The Internal Superior Laryngeal Nerve in Humans: Evidence for Pure Sensory Function.

OBJECTIVES: To determine if the internal branch of the superior laryngeal nerve (iSLN) provides direct motor innervation to the interarytenoid muscle, a laryngeal adductor critical for airway protection. We studied the iSLN-evoked motor response in the interarytenoid and other laryngeal muscles. If the iSLN is purely sensory, there will be no detectable short latency motor response upon supramaximal stimulation, indicating the absence of a direct efferent conduction path. STUDY DESIGN: Intraoperative case series. METHODS: In seven anesthetized patients undergoing laryngectomy for unilateral laryngeal carcinoma, the iSLN of the unaffected side was electrically stimulated intraoperatively with 0.1-ms pulses of progressive intensities until supramaximal stimulation was reached. Electromyographic responses were measured in the ipsilateral interarytenoid, thyroarytenoid, and cricothyroid muscles. RESULTS: None of the subjects exhibited short-latency interarytenoid motor responses to iSLN stimulation. Supramaximal electrical stimulation of the intact iSLN evoked ipsilateral motor responses with long latencies: 18.7-38.5 ms in the interarytenoid (n = 6) and 17.8-24.9 ms in the thyroarytenoid (n = 5). Supramaximal stimulation of the recurrent laryngeal nerve evoked ipsilateral motor responses with short latencies: 1.6-3.9 ms in the interarytenoid (n = 6) and 1.6-2.7 ms in the thyroarytenoid (n = 6). CONCLUSION: The iSLN provides no functional efferent motor innervation to the interarytenoid muscles. The iSLN exclusively evokes an interarytenoid motor response via afferent activation of central neural circuits that mediate the laryngeal reflex arc. These findings suggest that the role of the iSLN in vital laryngopharyngeal functions, such as normal swallowing and protection of the airway from aspiration, is purely sensory. LEVEL OF EVIDENCE: 4 Laryngoscope, 131:E207-E211, 2021.

Substantia Nigra Volume Dissociates Bradykinesia and Rigidity from Tremor in Parkinson's Disease: A 7 Tesla Imaging Study.

BACKGROUND: In postmortem analysis of late stage Parkinson's disease (PD) neuronal loss in the substantial nigra (SN) correlates with the antemortem severity of bradykinesia and rigidity, but not tremor. OBJECTIVE: To investigate the relationship between midbrain nuclei volume as an in vivo biomarker for surviving neurons in mild-to-moderate patients using 7.0 Tesla MRI. METHODS: We performed ultra-high resolution quantitative susceptibility mapping (QSM) on the midbrain in 32 PD participants with less than 10 years duration and 8 healthy controls. Following blinded manual segmentation, the individual volumes of the SN, subthalamic nucleus, and red nucleus were measured. We then determined the associations between the midbrain nuclei and clinical metrics (age, disease duration, MDS-UPDRS motor score, and subscores for bradykinesia/rigidity, tremor, and postural instability/gait difficulty). RESULTS: We found that smaller SN correlated with longer disease duration (r = -0.49, p = 0.004), more severe MDS-UPDRS motor score (r = -0.42, p = 0.016), and more severe bradykinesia-rigidity subscore (r = -0.47, p = 0.007), but not tremor or postural instability/gait difficulty subscores. In a hemi-body analysis, bradykinesia-rigidity severity only correlated with SN contralateral to the less-affected hemi-body, and not contralateral to the more-affected hemi-body, possibly reflecting the greatest change in dopamine neuron loss early in disease. Multivariate generalized estimating equation model confirmed that bradykinesia-rigidity severity, age, and disease duration, but not tremor severity, predicted SN volume. CONCLUSIONS: In mild-to-moderate PD, SN volume relates to motor manifestations in a motor domain-specific and laterality-dependent manner. Non-invasive in vivo 7.0 Tesla QSM may serve as a biomarker in longitudinal studies of SN atrophy and in studies of people at risk for developing PD.

Sensory dysphagia: A case series and proposed classification of an under recognized swallowing disorder.

BACKGROUND: Although sensory feedback is a vital regulator of deglutition, it is not comprehensively considered in the standard dysphagia evaluation. Difficulty swallowing secondary to sensory loss may be termed "sensory dysphagia" and may account for cases receiving diagnoses of exclusion, like functional or idiopathic dysphagia. METHODS AND RESULTS: Three cases of idiopathic dysphagia were suspected to have sensory dysphagia. The patients had (1) effortful swallowing, (2) globus sensation, and (3) aspiration. Endoscopic sensory mapping revealed laryngopharyngeal sensory loss. Despite normal laryngeal motor function during voluntary maneuvers, laryngeal closure was incomplete during swallowing. The causes of sensory loss were identified: cranial neuropathy from Chiari malformation, immune-mediated neuronopathy, and nerve damage from prior traumatic intubation. CONCLUSIONS: Sensory loss may cause dysphagia without primary motor dysfunction. Sensory dysphagia should be classified as a distinct form of swallowing motility disorder to improve diagnosis. Increasing awareness and developing appropriate assessment tools may advance dysphagia care.

Applying Machine Learning Algorithms for Automatic Detection of Swallowing from Sound.

Despite the severe consequences of dysfunctional swallowing, there is no simple method of monitoring swallowing outside of clinical settings. People who cannot swallow cannot eat safely, resulting in profound changes in quality of life and risk of death from aspiration pneumonia. A non-invasive swallowing detector may have widespread impact in both clinical care and research. Detection of swallowing from laryngeal sounds could become an ideal assessment tool because sounds are simple to record, quantifiable, and amenable to software analysis. The focus of this paper is to achieve high accuracy binary swallowing detection from sound recordings. A dataset with 2500 swallow sound samples and 1700 mixed laryngeal noise samples from 15 healthy adults was used to train and test three supervised machine learning algorithms. A decision tree, support vector machine (SVM), and neural network trained with the scaled conjugate gradient (SCG) method had areas under the receiver operating characteristic (ROC) curve of 0.970, 0.961, and 0.971 and average accuracies of 93.2 percent, 86.2 percent, and 93.7 percent respectively. While further work needs to be done to further optimize these algorithms and validate their efficacy, these initial results suggest machine learning strategies may be helpful to improve accuracy of swallowing detection.