Dataset Information

Longitudinal MRI atrophy biomarkers: relationship to conversion in the ADNI cohort.

ABSTRACT: Atrophic changes in early Alzheimer's disease (AD) and amnestic mild cognitive impairment (MCI) have been proposed as biomarkers for detection and monitoring. We analyzed magnetic resonance imaging (MRI) atrophy rate from baseline to 1 year in 4 groups of participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI): AD (n = 152), converters from MCI to probable AD (MCI-C, n = 60), stable MCI (MCI-S, n = 261), and healthy controls (HC, n = 200). Scans were analyzed using multiple methods, including voxel-based morphometry (VBM), regions of interest (ROIs), and automated parcellation, permitting comparison of annual percent change (APC) in neurodegeneration markers. Effect sizes and the sample required to detect 25% reduction in atrophy rates were calculated. The influence of APOE genotype on APC was also evaluated. AD patients and converters from MCI to probable AD demonstrated high atrophy APCs across regions compared with minimal change in healthy controls. Stable MCI subjects showed intermediate atrophy rates. APOE genotype was associated with APC in key regions. In sum, APC rates are influenced by APOE genotype, imminent MCI to AD conversion, and AD-related neurodegeneration.

SUBMITTER: Risacher SL

PROVIDER: S-EPMC2904350 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature

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Longitudinal MRI atrophy biomarkers: relationship to conversion in the ADNI cohort.

Risacher Shannon L SL Shen Li L West John D JD Kim Sungeun S McDonald Brenna C BC Beckett Laurel A LA Harvey Danielle J DJ Jack Clifford R CR Weiner Michael W MW Saykin Andrew J AJ

Neurobiology of aging 20100801 8

Atrophic changes in early Alzheimer's disease (AD) and amnestic mild cognitive impairment (MCI) have been proposed as biomarkers for detection and monitoring. We analyzed magnetic resonance imaging (MRI) atrophy rate from baseline to 1 year in 4 groups of participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI): AD (n = 152), converters from MCI to probable AD (MCI-C, n = 60), stable MCI (MCI-S, n = 261), and healthy controls (HC, n = 200). Scans were analyzed using multiple meth ...[more]

PMID: 20620664

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Project description:MotivationIdentifying the genetic basis of the brain structure, function and disorder by using the imaging quantitative traits (QTs) as endophenotypes is an important task in brain science. Brain QTs often change over time while the disorder progresses and thus understanding how the genetic factors play roles on the progressive brain QT changes is of great importance and meaning. Most existing imaging genetics methods only analyze the baseline neuroimaging data, and thus those longitudinal imaging data across multiple time points containing important disease progression information are omitted.ResultsWe propose a novel temporal imaging genetic model which performs the multi-task sparse canonical correlation analysis (T-MTSCCA). Our model uses longitudinal neuroimaging data to uncover that how single nucleotide polymorphisms (SNPs) play roles on affecting brain QTs over the time. Incorporating the relationship of the longitudinal imaging data and that within SNPs, T-MTSCCA could identify a trajectory of progressive imaging genetic patterns over the time. We propose an efficient algorithm to solve the problem and show its convergence. We evaluate T-MTSCCA on 408 subjects from the Alzheimer's Disease Neuroimaging Initiative database with longitudinal magnetic resonance imaging data and genetic data available. The experimental results show that T-MTSCCA performs either better than or equally to the state-of-the-art methods. In particular, T-MTSCCA could identify higher canonical correlation coefficients and capture clearer canonical weight patterns. This suggests that T-MTSCCA identifies time-consistent and time-dependent SNPs and imaging QTs, which further help understand the genetic basis of the brain QT changes over the time during the disease progression.Availability and implementationThe software and simulation data are publicly available at https://github.com/dulei323/TMTSCCA.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Longitudinal MRI atrophy biomarkers: relationship to conversion in the ADNI cohort.

Publications

Longitudinal MRI atrophy biomarkers: relationship to conversion in the ADNI cohort.

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