SPARK-ICA: Guided Frequency-Resolved Dynamic Spatial ICA Reveals Fine-Scale fMRI Network Dynamics

Fine-scale spatiotemporal dynamics of functional brain networks are increasingly recognized as critical markers of transient connectivity and neuropsychiatric pathology. However, many existing dynamic fMRI methods primarily focus on temporal variability without explicitly resolving the spatial-frequency content of network dynamics, potentially limiting characterization of subtle, high-frequency spatial reconfigurations. To address this gap, we propose SPARK-ICA (Spatial-frequency Partitioned, Adaptive Reconstruction using Knowledge-guided ICA), a frequency-resolved dynamic spatial independent component analysis (ICA) framework that integrates NeuroMark template guidance with spatial frequency decomposition and group-level ICA of time-resolved, band-specific spatial maps. Starting from template-guided NeuroMark networks, we first estimate time-varying spatial maps using a dynamic guided ICA approach, capturing subject-specific network expression over time. Each dynamic spatial map is then decomposed into multiple spatial frequency bands using Fourier-domain spatial filtering, isolating fine- to coarse-scale spatial features at each time point. Band-specific spatial maps are subsequently concatenated and subjected to a blind group-level multi-frequency ICA, yielding a compact latent representation in which each dynamic component is characterized by distinct spatial-frequency signatures. These group-level band-resolved components are then used as references to estimate subject- and band-specific time courses and maps via spatially constrained ICA, enabling frequency-specific analysis of both long-term correlations and short-term temporal variability while preserving reproducibility across subjects. Using resting-state fMRI data from schizophrenia (SZ) patients and healthy controls (HC), we evaluated both long-term correlations and short-term variability of band-resolved dynamic time courses. For the group-level analyses, we focused on a predefined subset of NeuroMark networks with well-established involvement in SZ-related dysconnectivity. Our results reveal frequency-specific abnormalities in SZ, with enhanced long-term connectivity within sensory and subcortical networks and pronounced short-term alterations involving predominantly higher-frequency spatial components, particularly in cerebellar and subcortical systems. These findings indicate that SZ-related dysregulation is differentially expressed across spatial-frequency and temporal scales, rather than reflecting uniform dysconnectivity. Together, this framework provides a unified approach for analyzing dynamic fMRI networks across spatial, temporal, and frequency dimensions, extending our previous NeuroMark-DyFICA framework from a predominantly high-frequency spatial representation to an explicitly frequency-resolved characterization of dynamic network organization. By explicitly modeling spatial frequency-dependent dynamics, the proposed method provides a more resolved characterization of network alterations and offers a framework for identifying candidate markers of neuropsychiatric disorders.