Evidence map›Paper›PMID 41493686›Full record

ArticleMagma (New York, N.Y.)2026

Inclusion of intracranial volume as a covariate feature improves MRI-based Alzheimer's disease classification.

Yongha Gi, A Hyun Jung, Hyungjin Lim, Sangyoon Park, Jeongwon Lee, Jong Hyun Kim, Byung-Jo Kim, Seol-Hee Baek, Myonggeun Yoon

Abstract read
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In one paragraph

Article in Magma (New York, N.Y.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Yongha GiDepartment of Biomedical Engineering, Korea University, Seoul, Republic of Korea.
A Hyun JungDepartment of Biomedical Engineering, Korea University, Seoul, Republic of Korea.
Hyungjin LimDepartment of Biomedical Engineering, Korea University, Seoul, Republic of Korea.
Sangyoon ParkDepartment of Biomedical Engineering, Korea University, Seoul, Republic of Korea.
Jeongwon LeeDepartment of Biomedical Engineering, Korea University, Seoul, Republic of Korea.
Jong Hyun KimFieldCure Co., Ltd., Seoul, Republic of Korea.
Byung-Jo KimCollege of Medicine, Korea University, Seoul, Republic of Korea.
Seol-Hee BaekCollege of Medicine, Korea University, Seoul, Republic of Korea. virgo0906@korea.ac.kr.
Myonggeun YoonDepartment of Biomedical Engineering, Korea University, Seoul, Republic of Korea. radioyoon@korea.ac.kr.ORCID http://orcid.org/0000-0002-4230-9458

Funding

Korea Medical Device Development Fund RS-2023-00254868Ministry of Science and ICT, South Korea NRF-2021R1A2C2008695
6 · The paper itself

Abstract

objectiveStructural MRI-based regional volumes are widely used for Alzheimer's disease (AD) classification, but inter-individual variability in intracranial volume (ICV) introduces confounding. Traditional adjustment methods use region-of-interest (ROI)/ICV ratios or residual adjustment during pre-processing, yet no consensus exists on the optimal method. This study tests whether explicitly including ICV as a covariate (ROI + ICV) improves classification compared with ratio, residual adjustment, and the unadjusted baseline. MATERIALS AND

methodsT1-weighted MRIs from ADNI1 (n = 1423) and MIRIAD (n = 69) were processed with FreeSurfer to extract eight AD-related ROI volumes and ICV. Four feature configurations (ROI-only, ROI/ICV, Residual ROI, ROI + ICV) were benchmarked across six classifiers for cognitive normal (CN)-AD, CN-mild cognitive impairment (MCI), and MCI-AD. Performance was assessed with AUROC and F1 using Friedman and post hoc tests. In addition, feature attribution was examined with permutation importance and SHAP.

resultsROI + ICV consistently produced the largest performance gains over ROI-only in CN-AD and CN-MCI, outperforming ratio and residual adjustment across most classifiers. These improvements generalized to the independent MIRIAD dataset. SHAP analyses showed that the directional effect of ICV reversed across strategies: under ratio or residual adjustment, larger ICV decreased AD probability, whereas in ROI + ICV, larger ICV increased it. This highlights ICV's contextual influence on model decisions. DISCUSSION: Pre-processing-based adjustments do not fully remove ICV effects and can distort ROI-ICV relationships. Explicit covariate inclusion avoids these issues and yields more consistent, generalizable improvements. Thus, ICV should be modeled rather than removed, making ROI + ICV the preferred default ICV-handling strategy for MRI-based AD classification.

Indexed as

Alzheimer DiseaseBrainMagnetic Resonance ImagingAgedAged, 80 and overAlgorithmsCognitive DysfunctionFemaleHumansImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedMaleOrgan SizeReproducibility of ResultsAlzheimer’s diseaseCovariate modelingIntracranial volumeMachine learningStructural MRI

Identifiers

PMID41493686

What OpenQuestion holds

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Registered trials

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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.