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While deep learning models have shown promise in brain\u00a0age prediction from MRI, data imbalance and model interpretability remain key challenges.\n<\/jats:p>\n          <jats:p>This study investigates the impact of data augmentation (DA) on\u00a0both predictive accuracy and explanation stability in convolutional neural networks (CNNs) for brain age prediction. We compare\u00a0three training strategies: (i) a baseline model, (ii) a model augmented with real MRI scans from OASIS-3, and (iii) a model trained\u00a0with synthetic data generated by a diffusion model. Model performance\u00a0is evaluated using mean absolute error (MAE), while interpretability\u00a0is assessed through Explainable AI (XAI) methods, including DeepSHAP, Grad-CAM, and Occlusion.<\/jats:p>\n          <jats:p>Our findings indicate that synthetic augmentation improves predictive accuracy, particularly for underrepresented age groups (individuals aged 40\u201380 years), while real-data augmentation provides more stable feature attributions. However, differences\u00a0in XAI methods suggest that explanation reliability varies across training strategies.<\/jats:p>\n          <jats:p>These results highlight the trade-offs between accuracy\u00a0and interpretability in AI-driven neuroimaging, emphasizing the need\u00a0for balanced augmentation strategies to develop clinically trustworthy models.<\/jats:p>","DOI":"10.1007\/978-3-032-08330-2_1","type":"book-chapter","created":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T03:10:33Z","timestamp":1760325033000},"page":"3-26","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparing XAI Explanations and\u00a0Synthetic Data Augmentation Strategies in\u00a0Neuroimaging AI"],"prefix":"10.1007","author":[{"given":"Danilo","family":"Danese","sequence":"first","affiliation":[]},{"given":"Giuseppe","family":"Fasano","sequence":"additional","affiliation":[]},{"given":"Angela","family":"Lombardi","sequence":"additional","affiliation":[]},{"given":"Eugenio","family":"Di Sciascio","sequence":"additional","affiliation":[]},{"given":"Tommaso","family":"Di Noia","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,14]]},"reference":[{"key":"1_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.101805","volume":"99","author":"S Ali","year":"2023","unstructured":"Ali, S., et al.: Explainable artificial intelligence (XAI): what we know and what is left to attain trustworthy artificial intelligence. 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