioning

confidence: 99%

The association between “Brain‐Age Score” (BAS) and traditional neuropsychological screening tools in Alzheimer's disease

2018

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“…Different imaging models, such as electroencephalography (EEG) [9], functional magnetic resonance imaging (fMRI) [10] and positron emission tomography (PET) [11], have been used to study the progression of disease. The majority of studies have investigated using the structural magnetic resonance imaging (MRI) [12][13][14] that assists in the visualization of degenerative histological changes caused by neurological disorders. The feature extracted from MRI is typically grey matter volumes.…”

Section: Introductionmentioning

confidence: 99%

White Matter Network Alterations in Alzheimer’s Disease Patients

Lama

Lee

2020

Applied Sciences

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Previous studies have revealed the occurrence of alterations of white matter (WM) and grey matter (GM) microstructures in Alzheimer’s disease (AD) and their prodromal state amnestic mild cognitive impairment (MCI). In general, these alterations can be studied comprehensively by modeling the brain as a complex network, which describes many important topological properties, such as the small-world property, modularity, and efficiency. In this study, we systematically investigated white matter abnormalities using unbiased whole brain network analysis. We compared regional and network related WM features between groups of 19 AD and 25 MCI patients and 22 healthy controls (HC) using tract-based spatial statistics (TBSS), network based statistics (NBS) and graph theoretical analysis. We did not find significant differences in fractional anisotropy (FA) between two groups on TBSS analysis. However, observable alterations were noticed at a network level. Brain network measures such as global efficiency and small world properties were low in AD patients compared to HCs.

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“…Various AD studies additionally used the Partial Least Squares technique to transfer the data from a high-dimensional space into a lower dimensional vector [50][51][52][53]. Parallel to these approaches, a feature ranking-based strategy has been proposed for sorting the extracted features from high-dimensional space on the basis of their importance and then selecting the optimal subset of top-ranked features using Fisher criterion [54] and classification error [55].…”

Section: Discussionmentioning

confidence: 99%

Identification of Alzheimer’s Disease on the Basis of a Voxel-Wise Approach

Beheshti

Hossein-Abad

2019

Applied Sciences

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Robust prediction of Alzheimer's disease (AD) helps in the early diagnosis of AD and may support the treatment of AD patients. In this study, for early detection of AD and prediction of mild cognitive impairment (MCI) conversion, we develop an automatic computer-aided diagnosis (CAD) framework based on a merit-based feature selection method through a whole-brain voxel-wise analysis using baseline magnetic resonance imaging (MRI) data. We also explore the impact of different MRI spatial resolution on the voxel-wise metric AD classification and MCI conversion prediction. We assessed the proposed CAD framework using the whole-brain voxel-wise MRI features of 507 J-ADNI participants (146 healthy controls [HCs], 102 individuals with stable MCI [sMCI], 112 with progressive MCI [pMCI], and 147 with AD) among four clinically relevant pairs of diagnostic groups at different imaging resolutions (i.e., 2, 4, 8, and 16 mm). Using a support vector machine classifier through a 10-fold cross-validation strategy at a spatial resolution of 2 mm, the proposed CAD framework yielded classification accuracies of 91.13%, 74.77%, 81.12%, and 81.78% in identifying AD/healthy control, sMCI/pMCI, sMCI/AD, and pMCI/HC, respectively. The experimental results show that a lower spatial resolution (i.e., 2 mm) may provide more robust information to trace the neuronal loss-related brain atrophy in AD.

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Structural MRI-based detection of Alzheimer's disease using feature ranking and classification error

Cited by 66 publications

References 68 publications

The association between “Brain‐Age Score” (BAS) and traditional neuropsychological screening tools in Alzheimer's disease

The association between “Brain‐Age Score” (BAS) and traditional neuropsychological screening tools in Alzheimer's disease

White Matter Network Alterations in Alzheimer’s Disease Patients

Identification of Alzheimer’s Disease on the Basis of a Voxel-Wise Approach

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