Pub Date : 2025-12-19DOI: 10.1109/MSP.2025.3640720
{"title":"SPS Social Media","authors":"","doi":"10.1109/MSP.2025.3640720","DOIUrl":"10.1109/MSP.2025.3640720","url":null,"abstract":"","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"42 5","pages":"7-7"},"PeriodicalIF":9.6,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11306192","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Brain foundation models (BFMs) represent a transformative paradigm in computational neuroscience that leverages large-scale pretraining on diverse neural signals to achieve robust generalization across tasks, modalities, and experimental contexts. This survey article establishes the first comprehensive definition and framework for BFMs, systematically examining their construction, core methodologies, and applications. We present key approaches for data processing and training strategies alongside diverse applications spanning brain decoding and scientific discovery. Through critical analysis of recent methodological innovations, we identify fundamental challenges that must be addressed to realize the full potential of BFMs, including advancing data quality and standardization, optimizing model architectures, improving training efficiency, and enhancing interpretability. By bridging the gap between neuroscience and artificial intelligence (AI), BFMs present unprecedented opportunities to revolutionize brain research, clinical diagnostics, and therapeutic interventions. This article serves as a foundational reference for researchers and practitioners seeking to understand and advance this emerging field.
脑基础模型（Brain foundation models，简称BFMs）代表了计算神经科学的一种变革范式，它利用对不同神经信号的大规模预训练来实现跨任务、模式和实验环境的鲁棒泛化。这篇综述文章建立了bfm的第一个综合定义和框架，系统地检查了它们的构造、核心方法和应用。我们提出了数据处理和训练策略的关键方法，以及跨越大脑解码和科学发现的各种应用。通过对最近方法创新的批判性分析，我们确定了必须解决的基本挑战，以实现bfm的全部潜力，包括提高数据质量和标准化，优化模型架构，提高训练效率和增强可解释性。通过弥合神经科学和人工智能（AI）之间的差距，bfm为彻底改变大脑研究、临床诊断和治疗干预提供了前所未有的机会。本文为寻求理解和推进这一新兴领域的研究人员和实践者提供了基础参考。
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Pub Date : 2025-12-19DOI: 10.1109/MSP.2025.3640755
{"title":"SPS Advance Your Career","authors":"","doi":"10.1109/MSP.2025.3640755","DOIUrl":"10.1109/MSP.2025.3640755","url":null,"abstract":"","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"42 5","pages":"C3-C3"},"PeriodicalIF":9.6,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11306187","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Domain-Randomized Deep Learning for Neuroimage Analysis: Selecting Training Strategies, Navigating Challenges, and Maximizing Benefits","authors":"Malte Hoffmann","doi":"10.1109/MSP.2025.3590806","DOIUrl":"10.1109/MSP.2025.3590806","url":null,"abstract":"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.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"42 4","pages":"78-90"},"PeriodicalIF":9.6,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145593217","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}
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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