Long latency responses in tongue muscle elicited by various stimulation sites in anesthetized humans - New insights into tongue-related brainstem reflexes

BACKGROUND

Long Latency Responses (LLR) in tongue muscles are a scarcely described phenomenon, the physiology of which is uncertain.

OBJECTIVES

The aim of this exploratory, observational study was to describe tongue-LLR elicited by direct trigeminal nerve (DTNS), dorsal column (DoColS), transcranial electric (TES) and peripheral median nerve (MNS) stimulation in a total of 93 patients undergoing neurosurgical procedures under general anesthesia.

METHODS

Bilateral tongue responses were derived concurrently after each of the following stimulations: (1) DTNS applied with single monophasic or train-of-three pulses, ≤5 mA; (2) DoColS applied with a train-of-three pulses, ≤10 mA; (3) TES consisting of an anodal train-of-five stimulation, ≤250 mA; (4) MNS at wrist consisting of single or train-of-three monophasic pulses, ≤50 mA. Polyphasic tongue muscle responses exceeding the latencies of tongue compound muscle action potentials or motor evoked potentials were classified as LLR.

RESULTS

Tongue-LLR were evoked from all stimulation sites, with latencies as follows: (1) DTNS: solely ipsilateral 20.2 ± 3.3 msec; (2) DoColS: ipsilateral 25.9 ± 1.6 msec, contralateral 25.1 ± 4.2 msec; (3) TES: contralateral 55.3 ± 10.2 msec, ipsilateral 54.9 ± 12.0 msec; (4) MNS: ipsilateral 37.8 ± 4.7 msec and contralateral 40.3 ± 3.5 msec.

CONCLUSION

The tongue muscles are a common efferent in brainstem pathways targeted by trigeminal and cervical sensory fibers. DTNS can elicit the "trigemino-hypoglossal-reflex". For the MNS elicited tongue-LLR, we propose the term "somatosensory-evoked tongue-reflex". Although the origin of the TES related tongue-LLR remains unclear, these data will help to interpret intraoperative tongue recordings.

Multidata Analysis Based on an Artificial Neural Network Model for Long-Term Pain Outcome and Key Predictors of Microvascular Decompression in Trigeminal Neuralgia

OBJECTIVE

To investigate use of multidata analysis based on an artificial neural network (ANN) to predict long-term pain outcomes after microvascular decompression (MVD) in patients with trigeminal neuralgia (TN) and to explore key predictors.

METHODS

Perioperative and long-term follow-up multidata of 1041 patients with TN who received MVD surgery at Hangzhou First People's Hospital from March 2013 to May 2018 were collected to construct an ANN model for prediction. The prediction results were compared with the actual follow-up outcomes, and the variables in each input layer were changed to test the effectiveness of ANN and explore the factors that had the greatest impact on prediction accuracy.

RESULTS

The ANN model could predict the long-term pain outcomes after MVD in patients with TN with an accuracy rate of 95.2% and area under the curve of 0.862. Four factors contributed the most to the predictive performance of the ANN: whether the neurovascular offending site of the trigeminal nerve corresponded the region of facial pain, immediate postoperative pain remission after MVD, degree of nerve compression by culprit vessels, and the type of culprit vessels. After these factors were sequentially removed, the accuracy of the ANN model decreased to 74.5%, 78.6%, 87.2%, and 90.1%, while the area under the curve was 0.705, 0.761, 0.793, and 0.810.

CONCLUSIONS

The ANN model, constructed using multiple data, predicted long-term pain prognosis after MVD in patients with TN objectively and accurately. The model was able to assess the importance of each factor in the prediction of pain outcome.

Microsurgical Anatomy of the Hypoglossal Nerve in the Lateral Approaches to the Craniovertebral Junction: A Study on Fresh Non-Formalin-Fixed Human Specimens

The aim of this anatomical study is to describe the anatomy of the hypoglossal nerve (HN) from its origin to the extracranial portion as it appears by performing a combined posterolateral and anterolateral approach to the craniovertebral junction (CVJ). Twelve fresh, non-formalin-fixed adult cadaveric heads (24 sides) were analyzed for the simulation of the combined lateral approach to the CVJ. The HN is divided into three main parts: cisternal, intracanalicular, and extracranial The anatomical relationships between the HN and other nerves, muscles, arteries and veins were carefully recorded, and some measurements were made between the HN and related structures. Thus, various landmarks were determined for the easy identification of the HN. Understanding the detailed anatomy of the HN and its relationships with the surrounding structures is crucial to prevent some complications during CVJ surgery.

Resolution of intractable retching following mobilization of a dolichoectatic vertebral artery: case report of a unique brainstem-cranial nerve compression syndrome

The authors present the case of a 53-year-old man who was referred with disabling retching provoked by left arm abduction. At the time of his initial evaluation, a cervical MRI study was available for review and revealed an anatomical variation of the ipsilateral juxtamedullary vertebrobasilar junction. After brain imaging revealed contact of the medulla by a dolichoectatic vertebral artery at the dorsal root entry zone of the glossopharyngeal and vagus nerves, the patient was successfully treated by microvascular decompression of the brainstem and cranial nerves. This case demonstrates how a dolichoectatic vertebral artery-a common anatomical variation that typically has no clinical consequence-should be considered in cases of cranial nerve dysfunction.