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ABSTRACT Spontaneous neural activity coherently relays information across the brain. Several efforts have been made to understand how spontaneous neural activity evolves at the macro‐scale level as measured by resting‐state functional magnetic resonance imaging (rsfMRI). Previous studies observe the global patterns and flow of information in rsfMRI using methods such as sliding window or temporal lags. However, to our knowledge, no studies have examined spatial propagation patterns evolving with time across multiple overlapping 4D networks. Here, we propose a novel approach to study how dynamic states of the brain networks spatially propagate and evaluate whether these propagating states contain information relevant to mental illness. We implement a lagged windowed correlation approach to capture voxel‐wise network‐specific spatial propagation patterns in dynamic states. Results show systematic spatial state changes over time, which we confirmed are replicable across multiple scan sessions using human connectome project data. We observe networks varying in propagation speed; for example, the default mode network (DMN) propagates slowly and remains positively correlated with blood oxygenation level‐dependent (BOLD) signal for 6–8 s, whereas the visual network propagates much quicker. We also show that summaries of network‐specific propagative patterns are linked to schizophrenia. More specifically, we find significant group differences in multiple dynamic parameters between patients with schizophrenia and controls within four large‐scale networks: default mode, temporal lobe, subcortical, and visual network. Individuals with schizophrenia spend more time in certain propagating states. In summary, this study introduces a promising general approach to exploring the spatial propagation in dynamic states of brain networks and their associated complexity and reveals novel insights into the neurobiology of schizophrenia. 

ABSTRACT With the increasing availability of large‐scale multimodal neuroimaging datasets, it is necessary to develop data fusion methods which can extract cross‐modal features. A general framework, multidataset independent subspace analysis (MISA), has been developed to encompass multiple blind source separation approaches and identify linked cross‐modal sources in multiple datasets. In this work, we utilized the multimodal independent vector analysis (MMIVA) model in MISA to directly identify meaningful linked features across three neuroimaging modalities—structural magnetic resonance imaging (MRI), resting state functional MRI and diffusion MRI—in two large independent datasets, one comprising of control subjects and the other including patients with schizophrenia. Results show several linked subject profiles (sources) that capture age‐associated decline, schizophrenia‐related biomarkers, sex effects, and cognitive performance. For sources associated with age, both shared and modality‐specific brain‐age deltas were evaluated for association with non‐imaging variables. In addition, each set of linked sources reveals a corresponding set of cross‐modal spatial patterns that can be studied jointly. We demonstrate that the MMIVA fusion model can identify linked sources across multiple modalities, and that at least one set of linked, age‐related sources replicates across two independent and separately analyzed datasets. The same set also presented age‐adjusted group differences, with schizophrenia patients indicating lower multimodal source levels. Linked sets associated with sex and cognition are also reported for the UK Biobank dataset.