Walking is a fundamental component of daily human activity, in which orderly execution of key gait events [such as heel strike (HS) and toe-off (TO) ] is essential for maintaining gait coordination and stability. However, the underlying brain–muscle neural coordination mechanisms associated with these events remain unclear. In this study, electroencephalography (EEG) signals from 19 channels and surface electromyography (sEMG) signals from 14 lower-limb muscles were synchronously recorded from 18 healthy participants during steady-state walking at a constant speed of 3.2 km/h. Brain–muscle connectivity networks were constructed using the adaptive directed transfer function (ADTF), and the dynamic connectivity characteristics associated with HS and TO events were quantitatively analyzed. The results showed that brain–muscle connectivity was strongest in the β frequency band. Intra-brain (EEG-EEG), brain-to-muscle (EEG-sEMG), and intra-muscle (sEMG-sEMG) connections were significantly stronger than muscle-to-brain (sEMG-EEG) connections. At both HS and TO events, information exchange between frontal-central cortical regions and the muscles of the supporting leg was markedly enhanced. Furthermore, compared with TO, the brain-muscle network at HS exhibited higher clustering coefficient, global efficiency, and betweenness centrality. These findings suggest that brain-muscle interactions during walking are predominantly mediated in the β band and dynamically modulated by gait events. Accurate characterization of gait event-related brain-muscle connectivity may provide important technical support for clarifying the neuromuscular coordination mechanisms underlying fine gait control.
Objective To evaluate the diagnostic value of antikeratin antibody (AKA) for rheumatoid arthritis (RA). Methods Systematic and comprehensive literature was searched in PubMed (1966 to June 2010), The Cochrane Library (Issue 6, 2010), CBM (1978 to June 2010), CNKI (1994 to June 2010), VIP (1989 to June 2010), and CMA Digital Periodicals (1997 to June 2010). The diagnosis studies of antikeratin antibody for rheumatoid arthritis were included. The quality assessment of diagnostic accuracy studies (QUADAS) items were used to assess the quality of the included studies. The Meta-Disc (version 1.4) software was used to analyze the data. Results A total of 69 trials involving 14 890 participants were included. The results of meta-analyses showed that compared with the RA classification criteria revised by American Rheumatism Association (ARA), the summary sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, OR value, and summary receiver operating characteristic curve of antikeratin antibody were 0.41 (0.39, 0.42), 0.94 (0.94, 0.95), 9.52 (7.21, 12.57), 0.63 (0.60, 0.66), 15.24 (11.62, 19.98), and 0.613 6, respectively. Conclusion Antikeratin antibody might be one of the most effective diagnoses for rheumatoid arthritis. The clinicians should combine other autoantibodies with AKA to diagnose rheumatoid arthritis.