Jinfeng Wen

Depression, a common mental illness that seriously affects the psychological health of patients, is also thought to be associated with abnormal brain functional connectivity. This study aimed to explore the differences in the sleep-state functional network topology in depressed patients. A total of 25 healthy participants and 26 depressed patients underwent overnight 16-channel electroencephalography (EEG) examination. The cortical networks were constructed by using functional connectivity metrics of participants based on the weighted phase lag index (WPLI) between the EEG signals. The results indicated that depressed patients exhibited higher global efficiency and node strength than healthy participants. Furthermore, the depressed group indicated right-lateralization in the δ band. The top 30% of connectivity in both groups were shown in undirected connectivity graphs, revealing the distinct link patterns between the depressed and control groups. Links between the hemispheres were noted in the patient group, while the links in the control group were only observed within each hemisphere, and there were many long-range links inside the hemisphere. The altered sleep-state functional network topology in depressed patients may provide clues for a better understanding of the depression pathology. Overall, functional network topology may become a powerful tool for the diagnosis of depression.

Depression is a common mental illness and a large number of researchers have been still devoted to exploring effective biomarkers for the identification of depression. Few researches have been conducted on functional connectivity (FC) during sleep in depression. In this paper, a novel depression characterization is proposed using specific spatial FC features of sleep electroencephalography (EEG). Overnight polysomnography recordings were obtained from 26 healthy individuals and 25 patients with depression. The weighted phase lag indexes (WPLIs) of four frequency bands and five sleep periods were obtained from 16 EEG channels. The high discriminative connections extracted via feature evaluation and the cross‐within variation (CW)—the spatial feature constructed to characterize the different performances in inter‐ and intra‐hemispheric FC based on WPLIs, were utilized to classify patients and normal controls. The results showed that enhanced average FC and spatial differences, higher inter‐hemispheric FC and lower intra‐hemispheric FC, were found in patients. Furthermore, abnormalities in the inter‐hemispheric connections of the temporal lobe in the theta band should be important indicators of depression. Finally, both CW and high discriminative WPLI features performed well in depression screening and CW was more specific for characterizing abnormal cortical EEG performance of depression. Our work investigated and characterized the abnormalities in sleep cortical activity in patients with depression, and may provide potential biomarkers for assisting with depression identification and new insights into the understanding of pathological mechanisms in depression.

Objective: We investigated the nature of interactions between the central nervous system (CNS) and the cardiorespiratory system during sleep. Methods: Overnight polysomnography recordings were obtained from 33 healthy individuals. The relative spectral powers of five frequency bands, three ECG morphological features and respiratory rate were obtained from six EEG channels, ECG, and oronasal airflow, respectively. The synchronous feature series were interpolated to 1 Hz to retain the high time-resolution required to detect rapid physiological variations. CNS-cardiorespiratory interaction networks were built for each EEG channel and a directionality analysis was conducted using multivariate transfer entropy. Finally, the difference in interaction between Deep, Light, and REM sleep (DS, LS, and REM) was studied. Results: Bidirectional interactions existed in central-cardiorespiratory networks, and the dominant direction was from the cardiorespiratory system to the brain during all sleep stages. Sleep stages had evident influence on these interactions, with the strength of information transfer from heart rate and respiration rate to the brain gradually increasing with the sequence of REM, LS, and DS. Furthermore, the occipital lobe appeared to receive the most input from the cardiorespiratory system during LS. Finally, different ECG morphological features were found to be involved with various central-cardiac and cardiac-respiratory interactions. Conclusion: These findings reveal detailed information regarding CNS-cardiorespiratory interactions during sleep and provide new insights into understanding of sleep control mechanisms. Significance: Our approach may facilitate the investigation of the pathological cardiorespiratory complications of sleep disorders.

Anhedonia, the loss or decline of the ability to enjoy pleasure, is an important clinical characteristic of schizophrenia. Schizotypal traits refer to the appearance of subclinical symptoms of schizophrenia across normal people. Still, few studies have investigated chemosensory anhedonia in schizophrenia patients and schizotypy individuals. Seventy-one schizophrenia patients (SCZ), 162 schizotypy individuals (SCT) as selected by the Schizotypal Personality Questionnaire (SPQ), and 182 healthy controls (HC) participated in our study. We used the Positive and Negative Syndrome Scale (PANSS) to measure the clinical symptoms of schizophrenia patients. All participants completed the Chemosensory Pleasure Scale (CPS), which was used to assess participants’ smell and taste hedonic capacities. We found that the three groups differed in chemosensory anhedonia. The SCZ group presented more severe chemosensory anhedonia than the SCT group, and the SCT group presented more severe chemosensory anhedonia than the HC group. We also found that chemosensory hedonic capacity was negatively correlated with negative schizotypal traits in the SCT group. Our results suggested that chemosensory anhedonia is an important characteristic of schizophrenia spectrum disorders.