Biomarker Investigation Using Multiple Brain Measures from MRI Through Explainable Artificial Intelligence in Alzheimer’s Disease Classification
As the leading cause of dementia worldwide, Alzheimer’s 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...
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MDPI AG
2025-01-01
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| author | Davide Coluzzi Valentina Bordin Massimo W. Rivolta Igor Fortel Liang Zhan Alex Leow Giuseppe Baselli |
| author_facet | Davide Coluzzi Valentina Bordin Massimo W. Rivolta Igor Fortel Liang Zhan Alex Leow Giuseppe Baselli |
| author_sort | Davide Coluzzi |
| collection | DOAJ |
| description | As the leading cause of dementia worldwide, Alzheimer’s 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 (<i>p</i> < 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. |
| format | Article |
| id | doaj-art-86125a9b98944497a6a0e995c6ca7e1f |
| institution | Kabale University |
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| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-86125a9b98944497a6a0e995c6ca7e1f2025-01-24T13:23:12ZengMDPI AGBioengineering2306-53542025-01-011218210.3390/bioengineering12010082Biomarker Investigation Using Multiple Brain Measures from MRI Through Explainable Artificial Intelligence in Alzheimer’s Disease ClassificationDavide Coluzzi0Valentina Bordin1Massimo W. Rivolta2Igor Fortel3Liang Zhan4Alex Leow5Giuseppe Baselli6Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, ItalyDipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, ItalyDipartimento di Informatica, Università degli Studi di Milano, 20133 Milan, ItalyDepartment of Biomedical Engineering, University of Illinois Chicago, Chicago, IL 60612, USADepartment of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USADepartment of Biomedical Engineering, University of Illinois Chicago, Chicago, IL 60612, USADipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, ItalyAs the leading cause of dementia worldwide, Alzheimer’s 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 (<i>p</i> < 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.https://www.mdpi.com/2306-5354/12/1/82explainable artificial intelligenceAlzheimer’s diseasemagnetic resonance imagingstructural connectivityneuroimaging biomarkers |
| spellingShingle | Davide Coluzzi Valentina Bordin Massimo W. Rivolta Igor Fortel Liang Zhan Alex Leow Giuseppe Baselli Biomarker Investigation Using Multiple Brain Measures from MRI Through Explainable Artificial Intelligence in Alzheimer’s Disease Classification Bioengineering explainable artificial intelligence Alzheimer’s disease magnetic resonance imaging structural connectivity neuroimaging biomarkers |
| title | Biomarker Investigation Using Multiple Brain Measures from MRI Through Explainable Artificial Intelligence in Alzheimer’s Disease Classification |
| title_full | Biomarker Investigation Using Multiple Brain Measures from MRI Through Explainable Artificial Intelligence in Alzheimer’s Disease Classification |
| title_fullStr | Biomarker Investigation Using Multiple Brain Measures from MRI Through Explainable Artificial Intelligence in Alzheimer’s Disease Classification |
| title_full_unstemmed | Biomarker Investigation Using Multiple Brain Measures from MRI Through Explainable Artificial Intelligence in Alzheimer’s Disease Classification |
| title_short | Biomarker Investigation Using Multiple Brain Measures from MRI Through Explainable Artificial Intelligence in Alzheimer’s Disease Classification |
| title_sort | biomarker investigation using multiple brain measures from mri through explainable artificial intelligence in alzheimer s disease classification |
| topic | explainable artificial intelligence Alzheimer’s disease magnetic resonance imaging structural connectivity neuroimaging biomarkers |
| url | https://www.mdpi.com/2306-5354/12/1/82 |
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