Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

Prospective Cohort Study

Healthy biological systems exhibit complex patterns of variability that can be described by mathematical chaos. Heart rate variability (HRV) consists of changes in the time intervals between consecutive heartbeats called interbeat intervals (IBIs). A healthy heart is not a metronome. The oscillations of a healthy heart are complex and constantly changing, which allow the cardiovascular system to rapidly adjust to sudden physical and psychological challenges to homeostasis. This article briefly reviews current perspectives on the mechanisms that generate 24 h, short-term (~5 min), and ultra-short-term (<5 min) HRV, the importance of HRV, and its implications for health and performance. The authors provide an overview of widely-used HRV time-domain, frequency-domain, and non-linear metrics. Time-domain indices quantify the amount of HRV observed during monitoring periods that may range from ~2 min to 24 h. Frequency-domain values calculate the absolute or relative amount of signal energy within component bands. Non-linear measurements quantify the unpredictability and complexity of a series of IBIs. The authors survey published normative values for clinical, healthy, and optimal performance populations. They stress the importance of measurement context, including recording period length, subject age, and sex, on baseline HRV values. They caution that 24 h, short-term, and ultra-short-term normative values are not interchangeable. They encourage professionals to supplement published norms with findings from their own specialized populations. Finally, the authors provide an overview of HRV assessment strategies for clinical and optimal performance interventions.

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An Overview of Heart Rate Variability Metrics and Norms.

The International League Against Epilepsy (ILAE) Classification of the Epilepsies has been updated to reflect our gain in understanding of the epilepsies and their underlying mechanisms following the major scientific advances that have taken place since the last ratified classification in 1989. As a critical tool for the practicing clinician, epilepsy classification must be relevant and dynamic to changes in thinking, yet robust and translatable to all areas of the globe. Its primary purpose is for diagnosis of patients, but it is also critical for epilepsy research, development of antiepileptic therapies, and communication around the world. The new classification originates from a draft document submitted for public comments in 2013, which was revised to incorporate extensive feedback from the international epilepsy community over several rounds of consultation. It presents three levels, starting with seizure type, where it assumes that the patient is having epileptic seizures as defined by the new 2017 ILAE Seizure Classification. After diagnosis of the seizure type, the next step is diagnosis of epilepsy type, including focal epilepsy, generalized epilepsy, combined generalized, and focal epilepsy, and also an unknown epilepsy group. The third level is that of epilepsy syndrome, where a specific syndromic diagnosis can be made. The new classification incorporates etiology along each stage, emphasizing the need to consider etiology at each step of diagnosis, as it often carries significant treatment implications. Etiology is broken into six subgroups, selected because of their potential therapeutic consequences. New terminology is introduced such as developmental and epileptic encephalopathy. The term benign is replaced by the terms self-limited and pharmacoresponsive, to be used where appropriate. It is hoped that this new framework will assist in improving epilepsy care and research in the 21st century.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/epi.13709

ILAE classification of the epilepsies: Position paper of the ILAE Commission for Classification and Terminology.

background Previous studies have suggested that the obstructive sleep apnea syndrome may be an important risk factor for stroke. It has not been determined, however, whether the syndrome is independently related to the risk of stroke or death from any cause after adjustment for other risk factors, including hypertension. methods In this observational cohort study, consecutive patients underwent polysomnography, and subsequent events (strokes and deaths) were verified. The diagnosis of the obstructive sleep apnea syndrome was based on an apnea–hypopnea index of 5 or higher (five or more events per hour); patients with an apnea–hypopnea index of less than 5 served as the comparison group. Proportional-hazards analysis was used to determine the independent effect of the obstructive sleep apnea syndrome on the composite outcome of stroke or death from any cause. results Among 1022 enrolled patients, 697 (68 percent) had the obstructive sleep apnea syndrome. At baseline, the mean apnea–hypopnea index in the patients with the syndrome was 35, as compared with a mean apnea–hypopnea index of 2 in the comparison group. In an unadjusted analysis, the obstructive sleep apnea syndrome was associated with stroke or death from any cause (hazard ratio, 2.24; 95 percent confidence interval, 1.30 to 3.86; P=0.004). After adjustment for age, sex, race, smoking status, alcohol-consumption status, body-mass index, and the presence or absence of diabetes mellitus, hyperlipidemia, atrial fibrillation, and hypertension, the obstructive sleep apnea syndrome retained a statistically significant association with stroke or death (hazard ratio, 1.97; 95 percent confidence interval, 1.12 to 3.48; P=0.01). In a trend analysis, increased severity of sleep apnea at baseline was associated with an increased risk of the development of the composite end point (P=0.005). conclusions The obstructive sleep apnea syndrome significantly increases the risk of stroke or death from any cause, and the increase is independent of other risk factors, including hypertension.

https://www.nejm.org/doi/pdf/10.1056/NEJMoa043104

Obstructive sleep apnea as a risk factor for stroke and death.

Neuronal oscillations underlie temporal coordination of neuronal processing and their synchronization enables neuronal communication across distributed brain areas to serve a variety of sensory, motor, and cognitive functions. The regulation and integration of neuronal processing between oscillating assemblies at distinct frequencies, and thereby the coordination of distinct computational functions, is thought to be achieved via cross-frequency coupling (CFC). Although many studies have observed CFC locally within a brain region during cognitive processing, the large-scale networks of CFC have remained largely uncharted. Critically, also the validity of prior CFC observations and the presence of true neuronal CFC has been recently questioned because non-sinusoidal or non-zero-mean waveforms that are commonplace in electrophysiological data cause filtering artefacts that lead to false positive CFC findings. We used a unique dataset of stereo-electroencephalography (SEEG) and source-reconstructed magnetoencephalography (MEG) data to chart whole-brain CFC networks from human resting-state brain dynamics. Using a novel graph theoretical method to distinguish true inter-areal CFC from potentially false positive CFC, we show that the resting state is characterized by two separable forms of true inter-areal CFC: phase-amplitude coupling (PAC) and n:m-cross-frequency phase synchrony (CFS). PAC and CFS large-scale networks coupled prefrontal, visual and sensorimotor cortices, but with opposing anatomical architectures. Crucially also directionalities between low- and high-frequency oscillations were opposite between CFS and PAC. We also found CFC to decay as a function of distance and to be stronger in the superficial than deep layers of the cortex. In conclusion, these results provide conclusive evidence for the presence of two forms of genuine inter-areal CFC and elucidate the large-scale organization of CFC resting-state networks.

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Resting-state cross-frequency coupling networks in human electrophysiological recordings

Sexsomnia: a diagnostic challenge, a case report.

Nocturnal sleep features in narcolepsy: a model-based approach.

Modules in brain connectomes are essential to balancing the functional segregation and integration crucial to brain operation. Connectomes are the set of structural or functional connections between each pair of brain regions. Non-invasive methodologies, Electroencephalography (EEG) and Magnetoencephalography (MEG), have been used to identify modules in connectomes of phase-synchronization, but have been compromised by spurious phase-synchronization due to EEG volume conduction or MEG field spread. In this study, we used invasive, intracerebral recordings with stereo-electroencephalography (SEEG, N = 67), to identify modules in connectomes of phase-synchronization. To do this, we used submillimetre localization of SEEG contacts and closest-white-matter referencing, to generate group-level connectomes of phase-synchronization minimally affected by volume conduction. Then, we employed community detection methods together with a novel consensus clustering approach, to identify modules in connectomes of phase-synchronization. The connectomes of phase-synchronization possessed significant modular organization at multiple spatial scales, from 3-320 Hz. These identified modules were highly similar within neurophysiologically meaningful frequency bands. Modules up to the high-gamma frequency band comprised only anatomically contiguous regions, unlike modules identified with functional Magnetic Resonance Imaging (fMRI). Strikingly, the identified modules comprised cortical regions involved in shared repertoires of cognitive functions including vision, language and attention. These results demonstrate the viability of combining SEEG with advanced methods, to identify modules in connectomes of phase-synchronization. The modules correspond to brain systems with specific functional roles in perceptual, cognitive, and motor processing.

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Modules in connectomes of phase-synchronization comprise anatomically contiguous, spatially related regions

Objective : A multiscale functional clustering approach is proposed to investigate the organization of the epileptic networks during different sleep stages and in relation with the occurrence of seizures. Method : Stereo-electroencephalographic signals from seven pharmaco-resistant epileptic patients (focal cortical dysplasia type II) were analyzed. The discrete wavelet transform provided a multiscale framework on which a data-driven functional clustering procedure was applied, based on multivariate measures of integration and mutual information. The most interacting functional clusters (FCs) within the sampled brain areas were extracted. Results : FCs characterized by strongly integrated activity were observed mostly in the beta and alpha frequency bands, immediately before seizure onset and in deep sleep stages. These FCs generally included the electrodes from the epileptogenic zone. Furthermore, repeatable patterns were found across ictal events in the same patient. Conclusion : In line with previous studies, our findings provide evidence of the important role of beta and alpha activity in seizures generation and support the relation between epileptic activity and sleep stages. Significance : Despite the small number of subjects included in the study, the present results suggest that the proposed multiscale functional clustering approach is a useful tool for the identification of the frequency-dependent mechanisms underlying seizure generation.

Lino Nobili

Papers

Resting-state cross-frequency coupling networks in human electrophysiological recordings

Sexsomnia: a diagnostic challenge, a case report.

Nocturnal sleep features in narcolepsy: a model-based approach.

Modules in connectomes of phase-synchronization comprise anatomically contiguous, spatially related regions

Multiscale Functional Clustering Reveals Frequency Dependent Brain Organization in Type II Focal Cortical Dysplasia With Sleep Hypermotor Epilepsy