Dynamic prediction of goal location by coordinated representation of prefrontal-hippocampal theta sequences.

Prefrontal (PFC) and hippocampal (HPC) sequences of neuronal firing modulated by theta rhythms could represent upcoming choices during spatial memory-guided decision-making. How the PFC-HPC network dynamically coordinates theta sequences to predict specific goal locations and how it is interrupted in memory impairments induced by amyloid beta (Aß) remain unclear. Here, we detected theta sequences of firing activities of PFC neurons and HPC place cells during goal-directed spatial memory tasks. We found that PFC ensembles exhibited predictive representation of the specific goal location since the starting phase of memory retrieval, earlier than the hippocampus. High predictive accuracy of PFC theta sequences existed during successful memory retrieval and positively correlated with memory performance. Coordinated PFC-HPC sequences showed PFC-dominant prediction of goal locations during successful memory retrieval. Furthermore, we found that theta sequences of both regions still existed under Aß accumulation, whereas their predictive representation of goal locations was weakened with disrupted spatial representation of HPC place cells and PFC neurons. These findings highlight the essential role of coordinated PFC-HPC sequences in successful memory retrieval of a precise goal location.

Nomogram-based geometric and hemodynamic parameters for predicting the growth of small untreated intracranial aneurysms.

Previous studies have shown that the growth status of intracranial aneurysms (IAs) predisposes to rupture. This study aimed to construct a nomogram for predicting the growth of small IAs based on geometric and hemodynamic parameters. We retrospectively collected the baseline and follow-up angiographic images (CTA/ MRA) of 96 small untreated saccular IAs, created patient-specific vascular models and performed computational fluid dynamics (CFD) simulations. Geometric and hemodynamic parameters were calculated. A stepwise Cox proportional hazards regression analysis was employed to construct a nomogram. IAs were stratified into low-, intermediate-, and high-risk groups based on the total points from the nomogram. Receiver operating characteristic (ROC) analysis, calibration curves, decision curve analysis (DCA) and Kaplan-Meier curves were evaluated for internal validation. In total, 30 untreated saccular IAs were grown (31.3%; 95%CI 21.8%-40.7%). The PHASES, ELAPSS, and UIATS performed poorly in distinguishing growth status. Hypertension (hazard ratio [HR] 4.26, 95%CI 1.61-11.28; P = 0.004), nonsphericity index (95%CI 4.10-25.26; P = 0.003), max relative residence time (HR 1.01, 95%CI 1.00-1.01; P = 0.032) were independently related to the growth status. A nomogram was constructed with the above predictors and achieved a satisfactory prediction in the validation cohort. The log-rank test showed significant discrimination among the Kaplan-Meier curves of different risk groups in the training and validation cohorts. A nomogram consisting of geometric and hemodynamic parameters presented an accurate prediction for the growth status of small IAs and achieved risk stratification. It showed higher predictive efficacy than the assessment tools.

An Efficient Multi-Task Synergetic Network for Polyp Segmentation and Classification.

Colonoscopy is considered the best diagnostic tool for early detection and resection of polyps, which can effectively prevent consequential colorectal cancer. In clinical practice, segmenting and classifying polyps from colonoscopic images have a great significance since they provide precious information for diagnosis and treatment. In this study, we propose an efficient multi-task synergetic network (EMTS-Net) for concurrent polyp segmentation and classification, and we introduce a polyp classification benchmark for exploring the potential correlations of the above-mentioned two tasks. This framework is composed of an enhanced multi-scale network (EMS-Net) for coarse-grained polyp segmentation, an EMTS-Net (Class) for accurate polyp classification, and an EMTS-Net (Seg) for fine-grained polyp segmentation. Specifically, we first obtain coarse segmentation masks by using EMS-Net. Then, we concatenate these rough masks with colonoscopic images to assist EMTS-Net (Class) in locating and classifying polyps precisely. To further enhance the segmentation performance of polyps, we propose a random multi-scale (RMS) training strategy to eliminate the interference caused by redundant information. In addition, we design an offline dynamic class activation mapping (OFLD CAM) generated by the combined effect of EMTS-Net (Class) and RMS strategy, which optimizes bottlenecks between multi-task networks efficiently and elegantly and helps EMTS-Net (Seg) to perform more accurate polyp segmentation. We evaluate the proposed EMTS-Net on the polyp segmentation and classification benchmarks, and it achieves an average mDice of 0.864 in polyp segmentation and an average AUC of 0.913 with an average accuracy of 0.924 in polyp classification. Quantitative and qualitative evaluations on the polyp segmentation and classification benchmarks demonstrate that our EMTS-Net achieves the best performance and outperforms previous state-of-the-art methods in terms of both efficiency and generalization.

Spontaneous alpha-band oscillations reflect individual differences in audiovisual temporal perception.

Light, and sound are persistently out of sync for subjective temporal perception called point of subjective simultaneity (PSS). It is stable within individuals but variable among individuals. Previous studies found that spontaneous alpha power, functioning in attention-related brain states, predicts individual PSS in the temporal order judgment (TOJ) task. However, the neural mechanisms underlying individual differences in audiovisual PSS have not been elucidated in the simultaneity judgment (SJ) task. A hypothesis that the spontaneous alpha band power might reflect the individual subjective temporal bias was proposed. We designed an SJ task EEG experiment where subjects judged whether the beep-flash stimuli are synchronous to test the above hypothesis. We primarily explored the correlation between the alpha-band power differences (visual- and auditory-leading conditions) with individual PSS. We used the V50A (~50% proportion of synchronous responses) to represent visual-leading conditions while A50V represents auditory-leading ones. We found the higher alpha power difference (V50A - A50V) predicted larger individual PSS. This study extends previous results and found that individual difference effects in the alpha band power also exist in the SJ task. The results suggested that alpha power might be associated with a spontaneous attentional state and reflect individuals' subjective temporal bias.

Prevalence and prognosis of acute ischemic stroke coexisting with unruptured intracranial aneurysms.

Objectives: The prevalence of unruptured intracranial aneurysms (UIAs) in the acute ischemic stroke (AIS) cohort is probably higher than in the general population. This study investigated the prevalence of UIAs in AIS patients and the management risk and prognosis when treating AIS. Methods: From January 2020 to January 2023, we conducted a single-center retrospective study at Tianjin Huanhu Hospital. Each patient underwent both brain MRI and MRA/CTA to diagnose AIS and UIAs. Clinical, radiologic, and therapeutic data during hospitalization and prognosis were analyzed. Propensity-score matching (PSM) was performed to evaluate the risk of in-hospital adverse events, unfavorable outcomes at discharge when receiving post-stroke treatment and stroke recurrence. Results: In all, 2,181 AIS patients were included, of whom 270 had UIAs (12.4%; 95%CI 11.0-13.8%). From the unmatched and matched cohort, the incidence of in-hospital adverse events and unfavorable outcomes at discharge in patients with UIAs were not significantly different; the risk of stroke recurrence was significantly higher in patients with UIAs than in those without (unmatched: aHR, 1.71 [1.08-2.70]; matched: aHR, 2.55 [1.16-5.58]). Multivariable Cox regression models showed that aneurysm size and the presence of homoregional infarction associated with higher risk of recurrence (unmatched: aHR, 1.31 [1.21-1.41] and aHR, 3.50 [1.52-8.10]; matched: aHR, 1.28 [1.18-1.40]; p < 0.001 and aHR, 3.71 [1.12-12.34]). Conclusion: The UIAs may not increase the risk of in-hospital adverse events and unfavorable outcomes at discharge in receiving post-stroke treatment, but it may associated with a high risk of stroke recurrence.

Neural oscillations reflect the individual differences in the temporal perception of audiovisual speech.

Multisensory integration occurs within a limited time interval between multimodal stimuli. Multisensory temporal perception varies widely among individuals and involves perceptual synchrony and temporal sensitivity processes. Previous studies explored the neural mechanisms of individual differences for beep-flash stimuli, whereas there was no study for speech. In this study, 28 subjects (16 male) performed an audiovisual speech/ba/simultaneity judgment task while recording their electroencephalography. We examined the relationship between prestimulus neural oscillations (i.e. the pre-pronunciation movement-related oscillations) and temporal perception. The perceptual synchrony was quantified using the Point of Subjective Simultaneity and temporal sensitivity using the Temporal Binding Window. Our results revealed dissociated neural mechanisms for individual differences in Temporal Binding Window and Point of Subjective Simultaneity. The frontocentral delta power, reflecting top-down attention control, is positively related to the magnitude of individual auditory leading Temporal Binding Windows (auditory Temporal Binding Windows; LTBWs), whereas the parieto-occipital theta power, indexing bottom-up visual temporal attention specific to speech, is negatively associated with the magnitude of individual visual leading Temporal Binding Windows (visual Temporal Binding Windows; RTBWs). In addition, increased left frontal and bilateral temporoparietal occipital alpha power, reflecting general attentional states, is associated with increased Points of Subjective Simultaneity. Strengthening attention abilities might improve the audiovisual temporal perception of speech and further impact speech integration.

Beyond alpha band: prestimulus local oscillation and interregional synchrony of the beta band shape the temporal perception of the audiovisual beep-flash stimulus.

OBJECTIVE: Sensory integration is modulated by local prestimulus ongoing oscillatory activity which was suggested to play a role in organizing general neural processes such as attention and neuronal excitability and relatively longer inter-areal poststimulus phase coupling, especially in the 8-12 Hz alpha band. Previous work has examined the modulation effect of phase in audiovisual temporal integration, but there is no unified conclusion whether there is phasic modulation in the visual leading condition (sound-flash pairs). Moreover, it is unknown whether temporal integration is also subject to prestimulus inter-areal phase coupling between localizer-defined auditory and visual regions. APPROACH: Here, we recorded brain activity with EEG while human participants of both sexes performed a simultaneity judgment (SJ) task with the beep-flash stimuli to explore the functional role of the ongoing local oscillation and inter-areal coupling in temporal integration. Main results: We found that the power and ITC of the alpha-band are larger in synchronous response in both the visual and auditory leading conditions in their respective occipital and central channels, suggesting that neuronal excitability and attention play a role in temporal integration. Critically, the simultaneous judgment was modulated by the phases of low beta (14-20 Hz) oscillations quantified by the phase bifurcation index (PBI). Posthoc Rayleigh test indicated that the beta phase encodes different time information rather than neuronal excitability. Furthermore, we also found the stronger spontaneous high beta (21-28 Hz) phasic coupling between audiovisual cortices for synchronous response in auditory leading condition. SIGNIFICANCE: Together, these results demonstrate that spontaneous local low-frequency (< 30 Hz) neural oscillations and functional connectivity between auditory and visual brain regions especially in the beta band collectively influence audiovisual temporal integration. .

The effect of prestimulus low-frequency neural oscillations on the temporal perception of audiovisual speech.

Objective: Perceptual integration and segregation are modulated by the phase of ongoing neural oscillation whose frequency period is broader than the size of the temporal binding window (TBW). Studies have shown that the abstract beep-flash stimuli with about 100 ms TBW were modulated by the alpha band phase. Therefore, we hypothesize that the temporal perception of speech with about hundreds of milliseconds of TBW might be affected by the delta-theta phase. Methods: Thus, we conducted a speech-stimuli-based audiovisual simultaneity judgment (SJ) experiment. Twenty human participants (12 females) attended this study, recording 62 channels of EEG. Results: Behavioral results showed that the visual leading TBWs are broader than the auditory leading ones [273.37 ± 24.24 ms vs. 198.05 ± 19.28 ms, (mean ± sem)]. We used Phase Opposition Sum (POS) to quantify the differences in mean phase angles and phase concentrations between synchronous and asynchronous responses. The POS results indicated that the delta-theta phase was significantly different between synchronous and asynchronous responses in the A50V condition (50% synchronous responses in auditory leading SOA). However, in the V50A condition (50% synchronous responses in visual leading SOA), we only found the delta band effect. In the two conditions, we did not find a consistency of phases over subjects for both perceptual responses by the post hoc Rayleigh test (all ps > 0.05). The Rayleigh test results suggested that the phase might not reflect the neuronal excitability which assumed that the phases within a perceptual response across subjects concentrated on the same angle but were not uniformly distributed. But V-test showed the phase difference between synchronous and asynchronous responses across subjects had a significant phase opposition (all ps < 0.05) which is compatible with the POS result. Conclusion: These results indicate that the speech temporal perception depends on the alignment of stimulus onset with an optimal phase of the neural oscillation whose frequency period might be broader than the size of TBW. The role of the oscillatory phase might be encoding the temporal information which varies across subjects rather than neuronal excitability. Given the enriched temporal structures of spoken language stimuli, the conclusion that phase encodes temporal information is plausible and valuable for future research.

Lack of Habituation in Migraine Patients Based on High-Density EEG Analysis Using the Steady State of Visual Evoked Potential.

Migraine is a periodic disorder in which a patient experiences changes in the morphological and functional brain, leading to the abnormal processing of repeated external stimuli in the inter-ictal phase, known as the habituation deficit. This is a significant feature clinically of migraine in both two types with aura or without aura and plays an essential role in studying pathophysiological differences between these two groups. Several studies indicated that the reason for migraine aura is cortical spreading depression (CSD) but did not clarify its impact on migraine without aura and lack of habituation. In this study, 22 migraine patients (MWA, N = 13), (MWoA, N = 9), and healthy controls (HC, N = 19) were the participants. Participants were exposed to the steady state of visual evoked potentials also known as (SSVEP), which are the signals for a natural response to the visual motivation at four Hz or six Hz for 2 s followed by the inter-stimulus interval that varies between 1 and 1.5 s. The order of the temporal frequencies was randomized, and each temporal frequency was shown 100 times. We recorded from 128 customized electrode locations using high-density electroencephalography (HD-EEG) and measured amplitude and habituation for the N1-P1 and P1-N2 from the first to the sixth blocks of 100 sweep features in patients and healthy controls. Using the entropy, a decrease in amplitude and SSVEP N1-P1 habituation between the first and the sixth block appeared in both MWA and MWoA (p = 0.0001, Slope = -0.4643), (p = 0.065, Slope = 0.1483), respectively, compared to HC. For SSVEP P1-N2 between the first and sixth block, it is varied in both MWA (p = 0.0029, Slope = -0.3597) and MWoA (p = 0.027, Slope = 0.2010) compared to HC. Therefore, migraine patients appear amplitude decrease and habituation deficit but with different rates between MWA, and MWoA compared to HCs. Our findings suggest this disparity between MWoA and MWA in the lack of habituation and amplitude decrease in the inter-ictal phase has a close relationship with CSD. In light of the fact that CSD manifests during the inter-ictal phase of migraine with aura, which is when migraine seizures are most likely to occur, multiple researchers have lately reached this conclusion. This investigation led us to the conclusion that CSD during the inter-ictal phase and migraine without aura are associated. In other words, even if previous research has not demonstrated it, CSD is the main contributor to both types of migraine (those with and without aura).

Generation of Schrödinger Cat States in a Hybrid Cavity Optomechanical System.

We present an alternative scheme to achieve Schrödinger cat states in a strong coupling hybrid cavity optomechanical system. Under the single-photon strong-coupling regime, the interaction between the atom-cavity-oscillator system can induce the mesoscopic mechanical oscillator to Schrödinger cat states. Comparing to previous schemes, the proposed proposal consider the second order approximation on the Lamb-Dicke parameter, which is more universal in the experiment. Numerical simulations confirm the validity of our derivation.

Auditory event-related potentials based on name stimuli: A pilot study.

In recent years, diagnostic studies of brain disorders based on auditory event-related potentials (AERP) have become a hot topic. Research showed that AERP might help to detect patient consciousness, especially using the subjects' own name (SON). In this study, we conducted a preliminary analysis of the brain response to Chinese name stimuli. Twelve subjects participated in this study. SONs were used as target stimuli for each trial. The names used for non-target stimuli were divided into three Chinese character names condition (3CC) and two Chinese characters names condition (2CC). Thus, each subject was required to be in active (silent counting) and passive mode (without counting) with four conditions [(passive, active) × (3CC, 2CC)]. We analyzed the spatio-temporal features for each condition, and we used SVM for target vs. non-target classification. The results showed that the passive mode under 3CC conditions showed a similar brain response to the active mode, and when 3CC was used as a non-target stimulus, the brain response induced by the target stimulus would have a better interaction than 2CC. We believe that the passive mode 3CC may be a good paradigm to replace the active mode which might need more attention from subjects. The results of this study can provide certain guidelines for the selection and optimization of the paradigm of auditory event-related potentials based on name stimulation.

A novel silent speech recognition approach based on parallel inception convolutional neural network and Mel frequency spectral coefficient.

Silent speech recognition breaks the limitations of automatic speech recognition when acoustic signals cannot be produced or captured clearly, but still has a long way to go before being ready for any real-life applications. To address this issue, we propose a novel silent speech recognition framework based on surface electromyography (sEMG) signals. In our approach, a new deep learning architecture Parallel Inception Convolutional Neural Network (PICNN) is proposed and implemented in our silent speech recognition system, with six inception modules processing six channels of sEMG data, separately and simultaneously. Meanwhile, Mel Frequency Spectral Coefficients (MFSCs) are employed to extract speech-related sEMG features for the first time. We further design and generate a 100-class dataset containing daily life assistance demands for the elderly and disabled individuals. The experimental results obtained from 28 subjects confirm that our silent speech recognition method outperforms state-of-the-art machine learning algorithms and deep learning architectures, achieving the best recognition accuracy of 90.76%. With sEMG data collected from four new subjects, efficient steps of subject-based transfer learning are conducted to further improve the cross-subject recognition ability of the proposed model. Promising results prove that our sEMG-based silent speech recognition system could have high recognition accuracy and steady performance in practical applications.

The application and perspective of hyperbaric oxygen therapy in acute ischemic stroke: From the bench to a starter?

Stroke has become a significant cause of death and disability globally. Along with the transition of the world's aging population, the incidence of acute ischemic stroke is increasing year by year. Even with effective treatment modalities, patients are not guaranteed to have a good prognosis. The treatment model combining intravenous thrombolysis/endovascular therapy and neuroprotection is gradually being recognized. After the clinical translation of pharmacological neuroprotective agents failed, non-pharmacological physical neuroprotective agents have rekindled hope. We performed a literature review using the National Center for Biotechnology Information (NCBI) PubMed database for studies that focused on the application of hyperbaric oxygen therapy in acute ischemic stroke. In this review, we present the history and mechanisms of hyperbaric oxygen therapy, focusing on the current status, outcomes, current challenges, perspective, safety, and complications of the application of hyperbaric oxygen in animal experiments and human clinical trials. Hyperbaric oxygen therapy, a non-pharmacological treatment, can improve the oxygenation level at the ischemic lesions in increased dissolved oxygen and oxygen diffusion radius to achieve salvage of neurological function, giving a new meaning to acute ischemic stroke.

Segmentation Method of Cerebral Aneurysms Based on Entropy Selection Strategy.

The segmentation of cerebral aneurysms is a challenging task because of their similar imaging features to blood vessels and the great imbalance between the foreground and background. However, the existing 2D segmentation methods do not make full use of 3D information and ignore the influence of global features. In this study, we propose an automatic solution for the segmentation of cerebral aneurysms. The proposed method relies on the 2D U-Net as the backbone and adds a Transformer block to capture remote information. Additionally, through the new entropy selection strategy, the network pays more attention to the indistinguishable blood vessels and aneurysms, so as to reduce the influence of class imbalance. In order to introduce global features, three continuous patches are taken as inputs, and a segmentation map corresponding to the central patch is generated. In the inference phase, using the proposed recombination strategy, the segmentation map was generated, and we verified the proposed method on the CADA dataset. We achieved a Dice coefficient (DSC) of 0.944, an IOU score of 0.941, recall of 0.946, an F2 score of 0.942, a mAP of 0.896 and a Hausdorff distance of 3.12 mm.

Four-Pyroptosis Gene-Based Nomogram as a Novel Strategy for Predicting the Effect of Immunotherapy in Hepatocellular Carcinoma.

Background: Immunotherapy has been considered as a promising cancer treatment for hepatocellular carcinoma (HCC). However, due to the particular immune environment of the liver, identifying patients who could benefit from immunotherapy is critical in clinical practice. Methods: The pyroptosis gene expression database of 54 candidates from The Cancer Genome Atlas (TCGA) were collected to discover the critical prognostic-related pyroptosis genes. A novel pyroptosis gene model was established to calculate the risk score. Kaplan-Meier analysis and receiver operating characteristic curve (ROC) were used to verify its predictive ability. The International Cancer Genome Consortium (ICGC) data was collected as external validation data to verify the model's accuracy. We employed multiple bioinformatics tools and algorithms to evaluate the tumor immune microenvironment (TIME) and the response to immunotherapy. Results: Our study found that most pyroptosis genes were expressed differently in normal and tumor tissues and that their expression was associated with the prognosis. Then, a precise four-pyroptosis gene model was generated. The one-year area under the curves (AUCs) among the training, internal, and external validation patients were 0.901, 0.727, and 0.671, respectively. An analysis of survival data revealed that individuals had a worse prognosis than patients with low risk. The analysis of TIME revealed that the low-risk group had more antitumor cells, fewer immunosuppressive cells, stronger immune function, less immune checkpoint gene expression, and better immunotherapy response than the high-risk group. Immunophenoscore (IPS) analysis also demonstrated that the low-risk score was related to superior immune checkpoint inhibitors therapy. Conclusion: A nomogram based on the four-pyroptosis gene signature was a novel tool to predict the effectiveness of immunotherapy for HCC. Therefore, individualized treatment targeting the pyroptosis genes may influence TIME and play an essential role in improving the prognosis in HCC patients.

Sensorimotor rhythm neurofeedback training relieves anxiety in healthy people.

Timely relief of anxiety in healthy people is important, but there is little research on this topic at present. Neurofeedback training allows subjects to regulate their specific brain activities autonomously and thus alter their corresponding cognitive functions. Inattention is a significant cognitive deficit in patients with anxiety. Sensorimotor rhythm (SMR) was reported to be closely related to attention. In this study, trainability, frequency specificity, and brain-behavior relationships were utilized to verify the validity of a relative SMR power protocol. An EEG neurofeedback training system was developed for alleviating anxiety levels in healthy people. The EEG data were collected from 33 subjects during SMR up-training sessions. Subjects attended six times neurofeedback training for about 2 weeks. The feedback value of the neurofeedback group was the relative SMR power at the feedback electrode (electrode C3), while the feedback values for the control group were pseudorandom numbers. The trainability index revealed that the learning trend showed an increase in SMR power activity at the C3 electrode, confirming effects across training. The frequency specificity index revealed only that SMR band activity increased significantly in the neurofeedback group. The brain-behavior relationships index revealed that increased SMR activity correlated negatively with the severity of anxiety. This study indicates that neurofeedback training using a relative SMR power protocol, based on activity at the C3 electrode, could relieve anxiety levels for healthy people and increase the SMR power. Preliminary studies support the feasibility and efficacy of the relative SMR power protocol for healthy people with anxiety. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-021-09732-8.

Anterior Circulation Acute Ischemic Stroke in the Plateau of China: Risk Factors and Clinical Characteristics.

Background and Purpose: Acute ischemic stroke has a high incidence in the plateau of China. It has unique characteristics compared to the plains, and the specific relationship with altitude has not yet been appreciated. This study aimed to investigate the specificity of the plateau's anterior circulation acute ischemic stroke in China. Methods: To retrospectively collect clinical data of patients with first-episode acute ischemic stroke in the anterior circulation in Tianjin and Xining city. The differences in clinical presentation, laboratory, and imaging examinations were compared. Results: Patients at high altitudes showed a significant trend toward lower age (61.0 ± 10.2 vs. 64.8 ± 8.1, P = 0.010) and had a history of dyslipidemia, higher levels of inflammatory markers, erythrocytosis, and alcohol abuse. The main manifestations were higher diastolic blood pressure (85.5 ± 14.0 mmHg vs. 76.8 ± 11.6 mmHg, P < 0.001), triglycerides [2.0 (1.8) mmol/L vs. 1.3 (0.9) mmol/L, P < 0.001], CRP [4.7 (4.4) mg/L vs. 2.1 (1.9) mg/L, P < 0.001], homocysteine levels [14.5 (11.7) µmol/L vs. 11.2 (5.2) µmol/L, P < 0.001]; larger infarct volume [3.5 (4.8) cm3 vs. 9.0 (6.9) cm3, P < 0.001] and worse prognosis. Patients at high altitudes had higher atherosclerotic indexes in cIMT and plaque than those in plains. Conclusions: The natural habituation and genetic adaptation of people to the particular geo-climatic environment of the plateau have resulted in significant differences in disease characteristics. Patients with the anterior circulation acute ischemic stroke in the plateau show more unfavorable clinical manifestations and prognosis. This study provides a preliminary interpretation of the effects of altitude and suggests developing preventive and therapeutic protocol measures that are more appropriate for the plateau of China.

Intracranial Aneurysm Rupture Risk Estimation With Multidimensional Feature Fusion.

The rupture of aneurysms is the main cause of spontaneous subarachnoid hemorrhage (SAH), which is a serious life-threatening disease with high mortality and permanent disability rates. Therefore, it is highly desirable to evaluate the rupture risk of aneurysms. In this study, we proposed a novel semiautomatic prediction model for the rupture risk estimation of aneurysms based on the CADA dataset, including 108 datasets with 125 annotated aneurysms. The model consisted of multidimensional feature fusion, feature selection, and the construction of classification methods. For the multidimensional feature fusion, we extracted four kinds of features and combined them into the feature set, including morphological features, radiomics features, clinical features, and deep learning features. Specifically, we applied the feature extractor 3D EfficientNet-B0 to extract and analyze the classification capabilities of three different deep learning features, namely, no-sigmoid features, sigmoid features, and binarization features. In the experiment, we constructed five distinct classification models, among which the k-nearest neighbor classifier showed the best performance for aneurysm rupture risk estimation, reaching an F2-score of 0.789. Our results suggest that the full use of multidimensional feature fusion can improve the performance of aneurysm rupture risk assessment. Compared with other methods, our method achieves the state-of-the-art performance for aneurysm rupture risk assessment methods based on CADA 2020.

A novel approach to automatic sleep stage classification using forehead electrophysiological signals.

Background: Sleep stage scoring is very important for the effective diagnosis and intervention of sleep disorders. However, the current automatic sleep staging methods generally have the problems of poor model generalization ability and non-portable acquisition equipment. Method: In this paper, we propose a novel automatic sleep scoring system based on forehead electrophysiological signals that is more effective and convenient than other systems. We extract 3 channel signals from the forehead, named forehead electroencephalogram 1 (Fh1), forehead electroencephalogram 2 (Fh2), and forehead center electrooculogram (Fhz). Spectral features, statistical features, and entropy features are extracted using the discrete wavelet transform (DWT) method. Light gradient boosting machine (LGB), random forest (RF), and support vector machine (SVM) are employed to classify four stages: awake, light sleep (LS), deep sleep (DS), and rapid eye movement (REM). Result: The performance of the proposed method is validated using databases of the Sleep-EDFX and our Own-data, which include polysomnograms (PSGs) and forehead signals of 28 subjects. The overall classification accuracy of using the combination of Fh1, Fh2, and Fhz can reach up to 90.25% accuracy with a kappa coefficient of 0.857. Conclusions: The proposed method could provide state-of-art multichannel sleep stage scoring performance with higher portability. This will facilitate the application of long-term monitoring of sleep quality in the future.

EMS-Net: Enhanced Multi-Scale Network for Polyp Segmentation.

In recent years, polyp segmentation plays an important role in the diagnosis and treatment of colorectal cancer. Accurate segmentation of polyps is very challenging due to different sizes, shapes, and unclear boundaries. Making full use of multi-scale contextual information to segment polyps may bring better results. In this paper, we propose an enhanced multi-scale network for accurate polyp segmentation. It is composed of a multi-scale connected baseline (U-Net+++), a multi-scale backbone (Res2Net), three Receptive Field Block (RFB) modules, and four Local Context Attention (LCA) modules. Specifically, the baseline's multi-scale skip connections can aggregate features in both low-level and high-level layers. We have evaluated our model on three publicly available and challenging datasets (EndoScene, CVC-ClinicDB, Kvasir-SEG). Compared with other methods, our model achieves SOTA performance. It is noteworthy that our model is the only network that has achieved over 0.900 mean Dice on EndoScene and CVC-ClinicDB.