BackgroundObstructive Sleep Apnea (OSA) characteristically leads to nocturnal hypoxia and sleep disturbance. Despite clear evidence of OSA-induced cognitive impairments, the literature offers no consensus on the relationship between these pathophysiological processes and brain structure alterations in patients.ObjectiveThis study leverages the robust technique of structural equation modeling to investigate how hypoxia and sleep disturbance exert differential effects on gray matter structures.MethodsSeventy-four Male participants were recruited to undergo overnight polysomnography and T1-weighted Magnetic Resonance Imaging. Four structural outcome parameters were extracted, namely, gray matter volume, cortical thickness, sulcal depth, and fractal dimension. Structural equation models were constructed with two latent variables (hypoxia, and sleep disturbance) and three covariates (age, body mass index, and education) to examine the association between gray matter structural changes in OSA and the two latent variables, hypoxia and sleep disturbance.ResultsThe structural equation models revealed hypoxia-associated changes in diverse regions, most significantly in increased gray matter volume, cortical thickness and sulcal depth. In contrast, sleep disturbance. Was shown to be largely associated with reduce gray matter volume and sulcal depth.ConclusionThis study provides new evidence showing significant effects of OSA-induced hypoxia and sleep disturbance on gray matter volume and morphology in male patients with obstructive sleep apnea. It also demonstrates the utility of robust structural equation models in examining obstructive sleep apnea pathophysiology.

IntroductionIntracerebral hemorrhage (ICH) is the most prevalent cause of death. We sought to explore whether serum Fibroblast growth factor 21 (FGF21) is of substantial benefit in predicting poor prognosis in ICH patient.MethodsA prospective, multicenter cohort analysis of serum FGF21 levels in 418 ICH patients was carried out. At three months following ICH start, the primary endpoint was death or major disability, whereas the secondary endpoint was death. We investigated the association between serum FGF21 and clinical outcomes. We added FGF21 to the existing rating scale to assess whether it enhanced the prediction ability of the original model. Effectiveness was determined by calculating the C-statistic, net reclassification index (NRI), absolute integrated discrimination improvement (IDI) index.ResultsAmong 418 enrolled patients, 217 (51.9%) of the all subjects had death or significant disability. Compared with patients in the lowest quartile group, those in the first quartile group had higher risk of the primary outcome (Odds ratio, 2.73 [95%CI,1.42–5.26, p < 0.05]) and second outcome (Hazard ratio, 4.28 [95%CI,1.61–11.42, p < 0.001]). The integration of FGF21 into many current ICH scales improved the discrimination and calibration quality for the integrated discrimination index’s prediction of main and secondary findings (all p < 0.05).ConclusionElevated serum FGF21 is associated with increased risks of adverse clinical outcomes at 3 months in ICH patients, suggesting FGF21 may be a valuable prognostic factor.

Brain-computer interface (BCI) based on lower-limb motor imagery (LMI) enables hemiplegic patients to stand and walk independently. However, LMI ability is usually poor for BCI-illiterate (e.g., some stroke patients), limiting BCI performance. This study proposed a novel LMI-BCI paradigm with kinesthetic illusion(KI) induced by vibratory stimulation on Achilles tendon to enhance LMI ability. Sixteen healthy subjects were recruited to carry out two research contents: (1) To verify the feasibility of induced KI by vibrating Achilles tendon and analyze the EEG features produced by KI, research 1 compared the subjective feeling and brain activity of participants during rest task with and without vibratory stimulation (V-rest, rest). (2) Research 2 compared the LMI-BCI performance with and without KI (KI-LMI, no-LMI) to explore whether KI enhances LMI ability. The analysis methods of both experiments included classification accuracy (V-rest vs. rest, no-LMI vs. rest, KI-LMI vs. rest, KI-LMI vs. V-rest), time-domain features, oral questionnaire, statistic analysis and brain functional connectivity analysis. Research 1 verified that induced KI by vibrating Achilles tendon might be feasible, and provided a theoretical basis for applying KI to LMI-BCI paradigm, evidenced by oral questionnaire (Q1) and the independent effect of vibratory stimulation during rest task. The results of research 2 that KI enhanced mesial cortex activation and induced more intensive EEG features, evidenced by ERD power, topographical distribution, oral questionnaire (Q2 and Q3), and brain functional connectivity map. Additionally, the KI increased the offline accuracy of no-LMI/rest task by 6.88 to 82.19% (p < 0.001). The simulated online accuracy was also improved for most subjects (average accuracy for all subjects: 77.23% > 75.31%, and average F1_score for all subjects: 76.4% > 74.3%). The LMI-BCI paradigm of this study provides a novel approach to enhance LMI ability and accelerates the practical applications of the LMI-BCI system.

BackgroundGlaucoma is the leading cause of irreversible vision loss. Accurate Optic Disc (OD) and Optic Cup (OC) segmentation is beneficial for glaucoma diagnosis. In recent years, deep learning has achieved remarkable performance in OD and OC segmentation. However, OC segmentation is more challenging than OD segmentation due to its large shape variability and cryptic boundaries that leads to performance degradation when applying the deep learning models to segment OC. Moreover, the OD and OC are segmented independently, or pre-requirement is necessary to extract the OD centered region with pre-processing procedures.MethodsIn this paper, we suggest a one-stage network named EfficientNet and Attention-based Residual Depth-wise Separable Convolution (EARDS) for joint OD and OC segmentation. In EARDS, EfficientNet-b0 is regarded as an encoder to capture more effective boundary representations. To suppress irrelevant regions and highlight features of fine OD and OC regions, Attention Gate (AG) is incorporated into the skip connection. Also, Residual Depth-wise Separable Convolution (RDSC) block is developed to improve the segmentation performance and computational efficiency. Further, a novel decoder network is proposed by combining AG, RDSC block and Batch Normalization (BN) layer, which is utilized to eliminate the vanishing gradient problem and accelerate the convergence speed. Finally, the focal loss and dice loss as a weighted combination is designed to guide the network for accurate OD and OC segmentation.Results and discussionExtensive experimental results on the Drishti-GS and REFUGE datasets indicate that the proposed EARDS outperforms the state-of-the-art approaches. The code is available at https://github.com/M4cheal/EARDS.

IntroductionEvidence suggests that spontaneous beta band (11–35 Hz) oscillations in the basal ganglia thalamocortical (BGTC) circuit are linked to Parkinson’s disease (PD) pathophysiology. Previous studies on neural responses in the motor cortex evoked by electrical stimulation in the subthalamic nucleus have suggested that circuit resonance may underlie the generation of spontaneous and stimulation-evoked beta oscillations in PD. Whether these stimulation-evoked, resonant oscillations are present across PD patients in the internal segment of the globus pallidus (GPi), a primary output nucleus in the BGTC circuit, is yet to be determined.MethodsWe characterized spontaneous and stimulation-evoked local field potentials (LFPs) in the GPi of four PD patients (five hemispheres) using deep brain stimulation (DBS) leads externalized after DBS implantation surgery.ResultsOur analyses show that low-frequency (2–4 Hz) stimulation in the GPi evoked long-latency (>50 ms) beta-band neural responses in the GPi in 4/5 hemispheres. We demonstrated that neural sources generating both stimulation-evoked and spontaneous beta oscillations were correlated in their frequency content and spatial localization.DiscussionOur results support the hypothesis that the same neuronal population and resonance phenomenon in the BGTC circuit generates both spontaneous and evoked pallidal beta oscillations. These data also support the development of closed-loop control systems that modulate the GPi spontaneous oscillations across PD patients using beta band stimulation-evoked responses.

IntroductionChronic pain negatively impacts a range of sensory and affective behaviors. Previous studies have shown that the presence of chronic pain not only causes hypersensitivity at the site of injury but may also be associated with pain-aversive experiences at anatomically unrelated sites. While animal studies have indicated that the cingulate and prefrontal cortices are involved in this generalized hyperalgesia, the mechanisms distinguishing increased sensitivity at the site of injury from a generalized site-nonspecific enhancement in the aversive response to nociceptive inputs are not well known.MethodsWe compared measured pain responses to peripheral mechanical stimuli applied to a site of chronic pain and at a pain-free site in participants suffering from chronic lower back pain (n = 15) versus pain-free control participants (n = 15) by analyzing behavioral and electroencephalographic (EEG) data.ResultsAs expected, participants with chronic pain endorsed enhanced pain with mechanical stimuli in both back and hand. We further analyzed electroencephalographic (EEG) recordings during these evoked pain episodes. Brain oscillations in theta and alpha bands in the medial orbitofrontal cortex (mOFC) were associated with localized hypersensitivity, while increased gamma oscillations in the anterior cingulate cortex (ACC) and increased theta oscillations in the dorsolateral prefrontal cortex (dlPFC) were associated with generalized hyperalgesia.DiscussionThese findings indicate that chronic pain may disrupt multiple cortical circuits to impact nociceptive processing.