{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T02:28:37Z","timestamp":1788316117908,"version":"build-2803163510"},"reference-count":82,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T00:00:00Z","timestamp":1737072000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union, NextGenerationEU"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>As the leading cause of dementia worldwide, Alzheimer\u2019s Disease (AD) has prompted significant interest in developing Deep Learning (DL) approaches for its classification. However, it currently remains unclear whether these models rely on established biological indicators. This work compares a novel DL model using structural connectivity (namely, BC-GCN-SE adapted from functional connectivity tasks) with an established model using structural magnetic resonance imaging (MRI) scans (namely, ResNet18). Unlike most studies primarily focusing on performance, our work places explainability at the forefront. Specifically, we define a novel Explainable Artificial Intelligence (XAI) metric, based on gradient-weighted class activation mapping. Its aim is quantitatively measuring how effectively these models fare against established AD biomarkers in their decision-making. The XAI assessment was conducted across 132 brain parcels. Results were compared to AD-relevant regions to measure adherence to domain knowledge. Then, differences in explainability patterns between the two models were assessed to explore the insights offered by each piece of data (i.e., MRI vs. connectivity). Classification performance was satisfactory in terms of both the median true positive (ResNet18: 0.817, BC-GCN-SE: 0.703) and true negative rates (ResNet18: 0.816; BC-GCN-SE: 0.738). Statistical tests (p &lt; 0.05) and ranking of the 15% most relevant parcels revealed the involvement of target areas: the medial temporal lobe for ResNet18 and the default mode network for BC-GCN-SE. Additionally, our findings suggest that different imaging modalities provide complementary information to DL models. This lays the foundation for bioengineering advancements in developing more comprehensive and trustworthy DL models, potentially enhancing their applicability as diagnostic support tools for neurodegenerative diseases.<\/jats:p>","DOI":"10.3390\/bioengineering12010082","type":"journal-article","created":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T03:33:02Z","timestamp":1737084782000},"page":"82","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Biomarker Investigation Using Multiple Brain Measures from MRI Through Explainable Artificial Intelligence in Alzheimer\u2019s Disease Classification"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8237-2443","authenticated-orcid":false,"given":"Davide","family":"Coluzzi","sequence":"first","affiliation":[{"name":"Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy"},{"name":"Dipartimento di Informatica, Universit\u00e0 degli Studi di Milano, 20133 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3190-274X","authenticated-orcid":false,"given":"Valentina","family":"Bordin","sequence":"additional","affiliation":[{"name":"Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8553-2414","authenticated-orcid":false,"given":"Massimo W.","family":"Rivolta","sequence":"additional","affiliation":[{"name":"Dipartimento di Informatica, Universit\u00e0 degli Studi di Milano, 20133 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Igor","family":"Fortel","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, University of Illinois Chicago, Chicago, IL 60612, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7920-4828","authenticated-orcid":false,"given":"Liang","family":"Zhan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex","family":"Leow","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, University of Illinois Chicago, Chicago, IL 60612, USA"},{"name":"Department of Psychiatry, University of Illinois Chicago, Chicago, IL 60612, USA"},{"name":"Department of Computer Science, University of Illinois Chicago, Chicago, IL 60612, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2978-1704","authenticated-orcid":false,"given":"Giuseppe","family":"Baselli","sequence":"additional","affiliation":[{"name":"Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"323","DOI":"10.3233\/JAD-190426","article-title":"Tip of the Iceberg: Assessing the Global Socioeconomic Costs of Alzheimer\u2019s Disease and Related Dementias and Strategic Implications for Stakeholders","volume":"70","author":"Wiley","year":"2019","journal-title":"J. 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