Dataset Information

Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Cognitively Unimpaired Individuals Using Multi-feature Kernel Discriminant Dictionary Learning.

ABSTRACT: Accurate classification of either patients with Alzheimer's disease (AD) or patients with mild cognitive impairment (MCI), the prodromal stage of AD, from cognitively unimpaired (CU) individuals is important for clinical diagnosis and adequate intervention. The current study focused on distinguishing AD or MCI from CU based on the multi-feature kernel supervised within-Class-similar discriminative dictionary learning algorithm (MKSCDDL), which we introduced in a previous study, demonstrating that MKSCDDL had superior performance in face recognition. Structural magnetic resonance imaging (sMRI), fluorodeoxyglucose (FDG) positron emission tomography (PET), and florbetapir-PET data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were all included for classification of AD vs. CU, MCI vs. CU, as well as AD vs. MCI (113 AD patients, 110 MCI patients, and 117 CU subjects). By adopting MKSCDDL, we achieved a classification accuracy of 98.18% for AD vs. CU, 78.50% for MCI vs. CU, and 74.47% for AD vs. MCI, which in each instance was superior to results obtained using several other state-of-the-art approaches (MKL, JRC, mSRC, and mSCDDL). In addition, testing time results outperformed other high quality methods. Therefore, the results suggested that the MKSCDDL procedure is a promising tool for assisting early diagnosis of diseases using neuroimaging data.

SUBMITTER: Li Q

PROVIDER: S-EPMC5767247 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Cognitively Unimpaired Individuals Using Multi-feature Kernel Discriminant Dictionary Learning.

Li Qing Q Wu Xia X Xu Lele L Chen Kewei K Yao Li L

Frontiers in computational neuroscience 20180109

Accurate classification of either patients with Alzheimer's disease (AD) or patients with mild cognitive impairment (MCI), the prodromal stage of AD, from cognitively unimpaired (CU) individuals is important for clinical diagnosis and adequate intervention. The current study focused on distinguishing AD or MCI from CU based on the multi-feature kernel supervised within-Class-similar discriminative dictionary learning algorithm (MKSCDDL), which we introduced in a previous study, demonstrating tha ...[more]

PMID: 29375356

Similar Datasets

Project description:BackgroundUnderstanding the changes that occur in the transitional stage between absent and overt amyloid-β (Aβ) pathology within the Alzheimer's continuum is crucial to develop therapeutic and preventive strategies. The objective of this study is to test whether cognitively unimpaired individuals with a low burden of Aβ pathology have a distinct CSF, structural, and functional neuroimaging biomarker profile.MethodsCross-sectional study of 318 middle-aged, cognitively unimpaired individuals from the ALFA+ cohort. We measured CSF Aβ42/40, phosphorylated tau (p-tau), total tau (t-tau), neurofilament light (NfL), neurogranin, sTREM2, YKL40, GFAP, IL6, S100B, and α-synuclein. Participants also underwent cognitive assessments, APOE genotyping, structural MRI, [18F]-FDG, and [18F]-flutemetamol PET. To ensure the robustness of our results, we used three definitions of low burden of Aβ pathology: (1) positive CSF Aβ42/40 and < 30 Centiloids in Aβ PET, (2) positive CSF Aβ42/40 and negative Aβ PET visual read, and (3) 20-40 Centiloid range in Aβ PET. We tested CSF and neuroimaging biomarker differences between the low burden group and the corresponding Aβ-negative group, adjusted by age and sex.ResultsThe prevalence and demographic characteristics of the low burden group differed between the three definitions. CSF p-tau and t-tau were increased in the low burden group compared to the Aβ-negative in all definitions. CSF neurogranin was increased in the low burden group definitions 1 and 3, while CSF NfL was only increased in the low burden group definition 1. None of the defined low burden groups showed signs of atrophy or glucose hypometabolism. Instead, we found slight increases in cortical thickness and metabolism in definition 2.ConclusionsThere are biologically meaningful Aβ-downstream effects in individuals with a low burden of Aβ pathology, while structural and functional changes are still subtle or absent. These findings support considering individuals with a low burden of Aβ pathology for clinical trials.Trial registrationNCT02485730.

Project description:BackgroundMagnetic resonance imaging (MRI) has unveiled specific alterations at different stages of Alzheimer's disease (AD) pathophysiologic continuum constituting what has been established as "AD signature". To what extent MRI can detect amyloid-related cerebral changes from structural MRI in cognitively unimpaired individuals is still an area open for exploration.MethodLongitudinal 3D-T1 MRI scans were acquired from a subset of the ADNI cohort comprising 403 subjects: 79 controls (Ctrls), 50 preclinical AD (PreAD), and 274 MCI and dementia due to AD (MCI/AD). Amyloid CSF was used as gold-standard measure with established cutoffs (<?192?pg/mL) to establish diagnostic categories. Cognitively unimpaired individuals were defined as Ctrls if were amyloid negative and PreAD otherwise. The MCI/AD group was amyloid positive. Only subjects with the same diagnostic category at baseline and follow-up visits were considered for the study. Longitudinal morphometric analysis was performed using SPM12 to calculate Jacobian determinant maps. Statistical analysis was carried out on these Jacobian maps to identify structural changes that were significantly different between diagnostic categories. A machine learning classifier was applied on Jacobian determinant maps to predict the presence of abnormal amyloid levels in cognitively unimpaired individuals. The performance of this classifier was evaluated using receiver operating characteristic curve analysis and as a function of the follow-up time between MRI scans. We applied a cost function to assess the benefit of using this classifier in the triaging of individuals in a clinical trial-recruitment setting.ResultsThe optimal follow-up time for classification of Ctrls vs PreAD was ?t?>?2.5?years, and hence, only subjects within this temporal span are used for evaluation (15 Ctrls, 10 PreAD). The longitudinal voxel-based classifier achieved an AUC?=?0.87 (95%CI 0.72-0.97). The brain regions that showed the highest discriminative power to detect amyloid abnormalities were the medial, inferior, and lateral temporal lobes; precuneus; caudate heads; basal forebrain; and lateral ventricles.ConclusionsOur work supports that machine learning applied to longitudinal brain volumetric changes can be used to predict, with high precision, the presence of amyloid abnormalities in cognitively unimpaired subjects. Used as a triaging method to identify a fixed number of amyloid-positive individuals, this longitudinal voxel-wise classifier is expected to avoid 55% of unnecessary CSF and/or PET scans and reduce economic cost by 40%.

Project description:Cerebrospinal fluid (CSF) YKL40 and sTREM2 are astroglial and microglial activity biomarkers, respectively. We assessed whether CSF YKL40 and sTREM2 baseline levels are associated with longitudinal brain volume and diffusivity changes in cognitively unimpaired adults. Two brain MRI scans of 36 participants (57 to 78-years old, 12 male) were acquired in a 2-year interval. A?42, p-tau, YKL40 and sTREM2 concentrations in CSF were determined at baseline. We calculated gray and white matter volume changes per year maps (?GM and ?WM, respectively) by means of longitudinal pairwise registration, and mean diffusivity variation per year (?MD) by subtraction. We checked voxel-wise for associations between ?GM, ?WM and ?MD and baseline CSF level of YKL40 and sTREM2 and verified to what extent these associations were modulated by age (YKL40xAGE and sTREM2xAGE interactions). We found a positive association between ?GM and YKL40 in the left inferior parietal region and no association between sTREM2 and ?GM. Negative associations were also observed between ?GM and YKL40xAGE (bilateral frontal areas, left precuneus and left postcentral and supramarginal gyri) and sTREM2xAGE (bilateral temporal and frontal cortex, putamen and left middle cingulate gyrus). We found negative associations between ?WM and YKL40xAGE (bilateral superior longitudinal fasciculus) and sTREM2xAGE (bilateral superior longitudinal fasciculus, left superior corona radiata, retrolenticular external capsule and forceps minor, among other regions) but none between ?WM and neither YKL40 nor sTREM2. ?MD was positively correlated with YKL40 in right orbital region and negatively with sTREM2 in left lingual gyrus and precuneus. In addition, significant associations were found between ?MD and YKL40xAGE (tail of left hippocampus and surrounding areas and right anterior cingulate gyrus) and sTREM2xAGE (right superior temporal gyrus). Areas showing statistically significant differences were disjoint in analyses involving YKL40 and sTREM2. These results suggest that glial biomarkers exert a relevant and distinct influence in longitudinal brain macro- and microstructural changes in cognitively unimpaired adults, which appears to be modulated by age. In younger subjects increased glial markers (both YKL40 and sTREM2) predict a better outcome, as indicated by a decrease in ?GM and ?WM and an increase in ?MD, whereas in older subjects this association is inverted and higher levels of glial markers are associated with a poorer neuroimaging outcome.

Dataset Information

Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Cognitively Unimpaired Individuals Using Multi-feature Kernel Discriminant Dictionary Learning.

Publications

Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Cognitively Unimpaired Individuals Using Multi-feature Kernel Discriminant Dictionary Learning.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure