ArticleJournal of Alzheimer's disease : JAD2019
Longitudinal Mapping of Cortical Thickness Measurements: An Alzheimer's Disease Neuroimaging Initiative-Based Evaluation Study.
Article in Journal of Alzheimer's disease : JAD, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
- Article
- ComBat-Predict Enhances Generalizability of Neuroimaging Models to New Sites.Human brain mapping · 2026Article
- Test-retest reliability of FreeSurfer measures of neurodegeneration.NeuroImage · 2026Article
- Medial temporal lobe Tau-Neurodegeneration mismatch from structural imaging and plasma biomarkers.Brain : a journal of neurology · 2026Article
- Reproducible Tools and Enhanced Computational Workflows for Batch Effect Evaluation of High-Throughput Data Using BatchQC.bioRxiv : the preprint server for biology · 2026Article
- Spatial covariance of mGluR5 density and structural degeneration in behavioral variant frontotemporal degeneration.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Sex-dependent grey matter atrophy in Alzheimer's disease progression.Brain communications · 2026Article
- Contusions bias cortical thickness estimates after traumatic brain injury: A TRACK-TBI study.NeuroImage. Clinical · 2026Article
- Enrichment of patients with concomitant limbic-predominant age-related TDP-43 encephalopathy (LATE) on the Alzheimer's disease continuum using hippocampal volume.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Tau-Clinical Mismatch Identifies Individuals with Co-Pathology and Predicts Clinical Trajectory.medRxiv : the preprint server for health sciences · 2025Article
- Disparate and shared transcriptomic signatures associated with cortical atrophy in genetic behavioral variant frontotemporal degeneration.Molecular neurodegeneration · 2025Article
- Regional CSF volume quantification using deep learning for comparative analysis of brain atrophy in frontotemporal dementia subtypes.Frontiers in aging neuroscience · 2025Article
- Assessing the role of volumetric brain information in multiple sclerosis progression.Computational and structural biotechnology journal · 2025Article
- Bayesian Tensor Modeling for Image-based Classification of Alzheimer's Disease.Neuroinformatics · 2024Article
- Longitudinally consistent registration and parcellation of cortical surfaces using semi-supervised learning.Medical image analysis · 2024Article
- DeepComBat: A statistically motivated, hyperparameter-robust, deep learning approach to harmonization of neuroimaging data.Human brain mapping · 2024Article
- PARE: A framework for removal of confounding effects from any distance-based dimension reduction method.PLoS computational biology · 2024Article
- Age-Related Brain Atrophy and the Positive Effects of Behavioral Enrichment in Middle-Aged Beagles.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- ANTsX neuroimaging-derived structural phenotypes of UK Biobank.Scientific reports · 2024Article
- Neurological Effects of Repeated Blast Exposure in Special Operations Personnel.Journal of neurotrauma · 2024Article
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11 authors.
Funding
Abstract
Longitudinal studies of development and disease in the human brain have motivated the acquisition of large neuroimaging data sets and the concomitant development of robust methodological and statistical tools for quantifying neurostructural changes. Longitudinal-specific strategies for acquisition and processing have potentially significant benefits including more consistent estimates of intra-subject measurements while retaining predictive power. Using the first phase of the Alzheimer's Disease Neuroimaging Initiative (ADNI-1) data, comprising over 600 subjects with multiple time points from baseline to 36 months, we evaluate the utility of longitudinal FreeSurfer and Advanced Normalization Tools (ANTs) surrogate thickness values in the context of a linear mixed-effects (LME) modeling strategy. Specifically, we estimate the residual variability and between-subject variability associated with each processing stream as it is known from the statistical literature that minimizing the former while simultaneously maximizing the latter leads to greater scientific interpretability in terms of tighter confidence intervals in calculated mean trends, smaller prediction intervals, and narrower confidence intervals for determining cross-sectional effects. This strategy is evaluated over the entire cortex, as defined by the Desikan-Killiany-Tourville labeling protocol, where comparisons are made with the cross-sectional and longitudinal FreeSurfer processing streams. Subsequent linear mixed effects modeling for identifying diagnostic groupings within the ADNI cohort is provided as supporting evidence for the utility of the proposed ANTs longitudinal framework which provides unbiased structural neuroimage processing and competitive to superior power for longitudinal structural change detection.
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Registered trials
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