{"doi":"10.1101/2025.05.02.651877","title":"NeuroMark-HiFi: A Data-Driven Method for Detecting High-Spatial-Frequency Functional Brain Networks","abstract":"Objective: The Traditional functional neuroimaging approaches typically focus on low-frequency spatial structures, potentially overlooking critical fine-scale connectivity disruptions associated with brain disorders. Methods: We introduce NeuroMark-HiFi, a fully automated algorithm designed to enhance the detection of high-spatial-frequency functional brain network patterns. NeuroMark-HiFi systematically preserves and analyzes fine-grained network variations by integrating reference-informed independent component analysis (ICA), 3D high-frequency spatial filtering, and a frequency-informed ICA decomposition to extract high-frequency functional components with greater precision. Results: Simulation studies and mathematical evaluations demonstrate that NeuroMark-HiFi significantly improves sensitivity to both individual and group differences driven by small local shifts in spatial patterns of intrinsic connectivity networks (ICNs). Compared to traditional methods, NeuroMark-HiFi revealed additional group differences between individuals with schizophrenia (SZ) and healthy controls (HC), particularly in the visual, sensorimotor, frontal, temporal, and insular networks. Conclusion: NeuroMark-HiFi successfully captures biologically meaningful alterations in spatial network patterns that conventional approaches may miss. Significance: By improving sensitivity to subtle brain network alterations, NeuroMark-HiFi holds promise for early diagnosis, treatment monitoring, neurodevelopment studies, aging research, and multimodal biomarker discovery, advancing the goals of precision psychiatry and neuroscience.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2025,"id":565535,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9495,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":254824,"name":"Armin Iraji","orcid":"0000-0002-0605-593X","position":1,"is_corresponding":false},{"id":227761,"name":"Vince D. Calhoun","orcid":"0000-0001-9058-0747","position":2,"is_corresponding":false},{"id":1468881,"name":"Neda Behzadfar","orcid":"0000-0002-3679-5664","position":0,"is_corresponding":true}],"reference_count":36,"raw_metadata":null,"created_at":"2026-07-19T02:56:28.709936Z","pmid":"40654930","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}