Brain Responses to Propofol in Advance of Recovery from Coma and Disorders of Consciousness: A Preliminary Study.

Rationale: Predicting recovery of consciousness in unresponsive, brain-injured individuals has crucial implications for clinical decision-making. Propofol induces distinctive brain network reconfiguration in the healthy brain as it loses consciousness. In patients with disorders of consciousness, the brain network's reconfiguration to propofol may reveal the patient's underlying capacity for consciousness. Objectives: To design and test a new metric for the prognostication of consciousness recovery in disorders of consciousness. Methods: Using a within-subject design, we conducted an anesthetic protocol with concomitant high-density EEG in 12 patients with a disorder of consciousness after a brain injury. We quantified the reconfiguration of EEG network hubs and directed functional connectivity before, during, and after propofol exposure and obtained an index of propofol-induced network reconfiguration: the adaptive reconfiguration index. We compared the index of patients who recovered consciousness 3 months after EEG (n = 3) to that of patients who did not recover or remained in a chronic disorder of consciousness (n = 7) and conducted a logistic regression to assess prognostic accuracy. Measurements and Main Results: The adaptive reconfiguration index was significantly higher in patients who later recovered full consciousness (U value = 21, P = 0.008) and able to discriminate with 100% accuracy whether the patient recovered consciousness. Conclusions: The adaptive reconfiguration index of patients who recovered from a disorder of consciousness at 3-month follow-up was linearly separable from that of patients who did not recover or remained in a chronic disorder of consciousness on the single-subject level. EEG and propofol can be administered at the bedside with few contraindications, affording the adaptive reconfiguration index tremendous translational potential as a prognostic measure of consciousness recovery in acute clinical settings.

Clinical use of Electroencephalography in the Assessment of Acute Thermal Pain: A Narrative Review Based on Articles From 2009 to 2019.

Nowadays, no practical system has successfully been able to decode and predict pain in clinical settings. The inability of some patients to verbally express their pain creates the need for a tool that could objectively assess pain in these individuals. Neuroimaging techniques combined with machine learning are seen as possible candidates for the identification of pain biomarkers. This review aimed to address the potential use of electroencephalographic features as predictors of acute experimental pain. Twenty-six studies using only thermal stimulations were identified using a PubMed and Scopus search. Combinations of the following terms were used: "EEG," "Electroencephalography," "Acute," "Pain," "Tonic," "Noxious," "Thermal," "Stimulation," "Brain," "Activity," "Cold," "Subjective," and "Perception." Results revealed that contact-heat-evoked potentials have been widely recorded over central areas during noxious heat stimulations. Furthermore, a decrease in alpha power over central regions was revealed, as well as increased theta and gamma powers over frontal areas. Gamma and theta rhythms were associated with connectivity between sensory and affective regions involved in pain processing. A machine learning analysis revealed that the gamma band is a predominant predictor of acute thermal pain. This review also addressed the need of supplementing current spectral features with techniques that allow the investigation of network dynamics.

Electroencephalographic characteristics of children and adolescents with chronic musculoskeletal pain.

Introduction: The pathophysiology of pediatric musculoskeletal (MSK) pain is unclear, contributing to persistent challenges to its management. Objectives: This study hypothesizes that children and adolescents with chronic MSK pain (CPs) will show differences in electroencephalography (EEG) features at rest and during thermal pain modalities when compared with age-matched controls. Methods: One hundred forty-two CP patients and 45 age-matched healthy controls (HCs) underwent a standardized thermal tonic heat and cold stimulations, while a 21-electrode headset collected EEG data. Cohorts were compared with respect to their EEG features of spectral power, peak frequency, permutation entropy, weight phase-lag index, directed phase-lag index, and node degree at 4 frequency bands, namely, delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz), at rest and during the thermal conditions. Results: At rest, CPs showed increased global delta (P = 0.0493) and beta (P = 0.0002) power in comparison with HCs. These findings provide further impetus for the investigation and prevention of long-lasting developmental sequalae of early life chronic pain processes. Although no cohort differences in pain intensity scores were found during the thermal pain modalities, CPs and HCs showed significant difference in changes in EEG spectral power, peak frequency, permutation entropy, and network functional connectivity at specific frequency bands (P < 0.05) during the tonic heat and cold stimulations. Conclusion: This suggests that EEG can characterize subtle differences in heat and cold pain sensitivity in CPs. The complementation of EEG and evoked pain in the clinical assessment of pediatric chronic MSK pain can better detect underlying pain mechanisms and changes in pain sensitivity.

Differential classification of states of consciousness using envelope- and phase-based functional connectivity.

The development of sophisticated computational tools to quantify changes in the brain's oscillatory dynamics across states of consciousness have included both envelope- and phase-based measures of functional connectivity (FC), but there are very few direct comparisons of these techniques using the same dataset. The goal of this study was to compare an envelope-based (i.e. Amplitude Envelope Correlation, AEC) and a phase-based (i.e. weighted Phase Lag Index, wPLI) measure of FC in their classification of states of consciousness. Nine healthy participants underwent a three-hour experimental anesthetic protocol with propofol induction and isoflurane maintenance, in which five minutes of 128-channel electroencephalography were recorded before, during, and after anesthetic-induced unconsciousness, at the following time points: Baseline; light sedation with propofol (Light Sedation); deep unconsciousness following three hours of surgical levels of anesthesia with isoflurane (Unconscious); five minutes prior to the recovery of consciousness (Pre-ROC); and three hours following the recovery of consciousness (Recovery). Support vector machine classification was applied to the source-localized EEG in the alpha (8-13 Hz) frequency band in order to investigate the ability of AEC and wPLI (separately and together) to discriminate i) the four states from Baseline; ii) Unconscious ("deep" unconsciousness) vs. Pre-ROC ("light" unconsciousness); and iii) responsiveness (Baseline, Light Sedation, Recovery) vs. unresponsiveness (Unconscious, Pre-ROC). AEC and wPLI yielded different patterns of global connectivity across states of consciousness, with AEC showing the strongest network connectivity during the Unconscious epoch, and wPLI showing the strongest connectivity during full consciousness (i.e., Baseline and Recovery). Both measures also demonstrated differential predictive contributions across participants and used different brain regions for classification. AEC showed higher classification accuracy overall, particularly for distinguishing anesthetic-induced unconsciousness from Baseline (83.7 ± 0.8%). AEC also showed stronger classification accuracy than wPLI when distinguishing Unconscious from Pre-ROC (i.e., "deep" from "light" unconsciousness) (AEC: 66.3 ± 1.2%; wPLI: 56.2 ± 1.3%), and when distinguishing between responsiveness and unresponsiveness (AEC: 76.0 ± 1.3%; wPLI: 63.6 ± 1.8%). Classification accuracy was not improved compared to AEC when both AEC and wPLI were combined. This analysis of source-localized EEG data demonstrates that envelope- and phase-based FC provide different information about states of consciousness but that, on a group level, AEC is better able to detect relative alterations in brain FC across levels of anesthetic-induced unconsciousness compared to wPLI.

Brain network motifs are markers of loss and recovery of consciousness.

Motifs are patterns of inter-connections between nodes of a network, and have been investigated as building blocks of directed networks. This study explored the re-organization of 3-node motifs during loss and recovery of consciousness. Nine healthy subjects underwent a 3-h anesthetic protocol while 128-channel electroencephalography (EEG) was recorded. In the alpha (8-13 Hz) band, 5-min epochs of EEG were extracted for: Baseline; Induction; Unconscious; 30-, 10- and 5-min pre-recovery of responsiveness; 30- and 180-min post-recovery of responsiveness. We constructed a functional brain network using the weighted and directed phase lag index, on which we calculated the frequency and topology of 3-node motifs. Three motifs (motifs 1, 2 and 5) were significantly present across participants and epochs, when compared to random networks (p < 0.05). The topology of motifs 1 and 5 changed significantly between responsive and unresponsive epochs (p-values < 0.01; Kendall's W = 0.664 (motif 1) and 0.529 (motif 5)). Motif 1 was constituted of long-range chain-like connections, while motif 5 was constituted of short-range, loop-like connections. Our results suggest that anesthetic-induced unconsciousness is associated with a topological re-organization of network motifs. As motif topological re-organization may precede (motif 5) or accompany (motif 1) the return of responsiveness, motifs could contribute to the understanding of the neural correlates of consciousness.

A Feasibility Study on the Use of the Method of Loci for Improving Episodic Memory Performance in Schizophrenia and Non-clinical Subjects.

We investigated the feasibility of a short intervention using the Method of Loci (MoL), a well-known visuospatial mnemonic, to improve episodic memory recall performance in schizophrenia. The MoL training protocol comprised encoding and recall of two lists of items (words and images), a training session and practice with MoL. Then, participants had the opportunity to put into practice the newly learned MoL and were instructed to encode and recall two new lists of items using. This approach was first validated with healthy individuals (N = 71). Subsequently, five individuals with schizophrenia completed the protocol. Improvement in healthy individuals was observed for the word list (Wilcoxon effect size r = 0.15). No significant memory improvement was denoted in the schizophrenia group, possibly due to participants' difficulties using the method efficiently and due to fatigue. The MoL seems to require episodic memory, working memory monitoring and executive functions, making it suboptimal for a population with impairments in all those domains. Future research should examine the use of other strategies, better suited for individuals with cognitive impairments like those found in schizophrenia.

Brain network motif topography may predict emergence from disorders of consciousness: a case series.

Neuroimaging methods have improved the accuracy of diagnosis in patients with disorders of consciousness (DOC), but novel, clinically translatable methods for prognosticating this population are still needed. In this case series, we explored the association between topographic and global brain network properties and prognosis in patients with DOC. We recorded high-density electroencephalograms in three patients with acute or chronic DOC, two of whom also underwent an anesthetic protocol. In these two cases, we compared functional network motifs, network hubs and power topography (i.e. topographic network properties), as well as relative power and graph theoretical measures (i.e. global network properties), at baseline, during exposure to anesthesia and after recovery from anesthesia. We also compared these properties to a group of healthy, conscious controls. At baseline, the topographic distribution of nodes participating in alpha motifs resembled conscious controls in patients who later recovered consciousness and high relative power in the delta band was associated with a negative outcome. Strikingly, the reorganization of network motifs, network hubs and power topography under anesthesia followed by their return to a baseline patterns upon recovery from anesthesia, was associated with recovery of consciousness. Our findings suggest that topographic network properties measured at the single-electrode level might provide more prognostic information than global network properties that are averaged across the brain network. In addition, we propose that the brain network's capacity to reorganize in response to a perturbation is a precursor to the recovery of consciousness in DOC patients.

Wearable Technology for Detecting Significant Moments in Individuals with Dementia.

The detection of significant moments can support the care of individuals with dementia by making visible what is most meaningful to them and maintaining a sense of interpersonal connection. We present a novel intelligent assistive technology (IAT) for the detection of significant moments based on patterns of physiological signal changes in individuals with dementia and their caregivers. The parameters of the IAT are tailored to each individual's idiosyncratic physiological response patterns through an iterative process of incorporating subjective feedback on videos extracted from candidate significant moments identified through the IAT algorithm. The IAT was tested on three dyads (individual with dementia and their primary caregiver) during an eight-week movement program. Upon completion of the program, the IAT identified distinct, personal characteristics of physiological responsiveness in each participant. Tailored algorithms could detect moments of significance experienced by either member of the dyad with an agreement with subjective reports of 70%. These moments were constituted by both physical and emotional significances (e.g., experiences of pain or anxiety) and interpersonal significance (e.g., moments of heighted connection). We provide a freely available MATLAB toolbox with the IAT software in hopes that the assistive technology community can benefit from and contribute to these tools for understanding the subjective experiences of individuals with dementia.

The zinc fingers of the small optic lobes calpain bind polyubiquitin.

The small optic lobes (SOL) calpain is a highly conserved member of the calpain family expressed in the nervous system. A dominant negative form of the SOL calpain inhibited consolidation of one form of synaptic plasticity, non-associative facilitation, in sensory-motor neuronal cultures in Aplysia, presumably by inhibiting cleavage of protein kinase Cs (PKCs) into constitutively active protein kinase Ms (PKMs) (Hu et al. 2017a). SOL calpains have a conserved set of 5-6 N-terminal zinc fingers. Bioinformatic analysis suggests that these zinc fingers could bind to ubiquitin. In this study, we show that both the Aplysia and mouse SOL calpain (also known as Calpain 15) zinc fingers bind ubiquitinated proteins, and we confirm that Aplysia SOL binds poly- but not mono- or diubiquitin. No specific zinc finger is required for polyubiquitin binding. Neither polyubiquitin nor calcium was sufficient to induce purified Aplysia SOL calpain to autolyse or to cleave the atypical PKC to PKM in vitro. In Aplysia, over-expression of the atypical PKC in sensory neurons leads to an activity-dependent cleavage event and an increase in nuclear ubiquitin staining. Activity-dependent cleavage is partially blocked by a dominant negative SOL calpain, but not by a dominant negative classical calpain. The cleaved PKM was stabilized by the dominant negative classical calpain and destabilized by a dominant negative form of the PKM stabilizing protein KIdney/BRAin protein. These studies provide new insight into SOL calpain's function and regulation. Open Data: Materials are available on https://cos.io/our-services/open-science-badges/ https://osf.io/93n6m/.