White matter structural bases for phase accuracy during tapping synchronization.

We determined the intersubject association between the rhythmic entrainment abilities of human subjects during a synchronization-continuation tapping task (SCT) and the macro- and microstructural properties of their superficial (SWM) and deep (DWM) white matter. Diffusion-weighted images were obtained from 32 subjects who performed the SCT with auditory or visual metronomes and five tempos ranging from 550 to 950 ms. We developed a method to determine the density of short-range fibers that run underneath the cortical mantle, interconnecting nearby cortical regions (U-fibers). Notably, individual differences in the density of U-fibers in the right audiomotor system were correlated with the degree of phase accuracy between the stimuli and taps across subjects. These correlations were specific to the synchronization epoch with auditory metronomes and tempos around 1.5 Hz. In addition, a significant association was found between phase accuracy and the density and bundle diameter of the corpus callosum, forming an interval-selective map where short and long intervals were behaviorally correlated with the anterior and posterior portions of the corpus callosum. These findings suggest that the structural properties of the SWM and DWM in the audiomotor system support the tapping synchronization abilities of subjects, as cortical U-fiber density is linked to the preferred tapping tempo and the bundle properties of the corpus callosum define an interval-selective topography.

Interactions between neural representations of the social and spatial environment.

Even in our highly interconnected modern world, geographic factors play an important role in human social connections. Similarly, social relationships influence how and where we travel, and how we think about our spatial world. Here, we review the growing body of neuroscience research that is revealing multiple interactions between social and spatial processes in both humans and non-human animals. We review research on the cognitive and neural representation of spatial and social information, and highlight recent findings suggesting that underlying mechanisms might be common to both. We discuss how spatial factors can influence social behaviour, and how social concepts modify representations of space. In so doing, this review elucidates not only how neural representations of social and spatial information interact but also similarities in how the brain represents and operates on analogous information about its social and spatial surroundings.This article is part of the theme issue 'The spatial-social interface: a theoretical and empirical integration'.

Perpetual step-like restructuring of hippocampal circuit dynamics.

Representation of the environment by hippocampal populations is known to drift even within a familiar environment, which could reflect gradual changes in single-cell activity or result from averaging across discrete switches of single neurons. Disambiguating these possibilities is crucial, as they each imply distinct mechanisms. Leveraging change point detection and model comparison, we find that CA1 population vectors decorrelate gradually within a session. In contrast, individual neurons exhibit predominantly step-like emergence and disappearance of place fields or sustained changes in within-field firing. The changes are not restricted to particular parts of the maze or trials and do not require apparent behavioral changes. The same place fields emerge, disappear, and reappear across days, suggesting that the hippocampus reuses pre-existing assemblies, rather than forming new fields de novo. Our results suggest an internally driven perpetual step-like reorganization of the neuronal assemblies.

Emerging Trends and Hot Topics of Non-Invasive Electroencephalography Research in the Elderly: A Bibliometric Analysis from 2014 to 2023.

Background: Electroencephalography (EEG) has been rapidly developed and is widely used in both clinical and scientific fields. Original studies on non-invasive EEG in the elderly have been of great importance owing to the global aging trend. The present study aimed to provide a bibliometric overview on current status and trends in this research field. Methods: We searched the Web of Science Core Collection for articles published during 2014 and 2023. Synonyms for EEG and the elderly were combined as a retrieval strategy. Invasive EEG and secondary studies were excluded. Online filters and manual reviews were applied to select eligible articles. Basic bibliometric parameters were analyzed and visualized using VOSviewer and Excel software. Results: A total of 1656 publications were filtered, and 655 of which were finally included. In general, publication counts have steadily increased over the last 10 years. A sharp rise in publications occurred in 2021, and then remained at a high level. Authors and institutions from high-income countries/regions such as the United States of America (USA), China, and Germany were more productive and made significant contributions. Journals specialized in neuroscience, such as Frontiers in Aging Neuroscience, Neurobiology of Aging, and Clinical Neurophysiology, were popular among authors. Articles on aging, Alzheimer's disease (AD), mild cognitive impairment (MCI), dementia, memory, event-related potentials, attention, and the brain were more likely to use EEG. The newer topics included anesthesia, postoperative delirium (POD), confusion assessment method, connectivity, validation, and power. Conclusion: This bibliometric study provides fundamental knowledge on the current status and hot spots of the original studies on EEG in elderly, which is beneficial to researchers in paving future investigations of neuroscience and neural diseases.

Ultra-overt therapy: a novel medical approach centered on patient consciousness.

Within the realms of human and artificial intelligence, the concepts of consciousness and comprehension are fundamental distinctions. In the clinical sphere, patient awareness regarding medication and its physiological processes plays a crucial role in determining drug efficacy and outcomes. This article introduces a novel perspective on prescription practices termed "Ultra-Overt Therapy" (UOT). A review of current supporting evidence was conducted through a non-systematic search in PubMed and Google Scholar, focusing on concepts such as the "mind-body relationship," "placebo response," "neuroscience," and "complementary medicine." Our findings, rooted in the mechanisms of the "placebo effect," the intricacies of "intersubjective therapy," the potency of "interoceptive awareness," and other domains of medical science, suggest that UOT holds theoretical promise. Future research endeavors focusing on these areas may elucidate the global impact of this method on medical treatment and patient care.

Quickly diagnosing Bietti crystalline dystrophy with deep learning.

Bietti crystalline dystrophy (BCD) is an autosomal recessive inherited retinal disease (IRD) and its early precise diagnosis is much challenging. This study aims to diagnose BCD and classify the clinical stage based on ultra-wide-field (UWF) color fundus photographs (CFPs) via deep learning (DL). All CFPs were labeled as BCD, retinitis pigmentosa (RP) or normal, and the BCD patients were further divided into three stages. DL models ResNeXt, Wide ResNet, and ResNeSt were developed, and model performance was evaluated using accuracy and confusion matrix. Then the diagnostic interpretability was verified by the heatmaps. The models achieved good classification results. Our study established the largest BCD database of Chinese population. We developed a quick diagnosing method for BCD and evaluated the potential efficacy of an automatic diagnosis and grading DL algorithm based on UWF fundus photography in a Chinese cohort of BCD patients.

Illumination by short-wavelength light inside the blind spot decreases light detectability.

Although the optic disk corresponding to the blind spot contains no classical photoreceptors, it contains photopigment melanopsin. To clarify whether melanopsin is involved in light detection, we conducted detection tasks for light stimuli presented in the normal visual field, with and without another illumination inside the blind spot. We found that a blue blind-spot illumination decreased the light detectability on a dark background. This effect was replicable when it was determined immediately after the blind-spot illumination was turned off, suggesting the contribution of a sluggish system rather than scattering. Moreover, the aforementioned effect was not observed when the blind-spot illumination was in red, indicating wavelength specificity in favor of melanopsin's sensitivity profile. These findings suggest that melanopsin is activated by the blind-spot illumination and thereby interferes with light detection near the absolute threshold. Light detection originating from conventional photoreceptors is modulated by melanopsin-based computation presumably estimating a baseline noise level.

Intergenerational epigenetic inheritance mediated by MYS-2/MOF in the pathogenesis of Alzheimer's disease.

Although autosomal-dominant inheritance is believed an important cause of familial clustering Alzheimer's disease (FAD), it covers only a small proportion of FAD incidence, and so we investigated epigenetic memory as an alternative mechanism to contribute for intergenerational AD pathogenesis. Our data in vivo showed that mys-2 of Caenorhabditis elegans that encodes a putative MYST acetyltransferase responsible for H4K16 acetylation modulated AD occurrence. The phenotypic improvements in the parent generation caused by mys-2 disfunction were passed to their progeny due to epigenetic memory, which resulted in similar H4K16ac levels among the candidate target genes of MYS-2 and similar gene expression patterns of the AD-related pathways. Furthermore, the ROS/CDK-5/ATM pathway functioned as an upstream activator of MYS-2. Our study indicated that MYS-2/MOF could be inherited intergenerationally via epigenetic mechanisms in C. elegans and mammalian cell of AD model, providing a new insight into our understanding of the etiology and inheritance of FAD.

Innovative 3D bioprinting approaches for advancing brain science and medicine: a literature review.

This paper reviews 3D bioprinting technologies and Bio-inks materials in brain neuroscience applications. The integration of 3D bioprinting technology in neuroscience research offers a unique platform to create complex brain and tissue architectures that mimic the mechanical, architectural, and biochemical properties of native tissues, providing a robust tool for modeling, repair, and drug screening applications. The review provides discussions and conclusions to highlight the current research, research gaps and recommendations for the future research on 3D bioprinting in neuroscience. The investigation shows that 3D bioprinting has a great potential to fabricate brain-like tissue constructs, holds great promise for regenerative medicine and drug testing models, offering new avenues for studying brain diseases and potential treatments. It is also found that the future of bioinks requires continuous improvement and innovation to meet the needs of applications in the field of neuroscience, aiming to improve the functionality and performance of bioink materials for neural tissue engineering. .

Pain neuroscience education and motor imagery-based exercise protocol for patients with fibromyalgia: A randomized controlled trial.

BACKGROUND: This study is a randomized controlled, biopsychosocial study investigating the effectiveness of pain neuroscience education (PNE) and motor imagery-based exercise protocol (MIEP) on fibromyalgia pain. METHODS: Our study has four groups (MIEP n = 12, PNE n = 12, MIEP + PNE n = 14, Control n = 12) and all participants (n = 50) consist of patients diagnosed with fibromyalgia with chronic back pain. The primary outcome measure was pain intensity, and secondary outcome measures were beliefs, kinesiophobia, anxiety-depression, cognitive-mood, self-esteem, and body awareness. RESULTS: A statistically significant decrease in pain intensity was observed in all experimental groups, without any group being superior (Visual Analog Scale [VAS]: MIEP + PNE p = .003, 95% confidence interval [CI], -4.7078 to -0.9922; MIEP p = .003, 95% CI, -5.4806 to -1.0194; PNE p = .002, 95% CI, -3.6139 to -1.5461). There was a significant improvement in organic beliefs in both groups where PNE was applied (MIEP + PNE: p = .017, 95% CI, -7.8211 to -0.3189; PNE: p = .003, 95% CI, -9.7999 to -0.0401). A significant superiority in organic pain beliefs was detected in the MIEP + PNE group compared to the control group (p = .008, 95% CI, 1.7241-9.4959). CONCLUSIONS: According to this study, in which MIEP and PNE were combined, there was a decrease in pain intensity when both applications were applied together and when they were applied one by one. MIEP has improved her motor imagery ability, improved pain and increased body awareness. PNE has improved people's organic pain beliefs; removed people from fears, catastrophizing, and negative thoughts about pain; improved easier management of psychological processes and cognitive-emotion regulation ability.

Bridging auditory perception and natural language processing with semantically informed deep neural networks.

Sound recognition is effortless for humans but poses a significant challenge for artificial hearing systems. Deep neural networks (DNNs), especially convolutional neural networks (CNNs), have recently surpassed traditional machine learning in sound classification. However, current DNNs map sounds to labels using binary categorical variables, neglecting the semantic relations between labels. Cognitive neuroscience research suggests that human listeners exploit such semantic information besides acoustic cues. Hence, our hypothesis is that incorporating semantic information improves DNN's sound recognition performance, emulating human behaviour. In our approach, sound recognition is framed as a regression problem, with CNNs trained to map spectrograms to continuous semantic representations from NLP models (Word2Vec, BERT, and CLAP text encoder). Two DNN types were trained: semDNN with continuous embeddings and catDNN with categorical labels, both with a dataset extracted from a collection of 388,211 sounds enriched with semantic descriptions. Evaluations across four external datasets, confirmed the superiority of semantic labeling from semDNN compared to catDNN, preserving higher-level relations. Importantly, an analysis of human similarity ratings for natural sounds, showed that semDNN approximated human listener behaviour better than catDNN, other DNNs, and NLP models. Our work contributes to understanding the role of semantics in sound recognition, bridging the gap between artificial systems and human auditory perception.

Opposing serial effects of stimulus and choice in speech perception scale with context variability.

In this study, we investigated serial effects on the perception of auditory vowel stimuli across three experimental setups with different degrees of context variability. Aligning with recent findings in visual perception, our results confirm the existence of two distinct processes in serial dependence: a repulsive sensory effect coupled with an attractive decisional effect. Importantly, our study extends these observations to the auditory domain, demonstrating parallel serial effects in audition. Furthermore, we uncover context variability effects, revealing a linear pattern for the repulsive perceptual effect and a quadratic pattern for the attractive decisional effect. These findings support the presence of adaptive sensory mechanisms underlying the repulsive effects, while higher-level mechanisms appear to govern the attractive decisional effect. The study provides valuable insights into the interplay of attractive and repulsive serial effects in auditory perception and contributes to our understanding of the underlying mechanisms.

Neuropeptidergic regulation of neuromuscular signaling in larval zebrafish alters swimming behavior and synaptic transmission.

Chemical synaptic transmission is modulated to accommodate different activity levels, thus enabling homeostatic scaling in pre- and postsynaptic compartments. In nematodes, cholinergic neurons use neuropeptide signaling to modulate synaptic vesicle content. To explore if this mechanism is conserved in vertebrates, we studied the involvement of neuropeptides in cholinergic transmission at the neuromuscular junction of larval zebrafish. Optogenetic stimulation by photoactivated adenylyl cyclase evoked locomotion. We generated mutants lacking the neuropeptide-processing enzyme carboxypeptidase E (cpe), and the most abundant neuropeptide precursor in motor neurons, tachykinin (tac1). Both mutants showed exaggerated locomotion after photostimulation. Recording excitatory postsynaptic currents demonstrated overall larger amplitudes in the wild type. Exaggerated locomotion in the mutants thus reflected upscaling of postsynaptic excitability. Both mutant muscles expressed more nicotinic acetylcholine receptors (nAChRs) on their surface; thus, neuropeptide signaling regulates synaptic transmitter output in zebrafish motor neurons, and muscle cells homeostatically regulate nAChR surface expression, compensating reduced presynaptic input.

Serum lipidome associates with neuroimaging features in patients with traumatic brain injury.

Acute traumatic brain injury (TBI) is associated with substantial abnormalities in lipid biology, including changes in the structural lipids that are present in the myelin in the brain. We investigated the relationship between traumatic microstructural changes in white matter from magnetic resonance imaging (MRI) and quantitative lipidomic changes from blood serum. The study cohort included 103 patients from the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) study. Diffusion tensor fitting generated fractional anisotropy (FA) and mean diffusivity (MD) maps for the MRI scans while ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry was applied to analyze the lipidome. Increasing severity of TBI was associated with higher MD and lower FA values, which scaled with different lipidomic signatures. There appears to be consistent patterns of lipid changes associating with the specific microstructure changes in the CNS white matter, but also regional specificity, suggesting that blood-based lipidomics may provide an insight into the underlying pathophysiology of TBI.

Sensorimotor variability distinguishes early features of cognition in toddlers with autism.

The potential role of early sensorimotor features to atypical human cognition in autistic children has received surprisingly little attention given that appropriate movements are a crucial element that connects us to other people. We examined quantitative and observation-based movements in over 1,000 toddlers diagnosed with autism spectrum disorder (ASD) with different levels of cognitive abilities (intelligence quotient, IQ). Relative to higher-IQ ASD toddlers, those with lower-IQ had significantly altered sensorimotor features. Remarkably, we found that higher IQ in autistic toddlers confers resilience to atypical movement, as sensorimotor features in higher-IQ ASD children were indistinguishable from those of typically developing healthy control toddlers. We suggest that the altered movement patterns may affect key autistic behaviors in those with lower intelligence by affecting sensorimotor learning mechanisms. Atypical sensorimotor functioning is a key feature in lower-IQ early childhood autism. These findings have implications for the development of individualized interventions for subtypes of autism.

Mechanical force of uterine occupation enables large vesicle extrusion from proteostressed maternal neurons.

Large vesicle extrusion from neurons may contribute to spreading pathogenic protein aggregates and promoting inflammatory responses, two mechanisms leading to neurodegenerative disease. Factors that regulate the extrusion of large vesicles, such as exophers produced by proteostressed C. elegans touch neurons, are poorly understood. Here, we document that mechanical force can significantly potentiate exopher extrusion from proteostressed neurons. Exopher production from the C. elegans ALMR neuron peaks at adult day 2 or 3, coinciding with the C. elegans reproductive peak. Genetic disruption of C. elegans germline, sperm, oocytes, or egg/early embryo production can strongly suppress exopher extrusion from the ALMR neurons during the peak period. Conversely, restoring egg production at the late reproductive phase through mating with males or inducing egg retention via genetic interventions that block egg-laying can strongly increase ALMR exopher production. Overall, genetic interventions that promote ALMR exopher production are associated with expanded uterus lengths and genetic interventions that suppress ALMR exopher production are associated with shorter uterus lengths. In addition to the impact of fertilized eggs, ALMR exopher production can be enhanced by filling the uterus with oocytes, dead eggs, or even fluid, supporting that distention consequences, rather than the presence of fertilized eggs, constitute the exopher-inducing stimulus. We conclude that the mechanical force of uterine occupation potentiates exopher extrusion from proximal proteostressed maternal neurons. Our observations draw attention to the potential importance of mechanical signaling in extracellular vesicle production and in aggregate spreading mechanisms, making a case for enhanced attention to mechanobiology in neurodegenerative disease.

Neurons are specialized cells in the brain and nervous system that transmit signals between the brain and the rest of the body, enabling humans and animals to react to internal and external stimuli. For this communication system to function effectively, neurons must remain healthy. Neurons maintain their function in a variety of ways, including by removing excess or damaged cellular components (such as organelles and protein aggregates) that could compromise neuron function. One way to do this is by extruding organelles and aggregates. During 'extrusion events', the material to be removed is gathered within a budding portion of the plasma membrane, which forms a vesicle that ejects the material from the neuron. However, the factors driving the extrusion process remained unknown. To investigate, Wang, Guasp, Salam et al. conducted experiments in the roundworm Caenorhabditis elegans, finding that the number of extrusion events in a certain type of neuron increases at the peak of reproduction. More specifically, a greater number of extrusion events were associated with the presence of fertilized eggs, which accumulate in the uterus before they are laid. Disrupting eggs, sperm or the fertilization process suppressed the increase in extrusion events, suggesting the presence of fertilized eggs is responsible. To determine how the eggs might trigger extrusion events, Wang et al. stretched the uterus using dead eggs, unfertilized eggs or by injecting fluid, finding that each of these approaches increased the number of extrusion events. Further analysis suggests that this mechanical stretching of the uterus signals to the neurons that reproduction has started, encouraging the neurons to remove old components and optimize their function. Wang et al. hypothesize that this stretch response could support neuronal behaviors that aid in successful reproduction, such as sensing food and selecting where to lay eggs. The findings increase our understanding of the factors that trigger vesicle extrusion in living organisms. These observations could have implications for human neurodegenerative diseases such as Alzheimer's disease, in which protein aggregates accumulate in neurons. It is possible that mechanical signals generated by factors associated with Alzheimer's disease, such as high blood pressure, could influence neuronal extrusion and contribute to some of the mechanisms underlying aggregate transfer in neurodegenerative diseases.

Individuals with anxiety and depression use atypical decision strategies in an uncertain world.

Previous studies on reinforcement learning have identified three prominent phenomena: (1) individuals with anxiety or depression exhibit a reduced learning rate compared to healthy subjects; (2) learning rates may increase or decrease in environments with rapidly changing (i.e. volatile) or stable feedback conditions, a phenomenon termed learning rate adaptation; and (3) reduced learning rate adaptation is associated with several psychiatric disorders. In other words, multiple learning rate parameters are needed to account for behavioral differences across participant populations and volatility contexts in this flexible learning rate (FLR) model. Here, we propose an alternative explanation, suggesting that behavioral variation across participant populations and volatile contexts arises from the use of mixed decision strategies. To test this hypothesis, we constructed a mixture-of-strategies (MOS) model and used it to analyze the behaviors of 54 healthy controls and 32 patients with anxiety and depression in volatile reversal learning tasks. Compared to the FLR model, the MOS model can reproduce the three classic phenomena by using a single set of strategy preference parameters without introducing any learning rate differences. In addition, the MOS model can successfully account for several novel behavioral patterns that cannot be explained by the FLR model. Preferences for different strategies also predict individual variations in symptom severity. These findings underscore the importance of considering mixed strategy use in human learning and decision-making and suggest atypical strategy preference as a potential mechanism for learning deficits in psychiatric disorders.