AD diagnosis model based on fusion of heterogeneous brain imaging and genomic data.

Overview

1. School of Computer Science and Technology, Xinjiang University, Urumqi, China
2. Institute of Medical Engineering Interdisciplinary Research, Xinjiang Medical University, Urumqi, China

Abstract

Alzheimer's disease (AD) is a common neurodegenerative disorder in the elderly population, and early screening can effectively delay the progression of the disease. Mild cognitive impairment (MCI) occurs prior to the onset of AD; however, the accuracy of existing MCI-to-AD prediction methods remains relatively low. Additionally, small sample sizes and high feature dimensions often lead to model overfitting, highlighting the need for effective early screening approaches. To address the aforementioned issues, this study integrated non-paired multi-modal features—including clinical indicators from the ADNI database, blood biomarkers, brain region volume features extracted from MRI, and genetic biomarkers from the GEO database—and proposed a gender-corrected random matching strategy. The Random Forest algorithm was adopted to evaluate this strategy, analyze feature importance, and compare the performance of 9 machine learning algorithms based on the top 40 ranked features. The predictive performance of multi-modal data was superior to that of single-modal data, and the proposed strategy achieved favorable results in early AD screening. 16 specific genetic features (e.g., IFI27, EDF1, RAP2A, KIF5C, SERPINA3, FBXW7, IFITM1, ISG15, PSMB3, APOE4, KCNB1, PSPH, HMGN2, S100A13, IFIT3, and CALM1) and 6 brain region volume features ranked high in terms of importance. When validated using paired datasets from ADNI across the 9 algorithms, ensemble learning models demonstrated significantly stronger fitting capabilities. The non-paired multi-modal fusion approach not only expands the sample size but also enhances the generalization ability and robustness of the model. This provides a theoretical basis for the application of this strategy in the field of small-sample medical research.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• geo:GSE84422, at NCBI GEO; found in the end of the paper

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 6 authors, 5 keywords, 2 funders, 34 references.

Cite

This paper

Zhang, Z., Zhang, R., Yang, W., lv, K., Wu, M., & Xu, L. (2026). AD diagnosis model based on fusion of heterogeneous brain imaging and genomic data. Frontiers in neuroscience, 20, 1719390. https://doi.org/10.3389/fnins.2026.1719390

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