Disorders of consciousness are a heterogeneous mixture of different diseases or injuries. Although some indicators and models have been proposed for prognostication, any single method when used alone carries a high risk of false prediction. This study aimed to develop a multidomain prognostic model that combines resting state functional MRI with three clinical characteristics to predict one year-outcomes at the single-subject level. The model discriminated between patients who would later recover consciousness and those who would not with an accuracy of around 88% on three datasets from two medical centers. It was also able to identify the prognostic importance of different predictors, including brain functions and clinical characteristics. To our knowledge, this is the first reported implementation of a multidomain prognostic model that is based on resting state functional MRI and clinical characteristics in chronic disorders of consciousness, which we suggest is accurate, robust, and interpretable.

In recent years, advances in neuroscience and artificial intelligence have paved the way for unprecedented opportunities to understand the complexity of the brain and its emulation using computational systems. Cutting-edge advancements in neuroscience research have revealed the intricate relationship between brain structure and function, and the success of artificial neural networks has highlighted the importance of network architecture. It is now time to bring these together to better understand how intelligence emerges from the multiscale repositories in the brain. In this article, we propose the Digital Twin Brain (DTB)—a transformative platform that bridges the gap between biological and artificial intelligence. It comprises three core elements: the brain structure, which is fundamental to the twinning process, bottom-layer models for generating brain functions, and its wide spectrum of applications. Crucially, brain atlases provide a vital constraint that preserves the brain’s network organization within the DTB. Furthermore, we highlight open questions that invite joint efforts from interdisciplinary fields and emphasize the far-reaching implications of the DTB. The DTB can offer unprecedented insights into the emergence of intelligence and neurological disorders, holds tremendous promise for advancing our understanding of both biological and artificial intelligence, and ultimately can propel the development of artificial general intelligence and facilitate precision mental healthcare.

Neuroimaging data typically include multiple modalities, such as structural or functional magnetic resonance imaging, diffusion tensor imaging, and positron emission tomography, which provide multiple views for observing and analyzing the brain. To leverage the complementary representations of different modalities, multimodal fusion is consequently needed to dig out both inter-modality and intra-modality information. With the exploited rich information, it is becoming popular to combine multiple modality data to explore the structural and functional characteristics of the brain in both health and disease status. In this paper, we first review a wide spectrum of advanced machine learning methodologies for fusing multimodal brain imaging data, broadly categorized into unsupervised and supervised learning strategies. Followed by this, some representative applications are discussed, including how they help to understand the brain arealization, how they improve the prediction of behavioral phenotypes and brain aging, and how they accelerate the biomarker exploration of brain diseases. Finally, we discuss some exciting emerging trends and important future directions. Collectively, we intend to offer a comprehensive overview of brain imaging fusion methods and their successful applications, along with the challenges imposed by multi-scale and big data, which arises an urgent demand on developing new models and platforms.