Association between Elastic Modulus of Foot Soft Tissues and Gait Characteristics in Young Individuals with Flatfoot.

Flatfoot is a common foot deformity, causing foot pain, osteoarthritis of the midfoot, and even knee and hip dysfunction. The elastic modulus of foot soft tissues and its association with gait biomechanics still remain unclear. For this study, we recruited 20 young individuals with flatfoot and 22 age-matched individuals with normal foot arches. The elastic modulus of foot soft tissues (posterior tibial tendon, flexor digitorum brevis, plantar fascia, heel fat pad) was obtained via ultrasound elastography. Gait data were acquired using an optical motion capture system. The association between elastic modulus and gait data was analyzed via correlation analysis. The elastic modulus of the plantar fascia (PF) in individuals with flatfoot was higher than that in individuals with normal foot arches. There was no significant difference in the elastic modulus of the posterior tibial tendon (PTT), the flexor digitorum brevis (FDB), or the heel fat pad (HFD), or the thickness of the PF, PTT, FDB, and HFD. Individuals with flatfoot showed greater motion of the hip and pelvis in the coronal plane, longer double-support phase time, and greater maximum hip adduction moment during walking. The elastic modulus of the PF in individuals with flatfoot was positively correlated with the maximum hip extension angle (r = 0.352, p = 0.033) and the maximum hip adduction moment (r = 0.429, p = 0.039). The plantar fascia is an important plantar structure in flatfoot. The alteration of the plantar fascia's elastic modulus is likely a significant contributing factor to gait abnormalities in people with flatfoot. More attention should be given to the plantar fascia in the young population with flatfoot.

Neural Correlates of Abnormal Cortical Gait Control in Parkinson's Disease: A Whole-Gait-Cycle EEG Study.

OBJECTIVE: Electroencephalography (EEG) with high time-resolution allows for recording dynamic cortical activity during walking and provides new insight into the underlying pathophysiology of gait impairments in PD. However, traditional gait-phase-specific EEG analysis only measures the brain activities in the isolated gait phase, but neglects the between-gait-phase interactions as well as the whole-gait-cycle characteristics, and therefore is unable to effectively reflect the abnormal cortical gait control. METHODS: In this study, we introduced three whole-gait-cycle measures of intra-stride EEG activity (i.e., mean desynchronization, amplitude of fluctuations, and coupling to the gait phase), and investigated their abnormalities in PD and relationships with gait impairments, which were further compared with the traditional gait-phase-specific measures. RESULTS: Compared with healthy controls, PD patients showed overwhelming stronger desynchronizations (ERD) across the whole gait cycle in θ, α and low-ß bands, implying a cortical compensatory strategy in response to the low efficiency of the motor network. Patients also exhibited weaker intra-stride ERD fluctuations in the central area in α and low-ß bands, with reduced amplitude and less coupling to the gait phase, which were correlated with gait impairments in walking speed, gait rhythm and walking stability. However, gait-phase-specific EEG measures did not show any significant correlation with gait impairments in PD. CONCLUSION: Our results demonstrated the efficiency of whole-gait-cycle EEG measures in characterizing the abnormal cortical gait control, and for the first time, associated gait impairments with weak intra-stride electrocortical fluctuations. SIGNIFICANCE: The findings may shed light on the development of cortical-targeted interventions for PD.

Brain Network Analysis by Stable and Unstable EEG Components.

OBJECTIVE: Previous studies have already shown that electroencephalography (EEG) brain network (BN) can reflect the health status of individuals. However, novel methods are still needed for BN analysis. Therefore, in this study, BNs were constructed based on stable and unstable EEG components, and these may be implemented for disease diagnosis. METHODS: Parkinson's disease (PD) was used as an example to illustrate this method. First, EEG signals were decomposed into dynamic modes (DMs). Each DM contains one eigenvalue that can determine not only the stability of that mode, but also its corresponding oscillatory frequency. Second, the stable and unstable components of EEG signals in each frequency band (delta, theta, alpha and beta) were calculated, which are based on the stable and unstable DMs within each respective frequency band. Third, newly developed BNs were constructed, including stable brain network (SBN), unstable brain network (UBN) and inter-connected brain network (IBN). Finally, their topological attributes were extracted in order to differentiate between PD patients and healthy controls (HC). Furthermore, topological attributes were also derived from traditional brain network (TBN) for comparison. RESULTS: Most topological attributes of SBN, UBN and IBN can significantly differentiate between PD patients and HC ( p value 0.05). Furthermore, the area under the curve (AUC), precision and recall values of SBN analysis are all significantly higher than TBN. CONCLUSION: We proposed a new perspective on EEG BN analysis. SIGNIFICANCE: These newly developed BNs not only have biological significance, but also could be widely applied in most medical and engineering fields.