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.

The brain requires coordination among different regions to execute cognitive tasks, which may involve both positive- and negative-correlations. The topology of these correlations may indicate the mechanism underlying brain functioning in a given state. Here, we study changes in the functional connectomes (FCs) of both the positive and negative-correlations across various cognitive task states relative to the resting state, using publicly available electroencephalographic (EEG) data. Considering the EEG-specific topographical cortical regions as topographical modules (TMs), we find that the FC comprising positive correlations (G+) is modular. In contrast, networks of negative-correlations (G-) are anti-modular, with more connections between TMs than within them, and are associated with improved overall topological efficiency. These functional networks also show variability across frequency bands and brain states. In the low-frequency delta band, resting states exhibit higher modularity and anti-modularity than task states; in contrast, in the high-frequency Gamma band, modularity and anti-modularity are much higher during task states than in the resting state. The k-core analysis of all networks further reveals differences: G+ is more hierarchical and robust than G- across all states. Moreover, the task-state networks are always more hierarchical than the resting-state networks across all frequency bands. In the high-frequency gamma band, they are also significantly more robust than the resting-state networks. These networks also differ in the topology of their innermost core constituents: the innermost core regions of G+ are randomly connected and spatially localized, mostly in posterior brain regions across subjects, in the high-frequency gamma band. Whereas those in G- are spatially de-localized, cover the extreme anterior and extreme posterior brain regions, and remain anti-modular in all the frequency bands. Overall, our analysis reveals the presence of an anti-modular organization of functionally specialized TMs alongside their modular organization and points to task- and resting-state differences in their topologies.

Learning new information in the presence of distracters and changing conditions requires the ability to adapt. In the brain, this adaptive capability has been linked to dynamic interactions between attention and working memory, which enable the selective filtering of irrelevant input while preserving behaviorally relevant information. Specific neural oscillations have been implicated in this process. Here, we introduce a phenomenological data-driven framework for oscillatory network modeling that learns condition-dependent coupling laws directly from neural recordings and enables inference of condition-dependent directed pathways. We apply our approach to magnetoen-cephalography (MEG) data collected while participants performed a working-memory task with and without distracters. Recall dynamics in the non-distracter condition are first modeled using a linear oscillatory network in which each region of interest is represented by two alpha-band harmonic oscillators. We use universal differential equations (UDE), an extension of neural differential equations, to capture distracter-induced changes in coupling laws. Symbolic regression is then used to interpret the modifications identified by UDE as nonlinear functions, and an additional method is proposed to identify the directed pathway from the newly emerging nonlinear terms in the dynamics of brain regions of interest. Despite inter-subject variability, working memory recall data from all four participants examined under distraction showed the emergence of a pathway from the dorsolateral prefrontal cortex (dlPFC) to the primary visual cortex (V1). This finding is consistent with the established role of the dlPFC in cognitive control and suggests that distracter processing recruits a directed interaction from prefrontal to visual regions. More broadly, our results illustrate that combining linear models whose parameters are learned from the data with universal differential equations augmented by interpretability methods enables the identification of condition-dependent coupling laws, their representation as interpretable mathematical functions, and the discovery of candidate directed pathways underlying adaptive changes in oscillatory networks without requiring strong prior assumptions about the underlying mechanisms.

We have studied the emergence of slow relaxation oscillations in next generation neural mass models with spike frequency adaptation. Relaxation oscillations connect low firing state (Down state) to high firing state (Up state) via the slow adaptation. In the examined cases, the orbit relaxes towards the Up State via a sequence of collective damped oscillations (peaks of activity), thus revealing population bursting dynamics. The slower is the adaptation time scale the higher is the complexity (number of peaks) displayed by the relaxation oscillations. In particular, a chaos-induced spike-adding mechanism regulates the increase in the number of peaks. In analogy to what found in the Hidmarsh-Rose neuron model, two different types of chaotic behaviors have been identified: Population Spiking and Population Bursting Chaos. The increase of the adaptation strength leads to shorter (longer) Up (Down) state durations somehow mimicking the effect of charbachol in in vitro experiments, where spontaneous slow waves are observed. Indeed, the scenario depicted in [1], where an increase of the concentration of carbachol induces a transition from anesthesia-like to sleep-like dynamics is consistent with our results based on the variation of the adaptation strength. HighlightsO_LISpike Frequency Adaptation (SFA) promotes the emergence of Slow Relaxation Oscillations C_LIO_LISpike-adding mechanisms, controlled by SFA, lead to Relaxation Oscillations of increasing complexity C_LIO_LITwo types of chaotic behaviours: Population Spiking and Population Bursting Chaos C_LIO_LISFA regulates Up and Down States durations and their correlation C_LI

Abnormal states such as erroneous behaviors are generally difficult to represent from neural data. However, such states are also known to have specific spatiotemporal features, indicating a feasibility of developing a method to focus on them. If a method can highlight these spatiotemporal features, it may effectively represent such abnormal states, helping evaluate abnormal brain functions. In the present study, we proposed the hierarchy of supported modules (HSM) to highlight spatiotemporal features that can represent abnormal states. HSM spatiotemporally transforms multidimensional neural time-series based on their spatiotemporal context. We evaluated HSM through decoding and similarity analyses using multiple publicly available datasets. In the HSM results, decoding accuracies were higher for erroneous behaviors than for normal behaviors, and similarities were lower between erroneous behaviors and normal behaviors than between normal behaviors, demonstrating the ability of HSM to capture the spatiotemporal features of erroneous behaviors. Surprisingly, many parts of these results were also present even before HSM learning, showing the virtue of HSM as a simple-to-use method. The proposed HSM method may help elucidate the mechanisms underlying erroneous behaviors.

Transformer-based deep learning models have shown great potential for decoding visual EEG signals. However, their internal attention mechanisms are often evaluated primarily on optimization objectives, leaving their alignment with biological brain connectivity an open question. This study empirically evaluates how variations in EEG preprocessing strategies affect these attention representations using the Adaptive Thinking Mapper (ATM) model as a framework. We compared a baseline pipeline (MVNN only) against a comprehensive cleaning pipeline integrating ICA and notch filtering. The models were evaluated through cross-generalization, noise robustness, and spectral-temporal ablation analyses. Furthermore, we investigated the structural correspondence between the model's data-driven attention weights and neurophysiological reference networks (GPDC, PDC, and DTF) using Node Strength Correlation and Representational Similarity Analysis (RSA). The results show that the comprehensive preprocessing successfully suppresses non-neural artifacts, such as frontal noise and electrical interference, while maintaining comparable decoding accuracy and baseline robustness. Alignment analyses revealed that the broad spatial organization of the learned attention patterns remains highly stable across pipelines, capturing key directed connectivity dynamics with subtle, metric-dependent variations in global representational geometry. This work provides an empirical exploration into bridging data-driven attention weights with neurophysiological consistency, offering insights toward more transparent brain-computer interfaces.

Wave recurrent neural networks (wRNNs) are biologically inspired recurrent architectures that use traveling-wave dynamics to support sequence learning and memory. However, their input-to-hidden pathway remains relatively simple compared with biological neurons, where dendrites perform nonlinear input integration. In this study, we introduce the Dendritic Wave Recurrent Neural Network (DWRNN), which augments the input pathway of the wRNN with nonlinear basal dendritic branches while preserving the original recurrent wave dynamics. We evaluate DW-RNN on a simple copy task, sequential MNIST (sMNIST), permuted sequential MNIST (psMNIST), and noisy sequential CIFAR-10 (nsCIFAR-10). On the copy task, DW-RNN shows learning behavior comparable to the standard wRNN, suggesting that dendritic input integration does not disrupt the recurrent wave-based memory mechanism. On the three sequential image-classification benchmarks, DW-RNN outperforms the standard wRNN, improving accuracy from 97.27 {+/-} 0.15% to 97.82 {+/-} 0.12% on sMNIST, from 96.74 {+/-} 0.17% to 96.92 {+/-} 0.10% on psMNIST, and from 54.30 {+/-} 0.79% to 55.65 {+/-} 0.55% on nsCIFAR-10. In addition to improving mean accuracy, DW-RNN exhibits lower across-seed variability on all three classification benchmarks, suggesting that dendritic input integration may improve the stability of wRNN training. Hidden-activity visualizations further show that DW-RNN preserves the characteristic traveling-wave patterns of the original wRNN. These results suggest that dendritic computation and traveling-wave recurrent dynamics provide complementary mechanisms for biologically inspired sequence learning.

Facial color is associated with the perceptual evaluation of emotions, and compared with faces with original facial or greenish color, reddish angry faces are often judged as having higher emotion intensity. Although perceptual modulation by the relationship between anger and red has also been reported from the perspective of electroencephalography (EEG), how variations in perceived emotion intensity are reflected in brain activity remains unclear. This study investigated whether EEG activity associated with face and facial expression processing is modulated as a function of the interaction between facial color and perceived emotion intensity. In the experiment, we recorded EEGs while participants evaluated emotion intensity using facial stimuli created by combining morph continua from neutral to angry expressions with three types of facial color conditions (original, red, and green). The results revealed that, in the red facial color condition, the early posterior negativity (EPN) amplitude significantly increased as a function of emotion intensity compared with those in the original and green facial color conditions. These findings suggest that the early, automatic affective processing of facial expressions, reflected in the EPN, is modulated by the combination of facial color and emotion intensity. Our findings provide new evidence that early, automatic affective processing of facial expressions, as indexed by the EPN, is modulated by the congruency between high anger intensity and a reddish facial color. HighlightO_LIReddish angry faces increase the ERP component associated with emotion evaluation. C_LIO_LIThe relationship between anger and red is evident in the left hemisphere. C_LIO_LIThe interaction between facial expression and color occurs at a later cognitive processing stage than facial expression or facial color processing alone. C_LI

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.

Equilibrium propagation (EP) is a biologically plausible alternative to backpropagation for training neural networks. EP typically relies on free and nudged dynamical phases: during the free phase, the network relaxes toward an equilibrium state, whereas during nudging, the output state is perturbed toward a target using a teaching signal. However, it remains unclear whether the brain has access to such explicit target signals. Inspired by Attention-Gated Brain Propagation (BrainProp), a reward-based learning framework proposed by Pozzi et al. (2020), we introduce a reward-based variant of EP that replaces full target-based nudging with a selected-output binary reward signal. The proposed method updates the network using only the chosen class and whether that choice is correct, without directly revealing the full target vector. We evaluate the method on MNIST, Fashion-MNIST, and CIFAR-10 using both multilayer perceptrons and convolutional neural networks. The proposed reward-based EP achieves performance close to that of conventional EP across all three datasets, although it generally converges more slowly during the early stages of training. Generalization-gap analyses show similar behavior for the two methods on MNIST and Fashion-MNIST, while reward-based EP exhibits a smaller training-test accuracy gap during later training on CIFAR-10. We further investigate the effect of the exploration probability used during stochastic class selection and find that moderate exploration can provide small performance improvements, although its effect is dataset-dependent. These results demonstrate that EP can learn effectively from sparse, action-specific reward feedback rather than a complete supervised target.

Repetitive transcranial magnetic stimulation (rTMS) is an established treatment for major depressive disorder (MDD), yet variability in treatment response remains a significant challenge. Network control theory provides a framework to quantify how brain networks facilitate state transitions, but prior work has focused primarily on node level metrics. Here, I investigate whether edge based controllability of the structural connectome is associated with rTMS outcomes. Twenty five patients with treatment-resistant depression underwent diffusion MRI prior to a five week course of high frequency rTMS targeting the dorsolateral prefrontal cortex. Structural connectomes were constructed using MRtrix3 and the Destrieux atlas, and edge based controllability metrics were computed at baseline. Controllability of specific middle frontal gyrus centered edges showed significant associations with changes in HAMD-24 scores, including connections to the superior frontal gyrus, hippocampus, angular gyrus, and orbital gyrus (r = 0.470-0.597, p < 0.05). These findings suggest that edge based controllability captures circuit level properties relevant to treatment response and may inform personalized neuromodulation strategies.

Introduction - Neurofeedback (NFB) has demonstrated efficacy in treating various disorders, often achieving substantial symptom reductions. Despite its effectiveness, a significant percentage of users (non-responders), fail to benefit from NFB. Addressing this issue, the study at hand investigates the role of NFB design on neuro-physiological responses. Method - event related potentials (ERPs) are examined in response to the application of different aesthetic principles in the context of NFB stimulus design. Drawing from Self-Determination Theory and ERP studies in the field of web design, 16 feedback stimuli were developed according to specific design principles. Stimulus design effects were inspected by means of ERPs allowing for the assessment of implicit and fast electroencephalogram (EEG) reactions. Results of n = 38 participants indicated distinct ERP response patterns at time window of interest 1 (TWOI-1; 100-200 ms) and TWOI-2 (200-300 ms), predicted by beholder-based liking and complexity of stimuli. The findings align with the proposed hypotheses suggesting that aesthetic evaluation of NFB stimuli occurs rapidly and implicitly. In response to the aesthetic vs. non-aesthetic categories, the findings were mixed. The results underscore the importance of further exploration of aesthetic design guidelines in the context of NFB applications. It is discussed how NFB aesthetics relate to Processing Fluency and Affective Prediction Error Theory, while the theoretical methodological issue of the Fixed Effect Fallacy is taken into consideration. The study contributes to the broader understanding of how design elements can affect therapeutic efficacy and engagement in NFB and Human Computer Interaction in therapeutic contexts in general.

AbstractAI-generated images, videos, and news have become an inseparable part of daily life, leading to increasing skepticism toward online information. Understanding how expectations about the presence of AI-generated content influence perception is crucial for elucidating the underlying neural mechanisms and facilitating the generation of naturalistic avatars. In this study, we analyzed an existing EEG dataset (N = 29) in which participants viewed emotional facial photographs of only real individuals while being informed that upcoming faces were either real ("REAL") or AI-generated ("FAKE"). We examined pre-stimulus oscillatory activities in the one-second EEG interval before stimulus onset and found significantly lower alpha power when participants expected "FAKE" compared to "REAL" faces. This effect was region-specific, particularly in right occipito-parietal and temporo-parietal regions, as identified by both sensor- and source- level analyses. In addition, the modulation effect between pre-stimulus alpha activity and post-stimulus event-related potentials (ERPs) was measured. A significant correlation between changes in late positive potential (LPP) amplitudes and pre-stimulus alpha power was observed exclusively for "FAKE" smiling faces, consistent with our previous findings. These results suggest that AI-related expectations modulate neural preparatory states, with lower alpha activity presumably reflecting increased attentional demands for stimuli believed to be artificially generated. This study demonstrates that top-down beliefs systematically shape both pre- and post-stimulus neural dynamics, providing new insights into how cognitive expectations bias perceptual processing, with implications for understanding human-AI interaction and improving the design and evaluation of AI- generated content in real-world contexts.

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.

Approximately 50% of the population in India is estimated to experience sleep-related disorders. Sleep deprivation is a prevalent condition that adversely impacts cognitive performance, neural functioning, and overall health. Electroencephalography (EEG) offers an objective means of capturing neural alterations associated with sleep loss, making it well-suited for automated detection frameworks. In this study, we explore the application of a Quantum Support Vector Machine and Hybrid Quantum Neural Networks to classify sleep-deprived and well-rested states using resting-state EEG signals. A comprehensive feature extraction pipeline is employed, incorporating spectral band power, band ratios, Hjorth parameters, and functional connectivity measures. These features are subsequently encoded into quantum states to construct a quantum kernel, which is then utilized for classification. Model performance is evaluated under both epoch-level and subject-level data partitioning schemes. The Hybrid Quantum Neural Network (HQNN) achieves the highest performance across both evaluation settings, attaining an accuracy of 96.88% at the epoch level and 81.25% at the subject level. The QSVM model achieves accuracies of 93.75% and 75.00% for epoch-level and subject-level evaluations, respectively. At subject-level and epoch -level evaluation, HQNN outperforms previously reported results (68.23% and 95.72%). Overall, these findings highlight the potential of quantum machine learning as a competitive approach for EEG-based sleep deprivation detection, with promising implications for real-world biomedical applications.

Classification of motor imagery (MI) tasks through EEG is valuable in brain-computer interfacing and rehabilitation engineering. EEG channels selection for MI task classification is well discussed problem and is challenging due to its combinatorial nature. Most of the existing methods are subject and task-dependent. This paper introduces a subject-independent EEG channel selection. The proposed approach consists of two stages. First, we rank channels based on their divergence from a reference channel Cz. We hypothesize that channels less divergent from Cz are more relevant for MI task classification. In the second stage, we employ a three-stage feature selection and classification model to evaluate the selected channels. It consists of a bandpass filter, followed by common spatial pattern (CSP) filter and three classifiers viz. SVM, 1-NN and 5-NN. Two publicly available datasets viz. PhysioNet and BCI Competition III IVa datasets have been used to assess the method. It performs 15.21\% more than 3Cs and just 2.91\% less than all-channels accuracy with as few as 20/118 channels on BCI Competition data and 19.64\% more than 3Cs on the PhysioNet dataset with 16/64 channels. Empirical comparison implies that the method performs better than classical models such as CSP Rank, fishers rank, and normalized mutual information, significantly. Results support that our hypothesis that divergence between channels and a reference channel Cz can be used as a ranking measure for channel selection.

Neural synchronization is fundamental to brain function and, when it becomes excessive, underlies pathological conditions such as epilepsy. Among brain regions, the temporal lobes, and the hippocampus in particular, exhibit the highest epileptogenic potential, with mesial temporal lobe epilepsy representing the most prevalent form of the condition in humans. Within the hippocampus, extracellular potassium dynamics are central to non-synaptic epileptiform activity, and astrocytic potassium buffering mechanisms have emerged as key regulators of network excitability. Yet the specific contributions of astrocytic gap-junction coupling and potassium spatial buffering to neuronal synchronization across different spatial scales remain poorly understood. To address this gap, we developed a microcircuit biophysical model consisting of two astrocyte-neuron modules, each comprising one astrocyte coupled to five neurons. Astrocyte-neuron interactions are mediated exclusively through shared extracellular potassium dynamics. Using a reduced astrocyte model that captures both local membrane and syncytial potassium buffering, we systematically investigated how astrocytic potassium handling shapes neuronal activity patterns and inter-module synchronization. Our results demonstrate that astrocytes prevent the emergence of pathological states -- such as sustained ictal activity and depolarization block, by stabilizing extracellular potassium levels. Furthermore, we show that astrocytic gap-junction coupling strength critically regulates phase synchronization between neuronal modules: stronger coupling promotes inter-module synchrony under physiological conditions, whereas impaired astrocytic function drives networks toward pathological hypersynchronization when extracellular potassium is elevated. These findings support the hypothesis that astrocytic networks impose modularity on hippocampal neuronal assemblies, and suggest that astrocytic connexins may represent a relevant therapeutic target in epilepsy and other disorders characterized by aberrant neural synchronization. Author summary

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.

While extensive research has been conducted on emotion recognition from facial stimuli, it remains unclear how embodied emotional cues conveyed through body postures, gestures and actions (e.g., stick figures) compare with facial (human faces) and face-like symbolic representations (emoji faces) in shaping behavioral and neural responses. We employed a multimodal approach, combining behavioral measures (accuracy and reaction time) with electroencephalography (EEG) to examine the electrophysiological correlates of emotion recognition. Overall, sixty-six (equal number of males and females; 18-30 year old) participants identified positive, negative, and neutral emotions depicted in the three formats. Behavioral results showed significant effects of both emotion and format on accuracy and reaction time. Emoji faces were recognized with the highest accuracy and fastest reaction times, followed by stick figures and then human faces (p < .001, some comparisons p < .05). Thirty-four participants (17 males and 17 females) underwent EEG, which revealed distinct patterns of event-related potentials (ERPs). The Early Posterior Negativity (EPN) amplitude showed a significant overall effect of format. Post-hoc comparisons indicated that stick figures elicited greater (more negative) EPN amplitudes than human faces during positive emotion recognition (p = .003), and greater amplitudes than both emoji faces (p = .027) and human faces (p = .007) during negative emotion recognition. No significant differences were observed between emoji and human faces. N170 and Late Positive Potential (LPP) amplitudes did not reveal significant differences (all p > .05). Correlation analyses revealed no significant associations between ERP and behavioral measures. Thus, abstract, minimalistic representations like stick figures elicit enhanced early emotion-related processing despite similar early and later processing across formats. HighlightsO_LIEmotion recognition across realistic (human faces), symbolic (emoji faces) and embodied (stick figures) modalities were compared. C_LIO_LIEmotions were recognized fastest and most accurately in emoji faces format. C_LIO_LIStick figures format elicited significantly higher EPN compared to other two formats across positive and negative emotions. C_LIO_LINo significant differences were observed in terms of N170 and LPP. C_LIO_LIEmbodied, abstract emotional cues modulate automatic emotional appraisal, rather than initial sensory encoding and sustained cognitive evaluation. C_LI

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.