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Abstract: Despite its role as a keystone species, few studies have investigated the habitat requirements of the Yellow-bellied Sapsucker (Sphyrapicus varius (L., 1766)) in the northwestern part of its range, [...]

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., Competing Interests: The authors have no conflicts of interest to declare.Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article., (Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The International Association for the Study of Pain.)

Alzheimer's disease is marked by intracellular tau aggregates in the medial temporal lobe (MTL) and extracellular amyloid aggregates in the default network (DN). Here, we examined codependent structural variations between the MTL's most vulnerable structure, the hippocampus (HC), and the DN at subregion resolution in individuals with Alzheimer's disease and related dementia (ADRD). By leveraging the power of the approximately 40,000 participants of the UK Biobank cohort, we assessed impacts from the protective APOE ɛ2 and the deleterious APOE ɛ4 Alzheimer's disease alleles on these structural relationships. We demonstrate ɛ2 and ɛ4 genotype effects on the inter-individual expression of HC-DN co-variation structural patterns at the population level. Across these HC-DN signatures, recurrent deviations in the CA1, CA2/3, molecular layer, fornix's fimbria, and their cortical partners related to ADRD risk. Analyses of the rich phenotypic profiles in the UK Biobank cohort further revealed male-specific HC-DN associations with air pollution and female-specific associations with cardiovascular traits. We also showed that APOE ɛ2/2 interacts preferentially with HC-DN co-variation patterns in estimating social lifestyle in males and physical activity in females. Our structural, genetic, and phenotypic analyses in this large epidemiological cohort reinvigorate the often-neglected interplay between APOE ɛ2 dosage and sex and link APOE alleles to inter-individual brain structural differences indicative of ADRD familial risk., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Savignac et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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.