Ahmad Mayeli

Sigma power and sleep spindles are key elements of Non‐Rapid Eye Movement (NREM) sleep. They reflect anatomical and physiological properties of brain circuits, are linked with various behavioral outcomes in typically development (TD) children, and undergo significant modifications during development. Furthermore, recent studies have highlighted the potential of NREM sigma power and sleep spindles as early neurophysiological markers for autism spectrum disability (ASD). Here, we conducted polysomnography (PSG)/EEG recordings during afternoon naps on 50 children aged between 2 and 6 years, diagnosed with ASD or TD. EEG recordings from 19 scalp leads were analyzed, focusing on sigma power and sleep spindle parameters. EEG analyses revealed significant differences in power spectral density between ASD and TD children, particularly in the sigma band and adjacent alpha and beta bands, with increased power localized to anterior EEG leads in ASD children. Higher spindle amplitude and integrated spindle activity (ISA) were found in the ASD group, especially in frontal regions. Additional frequency‐specific analyses (10–12 Hz, 12–14 Hz, 14–16 Hz) confirmed significant differences in spindle amplitude and distribution patterns, emphasizing the role of brain regions that are detectable from anterior EEG leads in ASD‐related sleep abnormalities. No significant differences were found in spindle density, duration, or frequency outside specific clusters. These findings indicate that some sleep spindle parameters, particularly in frontal areas, are altered in ASD. The study highlights the feasibility of using afternoon nap PSG as a practical and effective method to detect these abnormalities in clinical settings. Future research should investigate the developmental trajectory of spindles in ASD and their potential role as neurophysiological biomarkers, offering valuable insights for diagnosis and prognosis.

Deficits in mediodorsal thalamus-dorsolateral prefrontal cortex (MDT-DLPFC) resting-state functional magnetic resonance imaging (rs-fMRI) connectivity and prefrontal sleep spindles have been reported in chronic and early course schizophrenia. However, the presence of these alterations in clinical high-risk for psychosis (CHR), alongside their relationships with underlying neurotransmission and cognitive function, remains to be established. Thirty-one CHR and thirty-two HC underwent: 1) 7 T rs-fMRI; 2) 7 T magnetic resonance spectroscopy imaging (MRSI); and 3) sleep electroencephalography (EEG). Rs-fMRI connectivity was analyzed by seeding the whole thalamus (WT) and seven thalamic subsections. Spindle duration was computed across all EEG channels. GABA/creatine (Cr) and glutamate/Cr were calculated in DLPFC and MDT. Relative to HC, CHR showed WT-DLPFC hypoconnectivity (p-FDR = 0.001), especially involving MDT-DLPFC (p-FDR < 0.001) and reduced prefrontal spindle duration (t-stat = -2.64, p = 0.010), while no differences were found for MRSI neuro-metabolites. We then performed clustering analysis using rs-fMRI connectivity and spindle duration to identify CHR and HC subgroups and predict their working memory (WM) performance. A cluster with intact rs-fMRI and spindle duration included mostly HC (83.33% purity), while a cluster with both measures altered involved almost entirely CHR (91.66% purity) and showed worse WM performances. We also examined MRSI metabolites' contribution to spindles and rs-fMRI connectivity with a within-group multivariable regression analysis. In HC, but not in CHR, MDT glutamate/Cr negatively predicted spindle duration and positively predicted MDT-DLPFC connectivity. Combined, these findings indicate that a multimodal neuroimaging approach can identify distinct thalamocortical dysfunctions in CHR individuals, thus informing future research aimed at developing personalized interventions in these individuals.

Non-invasive brain stimulation (NIBS) techniques are designed to precisely and selectively target specific brain regions, thus enabling focused modulation of neural activity. Among NIBS technologies, low-intensity transcranial focused ultrasound (tFUS) has emerged as a promising new modality. The application of tFUS can safely and non-invasively stimulate deep brain structures with millimetric precision, offering distinct advantages in terms of accessibility to non-cortical regions over other NIBS methods. However, to date, several tFUS aspects still need to be characterized; furthermore, there are only a handful of studies that have utilized tFUS in psychiatric populations. This narrative review provides an up-to-date overview of key aspects of this NIBS technique, including the main components of a tFUS system, the neuronavigational tools used to precisely target deep brain regions, the simulations utilized to optimize the stimulation parameters and delivery of tFUS, and the experimental protocols employed to evaluate the efficacy of tFUS in psychiatric disorders. The main findings from studies in psychiatric populations are presented and discussed, and future directions are highlighted.

Ahmad Mayeli, Fabio Ferrarelli; Addressing the First-Night Effect: It Is More Than the Environment, Sleep, , zsae198, https://doi.org/10.1093/sleep/zsae198

Deficits in memory performance have been linked to a wide range of neurological and neuropsychiatric conditions. While many studies have assessed the memory impacts of individual conditions, this study considers a broader perspective by evaluating how memory recall is differentially associated with nine common neuropsychiatric conditions using data drawn from 55 international studies, aggregating 15,883 unique participants aged 15–90. The effects of dementia, mild cognitive impairment, Parkinson’s disease, traumatic brain injury, stroke, depression, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder on immediate, short-, and long-delay verbal learning and memory (VLM) scores were estimated relative to matched healthy individuals. Random forest models identified age, years of education, and site as important VLM covariates. A Bayesian harmonization approach was used to isolate and remove site effects. Regression estimated the adjusted association of each clinical group with VLM scores. Memory deficits were strongly associated with dementia and schizophrenia (p < 0.001), while neither depression nor ADHD showed consistent associations with VLM scores (p > 0.05). Differences associated with clinical conditions were larger for longer delayed recall duration items. By comparing VLM across clinical conditions, this study provides a foundation for enhanced diagnostic precision and offers new insights into disease management of comorbid disorders.

Ahmad Mayeli, Fabio Ferrarelli; Can Sleep Tell Us How Mindful We Are?, Sleep, , zsae124, https://doi.org/10.1093/sleep/zsae124

Study Objectives: Microstates are semi-stable voltage topographies that account for most of electroencephalogram (EEG) variance. However, the impact of time of the day and sleep on microstates has not been examined. To address this gap, we assessed whether microstates differed between the evening and morning and whether sleep slow waves correlated with microstate changes in healthy participants. Methods: Forty-five healthy participants were recruited. Each participant underwent 6 minutes of resting state EEG recordings in the evening and morning, interleaved by sleep EEGs. Evening-to-morning changes in microstate duration, coverage, and occurrence were assessed. Furthermore, correlation between microstate changes and sleep slow-wave activity (SWA) and slow-wave density (SWD) were performed. Results: Two-way ANOVAs with microstate class (A, B, C, and D) and time (evening and morning) revealed significant microstate class × time interaction for duration (F(44) = 5.571, p = 0.002), coverage (F(44) = 6.833, p = 0.001), and occurrence (F(44) = 5.715, p = 0.002). Post hoc comparisons showed significant effects for microstate C duration (padj = 0.048, Cohen’s d = −0.389), coverage (padj = 0.002, Cohen’s d = −0.580), and occurrence (padj = 0.002, Cohen’s d = -0.606). Topographic analyses revealed inverse correlations between SWD, but not SWA, and evening-to-morning changes in microstate C duration (r = −0.51, padj = 0.002), coverage (r = −0.45, padj = 0.006), and occurrence (r = −0.38, padj = 0.033). Conclusions: Microstate characteristics showed significant evening-to-morning changes associated with, and possibly regulated by, sleep slow waves. These findings suggest that future microstate studies should control for time of day and sleep effects.