Artificial intelligence and machine learning (AI/ML) are paving the way to help unravel the complexities of the human brain and shed light on the underpinnings of atypical behaviors. This is critical for early detection of mental illnesses and personalized treatment prescription. Here, key AI/ML strategies to leverage the richness of brain data are discussed, including multimodal fusion, multidimensional and deep latent representations, as well as multi-level linkage detection. Each of these is presented through a general overarching concept of subspaces. Examples from original research and the brain imaging field at large illustrate the application of each strategy to brain data from multiple modalities. Markedly, these approaches tackle challenges that extend readily to other fields outside of brain imaging.
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