Time series motifs are pairs of individual time series, or subsequences of a longer time series, which are very similar to each other. As with their discrete analogues in computational biology, this similarity hints at structure which has been conserved for some reason and may therefore be of interest. Since the formalism of time series motifs in 2002, dozens of researchers have used them for diverse applications in many different domains. Because the obvious algorithm for computing motifs is quadratic in the number of items, more than a dozen approximate algorithms to discover motifs have been proposed in the literature. In this work, for the first time, we show a tractable exact algorithm to find time series motifs. As we shall show through extensive experiments, our algorithm is up to three orders of magnitude faster than brute-force search in large datasets. We further show that our algorithm is fast enough to be used as a subroutine in higher level data mining algorithms for anytime classification, near-duplicate detection and summarization, and we consider detailed case studies in domains as diverse as electroencephalograph interpretation and entomological telemetry data mining.

Spreading depolarizations (SD) are waves of abrupt, near-complete breakdown of neuronal transmembrane ion gradients, are the largest possible pathophysiologic disruption of viable cerebral gray matter, and are a crucial mechanism of lesion development. Spreading depolarizations are increasingly recorded during multimodal neuromonitoring in neurocritical care as a causal biomarker providing a diagnostic summary measure of metabolic failure and excitotoxic injury. Focal ischemia causes spreading depolarization within minutes. Further spreading depolarizations arise for hours to days due to energy supply-demand mismatch in viable tissue. Spreading depolarizations exacerbate neuronal injury through prolonged ionic breakdown and spreading depolarization-related hypoperfusion (spreading ischemia). Local duration of the depolarization indicates local tissue energy status and risk of injury. Regional electrocorticographic monitoring affords even remote detection of injury because spreading depolarizations propagate widely from ischemic or metabolically stressed zones; characteristic patterns, including temporal clusters of spreading depolarizations and persistent depression of spontaneous cortical activity, can be recognized and quantified. Here, we describe the experimental basis for interpreting these patterns and illustrate their translation to human disease. We further provide consensus recommendations for electrocorticographic methods to record, classify, and score spreading depolarizations and associated spreading depressions. These methods offer distinct advantages over other neuromonitoring modalities and allow for future refinement through less invasive and more automated approaches.

See Eissa and Schevon (doi:) for a scientific commentary on this article.Neurosurgical treatment of focal epilepsy is highly unpredictable in cases where a lesion is not apparent by any imaging modality. Sinha et al. propose a computational approach to predict the outcome of a planned resection prior to surgery and investigate the pathophysiology of epileptic networks to delineate cortical areas crucial for ictogenesis.

A modern understanding of how cerebral cortical lesions develop after acute brain injury is based on Aristides Leão's historic discoveries of spreading depression and asphyxial/anoxic depolarization. Treated as separate entities for decades, we now appreciate that these events define a continuum of spreading mass depolarizations, a concept that is central to understanding their pathologic effects. Within minutes of acute severe ischemia, the onset of persistent depolarization triggers the breakdown of ion homeostasis and development of cytotoxic edema. These persistent changes are diagnosed as diffusion restriction in magnetic resonance imaging and define the ischemic core. In delayed lesion growth, transient spreading depolarizations arise spontaneously in the ischemic penumbra and induce further persistent depolarization and excitotoxic damage, progressively expanding the ischemic core. The causal role of these waves in lesion development has been proven by real-time monitoring of electrophysiology, blood flow, and cytotoxic edema. The spreading depolarization continuum further applies to other models of acute cortical lesions, suggesting that it is a universal principle of cortical lesion development. These pathophysiologic concepts establish a working hypothesis for translation to human disease, where complex patterns of depolarizations are observed in acute brain injury and appear to mediate and signal ongoing secondary damage.

Background The neural mechanisms of anesthetic vapors have not been studied in depth. However, modeling and experimental studies on the intravenous anesthetic propofol indicate that potentiation of γ-Aminobutyric acid receptors leads to a state of thalamocortical synchrony, observed as coherent frontal alpha oscillations, associated with unconsciousness. Sevoflurane, an ether derivative, also potentiates γ-Aminobutyric acid receptors. However, in humans, sevoflurane-induced coherent frontal alpha oscillations have not been well detailed. Methods To study the electroencephalogram dynamics induced by sevoflurane, we identified age and gender matched patients in which sevoflurane (n = 30) or propofol (n = 30) were used as the sole agent for maintenance of general anesthesia during routine surgery. We compared the electroencephalogram signatures of sevoflurane to propofol using time-varying spectral and coherence methods. Results Sevoflurane general anesthesia is characterized by alpha oscillations with maximum power and coherence at ~10 Hz, (mean±std; peak power, 4.3dB ± 3.5; peak coherence, 0.73 ± 0.1). These alpha oscillations are similar to those observed during propofol general anesthesia, which also has maximum power and coherence at ~10 Hz (peak power, 2.1dB ± 4.3; peak coherence, 0.71 ± 0.1). However, sevoflurane also exhibited a distinct theta coherence signature (peak frequency, 4.9Hz ± 0.6; peak coherence, 0.58 ± 0.1). Slow oscillations were observed in both cases, with no significant difference in power or coherence. Conclusion Our results indicate that sevoflurane, like propofol, induces coherent frontal alpha oscillations and slow oscillations in humans to sustain the anesthesia-induced unconscious state. These results suggest a shared molecular and systems-level mechanism for the unconscious state induced by these drugs.

This cohort study assesses the potential use of red blood cell distribution width for risk stratification of patients with coronavirus disease 2019.

Background Electroencephalogram patterns observed during sedation with dexmedetomidine appear similar to those observed during general anesthesia with propofol. This is evident with the occurrence of slow (0.1–1 Hz), delta (1–4 Hz), propofol-induced alpha (8–12 Hz), and dexmedetomidine-induced spindle (12–16 Hz) oscillations. However, these drugs have different molecular mechanisms and behavioral properties, and are likely accompanied by distinguishing neural circuit dynamics. Methods We measured 64-channel electroencephalogram under dexmedetomidine (n = 9) and propofol (n = 8) in healthy volunteers, 18–36 years of age. We administered dexmedetomidine with a 1mcg/kg loading bolus over 10 minutes, followed by a 0.7mcg/kg/hr infusion. For propofol, we used a computer controlled infusion to target the effect-site concentration gradually from and 0 µg/mL to 5 µg/mL. Volunteers listened to auditory stimuli and responded by button-press to determine unconsciousness. We analyzed the electroencephalogram using multitaper spectral and coherence analysis. Results Dexmedetomidine was characterized by spindles with maximum power and coherence at ~13 Hz, (mean±std; power, −10.8dB±3.6; coherence, 0.8±0.08), while propofol was characterized with frontal alpha oscillations with peak frequency at ~11 Hz (power, 1.1dB±4.5; coherence, 0.9±0.05). Notably, slow oscillation power during a general anesthetic state under propofol (power, 13.2dB±2.4) was much larger than during sedative states under both propofol (power, −2.5dB±3.5) and dexmedetomidine (power, −0.4dB±3.1). Conclusion Our results indicate that dexmedetomidine and propofol place patients into different brain states, and suggests that propofol enables a deeper state of unconsciousness by inducing large amplitude slow oscillations that produce prolonged states of neuronal silence.

Functional connectivity networks have become a central focus in neuroscience as they reveal key higher dimensional features of normal and abnormal nervous system physiology. Functional networks reflect activity-based coupling between brain regions that may be constrained by relatively static anatomical connections, yet these networks appear to support tremendously dynamic behaviors. Within this growing field, the stability and temporal characteristics of functional connectivity brain networks have not been well characterized. We evaluated the temporal stability of spontaneous functional connectivity networks derived from multi-day scalp encephalogram (EEG) recordings in five healthy human subjects. Topological stability and graph characteristics of networks derived from averaged data epochs ranging from one second to multiple hours across different states of consciousness were compared. We show that although functional networks are highly variable on the order of seconds, stable network templates emerge after as little as ~100s of recording and persist across different states and frequency bands (albeit with slightly different characteristics in different states and frequencies). Within these network templates, the most common edges are markedly consistent, constituting a network “core”. Although average network topologies persist across time, measures of global network connectivity, density and clustering coefficient, are state- and frequency- specific, with sparsest but most highly clustered networks seen during sleep and in the gamma frequency band. These findings support the notion that a core functional organization underlies spontaneous cortical processing and may provide a reference template upon which unstable, transient, and rapidly adaptive long-range assemblies are overlaid in a frequency dependent manner.