{"doi":"10.1002/mus.28197","title":"F wave analysis based on the compound muscle action potential scan","abstract":"INTRODUCTION/AIMS: Conventional F wave analysis involves a relatively uniform physiological environment induced by supramaximal stimulations. The F wave characteristics in a dynamic physiological condition, however, are rarely investigated. This study aimed to improve understanding of F wave properties in the more dynamic process by introducing a novel method to analyze F waves based on the compound muscle action potential (CMAP) scan technique. METHODS: Twenty four healthy subjects participated in the study. The CMAP scan was applied to record muscle responses in the abductor pollicis brevis (APB) and abductor digiti minimi (ADM) muscles, respectively. F wave characteristics including mean F wave amplitude and latency (F-M latency), persistence and activating threshold were quantified. RESULTS: An average of 200 F waves per muscle were obtained from the CMAP scan recording. Weak to moderate correlations between F wave amplitude and stimulating intensity were observed in most of the APB (19 muscles; r = 0.33 ± 0.14, all p < .05) and ADM (23 muscles, r = 0.46 ± 0.16, all p < .05) muscles. Significantly longer mean F latency and lower activating F-threshold were found in the ADM muscles (F-M latency: APB: 25.43 ± 2.39 ms, ADM: 26.15 ± 2.32 ms, p < .05; F-threshold: APB: 7.68 ± 8.96% CMAP, ADM: 2.35 ± 2.42% CMAP, p < .05). DISCUSSION: This study introduces new features of F waves using the CMAP scan technique and identifies differences of F wave characteristics between the hand muscles. The CMAP scan based F waves analysis can be combined with the motor unit number estimation to assess functional alterations in motor neurons in neurological disorders.","journal":"Muscle & Nerve","year":2024,"id":477085,"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":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9647,"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":727044,"name":"Maoqi Chen","orcid":"0000-0001-9681-1876","position":1,"is_corresponding":false},{"id":1314917,"name":"Paul E. Barkhaus","orcid":"0000-0001-8268-8643","position":2,"is_corresponding":false},{"id":1314918,"name":"Sanjeev D. Nandedkar","orcid":"0000-0003-3310-4386","position":3,"is_corresponding":false},{"id":1149344,"name":"Brian D. Schmit","orcid":"0000-0002-3616-8388","position":4,"is_corresponding":false},{"id":727047,"name":"Ping Zhou","orcid":"0000-0002-4394-2677","position":5,"is_corresponding":false},{"id":727045,"name":"Xiaoyan Li","orcid":"0000-0002-1144-0648","position":0,"is_corresponding":true}],"reference_count":38,"raw_metadata":null,"created_at":"2026-07-19T02:06:33.741984Z","pmid":"38963007","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":[]}