The paper "Multimodal Fusion of Brain Imaging Data: Methods and Applications" by Na Luo, Weiyang Shi, Zhengyi Yang, Ming Song, and Tianzi Jiang reviews advanced machine learning methodologies for fusing multimodal brain imaging data, categorized into unsupervised and supervised learning strategies. The authors discuss the importance of combining multiple modalities to capture both inter-modality and intra-modality information, which is crucial for understanding the structural and functional characteristics of the brain in health and disease. The paper covers four main topics: 1. **Methodologies**: It reviews representative multimodal brain imaging fusion technologies, including correlation-based, clustering-based, and data reconstruction methods. 2. **Atlasing via multimodal brain imaging**: It discusses brain parcellations at both macro-level and micro-level based on anatomical structure, function activation, connectivity, or multiple modalities. 3. **Multimodal fusion in studying cognition and development**: It explores how multimodal fusion methods improve the prediction and understanding of behavioral phenotypes and brain aging. 4. **Multimodal fusion in brain disorders**: It elaborates on how multimodal fusion helps accelerate the exploration of underlying biological mechanisms of brain diseases. The authors highlight the benefits of multimodal fusion in providing a cumulative understanding of complex brain networks on different temporal and spatial scales. They also discuss the challenges posed by multi-scale and big data, emphasizing the need for developing new models and platforms to address these challenges. The paper concludes with a discussion on emerging trends and future directions in the field of multimodal brain imaging fusion.The paper "Multimodal Fusion of Brain Imaging Data: Methods and Applications" by Na Luo, Weiyang Shi, Zhengyi Yang, Ming Song, and Tianzi Jiang reviews advanced machine learning methodologies for fusing multimodal brain imaging data, categorized into unsupervised and supervised learning strategies. The authors discuss the importance of combining multiple modalities to capture both inter-modality and intra-modality information, which is crucial for understanding the structural and functional characteristics of the brain in health and disease. The paper covers four main topics: 1. **Methodologies**: It reviews representative multimodal brain imaging fusion technologies, including correlation-based, clustering-based, and data reconstruction methods. 2. **Atlasing via multimodal brain imaging**: It discusses brain parcellations at both macro-level and micro-level based on anatomical structure, function activation, connectivity, or multiple modalities. 3. **Multimodal fusion in studying cognition and development**: It explores how multimodal fusion methods improve the prediction and understanding of behavioral phenotypes and brain aging. 4. **Multimodal fusion in brain disorders**: It elaborates on how multimodal fusion helps accelerate the exploration of underlying biological mechanisms of brain diseases. The authors highlight the benefits of multimodal fusion in providing a cumulative understanding of complex brain networks on different temporal and spatial scales. They also discuss the challenges posed by multi-scale and big data, emphasizing the need for developing new models and platforms to address these challenges. The paper concludes with a discussion on emerging trends and future directions in the field of multimodal brain imaging fusion.