Neuromodulation techniques for modulating cognitive function: Enhancing stimulation precision and intervention effects

Neuromodulation techniques effectively intervene in cognitive function, holding considerable scientific and practical value in fields such as aerospace, medicine, life sciences, and brain research. These techniques utilize electrical stimulation to directly or indirectly target specific brain regions, modulating neural activity and influencing broader brain networks, thereby regulating cognitive function. Regulating cognitive function involves an understanding of aspects such as perception, learning and memory, attention, spatial cognition, and physical function. To enhance the application of cognitive regulation in the general population, this paper reviews recent publications from the Web of Science to assess the advancements and challenges of invasive and non-invasive stimulation methods in modulating cognitive functions. This review covers various neuromodulation techniques for cognitive intervention, including deep brain stimulation, vagus nerve stimulation, and invasive methods using microelectrode arrays. The non-invasive techniques discussed include transcranial magnetic stimulation, transcranial direct current stimulation, transcranial alternating current stimulation, transcutaneous electrical acupoint stimulation, and time interference stimulation for activating deep targets. Invasive stimulation methods, which are ideal for studying the pathogenesis of neurological diseases, tend to cause greater trauma and have been less researched in the context of cognitive function regulation. Non-invasive methods, particularly newer transcranial stimulation techniques, are gentler and more appropriate for regulating cognitive functions in the general population. These include transcutaneous acupoint electrical stimulation using acupoints and time interference methods for activating deep targets. This paper also discusses current technical challenges and potential future breakthroughs in neuromodulation technology. It is recommended that neuromodulation techniques be combined with neural detection methods to better assess their effects and improve the accuracy of non-invasive neuromodulation. Additionally, researching closed-loop feedback neuromodulation methods is identified as a promising direction for future development.

Neural circuit mechanisms of epilepsy: Maintenance of homeostasis at the cellular, synaptic, and neurotransmitter levels

Epilepsy, a common neurological disorder, is characterized by recurrent seizures that can lead to cognitive, psychological, and neurobiological consequences. The pathogenesis of epilepsy involves neuronal dysfunction at the molecular, cellular, and neural circuit levels. Abnormal molecular signaling pathways or dysfunction of specific cell types can lead to epilepsy by disrupting the normal functioning of neural circuits. The continuous emergence of new technologies and the rapid advancement of existing ones have facilitated the discovery and comprehensive understanding of the neural circuit mechanisms underlying epilepsy. Therefore, this review aims to investigate the current understanding of the neural circuit mechanisms in epilepsy based on various technologies, including electroencephalography, magnetic resonance imaging, optogenetics, chemogenetics, deep brain stimulation, and brain-computer interfaces. Additionally, this review discusses these mechanisms from three perspectives: structural, synaptic, and transmitter circuits. The findings reveal that the neural circuit mechanisms of epilepsy encompass information transmission among different structures, interactions within the same structure, and the maintenance of homeostasis at the cellular, synaptic, and neurotransmitter levels. These findings offer new insights for investigating the pathophysiological mechanisms of epilepsy and enhancing its clinical diagnosis and treatment.

Gut-derived lactic acid enhances tryptophan to 5-hydroxytryptamine in regulation of anxiety via Akkermansia muciniphila

The gut microbiota plays a pivotal role in anxiety regulation through pathways involving neurotransmitter production, immune signaling, and metabolic interactions. Among these, gut-derived serotonin (5-hydroxytryptamine, 5-HT), synthesized from tryptophan metabolism, has been identified as a key mediator. However, it remains unclear whether specific microbial factors regulate tryptophan metabolism to influence 5-HT production and anxiety regulation. In this study, we analyzed 110 athletes undergoing closed training and found that fecal lactate levels were significantly associated with anxiety indicators. We observed a significant negative correlation between Akkermansia abundance and anxiety levels in athletes. Co-supplementation with lactate and Akkermansia muciniphila (A. muciniphila) modulated tryptophan metabolism by increasing key enzyme TPH1 and reducing IDO1, thus shifting metabolism from kynurenine (Kyn) to 5-HT. In addition, lactate enhanced the propionate production capacity of A. muciniphila, potentially contributing to anxiety reduction in mice. Taken together, these findings suggest that enteric lactate and A. muciniphila collaboratively restore the imbalance in tryptophan metabolism, leading to increased 5-HT activity and alleviating anxiety phenotypes. This study highlights the intricate interplay between gut metabolites and anxiety regulation, offering potential avenues for microbiota-targeted therapeutic strategies for anxiety.

The influence of national origin cues in HPV vaccination advertising: An eye-tracking study of visual attention and vaccine perception using quantitative and qualitative analysis

This study is among the first to investigate how national origin cues influence visual attention and perception in HPV vaccine advertisements, using eye-tracking technology to provide objective insights into consumer responses. By integrating methods from public health, psychology, and advertising research, this study explores how visual attention is shaped by national affiliation cues. In a controlled experimental setting with a sample of 40 UK university students, we investigated visual attention and effectiveness of HPV vaccination advertisements by comparing ads disclosing the national origin of the vaccine and without any origin information. We assessed total fixation duration and time to first fixation to various elements of the ad, along with intention and attitude measures. Contrary to one of our hypotheses, we did not find significant differences in intention (p = .758) and attitude (p = .620) measures. However, there was significant difference in total fixation duration toward one of the ad images between conditions (p = .043). The qualitative analysis reveals the role of country-of-origin (COO) in HPV vaccination advertising, suggesting a shift in attention from that image to the COO cue. Furthermore, eight out of the 20 participants in the treatment condition did not fixate at the COO cue. Findings provide critical insights for public health communication strategies, suggesting that the use (or omission) of national origin cues in vaccine advertisements could influence vaccine perception and hesitancy. These results highlight the need for strategic messaging approaches to enhance HPV vaccine acceptance and improve public trust in domestic and international vaccines.

Teaching CORnet human fMRI representations for enhanced model-brain alignment

Deep convolutional neural networks (DCNNs) have demonstrated excellent performance in object recognition and have been found to share some similarities with brain visual processing. However, the substantial gap between DCNNs and human visual perception still exists. Functional magnetic resonance imaging (fMRI) as a widely used technique in cognitive neuroscience can record neural activation in the human visual cortex during the process of visual perception. Can we teach DCNNs human fMRI signals to achieve a more brain-like model? To answer this question, this study proposed ReAlnet-fMRI, a model based on the SOTA vision model CORnet but optimized using human fMRI data through a multi-layer encoding-based alignment framework. This framework has been shown to effectively enable the model to learn human brain representations. The fMRI-optimized ReAlnet-fMRI exhibited higher similarity to the human brain than both CORnet and the control model in within- and across-subject as well as within- and across-modality model-brain (fMRI and EEG) alignment evaluations. Additionally, we conducted an in-depth analysis to investigate how the internal representations of ReAlnet-fMRI differ from CORnet in encoding various object dimensions. These findings provide the possibility of enhancing the brain-likeness of visual models by integrating human neural data, helping to bridge the gap between computer vision and visual neuroscience.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-025-10252-y.

The role of beta band phase resetting in audio-visual temporal order judgment

The integration of auditory and visual stimuli is essential for effective language processing and social perception. The present study aimed to elucidate the mechanisms underlying audio-visual (A-V) integration by investigating the temporal dynamics of multisensory regions in the human brain. Specifically, we evaluated inter-trial coherence (ITC), a neural index indicative of phase resetting, through scalp electroencephalography (EEG) while participants performed a temporal-order judgment task that involved auditory (beep, A) and visual (flash, V) stimuli. The results indicated that ITC phase resetting was greater for bimodal (A + V) stimuli compared to unimodal (A or V) stimuli in the posterior temporal region, which resembled the responses of A-V multisensory neurons reported in animal studies. Furthermore, the ITC got lager as the stimulus-onset asynchrony (SOA) between beep and flash approached 0 ms. This enhancement in ITC was most clearly seen in the beta band (13-30 Hz). Overall, these findings highlight the importance of beta rhythm activity in the posterior temporal cortex for the detection of synchronous audiovisual stimuli, as assessed through temporal order judgment tasks.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-024-10183-0.

Alterations of synaptic plasticity and brain oscillation are associated with autophagy induced synaptic pruning during adolescence

Adolescent brain development is characterized by significant anatomical and physiological alterations, but little is known whether and how these alterations impact the neural network. Here we investigated the development of functional networks by measuring synaptic plasticity and neural synchrony of local filed potentials (LFPs), and further explored the underlying mechanisms. LFPs in the hippocampus were recorded in young (21 ~ 25 days), adolescent (1.5 months) and adult (3 months) rats. Long term potentiation (LTP) and neural synchrony were analyzed. The results showed that the LTP was the lowest in adolescent rats. During development, the theta coupling strength was increased progressively but there was no significant change of gamma coupling between young rats and adolescent rats. The density of dendrite spines was decreased progressively during development. The lowest levels of NR2A, NR2B and PSD95 were detected in adolescent rats. Importantly, it was found that the expression levels of autophagy markers were the highest during adolescent compared to that in other developmental stages. Moreover, there were more co-localization of autophagosome and PSD95 in adolescent rats. It suggests that autophagy is possibly involved in synaptic elimination during adolescence, and further impacts synaptic plasticity and neural synchrony.

Bidirectional causality of physical exercise in retinal neuroprotection

Physical exercise is recognized as an effective intervention to improve mood, physical performance, and general well-being. It achieves these benefits through cellular and molecular mechanisms that promote the release of neuroprotective factors. Interestingly, reduced levels of physical exercise have been implicated in several central nervous system diseases, including ocular disorders. Emerging evidence has suggested that physical exercise levels are significantly lower in individuals with ocular diseases such as glaucoma, age-related macular degeneration, retinitis pigmentosa, and diabetic retinopathy. Physical exercise may have a neuroprotective effect on the retina. Therefore, the association between reduced physical exercise and ocular diseases may involve a bidirectional causal relationship whereby visual impairment leads to reduced physical exercise and decreased exercise exacerbates the development of ocular disease. In this review, we summarize the evidence linking physical exercise to eye disease and identify potential mediators of physical exercise-induced retinal neuroprotection. Finally, we discuss future directions for preclinical and clinical research in exercise and eye health.

Hippocampal connectivity changes after traumatic memory reactivation with propranolol for posttraumatic stress disorder: a randomized fMRI study

Background: Reactivation of traumatic memory under the influence of propranolol has shown encouraging clinical results in the treatment of posttraumatic stress disorder (PTSD), but the neural correlates remain unknown. To identify these correlates, we examined the changes in brain functional connectivity specifically associated with the influence of propranolol and their correlation with improvement in PTSD symptoms.Objectives: To identify resting-state functional connectivity (rs-FC) changes specifically associated with propranolol after a traumatic memory reactivation procedure (TMRP) in PTSD patients.Method: Thirty patients (50% of women) with PTSD were enrolled in a randomized controlled study comprised of six sessions of a traumatic memory reactivation procedure (TMRP) under the influence of propranolol (n = 16), compared to the same reactivation under a placebo (n = 14). Patients were scanned twice by functional magnetic resonance before and after treatment. Resting state functional connectivity (rs-FC) was compared across groups and over time.Results: Post versus pretreatment comparisons found an increase in rs-FC between the right hippocampus and the left parahippocampal gyrus in the propranolol group, but not in the placebo group. Symptom improvement in both groups were associated with an increase in rs-FC between the parahippocampal gyrus and both the supramarginal gyrus and the amygdala.Conclusions: During TMRP treatment, propranolol appears to constrain functional connectivity changes in the explicit memory brain system. These findings require further replication and exploration but could distinguish the effect of TMRP on the brain from other forms of PTSD psychotherapy.

Gut dysbiosis was inevitable, but tolerance was not: temporal responses of the murine microbiota that maintain its capacity for butyrate production correlate with sustained antinociception to chronic morphine

The therapeutic benefits of opioids are compromised by the development of analgesic tolerance, which necessitates higher dosing for pain management thereby increasing the liability for drug dependence and addiction. Rodent models indicate opposing roles of the gut microbiota in tolerance: morphine-induced gut dysbiosis exacerbates tolerance, whereas probiotics ameliorate tolerance. Not all individuals develop tolerance, which could be influenced by differences in microbiota, and yet no study design has capitalized upon this natural variation. We leveraged natural behavioral variation in a murine model of voluntary oral morphine self-administration to elucidate the mechanisms by which microbiota influences tolerance. Although all mice shared similar morphine-driven microbiota changes that largely masked informative associations with variability in tolerance, our high-resolution temporal analyses revealed a divergence in the progression of dysbiosis that best explained sustained antinociception. Mice that did not develop tolerance maintained a higher capacity for production of the short-chain fatty acid (SCFA) butyrate known to bolster intestinal barriers and promote neuronal homeostasis. Both fecal microbial transplantation (FMT) from donor mice that did not develop tolerance and dietary butyrate supplementation significantly reduced the development of tolerance independently of suppression of systemic inflammation. These findings could inform immediate therapies to extend the analgesic efficacy of opioids.

Reducing calibration efforts of SSVEP-BCIs by shallow fine-tuning-based transfer learning

The utilization of transfer learning (TL), particularly through pre-training and fine-tuning, in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has substantially reduced the calibration efforts. However, commonly employed fine-tuning approaches, including end-to-end fine-tuning and last-layer fine-tuning, require data from target subjects that encompass all categories (stimuli), resulting in a time-consuming data collection process, especially in systems with numerous categories. To address this challenge, this study introduces a straightforward yet effective ShallOw Fine-Tuning (SOFT) method to substantially reduce the number of calibration categories needed for model fine-tuning, thereby further mitigating the calibration efforts for target subjects. Specifically, SOFT involves freezing the parameters of the deeper layers while updating those of the shallow layers during fine-tuning. Freezing the parameters of deeper layers preserves the model's ability to recognize semantic and high-level features across all categories, as established during pre-training. Moreover, data from different categories exhibit similar individual-specific low-level features in SSVEP-BCIs. Consequently, updating the parameters of shallow layers-responsible for processing low-level features-with data solely from partial categories enables the fine-tuned model to efficiently capture the individual-related features shared by all categories. The effectiveness of SOFT is validated using two public datasets. Comparative analysis with commonly used end-to-end and last-layer fine-tuning methods reveals that SOFT achieves higher classification accuracy while requiring fewer calibration categories. The proposed SOFT method further decreases the calibration efforts for target subjects by reducing the calibration category requirements, thereby improving the feasibility of SSVEP-BCIs for real-world applications.

Brain analysis to approach human muscles synergy using deep learning

Brain signals and muscle movements have been analyzed using electroencephalogram (EEG) data in several studies. EEG signals contain a lot of noise, such as electromyographic (EMG) waves. Further studies have been done to improve the quality of the results, though it is thought that the combination of these two signals can lead to a significant improvement in the synergistic analysis of muscle movements and muscle connections. Using graph theory, this study examined the interaction of EMG and EEG signals during hand movement and estimated the synergy between muscle and brain signals. Mapping of the brain diagram was also developed to reconstruct the muscle signals from the muscle connections in the brain diagram. The proposed method included noise removal from EEG and EMG signals, graph feature analysis from EEG, and synergy calculation from EMG. Two methods were used to estimate synergy. In the first method, after calculating the brain connections, the features of the communication graph were extracted and then synergy estimating was made with neural networks. In the second method, a convolutional network created a transition from the matrix of brain connections to the synergistic EMG signal. This study reached the high correlation values of 99.8% and maximum MSE error of 0.0084. Compared to other graph-based methods, this method based on regression analysis had a very significant performance. This research can lead to the improvement of rehabilitation methods and brain-computer interfaces.

Neural dynamics of deception: insights from fMRI studies of brain states

Deception is a complex behavior that requires greater cognitive effort than truth-telling, with brain states dynamically adapting to external stimuli and cognitive demands. Investigating these brain states provides valuable insights into the brain's temporal and spatial dynamics. In this study, we designed an experiment paradigm to efficiently simulate lying and constructed a temporal network of brain states. We applied the Louvain community clustering algorithm to identify characteristic brain states associated with lie-telling, inverse-telling, and truth-telling. Our analysis revealed six representative brain states with unique spatial characteristics. Notably, two distinct states-termed truth-preferred and lie-preferred-exhibited significant differences in fractional occupancy and average dwelling time. The truth-preferred state showed higher occupancy and dwelling time during truth-telling, while the lie-preferred state demonstrated these characteristics during lie-telling. Using the average z-score BOLD signals of these two states, we applied generalized linear models with elastic net regularization, achieving a classification accuracy of 88.46%, with a sensitivity of 92.31% and a specificity of 84.62% in distinguishing deception from truth-telling. These findings revealed representative brain states for lie-telling, inverse-telling, and truth-telling, highlighting two states specifically associated with truthful and deceptive behaviors. The spatial characteristics and dynamic attributes of these brain states indicate their potential as biomarkers of cognitive engagement in deception.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-025-10222-4.

Joint disentangled representation and domain adversarial training for EEG-based cross-session biometric recognition in single-task protocols

The increasing adoption of wearable technologies highlights the potential of electroencephalogram (EEG) signals for biometric recognition. However, the intrinsic variability in cross-session EEG data presents substantial challenges in maintaining model stability and reliability. Moreover, the diversity within single-task protocols complicates achieving consistent and generalized model performance. To address these issues, we propose the Joint Disentangled Representation with Domain Adversarial Training (JDR-DAT) framework for EEG-based cross-session biometric recognition within single-task protocols. The JDR-DAT framework disentangles identity-specific features through mutual information estimation and incorporates domain adversarial training to enhance longitudinal robustness. Extensive experiments on longitudinal EEG data from two publicly available single-task protocol datasets-RSVP-based (Rapid Serial Visual Presentation) and MI-based (Motor Imagery)-demonstrate the efficacy of the JDR-DAT framework, with the proposed method achieving average accuracies of 85.83% and 96.72%, respectively.

Plants have two minds as we do

This discussion paper carefully analyzes the cognition-related theories proposed for behavioral economics, to expand the concepts from human behaviors to those of plants. Behavioral economists analyze the roles of the intuitive sense and the rational thoughts affecting the human behavior, by employing the psychology-based models such as Two Minds theory (TMT) highlighting intuitive rapid thoughts (System 1) and rational slower thoughts (System 2) and Prospect theory (PT) with probability (p)-weighting functions explaining the human tendencies to overrate the low p events and to underrate the high p events. There are similarities between non-consciously processed System 1 (of TMT) and overweighing of low-p events (as in PT) and also, between the consciously processed System 2 (of TMT) and underrating of high-p events (as in PT). While most known p-weighting mathematical models employed single functions, we propose a pair of Hill-type functions reflecting the collective behaviors of two types of automata corresponding to intuition (System 1) and rationality (System 2), as a metaphor to the natural light processing in layered plant leaves. Then, the model was applied to two different TMT/PT-related behaviors, namely, preference reversal and habituation. Furthermore, we highlight the behaviors of plants through the above conceptual frameworks implying that plants behave as if they have Two Minds. Lastly, the possible evolutionary origins of the nature of Two Minds are discussed.

Visual image reconstructed without semantics from human brain activity using linear image decoders and nonlinear noise suppression

In recent years, substantial strides have been made in the field of visual image reconstruction, particularly in its capacity to generate high-quality visual representations from human brain activity while considering semantic information. This advancement not only enables the recreation of visual content but also provides valuable insights into the intricate processes occurring within high-order functional brain regions, contributing to a deeper understanding of brain function. However, considering fusion semantics in reconstructing visual images from brain activity involves semantic-to-image guide reconstruction and may ignore underlying neural computational mechanisms, which does not represent true reconstruction from brain activity. In response to this limitation, our study introduces a novel approach that combines linear mapping with nonlinear noise suppression to reconstruct visual images perceived by subjects based on their brain activity patterns. The primary challenge associated with linear mapping lies in its susceptibility to noise interference. To address this issue, we leverage a flexible denoised deep convolutional neural network, which can suppress noise from linear mapping. Our investigation encompasses linear mapping as well as the training of shallow and deep autoencoder denoised neural networks, including a pre-trained, state-of-the-art denoised neural network. The outcome of our study reveals that combining linear image decoding with nonlinear noise reduction significantly enhances the quality of reconstructed images from human brain activity. This suggests that our methodology holds promise for decoding intricate perceptual experiences directly from brain activity patterns without semantic information. Moreover, the model has strong neural explanatory power because it shares structural and functional similarities with the visual brain.

Neural oscillations predict flow experience

Flow experience, characterized by immersion in the activity at hand, provides a motivational boost and promotes positive behaviors. However, the oscillatory representations of flow experience are still poorly understood. In this study, the difficulty of the video game was adjusted to manipulate the individual's personalized flow or non-flow state, and EEG data was recorded throughout. Our results show that, compared to non-flow tasks, flow tasks exhibit higher theta power, moderate alpha power, and lower beta power, providing evidence for a focused yet effortless brain pattern during flow. Additionally, we employed Lasso regression to predict individual subjective flow scores based on neural data, achieving a correlation coefficient of 0.571 (p < 0.01) between the EEG-predicted scores and the actual self-reported scores. Our findings offer new insights into the oscillatory representation of flow and emphasize that flow, as a measure of individual experience quality, can be objectively and quantitatively predicted through neural oscillations.

Community engagement to support public health: mixed-method evaluation evidence on COVID-19 attitudes and practices in Lao PDR

BACKGROUND

Community engagement has been recognized as a key tool for supporting national health agendas, and experiences from the COVID-19 pandemic can offer important lessons for tackling future global health challenges such as antimicrobial resistance. This paper provides much-needed evaluation knowledge on relational community engagement initiatives and their impact on COVID-19-related attitudes and practices.

METHODS

A two-round mixed-method evaluative study to examine outcome indicators related to COVID-19-prevention and health-seeking behavior was implemented from October 2022 to December 2023 among 14 diverse case study communities in four Lao provinces. Data involved 50 semi-structured interviews with villagers, 50 key informant interviews, and two rounds of complete census surveys (3,161 survey observations incl. matched panel data from 618 individuals) to discern outcomes among villagers with different levels of activity participation in a difference-in-difference analysis.

RESULTS

Relative to non-participating villagers, villagers participating in the activities had higher COVID-19 vaccine uptake (+0.13 doses), higher public healthcare utilization for presentations consistent with COVID-19 (e.g. fever and neurological and/or respiratory symptoms; +69.4% points), and less antibiotic use per illness episode (-0.2 antibiotic use episodes). However, the activity raised worries to disclose a COVID-19-positive status and was often interpreted as a health education campaign.

CONCLUSIONS

Relational community engagement offers a respectful way of addressing persistent healthcare challenges and supporting vulnerable populations - and thus holds key for ongoing global health priorities such as emerging infectious disease responses and antimicrobial resistance. We recommend that community engagement initiatives become a standard component of national health policy portfolios beyond the scope of COVID-19.

Event-related potentials associated with cognitive control in adolescents exposed to complex childhood trauma

ABSTRACTBackground: Complex childhood trauma (CCT), characterized by repeated and prolonged exposure to adverse experiences, disrupts cognitive, emotional, and neural development. Adolescence, a critical developmental period, is particularly vulnerable to these effects, with CCT increasing the risk of psychopathologies, including post-traumatic stress disorder (PTSD). Despite this, the neurophysiological underpinnings of trauma-related deficits in cognitive control remain insufficiently explored, particularly in the developing brains of children and adolescents. This study aimed to investigate the neurophysiological markers of cognitive control in adolescents with CCT using event-related potential (ERP) components to propose an electrophysiological phenotype associated with CCT, as a vulnerability for PTSD.Methods: Twenty adolescents with CCT and 40 age- and gender-matched healthy controls performed a cued GO/NOGO task. ERP components - contingent negative variation (CNV), NoGo-N2, and NoGo-P3 - were analysed alongside behavioural measures such as omission and commission errors and reaction time, using a preregistered protocol. Statistical analysis included Mann-Whitney tests and cluster-based permutation tests for ERP comparisons.Results: Adolescents with CCT showed significant impairments in both proactive (reduced CNV amplitudes) and reactive (diminished NoGo-N2 and NoGo-P3 amplitudes) control mechanisms. Behaviourally, the CCT group exhibited higher omission errors and shorter reaction times than controls. Exploratory analysis revealed reduced amplitudes in the visual negativity (VN) component, suggesting disruptions in predictive processing. Latent component analysis identified ERP markers with potential diagnostic utility, linking deficits to key neural circuits associated with cognitive control and predictive processing.Conclusion: Study findings highlight significant impairments in cognitive control mechanisms and disrupted predictive processing in adolescents with CCT, emphasizing the importance of addressing trauma-related neural deficits during adolescence. Given that CCT is a significant risk factor for PTSD, the study provides insights into shared neurobiological pathways, supporting the development of targeted interventions. ERP markers like CNV, NoGo-N2, NoGo-P3, and VN show promise for improving diagnostic precision and monitoring therapeutic outcomes in trauma-exposed youth.

Interventions targeting the gut microbiota and their possible effect on gastrointestinal and neurobehavioral symptoms in autism spectrum disorder

Autism spectrum disorder (ASD) is a developmental disorder that is characterized by deficits in social communication and restricted, repetitive, and stereotyped behaviors. In addition to neurobehavioral symptoms, children with ASD often have gastrointestinal symptoms (e.g. constipation, diarrhea, gas, abdominal pain, reflux). Several studies have proposed the role of gut microbiota and metabolic disorders in gastrointestinal symptoms and neurodevelopmental dysfunction in ASD patients; these results offer promising avenues for novel treatments of this disorder. Interventions targeting the gut microbiota - such as fecal microbiota transplant (FMT), microbiota transplant therapy (MTT), probiotics, prebiotics, synbiotics, antibiotics, antifungals, and diet - promise to improve gut health and can potentially improve neurological symptoms. The modulation of the gut microbiota using MTT in ASD has shown beneficial and long-term effects on GI symptoms and core symptoms of autism. Also, the modulation of the gut microbiota to resemble that of typically developing individuals seems to be the most promising intervention. As most of the studies carried out with MTT are open-label studies, more extensive double-blinded randomized control trials are needed to confirm the efficacy of MTT as a therapeutic option for ASD. This review examines the current clinical research evidence for the use of interventions that target the microbiome - such as antibiotics, antifungals, probiotics/prebiotics, synbiotics, and MTT - and their effectiveness in changing the gut microbiota and improving gastrointestinal and neurobehavioral symptoms in ASD.

Implementation of memristive emotion associative learning circuit

Psychological studies have demonstrated that the music can affect memory by triggering different emotions. Building on the relationships among music, emotion, and memory, a memristor-based emotion associative learning circuit is designed by utilizing the nonlinear and non-volatile characteristics of memristors, which includes a music judgment module, three emotion generation modules, three emotional homeostasis modules, and a memory module to implement functions such as learning, second learning, forgetting, emotion generation, and emotional homeostasis. The experimental results indicate that the proposed circuit can simulate the learning and forgetting processes of human under different music circumstances, demonstrate the feasibility of memristors in biomimetic circuits, verify the impact of music on memory, and provide a foundation for in-depth research and application development of the interaction mechanism between emotion and memory.

Pragmatic information of aesthetic appraisal

A phenomenological model for aesthetic appraisal is proposed in terms of pragmatic information for a dynamic update semantics over belief states of an aesthetic appreciator. The model qualitatively correlates with aesthetic pleasure ratings in an experimental study on cadential effects in Western tonal music, conducted by Cheung et al. (Curr Biol 29(23):4084-4092.e4, 2019). Finally, related computational and neurodynamical accounts are discussed.

EEG microstate biomarkers for schizophrenia: a novel approach using deep neural networks

Schizophrenia remains a challenging neuropsychiatric disorder with complex diagnostic processes. Current clinical approaches often rely on subjective assessments, highlighting the critical need for objective, quantitative diagnostic methods. This study aimed to develop a robust classification approach for schizophrenia using EEG microstate analysis and advanced machine learning techniques. We analyzed EEG signals from 14 healthy individuals and 14 patients with schizophrenia during a 15-min resting-state session across 19 EEG channels. A data augmentation strategy expanded the dataset to 56 subjects in each group. The signals were preprocessed and segmented into five frequency bands (delta, theta, alpha, beta, gamma) and five microstates (A, B, C, D, E) using k-means clustering. Five key features were extracted from each microstate: duration, occurrence, standard deviation, coverage, and frequency. A Deep Neural Network (DNN) model, along with other machine learning classifiers, was developed to classify the data. A comprehensive fivefold cross-validation approach evaluated model performance across various EEG channels, frequency bands, and feature combinations. Significant alterations in microstate transition probabilities were observed, particularly in higher frequency bands. The gamma band showed the most pronounced differences, with a notable disruption in D → A transitions (absolute difference = 0.100). The Random Forest classifier achieved the highest accuracy of 99.94% ± 0.12%, utilizing theta band features from the F8 frontal channel. The deep neural network model demonstrated robust performance with 98.31% ± 0.68% accuracy, primarily in the occipital region. Feature size 2 consistently provided optimal classification across most models. Our study introduces a novel, high-precision EEG microstate analysis approach for schizophrenia diagnosis, offering an objective diagnostic tool with potential applications in neuropsychiatric disorders. The findings reveal critical insights into neural dynamics associated with schizophrenia, demonstrating the potential for transforming clinical diagnostic practices through advanced machine learning and neurophysiological feature extraction.

Formation of cognitive maps in large-scale environments by sensorimotor integration

Hippocampus in the mammalian brain supports navigation by building a cognitive map of the environment. However, only a few studies have investigated cognitive maps in large-scale arenas. To reveal the computational mechanisms underlying the formation of cognitive maps in large-scale environments, we propose a neural network model of the entorhinal-hippocampal neural circuit that integrates both spatial and non-spatial information. Spatial information is relayed from the grid units in medial entorhinal cortex (MEC) by integrating multimodal sensory-motor signals. Non-spatial, such as object, information is imparted from the visual units in lateral entorhinal cortex (LEC) by encoding visual scenes through a deep neural network. The synaptic weights from the grid units and the visual units to the place units in the hippocampus are learned by a competitive learning rule. We simulated the model in a large box maze. The place units in the model form irregularly-spaced multiple fields across the environment. When the strength of visual inputs is dominant, the responses of place units become conjunctive and egocentric. These results point to the key role of the hippocampus in balancing spatial and non-spatial information relayed via LEC and MEC.

PTSD symptoms moderate predictors of psychophysiological arousal during fear inhibition: Evidence from a fear, reward, and neutral discrimination task

The ability to distinguish between threatening, rewarding, and neutral cues is adaptative and crucial for survival. However, individuals with posttraumatic stress disorder (PTSD) often show poor knowledge of cue contingencies and heightened fear responses even in the presence of cues that signify safety, potentially due to atypical perceptions of neutral cues. We investigated whether perceiving neutral cues as more rewarding or threatening influences conditioned inhibition of fear and whether PTSD symptoms moderate this relationship. Trauma-exposed adults (N = 84; 64 % female; 76 % non-Hispanic white) completed a Fear, Reward, and Neutral Discrimination (FRND) Task involving geometric shapes paired with outcomes (Fear: white noise; Reward: monetary gain; Neutral: no outcome) and conditioned inhibition trials (Fear+Neutral and Reward+Neutral: no outcome). Skin conductance responses (SCR) quantified psychophysiological arousal, and participants rated the valence of each cue. PTSD symptoms were evaluated with the PTSD Checklist for DSM-5. Linear regressions examined PTSD severity as a moderator of the relationship between Reward vs. Neutral or Fear vs. Neutral valence difference and SCR during inhibition. Among individuals with less severe PTSD symptoms, stronger fear inhibition effects were observed when neutral cues were rated more similarly to reward cues (β = 0.12, p = .022); however, this relationship was not significant at average or higher PTSD severity. Our results emphasize that perceptions of neutral cues contribute to fear inhibition and may underlie PTSD-related deficits in safety learning. Future investigations on PTSD and fear inhibition should consider incorporating measures of reward-related processing to examine the overlap between rewarding and inhibitory qualities of safety signals.

Mental imagery abilities in different modalities moderate the efficacy of cognitive bias modification for interpretation bias in social anxiety

OBJECTIVES

Cognitive bias modification for interpretation bias (CBM-I) is an effective low-intensity intervention that targets interpretation biases associated with the development and maintenance of social anxiety. Few studies to-date have examined the extent to which individual mental imagery ability affects the efficacy of CBM-I.

METHODS

A total of 666 individuals were screened. Seventy-two participants with high levels of social anxiety and elevated baseline interpretation bias were randomly assigned to either CBM-I (n = 36) or control groups (n = 36). They completed 5-day internet-delivered training in modifying their interpretation bias associated with ambiguous social scenarios (CBM-I) or reading neutral text passages (control).

RESULTS

Intent-to-treat analyses revealed that participants in the CBM-I group had a significant reduction in their interpretation bias compared to the controls. They had a reduction of 11 %-18 % on the social anxiety measures. Participants' mental imagery ability was significantly associated with the reduction of interpretation bias and social anxiety symptoms in the CBM-I group. Specifically, participants with higher mental imagery ability in emotional feelings benefited the most from the intervention.

CONCLUSIONS

CBM-I is an efficacious intervention for modulating social anxiety-related biases and symptoms. Mental imagery ability facilitated the efficacy of CBM-I.

Investigating the role of top-down regulation and bottom-up cues in eating styles transitions: a one-year cohort study with young adults

Young adults often experience deterioration in eating habits during transition periods. However, longitudinal evidence on the changes of eating styles and associated determinants remains limited. This study aimed to explore the eating style transitions among young adults in their graduate transitions and investigate the influences of top-down regulatory factors and bottom-up environmental cues on their eating style transitions. This is a two-wave cohort study involving 594 Hong Kong young adults completing the baseline assessment during their post-secondary graduation year, of whom, 424 completed the one-year follow-up survey. Eating behaviours were measured at both points. Executive function (EF), coping style, exposure to digital food environments, responsiveness to food cues, perceived stress and demographics were also measured. Latent profile analysis was used to explore main eating styles among participants while multinomial logistic regression models were used to assess determinants of eating style transitions. In follow-up assessment, 5.9 % of participants were consistently approaching eaters (APE) across two time points, while 28.8 % have transitioned from moderate eaters (MOE) or APE to mixed eaters (MIE). The multinominal logistic regression model revealed that although EF and coping style were no longer significantly associated with participants' eating style transitions outcomes, greater exposure to digital food environments (OR = 2.60, p = 0.028) and higher responsiveness to food cues (OR = 5.86, p = 0.005) were associated with Persistent APE, while higher responsiveness to food cues (OR = 2.36, p = 0.009) and higher perceived stress (OR = 1.05, p = 0.046) were associated with Converted MIE. Bottom-up cues may dominate eating style transitions compared to top-down regulations. Future interventions should leverage environmental cues, thereby targeting the automatic decision-making process and supporting healthy eating habits during stressful life stages.

A robot's efficient demonstration cannot reduce 5- to 6-year-old children's over-imitation

Children tend to imitate inefficient behaviors containing causally irrelevant actions-they over-imitate. Out-group members' efficient demonstration cannot reduce children's over-imitation of in-group members, due to their interpretation of irrelevant actions as norms which in-group members should follow. Children may perceive robots as culture-specific behavior transmitters since they also over-imitate robots. This study explores whether a robot's efficient demonstration can reduce 5- to 6-year-old children's over-imitation. In Experiment 1, most of 64 children imitated a human's irrelevant actions in Phase 1, then reduced over-imitation after watching an efficient demonstration modeled by a robot or a human in Phase 2, but the rate of over-imitation decreased more when the model was a human. In Experiment 2, 64 children only had one chance to imitate after watching two demonstrations (an efficient one demonstrated by a human and an inefficient one demonstrated by a robot or another human), the over-imitation occurred more when the efficient model was a robot than a human. Compared with over-imitation rate of Phase 1 in Experiment 1, that was significantly decreased only when the efficient model was a human. The results indicate that children don't perceive robots as social learning models, at least in the presence of alternative human models.

Implementing feature binding through dendritic networks of a single neuron

A single neuron receives an extensive array of synaptic inputs through its dendrites, raising the fundamental question of how these inputs undergo integration and summation, culminating in the initiation of spikes in the soma. Experimental and computational investigations have revealed various modes of integration operations that include linear, superlinear, and sublinear summation. Interestingly, different types of neurons exhibit diverse patterns of dendritic integration depending on the spatial distribution of dendrites. The functional implications of these specific integration modalities remain largely unexplored. In this study, we employ the Purkinje cell (PC) as a model system to investigate these complex questions. Our findings reveal that PCs generally exhibit sublinear summation across their expansive dendrites. Both spatial and temporal input dynamically modulates the degree of sublinearity. Strong sublinearity necessitates the synaptic distribution in PCs to be globally scattered sensitive, whereas weak sublinearity facilitates the generation of complex firing patterns in PCs. Using dendritic branches characterized by strong sublinearity as computational units, we demonstrate that a neuron can successfully address the feature binding problem. Taken together, these results offer a systematic perspective on the functional role of dendritic sublinearity, inspiring a broader understanding of dendritic integration in various neuronal types.

Internal sensory models allow for balance control using muscle spindle acceleration feedback

Motor control requires sensory feedback, and the nature of this feedback has implications for the tasks of the central nervous system (CNS): for an approximately linear mechanical system (e.g., a freely standing person, a rider on a bicycle), if the sensory feedback does not contain the state variables (i.e., joint position and velocity), then optimal control actions are based on an internal dynamical system that estimates these states from the available incomplete sensory feedback. Such a computational system can be implemented as a recurrent neural network (RNN), and it uses a sensory model to update the state estimates. This is highly relevant for muscle spindle primary afferents whose firing rates scale with acceleration: if fusimotor and skeletomotor control are perfectly coordinated, these firing rates scale with the exafferent joint acceleration component, and in the absence of fusimotor control, they scale with the total joint acceleration (exafferent plus reafferent). For both scenarios, a sensory model exists that expresses the exafferent joint acceleration as a function of the state variables, and for the second scenario, a sensory model exists that corrects for the reafferent joint acceleration. Simulations of standing and bicycle balance control under realistic conditions show that joint acceleration feedback is sufficient for balance control, but only if the reafferent acceleration component is either absent from the feedback or is corrected for in the computational system.

The development of letter representations in preschool children is affected by visuomotor integration skills and visual field asymmetries

One essential skill believed to consolidate during the preschool years is children's ability to recognize the different letters of the alphabet. The aim of the present study was to track how visual representations of letters change and are consolidated with exposure to print and the graphomotor experience a child has. A secondary goal of this study was to investigate the emergence of the right visual field advantage for letter identification, reflecting children's sensitivity to the directionality of print. Eighty-one preschool children (aged 4 to 5 years) participated in a longitudinal study where they were shown isolated uppercase letters in both normal upright format and rotated 180°. The letter stimuli were mixed randomly with symbol stimuli in a letter/non-letter lateralized classification task. The results indicated that accuracy in classifying rotated letters as letters-rather than symbols-significantly improved among 4-year-old preschoolers between testing in December (mid-year) and in June (end of the school year). In contrast, little further development was observed in 5-year-old preschoolers, although they still exhibited a slight disadvantage in accuracy when classifying rotated letters. Additionally, behavioral and eye-movement data highlighted a left-to-right deployment of attention by the end of the second year of formal preschool education, evidenced by the emergence of a right visual field advantage. Our results suggest that letter representations undergo significant consolidation during the second year of formal preschool education, which typically corresponds to 4-year-old children in France, with a close relationship between letter identification skills, sensitivity to the directionality of print, and visuo-motor integration skills.

Age and body mass index are associated with dorsolateral prefrontal cortex activation in response to unhealthy food cues

Unhealthy food cues are omnipresent and promote overconsumption. Although childhood obesity rates are increasing, there is no strict regulation of the marketing of unhealthy foods towards children. This is problematic since the human brain, especially areas important for cognitive control, continues to develop into the 30s. It is not known in how far the brain response to unhealthy food cues varies with body mass index (BMI) and age. To investigate this, 168 children (10-17 y) and 182 adults (30-67 y) from the European IDEFICS cohort were scanned with the use of fMRI while viewing pictures of healthy and unhealthy foods. Children exhibited lower activation in the right dorsolateral prefrontal cortex (dlPFC) compared to adults when exposed to unhealthy food cues. Across all age groups, individuals with higher BMI demonstrated reduced activation in the middle cingulum in response to unhealthy food stimuli. Lastly, the relation between BMI and brain activation in response to unhealthy compared with healthy food stimuli varied with development: in children, higher BMI was correlated with decreased activation in right anterior insula and right dlPFC, whereas no such relationship was observed in adults. These findings suggest that children with higher BMI may be particularly vulnerable to unhealthy food cues. In this light, the lack of regulation regarding unhealthy food marketing targeted at children is concerning, especially considering the global increase in obesity rates.

Attend to the positive while feeling anxious: The effect of state anxiety on the effectiveness of Attentional Bias Modification

BACKGROUND AND OBJECTIVES

Elevating state anxiety during Attentional Bias Modification (ABM) may improve its effectiveness by matching the emotional state experienced during the training with the emotional state under which it is intended that the learned pattern of attentional bias will subsequently operate. This study examined whether inducing elevated levels of state anxiety during ABM enhanced the effectiveness in modifying an attentional bias to socially threatening information.

METHODS

Participants (n = 160) were randomized to a single session of attend-negative or attend-positive dot-probe training which was interspersed with either a state anxiety induction or control condition. Attentional bias was assessed post-training by means of a dot-probe task and a visual search task.

RESULTS

ABM was effective in modifying attentional bias in the direction of the allocated training condition as assessed with a dot-probe task, but did not generalize to a visual search task. Importantly, state anxiety did not moderate ABM's training effects.

LIMITATIONS

Although the state anxiety manipulation successfully induced state anxiety, state anxiety levels were modest which potentially limited the chance to detect a moderating effect of state anxiety.

CONCLUSIONS

Although these findings suggest that inducing state anxiety during ABM does not improve its effectiveness, more studies are needed to confirm this preliminary conclusion. Future studies should examine whether larger state anxiety elevations and state anxiety manipulations that are more integrated into the ABM procedure do enhance training effects.

Learning performance of cerebellar circuit depends on diversity and chaoticity of spiking patterns in granule cells: A simulation study

The cerebellum, composed of numerous neurons, plays various roles in motor control. Although it is functionally subdivided, the cerebellar cortex has a canonical structural pattern in neuronal circuits including a recurrent circuit pattern formed by granule cells (GrCs) and Golgi cells (GoCs). The canonical circuital pattern suggests the existence of a fundamental computational algorithm, although it remains unclear. Modeling and simulation studies are useful for verifying hypotheses about complex systems. Previous models have shown that they could reproduced the neurophysiological data of the cerebellum; however, the dynamic characteristics of the system have not been fully elucidated. Understanding the dynamic characteristics of the circuital pattern is necessary to reveal the computational algorithm embedded in the circuit. This study conducted numerical simulations using the cerebellar circuit model to investigate dynamic characteristics in a simplified model of cerebellar microcircuits. First, the diversity and chaoticity of the patterns of spike trains generated from GrCs depending on the synaptic strength between the GrCs and GoCs were investigated based on cluster analysis and the Lyapunov exponent, respectively. Then the effect of synaptic strength on learning tasks was investigated based on the convergence properties of the output signals from Purkinje cells. The synaptic strength for high learning performance was almost consistent with that for the high diversity of the generated patterns and the edge of chaos. These results suggest that the learning performance of the cerebellar circuit depends on the diversity and the chaoticity of the spiking patterns from the GrC-GoC recurrent circuit.

Does the lateral hypothalamus govern the transition between appetitive and consummatory feeding?

Feeding is a cyclic behaviour that includes appetitive, consummatory and termination phases. Identifying the neural circuits controlling these phases and triggering specific transitions between phases would be a key advance in understanding feeding behaviour. The lateral hypothalamus (LH) has long been recognized for its central role in feeding. We review evidence suggesting that the LH acts as a regulator of the appetitive-consummatory transition using a switchboard-like circuit architecture. Within the LH, several neuronal subpopulations can be defined based on molecular markers, and - although these subpopulations are functionally diverse - they contribute to appetitive and consummatory behaviours to varying extents. We summarise the current evidence on whether these subpopulations have functional identities and speculate on the role of the LH as a controller of behavioural transitions.

Leveraging social media and large language models for scalable alcohol risk assessment: Examining validity with AUDIT-C and post recency effects

Risky alcohol consumption is a major public health concern, yet significant barriers exist to effective screening. The present study examines the potential of Large Language Models (LLMs) to infer risky alcohol use from social media text. The unobtrusive nature of this approach could provide a more scalable way to assess alcohol risk in large populations. To this aim, we analyzed Facebook status updates from 208 adults from Italy (mean age = 26.8, 70.7 % female) who also completed the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C), a brief validated self-report measure of risky drinking. Two state-of-the-art LLMs, Gemini 1.5 Pro and GPT-4o, were used to assess alcohol risk and to quantify alcohol references. Results demonstrated strong inter-model agreement between risk inferences (ρ = 0.572, p < 0.001). LLM-inferred risk scores showed moderate correlations with AUDIT-C scores (Gemini 1.5 Pro: ρ = 0.344, p < 0.001; GPT-4o: ρ = 0.375, p < 0.001; Average: ρ = 0.405, p < 0.001). These correlations were significantly stronger among participants with recent posts (Average risk score: ρ = 0.500, p < 0.001) than among those without (ρ = 0.294, p = 0.008). The strongest correlation was observed between average LLM-inferred risk scores and AUDIT-C in the recent posts group (disattenuated ρ = 0.606). These findings suggest that LLMs offer a promising tool for identifying risky alcohol use when analyzing recent social media activity. Their accuracy is comparable to some traditional alcohol assessment methods, highlighting their potential to enhance early detection efforts. Limitations and future research directions are discussed.

Obesity is associated with reduced sensitivity to stimulus rewarding value, but unaltered effects of fasting and contextual modulation during action prediction

Previous studies have shown that our perception of others' actions is influenced by both the reward value of target objects and our internal motivational state. In the present study, we investigated how the energy content of the target object and the physiological state of hunger influence the prediction of food-oriented actions in people with a healthy weight or obesity. Thirty-one participants with normal-weight and 31 participants with obesity performed a social perception task in which, under a fasting or a satiety state, they had to predict the intention of actions directed to high- or low-energy food objects in the context of a breakfast table that could suggest congruent or incongruent actions compared to action kinematics. The results showed that action prediction performance was greater: 1) for high-compared to low-energy food objects in the group with healthy weight but not in the group with obesity, with lower sensitivity to the energy content of the target object in individuals with more weight; 2) under a fasting compared to a satiety state in both groups; 3) for actions embedded in congruent compared to incongruent contexts when directed to high- but not low-energy food objects, independently of group and hunger state. The findings document an altered sensitivity to the reward value of action stimuli in people with obesity, despite a conserved sensitivity to hunger and contextual modulation. This supports a reduced sensitivity to the reward value of food stimuli in people with obesity pointing at a dysfunctional reward system during action perception. Interestingly, being under a food deprivation state comparably boosted performance in both groups, providing evidence that fasting may sharpen senses independently from individual weight.

Lying behavior in adolescents with conduct disorder: An experimental study of the role of executive functioning

Children with conduct disorder (CD) are often reported to engage in higher rates of lying compared to their typically developing (TD) peers. However, there is a paucity of experimental evidence exploring the specific characteristics and underlying mechanisms driving the propensity for lying in this population. To address these gaps, this study aimed to investigate the relationship between executive functioning (EF) and lying behavior in adolescents with CD compared to TD adolescents. To assess spontaneous lying for personal gain, we used a spot-the-differences task with adolescents aged 13 to 16 (N = 115). EF abilities-including cognitive flexibility, inhibitory control, and working memory-were measured using the Trail Making Test, Color-Word Stroop Task, and Digit Span Test, respectively. We hypothesized that CD adolescents would lie more frequently for personal gain than TD adolescents. Additionally, we expected EF to negatively correlate with lying frequency in TD adolescents but positively correlate with lying frequency in CD adolescents. The results partially supported these hypotheses. While adolescents with CD did not lie significantly more often than TD adolescents, EF was differently related to lying frequency. Specifically, cognitive flexibility was positively associated with lying frequency in CD adolescents but was not significantly related to lying frequency in TD adolescents. Additionally, better inhibitory control was associated with less frequent lying across both groups. These findings provide new insights into the role of EF in adolescent dishonesty and suggest that EF may influence lying behavior differently in CD and TD populations.

Avoiding automation surprise: Identifying requirements to support pilot intervention in automated Uncrewed Aerial Vehicle (UAV) flight

The breadth and depth of Uncrewed Aerial Vehicle (UAV) operations are expanding at a considerable rate. With expansion comes challenges for the design of automation to support decision making. This research takes the perceptual cycle model (PCM) and the derived trust version of the Schema World Action Research Method (T-SWARM), to identify the issues and challenges of pilot intervention in UAVs operating during highly automated states. Nine UAV pilots with current experience operating medium to large UAVs were interviewed, using T-SWARM, about incidents in which they initiated an intervention in system operation (i.e. to avoid weather or collision) and an event where the system initiated the intervention (i.e. due to system failure). The coded responses highlighted the challenges with what information is displayed, how it is displayed and how it influences decision-making in the UAV context. In addition, the responses also identified aspects that influence trust in the system, including personal disposition, affect interventions with the automation. Against each of the key factors identified recommendations are made to increase safety and operational efficiency of UAV operations. This research adds to the growing body of literature that supports the application of T-SWARM for eliciting knowledge in the aviation domain and specifically within the UAV domain.

Cortical-subcortical neural networks for motor learning and storing sequence memory

Motor sequence learning relies on the synergistic collaboration of multiple brain regions. However, most existing models for motor sequence learning primarily focus on functional-level analyses of sequence memory mechanisms, providing limited neurophysiological insights into how biological neural systems intrinsically encode the ordering of sequential element. Based on physiological and anatomical evidence, this study establishes a cortico-subcortical neuronal network model that differs from existing functional frameworks, emphasizing the neural mechanisms of sequence learning in the brain. The proposed model is biological plausibility and represents a potential mechanism for human sequential learning. It achieves the sequential selection and learning of elements through the cortico-basal ganglia-thalamic circuit, where the working memory function of the prefrontal cortex serves as the basis for Hebbian learning among cortical neurons, enabling the encoding of sequential order. The model successfully reproduces physiological experimental phenomena, validating its biological rationality. Furthermore, we explore the role of cholinergic interneurons in sequence learning, revealing their ability to enhance the robustness of learning. Finally, we demonstrate the model's applicability by deploying it to control a robotic arm in drawing and handwriting tasks, highlighting its adaptability to complex real-world scenarios. These biologically inspired results aim to offer a mechanistic explanation for sequence learning and memory formation in the human brain, providing valuable insights into brain-like control systems and neural networks.

Amphetamine increases timing variability by degrading prefrontal temporal encoding

Amphetamine is a commonly abused psychostimulant that increases synaptic catecholamine levels and impairs executive functions. However, it is unknown how acute amphetamine affects brain areas involved in executive control, such as the prefrontal cortex. We studied this problem in mice using interval timing, which requires participants to estimate an interval of several seconds with a motor response. Rodent prefrontal cortex ensembles are required for interval timing. We tested the hypothesis that amphetamine disrupts interval timing by degrading prefrontal cortex temporal encoding. We first quantified the effects of amphetamine on interval timing performance by conducting a meta-analysis of 15 prior rodent studies. We also implanted multielectrode recording arrays in the dorsomedial prefrontal cortex of 7 mice and then examined the effects of 1.5 mg/kg D-amphetamine injected intraperitoneally on interval timing behavior and prefrontal neuronal ensemble activity. A meta-analysis of previous literature revealed that amphetamine produces a large effect size on interval timing variability across studies but only a medium effect size on central tendencies of interval timing. We found a similar effect on interval timing variability in our task, which was accompanied by greater trial-to-trial variability in prefrontal ramping, attenuated interactions between pairs of ramping neurons, and dampened low-frequency oscillations. These findings suggest that amphetamine alters prefrontal temporal processing by increasing the variability of prefrontal temporal encoding. Our work provides insight into how amphetamine affects prefrontal activity, which may be useful in developing new neurophysiological markers for amphetamine use and novel treatments targeting the prefrontal cortex.

Abnormalities of brain dynamics based on large-scale cortical network modeling in autism spectrum disorder

Synaptic increase is a common phenomenon in the brain of autism spectrum disorder (ASD). However, the impact of increased synapses on the neurophysiological activity of ASD remains unclear. To address this, we propose a large-scale cortical network model based on empirical structural connectivity data using the Wendling model, which successfully simulates both pathological and physiological electroencephalography (EEG) signals. Building on this, the EEG functional network is constructed using the phase lag index, effectively characterizing the functional connectivity. Our modeling results indicate that EEG activity and functional network properties undergo significant changes by globally increasing synaptic coupling strength. Specifically, it leads to abnormal neural oscillations clinically reported in ASD, including the decreased dominant frequency, the decreased relative power in the α band and the increased relative power in the δ+θ band, particularly in the frontal lobe. At the same time, the clustering coefficient and global efficiency of the functional network decrease, while the characteristic path length increases, suggesting that the functional network of ASD is inefficient and poorly integrated. Additionally, we find insufficient functional connectivity across multiple brain regions in ASD, along with decreased wavelet coherence in the α band within the frontal lobe and between the frontal and temporal lobes. Considering that most of the synaptic increases in ASD are limited, brain regions are further randomly selected to increase the local synaptic coupling strength. The results show that disturbances in local brain regions can also facilitate the development of ASD. This study reveals the intrinsic link between synapse increase and abnormal brain activity in ASD, and inspires treatments related to synapse pruning.

Visual reasoning in object-centric deep neural networks: A comparative cognition approach

Achieving visual reasoning is a long-term goal of artificial intelligence. In the last decade, several studies have applied deep neural networks (DNNs) to the task of learning visual relations from images, with modest results in terms of generalization of the relations learned. However, in recent years, object-centric representation learning has been put forward as a way to achieve visual reasoning within the deep learning framework. Object-centric models attempt to model input scenes as compositions of objects and relations between them. To this end, these models use several kinds of attention mechanisms to segregate the individual objects in a scene from the background and from other objects. In this work we tested relation learning and generalization in several object-centric models, as well as a ResNet-50 baseline. In contrast to previous research, which has focused heavily in the same-different task in order to asses relational reasoning in DNNs, we use a set of tasks - with varying degrees of complexity - derived from the comparative cognition literature. Our results show that object-centric models are able to segregate the different objects in a scene, even in many out-of-distribution cases. In our simpler tasks, this improves their capacity to learn and generalize visual relations in comparison to the ResNet-50 baseline. However, object-centric models still struggle in our more difficult tasks and conditions. We conclude that abstract visual reasoning remains an open challenge for DNNs, including object-centric models.

A network analysis of depressive symptoms and cognitive performance in older adults with multimorbidity: A nationwide population-based study

BACKGROUND

Depression and cognitive impairment are prevalent mental health issues. Older adults in China exhibits a higher prevalence of multimorbidity, which is linked to an increased risk of depression and cognitive impairment. This study aims to investigate association between depressive symptoms and cognitive impairment in older adults with multimorbidity using network analysis, and to identify important bridge symptoms as potential intervention targets.

METHOD

The study included 5729 individuals aged 60 years and above with multimorbidity, drawn from the China Health and Retirement Longitudinal Survey (CHARLS) dataset. Depressive symptoms and cognitive performance were assessed utilizing the CESD-10 (10-item Center for Epidemiologic Studies Depression) and MMSE (Mini Mental State Examination) scales, respectively. We constructed a network structure of depressive symptoms and cognitive performance, and calculated index of strength and bridge strength for each symptom. Furthermore, a comparative analysis of the network structure across gender and age groups were conducted.

RESULTS

D3 (Felt depressed), C1 (Orientation), and D10 (Could not get going) were identified as the central symptoms of the depressive symptoms - cognitive performance network. C1 (Orientation), C5 (Linguistic skills), and D10 (Could not get going) were bridge symptoms connecting the two illnesses. Moreover, significant differences in edge weights were observed across gender and age groups.

CONCLUSIONS

The central symptoms and bridge symptoms in the network may represent the most effective intervention pathway for addressing cognitive impairment and depression in older adults with multimorbidity. Clinical interventions should properly focus on gender and age differences in symptom presentation.

A systematic review of resting-state functional-MRI studies in the diagnosis, comorbidity and treatment of postpartum depression

BACKGROUND

Postpartum depression (PPD) is a common and serious mental health problem that affects many new mothers and their families worldwide. In recent years, there has been an increasing number of studies using magnetic resonance techniques (MRI), particularly functional MRI (fMRI), to explore the neuroimaging biomarker of this disease.

METHODS

PubMed database was used to search for English literature focusing on resting-state fMRI and PPD published up to June 2024.

RESULTS

After screening, 17 studies were finally identified, among which all 17 studies reported abnormal regions or connectivity compared to health controls (HC), 4 studies reported results considering the differences between PPD and PPD with anxiety (PPD-A), and 2 studies reported biomarkers for the treatment of PPD. The existing studies indicate that PPD is characterized by functional impairments in multiple brain regions, especially the medial prefrontal cortex (MPFC), precentral gyrus and cerebellum. Abnormal functional connectivity has been widely reported in the dorsomedial prefrontal cortex (dmPFC), anterior cingulate cortex (ACC) and the orbitofrontal cortex (OFC). However, none of the four comorbidity studies identified overlapping discriminative biomarkers between PPD and PPD-A. Additionally, the two treatment-related studies consistently reported functional improvements in the amygdala after effective treatment.

CONCLUSION

The affected brain regions were highly overlapped with major depressive disorder (MDD), suggesting that PPD may be categorized as a potential subtype of MDD. Considering the negative effects of medication on PPD, future efforts should focus on developing non-pharmacological therapies, such as transcranial magnetic stimulation (TMS) and acupuncture, to support women with PPD in overcoming this unique and important phase.

Effectiveness of cognitive rehabilitation in children and adolescents with ADHD: A review of EEG studies

BACKGROUND

Attention-Deficit/Hyperactivity Disorder (ADHD) is characterized by behavioral, cognitive, and neural differences that can significantly impact individuals in multiple areas of their lives. For a therapeutic intervention to be considered successful, it should not only address the targeted areas but also address other challenges faced by individuals with ADHD. This comprehensive approach can help reduce both the cost and duration of treatment. Thus, the objective of this mini review is to examine the transferability of cognitive rehabilitation methods that focus on working memory and/or inhibitory control on resting-state electroencephalography (EEG) findings.

METHOD

A systematic search was performed in PubMed, Web of Science, and Scopus to identify relevant studies published before 30th of July 2024. A total of 7 studies were included in our data extraction process.

RESULTS

Cognitive rehabilitation methods targeting working memory and/or inhibitory control generally assisted in change EEG frequencies in children and adolescents with ADHD. However, its efficiency was different in diverse brain frequency bands.

LIMITATIONS

The analysis of the available literature was restricted due to the small number of studies with non-homogeneous characteristics - both in terms of methodology and clinical aspects - which restricted our ability to draw comprehensive conclusions.

CONCLUSION

Notably, there was a consistent decrease in theta power observed among participants with ADHD when contrasted with an age- and gender-matched control group. However, there was no significant effect on beta power. Further research is necessary to comprehensively evaluate the effectiveness of these interventions across alpha and delta frequencies.

Machine learning based differential diagnosis of schizophrenia, major depression disorder and bipolar disorder using structural magnetic resonance imaging

BACKGROUND

Cortical morphological abnormalities in schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD) have been identified in past research. However, their potential as objective biomarkers to differentiate these disorders remains uncertain. Machine learning models may offer a novel diagnostic tool.

METHODS

Structural MRI (sMRI) of 220 SCZ, 220 MDD, 220 BD, and 220 healthy controls were obtained using a 3T scanner. Volume, thickness, surface area, and mean curvature of 68 cerebral cortices were extracted using FreeSurfer. 272 features underwent 3 feature selection techniques to isolate important variables for model construction. These features were incorporated into 3 classifiers for classification. After model evaluation and hyperparameter tuning, the best-performing model was identified, along with the most significant brain measures.

RESULTS

The univariate feature selection-Naive Bayes model achieved the best performance, with an accuracy of 0.66, macro-average AUC of 0.86, and sensitivities and specificities ranging from 0.58-0.86 to 0.81-0.93, respectively. Key features included thickness of right isthmus-cingulate cortex, area of left inferior temporal gyrus, thickness of right superior temporal gyrus, mean curvature of right pars orbitalis, thickness of left transverse temporal cortex, volume of left caudal anterior-cingulate cortex, area of right banks superior temporal sulcus, and thickness of right temporal pole.

CONCLUSION

The machine learning model based on sMRI data shows promise for aiding in the differential diagnosis of SCZ, MDD, and BD. Cortical features from the cingulate and temporal lobes may highlight distinct biological mechanisms underlying each disorder.

The role of testosterone in modulating positive and negative empathy in social interactions

Empathy encompasses both negative (e.g., distress) and positive (e.g., shared joy) dimensions, yet the effects of testosterone on positive empathy and its modulation of intrinsic neural dynamics remain underexplored. This double-blind, placebo-controlled study examined how testosterone influences neural sensitivity to empathy within social inclusion and exclusion contexts, as well as its impact on resting-state EEG microstates-millisecond-scale transient patterns of brain activity. Healthy male participants received either testosterone or placebo before completing resting-state EEG recordings and an empathy task featuring social scenarios. While self-reported empathy ratings remained unchanged, testosterone amplified neurophysiological responses: it enhanced anterior N2 amplitude (250-310 ms), associated with negative empathy toward social exclusion, and increased posterior α-event-related desynchronization (8.28-10 Hz; 1226-1901 ms), linked to positive empathy during social inclusion. These findings suggest that testosterone enhances neural responsiveness to both threatening and affiliative social cues, reinforcing its role in adaptive social vigilance. Resting-state EEG microstate analysis further revealed that testosterone prolonged the temporal dominance of microstate E-a centro-parietal activity pattern associated with interoceptive awareness and emotional processing. Notably, these microstate E changes predicted increased emotional empathy across both positive and negative contexts. Together, our findings suggest that testosterone indirectly enhances empathy-related responsiveness by amplifying baseline interoceptive sensitivity to socially salient stimuli. These dual effects-enhanced intrinsic interoceptive processing and heightened neural reactivity to social cues-position testosterone as a key neuromodulator of context-adaptive social perception.

Glutamate plus glutamine to GABA ratio as a predictor of ketamine response in treatment-resistant depression: A double-blind, randomized, open-label extension study

BACKGROUND

Approximately 30 % of patients with treatment-resistant depression (TRD) respond to ketamine; however, no replicable predictors of response have been reported. The imbalance between excitatory and inhibitory neurotransmissions may be implicated in the mechanism of action of ketamine. This study aimed to evaluate whether the ratio of glutamate and glutamine (Glx) to GABA levels at baseline in the dorsal anterior cingulate cortex (dACC) could predict ketamine response in patients with TRD.

METHOD

This exploratory study analyzed data from a double-blind randomized clinical trial with an open-label extension study (jRCTs031210124). Fifteen participants in the ketamine group and 15 of 16 participants in the placebo group received repeated intravenous ketamine during the double-blind and open-label extension periods, respectively. We measured Glx and GABA levels in the dACC before and after treatment during the double-blind period using proton magnetic resonance spectroscopy. The 17-item Hamilton Depression Rating Scale (HDRS-17) was measured for depressive symptomatology. General linear models were used to examine the relationship between baseline Glx/GABA ratio and HDRS-17 score changes.

RESULT

Changes in HDRS-17 scores (mean (±SD)) following ketamine treatment were -4.9 (6.5) and -4.9 (5.2) in the double-blind and open-label periods, respectively. A higher baseline dACC Glx/GABA ratio was correlated with greater improvement in HDRS-17 (β = -0.42, p = 0.040). In the ketamine group, a reduction in the dACC Glx/GABA ratio was correlated with greater HDRS-17 improvement (β = 0.74, p = 0.009) with no such association in the placebo group.

CONCLUSION

These results suggest that excitatory-inhibitory imbalance in the dACC may predict the efficacy of ketamine in TRD.