Objective This study investigated the functional neural activity and connectivity correlates of disrupted sensorimotor integration underlying vocal production and motor control deficits in post-stroke aphasia. Methods fMRI data were acquired from 15 aphasia and 15 control participants during vocalization of speech vowel sounds under altered auditory feedback with randomized ± 100-cents pitch-shift stimuli. Results We found that stroke-induced brain damage in aphasia was associated with diminished activation within left-hemisphere dorsal stream networks, including premotor and motor cortices, posterior superior temporal regions, and supramarginal gyrus. Functional connectivity analysis revealed reduced left-hemisphere coupling between posterior superior temporal gyrus (pSTG) and dorsal and ventral precentral speech areas (dPSA and vPSA) in aphasia, indicating disrupted auditory–motor integration. Aphasia participants exhibited reduced following and opposing (i.e., compensatory) vocal responses to pitch-shifted auditory feedback, reflecting impairments in both rapid feedforward and slower feedback-based control mechanisms. In addition, impaired vocal compensatory behavior was associated with reduced functional connectivity between pSTG and the left dorsal precentral speech areas (dPSA) as well as the right dorsal laryngeal motor cortex (dLMC), suggesting the critical role of these networks in auditory-feedback-based laryngeal motor control during vocal production and their stroke-induced impairment in aphasia. Conclusions Our data provide converging evidence of disrupted auditory–motor integration and impaired neural communications within dorsal stream networks in aphasia. Significance These findings emphasize that clinical interventions for aphasia therapy benefit from targeting sensorimotor mechanisms supporting vocal auditory feedback processing and control. Personalized interventions that strengthen sensorimotor integration pathways could further enhance recovery of fluent verbal communication.
Background and objectives To identify biomarkers of long-term functional recovery after cardiac arrest (CA). Methods This is a prospective longitudinal cohort study based on the Veneto Registry of CA (July 2021- June 2023). We collected demographic, clinical, biochemical, and electroencephalography and short- and middle-latencysomatosensory-evoked-potentials (S-SSEP and ML-SSEP) within 72 hours. Functional scales were collected at discharge, 3, 6, and 12 months after the event. Multiple imputation was used to avoid statistical loss. Calibration was verified. A penalised logistic regression and a penalised continuation ratio were fitted to model the effect of covariates. The quality of prediction or fitting was verified computing the rank probability score for ordinal outcomes, the Brier score, and area under the ROC curve for binary outcomes. Decision curve analysis confirmed clinical utility. Results We included 160 persons (mean 62.80 ± SD 14.36 years; 123 males) out of 186. The interaction between continuous/nearly continuous EEG and N20/P25 predictor of survival (β = 0.840) and recovery (β = 1.160) of consciousness at 12 months after CA. ML-SSEP was a predictor of good outcome on all scales at 12 months after CA (0.739<β <1.708). Conclusion ML-SSEPs associated with standard neurophysiological assessments are robust predictors of long-term functional outcomes after CA. Significance ML-SSEPs associated with EEG and standard SSEP might be incorporated in routine clinical practice in ICU post-anoxic coma assessment.
Objective Turns-amplitude analysis (TAA) is a rapid quantitative electromyographic technique used in neuromuscular disorders, but its categorical interpretation (normal, myopathic, neurogenic) limits the granularity needed for longitudinal monitoring. We investigated whether continuous TAA-derived metrics could reflect longitudinal clinical change in idiopathic inflammatory myopathies (IIM). Methods In this prospective single-center study, 30 adults with IIM underwent TAA of biceps brachii, quadriceps, and iliopsoas at baseline, 6, and 12 months. From each cloud, we extracted descriptors of cloud position, shape, point distribution, and the turns-amplitude relationship. Clinical improvement was defined using EULAR/ACR response criteria. Correlations with motor unit action potential descriptors, clinical and biopsy variables were assessed. Longitudinal trajectories were compared between improving and non-improving patients. Results Percentage of myopathic points (mean |ρ|=.41), centroid Y (0.35), and regression-based metrics (0.34) showed the strongest alignment with clinical severity. Group-by-time interactions were found for percentage of myopathic points, slope of the linear regression constrained through the origin, and centroid Y. In an exploratory ranking, regression-based metrics performed best overall. Conclusions Continuous TAA cloud metrics showed clinicopathological associations and captured longitudinal differences between improving and non-improving patients. Significance These findings support cloud analysis as a rapid, reproducible adjunct for tracking IIM recovery in research settings.
