Dataset Information

Structural Brain Imaging Phenotypes of Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) Found by Hierarchical Clustering.

ABSTRACT: A hierarchical clustering algorithm was applied to magnetic resonance images (MRI) of a cohort of 751 subjects having a mild cognitive impairment (MCI), 282 subjects having received Alzheimer's disease (AD) diagnosis, and 428 normal controls (NC). MRIs were preprocessed to gray matter density maps and registered to a stereotactic space. By first rendering the gray matter density maps comparable by regressing out age, gender, and years of education, and then performing the hierarchical clustering, we found clusters displaying structural features of typical AD, cortically-driven atypical AD, limbic-predominant AD, and early-onset AD (EOAD). Among these clusters, EOAD subjects displayed marked cortical gray matter atrophy and atrophy of the precuneus. Furthermore, EOAD subjects had the highest progression rates as measured with ADAS slopes during the longitudinal follow-up of 36 months. Striking heterogeneities in brain atrophy patterns were observed with MCI subjects. We found clusters of stable MCI, clusters of diffuse brain atrophy with fast progression, and MCI subjects displaying similar atrophy patterns as the typical or atypical AD subjects. Bidirectional differences in structural phenotypes were found with MCI subjects involving the anterior cerebellum and the frontal cortex. The diversity of the MCI subjects suggests that the structural phenotypes of MCI subjects would deserve a more detailed investigation with a significantly larger cohort. Our results demonstrate that the hierarchical agglomerative clustering method is an efficient tool in dividing a cohort of subjects with gray matter atrophy into coherent clusters manifesting different structural phenotypes.

SUBMITTER: Karkkainen M

PROVIDER: S-EPMC7708019 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Structural Brain Imaging Phenotypes of Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) Found by Hierarchical Clustering.

Kärkkäinen Mikko M Prakash Mithilesh M Zare Marzieh M Tohka Jussi J Tohka Jussi J For The Alzheimer's Disease Neuroimaging Initiative

International journal of Alzheimer's disease 20201113

A hierarchical clustering algorithm was applied to magnetic resonance images (MRI) of a cohort of 751 subjects having a mild cognitive impairment (MCI), 282 subjects having received Alzheimer's disease (AD) diagnosis, and 428 normal controls (NC). MRIs were preprocessed to gray matter density maps and registered to a stereotactic space. By first rendering the gray matter density maps comparable by regressing out age, gender, and years of education, and then performing the hierarchical clustering ...[more]

PMID: 33299603

Similar Datasets

Project description:To elucidate the key molecules, functions, and pathways that bridge mild cognitive impairment (MCI) and Alzheimer's disease (AD), we investigated open gene expression data sets. Differential gene expression profiles were analyzed and combined with potential MCI- and AD-related gene expression profiles in public databases. Then, weighted gene co-expression network analysis was performed to identify the gene co-expression modules. One module was significantly negatively associated with MCI samples, in which gene ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed that these genes were related to cytosolic ribosome, ribosomal structure, oxidative phosphorylation, AD, and metabolic pathway. The other two modules correlated significantly with AD samples, in which functional and pathway enrichment analysis revealed strong relationships of these genes with cytoplasmic ribosome, protein binding, AD, cancer, and apoptosis. In addition, we regarded the core genes in the module network closely related to MCI and AD as bridge genes and submitted them to protein interaction network analysis to screen for major pathogenic genes according to the connectivity information. Among them, small nuclear ribonucleoprotein D2 polypeptide (SNRPD2), ribosomal protein S3a (RPS3A), S100 calcium binding protein A8 (S100A8), small nuclear ribonucleoprotein polypeptide G (SNRPG), U6 snRNA-associated Sm-like protein LSm3 (LSM3), ribosomal protein S27a (RPS27A), and ATP synthase F1 subunit gamma (ATP5C1) were not only major pathogenic genes of MCI, but also bridge genes. In addition, SNRPD2, RPS3A, S100A8, SNRPG, LSM3, thioredoxin (TXN), proteasome 20S subunit alpha 4 (PSMA4), annexin A1 (ANXA1), DnaJ heat shock protein family member A1 (DNAJA1), and prefoldin subunit 5 (PFDN5) were not only major pathogenic genes of AD, but also bridge genes. Next, we screened for differentially expressed microRNAs (miRNAs) to predict the miRNAs and transcription factors related the MCI and AD modules, respectively. The significance score of miRNAs in each module was calculated using a hypergeometric test to obtain the miRNApivot-Module interaction pair. Thirty-four bridge regulators were analyzed, among which hsa-miR-519d-3p was recognized as the bridge regulator between MCI and AD. Our study contributed to a better understanding of the pathogenic mechanisms of MCI and AD, and might lead to the development of a new strategy for clinical diagnosis and treatment.

Project description:INTRODUCTION:The diagnosis of mild cognitive impairment (MCI) refers to cognitive impairment not meeting dementia criteria. A survey among members of the American Association of Neurology (AAN) showed that MCI was considered a useful diagnosis. Recently, research criteria have been proposed for the diagnosis of Alzheimer's disease (AD) in MCI based on AD biomarkers (prodromal AD/MCI due to AD). The aim of this study was to investigate the attitudes of clinicians in Europe on the clinical utility of MCI and prodromal AD/MCI due to AD criteria. We also investigated whether the prodromal AD/MCI due to AD criteria impacted management of MCI patients. METHODS:An online survey was performed in 2015 among 102 members of the European Academy of Neurology (EAN) and the European Alzheimer's Disease Consortium (EADC). Questions were asked on how often criteria were used, how they were operationalized, how they changed patient management, and what were considered advantages and limitations of MCI and prodromal AD/MCI due to AD. The questionnaire consisted of 47 questions scored on a Likert scale. RESULTS:Almost all respondents (92%) used the MCI diagnosis in clinical practice. Over 80% of the EAN/EADC respondents found a MCI diagnosis useful because it helped to label the cognitive problem, involve patients in planning for the future, and start risk reduction activities. These findings were similar to those reported in the AAN survey. Research criteria for prodromal AD/MCI due to AD were used by 68% of the EAN/EADC respondents. The most common reasons to use the criteria were increased certainty of diagnosis (86%), increased possibilities to provide counseling (51%), facilitation of follow-up planning (48%), start of medical intervention (49%), and response to patients' wish for a diagnosis (41%). Over 70% of the physicians considered that a diagnosis of prodromal AD/MCI due to AD had an added value over the MCI diagnosis. CONCLUSIONS:The diagnostic criteria of MCI and prodromal AD/MCI due to AD are commonly used among EAN/EADC members. The prodromal AD/MCI due to AD were considered clinically useful and impacted patient management and communication.

Dataset Information

Structural Brain Imaging Phenotypes of Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) Found by Hierarchical Clustering.

Publications

Structural Brain Imaging Phenotypes of Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) Found by Hierarchical Clustering.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets