Frontiers in Systems Neuroscience

Derived from previous observations on equal and cross-frequency coupling, we evaluated the proposal that equal and cross-frequency phase synchronization may characterize the integration-segregation perspective of cerebral sensory-motor processing. Using brain recordings obtained in normal conditions and in conditions of diminished sensory input (eyes closed wakefulness, sleep and coma, when there is presumably less functional segregation of sensory-motor processing in neural networks), we assessed potential differences in partitioning of the synchrony state space linked to cross-frequency synchronization. More partitions were found in conditions of decreased sensory input. In addition, there was a less complex synchrony state space in cross-frequency as compared with equal-frequency coupling, in terms of fewer connectivity configurations. These results support the idea that equal-frequency coupling favours integration from multiple brain regions occurring in a complex synchrony state space rich in possible connectivity configurations, whereas cross-frequency coupling contributes to segregation, or localized sensory-motor transformations taking place in specific brain areas. This evidence may contribute to new considerations about the much-discussed role of multi-frequency relations in neuronal activity, and how the structural and functional modular organization of the nervous system is able to generate the coordinated activity needed for conscious and appropriate cognitive behaviors in complex environments.

Economic choices are believed to depend on the orbitofrontal cortex (OFC). Work in primates and rodents indicates that neurons in OFC participate in computing and comparing subjective values, suggesting that different groups of cells constitute the building blocks of a decision circuit. In a recent study (Livi et al., 2025), we examined the laminar organization of this circuit in mice. We found that different decision variables were differentially represented in layer 2/3 (L2/3) and layer 5 (L5). Furthermore, the temporal dynamics of decision signals indicated a combination of feed-forward and feed-back across layers, and pointed to L5 as the locus for winner-take-all value comparison. Importantly, these results were obtained under the constraint that each neuron encoded a single variable. Here, we tested whether our results on laminar organization depended on the categorical framework. We applied LASSO regression to identify a minimal set of variables explaining each neurons activity. Even with approximately half of all neurons representing two or more variables, the layer specificity of decision variables was preserved. In addition, Granger Causality Analysis and activity profiles reached similar conclusions as for analyses conducted under the single-variable constraint. We conclude that the decision circuit in OFC exhibits a laminar architecture, independently of whether the representation of decision variables in this area is categorical or mixed.

Vocal communication involves a series of cognitive processes, which can be broadly categorized into three components: perceiving communicative signals; deciding whether and how to respond; and generating vocal motor output. These processes must work harmoniously, with integration and bridging between components being crucial for effective communication. Previous research on vocal communication has typically focused on specific brain regions or isolated cognitive functions, often lacking a holistic perspective of macro-scale, whole-cortical dynamics and their role in the complete communication process. Therefore, although the cortical areas associated with each cognitive component have been localized in humans, the macro-scale cortical dynamics underlying the integration of these cognitive processes remain unknown. Building on recent findings linking macro-scale cortical dynamics to behavioral performance, we hypothesized that traveling wave like cross-areal interactions play a role in integrating the three communicative components. To test this hypothesis, we recorded whole-cortical activity using epidural electrocorticography (ECoG) while subject marmosets vocally interacted with partners. We found theta-band activation in several cortical areas, including the parietal and auditory cortices, while listening to partners calls. This activity was further modulated depending on whether the subjects engaged in vocal interactions, potentially representing the transformation of sensory processing into decision-making and vocal motor preparation. Given the widespread nature of this modulation, we next characterized whole-brain activity patterns by employing a novel analytical method, Weakly Orthogonal Conjugate Contrast Analysis (WOCCA). This analysis revealed that cortical activity could be decomposed into two distinct traveling wave like propagation patterns, a rotational and a translational wave, and both waves discriminated communicative conditions consistent with localized activity. The rotational wave further represented vocal motor preparation through trigger-like temporal pattern. In addition, the magnitude of the translational wave immediately before subjects vocal production correlated with the vocal production-induced suppression of high-gamma-band activity, particularly in the prefrontal and auditory cortices. As vocalization-induced suppression is believed to reflect sensory prediction, the translational wave may propagate specific decision-related or acoustic information necessary for subsequent vocal production to local cortical areas. These findings suggest that the brain orchestrates the sequential cognitive processes underlying vocal communication through macro-scale traveling waves.

Growing interest to describe the electrical behavior of glial cells, mainly astrocytes, in intact brain tissue poses more and more challenges to commonly accepted belief they only respond in a linear manner in uptake of the excess of extracellular potassium and maintenance of their network equipotentiality. Their highly conductive mutual interconnections via gap junction (GJ) connections introduce yet another class of nonlinear elements. As more studies report nonlinearities in membrane voltage Vm dependence of both, the membrane and junctional conductances, the need to formulate minimal dynamical models of their transient behavior is getting more acute. Since ODE models of coupled cells, even in simplest 1-d arrays, require simplified descriptions and small set of parameters, rare quantitative studies on glia makes the task even more difficult. This study attempts to qualify a self-coupled cell, or a glial cell coupled to fixed voltage as useful system for detecting the nature of instabilities and transitions coming from coupling. In a novel biophysical model of coupled astrocyte, we introduce nonlinear kinetics of deactivation for large junctional voltages for the first time. We found that N-shaped nonlinearities and corresponding fold structure in the vector field of isolated cell serves as a baseline on top of which coupling nonlinearities enrich the bifurcation picture. Numerical simulations of 1-d array of coupled astrocytes show that coupling increases the propensity of astrocytic Vm to bistability and front propagation. We believe that presented illustrations of possible effects of coupling nonlinearities will motivate neurobiologists to further explore their impact in disease. Significance statementTransient changes in membrane voltage of glial cells may produce significant transient voltage difference between directly coupled cells. Nonlinear steady-state conductance of their interconnection elements, the gap junctions, introduce nonlinear current profiles which are very difficult to measure and quantitate using the available methods due to marked permeability of the junctions and leakiness of glial membrane in general. We propose a minimal model of glial membrane extended with a self-coupled feedback loop, which under realistic simplifying assumptions could serve for qualitative analysis of the impact of coupling, on the stability of resting membrane voltage. Neuronal cells of the brain and spinal cord cannot exist and function without supportive and neuromodulatory functions of the diverse population of glial cells. This applies to virtually all physiological processes on cell level - from cell development, metabolic support, membrane signaling, slow molecular signal transduction, ion homeostasis, neurovascular coupling, myelination, to mention only a few, manifest neuro-glial interaction. Even though all glial cell types are interconnected, the most abundant ones, the astrocytes are massively interconnected by gap junctions to form ordered networks. Electrically, astrocytic networks display membrane voltage equipotentiality, which is considered system-wide resting state for given neuro-glial circuit or unit. With molecular and cellular substrates of glial connectivity being slowly elucidated, network science and dynamical modeling are slowly "invading" that area with many important issues left open. In this study using classical dynamical systems approaches we give indications how nonlinear intercellular coupling between astrocytes affects physiological resting state and its instabilities compared to isolated, uncoupled cell. We strongly believe the suggested minimal model could fill the gap in ODE modeling of neuro-glial circuits, within broadest scope of hypothesis-driven research in cell-level neuroscience.

The brain is the most captivating chef doeuvre of nature. Naturally then, the mind wonders about the process that births such a fascinating organ. Neurodevelopment is a complex yet robust phenomenon that conceals answers to our questions in its intricacies. In an attempt to shed some light on this matter, we study the developing brain connectome of the nematode, C. elegans across the post-embryonic phase. A tiny organism with only around 200 neurons comprising its brain and yet a diverse array of behaviors to display, it makes for a great model. Starting with most of its head neurons already present at hatching, the worm brain accumulates numerous more synaptic connections increasing the edge density. It maintains a weak connectivity throughout thereby, balancing global communication as well as hierarchy. At the mesoscopic level, we find that the core has a conserved backbone of persistent neurons along with a dynamic component formed of transient/recurring neurons. Moreover, the connectome has a rich club organization since the early stage which selectively strengthens indicating progressively denser connectivity among the integrators due to the previously reported asymmetric synapse addition. This asymmetry also shows up in the preservation of input hubs across development and the progressively more centralized organization of the in-degree k-core. Our work provides a new perspective into the neurodevelopment of the brain that may facilitate our understanding of its functioning.

Theta (4-10 Hz) oscillations seem well-suited for coordinating neural activity. Many studies have focused on thetas role in long-range coordination across brain regions (e.g., connectivity between the prefrontal cortex and the hippocampus). It remains unclear how theta coordinates neural activity more locally within prefrontal subareas. We examined neural activity in three frontal areas (i.e., dorsolateral prefrontal cortex (dlPFC), ventrolateral prefrontal cortex (vlPFC), and frontal eye fields (FEF)) as non-human primates categorized dot patterns. We found that theta flexibly coordinated spiking activity and higher-frequency oscillations within and between frontal areas. First, theta phase in all areas was coupled to spiking information in the FEF and seemed to gate information that was behaviorally relevant. Second, theta influences were routed in opposite directions depending on feedback. Theta flowed in a posterior direction to the FEF during choices and after correct outcomes. Theta influences reversed directionality and flowed in an anterior direction after incorrect outcomes. Third, theta organized nested cross-frequency, phase-amplitude interactions. Theta was coupled to beta (15-30Hz) oscillations, both within and between areas. Beta, in turn, was coupled to gamma (40-90Hz) oscillations, but mainly locally. Together, our results position theta as a critical mechanism that flexibly and dynamically coordinates neural activity within and across the frontal cortex. HIGHLIGHTS- Neural activity was recorded in three frontal areas as non-human primates categorized dot patterns. - Theta (4-10Hz) oscillations gated relevant information in spiking. - Theta influences were routed in opposite directions depending on feedback. - Theta organized nested cross-frequency interactions with beta (15-30Hz) and gamma (40-90Hz) oscillations. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/729810v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@1b4a6a4org.highwire.dtl.DTLVardef@12886feorg.highwire.dtl.DTLVardef@1b6c08dorg.highwire.dtl.DTLVardef@11ba768_HPS_FORMAT_FIGEXP M_FIG C_FIG

Homeostatic plasticity of intrinsic excitability (IE) in the visual system has been essentially shown at the cortical level but whether thalamic nuclei also express homeostatic plasticity of IE is unknown. We show here that 4 days of monocular deprivation (MD) at eye opening induces a homeostatic change in IE in dorsal lateral geniculate nucleus (dLGN) neurons. Neurons recorded in the dLGN region activated by the deprived eye are more excitable than neurons recorded in the dLGN region activated by the open eye. No significant changes were observed following 7 days of MD, however. Enhanced excitability in neurons from the deprived side after 4 days of MD was associated with a reduced Kv1-dependent LTP-IE, a smaller voltage ramp, and a reduced inter-spike interval, suggesting that Kv1 channels are down-regulated in deprived dLGN neurons. Furthermore, the ankyrin G signal of the axon initial segment was larger in deprived dLGN neurons compared with open ones, indicating that Nav1 channel number also undergoes homeostatic regulation, and Kv1.1 channel signals were lower in deprived neurons compared to open ones. In addition, electrical coupling was found to be strengthened in neurons displaying enhanced IE following either brief (4 days) or long (10 days) MD. These results suggest that homeostatic and Hebbian plasticity in the dLGN share common expression mechanisms involving the regulation of Kv1 channels, Nav1 channels and electrical coupling between relay neurons.

Vestibular neurons are core elements of the pathways involved in vestibulo-motor functions, such as vestibulo-spinal and vestibulo-ocular reflexes. To meet behavioral needs, electrophysiological neuronal properties are adequately adapted to the sensory-motor computation sustaining these distinct vestibular reflexes. During frog metamorphosis, there is a complete reorganization of the posturo-locomotor system while the oculomotor system remains minimally changed, probably associated to so far unknown changes in vestibular neuronal properties. We used this unique model to investigate the central developmental mechanisms underlying such a reconfiguration of vestibular-associated behaviors. Central vestibular neurons exhibit two types of electrophysiological phenotypes: tonic neurons with a continuous discharge and phasic neurons with a transitory discharge mainly due to the activation of Kv1.1 channel. Electrophysiological recordings and Kv1.1 immunolabeling of vestibulospinal (VS) and vestibulo-ocular (VO) neurons at both larval and juvenile stages revealed that the majority of VS neurons exhibited a tonic discharge in larvae but a phasic discharge in juvenile, while VO neurons remained mainly tonic throughout development. Changes in phasic and tonic neurons proportions in VS population are partly explained by neurogenesis. But we provide evidences that an electrophysiological phenotype switch is a concomitant developmental mechanism participating in the maturation of these central vestibular neurons. All together our results showed that the maturation process in central vestibular neuronal groups is highly related to the metamorphosis-induced remodeling of vestibulo-motor functions they are involved in, with the ultimate purpose of ensuring an adequate adaptation of neuronal elements properties to the developmental changes of behavioral constrains.

The phenomenon of splitting was originally observed in hamsters which, after prolonged exposure to constant light, exhibit two rest/wake cycles within a subjective day. Splitting is a consequence of the left and right suprachiasmatic nuclei (SCN) falling out of synchrony. While it is known that split activity is characterized by an antiphase relationship between the left and right SCN and between the core and shell within each hemisphere, the role of the commissural projections that connect the right and left SCN is not known. In the present study, we investigate the impact of the inter-hemispheric connections on the split and unsplit dynamics of a computational model of the bilateral SCN. Our model has 4 nodes corresponding to each right and left core and shell. We simulated our bilateral model under different lighting conditions and measured its period and the phase relationships among the 4 nodes. To further characterize the dynamics of the system, we performed a bifurcation analysis. We found that the bilateral model automatically splits unless entrained by bright light/dark cycles, or unless it has excitatory inter-hemispheric connections. This suggests that excitatory cross-connections may be important for freerunning behavior. We found that constant light of varying intensities transitions the model between split and unsplit activity only in very limited conditions, but the strength and polarity of the contralateral connections play a much greater role in this dynamical transition. These findings suggest that splitting may involve plasticity of the inter-hemispheric connections of the SCN.

The goal of the resting-state functional connectivity studies is to determine the inherent dynamics of the brain networks while the body is at rest. These networks get differentially activated when the brain is involved in various tasks such as processing of sensory inputs, initiating motor activities, or various cognitive tasks. Resting state functional connectivity networks are commonly revealed by determining Pearson Correlation Coefficients of the Blood Oxygenation Level Dependent (BOLD) signals collected from different brain regions using functional Magnetic Resonance Imaging (fMRI) while the subject is not actively performing any task. However, the functional connectivity thus determined does not correlate well with the known structural connectivity between different brain regions. Here, we used Empirical Mode decomposition (EMD), followed by Hilbert Transformation (HT), to determine the resting state functional connectivity of the somatomotor network in the human brains. We show that the time series data decomposed by this method improves correlation of the derived functional connectivity with the known structural connectivity (especially for low -TR fMRI data) as compared to the methods commonly used.

The hippocampus participates in crucial functions such as memory consolidation, spatial processing and emotional regulation that require diverse input from multiple cortical areas that is funneled through the upper layers of the entorhinal cortex (EC), mostly from layer II to the dentate gyrus (DG). Traditional models described 200,000 EC layer II neurons projecting to 1 million granule cells (GCs) in the rat, rendering low divergence (1:5), with each EC neuron establishing about 18,000 synapses with GCs and each GC receiving about 4,000 synapses from EC neurons. In this manuscript, we update this model of connectivity incorporating new features described in the last three decades that include updated populations of EC layer II neurons obtained with design-based stereology, a revised definition of EC layer II based on molecular criteria and selecting reelin expressing neurons as the only layer II neurons projecting to the hippocampus. The updated model shows [~]80,000 neurons from EC layer II projecting to the DG, [~]45,000 from the medial entorhinal cortex (MEC) and [~]35,000 from the lateral entorhinal cortex (LEC) with high divergence of 1:20 and 1:30. We also show that EC layer II neurons may establish [~]90,000-115,000 synapses on GCs, while GCs receive about 8,000 synapses from EC layer II neurons. We estimate a [~]25% redundancy in the connectivity, so each EC neuron may contact [~]68,000-86,000 GCs and each GC would be contacted by [~]3000 neurons from MEC and 3,000 from LEC. In addition, we quantitatively assess a potential projection of mossy cells to the medial molecular layer described in mice, which could have a potential impact on GC inhibition. Overall, we produced a detailed, complete, and updated quantitative model of EC projections to the DG that reveals a much more divergent and richer projection than previously described, with implications for functional models (e.g.: pattern separation) and more widely for building realistic hippocampal models or establishing comparisons across species.

Cortico-thalamo-cortical circuits entail extensive trans-thalamic connectivity between cortical areas, yet their structural organization and function remain poorly understood. Here, the thalamocortical projections of several higher-order thalamic nuclei were characterized by retrograde tracing from two cortical areas, the primary somatosensory (S1) and motor (M1) cortices. Cholera toxin B conjugated with different fluorophores allowed for simultaneous detection of projection neurons targeting S1 and M1. A cell detection pipeline based on neural networks was developed to allow semi-automated analysis of large thalamic imaging volumes to quantitatively infer the spatial distribution of projection neurons in the posterior complex (PO) and the adjacent ethmoid nucleus (Eth), nucleus centrolateralis (CL), nucleus paracentralis (PCN), and the nucleus parafascicularis (PF). The arrangement of neurons projecting to both, primary somatosensory and motor cortices, occurs at different connection strengths and was topographically organized in all nuclei studied. Co-injections into both cortical areas revealed projection neurons with axons branching into both S1 and M1 cortices. Our work introduces a pipeline for semi-automated quantitative analysis of thalamic projection patterns that could be useful for connectivity analyses in general. This approach revealed repetitive anatomical patterns in different thalamic nuclei with regard to projection strength, spatial organization and fraction of projection neurons targeting two cortical areas simultaneously.

Interactions between distinct populations of excitatory (E) and inhibitory (I) neurons can produce complex dynamical landscapes, featuring multistability, oscillations, and paradoxical perturbation responses. By employing an elementary model, the threshold-linear network (TLN), we indicate mathematical conditions for each dynamical regime across fundamental microcircuit architectures, thereby mapping previously unrelated systems neuroscience hypotheses to a common reference space and obtaining novel insights on inputs and connectivity. Namely, we compare balancing strategies in inhibition-stabilized E-I networks, we interpret experiments on gamma oscillations in a canonical neocortical E-I-I circuit, and we discuss bistability in hippocampal E-I-I networks. Then, we show that connectivity determines three fundamentally different kinds of interactions between assemblies in E-E-I circuits. Moreover in, E-E-I-I circuits we find that balanced clustering hinders lateral inhibition, while opponent clustering can produce different bistable configurations, even between completely unstructured assemblies. We conclude that TLNs allow to grasp deep and universal aspects of microcircuit dynamics.

The primary objective of system neuroscience is to understand the functional mapping and its causation in the dynamics of the brain network. Some experimental and methodological studies suggest that functional modularity and its hierarchical information processing in the brain network are crucial to understanding the functional role of task-specific or state-specific information flow in the brain. However, because most of the established techniques for detecting effective network structures in the neuroscience research field are strongly based on the "Granger causality" perspective, existing causal discovery methods specified for brain network analysis cannot identify the causal hierarchy in the modular network in the brain due to spurious correlation issues and indistinguishability of causal direction under the Gaussianity of observational noise in a linear system. To address the issues, we developed a causal discovery method for synchronous neural dynamics, called the Jacobian-informed linear non-Gaussian acyclic model, "j-VAR-LiNGAM", by incorporating the information of the Jacobian matrix determined from a phase-coupled oscillator model estimated from observed neural data into the VAR-LiNGAM algorithms. The method was validated by showing that it could extract causal ordering in both synthetic data and empirical neural observed data. Moreover, by analyzing the observed neural oscillatory signals obtained from mice and humans, we confirmed that our method identified causally hierarchical structures in the brain, which aligned with the neurophysiological interpretations. These findings suggested that our proposed method can reveal the neural basis of hierarchical information processing in the brain network.

BackgroundThe relevance of spontaneous activity has been unlocked thanks to recent large scale recordings that revealed, via Shared Variance Component Analysis (SVCA), the high-dimensional nature of the ongoing activity. A fundamental problem is how the dimension modifies when more neurons are included in the analysis. Contradictory results have been reported on this subject based on SVCA and Principal Component Analysis (PCA). New MethodWe investigate pro et contra of SVCA and PCA for the identification of reliable responses encoding underlying state variables. We focus on common features of the spectra of the reliable variances (RVs) and on their dimensionality. The analysis is demonstrated on previously published Ca2+ data from the visual and the dorsal cortex in head fixed mice during spontaneous behavior. ResultsRVs grow proportionally to the number N of neurons and show a power-law decay k- with the k-th SVC dimension over a range bounded by a maximal dimension kc, initially diverging as N 1/ and then saturating at sufficiently large N. The reliable dimensionality, estimated with different methodologies, also shows a clear saturation to an asymptotic value for large N. Furthermore, its value decreases when becomes larger, as demonstrated by employing experimental data as well as theoretical predictions. ConclusionWe have shown that SVCA is an extremely effective tool to extract reliable features from the neural signals, and that the exponent represents a biomarker able to reveal the level of correlation of the neurons as well as the dimensionality of the reliable space. HighlightsO_LIAdvantages and drawbacks of Shared Variance Component Analysis to extract reliable signals from neural data C_LIO_LIComparison of different methods to estimate reliable neural dimensionality associated to spontaneous activity C_LIO_LIAnalytical expressions of embedding dimensionality for power-law decaying reliable variances C_LIO_LIBounded growth of the dimensionality with the number of neurons C_LI

Hippocampal area CA2 has recently emerged as a critical region for social recognition memory. Furthermore, this understudied region has been implicated in psychiatric diseases and neurodegenerative diseases. There has been accumulating evidence indicating that the pyramidal neurons (PNs) in area CA2 exhibit functional specializations that correlate with somatic position in stratum pyramidale (sp). In this study, we investigated the morphological differences in dendritic architecture of CA2 PNs with a focus on the radial gradient, i.e., along the deep-superficial axis of the sp. We conducted a comprehensive morphological analysis including Sholl intersection profiles, branching order distributions, root angle distributions, and dendritic cable lengths. We found that CA2 PNs have fewer oblique dendrites and a larger number of tuft-like dendrites as compared to CA1 PNs. Furthermore, within the CA2 population, we found that many of the dendritic structural features gradually changed along the radial axis from deep to superficial somatic location, indicating a continuum of dendritic morphology rather than two sharply defined subtypes of pyramidal neurons. This morphological characterization may serve as a starting point to better understand the corresponding functional organization of CA2. The gradual difference between deeper and superficial CA2 PNs suggests a continuum of their computational capabilities beyond two binary functional classes. In briefUsing several methods, we examine the dendritic morphology of over 130 CA2 and CA1 pyramidal neurons and find that many properties such as the cable length and terminal numbers of the dendritic arbors vary as a with the location of the soma in the pyramidal layer. HighlightsO_LIWe use scholl analysis, graph theory and machine learning techniques to quantify the different dendritic morphologies of CA2 pyramidal neurons. C_LIO_LIMany properties of CA2 pyramidal neuron apical dendrites vary as a function of somatic location in the pyramidal layer. C_LIO_LIMore superficial CA2 pyramidal neurons have longer oblique apical dendrites, and shorter tuft dendrites. C_LI

Proprioception refers to the sense of position and movement of the body and is essential for the planning and execution of movements, especially skilled hand movements. The somatotopic organization of the fingers in the human sensorimotor (SM1) cortex has previously been studied using passive tactile stimulation and active movements. However, the finger representations in response to proprioceptive stimulation in the SM1 cortex are incompletely understood. To address this, we investigated cortical representations of the index and ring fingers during kinematically matched passive and active finger movements using 3T fMRI and an MRI-compatible proprioceptive stimulator. Both active and passive conditions elicited significant activations in the SM1 cortex, with stronger and more extensive responses observed during the active condition. The results indicated that volitional activation of the fingers intensifies cortical proprioceptive processing in the SM1 cortex. This observation could be explained by the active state of the SM1 cortex and the increased sensitivity of peripheral proprioceptors during the active condition. Key pointsVolitional activation of the fingers intensifies cortical processing of proprioceptive afference and modulates the organization of finger representations in the SM1 cortex. This could be explained by the active state of the SM1 cortex and the increased sensitivity of peripheral proprioceptors during the active condition. Proprioceptive finger representations followed the classical somatotopic organization at the group level, although this organization was not consistently observed across all subjects.

Elite athletes exhibit sport-specific neural adaptations, yet it remains unclear whether such changes reflect general effects of training or the unique demands of individual sports. Skiing requires postural control and whole-body coordination under dynamically unstable environments, placing high demands on somatosensory processing and sensorimotor integration. The present study aimed to identify structural brain characteristics specific to elite skiers by comparing them with athletes from other sports disciplines and non-athletes. T1-weighted MRI data were analyzed using voxel-based morphometry in 13 skiers, 23 non-ski control athletes and 25 non-athletes. Whole-brain analysis comparing skiers with non-ski athletes revealed a significant cluster showing greater gray matter volume in skiers compared with non-ski athletes in the left postcentral gyrus, extending into the superior parietal lobule. The identified cluster primarily encompassed cytoarchitectonic Areas 2 and 5L. These regions are involved in higher-order somatosensory processing and multisensory integration. Importantly, region-of-interest analysis demonstrated that gray matter volume within this cluster was greater in skiers compared with non-ski athletes and non-athletes, with no difference between non-ski athletes and non-athletes. These findings highlight the relative prominence of structural adaptations within somatosensory-parietal networks, reflecting the unique integration of proprioceptive and other sensory information required for elite skiing. Overall, these findings provide evidence for sport-specific structural brain differences in elite athletes and highlight the importance of somatosensory and parietal regions in sensorimotor integration relevant to skiing. These findings may have implications for understanding neural markers of expertise and may inform future approaches to training and performance evaluation in skiing.

Understanding how complex systems self-organize, exhibit emergent properties beyond their constituent elements remains a challenge across physics, biology, and cognitive science. In resource-constrained neuronal systems, existing theoretical approaches, including gauge theoretic formulations, statistical physics-inspired methods, dynamical population models, and variational principles such as the Free Energy Principle, address important aspects of this problem but do not fully specify the physical conditions and thermodynamic costs under which self-organizing behavior occurs. Here, we introduce Dynamic Resource Theory (DRT) as a general physical framework for describing self-organization under constrained resource availability. DRT formalizes complexity as a physical property of self-organizing systems arising from coupled mechanisms of resource allocation and dynamic reallocation of internal resources. This framework provides a thermodynamic and variational account of how stability is preserved while adaptive reconfiguration remains possible, consistent with stationary action and thermodynamic constraints. DRT is formulated within a gauge theoretic setting and directly incorporates the energetic costs associated with maintaining structure and enabling system-level reconfiguration. Within DRT, baseline resource allocation preserves system stability, while internal and external demands perturb the system, driving self-organization through dynamic resource reallocation across a coupled free energy landscape without assuming subsystem separability. We then develop Neural Resource Theory (NRT) and Cognitive Resource Theory (CRT) as principled specializations of DRT, illustrating how this structure is instantiated in resource constrained neuronal and cognitive systems. We conclude by discussing the broader implications of DRT for understanding how complexity, emergence, and adaptive capacity arise over time through thermodynamically permissible reallocation processes across scales.

Sudden and isolated sensory stimuli (SISS) engage the extralemniscal system and elicit widespread electrocortical responses in the brain. These responses, consisting of both time-domain transients and spectral changes, reflect a switch of the global brain state that likely prepares the organism for subsequent urgent behaviours. Crucially, SISS also elicit a short-latency phasic response in a key component of the extralemniscal system in the brainstem, the noradrenergic Locus Coeruleus (LC) nucleus. Such stimulus-evoked LC firing is associated with the electrocortical markers of extralemniscal activation. LC neurons also display burst-like firing spontaneously, i.e., without imposed sensory stimuli, for example, during quiet wakefulness, sleep, or anaesthesia. However, this phenomenon remains underexplored. We therefore measured, in freely behaving rats, the prefrontal electrocorticogram (ECoG) responses following spontaneous LC bursts. In addition, we compared these ECoG responses to those triggered by electrical LC stimulation or auditory SISS. We found that ECoG responses were proportional to the magnitude of the spontaneous LC bursts or microstimulation, and remarkably similar to those elicited by SISS. Finally, suppression of noradrenergic transmission with systemic clonidine administration attenuated the auditory-evoked ECoG response. These results suggest that LC plays a role in generating the transient brain state changes elicited by SISS.