In Reply: We appreciate the opportunity to respond to Velazquez et al.'s thoughtful comments. Although we agree with their main points about using the entropy and complexity metric for EEG analysis, we wanted to explain further the choices made for the study.1 In Velazquez et al., the authors highlight that our specific pipeline of analysis was prepared for a specific use case, and this choice has impacted the estimate of the sample entropy measure. Although we agree with this comment, the reason behind these choices was to focus on the alpha rhythm per se and both in terms of power2,3 and entropy.4,5 First, the bandpass filtering and, importantly, the resampling factor of the EEG were scaled to an appropriate and predefined range to better track alpha fluctuation. This was an essential step in the analysis because the study's goal was not to simply investigate the sample entropy across multiple scales, as done in previous work on coma states,6,7 but the entropy related to alpha oscillation itself. Second, the selection of the region of interest was again used to assess a fair comparison between the two cohorts. Indeed, because alpha-coma state is often characterized by an anteriorization of the alpha network, we decided to select the EEG channels that presented the strongest alpha activity for the pathologic and healthy cohort in a complete data-driven way. We believe that this agnostic approach allowed for a better understanding of the underling differences across the two different entropy levels. It is worth noting that the result remained significant using also different regions (see Supplementary materials, Supplemental Digital Content 1, https://links.lww.com/JCNP/A306). Finally, we chose to compare alpha-coma activity with the eyes-closed condition in healthy subjects primarily because our goal was to investigate entropy within the alpha rhythm as an oscillatory pattern readily identifiable through standard visual raw EEG analysis. There are three clinical contexts in which alpha rhythm is typically appreciated via standard EEG inspection: alpha-coma, posterior background alpha in awake eyes-closed individuals, and drug-induced alpha activity. As emphasized by Velazquez et al., entropy levels differ significantly between eyes-open and eyes-closed states in healthy individuals. This variation reflects the phenomenon of “EEG reactivity,” wherein posterior alpha activity diminishes or disappears upon eye opening.2,3 However, including an eyes-open condition as a control was neither feasible nor appropriate for our study. Our patients were unable to open their eyes or receive visual stimulation, and since our interest lay in entropy associated with visible alpha rhythms at raw EEG, the eyes-open condition would have introduced a substantial confounding factor. We, therefore, compared alpha-coma with the posterior background alpha observed in awake eyes-closed individuals. Despite differences in network spatialization,8–10 both rhythms appear quite similar under standard clinical EEG analysis. In both cases, the alpha activity is spontaneous, and in the alpha-coma condition, the patients also had their eyes closed, further supporting the rationale for this comparison. It is also unlikely that adding an additional cohort of subjects under different levels of anesthesia would have changed the results, because the alpha rhythms in such cases would no longer be spontaneous but drug-induced. Moreover, drug-induced alpha activity has been repeatedly shown to be more predictable than that observed in healthy, awake individuals.11–13 We agree with Velazquez et al. that these steps inevitably affect the assessment of the variability of an EEG signal (as also shown by their simulations), but because the choices of analysis were statistically orthogonal to the two cohorts, we believe that the study's implications are still valid. Like Velazquez et al., we also believe that this difference in variability across the two cohorts is intriguing, but our interpretation differs slightly from what is suggested by Velazquez et al. We indeed interpret the higher level of entropy in alpha-coma as an underlying generator of cortical activity that is, by its nature, unpredictable. In other words, the lower entropy observed during eyes-closed conditions in healthy participants should not be interpreted as a deeper shutdown compared with alpha-coma. Instead, the variability of the alpha rhythm in alpha-coma states likely reflects an epiphenomenon of an autonomous and erratic rhythmic activity, which is inherently more variable because of its pathologic origin.

Summary: Stereoelectroencephalography is an established, hypothesis-driven method for investigating refractory epilepsy. There are special considerations and some limitations that apply to children who undergo stereoelectroencephalography. A key principle in stereoelectroencephalography is taking an individualized approach to investigating refractory epilepsy. A crucial factor for success in a personalized pediatric epilepsy surgery is understanding some of the fundamental and unique aspects of it, including, but not limited to, diverse etiology, epilepsy syndromes, maturation, and age-related characteristics as well as neural plasticity. Such features are reflected in the ontogeny of semiology and electrophysiology. In addition, special considerations are taken into account during cortical stimulation in children. Stereoelectroencephalography can guide a tailored surgical intervention where it is sufficient to render the patient seizure-free but it also lessens collateral damage with a minimum or no functional deficit. Epilepsy surgery outcomes remain stagnant despite advances in noninvasive testing modalities. A stereoelectroencephalography “way of thinking” and guided mentorship may influence outcomes positively.

Summary:The Guidelines for Qualifications of Neurodiagnostic Personnel (QNP) document has been created through the collaboration of the American Clinical Neurophysiology Society (ACNS), the American Society of Neurophysiological Monitoring (ASNM), the American Association of Neuromuscular & Electrodiagnostic Medicine (AANEM), and ASET—The Neurodiagnostic Society (ASET). The quality of patient care is optimized when neurophysiological procedures are performed and interpreted by appropriately trained and qualified practitioners at every level. These societies recognize that neurodiagnostics is a large field with practitioners who have entered the field through a variety of training paths. This document suggests job titles, associated job responsibilities, and the recommended levels of education, certification, experience, and ongoing education appropriate for each job. This is important because of the growth and development of standardized training programs, board certifications, and continuing education in recent years. This document matches training, education, and credentials to the various tasks required for performing and interpreting neurodiagnostic procedures. This document does not intend to restrict the practice of those already working in neurodiagnostics. It represents recommendations of these societies with the understanding that federal, state, and local regulations, as well as individual hospital bylaws, supersede these recommendations. Because neurodiagnostics is a growing and dynamic field, the authors fully intend this document to change over time.

Central, peripheral, and root motor conduction times (CMCTs, PMCTs, and RMCTs, respectively) are valuable diagnostic tools for spinal cord and motor nerve root lesions. We investigated the normal values and the effects of age and height on each motor conduction time. This study included 190 healthy Korean subjects who underwent magnetic stimulation of the cortex and spinous processes at the C7 and L1 levels. Recording muscles were abductor pollicis brevis and abductor digiti minimi in the unilateral upper limb and extensor digitorum brevis and abductor hallucis in the contralateral lower limb. F-wave and compound motor nerve action potentials were also recorded. Central motor conduction time was evaluated as the difference between cortical motor evoked potential onset latency and PMCT using calculation and spinal stimulation methods. Root motor conduction time was computed as the difference between spinal stimulated and calculated CMCTs. The average age and height of the participants were 41.21 ± 14.39 years and 164.64 ± 8.27 cm, respectively; 39.5% (75/190) patients were men. In the linear regression analyses, upper limb CMCTs showed a significant and weak positive relationship with height. Lower limb CMCTs demonstrated a significant and weak positive relationship with age and height. Peripheral motor conduction times were significantly and positively correlated with age and height. Root motor conduction times showed no significant relationship with age and height, except for abductor pollicis brevis-RMCT, which had a weak negative correlation with height. This study provides normal values of CMCTs, PMCTs, and RCMTs, which have potential clinical applications. When interpreting CMCTs, age and height should be considered.

Diagnosing and characterizing myoclonus can be challenging. Many authors agree on the need to complement the clinical findings with an electrophysiological study to characterize the movements. Besides helping to rule out other movements that may look like myoclonus, electrophysiology can help localize the source of the movement. This article aims to serve as a practical manual on how to do a myoclonus study. For this purpose, the authors combine their experience with available evidence. The authors provide detailed descriptions of recording poly-electromyography, combining electroencephalography and electromyography, Bereitschaftspotentials, somatosensory evoked potentials, and startle techniques. The authors discuss analysis considerations for these data and provide a simplified algorithm for their interpretation. Finally, the authors discuss some factors that they believe have hindered the broader use of these useful techniques.

Prognostication following cardiorespiratory arrest relies on the neurological examination, which is supported by neuroimaging and neurophysiological testing. Acute posthypoxic myoclonus (PHM) is a clinical entity that has prognostic significance and historically has been considered an indicator of poor outcome, but this is not invariably the case. "Malignant" and more "benign" forms of acute PHM have been described and differentiating them is key in understanding their meaning in prognosis. Neurophysiological tests, electroencephalogram in particular, and clinical phenotyping are crucial in defining subtypes of acute PHM. This review describes the neurophysiological and phenotypic markers of malignant and benign forms of acute PHM, a clinical approach to evaluating acute PHM following cardiorespiratory arrest in determining prognosis, and gaps in our understanding of acute PHM that require further study.

The Accreditation Council for Graduate Medical Education and the American Board of Psychiatry and Neurology first developed milestones for the clinical neurophysiology (CNP) fellowship in 2015. The milestones provide a comprehensive evaluation of the fellow's development based on six domains of competency. Here, we describe the development of a new set of milestones for CNP fellowship with level 1 as the incoming level, level 4 as the goal for graduation, and level 5 as the aspirational level that may not be achieved. Committee members were nominated or volunteered to participate in the milestones update. Milestone development began with the creation of a shared mental model of the ideal skills and knowledge a graduating CNP fellow should attain. The CNP committee met virtually 7 times for a total of 14 meeting hours. Nine Patient Care and five Medical Knowledge milestones evolved from the seven Patient Care and six Medical Knowledge milestones that were in the first iteration. The committee incorporated 11 "Harmonized Milestones" into the revision and a supplemental guide was created. The revised Accreditation Council for Graduate Medical Education milestones for CNP fellowship contain important updates that program directors should review against their curricula to identify any gaps in learning. Program leadership should take note of two new Patient Care milestones for telemedicine and intraoperative monitoring. Clinical neurophysiology fellowships are not designed to provide level 4 competency across all milestones. The revised milestones should be viewed within the context of an individual program's goals.

Sleep/wake disorders are common in patients with autoimmune encephalitis, sometimes the most prominent or sole initial symptom, then delaying diagnosis. Sleep/wake disorders in autoimmune encephalitis vary and include severe sleeplessness, hypersomnia, central and/or obstructive sleep apnea, rapid eye movement sleep behavior disorder, indeterminate sleep/wake states, and loss of circadian sleep/wake rhythms. N-methyl- d aspartate receptor encephalitis (NMDAR) is often associated with insomnia, then hypersomnia and sleep-related central hypoventilation. Profound sleeplessness and rapid eye movement sleep behavior disorder are seen in patients with voltage-gated potassium channel-complex antibodies. Fragmented sleep and hypersomnia are common in paraneoplastic syndromes associated with anti-MA protein encephalitis; rapid eye movement sleep behavior disorder in those with antibodies against leucine-rich glioma inactivated protein (LGI1) or contactin-associated protein 2 (CASPR2) antibodies. Antibodies against a cell adhesion protein IGLON5 may result in obstructive sleep apnea, inspiratory stridor, disorganized nonrapid eye movement sleep, and excessive movements and parasomnias fragmenting nonrapid and rapid eye movement sleep. Recognizing a particular sleep/wake disorder is often a presenting or prominent feature in certain autoimmune encephalitis permit for earlier diagnosis. This is important because reduced morbidity and better short- and long-term outcomes are associated with earlier diagnosis and immunotherapies.

Sleep disorders are common after traumatic brain injury (TBI). This study will review the spectrum and proposed mechanisms of TBI-associated sleep disorders and discuss the clinical approach to diagnosis and management of them. Disordered and fragmented sleep with insomnia and daytime sleepiness is very common after TBI. Sleep disruption contributes to morbidity and neurocognitive and neurobehavioral deficits and prolongs the recovery phase after injury. Early recognition and correction of these problems may limit the secondary effects of TBI and improve patient outcomes. Evaluating sleep disorders in TBI should be an important component of TBI assessment and management. Finally, newer research techniques for early diagnosis, prognosis, and improved outcomes after TBI will also be addressed.