The Neurodiagnostic Journal最新文献

Selective dorsal rhizotomy (SDR) is a treatment for lower-extremity spasticity in disorders such as cerebral palsy (CP). "Selective" refers to sectioning nerve rootlets with the most abnormal responses on electromyography (EMG) upon intraoperative stimulation. EMG abnormalities can be classified by waveform appearance or by degree of spread throughout lower extremity muscles. We examine the relationship between different EMG waveforms and grades of spread. Intraoperative SDR EMG records from November 2009 through December 2021 were analyzed for waveform types and degrees of spread. Irregular, incremental, multiphasic, sustained, and clonic waveform patterns were considered more abnormal. Decremental, squared decremental, and squared waveforms were less abnormal. Degrees of spread were graded 0-4+, 4+ signifying the most abnormal spread. Distribution of grades of spread was compared between waveform patterns using pairwise Cochran-Armitage tests with Holm-Bonferroni correction. We hypothesized that more abnormal EMG waveform patterns would correlate with higher grades of spread. Sixty-three patients were included, with an average age of 8 years. Most had cerebral palsy (86%, n = 54). The remainder had brain malformations (8%, n = 5) and other etiologies (6%, n = 4). Higher grades of spread significantly increased the likelihood of multiphasic, sustained, or clonic patterns, compared to decremental, irregular, and squared patterns (p < .05). Squared waveforms decreased with higher grades of spread relative to other patterns (p < .05). Different EMG waveform patterns are associated with varying grades of spread in SDR, suggesting that evaluating both waveform pattern and degree of spread together can be useful in guiding rootlet sectioning.

A 42-year-old female presented with acute onset of asymmetric lower extremity weakness, bilateral lower extremity areflexia and sensory loss, and urinary incontinence. Initial investigation with a spine MRI for a spinal cord process was negative, leading to further investigation with nerve conduction studies/electromyography (NCS/EMG). This revealed absent F waves bilaterally in the fibular nerves, and there was only one isolated F wave with decreased amplitude and increased latency in the left tibial nerve and had lack of heterogeneity in the right tibial nerve. Given the early presentation of her symptoms, this led to a working diagnosis of Guillain-Barré Syndrome, which was treated with plasmapheresis without improvement. After plasmapheresis, the diagnosis was revisited, and a repeat spine MRI revealed an extensive lower thoracic and lumbar spinal cord infarction. In this unique scenario, the properties and abnormalities of F waves are discussed and reviewed, along with the influence of acute central nervous system lesions on their configuration.

Artificial intelligence (AI) is revolutionizing clinical neurophysiology (CNP), particularly in its applications to electroencephalography (EEG), electromyography (EMG), and polysomnography (PSG). AI enhances diagnostic accuracy and efficiency while addressing interrater variability and the growing data volume. The evolution of AI tools, from early mimetic methods to advanced deep learning techniques, has significantly improved spike and seizure detection in EEG and facilitated whole EEG evaluations, reducing the workload on clinicians. In EMG, AI demonstrates promise in identifying motor unit abnormalities and analyzing audio signals, though challenges persist due to limited datasets and clinical context considerations. PSG scoring has seen substantial integration of AI, with systems achieving high accuracy through uncertainty estimation and selective manual review, but limitations remain in analyzing epileptic activity and classifying certain sleep stages. As a "co-pilot," AI augments human expertise by improving quality control, standardizing clinical trials, and enabling rapid data review, particularly for less experienced providers. Future AI advancements in CNP aim to shift from isolated data interpretation to providing clinical context, considering patient history, treatment options, and prognostic implications. While the potential of generative AI and "AI-omics" is transformative, the importance of thoughtful integration to augment rather than replace human expertise must be emphasized, ensuring that AI becomes a tool for collaboration and innovation in medicine.

Intraoperative neurophysiological monitoring (IONM) is instrumental in mitigating neurological deficits following cranial and spinal procedures. Despite extensive research on IONM's ability to recognize limb-malposition-related issues, less attention has been given to other secondary neural injuries in cranial surgeries. A comprehensive multimodal neuromonitoring approach was employed during a left frontal craniotomy for tumor resection. The electronic medical record was reviewed in detail in order to describe the patient's clinical course. The patient, a 46-year-old female, underwent craniotomy for excision of a meningioma. Deteriorations in somatosensory evoked potential and transcranial motor evoked potential recordings identified both a mal-positioned limb as well as an infiltrated intravenous (IV) line in the arm contralateral to the surgical site. The IONM findings for the infiltrated IV were initially attributed to potential limb malposition until swelling and blistering of the limb were appreciated and investigated. The timely identification and management of the infiltrated IV and adjustment of limb positioning contributed to the patient's recovery, avoiding fasciotomy, with no postoperative neurological deficits. This case is the first published demonstration of the utility of IONM in detecting IV infiltration. This early recognition facilitated early intervention, saving the patient from a potential fasciotomy and enabling their recovery with no postoperative neurological deficits. The findings from this single case highlight the necessity for vigilant and dynamic application of IONM techniques to enhance patient safety and outcomes in neurosurgical procedures. Further research is needed to explore broader applications and further optimize the detection capabilities of IONM.

Graphogenic, or writing epilepsy, is a rare type of reflex epilepsy (RE) in which seizures are provoked by the act of writing. RE is a condition that affects between 4% and 7% of all patients diagnosed with epilepsy. In this case study, we provide a case report on a patient whose seizures were generated by the act of writing. We also present evidence that the cortical mechanisms by which writing-induced seizures occur are focal with possible secondary generalization.