{"doi":"10.1162/imag_a_00099","title":"BNPower: a power calculation tool for data-driven network analysis for whole-brain connectome data","abstract":"Network analysis of whole-brain connectome data is widely employed to examine systematic changes in connections among brain areas caused by clinical and experimental conditions. In these analyses, the connectome data, represented as a matrix, are treated as outcomes, while the subject conditions serve as predictors. The objective of network analysis is to identify connectome subnetworks whose edges are associated with the predictors. Data-driven network analysis is a powerful approach that automatically organizes individual predictor-related connections (edges) into subnetworks, rather than relying on pre-specified subnetworks, thereby enabling network-level inference. However, power calculation for data-driven network analysis presents a challenge due to the data-driven nature of subnetwork identification, where nodes, edges, and model parameters cannot be pre-specified before the analysis. Additionally, data-driven network analysis involves multivariate edge variables and may entail multiple subnetworks, necessitating the correction for multiple testing (e.g., family-wise error rate (FWER) control). To address this issue, we developed BNPower, a user-friendly power calculation tool for data-driven network analysis. BNPower utilizes simulation analysis, taking into account the complexity of the data-driven network analysis model. We have implemented efficient computational strategies to facilitate data-driven network analysis, including subnetwork extraction and permutation tests for controlling FWER, while maintaining low computational costs. The toolkit, which includes a graphical user interface and source codes, is publicly available at the following GitHub repository: https://github.com/bichuan0419/brain_connectome_power_tool.","journal":"Imaging Neuroscience","year":2024,"id":459204,"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":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9411,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":44930,"name":"Thomas E. Nichols","orcid":"0000-0002-4516-5103","position":1,"is_corresponding":false},{"id":975683,"name":"Hwiyoung Lee","orcid":"0000-0002-3855-2316","position":2,"is_corresponding":false},{"id":1285982,"name":"Yifan Yang","orcid":"0000-0003-3313-5473","position":3,"is_corresponding":false},{"id":772630,"name":"Zhenyao Ye","orcid":"0000-0002-6530-1088","position":4,"is_corresponding":false},{"id":1053135,"name":"Yezhi Pan","orcid":"0009-0001-4014-0302","position":5,"is_corresponding":false},{"id":239039,"name":"Elliot Hong","orcid":null,"position":6,"is_corresponding":false},{"id":227759,"name":"Peter Kochunov","orcid":"0000-0003-3656-4281","position":7,"is_corresponding":false},{"id":270738,"name":"Shuo Chen","orcid":"0000-0002-7990-4947","position":8,"is_corresponding":false},{"id":916749,"name":"Chuan Bi","orcid":"0000-0001-8498-5422","position":0,"is_corresponding":true}],"reference_count":38,"raw_metadata":null,"created_at":"2026-07-19T02:03:55.280882Z","pmid":"40800270","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":[]}