In human postural control, touching a fingertip to a stable object with a slight force (<1 N) reduces postural sway independent of mechanical support, which is referred to as the effect of light touch (LT effect). The LT effect is achieved by the spatial orientation according to haptic feedback acquired from an external spatial reference. However, the neural mechanism of the LT effect is incompletely understood. Therefore, the purpose of this study was to employ EEG frequency analysis to investigate the cortical brain activity associated with the LT effect when attentional focus was strictly controlled with the eyes closed during standing (i.e., control, fixed-point touch, sway-referenced touch, and only fingertip attention). We used EEG to measure low-alpha (about 8-10 Hz) and high-alpha rhythm (about 10-12 Hz) task-related power decrease/increase (TRPD/TRPI). The LT effect was apparent only when the subject acquired the stable external spatial reference (i.e., fixed-point touch). Furthermore, the LT-specific effect increased the high-alpha TRPD of two electrodes (C3, P3), which were mainly projected from cortical brain activities of the left primary sensorimotor cortex area and left posterior parietal cortex area. Furthermore, there was a negative correlation between the LT effect and increased TRPD of C3. In contrast, the LT effect correlated positively with increased TRPD of P3. These results suggest that central and parietal high-alpha TRPD of the contralateral hemisphere reflects the sensorimotor information processing and sensory integration for the LT effect. These novel findings reveal a partial contribution of a cortical neural mechanism for the LT effect.
Objectives We quantitatively examined the motor‐imagery ability in stroke patients using a bimanual circle‐line coordination task (BCT) and clarified the relationship between motor‐imagery ability and motor function of hemiplegic upper limbs and the level of use of paralyzed limbs. Methods We enrolled 31 stroke patients. Tasks included unimanual‐line (U‐L)—drawing straight lines on the nonparalyzed side; bimanual circle‐line (B‐CL)—drawing straight lines with the nonparalyzed limb while drawing circles with the paralyzed limb; and imagery circle‐line (I‐CL)—drawing straight lines on the nonparalyzed side during imagery drawing on the paralyzed side, using a tablet personal computer. We calculated the ovalization index (OI) and motor‐imagery ability (image OI). We used the Fugl–Meyer motor assessment (FMA), amount of use (AOU), and quality of motion (QOM) of the motor activity log (MAL) as the three variables for cluster analysis and performed mediation analysis. Results Clusters 1 (FMA <26 points) and 2 (FMA ≥26 points) were formed. In cluster 2, we found significant associations between image OI and FMA, AOU, and QOM. When AOU and QOM were mediated between image OI and FMA, we observed no significant direct association between image OI and FMA, and a significant indirect effect of AOU and QOM. Interpretation In stroke patients with moderate‐to‐mild movement disorder, image OI directly affects AOU of hemiplegic upper limbs and their QOM in daily life and indirectly influences the motor functions via those parameters.
Background A method for modeling the acute pain trajectory using the simple linear fit of an individual’s pain intensity scores after surgery was developed and affords more precise measurement than conventional pain assessment. However, the method has the disadvantage of using only the slope without considering the intercept. The purpose of this study was to verify our modification of the pain trajectory model including slope and intercept and to identify clusters. Methods The pain intensity was measured in 60 patients after surgery, and we calculated their pain trajectories. The pain trajectory normally resolves in intensity over a period of days, and the linear fit of an individual patient’s pain intensity score defines the trajectory. In this simple linear model (x axis, days; y axis, pain intensity), each patient’s trajectory has the slope and the intercept. A multiple regression analysis model known as structural equation modeling was used to predict postoperative pain at 30 days after surgery. Finally, we performed hierarchical cluster analysis using the pain trajectory. Results The slope and intercept model was the best fit among the models. Based on cluster analysis results, we created 4 pain trajectory groups (slope and intercept). Conclusion Our results suggest that the pain trajectory using the slope and intercept is quite useful for predicting postoperative pain at 30 days after surgery. Additionally, patients were classified into 4 groups using the slope and intercept. By considering both the slope and intercept, clinicians may be able to detect the risk for prolonged pain earlier than other methods.
Background: The pain trajectory is an early detection/prediction method for chronic postsurgical pain (CPSP). It is unclear whether a pain trajectory can predict CPSP in patients who have undergone a total knee arthroplasty (TKA). Here we investigated (1) whether CPSP can be predicted in TKA patients, and (2) the values that can be used to predict CPSP. Methods: We studied 211 postoperative TKA patients. We calculated the pain trajectory (pain curve slope and intercept) using the patients' self-reported pain intensity values at 1, 3, 5, and 7 days post-TKA. Using structural equation modeling (SEM), we performed a multiple regression analysis to investigate appropriate prediction models for the pain trajectory. Classification and regression tree (CHAID) methodology was used to calculate values to predict CPSP by a decision tree model. CPSP (dependent variable) was defined as >30 mm on a visual analog scale for pain intensity at 1 year post-TKA. The predictor variables were pain curve slope, intercept, age, sex, body mass index, and preoperative pain intensity. Results: The pain trajectory was the best fit among the models to predict pain intensity at 1 year post-TKA. When the pain curve slope (pain trajectory) was greater than 2.8, the probability of CPSP at 1 year post-TKA was 33.3%. Conclusion: Our results suggest that the pain trajectory could be applied to post-TKA patients and used to calculate clinical values to predict CPSP. Our findings also indicate the possibility that patients with a positive pain curve slope in the first postoperative week may need early intervention to avoid CPSP.
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