Cognitive Neurodynamics

Reading comprehension is a complex cognitive task that involves dynamic interactions between the brain and external information. Previous studies on reading development primarily focused on localized or static brain activities. However, it remains an enigma how brain state dynamics evolve with development underlying reading comprehension. This study aims to address this issue by combining functional magnetic resonance imaging (fMRI) with Hidden Markov Model (HMM) to explore brain state dynamics. A total of 35 typically developing children and 31 adults were scanned while reading a story. Our results demonstrated a tripartite brain state organization, characterized respectively by high activities in the visual (State #1), language (State #2), and default mode network (DMN, State #3) regions. Children exhibited significantly longer dwell time in the DMN state (State #3) compared to adults, along with a higher probability of transitioning from the language state (State #2) to the DMN state (State #3). In addition, adults exhibited greater flexibility in state transitions during reading comprehension. Finally, the alignment between the dynamic states of children and the average states of adults was a significant positive predictor of their reading comprehension performance. This study provides a novel, intuitive perspective on how brain state dynamics evolve during the development of reading comprehension.

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.

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.

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.

The Component Process Model of Emotion conceptualizes any emotional episode (e.g., the discrete emotions of sadness, anger, fear, or interest) as being driven by the multiple appraisal components. However, both the specificity of the neural mechanisms underlying appraisal processes and the way these appraisal networks relate to the neural circuits underlying discrete emotions remain unclear. Here we investigated the neural correlates of appraisal processes and compared them with those of discrete emotions. Participants (n = 97) were scanned with functional magnetic resonance imaging (fMRI) while watching short movie clips with varying emotional contents. Intensity for 12 appraisals and 12 basic and epistemic emotions evoked by the movie clips were rated by independent participants (n = 444). The neural responses were modelled with convolved ratings of appraisals and discrete emotions. The results indicated that appraisals and discrete emotions are supported by a shared set of distributed brain regions that extend beyond typically reported emotion-related areas, encompassing perceptual, action-related, and higher-order cognitive systems. Activations were more consistent for and better explained by appraisals versus discrete emotions. Within this network, epistemic emotions elicited less consistent activations than basic emotions, particularly in limbic regions. Our results highlight the functional organization of appraisals and discrete emotions under dynamic and complex conditions and indicate that appraisal theories better explain neural responses than discrete emotion models.

Introduction: Decision making for selecting an object or a course of action from possible alternatives largely depends on our perceptual ability modulated by attention. When multiple stimuli appear close together in time, processing one stimulus can temporarily impair the processing of another due to temporal limitations of attention. Observers frequently fail to detect the second target (T2) presented within a few hundred milliseconds after the first target (T1) in a stream of stimuli, which is commonly known as attentional blink (AB). Existing theories attribute this perceptual lapse to T1 processing, distractor interference, or transient attentional gating; however, the computations underlying suppressive mechanism remains unresolved. We investigated whether pupil-size could reveal the underlying mechanisms of AB and predict conscious perception on a trial-by-trial basis. Methods: Pupil diameter and gaze locations were recorded using an infrared eye tracker. Machine learning techniques were used to classify trials when T2 was detected versus when it was not, after correct identification of T1, during an AB task from the pupil dynamics, which also yielded attentional episode (AE) associated with each element in the stream of visual stimuli when deconvolved. Results: Cross-validating classifiers achieved near-perfect accuracy not only in distinguishing but also predicting perceptual outcomes on a single-trial basis. AEs exhibited greater power when T2 was detected than when it was missed; the differential power in AEs on a logarithmic scale was highly synced with the differential pupil size. Conclusions: Collectively, these findings establish a framework for predicting attention-driven perceptual outcomes from pupil-dynamics at finer time-scale.

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.

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.

How internal speech relates to overt speech remains a fundamental question in speech production: do different forms of speech preserve a common neural representation of the intended word, or does that representation change as speech becomes articulated? We used time-resolved electroencephalography to characterize representations of 10 Chinese words during imagined, silent, and overt speech. Word identity was reliably decodable in all three modes, but its temporal dynamics differed: imagined-speech representations peaked earlier and were less temporally stable, whereas silent and overt speech showed stronger and more sustained representations. Cross-mode decoding revealed word-discriminative information shared across all three mode pairs, with substantially stronger generalization between silent and overt speech. However, direct comparison of word-level representational geometry revealed robust correspondence only between silent and overt speech, indicating that transferable information across modes does not necessarily imply preservation of the broader relational structure among words. Representational similarity analyses further showed distinct visual-form, semantic, and phonetic dynamics across speech modes, with late visual-form and phonetic information contributing uniquely to the geometry shared by silent and overt speech. Controlling for time-matched surface electromyography preserved the overall silent-overt neural correspondence and within-mode phonetic representations, while eliminating the unique phonetic contribution to their shared geometry, suggesting that peripheral articulation accounts for part, but not all, of this common structure. Together, these findings show that imagined, silent, and overt speech share word representations at different levels and suggest that representational geometry and temporal stability are progressively reorganized as internal speech is translated into articulation.

Despite the importance of sustaining attention focus throughout a task, sustained attention research demonstrates a rapid decline of task-focus with time-on-task. Separate research body highlights perceptual load as critical determinant of focused attention, showing that increased perceptual load draws more neural energy into task-relevant processing (Bruckmaier et al., 2020) and improves attention focus (Lavie, 2005). However, the effect of perceptual load on the neurophysiological mechanisms underlying time-on-task impact on sustained attention remains unknown. This was the aim of the present study. Participants performed a gradual continuous-performance task, detecting infrequent mountain scenes, among streams of city scenes, under either high or low perceptual load (with or without overlaid salt-and-pepper noise, respectively). EEG was recorded and parameterised into periodic and aperiodic components; the aperiodic 1/f slope linked with excitation-inhibition (E/I) balance: steeper slopes reflecting reduced E/I ratio (Gao et al., 2017). Time-on-task resulted in a wide-spread increase in alpha power, and a steeper 1/f slope in a left temporal-parietal cluster, accompanied by reduced detection sensitivity and increased response variability, as well as increased mind wandering, with reduced thoughts detail. Perceptual load improved task focus, as indexed by reduced mind wandering, but exacerbated the effect of time-on-task on detection sensitivity, and the 1/f slope, which was steeper with time-on-task in a right parieto-occipital cluster with increased load. Overall, the findings suggest that sustained attention decline with time-on-task can be attributed to depletion of neural energy needed for excitatory signalling, which is further drained with increased processing demands in tasks of high perceptual load.

The superior temporal sulcus (STS) is selectively responsive to multimodal social interactions. Yet studies so far have relied on pre-defined, simplified stimuli or features to uncover the type of information that drives STS activity. We hypothesized that high-dimensional representations from visual and auditory deep learning models would better predict STS responses to naturalistic social interactions. We used self-supervised visual and auditory deep learning models to extract representations of movie frames and audio, respectively, of a TV series participants watched during fMRI scanning. Voxel-wise encoding models of a joint visual-auditory representation outperformed human-made social-affective annotations in predicting STS. Variance partition further revealed visual-auditory posterior-to-anterior gradient within the STS. To interpret what these models encode, we applied Principal Component Analysis to the encoding model weights. In both the visual and auditory models the first dimension tracked social interaction and peaked in the STS, indicating that social interaction is a dominant dimension of STS representation across both modalities. We conclude that the STS represents naturalistic social interaction in a multimodal manner, integrating visual and auditory information, and that visual and auditory deep learning models capture key representational properties of these responses.

Intergroup conflict is one of the most significant issues in human society. In the 1950s, Sherif et al. reported that intergroup conflict could be artificially induced in boys through intergroup competition with tug-of-war and ball games. Since this iconic study, researchers have developed various experimental methods to replicate intergroup competition and/or conflicts. However, although intergroup conflicts in wild animals are often reported, it has been difficult to establish a situation of intergroup conflict in laboratory rodents that is discriminable from aggressive behavior individually. In this study, we established a novel experimental paradigm for intergroup competition in mice in which the members of each group shared objectives and tasks. Adult male ICR/Jcl mice were housed in groups of six, divided into two teams of three and repeatedly performed a competitive Tsunahiki task (tsunahiki means tug-of-war in Japanese). The competitive Tsunahiki task was conducted in an open field divided into two experimental fields, with three ropes stuck to a wall separating the fields. The mice were required to pull two ropes out faster than their opponent team to win, and only the winners could proceed to the reward area separated by a guillotine door. We demonstrated that the experience of the competitive Tsunahiki task induced attack bites selectively toward members of the other team (out-group members). Our findings suggest that intergroup competition induces intergroup conflict in mice, providing a technical breakthrough in elucidating the detailed neuroscientific mechanisms underlying intergroup conflict.

Behavioral experiments are often infeasible when stimulus spaces have many dimensions or when testing time is limited. One way to address this challenge is adaptive stimulus selection, where informative stimuli are chosen dynamically based on participants responses. However, in high-dimensional spaces, identifying such stimuli is computationally demanding. Here, we describe High-dimensional Online Particle Estimation (HOPE), which selects informative stimuli in less than a second for up to 50 dimensions, enabling efficient estimation of high-dimensional psychometric functions. We validate HOPE through simulations and a face-categorization experiment in an 18-dimensional parameter space with human participants. Compared to uniform stimulus presentation, HOPE reduces uncertainty over model parameters two-to three-times faster, reaching the same certainty in half the trials or fewer. This efficiency enables psychophysical studies that were previously impractical due to the exponential scaling of trial requirements.

Analysis does not always enhance aesthetic experience. Philosophical accounts have long suggested that decomposing an aesthetic experience into determinate components may weaken it, yet this possibility has rarely been tested experimentally. To examine whether, when, and how analysis produces divergent effects on aesthetic experience, we conducted two experiments manipulating analysis depth. Experiment 1 showed that, during affective analysis of visual art, deep analysis produced a significantly weaker increase in aesthetic ratings than shallow analysis. In Experiment 2, we selected this condition to investigate the underlying mechanism. The behavioral effect was replicated: deep analysis removed the increase produced by shallow analysis without reducing ratings below the image baseline. Frequency-resolved brain network analysis further revealed a stronger task-related component and higher spatial entropy within the default mode network under deep analysis. Network-behavior correlations observed under shallow analysis were absent under deep analysis, suggesting reduced correspondence between the default-mode network (DMN) organization and aesthetic experience. Exploratory analyses further showed that spatial weights in the lateral temporal cortex and inferior parietal lobule were associated with smaller increases in aesthetic ratings. Together, these findings indicate that deeper analysis can selectively weaken improvements in aesthetic experience by altering how affective information is organized within the DMN.

In this study, we investigated the neural and behavioral basis of motivationally objective versus subjective value-based decisions. Using a within-subject fMRI design, healthy participants performed a risky decision-making task that elicited different levels of subjectivity in decision-making in two task conditions. In the Best or objective condition, choices were rewarded only when they were objectively best on a given trial, incentivizing decisions based on externally specified per-trial point maximization. In the Choice or subjective condition, participants received the reward associated with the chosen option, irrespective of how it compared to the unchosen option, allowing greater freedom to exercise subjective preferences in decision policy. Behaviorally, participants relied more on objectively optimal policy in the Best than the Choice condition. There was also a greater consensus across participants in behaviorally displayed and self-reported policies in the Best condition as well as a greater commitment to a single policy by individual participants in this condition, further confirming more objective behavior in the Best condition, compared to Choice. Moreover, behavioral inferences showed a greater agreement with self-report in the Best condition. Our fMRI results showed that the decision-making in Choice, relative to the Best condition, was associated with greater BOLD response in mid-cingulum/posterior cingulate cortex and dorsal anterior cingulate cortex, suggesting their involvement in less externally constrained, or motivationally subjective, decision-making.

EEG microstates are a distinct number of quasi-stable spatial distributions of brain activity. Microstate trajectories are strongly suspected to reflect the underlying neural mechanisms during information processing and are therefore also called the "building blocks" of human thought. In this study, we examined, if EEG microstate sequences can serve as potential triggers for a Brain-Computer Interface (BCI). To this end, a semi-supervised deep learning model architecture consisting of an LSTM-based autoencoder and a dense neural network was utilized to classify between left- and right-hand motor imagery EEG data, with the resulting classification output serving as the BCI trigger. On the one hand, this was done in a 2-step approach, in which the autoencoder and classifer have been trained separately. On the other hand, an end-to-end approach was employed, where training was performed by combining reconstruction and classification losses. Results show that the proposed model architecture was able to extract relevant features from microstate sequences and exploit them for within subjects and sessions classification. Applying transfer learning to session-to-session or across-subject transfer resulted in peak classification accuracies around 89%. We also investigated to what extent transfer learning has to be applied to reach considerable classification accuracies serving as the calibration time representative. We found that on average around 400s are needed for BCI calibration when emplyoing our approach to reach 80% classification accuracy. The present study signifies that the investigation of EEG microstate trajectories can be a promising approach for extracting BCI triggers, as it reduces the dimensionality of multi-channel recorded EEG signals to a distinct number of brain states over time. Deep learning methods, especially transfer learning, applied to EEG microstate trajectories seem promising regarding user-convenient and calibration-free BCIs in real-world applications.

IntroductionExecutive functions (EF) are higher-order cognitive processes essential for academic performance in university settings. Although there is extensive research on EF training in children, studies in young adults are scarce, particularly those involving interventions tailored to specific needs. ObjectiveTo evaluate the effect of the University Executive Network-Training Program (NExT-U), based on specific needs, on the executive functioning of Cuban university students. MethodologyA quasi-experimental study with a non-equivalent control group and pretest-posttest measurements. Participants were 27 second-year Psychology students (74% female; mean age = 19 years). The experimental group (n=7) received three training sessions focused on Conscious Regulation of Behavior, Decision-Making, Emotional Regulation, and Monitoring of Responsibilities, identified through an initial assessment using the UEF-1 Scale. The control group (n=20) continued with their usual academic activities. Non-parametric analyses and the residual gain method were employed. ResultsThe experimental group showed significant improvements in Conscious Regulation of Behavior (p = .026; r = .51), Emotional Regulation (p = .030; r = .49), and the Supervisory Attention System (p = .046; r = .44), with large effect sizes. The control group experienced no significant changes in any of the functions evaluated. ConclusionsA brief, personalized program can enhance specific executive functions in university students, demonstrating cognitive plasticity in young adults. The findings support the design of contextually relevant interventions to strengthen transversal competencies in higher education.

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.

Theft, including shoplifting, extorts a pervasive societal and economic burden. However, the neurobehavioral mechanisms underlying recurrent theft remain sparsely understood. In this study, we investigated social cognition deficits in theft recidivists with kleptomania (TR+K) and instrumental theft recidivists without kleptomania (TR-K) compared to control subjects without criminal records (CT), for which the Social Norms Questionnaire (SNQ-22) to assess explicit moral knowledge, alongside the Dictator Game (DG) and Hawk-Dove Game (HDG) to evaluate discretionary and competitive resource allocation with others, respectively, were administered. Bayesian statistical analyses revealed that all groups demonstrated comparable social norm recognition in SNQ-22 and prosociality in the DG. However, distinct behavioral profiles emerged in specific contexts, such that TR+K exhibited more unfairness than CT and TR-K at discretionary resource allocations in the DG, whereas in the HDG, TR-K demonstrated more aggressive, resource-monopolizing responses, particularly when against an aggressive opponent, than CT and TR+K. These results suggest that theft recidivism may stem from contextual failures rather than general deficits in moral knowledge, which are distinct between TR+K rooted in the internal factor, such as heightened loss aversion, and TR-K characterized by impulsivity over the external factor, such as social conflicts with others.

Classical neuronal cable theory relies on quasi-static electric field approximations and neglects magnetic induction, Lorentz force coupling, and transient electromagnetic currents, limiting its ability to fully characterize action potential propagation within geometrically branched axons and dendrites. This work develops a coupled Maxwell-electromagnetic cable framework by integrating finite-difference time-domain (FDTD) solutions of Maxwells equations with extended Hodgkin-Huxley and Fitzhugh-Nagumo membrane dynamics, incorporating magnetic gating perturbations, electromagnetic trans-membrane currents IEM, and nanoscale quantum corrections for thin neural segments. Controlled propagation experiments are designed to quantify deviations from standard cable predictions across asymmetric and symmetric axonal bifurcation geometries. Numerical results demonstrate that inductive magnetic effects lower the critical branch radius for junction conduction failure and break symmetric action potential invasion in geometrically identical child branches under external transverse magnetic fields. An electromagnetic corrected geometric ratio GREM is proposed to revise impedance-matching conditions at branch points, accounting for size-dependent axial current imbalance induced by magnetic and displacement currents. Parent axon conduction velocity deviates substantially from the canonical [Formula] scaling law when electromagnetic feedback and quantum charge distributions are included, triggering early signal blockage at large cable diameters. Collectively, this study establishes that quasi-static cable models underestimate electromagnetic corrections to propagation speed, waveform shape, and bifurcation transmission fidelity; the coupled Maxwell-cable framework provides a comprehensive multi-physics tool for modeling electrodynamic signal behavior in complex neuronal architectures.