ArticleNeuroImage2013
Maximizing power to track Alzheimer's disease and MCI progression by LDA-based weighting of longitudinal ventricular surface features.
Article in NeuroImage, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Shape analysis of subcortical structures in obsessive-compulsive disorder and the relationship with comorbid anxiety, depression, and medication use: A meta-analysis by the OCD Brain Imaging Consortium.Brain and behavior · 2022Pooled it
- Clinical dementia severity associated with ventricular size is differentially moderated by cognitive reserve in men and women.Alzheimer's research & therapy · 2018Trial
- A 3D Brain Geometry Toolkit for Multisite Neuroimaging Analysis.bioRxiv : the preprint server for biology · 2026Article
- Regional, functional and transcriptomic decoding of multidimensional brain structure alterations in obsessive-compulsive disorder.Nature communications · 2026Article
- Aging-related lateral ventricular shape changes and corresponding mechanical loading derived from longitudinal image registration.Biomechanics and modeling in mechanobiology · 2026Article
- Chemoarchitectural signatures of subcortical shape alterations in generalized epilepsy.Communications biology · 2024Article
- Improved Prediction of Amyloid-β and Tau Burden Using Hippocampal Surface Multivariate Morphometry Statistics and Sparse Coding.Journal of Alzheimer's disease : JAD · 2023Article
- Limitations of clinical trial sample size estimate by subtraction of two measurements.Statistics in medicine · 2022Article
- Predicting Brain Amyloid Using Multivariate Morphometry Statistics, Sparse Coding, and Correntropy: Validation in 1,101 Individuals From the ADNI and OASIS Databases.Frontiers in neuroscience · 2021Article
- Computing Univariate Neurodegenerative Biomarkers with Volumetric Optimal Transportation: A Pilot Study.Neuroinformatics · 2020Article
- Mapping Subcortical Brain Alterations in 22q11.2 Deletion Syndrome: Effects of Deletion Size and Convergence With Idiopathic Neuropsychiatric Illness.The American journal of psychiatry · 2020Article
- Early Detection of Alzheimer's Disease: Detecting Asymmetries with a Return Random Walk Link Predictor.Entropy (Basel, Switzerland) · 2020Article
- Applying surface-based morphometry to study ventricular abnormalities of cognitively unimpaired subjects prior to clinically significant memory decline.NeuroImage. Clinical · 2020Article
- A concise and persistent feature to study brain resting-state network dynamics: Findings from the Alzheimer's Disease Neuroimaging Initiative.Human brain mapping · 2019Article
- Changes in the Hippocampal Volume and Shape in Early-Onset Mild Cognitive Impairment.Psychiatry investigation · 2018Article
- Uncovering Biologically Coherent Peripheral Signatures of Health and Risk for Alzheimer's Disease in the Aging Brain.Frontiers in aging neuroscience · 2018Article
- Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2017Review
- APPROXIMATING PRINCIPAL GENETIC COMPONENTS OF SUBCORTICAL SHAPE.Proceedings. IEEE International Symposium on Biomedical Imaging · 2017Article
- Heritability of the shape of subcortical brain structures in the general population.Nature communications · 2016Article
- Domain adaptation for Alzheimer's disease diagnostics.NeuroImage · 2016Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
We propose a new method to maximize biomarker efficiency for detecting anatomical change over time in serial MRI. Drug trials using neuroimaging become prohibitively costly if vast numbers of subjects must be assessed, so it is vital to develop efficient measures of brain change. A popular measure of efficiency is the minimal sample size (n80) needed to detect 25% change in a biomarker, with 95% confidence and 80% power. For multivariate measures of brain change, we can directly optimize n80 based on a Linear Discriminant Analysis (LDA). Here we use a supervised learning framework to optimize n80, offering two alternative solutions. With a new medial surface modeling method, we track 3D dynamic changes in the lateral ventricles in 2065 ADNI scans. We apply our LDA-based weighting to the results. Our best average n80-in two-fold nested cross-validation-is 104 MCI subjects (95% CI: [94,139]) for a 1-year drug trial, and 75AD subjects [64,102]. This compares favorably with other MRI analysis methods. The standard "statistical ROI" approach applied to the same ventricular surfaces requires 165 MCI or 94AD subjects. At 2 years, the best LDA measure needs only 67 MCI and 52AD subjects, versus 119 MCI and 80AD subjects for the stat-ROI method. Our surface-based measures are unbiased: they give no artifactual additive atrophy over three time points. Our results suggest that statistical weighting may boost efficiency of drug trials that use brain maps.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.