Pub Date : 2025-12-19DOI: 10.1109/MSP.2025.3640755
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Pub Date : 2025-11-24DOI: 10.1109/MSP.2025.3590806
Malte Hoffmann
Deep learning has revolutionized neuroimage analysis by delivering unprecedented speed and accuracy. However, the narrow scope of many training datasets constrains model robustness and generalizability. This challenge is particularly acute in magnetic resonance imaging (MRI), where image appearance varies widely across pulse sequences and scanner hardware. A recent domain-randomization strategy addresses the generalization problem by training deep neural networks on synthetic images with randomized intensities and anatomical content. By generating diverse data from anatomical segmentation maps, the approach enables models to accurately process image types unseen during training, without retraining or fine-tuning. It has demonstrated effectiveness across modalities including MRI, computed tomography, positron emission tomography, and optical coherence tomography, as well as beyond neuroimaging in ultrasound, electron and fluorescence microscopy, and X-ray microtomography. This tutorial paper reviews the principles, implementation, and potential of the synthesis-driven training paradigm. It highlights key benefits, such as improved generalization and resistance to overfitting, while discussing trade-offs such as increased computational demands. Finally, the article explores practical considerations for adopting the technique, aiming to accelerate the development of generalizable tools that make deep learning more accessible to domain experts without extensive computational resources or machine learning knowledge.
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Pub Date : 2025-11-21DOI: 10.1109/MSP.2025.3605746
Morten Mørup;Evrim Acar;Tülay Adalı
Functional neuroimaging has become a central window into our working mind and how it changes by aging and disease. However, the different measurement modalities in functional neuroimaging are challenged by high dimensionality when compared with sample sizes. Furthermore, they exhibit high degrees of variability across individuals when analyzing multiset functional neuroimaging datasets. Whereas the multiple functional neuroimaging modalities currently available provide complementary views of brain function, their joint analysis remains an important challenge in neuroscience. This survey article highlights prominent modeling methodologies for the discovery of interpretable patterns in such multiset and multimodal functional neuroimaging datasets. The survey highlights prominent modeling strategies from hard to soft coupling for the modeling of these high-dimensional multiset and multimodal functional neuroimaging datasets while emphasizing the importance of model uniqueness as a prerequisite for reliable and reproducible pattern discovery. We also provide future directions of research for interpretable pattern discovery in functional neuroimaging that ultimately can further our understanding of perhaps one of nature’s most intriguing organs, the human brain.
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Pub Date : 2025-11-21DOI: 10.1109/MSP.2025.3596731
Saurabh Sihag;Gonzalo Mateos;Alejandro Ribeiro
Neurodegenerative disorders exhibit correlated patterns of cortical atrophy across the brain, where the degree of atrophy deviates from what is expected in a typically healthy individual. Brain age is a data-driven estimate of biological age derived from neuroimaging datasets. An increasing brain age gap—defined as a higher predicted brain age relative to chronological age—can indicate greater vulnerability to neurodegeneration and cognitive decline. As such, the brain age gap is a promising biomarker for monitoring brain health. However, the practical adoption of brain age gap metrics derived from prevalent machine learning (ML) approaches is limited by various methodological obscurities that stem from opaque decision-making processes and insufficient handling of statistical phenomena inherent to neurodegeneration. This article introduces key mathematical principles for brain age gap prediction from the perspective of graph signal processing (GSP), aiming to address the core challenges hindering the practical use of the brain age gap as a biomarker. In this context, we survey a principled deep learning framework based on coVariance Neural Networks (VNNs) to infer the brain age gap from anatomical features extracted from structural neuroimaging. VNNs operate on the covariance matrix as a graph, and their theoretical foundations are inspired by recent advances in GSP. We demonstrate that brain age gap derived from a VNN-based ML pipeline exhibit stability, transferability across multi-scale datasets, and improved interpretability; all key properties that enhance the reproducibility and transparency required for principled brain age gap prediction in clinical applications.
{"title":"Disentangling Neurodegeneration With Brain Age Gap Prediction Models: A graph signal processing perspective","authors":"Saurabh Sihag;Gonzalo Mateos;Alejandro Ribeiro","doi":"10.1109/MSP.2025.3596731","DOIUrl":"https://doi.org/10.1109/MSP.2025.3596731","url":null,"abstract":"Neurodegenerative disorders exhibit correlated patterns of cortical atrophy across the brain, where the degree of atrophy deviates from what is expected in a typically healthy individual. Brain age is a data-driven estimate of biological age derived from neuroimaging datasets. An increasing brain age gap—defined as a higher predicted brain age relative to chronological age—can indicate greater vulnerability to neurodegeneration and cognitive decline. As such, the brain age gap is a promising biomarker for monitoring brain health. However, the practical adoption of brain age gap metrics derived from prevalent machine learning (ML) approaches is limited by various methodological obscurities that stem from opaque decision-making processes and insufficient handling of statistical phenomena inherent to neurodegeneration. This article introduces key mathematical principles for brain age gap prediction from the perspective of graph signal processing (GSP), aiming to address the core challenges hindering the practical use of the brain age gap as a biomarker. In this context, we survey a principled deep learning framework based on coVariance Neural Networks (VNNs) to infer the brain age gap from anatomical features extracted from structural neuroimaging. VNNs operate on the covariance matrix as a graph, and their theoretical foundations are inspired by recent advances in GSP. We demonstrate that brain age gap derived from a VNN-based ML pipeline exhibit stability, transferability across multi-scale datasets, and improved interpretability; all key properties that enhance the reproducibility and transparency required for principled brain age gap prediction in clinical applications.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"42 4","pages":"58-77"},"PeriodicalIF":9.6,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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