The miR-183/96/182 cluster regulates sensory innervation, resident myeloid cells and functions of the cornea through cell type-specific target genes.

The conserved miR-183/96/182 cluster (miR-183C) is expressed in both corneal resident myeloid cells (CRMCs) and sensory nerves (CSN) and modulates corneal immune/inflammatory responses. To uncover cell type-specific roles of miR-183C in CRMC and CSN and their contributions to corneal physiology, myeloid-specific miR-183C conditional knockout (MS-CKO), and sensory nerve-specific CKO (SNS-CKO) mice were produced and characterized in comparison to the conventional miR-183C KO. Immunofluorescence and confocal microscopy of flatmount corneas, corneal sensitivity, and tear volume assays were performed in young adult naïve mice; 3' RNA sequencing (Seq) and proteomics in the trigeminal ganglion (TG), cornea and CRMCs. Our results showed that, similar to conventional KO mice, the numbers of CRMCs were increased in both MS-CKO and SNS-CKO vs age- and sex-matched WT control littermates, suggesting intrinsic and extrinsic regulations of miR-183C on CRMCs. The number of CRMCs was increased in male vs female MS-CKO mice, suggesting sex-dependent regulation of miR-183C on CRMCs. In the miR-183C KO and SNS-CKO, but not the MS-CKO mice, CSN density was decreased in the epithelial layer of the cornea, but not the stromal layer. Functionally, corneal sensitivity and basal tear volume were reduced in the KO and SNS-CKO, but not the MS-CKO mice. Tear volume in males is consistently higher than female WT mice. Bioinformatic analyses of the transcriptomes revealed a series of cell-type specific target genes of miR-183C in TG sensory neurons and CRMCs. Our data elucidate that miR-183C imposes intrinsic and extrinsic regulation on the establishment and function of CSN and CRMCs by cell-specific target genes. miR-183C modulates corneal sensitivity and tear production through its regulation of corneal sensory innervation.

Developmental dyslexia susceptibility genes DNAAF4, DCDC2, and NRSN1 are associated with brain function in fluently reading adolescents and young adults.

Reading skills and developmental dyslexia, characterized by difficulties in developing reading skills, have been associated with brain anomalies within the language network. Genetic factors contribute to developmental dyslexia risk, but the mechanisms by which these genes influence reading skills remain unclear. In this preregistered study (https://osf.io/7sehx), we explored if developmental dyslexia susceptibility genes DNAAF4, DCDC2, NRSN1, and KIAA0319 are associated with brain function in fluently reading adolescents and young adults. Functional MRI and task performance data were collected during tasks involving written and spoken sentence processing, and DNA sequence variants of developmental dyslexia susceptibility genes previously associated with brain structure anomalies were genotyped. The results revealed that variation in DNAAF4, DCDC2, and NRSN1 is associated with brain activity in key language regions: the left inferior frontal gyrus, middle temporal gyrus, and intraparietal sulcus. Furthermore, NRSN1 was associated with task performance, but KIAA0319 did not yield any significant associations. Our findings suggest that individuals with a genetic predisposition to developmental dyslexia may partly employ compensatory neural and behavioral mechanisms to maintain typical task performance. Our study highlights the relevance of these developmental dyslexia susceptibility genes in language-related brain function, even in individuals without developmental dyslexia, providing valuable insights into the genetic factors influencing language processing.

Brain activity of professional investors signals future stock performance.

A major aspiration of investors is to better forecast stock performance. Interestingly, emerging "neuroforecasting" research suggests that brain activity associated with anticipatory reward relates to market behavior and population-wide preferences, including stock price dynamics. In this study, we extend these findings to professional investors processing comprehensive real-world information on stock investment options while making predictions of long-term stock performance. Using functional MRI, we sampled investors' neural responses to investment cases and assessed whether these responses relate to future performance on the stock market. We found that our sample of investors could not successfully predict future market performance of the investment cases, confirming that stated preferences do not predict the market. Stock metrics of the investment cases were not predictive of future stock performance either. However, as investors processed case information, nucleus accumbens (NAcc) activity was higher for investment cases that ended up overperforming in the market. These findings remained robust, even when controlling for stock metrics and investors' predictions made in the scanner. Cross-validated prediction analysis indicated that NAcc activity could significantly predict future stock performance out-of-sample above chance. Our findings resonate with recent neuroforecasting studies and suggest that brain activity of professional investors may help in forecasting future stock performance.

Cross-hemispheric communication: Insights on lateralized brain functions.

On the surface, the two hemispheres of vertebrate brains look almost perfectly symmetrical, but several motor, sensory, and cognitive systems show a deeply lateralized organization. Importantly, the two hemispheres are connected by various commissures, white matter tracts that cross the brain's midline and enable cross-hemispheric communication. Cross-hemispheric communication has been suggested to play an important role in the emergence of lateralized brain functions. Here, we review current advances in understanding cross-hemispheric communication that have been made using modern neuroscientific tools in rodents and other model species, such as genetic labeling, large-scale recordings of neuronal activity, spatiotemporally precise perturbation, and quantitative behavior analyses. These findings suggest that the emergence of lateralized brain functions cannot be fully explained by largely static factors such as genetic variation and differences in structural brain asymmetries. In addition, learning-dependent asymmetric interactions between the left and right hemispheres shape lateralized brain functions.

Ensemble dynamics and information flow deduction from whole-brain imaging data.

The recent advancements in large-scale activity imaging of neuronal ensembles offer valuable opportunities to comprehend the process involved in generating brain activity patterns and understanding how information is transmitted between neurons or neuronal ensembles. However, existing methodologies for extracting the underlying properties that generate overall dynamics are still limited. In this study, we applied previously unexplored methodologies to analyze time-lapse 3D imaging (4D imaging) data of head neurons of the nematode Caenorhabditis elegans. By combining time-delay embedding with the independent component analysis, we successfully decomposed whole-brain activities into a small number of component dynamics. Through the integration of results from multiple samples, we extracted common dynamics from neuronal activities that exhibit apparent divergence across different animals. Notably, while several components show common cooperativity across samples, some component pairs exhibited distinct relationships between individual samples. We further developed time series prediction models of synaptic communications. By combining dimension reduction using the general framework, gradient kernel dimension reduction, and probabilistic modeling, the overall relationships of neural activities were incorporated. By this approach, the stochastic but coordinated dynamics were reproduced in the simulated whole-brain neural network. We found that noise in the nervous system is crucial for generating realistic whole-brain dynamics. Furthermore, by evaluating synaptic interaction properties in the models, strong interactions within the core neural circuit, variable sensory transmission and importance of gap junctions were inferred. Virtual optogenetics can be also performed using the model. These analyses provide a solid foundation for understanding information flow in real neural networks.

Early Unguided Human Brain Organoid Neurovascular Niche Modeling into the Permissive Chick Embryo Chorioallantoic Membrane.

Engrafting organoids into vascularized tissues in model animals, such as the immunodeficient mouse or chick embryo chorioallantoic membrane (CAM), has proven efficient for neovascularization modeling. The CAM is a richly vascularized extraembryonic membrane, which shows limited immunoreactivity, thus becoming an excellent hosting model for human origin cell transplants. This paper describes the strategy to engraft human brain organoids differentiated at multiple maturation stages into the CAM. The cellular composition of brain organoids changes with time, reflecting the milestones of human brain development. We grafted brain organoids at relevant maturation stages: neuroepithelial expansion (18 DIV), early neurogenesis (60 DIV), and early gliogenesis (180 DIV) into the CAM of embryonic day (E)7 chicken embryos. Engrafted brain organoids were harvested 5 days later and their histological features were analyzed. No histological signs of neovascularization in the grafted organoids or abnormal blood vessels adjacent to the graftings were detected. Moreover, remarkable changes were observed in the cellular composition of the grafted organoids, namely, an increase in the number of glial fibrillary acidic protein-positive-reactive astrocytes. However, the cytoarchitectural changes were dependent on the organoid maturation stage. Altogether, these results suggest that brain organoids can grow in the CAM, and they show differences in the cytoarchitecture depending on their maturation stage at grafting.

Touch receptor end-organ innervation and function require sensory neuron expression of the transcription factor Meis2.

Touch sensation is primarily encoded by mechanoreceptors, called low-threshold mechanoreceptors (LTMRs), with their cell bodies in the dorsal root ganglia. Because of their great diversity in terms of molecular signature, terminal endings morphology, and electrophysiological properties, mirroring the complexity of tactile experience, LTMRs are a model of choice to study the molecular cues differentially controlling neuronal diversification. While the transcriptional codes that define different LTMR subtypes have been extensively studied, the molecular players that participate in their late maturation and in particular in the striking diversity of their end-organ morphological specialization are largely unknown. Here we identified the TALE homeodomain transcription factor Meis2 as a key regulator of LTMRs target-field innervation in mice. Meis2 is specifically expressed in cutaneous LTMRs, and its expression depends on target-derived signals. While LTMRs lacking Meis2 survived and are normally specified, their end-organ innervations, electrophysiological properties, and transcriptome are differentially and markedly affected, resulting in impaired sensory-evoked behavioral responses. These data establish Meis2 as a major transcriptional regulator controlling the orderly formation of sensory neurons innervating peripheral end organs required for light touch.

How brain structure-function decoupling supports individual cognition and its molecular mechanism.

Functional signals emerge from the structural network, supporting multiple cognitive processes through underlying molecular mechanism. The link between human brain structure and function is region-specific and hierarchical across the neocortex. However, the relationship between hierarchical structure-function decoupling and the manifestation of individual behavior and cognition, along with the significance of the functional systems involved, and the specific molecular mechanism underlying structure-function decoupling remain incompletely characterized. Here, we used the structural-decoupling index (SDI) to quantify the dependency of functional signals on the structural connectome using a significantly larger cohort of healthy subjects. Canonical correlation analysis (CCA) was utilized to assess the general multivariate correlation pattern between region-specific SDIs across the whole brain and multiple cognitive traits. Then, we predicted five composite cognitive scores resulting from multivariate analysis using SDIs in primary networks, association networks, and all networks, respectively. Finally, we explored the molecular mechanism related to SDI by investigating its genetic factors and relationship with neurotransmitter receptors/transporters. We demonstrated that structure-function decoupling is hierarchical across the neocortex, spanning from primary networks to association networks. We revealed better performance in cognition prediction is achieved by using high-level hierarchical SDIs, with varying significance of different brain regions in predicting cognitive processes. We found that the SDIs were associated with the gene expression level of several receptor-related terms, and we also found the spatial distributions of four receptors/transporters significantly correlated with SDIs, namely D2, NET, MOR, and mGluR5, which play an important role in the flexibility of neuronal function. Collectively, our findings corroborate the association between hierarchical macroscale structure-function decoupling and individual cognition and provide implications for comprehending the molecular mechanism of structure-function decoupling. PRACTITIONER POINTS: Structure-function decoupling is hierarchical across the neocortex, spanning from primary networks to association networks. High-level hierarchical structure-function decoupling contributes much more than low-level decoupling to individual cognition. Structure-function decoupling could be regulated by genes associated with pivotal receptors that are crucial for neuronal function flexibility.

Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies.

Identifying different sleep stages in humans and other mammals has traditionally relied on electroencephalograms. Such an approach is not feasible in certain animals such as invertebrates, although these animals could also be sleeping in stages. Here, we perform long-term multichannel local field potential recordings in the brains of behaving flies undergoing spontaneous sleep bouts. We acquired consistent spatial recordings of local field potentials across multiple flies, allowing us to compare brain activity across awake and sleep periods. Using machine learning, we uncover distinct temporal stages of sleep and explore the associated spatial and spectral features across the fly brain. Further, we analyze the electrophysiological correlates of microbehaviors associated with certain sleep stages. We confirm the existence of a distinct sleep stage associated with rhythmic proboscis extensions and show that spectral features of this sleep-related behavior differ significantly from those associated with the same behavior during wakefulness, indicating a dissociation between behavior and the brain states wherein these behaviors reside.

Mapping dynamic spatial patterns of brain function with spatial-wise attention.

Objective: Using functional magnetic resonance imaging (fMRI) and deep learning to discover the spatial pattern of brain function, or functional brain networks (FBNs) has been attracted many reseachers. Most existing works focus on static FBNs or dynamic functional connectivity among fixed spatial network nodes, but ignore the potential dynamic/time-varying characteristics of the spatial networks themselves. And most of works based on the assumption of linearity and independence, that oversimplify the relationship between blood-oxygen level dependence signal changes and the heterogeneity of neuronal activity within voxels.Approach: To overcome these problems, we proposed a novel spatial-wise attention (SA) based method called Spatial and Channel-wise Attention Autoencoder (SCAAE) to discover the dynamic FBNs without the assumptions of linearity or independence. The core idea of SCAAE is to apply the SA to generate FBNs directly, relying solely on the spatial information present in fMRI volumes. Specifically, we trained the SCAAE in a self-supervised manner, using the autoencoder to guide the SA to focus on the activation regions. Experimental results show that the SA can generate multiple meaningful FBNs at each fMRI time point, which spatial similarity are close to the FBNs derived by known classical methods, such as independent component analysis.Main results: To validate the generalization of the method, we evaluate the approach on HCP-rest, HCP-task and ADHD-200 dataset. The results demonstrate that SA mechanism can be used to discover time-varying FBNs, and the identified dynamic FBNs over time clearly show the process of time-varying spatial patterns fading in and out.Significance: Thus we provide a novel method to understand human brain better. Code is available athttps://github.com/WhatAboutMyStar/SCAAE.

Autonomic nervous system function before and after trauma-focused psychotherapy in youth with (partial) posttraumatic stress disorder.

While trauma-focused psychotherapies have been shown effective in youth with PTSD, the relationship between treatment response and alterations in the autonomic nervous system (ANS) associated with PTSD, remains incompletely understood. During neutral and personalized trauma script imagery heart rate (HR), pre-ejection period (PEP) and respiratory sinus arrhythmia (RSA) were recorded in youth aged 8-18 with PTSD or partial PTSD (n = 76) and trauma-exposed controls (TEC) (n = 27) to determine ANS activity and stress reactivity. Within the patient group, 77.6% met the full DSM-IV diagnostic criteria for PTSD, the remaining 22.4% met the criteria for partial PTSD. Youth with (partial) PTSD were subsequently treated with eight sessions of either trauma-focused cognitive behavioral therapy or eye movement desensitization and reprocessing. PTSD severity was assessed using the Clinician-Administered PTSD scale for Children and Adolescents to divide patients into responders and non-responders. Youth with (partial) PTSD relative to TEC had higher overall HR during both neutral and trauma imagery (p = .05). Youth with (partial) PTSD showed RSA decrease during trauma imagery relative to neutral imagery, the reverse of TEC (p = .01). Relative to non-responders, responders demonstrated a significant baseline to posttreatment increase of RSA response to stress only when employing a ≥ 50% response criterion (p = .05) and not with the primary ≥ 30% criterion (p = .12). Our results suggest overall higher HR and sympathetic nervous system activity as well as vagal withdrawal in response to stress in youth with (partial) PTSD and only provide partial support for normalization of the latter with successful trauma-focused psychotherapy.

osl-dynamics, a toolbox for modeling fast dynamic brain activity.

Neural activity contains rich spatiotemporal structure that corresponds to cognition. This includes oscillatory bursting and dynamic activity that span across networks of brain regions, all of which can occur on timescales of tens of milliseconds. While these processes can be accessed through brain recordings and imaging, modeling them presents methodological challenges due to their fast and transient nature. Furthermore, the exact timing and duration of interesting cognitive events are often a priori unknown. Here, we present the OHBA Software Library Dynamics Toolbox (osl-dynamics), a Python-based package that can identify and describe recurrent dynamics in functional neuroimaging data on timescales as fast as tens of milliseconds. At its core are machine learning generative models that are able to adapt to the data and learn the timing, as well as the spatial and spectral characteristics, of brain activity with few assumptions. osl-dynamics incorporates state-of-the-art approaches that can be, and have been, used to elucidate brain dynamics in a wide range of data types, including magneto/electroencephalography, functional magnetic resonance imaging, invasive local field potential recordings, and electrocorticography. It also provides novel summary measures of brain dynamics that can be used to inform our understanding of cognition, behavior, and disease. We hope osl-dynamics will further our understanding of brain function, through its ability to enhance the modeling of fast dynamic processes.

A randomized controlled trial comparing different sites of high-velocity low amplitude thrust on sensorimotor integration parameters.

Increasing evidence suggests that a high-velocity, low-amplitude (HVLA) thrust directed at a dysfunctional vertebral segment in people with subclinical spinal pain alters various neurophysiological measures, including somatosensory evoked potentials (SEPs). We hypothesized that an HVLA thrust applied to a clinician chosen vertebral segment based on clinical indicators of vertebral dysfunction, in short, segment considered as "relevant" would significantly reduce the N30 amplitude compared to an HVLA thrust applied to a predetermined vertebral segment not based on clinical indicators of vertebral dysfunction or segment considered as "non-relevant". In this double-blinded, active-controlled, parallel-design study, 96 adults with recurrent mild neck pain, ache, or stiffness were randomly allocated to receiving a single thrust directed at either a segment considered as "relevant" or a segment considered as "non-relevant" in their upper cervical spine. SEPs of median nerve stimulation were recorded before and immediately after a single HVLA application delivered using an adjusting instrument (Activator). A linear mixed model was used to assess changes in the N30 amplitude. A significant interaction between the site of thrust delivery and session was found (F1,840 = 9.89, p < 0.002). Pairwise comparisons showed a significant immediate decrease in the N30 complex amplitude after the application of HVLA thrust to a segment considered "relevant" (- 16.76 ± 28.32%, p = 0.005). In contrast, no significant change was observed in the group that received HVLA thrust over a segment considered "non-relevant" (p = 0.757). Cervical HVLA thrust applied to the segment considered as "relevant" altered sensorimotor parameters, while cervical HVLA thrust over the segment considered as "non-relevant" did not. This finding supports the hypothesis that spinal site targeting of HVLA interventions is important when measuring neurophysiological responses. Further studies are needed to explore the potential clinical relevance of these findings.

Non-fibril form but not fibril form of human islet amyloid polypeptide 8-20 changes brain functions in mice.

Whether fibril formation increases or decreases cytotoxicity remains unclear. Aggregation of human islet amyloid polypeptide (hIAPP), a pivotal regulator of glucose homeostasis, impairs the function and viability of pancreatic ß cells. Evidence suggests that low-order oligomers of hIAPP are more toxic to ß cells than fibril. However, it remains unclear whether non-fibril form of hIAPP specifically alters brain functions. This study produced fibril and non-fibril forms from a single hIAPP 8-20 peptide. The non-fibril form-injected mice showed changes in spontaneous motor activities, preference for location in the open field and social behavior. In contrast, the fibril-injected mice showed no changes in these behavioral tests. In line with the behavioral changes, the non-fibril form led to impaired neurite outgrowth of cultured neuron-like cells and the loss of neurons in the mouse hippocampus. These findings suggest that non-fibril form but not fibril form of hIAPP changes brain functions.

The Hopf whole-brain model and its linear approximation.

Whole-brain models have proven to be useful to understand the emergence of collective activity among neural populations or brain regions. These models combine connectivity matrices, or connectomes, with local node dynamics, noise, and, eventually, transmission delays. Multiple choices for the local dynamics have been proposed. Among them, nonlinear oscillators corresponding to a supercritical Hopf bifurcation have been used to link brain connectivity and collective phase and amplitude dynamics in different brain states. Here, we studied the linear fluctuations of this model to estimate its stationary statistics, i.e., the instantaneous and lagged covariances and the power spectral densities. This linear approximation-that holds in the case of heterogeneous parameters and time-delays-allows analytical estimation of the statistics and it can be used for fast parameter explorations to study changes in brain state, changes in brain activity due to alterations in structural connectivity, and modulations of parameter due to non-equilibrium dynamics.

Putative role of endothelin receptor B in the development and maintenance of taste buds within the circumvallate papillae.

This study aimed to achieve a better understanding of taste receptor cell development relative to endothelin receptor B (ETB) in circumvallate papillae (CVP). ETB localization was assessed by immunohistochemistry during tongue development of the mouse. Co-localization of ETB with taste receptor type III cell marker, Synaptosomal-Associated Protein 25 kDa (SNAP25), was evident in both the developing and adult CVP. ETB was strongly localized in the stromal core region. As development progressed, ETB became localized in the CVP mesenchyme and partially in the epithelium. ETB and SNAP25 co-localization indicates that ETB may regulate innervation from the CVP mesenchyme to taste buds.

Neuronal sensorimotor integration guiding salt concentration navigation in Caenorhabditis elegans.

Animals navigate their environment by manipulating their movements and adjusting their trajectory which requires a sophisticated integration of sensory data with their current motor status. Here, we utilize the nematode Caenorhabditis elegans to explore the neural mechanisms of processing the sensory and motor information for navigation. We developed a microfluidic device which allows animals to freely move their heads while receiving temporal NaCl stimuli. We found that C. elegans regulates neck bending direction in response to temporal NaCl concentration changes in a way which is consistent with a C. elegans' navigational strategy which regulates traveling direction toward preferred NaCl concentrations. Our analysis also revealed that the activity of a neck motor neuron is significantly correlated with neck bending and activated by the decrease in NaCl concentration in a phase-dependent manner. By combining the analysis of behavioral and neural response to NaCl stimuli and optogenetic perturbation experiments, we revealed that NaCl decrease during ventral bending activates the neck motor neuron which counteracts ipsilateral bending. Simulations further suggest that this phase-dependent response of neck motor neurons can facilitate curving toward preferred salt concentrations.

Disinhibition Is an Essential Network Motif Coordinated by GABA Levels and GABA B Receptors.

Network dynamics are crucial for action and sensation. Changes in synaptic physiology lead to the reorganization of local microcircuits. Consequently, the functional state of the network impacts the output signal depending on the firing patterns of its units. Networks exhibit steady states in which neurons show various activities, producing many networks with diverse properties. Transitions between network states determine the output signal generated and its functional results. The temporal dynamics of excitation/inhibition allow a shift between states in an operational network. Therefore, a process capable of modulating the dynamics of excitation/inhibition may be functionally important. This process is known as disinhibition. In this review, we describe the effect of GABA levels and GABAB receptors on tonic inhibition, which causes changes (due to disinhibition) in network dynamics, leading to synchronous functional oscillations.