eScholarship
metadata only
Unbiased tensor-based morphometry: Improved robustness and sample size estimates for Alzheimer's disease clinical trials
Various neuroimaging measures are being evaluated for tracking Alzheimer's disease (AD) progression in therapeutic trials, including measures of structural brain change based on repeated scanning of patients with magnetic resonance imaging (MRI). Methods to compute brain change must be robust to sca
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Predicting categorical and continuous Alzheimer's disease outcomes from a single MRI scan.#218805
- Time’s up: Using data-driven phenotype-severity metrics not time to map progression in the dementias#422472
- Quantum Model Parallelism for MRI-Based Classification of Alzheimer's Disease Stages#1052087
- Utility of a Subdomain‑Based ACE‑III Online Dementia Screening Tool#276709
- Maximizing power to track Alzheimer's disease and MCI progression by LDA-based weighting of longitudinal ventricular surface features#1373305
Record · ID 1250107
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive