Electroencephalography connectome changes in chronic insomnia disorder are correlated with neurochemical signatures.

STUDY OBJECTIVES: This study aimed to investigate the alterations in resting-state electroencephalography (EEG) global brain connectivity (GBC) in patients with chronic insomnia disorder (CID) and to explore the correlation between macroscale connectomic variances and microscale neurotransmitter distributions. METHODS: We acquired 64-channel EEG from 35 female CID patients and 34 healthy females. EEG signals were source-localized using individual brain anatomy and orthogonalized to mitigate volume conduction. Correlation coefficients between band-limited source-space power envelopes of the DK 68 atlas were computed and averaged across regions to determine specific GBC values. A support vector machine (SVM) classifier utilizing GBC features was employed to differentiate CID patients from controls. We further used Neurosynth and a 3D atlas of neurotransmitter receptors/transporters to assess the cognitive functions and neurotransmitter landscape associated with CID cortical abnormality maps, respectively. RESULTS: CID patients exhibited elevated GBC within the medial prefrontal cortex and limbic cortex, particularly at the gamma carrier frequency, compared to controls (pFDR<0.05). GBC patterns were found to effectively distinguish CID patients from controls with a precision of 90.8% in the SVM model. The cortical abnormality maps were significantly correlated with meta-analytic terms like "cognitive control" and "emotion regulation." Notably, GBC patterns were associated with neurotransmitter profiles (pspin<0.05), with neurotransmitter systems such as norepinephrine, dopamine, and serotonin making significant contributions. CONCLUSIONS: This work characterizes the EEG connectomic profile of CID, facilitating the cost-effective clinical translation of EEG-derived markers. Additionally, the linkage between GBC patterns and neurotransmitter distribution offers promising avenues for developing targeted treatment strategies for CID.

Larger hypothalamic subfield volumes in patients with chronic insomnia disorder and relationships to levels of corticotropin-releasing hormone.

The hypothalamus is a well-established core structure in the sleep-wake cycle. While previous studies have not consistently found whole hypothalamus volume changes in chronic insomnia disorder (CID), differences may exist at the smaller substructural level of the hypothalamic nuclei. The study aimed to investigate the differences in total and subfield hypothalamic volumes, between CID patients and healthy controls (HCs) in vivo, through an advanced deep learning-based automated segmentation tool. A total of 150 patients with CID and 155 demographically matched HCs underwent T1-weighted structural magnetic resonance scanning. We utilized FreeSurfer v7.2 for automated segmentation of the hypothalamus and its five nuclei. Additionally, correlation and causal mediation analyses were performed to investigate the association between hypothalamic volume changes, insomnia symptom severity, and hypothalamus-pituitary-adrenal (HPA) axis-related blood biomarkers. CID patients exhibited larger volumes in the right anterior inferior, left anterior superior, and left posterior subunits of the hypothalamus compared to HCs. Moreover, we observed a positive association between blood corticotropin-releasing hormone (CRH) levels and insomnia severity, with anterior inferior hypothalamus (a-iHyp) hypertrophy mediating this relationship. In conclusion, we found significant volume increases in several hypothalamic subfield regions in CID patients, highlighting the central role of the HPA axis in the pathophysiology of insomnia.

Inhibitory Deficits of Insomnia Disorder: A Meta-Analysis on Event Related Potentials in Auditory Oddball Task.

BACKGROUND: Despite numerous studies on auditory event-related potentials (ERPs) in insomnia disorder (ID), the results are inconsistent across different ERP components (e.g. N1, P2, P3, and N350), types of auditory stimuli (e.g. standard and deviant), and stages of sleep (e.g. wakefulness, NREM sleep, and REM sleep). In light of this variability, we conducted a systematic meta-analysis of previous auditory ERP studies in ID to provide a quantitative review of the existing literature. METHODS: Relevant literatures were searched on the Embase, PubMed/MEDLINE, PsycINFO and Cochrane Library. A total of 12 studies comprising 497 participants were finally included in this meta-analysis. The study protocol was registered with PROSPERO under the registration number CRD42022308348. RESULTS: We found that patients with ID have significantly decreased N1 (Hedges' g = 0.34, 95%CI [0.04, 0.65]) and P3 (Hedges'g = -1.21, 95%CI [-2.37, -0.06]) amplitudes during wakefulness. In addition, decreases in P2 (Hedges'g = -0.57, 95%CI [-0.96, -0.17]) amplitude during wakefulness and N350 (Hedges' g = 0.73, 95%CI [0.36, 1.09]) amplitude during NREM. CONCLUSIONS: This meta-analysis represents the first systematic investigation of ERP features across different stages of sleep in individuals with ID. Our results suggest that in patients with insomnia, the absence or deficiency of arousal inhibition during the nighttime sleep initiation or maintenance process may interfere with the normal process of sleep.

Deficits in brain default mode network connectivity mediate the relationship between poor sleep quality and anxiety severity.

STUDY OBJECTIVES: Chronic insomnia disorder (CID) is a prevalent sleep disorder that frequently cooccurs with anxiety. The association between insomnia and anxiety has been established; however, the neurobiological basis of this relationship remains unclear. This study aimed to investigate the neural markers of CID patients with and without anxiety and to determine whether specific neural connectivity mediates the relationship between insomnia and anxiety. METHODS: This study included 180 participants, comprising CID patients with anxiety (CID-A), CID patients without anxiety (CID-NA), and good sleep controls. All participants completed self-reported measures of sleep quality and anxiety severity and underwent functional magnetic resonance imaging. Brain functional integration was measured using functional connectivity density (FCD) and resting-state functional connectivity (rsFC). Correlation and mediation analyses were used to examine the relationships among brain connectivity, sleep quality, and anxiety severity. RESULTS: The CID-NA and CID-A groups showed decreased local FCD in the medial prefrontal cortex (mPFC) and disrupted rsFC between the precuneus and other brain regions. Only the CID-A group exhibited altered long-range FCD in the precuneus and the rsFC between the anterior default mode network (DMN, e.g. mPFC) and posterior DMN (e.g. precuneus). Mediation analysis revealed DMN dysconnectivity underlying the association between poor sleep quality and anxiety symptoms. CONCLUSIONS: This study identified shared and distinct brain circuit disruptions in the CID-NA and CID-A groups, with deficits in DMN connectivity as a potential neural mechanism through which disrupted sleep augments anxiety. These findings may facilitate the development of personalized therapies for insomnia and associated anxiety problems.

Neural mechanisms of attentional bias to emotional faces in patients with chronic insomnia disorder.

OBJECTIVE: This study used event-related potential (ERP) and resting-state functional connectivity (rs-FC) approaches to investigate the neural mechanisms underlying the emotional attention bias in patients with chronic insomnia disorder (CID). METHODS: Twenty-five patients with CID and thirty-three demographically matched healthy controls (HCs) completed clinical questionnaires and underwent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) scans. EEG analysis examined the group differences in terms of reaction times, P3 amplitudes, event-related spectral perturbations, and inter-trial phase synchrony. Subsequently, seed-based rs-FC analysis of the amygdala nuclei (including the central-medial amygdala [CMA] and basolateral amygdala [BLA]) was performed. The relationship between P3 amplitude, rs-FC and clinical symptom severity in patients with CID was further investigated by correlation analysis. RESULTS: CID patients exhibited shorter reaction times than HCs in both standard and deviant stimuli, with the abnormalities becoming more pronounced as attention allocation increased. Compared to HCs, ERP analysis revealed increased P3 amplitude, theta wave power, and inter-trial synchrony in CID patients. The rs-FC analysis showed increased connectivity of the BLA-occipital pole, CMA-precuneus, and CMA-angular gyrus and decreased connectivity of the CMA-thalamus in CID patients. Notably, correlation analysis of the EEG and fMRI measurements showed a significant positive correlation between the P3 amplitude and the rs-FC of the CMA-PCU. CONCLUSION: This study confirms an emotional attention bias in CID, specifically in the neural mechanisms of attention processing that vary depending on the allocation of attentional resources. Abnormal connectivity in the emotion-cognition networks may constitute the neural basis of the abnormal scalp activation pattern.

Neural mechanisms of working memory dysfunction in patients with chronic insomnia disorder.

OBJECTIVE: This study aimed to investigate the neural mechanisms underlying working memory impairment in patients with chronic insomnia disorder (CID) using event-related potentials (ERP) and resting-state functional connectivity (rsFC) approaches. METHODS: Participants, including CID patients and healthy controls (HCs), completed clinical scales and underwent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). EEG analysis compared reaction times, P3 amplitudes, event-related spectral perturbations (ERSP), and inter-trial phase synchronisation (ITPS) between CID patients and HCs. Subsequently, frontal regions (i.e., the Superior Frontal Gyrus [SFG] and Middle Frontal Gyrus [MFG]) corresponding to the EEG were selected as seeds for rsFC analysis. Correlation analyses were conducted to further investigate the relationship between functional connectivity abnormalities in brain regions and clinical symptom severity and P3 amplitude in CID patients. RESULTS: Compared to HCs, CID patients exhibited slower reaction times across all working memory conditions, with the deficits becoming more pronounced as memory load increased. ERP analysis revealed increased P3 amplitude, theta wave power, and reduced inter-trial synchrony in CID patients. rsFC analysis showed decreased connectivity of SFG-posterior cingulated cortex (PCC), SFG-MFG, and MFG-frontal pole (FP), and increased connectivity of MFG- Middle Temporal Gyrus (MTG)in CID patients. Importantly, a significant correlation was found between the rsFC of SFG-MTG and P3 amplitude during 1-back. CONCLUSION: This study confirms deficits in working memory capacity in patients with CID, specifically in the neural mechanisms of cognitive processing that vary depending on the level of cognitive load. Alterations in connectivity patterns within and between the frontal and temporal regions may be the neural basis of the cognitive impairment.

Neuroimaging profiles of the negative affective network predict anxiety severity in patients with chronic insomnia disorder: A machine learning study.

BACKGROUND: Sleep is instrumental in safeguarding emotional well-being. While the susceptibility to both insomnia and anxiety has been demonstrated to involve intricate brain systems, the neuroimaging profile of chronic insomnia disorder with comorbid anxiety symptoms (CID-A) remains unexplored. Employing machine learning methodologies, this study aims to elucidate the distinct neural substrates underlying CID-A and to investigate whether these cerebral markers can prognosticate anxiety symptoms in patients with insomnia. METHODS: Functional magnetic resonance imaging (fMRI) data were procured from a relatively large cohort (dataset 1) comprised of 47 CID-A patients, 49 CID patients without anxiety (CID-NA), and 48 good sleeper controls (GSC). Aberrant cerebral functional alterations were assessed through functional connectivity strength (FCS) and resting-state functional connectivity (rsFC). Subsequently, Support Vector Regression (SVR) models were constructed to predict anxiety symptoms in CID patients based on neuroimaging features, which were validated utilizing an external cohort (dataset 2). RESULTS: In comparison to CID-NA and GSC subjects, CID-A patients exhibited heightened FCS in the right dorsomedial prefrontal cortex (DMPFC), a central hub within the negative affective network. Moreover, the SVR models revealed that DMPFC-related rsFC/FCS features could be employed to predict anxiety symptoms in two independent cohorts of CID patients. LIMITATION: Modifications in brain functionality might vary across insomnia subtypes. CONCLUSION: The present findings suggest a potential negative affective network model for the neuropathophysiology of CID accompanied by anxiety. Importantly, the negative affective network pattern may serve as a predictor for anxiety symptoms in CID patients.

Improving Performance and Adaptivity of Anchor-Based Detector Using Differentiable Anchoring With Efficient Target Generation.

Most anchor-based object detection methods have adopted predefined anchor boxes as regression references. However, the proper setting of anchor boxes may vary significantly across different datasets, improperly designed anchors severely limit the performances and adaptabilities of detectors. Recently, some works have tackled this problem by learning anchor shapes from datasets. However, all of these works explicitly or implicitly rely on predefined anchors, limiting universalities of detectors. In this paper, we propose a simple learning anchoring scheme with an effective target generation method to cast off predefined anchor dependencies. The proposed anchoring scheme, named as differentiable anchoring, simplifies learning anchor shape process by adding only one branch in parallel with the existing classification and bounding box regression branches. The proposed target generation method, including the Lp norm ball approximation and the optimization difficulty-based pyramid level assignment approach, generates positive samples for the new branch. Compared with existing learning anchoring-based approaches, the proposed method doesn't require any predefined anchors, while tremendously improving performances and adaptiveness of detectors. The proposed method can be seamlessly integrated to Faster RCNN, RetinaNet, and SSD, improving the detection mAP by 2.8%, 2.1% and 2.3% respectively on MS COCO 2017 test-dev set. Moreover, the differentiable anchoring-based detectors can be directly applied to specific scenarios without any modification of the hyperparameters or using a specialized optimization. Specifically, the differentiable anchoring-based RetinaNet achieves very competitive performances on tiny face detection and text detection tasks, which are not well handled by the conventional and guided anchoring based RetinaNets for the MS COCO dataset.

Realistic Image Rendition Using a Variable Exponent Functional Model for Retinex.

The goal of realistic image rendition is to recover the acquired image under imperfect illuminant conditions, where non-uniform illumination may degrade image quality with high contrast and low SNR. In this paper, the assumption regarding illumination is modified and a variable exponent functional model for Retinex is proposed to remove non-uniform illumination and reduce halo artifacts. The theoretical derivation is provided and experimental results are presented to illustrate the effectiveness of the proposed model.