Decreased brain iron deposition based on quantitative susceptibility mapping correlates with reduced neurodevelopmental status in children with autism spectrum disorder.

To investigate potential correlations between the susceptibility values of certain brain regions and the severity of disease or neurodevelopmental status in children with autism spectrum disorder (ASD), 18 ASD children and 15 healthy controls (HCs) were recruited. The neurodevelopmental status was assessed by the Gesell Developmental Schedules (GDS) and the severity of the disease was evaluated by the Autism Behavior Checklist (ABC). Eleven brain regions were selected as regions of interest and the susceptibility values were measured by quantitative susceptibility mapping. To evaluate the diagnostic capacity of susceptibility values in distinguishing ASD and HC, the receiver operating characteristic (ROC) curve was computed. Pearson and Spearman partial correlation analysis were used to depict the correlations between the susceptibility values, the ABC scores, and the GDS scores in the ASD group. ROC curves showed that the susceptibility values of the left and right frontal white matter had a larger area under the curve in the ASD group. The susceptibility value of the right globus pallidus was positively correlated with the GDS-fine motor scale score. These findings indicated that the susceptibility value of the right globus pallidus might be a viable imaging biomarker for evaluating the neurodevelopmental status of ASD children.

Lower Small-Worldness of Intrinsic Brain Networks Facilitates the Cognitive Protection of Intellectual Engagement in Elderly People Without Dementia: A Near-Infrared Spectroscopy Study.

OBJECTIVE: Lifetime intellectual engagement may be associated with cognitive ability late in life. However, the current evidence on whether cognitive activities will improve and/or maintain cognitive function is heterogeneous. Drawing on knowledge of the brain's intrinsic small-world organization which combines regional specialization and efficient global information transfer, we aimed to explore that whether individual differences in the small-worldness of resting-state functional connectivity (rsFC) networks would explain the variability in the strength of the association between intellectual engagement and cognitive functioning. METHODS: Sixty-five elderly people without dementia were enrolled and scanned with a 52-channel near-infrared spectroscopy system. The number, frequency, and participation hours of intellectual activities were investigated to measure intellectual engagement. Global cognition was assessed by the Montreal Cognitive Assessment. The general linear models and the simple slope analysis were employed to measure the modulatory role of network properties. RESULTS: The small-worldness of the brain network emerged as a moderator of the association between intellectual engagement and cognition. Exclusively among elderly people with lower small-worldness, greater intellectual engagement, including the frequency and participation hours of activities, was associated with greater global cognitive function. Furthermore, we observed that elderly people with lower small-worldness exhibited decreased rsFC across the bilateral frontopolar areas and increased rsFC across the bilateral parietal cortex. CONCLUSION: The individual differences in the small-worldness of rsFC networks might explain the varying strength of the association between intellectual engagement and cognitive functioning. Our findings imply that the intrinsic small-worldness of the brain network might be a potential neurobiological contributor that interacts with the intellectual engagement in enhancing the cognitive ability in late life.

Adaptive neuro-fuzzy inference system for classification of background EEG signals from ESES patients and controls.

Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3-9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved.

Classifying normal and abnormal status based on video recordings of epileptic patients.

Based on video recordings of the movement of the patients with epilepsy, this paper proposed a human action recognition scheme to detect distinct motion patterns and to distinguish the normal status from the abnormal status of epileptic patients. The scheme first extracts local features and holistic features, which are complementary to each other. Afterwards, a support vector machine is applied to classification. Based on the experimental results, this scheme obtains a satisfactory classification result and provides a fundamental analysis towards the human-robot interaction with socially assistive robots in caring the patients with epilepsy (or other patients with brain disorders) in order to protect them from injury.

Long-term HD-tDCS modulates dynamic changes of brain activity on patients with disorders of consciousness: A resting-state EEG study.

OBJECTIVE: High-definition transcranial direct current stimulation (HD-tDCS) has been an effective neurostimulation method in the treatment of disorders of consciousness (DOC). However, the effects and mechanism of HD-tDCS are still unclear. METHODS: This study recruited 8 DOC patients and applied 20-min sessions of 2 mA HD-tDCS (central anode electrode at Pz) for 14 consecutive days. We record DOC patients' EEG data and Coma Recovery Scale-Revised (CRS-R) values at four time point: baseline (T0), after 1 day's and 7,14 days' parietal HD-tDCS treatment (T1, T2, T3). Power spectral density (PSD), relative power (RP), spectral entropy and spectral exponent were calculated to evaluate the EEG dynamic changes of DOC patients during long-term parietal HD-tDCS. At last, we calculated the correlation between changes of EEG features and changes of CRS-R values. RESULT: After 1 day's parietal HD-tDCS, DOC patients' CRS-R value had not changed (8.25 ± 1.91). HD-tDCS improved DOC patients' CRS-R value at T2 (9.75 ± 1.91, p < 0.05) and at T3 (11.38 ± 2.77, p < 0.05), compared with that at T0 (8.25 ± 1.91). As the treatment time increased, the EEG PSD decayed more slowly. Specifically, the delta frequency band RP decreased, while the alpha, beta, and gamma frequency bands RP increased. EEG oscillation characteristics changed but not significant at T1 (p > 0.05), and showed significant changes at T2 and T3 (p < 0.05). The spectral entropy continuously increased and the spectral exponent continuously decreased from T0 to T3. Specifically, the spectral entropy and spectral exponent of the parietal and occipital regions were significantly higher at T2 and T3 than that at T0 (p < 0.05). In addition, The changes in EEG features of the parietal and occipital lobes were correlated with changes in CRS-R value, especially between T2 and T0. CONCLUSION: Long-term parietal HD-tDCS can improve the consciousness level and brain activity in DOC patients. Resting-state EEG can evaluate the dynamic changes of brain activity in DOC patients during HD-tDCS. EEG oscillation and non-oscillatory activity might be used to explain the mechanism of HD-tDCS on DOC patients.

The effect of EEG microstate on the characteristics of TMS-EEG.

OBJECTIVE: Differences in neural states at the time of transcranial magnetic stimulation (TMS) can lead to variations in the effectiveness of TMS stimulation. Strategies that aim to lock neural activity states and improve the precision of stimulation timing in TMS optimization should gradually receive attention. One feasible approach is to utilize microstate locking for TMS stimulation, and understanding the impact of microstates at the time of stimulation on TMS response forms the foundation of this approach. APPROACH: TMS-EEG data were extracted from 21 healthy subjects through experiments. Based on the different microstates at the time of stimulation, the trials were classified into four datasets. TMS-evoked potential (TEP), topographical distribution, and natural frequency, were computed for each dataset to explore the differences in TMS-EEG characteristics across different microstates. MAIN RESULTS: The N100 component of microstate C group (-2.376 µV) was significantly higher (p = 0.003) than of microstate D group (-1.739 µV), and the P180 component of microstate D group (2.482 µV) was significantly higher (p = 0.024) than of microstate B group (1.766 µV) and slightly higher (p = 0.058) than of microstate C group (1.863 µV) by calculating the ROI. The topographical distribution of TEP components during microstate C and microstate D still retained the template characteristics of the microstate at the time of stimulation, and the natural frequencies did not differ among the four classical microstates. SIGNIFICANCE: This study showed the potential for future closed-loop TMS based on microstates and would guiding the development of microstate-based closed-loop TMS techniques.

Identification of autism spectrum disorder based on electroencephalography: A systematic review.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by difficulties in social communication and repetitive and stereotyped behaviors. According to the World Health Organization, about 1 in 100 children worldwide has autism. With the global prevalence of ASD, timely and accurate diagnosis has been essential in enhancing the intervention effectiveness for ASD children. Traditional ASD diagnostic methods rely on clinical observations and behavioral assessment, with the disadvantages of time-consuming and lack of objective biological indicators. Therefore, automated diagnostic methods based on machine learning and deep learning technologies have emerged and become significant since they can achieve more objective, efficient, and accurate ASD diagnosis. Electroencephalography (EEG) is an electrophysiological monitoring method that records changes in brain spontaneous potential activity, which is of great significance for identifying ASD children. By analyzing EEG data, it is possible to detect abnormal synchronous neuronal activity of ASD children. This paper gives a comprehensive review of the EEG-based ASD identification using traditional machine learning methods and deep learning approaches, including their merits and potential pitfalls. Additionally, it highlights the challenges and the opportunities ahead in search of more effective and efficient methods to automatically diagnose autism based on EEG signals, which aims to facilitate automated ASD identification.

D348N Mutation of BFSP1 Gene in Congenital Cataract: it Does Matter.

Beaded filament structural protein 1 (BFSP1) gene plays important role in the development of congenital cataract. We aimed to investigate and analyze the molecular mechanism of congenital cataract caused by D348N mutation of BFSP1 gene, and to provide evidence for the intervention of congenital cataract. BFSP1 and CP49 genes were cloned, wild type and mutant expression plasmids of BFSP1 were constructed and transfected into 293T cells. The BFSP1 wild type and mutant (D348N) gene sequence (NM_001195) were constructed into pEGFP-N1 vector by the restriction site NheI/KpnI. The effect of mutation on cell proliferation and apoptosis was analyzed. There was no significant change between the expression site of BFSP1 D348N mutation and the wild type. The expression of BFSP1 protein in wild group was higher than that in mutant group. CCK8 detection showed that the proliferation ability of 293T cells in mutant group was weaker than that in BFSP1 group. The mutation led to an increase in apoptosis. BFSP1 mutation significantly decreases the expression of BFSP1 protein, weakened the ability of cell proliferation and increased apoptosis. BFSP1 D348N mutation may be closely associated with congenital cataract and is of great significance to the investigations of the mechanism and intervention of congenital cataract.

Preliminary study on the efficacy of lacrimal duct reconstruction with pedicled conjunctival flap in the treatment of severe lacrimal canalicular obstruction with conjunctivochalasis.

AIM: To determine the efficacy and safety of pedicled conjunctival lacrimal duct reconstruction in the treatment of severe obstruction of superior and inferior lacrimal canaliculi with conjunctivochalasis. METHODS: This study was performed as a retrospective analysis of patients who received conjunctival dacryocystorhinostomy with pedicled conjunctival flap reconstruction combined with tube intubation due to severe superior and inferior lacrimal canalicular obstruction with conjunctivochalasis from January 2019 to October 2019. The clinical data included the degree of preoperative epiphora and postoperative relief, preoperative examination of lacrimal duct computed tomography and ultrasound biomicroscopy, postoperative evaluation of lacrimal duct function by chloramphenicol taste and fluorescein dye disappearance test, etc. Syringing was carried out to determine the reconstruction and patency of the lacrimal duct. RESULTS: All 9 patients (9 eyes) had severe canalicular obstruction with conjunctivochalasis. The patients included 4 males and 5 females aged between 47-65y with an average age of 52.2±6.7y. At 3mo follow-up, the tube was removed and the patients were followed for a further 3mo. After tube removal, 6 patients showed no epiphora. These patients also had positive chloramphenicol tastes and normal fluorescein dye disappearance test results. Two patientshad epiphora. Also, syringing showed partial patency of the reconstructed lacrimal duct. One patient had no improvement in epiphora with negative chloramphenicol taste and fluorescein dye disappearance test results and obstruction of the reconstructed lacrimal duct. The total effective rate of the operation was 8/9, with no serious complications. CONCLUSION: Pedicled conjunctival lacrimal duct reconstruction conjunctival dacryocystorhinostomy is safe and effective for superior and inferior canalicular obstruction with conjunctivochalasis.

Task-independent auditory probes reveal changes in mental workload during simulated quadrotor UAV training.

Objective: The event-related potential (ERP) methods based on laboratory control scenes have been widely used to measure the level of mental workload during operational tasks. In this study, both task difficulty and test time were considered. Auditory probes (ignored task-irrelevant background sounds) were used to explore the changes in mental workload of unmanned aerial vehicle (UAV) operators during task execution and their ERP representations. Approach: 51 students participated in a 10-day training and test of simulated quadrotor UAV. During the experiment, background sound was played to induce ERP according to the requirements of oddball paradigm, and the relationship between mental workload and the amplitudes of N200 and P300 in ERP was explored. Main results: Our study shows that the mental workload during operational task training is multi-dimensional, and its changes are affected by bottom-up perception and top-down cognition. The N200 component of the ERP evoked by the auditory probe corresponds to the bottom-up perceptual part; while the P300 component corresponds to the top-down cognitive part, which is positively correlated with the improvement of skill level. Significance: This paper describes the relationship between ERP induced by auditory probes and mental workload from the perspective of multi-resource theory and human information processing. This suggests that the auditory probe can be used to reveal the mental workload during the training of operational tasks, which not only provides a possible reference for measuring the mental workload, but also provides a possibility for identifying the development of the operator's skill level and evaluating the training effect.

SRY-box transcription factor 9 modulates Müller cell gliosis in diabetic retinopathy by upregulating TXNIP transcription.

Diabetic retinopathy (DR), a common complication of diabetes, involves excessive proliferation and inflammation of Muller cells and ultimately leads to vision loss and blindness. SRY-box transcription factor 9 (SOX9) has been reported to be highly expressed in Müller cells in light-induced retinal damage rats, but the functional role of SOX9 in DR remains unclear. To explore this issue, the DR rat model was successfully constructed via injection with streptozotocin (65 mg/kg) and the retinal thicknesses and blood glucose levels were evaluated. Müller cells were treated with 25 mmol/l glucose to create a cell model in vitro. The results indicated that SOX9 expression was significantly increased in DR rat retinas and in Müller cells stimulated with a high glucose (HG) concentration. HG treatment promoted the proliferation and migration capabilities of Müller cells, whereas SOX9 knockdown reversed those behaviors. Moreover, SOX9 knockdown provided protection against an HG-induced inflammatory response, as evidenced by reduced tumor necrosis factor-α, IL-1ß, and IL-6 levels in serum and decreased NLRP3 inflammasome activation. Notably, SOX9 acted as a transcription factor that positively regulated thioredoxin-interacting protein (TXNIP), a positive regulator of Müller cells gliosis under HG conditions. A dual-luciferase assay demonstrated that SOX9 could enhance TXNIP expression at the transcriptional level through binding to the promoter of TXNIP. Moreover, TXNIP overexpression restored the effects caused by SOX9 silencing. In conclusion, these findings demonstrate that SOX9 may accelerate the progression of DR by promoting glial cell proliferation, metastasis, and inflammation, which involves the transcriptional regulation of TXNIP, providing new theoretical fundamentals for DR therapy.

A Multimodal Approach for Identifying Autism Spectrum Disorders in Children.

Identification of autism spectrum disorder (ASD) in children is challenging due to the complexity and heterogeneity of ASD. Currently, most existing methods mainly rely on a single modality with limited information and often cannot achieve satisfactory performance. To address this issue, this paper investigates from internal neurophysiological and external behavior perspectives simultaneously and proposes a new multimodal diagnosis framework for identifying ASD in children with fusion of electroencephalogram (EEG) and eye-tracking (ET) data. Specifically, we designed a two-step multimodal feature learning and fusion model based on a typical deep learning algorithm, stacked denoising autoencoder (SDAE). In the first step, two SDAE models are designed for feature learning for EEG and ET modality, respectively. Then, a third SDAE model in the second step is designed to perform multimodal fusion with learned EEG and ET features in a concatenated way. Our designed multimodal identification model can automatically capture correlations and complementarity from behavior modality and neurophysiological modality in a latent feature space, and generate informative feature representations with better discriminability and generalization for enhanced identification performance. We collected a multimodal dataset containing 40 ASD children and 50 typically developing (TD) children to evaluate our proposed method. Experimental results showed that our proposed method achieved superior performance compared with two unimodal methods and a simple feature-level fusion method, which has promising potential to provide an objective and accurate diagnosis to assist clinicians.

Application Value of Flexible Endoscopic Examination of Swallowing in Acute Stoke Patients With Dysphagia.

BACKGROUND: The aim was to study the application value of flexible endoscopic examination of swallowing (FEES) for the aspiration screening, the diagnosis of dysphagia and evaluation of the therapeutic effect in acute stoke patients with dysphagia. METHODS: A total of 525 patients with acute stoke who were hospitalized from October 2015 to January 2021 in the Rehabilitation Medicine Department of our hospital underwent FEES for analyzing the characteristic performance. Twenty-one cases of them were examined by video fluoroscopic swallow study and compared with the results of FEES for evaluating the reliability of the FEES, the reliability of diagnosis of dysphagia, and the consistency of the 2 methods. The effect of rehabilitation was evaluated by comparing the FEES test results before and after treatment. RESULTS: In 525 patients, the FEES revealed 378 cases of aspiration (139 cases were silent aspiration), showing a higher detection rate than water swallow test. Patients with potential cricopharyngeus achalasia got the same results through both of examinations. FEES can provide more positive indicators, guide clinical rehabilitation treatment and objectively assess the effect of rehabilitation. CONCLUSIONS: Acute stoke patients with dysphagia have characteristic pharyngeal and laryngeal performance. FEES is simple to operate and has high application value in the diagnosis and treatment of dysphagia.

Appearance-Based Gaze Estimation for ASD Diagnosis.

Biomarkers, such as magnetic resonance imaging (MRI) and electroencephalogram have been used to help diagnose autism spectrum disorder (ASD). However, the diagnosis needs the assist of specialized medical equipment in the hospital or laboratory. To diagnose ASD in a more effective and convenient way, in this article, we propose an appearance-based gaze estimation algorithm-AttentionGazeNet, to accurately estimate the subject's 3-D gaze from a raw video. The experimental results show its competitive performance on the MPIIGaze dataset and the improvement of 14.7% for static head pose and 46.7% for moving head pose on the EYEDIAP dataset compared with the state-of-the-art gaze estimation algorithms. After projecting the obtained gaze vector onto the screen coordinate, we apply accumulated histogram to taking into account both spatial and temporal information of estimated gaze-point and head-pose sequences. Finally, classification is conducted on our self-collected autistic children video dataset (ACVD), which contains 405 videos from 135 different ASD children, 135 typically developing (TD) children in a primary school, and 135 TD children in a kindergarten. The classification results on ACVD shows the effectiveness and efficiency of our proposed method, with the accuracy 94.8%, the sensitivity 91.1% and the specificity 96.7% for ASD.

Scale-free topology of the CA3 hippocampal network: a novel method to analyze functional neuronal assemblies.

Cognitive mapping functions of the hippocampus critically depend on the recurrent network of the CA3 pyramidal cells. However, it is still not known in detail how network activity patterns emerge, or how they encode information. By using functional multineuron calcium imaging, we simultaneously recorded the activity of >100 neurons in the CA3 region of hippocampal slice cultures. We utilized a novel computational method to analyze the multichannel spike trains and to depict functional neuronal assemblies. By means of event synchronization and the correlation matrix analysis method, we found that: 1), the average functional neuronal cluster consists of 23 neurons, and neurons could be part of multiple assemblies; 2), the clustering strength, size, and mean distance among cells in neuronal assemblies follow a power-law-like distribution; 3), the clustering strength and size of neuronal assemblies are not correlated with the total number of neurons and their physical distance; and 4), the clustering distance of neuronal assemblies is weakly correlated with the total number of neurons and their physical distance. These findings suggest that the functional organization of the spontaneously firing CA3 hippocampal network is a scale-free structure in slice culture.

Estimating coupling direction between neuronal populations with permutation conditional mutual information.

To further understand functional connectivity in the brain, we need to identify the coupling direction between neuronal signals recorded from different brain areas. In this paper, we present a novel methodology based on permutation analysis and conditional mutual information for estimation of a directionality index between two neuronal populations. First, the reliability of this method is numerically assessed with a coupled mass neural model; the simulations show that this method is superior to the conditional mutual information method and the Granger causality method for identifying the coupling direction between unidirectional or bidirectional neuronal populations that are generated by the mass neuronal model. The method is also applied to investigate the coupling direction between neuronal populations in CA1 and CA3 in the rat hippocampal tetanus toxin model of focal epilepsy; the propagation direction of the seizure events could be elucidated through this coupling direction estimation method. All together, these results suggest that the permutation conditional mutual information method is a promising technique for estimating directional coupling between mutually interconnected neuronal populations.

Closed-loop transcranial magnetic stimulation of real-time EEG based on the AR mode method.

OBJECTIVE: Transcranial magnetic stimulation (TMS) as a safe, noninvasive brain regulation technology has been gradually applied to clinical treatment. Traditional TMS devices do not adjust output based on real-time brain activity information when regulating the cerebral cortex, but the current activity information from the brain, especially the EEG phase, may affect the stimulation effect. It is necessary to calculate the synchronous EEG phase during TMS. APPROACH: In this study, a set of closed-loop TMS device a fast EEG phase prediction algorithm based on the AR model was designed to meet the demand. EEG data for twenty-seven healthy college students were collected to verify the accuracy of the algorithm. MAIN RESULTS: The calculation results showed that the prediction accuracy of the AR model algorithm is better than that of the conventional algorithm when the model order is lower, and the prediction accuracy will increase with improvements in the signal quality. SIGNIFICANCE: When the experimental environment is good, the EEG data with a high SNR can be recorded, and when the order of the AR model is properly set, the prediction algorithm can make correct judgments most of the time and the stimulation pulse can be output when the EEG phase reaches a set value.

Deterministic dynamics of neural activity during absence seizures in rats.

The study of brain electrical activities in terms of deterministic nonlinear dynamics has recently received much attention. Forbidden ordinal patterns (FOP) is a recently proposed method to investigate the determinism of a dynamical system through the analysis of intrinsic ordinal properties of a nonstationary time series. The advantages of this method in comparison to others include simplicity and low complexity in computation without further model assumptions. In this paper, the FOP of the EEG series of genetic absence epilepsy rats from Strasbourg was examined to demonstrate evidence of deterministic dynamics during epileptic states. Experiments showed that the number of FOP of the EEG series grew significantly from an interictal to an ictal state via a preictal state. These findings indicated that the deterministic dynamics of neural networks increased significantly in the transition from the interictal to the ictal states and also suggested that the FOP measures of the EEG series could be considered as a predictor of absence seizures.

Using recurrence plot for determinism analysis of EEG recordings in genetic absence epilepsy rats.

OBJECTIVE: Understanding the transition of brain activity towards an absence seizure is a challenging task. In this paper, we use recurrence quantification analysis to indicate the deterministic dynamics of EEG series at the seizure-free, pre-seizure and seizure states in genetic absence epilepsy rats. METHODS: The determinism measure, DET, based on recurrence plot, was applied to analyse these three EEG datasets, each dataset containing 300 single-channel EEG epochs of 5-s duration. Then, statistical analysis of the DET values in each dataset was carried out to determine whether their distributions over the three groups were significantly different. Furthermore, a surrogate technique was applied to calculate the significance level of determinism measures in EEG recordings. RESULTS: The mean (+/-SD) DET of EEG was 0.177+/-0.045 in pre-seizure intervals. The DET values of pre-seizure EEG data are significantly higher than those of seizure-free intervals, 0.123+/-0.023, (P<0.01), but lower than those of seizure intervals, 0.392+/-0.110, (P<0.01). Using surrogate data methods, the significance of determinism in EEG epochs was present in 25 of 300 (8.3%), 181 of 300 (60.3%) and 289 of 300 (96.3%) in seizure-free, pre-seizure and seizure intervals, respectively. CONCLUSIONS: Results provide some first indications that EEG epochs during pre-seizure intervals exhibit a higher degree of determinism than seizure-free EEG epochs, but lower than those in seizure EEG epochs in absence epilepsy. SIGNIFICANCE: The proposed methods have the potential of detecting the transition between normal brain activity and the absence seizure state, thus opening up the possibility of intervention, whether electrical or pharmacological, to prevent the oncoming seizure.