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Background: Although several researches investigated Default Mode Network (DMN) alterations in individuals with anxiety disorders, up to now no studies have investigated DMN functional connectivity in non-clinical individuals with high-trait-anxiety using quantitative electroencephalography (EEG). Here, the main aim was to extend previous findings investigating the association between trait anxiety and DMN EEG functional connectivity.Methods: Twenty-three individuals with high-trait-anxiety and twenty-four controls were enrolled. EEG was recorded during 5 min of resting state (RS). EEG analyses were conducted by means of the exact Low-Resolution Electromagnetic Tomography software (eLORETA).Results: Compared to controls, individuals with high-trait-anxiety showed a decrease of theta connectivity between right medial prefrontal cortex (mPFC) and right posterior cingulate/retrosplenial cortex. A decrease of beta connectivity was also observed between right mPFC and right anterior cingulate cortex. Furthermore, DMN functional connectivity strength was negatively related with STAI-T total score (i.e., lower connectivity was associated with higher trait anxiety), even when controlling for potential confounding variables (i.e., sex, age, and general psychopathology).Limitations: Small sample size makes it difficult to draw definitive conclusions. Furthermore, we did not assess state variation of anxiety, which make our interpretation specific to trait anxiety.Conclusions: Taken together, our results suggest that high-trait-anxiety individuals fail to synchronize DMN during RS, reflecting a possible top-down cognitive control deficit. These results may help in the understanding of the individual differences in functional brain networks associated with trait anxiety, a crucial aim in the prevention and in the early etiology understanding of clinical anxiety and related sequelae. [ABSTRACT FROM AUTHOR]

Recent studies suggest that parental overcontrol could be considered a specific form of childhood trauma (CT). Although previous research has shown that CT alters the functional and structural architecture of large-scale networks in the brain, the neural basis associated with parental overcontrol has not been sufficiently explored. Therefore, the main aim of the current study was to investigate the relationship between parental overcontrol and electroencephalography (EEG) triple network (TN) functional connectivity during the resting state (RS) condition in a non-clinical sample (N = 71; 39 females, mean age 23.94 ± 5.89 SD). EEG was recorded during 5 min of RS with eyes closed. All participants were asked to self-report maternal and paternal overcontrol, CT and general psychopathology. All EEG analyses were performed using the exact low-resolution electromagnetic tomography software (eLORETA). Our results showed a significant positive correlation between maternal overcontrol and theta connectivity between the salience network and the central executive network. This connectivity pattern was independently associated with maternal overcontrol even when controlling for relevant confounding variables, including the severity of CT and the general level of psychopathology. This neurophysiological pattern may reflect a predisposition to detect and respond to potentially threatening stimuli in the environment, which is typically associated with excessive overcontrol. Our findings support the hypothesis that parental overcontrol should be considered a form of CT in all respects independent of the forms traditionally studied in the literature (i.e., emotional abuse, physical abuse, sexual abuse, and physical and emotional neglect). [ABSTRACT FROM AUTHOR]

Childhood trauma (CT) is considered a major risk factor for several disorders as well as for the development of eating psychopathology and adult obesity. The main aims of the present study were to assess in overweight and obese women: (i) the independent association between CT and food addiction (FA), and (ii) CT in patients with both FA and clinical-level of binge eating (BE), versus patients who only engage in FA or BE. Participants were 301 overweight and obese women seeking low-energy-diet therapy. All of the patients were administered self-report measures investigating FA, BE, CT, anxiety and depressive symptoms. CT severity was moderately and positively associated with both FA ( r = 0.37; p < 0.001) and BE ( r = 0.36; p < 0.001) severity. The association between FA and CT remained significant after controlling for potential confounding variables. Furthermore, compared to patients without dysfunctional eating patterns, the co-occurrence of FA and BE was associated with more severe CT as well as with more severe psychopathology (i.e., anxiety and depressive symptoms) and higher BMI. Our results suggest that clinicians should carefully assess the presence of CT in individuals who report dysfunctional eating patterns in order to develop treatment approaches specifically for obese and overweight patients with a history of CT. [ABSTRACT FROM AUTHOR]

Abstract: Objective: The aim of the present study was to develop and validate a software tool for the detection of movements during sleep, based on the automated analysis of video recordings. This software is aimed to detect and quantify movements and to evaluate periods of sleep and wake. Methods: We applied an open-source software, previously distributed on the web (Zoneminder, ZM), meant for video surveillance. A validation study was performed: computed movement analysis was compared with two standardised, ‘gold standard’ methods for the analysis of sleep–wake cycles: actigraphy and laboratory-based video-polysomnography. Results: Sleep variables evaluated by ZM were not different from those measured by traditional sleep-scoring systems. Bland–Altman plots showed an overlap between the scores obtained with ZM, PSG and actigraphy, with a slight tendency of ZM to overestimate nocturnal awakenings. ZM showed a good degree of accuracy both with respect to PSG (79.9%) and actigraphy (83.1%); and had very high sensitivity (ZM vs. PSG: 90.4%; ZM vs. actigraphy: 89.5%) and relatively lower specificity (ZM vs. PSG: 42.3%; ZM vs. actigraphy: 65.4%). Conclusions: The computer-assisted motion analysis is reliable and reproducible, and it can allow a reliable esteem of some sleep and wake parameters. The motion-based sleep analysis shows a trend to overestimate wakefulness. Significance: The possibility to measure sleep from video recordings may be useful in those clinical and experimental conditions in which traditional PSG studies may not be performed. [Copyright &y& Elsevier]

Objectives: To evaluate cyclic alternating pattern (CAP) in sleep of school-age children in order to obtain a standardized database for CAP parameters in this age range.Methods: CAP parameters were quantified in 10 normal healthy subjects (6 males and 4 females, mean age 8.3 years; range 6–10 years). All subjects underwent polysomnography recordings for two consecutive nights in a standard laboratory setting. Sleep data were stored on computer using a 16-channel polysomnography digital system. Sleep macrostructure was visually scored according to the criteria by Rechtschaffen and Kales (Brain Information Service/Brain Research Institute, University of California, Los Angeles, 1968); CAP was visually scored following the criteria by Terzano et al. (Sleep Med 2 (2001) 537).Results: CAP rate showed a progressive increase with the deepness of sleep, with high values during slow wave sleep (SWS). CAP time showed its longest duration during non-REM (NREM) sleep stage 2 (S2), followed by SWS and sleep stage 1 (S1). No differences across NREM sleep stages were found for CAP cycle and phase B mean duration; on the contrary, phase A showed longer duration during SWS than in S1 and S2. Phases A1 were the most numerous (84.45%) followed by A3 (9.14%) and by A2 (6.44%). The distribution of phases A subtypes across NREM stages showed significant differences for the A1 subtypes that occurred more frequently during SWS than in S2 and S1 (and during S2 than in S1). Subtypes A3 were more frequent during S1 than SWS while no differences were found for subtype A2. The analysis of A1 interval distribution showed a log-normal-like distribution with a peak around 25 s for the A1 phases and no clear peak for A2–A3 phases.Conclusions: The analysis of CAP in school-age children is characterized by an increase of CAP rate during SWS and a high percentage of A1 phases. The distribution of interval between consecutive A1 phases showed a peak around 25 s. [ABSTRACT FROM AUTHOR]