Frontiers in Systems Neuroscience

Emerging evidence suggests that cortical activity is organized in traveling waves that coordinate neural activity across space and time. How anesthesia alters these waves remains underexplored. We recently showed that cortical activity undergoes spontaneous state transitions at steady-state anesthetic concentrations including a paradoxical state exhibiting awake-like spectral properties during deep anesthesia. Here, we investigated traveling wave dynamics across spontaneous cortical states using hemispheric electrocorticography in rats anesthetized with desflurane at inhaled concentrations of 6, 4, 2, and 0%. Compared with the awake state, delta-band traveling waves in cortical states predominantly associated with 4-6% desflurane were more frequent and exhibited more stereotyped propagation patterns, characterized by a greater prevalence of planar waves and a corresponding reduction in source/sink wave patterns. The occurrence rate and pattern complexity of theta- and gamma-band waves remained largely unchanged, whereas the propagation direction of planar waves became more variable. Feedforward-feedback organization was also altered: compared with the awake state, the feedback-dominance of theta-band diminished, and the feed-forward dominance of gamma-band was attenuated. Despite occurring predominantly in deep anesthesia associated with behavioral unresponsiveness, traveling-wave dynamics of the paradoxical state exhibited partial, frequency-dependent shifts toward those observed in the awake state. These findings demonstrate that spontaneous cortical states under anesthesia are associated with frequency-dependent reorganization of cortical traveling waves and identify the paradoxical state as a distinct dynamical regime of deep anesthesia. Significance StatementAnesthesia is commonly thought to alter cortical dynamics progressively with increasing anesthetic depth, yet cortical activity can transition spontaneously between distinct states even at constant anesthetic concentrations. Here, we show that cortical states spectrally derived from the electrocorticogram of rats are associated with distinct frequency-specific organization of cortical traveling waves, revealing spatiotemporal dynamics beyond conventional spectral measures. Notably, a paradoxical state, occurred predominantly in deep anesthesia associated with behavioral unresponsiveness, exhibited traveling-wave dynamics that approached those observed during wakefulness. These findings demonstrate that cortical traveling-wave organization changes dynamically with brain state rather than anesthetic concentration alone. They suggest that structured cortical dynamics can emerge during deep anesthesia, providing new insights into large-scale cortical dynamics associated with anesthetic modulation of consciousness.

This paper introduces a sensorimotor learning framework for a corticocerebellar network, grounded in the perspective of population dynamics. Using an optimal control theory approach, the cerebellum model enhances preparatory activity through premotor input, allowing the motor cortex to reach the desired initial conditions for movement more efficiently. Unlike traditional motor learning approaches that focus on acquiring new skills, this paradigm emphasizes automatization of already executable behaviors through repetition driven by intrinsic motivation. The proposed model is evaluated using a center-out reaching task, demonstrating that the role of the cerebellum is to shorten the preparatory period required for the successful execution of the movement. These findings suggest that corticocerebellar interactions play a crucial role in optimizing motor preparation, offering insight into the neural mechanisms underlying movement efficiency.

Background: Brain banks usually provide small tissue blocks fixed by immersion in neutral-buffered formalin (NBF). While still underexploited for research, gross anatomy laboratories could provide full brains fixed by perfusion with solutions better suited for gross anatomy dissection. However, the chemicals in these solutions might have a different impact on histology protocols for cell quantification than in NBF-fixed brains. The main goal of this study is to compare the effects on the number and size of labeled neurons of the primary motor cortex (PMC) of mouse brains fixed with three different solutions: (1) NBF, typical of brain banks, (2) a saturated salt solution (SSS), and (3) an alcohol-formaldehyde solution (AFS), both used in human anatomy laboratories. Methods: 27 C57BL/6J mouse brains were perfused with the NBF (N=9), SSS (N=9) or AFS (N=9), then cut in 40-m slices and processed with immunohistochemistry to target neurons. Various quantitative variables were assessed manually and automatically on photomicrographs of 3 regions of interest (ROIs) of the PMC per specimen, namely the total and individual neuronal profile areas, number and diameters. The effects of the three fixatives on these variables were compared using ANOVA or Kruskal-Wallis, depending on the distribution. For measures on individual cells, a generalized linear mixed model was applied. Dice coefficients and correlations were applied to evaluate the agreement of the manual and automatic methods. Results: There was no significant difference between the brains fixed by the three fixatives for the total and individual cell areas, the total cell count and the cell diameters. The values obtained from manual and automatic measures had an overall good agreement (Dice coefficients > 0.79). Conclusion: It was found that the SSS and AFS had similar impacts on the quantitative variables in the tissue as the NBF. These results are promising for neuroscientists interested in using brains from anatomy laboratories for quantitative research on neurons from the PMC.

Cueing paradigms are commonly used to study the neural mechanisms of visual spatial attention control. In these paradigms, each trial starts with an external cue, which instructs the subject to pay covert attention to a spatial location in anticipation of an impending stimulus (instructed attention). Recent work has introduced a new type of cue which prompts the subject to spontaneously decide which spatial location to attend (willed attention). We studied the neural mechanisms of willed attention control by analyzing fMRI and EEG data recorded at two institutions (UF and UC Davis) using the same willed attention paradigm. The findings include: (1) both instructional cues and the choice cue activated the DAN, (2) the choice cue additionally activated a frontoparietal decision network consisting of dorsal anterior cingulate cortex (dACC), anterior insula (AI), anterior prefrontal cortex (APFC), dorsal lateral prefrontal cortex (DLPFC), and inferior parietal lobule (IPL), (3) the decision about where to attend can be decoded in frontoparietal decision network in choice trials but not in instructional trials, and (4) EEG alpha oscillation patterns immediately preceding the choice cue, but not the instructional cues, predicted the postcue direction of attention and the frontoparietal decision network activity. Based on these findings we proposed a model of willed attention control suggesting how the direction of visual spatial attention was decided upon in the absence of external instructions.

Consciousness is a dynamic brain state, yet the systems-level mechanisms underlying transitions into and out of unconsciousness remain poorly understood. It is unclear whether neural dynamics during loss of consciousness (LOC) and recovery of consciousness (ROC) simply retrace the same trajectory or instead follow distinct paths across multiple spatial scales. Here, we simultaneously measured local electrophysiology, whole-brain functional MRI, and pupil dynamics in rats during graded propofol anesthesia to characterize consciousness transitions from local circuits to whole-brain networks. We found that local field potential, regional BOLD responses, and pairwise functional connectivity exhibited largely reversible changes between LOC and ROC. In contrast, the global brain organization showed distinct and asymmetric patterns during the two transitions, as consistently revealed by traveling-wave propagation, low-dimensional network trajectories, and graph-theoretical analyses. Importantly, brain-wide coupling between pupil dynamics and regional BOLD activity remained highly consistent during LOC and ROC, indicating that these distinct global trajectories cannot be simply explained by differences in neuromodulatory tone. Together, our findings identify scale-dependent reversibility as a systems-level organizing principle of consciousness transitions. These results suggest that recovery of consciousness is an active process of large-scale network reorganization rather than merely the reversal of anesthetic suppression.

Understanding how neural circuits transform sensory and bodily signals into motivational states and adaptive behavior is a central problem in neuroscience. In teleost fish, the dorsomedial telencephalon (Dm) is a key pallial region implicated in both sensory processing and aversive behavior, yet whether these functions arise from a functionally uniform region or from interactions among specialized pallial domains has remained unknown. Here we show that the teleost dorsomedial telencephalon exhibits a previously unrecognized functional organization in which distinct but interconnected pallial domains perform complementary computations that progressively transform multimodal sensory and bodily representations into aversive motivational value and adaptive behavioral control. Wide-field voltage-sensitive dye imaging revealed that tactile, auditory, and gustatory stimuli evoke spatially organized, modality-specific activity exclusively within the caudal subdivision of Dm (Dmc), whereas the rostral subdivision (Dmr) showed little or no sensory responsiveness. In contrast, focal intracerebral microstimulation demonstrated that activation of Dmr, but not Dmc, is sufficient to generate robust, flexible, and reversible conditioned place avoidance, identifying Dmr as a pallial node causally involved in the assignment of negative motivational value. Anatomical tracing revealed a circuit in which sensory and bodily-related inputs converge onto Dmc, are relayed intrapallially to Dmr, where they are transformed into an aversive motivational signal before being conveyed to hypothalamic and brainstem centers involved in autonomic and behavioral regulation. Immunohistochemical analyses confirmed the pallial identity of both subdivisions and their distinct rostrocaudal organization, while providing no evidence that Dm corresponds to a classical pallial amygdaloid territory. This functional architecture more closely resembles the distributed organization of mammalian corticolimbic networks than either a unitary pallial amygdala or a neocortical sensory hierarchy, suggesting that the transformation of sensory and bodily representations into motivational control may represent a conserved organizational feature of the pallium that emerged early during vertebrate evolution. Short abstract / Significance statementThis study shows that the teleost dorsomedial pallium is organized into complementary functional domains that dissociate multimodal sensory representation from negative motivational processing while forming an interconnected pallial circuit associated with adaptive behavioral control. Our findings reveal a distributed pallial organization resembling mammalian corticolimbic architectures and provide a new framework for understanding the evolution of vertebrate pallial function.

Objects moving between the left and right visual hemifields are naturally perceived as continuous entities, although early visual processing independently transmits information from the two hemifields. Therefore, interhemispheric integration of visual information is essential for maintaining an object's identity. Additionally, brain function is thought to be maintained through a dynamic balance between integration and segregation. In this study, we investigated the functional neural architecture underlying visual hemifield integration in healthy adults, using electroencephalography (EEG) and a visual integration task. To capture neural oscillatory networks without relying on prior assumptions regarding electrode pairs or frequency bands, we applied a frequency-inclusive, data-driven network analysis based on an extended network-based statistic. This analysis identified a broadband EEG phase synchronization network that emerged specifically under task conditions with high interhemispheric integration demands. Furthermore, individual differences in behavioral performance were associated with modulation of interhemispheric synchronization, with this relationship differing according to participants' relative performance across task conditions. These findings suggest that visual hemifield integration is supported by large-scale phase synchronization networks spanning multiple frequencies and are consistent with the importance of a balance between integration and segregation.

Classical neurogeometric models describe the primary visual cortex as a fibered structure in which retinal position and local orientation are coupled through the geometry of the roto-translation group. We extend this approach to the visuomotor cortex by modeling it as an assemblage of visual and motor cortical geometries. The model combines orientation-selective representations, analogous to those of the primary visual cortex, with movement-direction-selective representations, analogous to those of the primary motor cortex, in order to describe the mixed visual and motor selectivity observed in the visuomotor cortex. We introduce a coupled visuomotor structure in which visual orientation and motor direction coexist over a common spatial plane and interact through a relative-orientation constraint. Neural responses are modeled by orientation- and direction-dependent profile functions, and preference maps are obtained from vectorized population responses. Numerical simulations generate visual, motor, and mixed visuomotor response maps. A competition rule between visual and motor responses produces incidence ratios close to experimental observations in macaque visuomotor cortex. This framework provides a first neurogeometric approximation of visuomotor functional architecture and a mathematical setting for studying visually guided action.

The gastric network, comprised of brain regions whose activity synchronizes with the stomach's slow-wave rhythm, offers a unique window into the brain-body interaction involved in interoceptive processing. While previous work has established the existence of this network, its intrinsic organization and temporal unfolding remain poorly understood. Here, we reanalyzed resting-state fMRI-electrogastrogram data from 43 healthy adults of both sexes to characterize the time-averaged architecture and time-varying reconfiguration of the gastric network. We identified regions exhibiting phase-locked synchronization with the stomach slow electrical rhythm (0.05 Hz) and characterized cortical parcels comprising this network. Time-averaged graph-theoretical analysis revealed a fixed unimodal organization of functional communities, with primary visual, default mode network (DMN) and dorsal attention regions emerging as the principal time-averaged hubs. Next, we applied edge-centric functional connectivity (eFC) to capture the network state during transient high-amplitude "bursts". Time-varying community detection revealed communities whose compositions formed integrative combinations of DMN, visual, attentional and control elements. Edge-derived hubs shifted away from primary visual dominancy in the time-averaged analysis, and were instead directed by DMN regions, suggesting that moments of heightened connectivity in the network are coordinated by multisensory integration rather than passive sensory processing. These findings demonstrate that the gastric network is not merely a time-averaged, sensory-bound system, but rather a flexible and dynamically reconfiguring interoceptive network whose organization is selectively coordinated by transient cofluctuation events. This work provides a comprehensive network analysis of gastric-brain coupling and reveals a temporally structured mode of interoceptive integration that may support adaptive physiological and cognitive regulation.

Prediction errors (PEs) are commonly described as cortical signals generated within sensory hierarchies, but whether the thalamus participates in their encoding and transmission remains unclear. We recorded Local Field Potentials (LFP) from the medial and lateral geniculate nuclei and electrocorticography (ECoG) from multiple cortical regions in three awake cats during two auditory prediction tasks. Mutual information (MI) analyses revealed PE encoding in both thalamic and cortical signals. Co-information (co-I) analyses showed off-diagonal temporal synergy between early and later thalamic response components, consistent with an early response inducing a neural state change that shaped the informational content of subsequent activity. Multivariate co-information (MVCo-I) further revealed that thalamic and cortical population activity carried complementary PE information unavailable from either thalamic or cortical areas alone. These synergistic interactions were reliable across animals for violations of structured auditory sequences and weaker for repetition-based deviants. These findings show that auditory PEs are not simply relayed or duplicated across the thalamocortical hierarchy. Instead, they emerge through state-dependent transformations within the thalamus and complementary interactions between thalamic and cortical populations, identifying the thalamus as an active node of context-dependent PE processing.

With 302 neurons and a rigorously characterized connectome, the nematode Caenorhabditis elegans represents a powerful model organism to study the fundamental roles of neuronal circuits in behavior. However, despite the breadth of research, many questions remain unanswered regarding how these organisms are able to successfully navigate their environment. Here, we present a biologically grounded dynamical circuit model for the investigation of sensory-guided behavior during C. elegans chemotaxis. Our mathematical model consists of the chemosensory neuron AWA, interneurons RIM and RIA, motor neurons, including SMDs and RMDs, and body wall muscles that provide proprioceptive feedback through stretch receptors. After optimization with an evolutionary algorithm, the model locomotes effectively toward a chemical attractant, realistically capturing nematode chemotactic behavior. Chemotaxis is ensured by sharp turns, which resemble the omega turns of living nematodes, as a key emergent property of the model. The sharp turning behavior is triggered by decreases in the concentration of the attractant. These result in reduced AWA activity, which in turn triggers disinhibition of RIM and subsequent changes in RIA oscillations. The ensuing coordinated changes in downstream motor neurons activity patterns produce sharp turns, which correct the nematodes path, so that the model worm heads toward the attractant, and remains at its proximity, after it reaches the gradient peak. The proposed framework, along with its emergent dynamics, provides new insights into the minimum requirements for C. elegans circuitry to display major features of its chemotactic behavior, including omega turns. In parallel, it generates experimentally testable hypotheses with respect to the participating neuronal elements.

Gamma oscillations (30-90 Hz) are a prominent signature of cortical network state, but whether they facilitate or hinder inter-areal communication remains unresolved. The communication-through-coherence hypothesis posits that gamma enhances transmission between areas, whereas recent computational work suggests that high-amplitude gamma oscillations may instead filter incoming inputs and reduce their impact. To distinguish between these accounts, we used a well-defined physiological input--sharp-wave ripple (SWR) complexes--to probe cortical responsiveness via two parallel monosynaptic pathways: from ventral CA1 to prefrontal cortex (PFC), and from dorsal CA1 to retrosplenial cortex (RSC). By classifying the cortical state immediately preceding each ripple as low- or high-amplitude gamma, we found that PFC responses to ripples were significantly larger during low-amplitude gamma states, an effect carried by the ventral CA1-PFC pathway and driven primarily by ripples during quiet wakefulness. RSC showed no such state-dependent modulation. A mean-field model of PFC reproduced the enhanced responsiveness during low-amplitude gamma and revealed that this modulation depends on the excitatory-inhibitory balance of the afferent input and on the level of recurrent excitation--providing a mechanistic explanation for the distinct behaviors of PFC and RSC, which differ in their local recurrent connectivity. Extending the model to a chain of cortical areas predicted that low-amplitude gamma supports robust propagation of activity across regions, whereas high-amplitude gamma confines it locally. Together, these results argue that low-amplitude gamma, rather than strong gamma synchronization, constitutes a favorable substrate for communication between brain areas.

Dietary iron deficiency in the absence of anemia (IDNA) affects numerous people worldwide, with a wide range of negative effects on brain functioning and cognition. Although studies employing electroencephalography (EEG) have revealed a number of negative effects of IDNA in both the time- and frequency domains, to date there have been no attempts to characterize the effects of IDNA on the temporal dynamics of whole brain interactions. To address this issue, we applied multiscale entropy (MSE) analysis to resting-state EEG data collected from IDNA (n = 21) and iron sufficient (IS, n = 21) women. The MSE analysis on this data revealed that entropy was higher overall for the IS than the IDNA group, with significant differences appearing primarily at longer time scales and under right frontal and left and right parietal electrodes. These results suggest that IDNA may negatively affect long-distance interactions among brain regions and that this could conceivably be a source of diminished cognitive function and neural resilience in IDNA.

Exposure to chemical cues released by predator or pathogen can evoke anxiety or fear responses in prey/host animals such as fight, flight or freeze both at behavioral and molecular levels. Freezing is a fundamental anxiety response when fighting or fleeing arent feasible. Despite the potential relevance of freezing as a stress-coping mechanism, its behavioral and molecular underpinnings are not understood yet. At molecular level danger cues are perceived by chemosensory receptors expressed in sensory neurons which may further regulate the animals behavioral responses(Ye et al., 2024){Citation}. 2-nonanone (2-NA) is one of the principal volatile organic compounds secreted by many pathogenic bacteria infecting Caenorhabditis elegans as well as humans and may signal danger to worms. Here, we show that olfactory exposure to threat-associated cue 2-NA induces a reversible fear-like freezing response characterized by immobility and halted feeding in C. elegans. With the application of in silico and behavioral approaches we showed that 2-NA is one of the ligands for an olfactory G-protein Coupled Receptor (GPCR) STR-211 and RNAi knockdown of the receptor leads to a defect in 2-NA induced avoidance behavior in worms. We next discovered that STR-211 is required for immediate behavioral changes in C. elegans during freezing response against 2-NA. The study proposes an environment relevant animal model to mimic human anxiety and fear-like behavior, along with the identification of one of the olfactory GPCRs mediating this behavior. The model may help in understanding the neuromolecular basis of freezing response in human anxiety, contributing towards treatment of mental health disorders.

Sensory scenes contain many different stimuli. Two complementary theories about how the brain segregates signals from different stimuli concern (a) time division multiplexing, such that neurons switch between encoding each item over time; and/or (b) place coding, such that different populations of neurons encode each item. Such time division multiplexing would appear to be required when the population of neurons responsive to each stimulus overlaps, as place coding lacks the granularity to resolve the two stimuli. This predicts that as responses to component stimuli become more similar, and thus less well resolved by place coding, there would be a greater incidence of multiplexing. We tested this hypothesis using single-unit responses in the macaque inferior colliculus to combinations of two sounds of varying center frequencies (given that sound frequency is place coded in this structure). We found that neurons were more likely to multiplex when their responses to each individual sound was more similar, differentiating signals whose neural representations would otherwise be less distinct. This finding supports the theory that neurons multiplex to maintain information about concurrent stimuli when place coding is insufficient to prevent largely overlapping responses in the neural population.

Previous work suggests that different groups of neurons in orbitofrontal cortex (OFC) constitute the building blocks of a circuit in which economic decisions are formed. Here we used network inference analysis (Ising model) to shed light on the internal organization of this circuit. We examined populations of neurons recorded simultaneously, and inferred the functional couplings. We then computed a reduced, effective network (EN) where each node corresponded to an encoded variable. The EN had a recognizable structure, with enhanced couplings between input and output neurons supporting the same decision, and enhanced couplings between neurons encoding value variables with the same sign. This structure was highly reproducible across individuals and hemispheres. Importantly, it depended on the internal state of the animal and the behavioral conditions. The EN reproducibility decreased with the distance between cells but it increased with the number of cell pairs, suggesting that OFC operates as a single distributed assembly.

A novel mathematical framework to define the threshold of action potentials in excitable cells is presented. Unlike previously applied methods that rely on approximations or bifurcations, the approach focuses on the geometry of membrane potential trajectories. The changes in concavity during the upstroke of an action potential can be directly obtained from a time series of voltages. The concavity criterion is then extended to models based on autonomous dynamical systems where the changes in concavity can be obtained analytically from a curve of inflection points in phase space. The inflection point manifold defines a region required for excitability: all the orbits that cross it contain action potentials, and all the trajectories that contain action potentials are in it. This analytical principle can then be used to define excitability in a dynamical system, and also a measure of excitability that enables quantification and comparisons of excitability across dynamical system. The measure provides a way to compare the excitabilities of systems that model neurons with different electrophysiological phenotypes and consider different stimulus conditions. The traditionally vague physiological concept of electrical excitability is transformed into a rigorous analytical description by considering the time-dependent curvature of the membrane potential. The criterion is robust across smooth, single compartment models of electrical excitability and can be can be extended to single compartment models in higher dimensions, and multicompartment models as well.

This study presents improvements to the Hodgkin-Huxley (HH) models of ionic conductance and action potential generation. Sodium and potassium conductances are expressed by a single analytical formula describing the impulse response of a convolution of exponential distributions within a short-memory integration space. Treating transmembrane ion transit duration as a random variable, conductance profiles are interpreted as realizations of the probability density functions governing ionic movements. Applying the central limit theorem, the lognormal distribution emerges as the asymptotic profile of ionic conductances, constituting a fundamental primitive for such biosignals. A temporal state-transition paradigm describes the action potential waveform through four successive membrane potential transitions. Applied to electrophysiological recordings from lamprey reticulospinal neurons, this framework enables indirect estimation of key physiological quantities, including depolarization threshold, Nernst potentials, and net ion fluxes across the membrane. These advances open new perspectives for parameter estimation from experimental data and neuronal network simulation.

Sensory neuroscientists seek to model the neural computations that encode complex stimuli. Distinct encoding models often make similar predictions for natural stimuli such as speech, posing a challenge for model comparison. We developed a method to synthesize stimuli that decorrelate model predictions across a neural population, termed neural prediction decorrelation (NPD). Using fMRI responses to NPD sounds, we compared standard and adversarially robust deep neural network models of human auditory cortex. Prediction accuracy for NPD sounds was substantially better for the adversarially robust model in every region tested, an effect completely masked with natural sounds. Population responses to natural and synthesized NPD sounds shared an interpretable low-dimensional organization that was reproduced by the robust encoding model. NPD provides a general approach for comparing encoding models and reveals that adversarial robustness expands the predictive power of DNNs beyond natural stimuli, which is likely critical for targeting population activity through stimulus synthesis.

The human visual system integrates both static and dynamic information to support form and shape perception, yet the computational principles underlying the integration of motion for object recognition remain unclear. Artificial neural networks (ANNs) offer a computational framework for developing and testing hypotheses about these principles: if ANNs trained on motion-related tasks develop representations that align with brain activity and support object categorization, this would suggest that the training objectives and architectural constraints of these networks may capture key aspects of motion processing in biological visual systems in general, and motion processing for object recognition, in particular. Here, we investigated this question using "object kinematograms", stimuli in which object form is conveyed solely through motion cues. We measured neural responses of two higher regions of the lateral and the dorsal visual pathways, respectively, with strong sensitivity to dynamic cues from objects: lateral occipitotemporal cortex (LOTbio), and left supramarginal gyrus (SMGlh), as well as primary visual cortex (V1). We compared brain responses to representations extracted from two neural networks: SlowFast, a dual-pathway architecture trained on action recognition that processes slow- and fast-varying visual information with cross-pathway integration, and DorsalNet, a model of the primate dorsal visual pathway trained on embodied self-motion estimation. Representational similarity analysis revealed distinct representational profiles across brain areas, demonstrating functional specialization in motion-based form processing. LOTbio was best characterized by the slow pathway of the SlowFast model, whereas SMGlh showed strong similarity to both models. Critically, we found that representations aligned with brain activity also better supported behavioral function: the full SlowFast model, incorporating both slow and fast pathways, outperformed other models in few-shot categorization of object kinematograms and showed the highest similarity to human perceptual judgments. These findings demonstrate that with appropriate inductive biases, specifically, dual-pathway architectures for multi-scale motion processing and training objectives focused on dynamic visual tasks, ANNs can develop functionally useful representations of motion-defined forms that exhibit better alignment with the visual regions involved in processing dynamic visual signals.