Low-intensity ultrasound ameliorates brain organoid integration and rescues microcephaly deficits.

Human brain organoids represent a remarkable platform for modeling neurological disorders and a promising brain repair approach. However, the effects of physical stimulation on their development and integration remain unclear. Here, we report that low-intensity ultrasound significantly increases neural progenitor cell proliferation and neuronal maturation in cortical organoids. Histological assays and single-cell gene expression analyses reveal that low-intensity ultrasound improves the neural development in cortical organoids. Following organoid grafts transplantation into the injured somatosensory cortices of adult mice, longitudinal electrophysiological recordings and histological assays reveal that ultrasound-treated organoid grafts undergo advanced maturation. They also exhibit enhanced pain-related gamma-band activity and more disseminated projections into the host brain than the untreated groups. Finally, low-intensity ultrasound ameliorates neuropathological deficits in a microcephaly brain organoid model. Hence, low-intensity ultrasound stimulation advances the development and integration of brain organoids, providing a strategy for treating neurodevelopmental disorders and repairing cortical damage.

Evidence for modulation of EEG microstates by mental workload levels and task types.

Electroencephalography (EEG) microstate analysis has become a popular tool for studying the spatial and temporal dynamics of large-scale electrophysiological activities in the brain in recent years. Four canonical topographies of the electric field (classes A, B, C, and D) have been widely identified, and changes in microstate parameters are associated with several psychiatric disorders and cognitive functions. Recent studies have reported the modulation of EEG microstate by mental workload (MWL). However, the common practice of evaluating MWL is in a specific task. Whether the modulation of microstate by MWL is consistent across different types of tasks is still not clear. Here, we studied the topographies and dynamics of microstate in two independent MWL tasks: NBack and the multi-attribute task battery (MATB) and showed that the modulation of MWL on microstate topographies and parameters depended on tasks. We found that the parameters of microstates A and C, and the topographies of microstates A, B, and D were significantly different between the two tasks. Meanwhile, all four microstate topographies and parameters of microstates A and C were different during the NBack task, but no significant difference was found during the MATB task. Furthermore, we employed a support vector machine recursive feature elimination procedure to investigate whether microstate parameters were suitable for MWL classification. An averaged classification accuracy of 87% for within-task and 78% for cross-task MWL discrimination was achieved with at least 10 features. Collectively, our findings suggest that topographies and parameters of microstates can provide valuable information about neural activity patterns with a dynamic temporal structure at different levels of MWL, but the modulation of MWL depends on tasks and their corresponding functional systems. Moreover, as a potential indicator, microstate parameters could be used to distinguish MWL.

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.

The frontooccipital interaction mechanism of high-frequency acoustoelectric signal.

Based on acoustoelectric effect, acoustoelectric brain imaging has been proposed, which is a high spatiotemporal resolution neural imaging method. At the focal spot, brain electrical activity is encoded by focused ultrasound, and corresponding high-frequency acoustoelectric signal is generated. Previous studies have revealed that acoustoelectric signal can also be detected in other non-focal brain regions. However, the processing mechanism of acoustoelectric signal between different brain regions remains sparse. Here, with acoustoelectric signal generated in the left primary visual cortex, we investigated the spatial distribution characteristics and temporal propagation characteristics of acoustoelectric signal in the transmission. We observed a strongest transmission strength within the frontal lobe, and the global temporal statistics indicated that the frontal lobe features in acoustoelectric signal transmission. Then, cross-frequency phase-amplitude coupling was used to investigate the coordinated activity in the AE signal band range between frontal and occipital lobes. The results showed that intra-structural cross-frequency coupling and cross-structural coupling co-occurred between these two lobes, and, accordingly, high-frequency brain activity in the frontal lobe was effectively coordinated by distant occipital lobe. This study revealed the frontooccipital long-range interaction mechanism of acoustoelectric signal, which is the foundation of improving the performance of acoustoelectric brain imaging.

EEG-based assessment of temporal fine structure and envelope effect in mandarin syllable and tone perception.

In recent years, speech perception research has benefited from low-frequency rhythm entrainment tracking of the speech envelope. However, speech perception is still controversial regarding the role of speech envelope and temporal fine structure, especially in Mandarin. This study aimed to discuss the dependence of Mandarin syllables and tones perception on the speech envelope and the temporal fine structure. We recorded the electroencephalogram (EEG) of the subjects under three acoustic conditions using the sound chimerism analysis, including (i) the original speech, (ii) the speech envelope and the sinusoidal modulation, and (iii) the fine structure of time and the modulation of the non-speech (white noise) sound envelope. We found that syllable perception mainly depended on the speech envelope, while tone perception depended on the temporal fine structure. The delta bands were prominent, and the parietal and prefrontal lobes were the main activated brain areas, regardless of whether syllable or tone perception was involved. Finally, we decoded the spatiotemporal features of Mandarin perception from the microstate sequence. The spatiotemporal feature sequence of the EEG caused by speech material was found to be specific, suggesting a new perspective for the subsequent auditory brain-computer interface. These results provided a new scheme for the coding strategy of new hearing aids for native Mandarin speakers.

Gamma auditory steady-state response as a promising electrophysiological biomarker for depression: an in vivo study with chronic unpredictable mild stress (CUS)-induced rats.

Gamma oscillations play a functional role in brain cognitions. Recently, auditory steady-state response (ASSR) has been reported abnormally in depression clinically, particularly in the low-gamma band. However, clinical electroencephalography research has challenges obtaining pure signals straight from the source level, making information isolation and precise localization difficult. Besides, the ASSR deficits pattern remains unclear. Herein, we focused on the origin of ASSR-primary auditory cortex (A1), the central node in the auditory pathway. We assessed the evoked-power and phase-synchronization using local field potentials (LFP) in depression (n = 21) and control (n = 22) rats. Subsequent processing of the received auditory information was examined using event-related potentials (AEPs). Results showed that depressed rats exhibited significant gamma ASSR impairments in peak-to-peak amplitude, inter-trial phase coherence, and signal-to-noise ratio. These deficits were more pronounced during 40-Hz auditory stimuli in right-A1, indicating severe gamma network abnormalities in the right auditory pathway. Besides, increased N2 and P3 amplitudes in depression group were found, indicating excessive inhibitory control and contextual processing. Taken together, these ASSR abnormalities have a high specificity of more than 90% and high sensitivity of more than 80% to distinguish depression under 40-Hz auditory stimuli. Our findings provided an abnormal gamma network in the auditory pathway, as a promising diagnostic biomarker in the future.

Spatiotemporal connectivity maps abnormal communication pathways in major depressive disorder underlying gamma oscillations.

Auditory steady-state response underlying gamma oscillations (gamma-ASSR) have been explored in patients with major depressive disorder (MDD), while ignoring the spatiotemporal dynamic characteristics. This study aims to construct dynamic directed brain networks to explore the disruption of spatiotemporal dynamics underlying gamma-ASSR in MDD. This study recruited 29 MDD patients and 30 healthy controls for a 40 Hz auditory steady-state evoked experiment. The propagation of gamma-ASSR was divided into early, middle, and late time interval. Partial directed coherence was applied to construct dynamic directed brain networks based on graph theory. The results showed that MDD patients had lower global efficiency and out-strength in temporal, parietal, and occipital regions over three time intervals. Additionally, distinct disrupted connectivity patterns occurred in different time intervals with abnormalities in the early and middle gamma-ASSR in left parietal regions cascading forward to produce dysfunction of frontal brain regions necessary to support gamma oscillations. Furthermore, the early and middle local efficiency of frontal regions were negatively correlated with symptom severity. These findings highlight patterns of hypofunction in the generation and maintenance of gamma-band oscillations across parietal-to-frontal regions in MDD patients, which provides novel insights into the neuropathological mechanism underlying gamma oscillations associated with aberrant brain network dynamics of MDD.

Repetitive transcranial magnetic stimulation rescues simulated space complex environment-induced emotional and social impairments by enhancing neuronal excitability in the medial prefrontal cortex.

Studies have shown that spaceflight affects the emotional and social performance of astronauts. Identifying the neural mechanisms underlying the emotional and social effects of spacefaring-specific environments is essential to specify targeted treatment and prevention interventions. Repetitive transcranial magnetic stimulation (rTMS) has been shown to improve the neuronal excitability and is used to treat psychiatric disorders such as depression. To study the changes of excitatory neuron activity in medial prefrontal cortex (mPFC) in simulated space complex environment (SSCE), and to explore the role of rTMS in behavioral disorders caused by SSCE and the neural mechanism. We found that rTMS effectively ameliorated the emotional and social impairments of mice in SSCE, and acute rTMS could instantaneously enhance the excitability of mPFC neurons. During depression-like and social novelty behaviors, chronic rTMS enhanced the mPFC excitatory neuronal activity that was inhibited by SSCE. Above results suggested that rTMS can completely reverse the SSCE-induced mood and social impairment by enhancing the suppressed mPFC excitatory neuronal activity. It was further found that rTMS suppressed the SSCE-induced excessive dopamine D2 receptor expression, which may be the cellular mechanism by which rTMS potentiates the SSCE-evoked hypoactive mPFC excitatory neurons. Our current results raise the possibility of rTMS being applied as a novel neuromodulation for mental health protection in spaceflight.

Low-intensity focused ultrasound ameliorates depression-like behaviors associated with improving the synaptic plasticity in the vCA1-mPFC pathway.

It is of great social significance and clinical value to explore new effective treatments for depression. Low-intensity focused ultrasound stimulation (LIFUS) has been indicated to have notable neuroprotective effects on depression. However, little is known about how different strategies of LIFUS affect the therapeutic effect. Therefore, the purpose of this study is to investigate whether the effects of LIFUS on depression-like behaviors are associated with the intensity and the underlying mechanisms. We established the depression rats model using the chronic unpredictable stress (CUS) and applied the LIFUS with high/low intensity (Ispta = 500 and 230 mW/cm2, respectively) to the left medial prefrontal cortex (mPFC) after CUS. We found that two intensities of LIFUS both could significantly improve depression-like behaviors to a comparable degree. We further found that theta oscillation synchronization and synaptic functional plasticity in the hippocampal vCA1-mPFC pathway were significantly improved by chronic LIFUS which mainly due to the alternation of synaptic structural plasticity and the expression of post-synaptic proteins in the mPFC. These results suggest that LIFUS ameliorates the depression-like behaviors associated with improving the synaptic plasticity in the vCA1-mPFC pathway. Our study provides preclinical evidence and a theoretical basis for applying LIFUS for depression treatment.

Fusion of Multi-domain EEG Signatures Improves Emotion Recognition.

BACKGROUND: Affective computing has gained increasing attention in the area of the human-computer interface where electroencephalography (EEG)-based emotion recognition occupies an important position. Nevertheless, the diversity of emotions and the complexity of EEG signals result in unexplored relationships between emotion and multichannel EEG signal frequency, as well as spatial and temporal information. METHODS: Audio-video stimulus materials were used that elicited four types of emotions (sad, fearful, happy, neutral) in 32 male and female subjects (age 21-42 years) while collecting EEG signals. We developed a multidimensional analysis framework using a fusion of phase-locking value (PLV), microstates, and power spectral densities (PSDs) of EEG features to improve emotion recognition. RESULTS: An increasing trend of PSDs was observed as emotional valence increased, and connections in the prefrontal, temporal, and occipital lobes in high-frequency bands showed more differentiation between emotions. Transition probability between microstates was likely related to emotional valence. The average cross-subject classification accuracy of features fused by Discriminant Correlation Analysis achieved 64.69%, higher than that of single mode and direct-concatenated features, with an increase of more than 7%. CONCLUSIONS: Different types of EEG features have complementary properties in emotion recognition, and combining EEG data from three types of features in a correlated way, improves the performance of emotion classification.

Effect of Phase Clustering Bias on Phase-Amplitude Coupling for Emotional EEG.

BACKGROUND: Emotions are thought to be related to distinct patterns of neural oscillations, but the interactions among multi-frequency neural oscillations during different emotional states lack full exploration. Phase-amplitude coupling is a promising tool for understanding the complexity of the neurophysiological system, thereby playing a crucial role in revealing the physiological mechanisms underlying emotional electroencephalogram (EEG). However, the non-sinusoidal characteristics of EEG lead to the non-uniform distribution of phase angles, which could potentially affect the analysis of phase-amplitude coupling. Removing phase clustering bias (PCB) can uniform the distribution of phase angles, but the effect of this approach is unknown on emotional EEG phase-amplitude coupling. This study aims to explore the effect of PCB on cross-frequency phase-amplitude coupling for emotional EEG. METHODS: The technique of removing PCB was implemented on a publicly accessible emotional EEG dataset to calculate debiased phase-amplitude coupling. Statistical analysis and classification were conducted to compare the difference in emotional EEG phase-amplitude coupling prior to and post the removal of PCB. RESULTS: Emotional EEG phase-amplitude coupling values are overestimated due to PCB. Removing PCB enhances the difference in coupling strength between fear and happy emotions in the frontal lobe. Comparable emotion recognition performance was achieved with fewer features after removing PCB. CONCLUSIONS: These findings suggest that removing PCB enhances the difference in emotional EEG phase-amplitude coupling patterns and generates features that contain more emotional information. Removing PCB may be advantageous for analyzing emotional EEG phase-amplitude coupling and recognizing human emotions.

The inertial-based gait normalcy index of dual task cost during turning quantifies gait automaticity improvement in early-stage Parkinson's rehabilitation.

BACKGROUND: The loss of gait automaticity is a key cause of motor deficits in Parkinson's disease (PD) patients, even at the early stage of the disease. Action observation training (AOT) shows promise in enhancing gait automaticity. However, effective assessment methods are lacking. We aimed to propose a novel gait normalcy index based on dual task cost (NIDTC) and evaluate its validity and responsiveness for early-stage PD rehabilitation. METHODS: Thirty early-stage PD patients were recruited and randomly assigned to the AOT or active control (CON) group. The proposed NIDTC during straight walking and turning tasks and clinical scale scores were measured before and after 12 weeks of rehabilitation. The correlations between the NIDTCs and clinical scores were analyzed with Pearson correlation coefficient analysis to evaluate the construct validity. The rehabilitative changes were assessed using repeated-measures ANOVA, while the responsiveness of NIDTC was further compared by t tests. RESULTS: The turning-based NIDTC was significantly correlated with multiple clinical scales. Significant group-time interactions were observed for the turning-based NIDTC (F = 4.669, p = 0.042), BBS (F = 6.050, p = 0.022) and PDQ-39 (F = 7.772, p = 0.011) tests. The turning-based NIDTC reflected different rehabilitation effects between the AOT and CON groups, with the largest effect size (p = 0.020, Cohen's d = 0.933). CONCLUSION: The turning-based NIDTC exhibited the highest responsiveness for identifying gait automaticity improvement by providing a comprehensive representation of motor ability during dual tasks. It has great potential as a valid measure for early-stage PD diagnosis and rehabilitation assessment. Trial registration Chinese Clinical Trial Registry: ChiCTR2300067657.

Combining Network Pharmacology and Experimental Verification to Ascertain the Mechanism of Action of Asparagus officinalis Against the Brain Damage Caused by Fluorosis.

Asparagus officinalis (ASP) has antioxidation, anti-inflammatory, antiaging, and immune system-enhancing effects. We explored the preventive and therapeutic consequences of ASP on the brain damage elicited by fluorosis through network pharmacology and in vivo experimental validation. We ascertained the pharmaceutically active ingredients and drug targets of ASP from the Traditional Chinese Medicine Systems Pharmacology database, predicted the disease targets of fluorosis-induced brain injury using GeneCards and Online Mendelian Inheritance in Man databases, obtained target protein-protein interaction networks in the Search Tool for the Retrieval of Interacting Genes/Proteins database, used Cytoscape to obtain key targets and active ingredients, and conducted enrichment analyses of key targets in the Database for Annotation, Visualization and Integrated Discovery. Enrichment analyses showed that "mitogen-activated protein kinase" (MAPK), "phosphoinositide 3-kinase/protein kinase B" (PI3K-Akt), "nuclear factor-kappa B" (NF-κB), and the "neurotrophin signaling pathway" were the most enriched biological processes and signaling pathways. ASP could alleviate fluorosis-based injury, improve brain-tissue damage, increase urinary fluoride content, and improve oxidation levels and inflammatory-factor levels in the body. ASP could also reduce dental fluorosis, bone damage, fluoride concentrations in blood and bone, and accumulation of lipid peroxide. Upon ASP treatment, expression of silent information regulator (SIRT)1, brain-derived neurotrophic factor (BDNF), tropomyosin receptor kinase B (TrkB), MAPK, NF-κB, PI3K, Akt, and B-cell lymphoma-2 in rat brain tissue increased gradually, whereas that of Bax, caspase-3, and p53 decreased gradually. We demonstrated that ASP could regulate the brain damage caused by fluorosis through the SIRT1/BDNF/TrkB signaling pathway, and reported the possible part played by ASP in preventing and treating fluorosis.

The Estimation of Knee Medial Force with Substitution Parameters during Walking and Turning.

PURPOSE: Knee adduction, flexion moment, and adduction angle are often used as surrogate parameters of knee medial force. To verify whether these parameters are suitable as surrogates under different walking states, we investigated the correlation between knee medial loading with the surrogates during walking and turning. METHODS: Sixteen healthy subjects were recruited to complete straight walk (SW), step turn (ST), and crossover turn (CT). Knee joint moments were obtained using inverse dynamics, and knee medial force was computed using a previously validated musculoskeletal model, Freebody. Linear regression was used to predict the peak of knee medial force with the peaks of the surrogate parameters and walking speed. RESULTS: There was no significant difference in walking speed among these three tasks. The peak knee adduction moment (pKAM) was a significant predictor of the peak knee medial force (pKMF) for SW, ST, and CT (p < 0.001), while the peak knee flexion moment (pKFM) was only a significant predictor of the pKMF for SW (p = 0.034). The statistical analysis showed that the pKMF increased, while the pKFM and the peak knee adduction angle (pKAA) decreased significantly during CT compared to those of SW and ST (p < 0.001). The correlation analysis indicated that the knee parameters during SW and ST were quite similar. CONCLUSIONS: This study investigated the relationship between knee medial force and some surrogate parameters during walking and turning. KAM was still the best surrogate parameter for SW, ST, and CT. It is necessary to consider the type of movement when comparing the surrogate predictors of knee medial force, as the prediction equations differ significantly among movement types.

[Review of joint attention deficit intervention based on virtual reality for children with autism].

Joint attention deficit is one of the core disorders in children with autism, which seriously affects the development of multiple basic skills such as language and communication. Virtual reality scene intervention has great potential in improving joint attention skills in children with autism due to its good interactivity and immersion. This article reviewed the application of virtual reality based social and nonsocial scenarios in training joint attention skills for children with autism in recent years, summarized the problems and challenges of this intervention method, and proposed a new joint paradigm for social scenario assessment and nonsocial scenario training. Finally, it looked forward to the future development and application prospects of virtual reality technology in joint attention skill training for children with autism.

[Current progress on characteristics of intracranial electrophysiology related to prolonged disorders of consciousness].

Prolonged disorders of consciousness (pDOC) are pathological conditions of alterations in consciousness caused by various severe brain injuries, profoundly affecting patients' life ability and leading to a huge burden for both the family and society. Exploring the mechanisms underlying pDOC and accurately assessing the level of consciousness in the patients with pDOC provide the basis of developing therapeutic strategies. Research of non-invasive functional neuroimaging technologies, such as functional magnetic resonance (fMRI) and scalp electroencephalography (EEG), have demonstrated that the generation, maintenance and disorders of consciousness involve functions of multiple cortical and subcortical brain regions, and their networks. Invasive intracranial neuroelectrophysiological technique can directly record the electrical activity of subcortical or cortical neurons with high signal-to-noise ratio and spatial resolution, which has unique advantages and important significance for further revealing the brain function and disease mechanism of pDOC. Here we reviewed the current progress of pDOC research based on two intracranial electrophysiological signals, spikes reflecting single-unit activity and field potential reflecting multi-unit activities, and then discussed the current challenges and gave an outlook on future development, hoping to promote the study of pathophysiological mechanisms related to pDOC and provide guides for the future clinical diagnosis and therapy of pDOC.

Structural changes in Schwann cells and nerve fibres in type 1 diabetes: relationship with diabetic polyneuropathy.

AIMS/HYPOTHESIS: Our aim was to investigate structural changes of cutaneous Schwann cells (SCs), including nociceptive Schwann cells (nSCs) and axons, in individuals with diabetic polyneuropathy. We also aimed to investigate the relationship between these changes and peripheral neuropathic symptoms in type 1 diabetes. METHODS: Skin biopsies (3 mm) taken from carefully phenotyped participants with type 1 diabetes without polyneuropathy (T1D, n=25), type 1 diabetes with painless diabetic polyneuropathy (T1DPN, n=30) and type 1 diabetes with painful diabetic polyneuropathy (P-T1DPN, n=27), and from healthy control individuals (n=25) were immunostained with relevant antibodies to visualise SCs and nerve fibres. Stereological methods were used to quantify the expression of cutaneous SCs and nerve fibres. RESULTS: There was a difference in the number density of nSCs not abutting to nerve fibres between the groups (p=0.004) but not in the number density of nSCs abutting to nerve fibres, nor in solitary or total subepidermal SC soma number density. The overall dermal SC expression (measured by dermal SC area fraction and subepidermal SC process density) and peripheral nerve fibre expression (measured by intraepidermal nerve fibre density, dermal nerve fibre area fraction and subepidermal nerve fibre density) differed between the groups (all p<0.05): significant differences were seen in participants with T1DPN and P-T1DPN compared with those without diabetic polyneuropathy (healthy control and T1D groups) (all p<0.05). No difference was found between participants in the T1DPN and P-T1DPN group, nor between participants in the T1D and healthy control group (all p>0.05). Correlational analysis showed that cutaneous SC processes and nerve fibres were highly associated, and they were weakly negatively correlated with different neuropathy measures. CONCLUSIONS/INTERPRETATION: Cutaneous SC processes and nerves, but not SC soma, are degenerated and interdependent in individuals with diabetic polyneuropathy. However, an increase in structurally damaged nSCs was seen in individuals with diabetic polyneuropathy. Furthermore, dermal SC processes and nerve fibres correlate weakly with clinical measures of neuropathy and may play a partial role in the pathophysiology of diabetic polyneuropathy in type 1 diabetes.

LMDA-Net:A lightweight multi-dimensional attention network for general EEG-based brain-computer interfaces and interpretability.

Electroencephalography (EEG)-based brain-computer interfaces (BCIs) pose a challenge for decoding due to their low spatial resolution and signal-to-noise ratio. Typically, EEG-based recognition of activities and states involves the use of prior neuroscience knowledge to generate quantitative EEG features, which may limit BCI performance. Although neural network-based methods can effectively extract features, they often encounter issues such as poor generalization across datasets, high predicting volatility, and low model interpretability. To address these limitations, we propose a novel lightweight multi-dimensional attention network, called LMDA-Net. By incorporating two novel attention modules designed specifically for EEG signals, the channel attention module and the depth attention module, LMDA-Net is able to effectively integrate features from multiple dimensions, resulting in improved classification performance across various BCI tasks. LMDA-Net was evaluated on four high-impact public datasets, including motor imagery (MI) and P300-Speller, and was compared with other representative models. The experimental results demonstrate that LMDA-Net outperforms other representative methods in terms of classification accuracy and predicting volatility, achieving the highest accuracy in all datasets within 300 training epochs. Ablation experiments further confirm the effectiveness of the channel attention module and the depth attention module. To facilitate an in-depth understanding of the features extracted by LMDA-Net, we propose class-specific neural network feature interpretability algorithms that are suitable for evoked responses and endogenous activities. By mapping the output of the specific layer of LMDA-Net to the time or spatial domain through class activation maps, the resulting feature visualizations can provide interpretable analysis and establish connections with EEG time-spatial analysis in neuroscience. In summary, LMDA-Net shows great potential as a general decoding model for various EEG tasks.

Persulfate promoted carbamoylation of N-arylacrylamides and N-arylcinnamamides with 4-carbamoyl-Hantzsch esters.

Carbamoyl-Hantzsch esters were used as carbamoyl radical precursors for oxidative carbamoylation of N-arylacrylamides and N-arylcinnamamides in the presence of inexpensive persulfates. This protocol can be applied to a broad range of substrates with various functional groups, providing a variety of 3,3-disubstituted oxindoles and 3,4-disubstituted dihydroquinolin-2(1H)-ones in moderate to good yields via an intermolecular addition/cyclization process.

Decoding selective auditory attention with EEG using a transformer model.

The human auditory system extracts valid information in noisy environments while ignoring other distractions, relying primarily on auditory attention. Studies have shown that the cerebral cortex responds differently to the sound source locations and that auditory attention is time-varying. In this work, we proposed a data-driven encoder-decoder architecture model for auditory attention detection (AAD), denoted as AAD-transformer. The model contains temporal self-attention and channel attention modules and could reconstruct the speech envelope by dynamically assigning weights according to the temporal self-attention and channel attention mechanisms of electroencephalogram (EEG). In addition, the model is conducted based on data-driven without additional preprocessing steps. The proposed model was validated using a binaural listening dataset, in which the speech stimulus was Mandarin, and compared with other models. The results showed that the decoding accuracy of the AAD-transformer in the 0.15-second decoding time window was 76.35%, which was much higher than the accuracy of the linear model using temporal response function in the 3-second decoding time window (increased by 16.27%). This work provides a novel auditory attention detection method, and the data-driven characteristic makes it convenient for neural-steered hearing devices, especially those who speak tonal languages.