Cognitive Neurodynamics

Developing a reliable detection of olfactory performance for early Alzheimers disease (AD) diagnosis remains challenging. Existing methods, such as psychophysical and event-related potential approaches, provide limited consistency in quantifying olfactory function. This study introduces a novel and objective framework that analyzes olfactory-stimulus-evoked EEG synchronizations of the subjects for AD diagnosis. We calculated the time-resolved wavelet coherence between EEG signals and then determined the timings (i.e., latency and duration) that describe when olfactory-stimulus-induced EEG channel interactions begin and end for each channel and frequency band. These timings, as well as the mean synchronization values in these segments, were used as features for diagnosis. Our framework, when cross-correntropy was used as a synchronization measure, exhibited a notable diagnostic accuracy in mild AD detection. The most discriminating feature between mild AD and healthy subjects was found to be the latency of synchronization between Fp1 and Fz in the low{theta} band, which showed significantly high correlation with clinical test scores. Furthermore, our framework achieved 100% diagnosis accuracy when EEG features and clinical test scores were used together. Our findings show that inter-channel short-lived synchronization timings serve as useful and complementary metrics about subjects olfactory performance and their neurological conditions.

Voltage perturbations to a repetitively firing Hodgkin-Huxley (HH) model of neuronal spiking in the bistable regime with coexisting limit cycle and stable steady node can either lead to the spikes phase resetting or collapse to the stable steady state. The latter describes a non-firing hyperpolarized quiescent state of the neuron despite the presence of constant external current. Using asymptotic phase response curve (PRC), the impact of voltage perturbations on a repetitively firing HH model is studied here while it is diffusively coupled to another HH model under identical external stimulation. It is observed that the pre-perturbation state of synchronization and the coupling strength critically determine the PRC response of the perturbed HH dynamics. Higher coupling strengths of perfectly in-phase (anti-phase) synchronized HH models shrink (expand) the combinatorial space of perturbation strengths and the oscillation phases causing collapse to the quiescent state. This indicates reduced (enlarged) basin of attraction, viz. the null space, associated with the steady state in the HH phase space. The findings bear important implications to the spiking dynamics of diverse interneurons, as well as special cases of pyramidal neurons, coupled through electrical synapses via. gap junctions, and suggest the role of gap junction plasticity in tuning vulnerability to quiescent state in the presence of biological noise and spikelets.

As social beings, humans make decisions partly based on social interaction. Observing the behavior of others can lead to learning from and about them, potentially increasing trust and prompting trust-based behavioral changes. Observation-based decision making involves different neural structures. The orbitofrontal cortex (OFC) and lateral prefrontal cortex (LPFC) are known as neural structures mainly involved in processing emotional and cognitive decision values, respectively, while the anterior cingulate cortex (ACC) plays a pivotal role as a social hub, integrating the afferent expectancy signals from OFC and LPFC. This paper presents a neurocomputational model of the interplay between observational learning and trust, as well as their role in individual decision-making. Our model elucidates and predicts the emotional and rational behavioral changes of an individual influenced by observing the action-outcome association of an alleged expert. We have modeled the neurodynamics of three cortical structures (OFC, LPFC, and ACC) and their interactions, where the neural oscillatory properties, modeled with Dynamic Bayesian Probability, represent the observers attitude towards the expert and the decision options. As an example of an everyday behavioral situation related to climate change, we use the choice of transportation between home and work. The EEG-like simulation outputs from our model represent the presumed brain activity of an individual making such a choice, assuming the decision-maker is exposed to social information.

Mass-conserved reaction-diffusion systems are used as mathematical models for various phenomena such as cell polarity. Numerical simulations of this system present transient dynamics in which multiple stripe patterns converge to spatially monotonic patterns. Previous studies indicated that the transient dynamics are driven by a mass conservation law and by variations in the amount of substance contained in each pattern, which we refer to as "pattern flux". However, it is challenging to mathematically investigate these pattern dynamics. In this study, we introduce a reaction-diffusion compartment model to investigate the pattern dynamics in view of the conservation law and the pattern flux. This model is defined on multiple intervals (compartments), and diffusive couplings are imposed on each boundary of the compartments. Corresponding to the transient dynamics in the original system, we consider the dynamics around stripe patterns in the compartment model. We derive ordinary differential equations describing the pattern dynamics of the compartment model and analyze the existence and stability of equilibria for the reduced ODE with respect to the boundary parameters. For a specific parameter setting, we obtained results consistent with previous studies. Moreover, we present that the stripe patterns in the compartment model are potentially stabilized by changing the parameter, which is not observed in the original system. We expect that the methodology developed in this paper is extendable to various directions, such as membrane-induced pattern control.

For decades, electrical neuromodulation has been used as a therapeutic mechanism to disrupt and desynchronize pathological neural activity in various neurological disorders. Despite notable progress, however, patient outcomes remain highly variable, particularly in medically intractable epilepsy where surgery still provides the greatest chance of seizure freedom. Here we propose passive neuromodulation (PNM) as a radical alternative to conventional neurostimulation, whereby analogue feedback is used to drain energy from an epileptic circuit and thus suppress the initiation or spread of electrographic seizures. We provide pilot evidence on the efficacy and robustness of PNM using two computational models of epileptic dynamics: a detailed biophysical network model of dentate gyrus, and the Epileptor neural mass model of seizure dynamics. Despite the vast differences between these models, our results show the robust ability of PNM to suppress seizures in both models. We further demonstrate the efficacy and robustness of responsive PNM, whereby brief (50ms) windows of PNM are triggered by a simultaneously-running seizure detection algorithm, as well as the safe and tunable nature of PNM, where more robust seizure suppression can be achieved by parametrically titrating the amount of power drained from the tissue, without inducing any seizures even if applied interictally. Overall, our results provide strong evidence on the promise of PNM for the closed-loop control of epileptic seizures and other neurological disorders where damping pathological network activity can restore healthy dynamics.

Background: Atrial fibrillation (AFib) is the most common sustained arrhythmia in the world, imposing a heavy clinical and economic burden on global healthcare systems. Early detection of AFib can reduce mortality and morbidity, while helping to alleviate the growing economic burden of cardiovascular diseases. With the increasing availability of digital health technologies, computational solutions have great potential to support the timely diagnosis of cardiac abnormalities. Objectives: With the increasing availability of electrocardiogram (ECG) data from clinical and wearable devices, manual interpretation has become impractical due to its time-consuming and subjective nature. Existing automated approaches often rely on single classifiers or fixed ensembles that primarily optimize predictive accuracy while neglecting model diversity, which leads to limited robustness and generalization across heterogeneous datasets. Therefore, this study aims to develop a robust and diversity-aware framework for automatic AFib detection that simultaneously improves classification performance and model generalizability. To this end, we propose MOE-ECG, a multi-objective ensemble selection and fusion framework that explicitly optimizes both predictive performance and inter-model diversity for reliable AFib detection from ECG recordings. Methods: The proposed multi-objective ensemble (MOE) framework uses ensemble selection as a bi-objective optimization problem and employs multi-objective particle swarm optimization to identify complementary classifiers from a heterogeneous model pool. Unlike conventional ensembles, it explicitly optimizes both predictive performance and diversity and integrates Dempster-Shafer theory for uncertainty-aware decision fusion. After filtering the ECG signals to remove baseline wander and noise, they were segmented into windows of 20, 60, and 120 heartbeats with 50% overlap. The proposed approach was evaluated over five independent runs to assess its stability and generalization. Fifteen statistical and nonlinear features were obtained from the RR-intervals of the pre-processed ECG signals, of which eight features were selected with correlation analysis to capture subtle information from the ECG data. We trained and evaluated the performance of the proposed model in three open source databases, namely, the MIT-BIH Atrial Fibrillation Database, Saitama Heart Database Atrial Fibrillation, and Long-Term AF Database. Results: The proposed approach achieved the best overall performance on 60-beat segments, with an average accuracy of 89.85%, precision of 91.14%, recall of 94.19%, an F1-score of 92.64%, and area under the curve (AUC) of around 0.95. Statistical analysis using Holm-adjusted Wilcoxon tests confirmed significant improvements (p<0.05) compared to both the best individual classifier and the unoptimized average ensemble of all classifiers. These findings show that the proposed selection and evaluation methodology, rather than group aggregation alone, is the key driver of performance improvements. Conclusion: The results obtained demonstrate that the MOE-ECG model offers a robust, accurate, and reliable solution for the detection of AFib from short ECG segments. The empirical findings, in general, confirm that multi-objective ensemble fusion enhances diagnostic performance and offers robust predictions that will open up possibilities for real-time AFib detection in clinical and tele-health settings.

Autism spectrum disorder (ASD) is a neurodevelopmental condition for which timely and accurate detection remains a major clinical priority. Early and reliable identification is important because it can facilitate access to assessment, diagnosis, and appropriate support; however, current diagnostic pathways still rely largely on behavioural evaluation and clinical judgement. In this context, machine-learning (ML) approaches have attracted growing interest because they can identify subtle and complex patterns in speech data that may not be easily captured through conventional methods. The current study capitalizes on this potential by developing and evaluating ML models for distinguishing autistic individuals from neurotypical individuals based on speech features. More specifically, acoustic features of vowels, including fundamental frequency (F0), first three formants (F1, F2, F3), duration, jitter, shimmer, harmonics-to-noise ratio (HNR), and intensity, were elicited from 18 autistic adults and 18 neurotypical adults through a controlled production task. Then, four supervised ML models were trained and evaluated on these features: LightGBM, Random Forest, Support Vector Machine, and XGBoost. All models demonstrated good classification performance, with the best-performing model achieving a strong discriminability of 89%. The explainability analysis identified F0 as the most influential predictor by a substantial margin, followed by intensity, F3, and F1, while duration, shimmer, HNR, jitter, and F2 contributed more modestly. These findings demonstrate that vowel acoustics contain clinically relevant information for distinguishing autistic from neurotypical adult speech and highlight the potential of interpretable, speech-based ML as a transparent and scalable aid for ASD screening and assessment.

Both episodic memory and word retrieval have been linked to power decreases in the alpha and beta oscillatory bands, but these patterns have rarely been related to each other, partly due to a lack of methodological approaches available. In this explorative study, we investigate the similarities and dissimilarities in the oscillatory fingerprints of the retrieval of words and episodes by directly comparing the activity patterns across time, frequency, and space. We acquired electroencephalography (EEG) data of participants performing a language and an episodic memory task based on the same stimulus material. With a newly developed approach, we directly compared the source-reconstructed oscillatory activity using mutual information and a feature-impact analysis. While left temporal and frontal regions showed dissimilarities between the tasks, right-hemispheric parietal regions exhibited similarities. We speculate that this could indicate a homologous function of these regions, potentially sharing less-specific representations between the tasks. We further uncovered a dissociation of the alpha and beta bands regarding the similarity across tasks. While the beta band was dissimilar between word and episodic memory retrieval, the alpha band seemed to contribute to the similarity we observed in right parietal regions. Whether this points to a task-unspecific function of the alpha band or a functional role in the retrieval process of the presumed representations, remains to be determined. In summary, we present an approach to study similarity across tasks using the temporal, spectral, and spatial dimensions of EEG data, and present results of exploring the shared oscillatory fingerprints between episodic memory and word retrieval.

Human attention is inherently transient and limited in span to only a few moments without lapsing. The intrinsic dynamics of large-scale neurocognitive networks are thought to contribute to these lapses and result in the unavoidable fluctuations in attention that constrain its span. However, it remains unclear how the millisecond temporal dynamics of specific electrophysiological brain states contribute to the endogenous maintenance of attention or the onset of attentional lapses. In the present study, we investigated whether the strength and millisecond dynamics of brain electric microstates differentiate states of focus from inattention and contribute to the endogenous maintenance of attention over short and long timescales. We recorded 128-channel EEG while participants maintained their attention during the wait time delay of trials in the Sustained Attention to Cue Task (SACT) and segmented the EEG into a categorized time series of microstates based on data-driven clustering of topographic voltage patterns. The findings revealed that the prevalence and rate of occurrence of microstates C and E in the wait time delay of trials differentiated trials in which the target stimulus was correctly detected from incorrectly detected. These same microstates were also implicated in the maintenance of attention over short and long timescales, with their time-varying dynamics changing systematically during the wait time delay of trials and over the course of the task session. Together, these findings demonstrate the sensitivity of microstates to variation in attentional states and suggest that the millisecond dynamics of these brain states contribute to the maintenance of attention over time.

Background: Deep brain stimulation (DBS) is a treatment option for Parkinson disease (PD). However, the effect of DBS on the arterial pressure (AP) remains unexplored. We aimed to develop an artificial baroreflex system for treating orthostatic hypotension (OH) due to central baroreflex failure in patients with PD. To achieve this, we developed an appropriate algorithm after estimating the dynamic responses of the AP to DBS using a white noise system identification method. Methods: We randomly performed DBS while measuring the AP tonometrically in 3 trials involving 3 patients with PD treated with DBS. We calculated the frequency response of the AP to the DBS using a fast Fourier transform algorithm. Finally, the feedback correction factors were determined via numerical simulation. Results: The frequency responses of the systolic AP to random DBS were identifiable in all 3 trials, and the steady state gain was 8.24 mmHg/STM. Based on these results, the proportional correction factor was set to 0.12, and the integral correction factor was set to 0.018. The computer simulation revealed that the system could quickly and effectively attenuate a sudden AP drop induced by external disturbances such as head-up tilting. Conclusion: An artificial baroreflex system with DBS may be a novel therapeutic approach for OH caused by central baroreflex failure.

BackgroundFlexibility and robustness of neuronal function are closely linked to degeneracy, the ability of distinct structural or parametric configurations to produce similar functional outcomes. At the cellular level, this often manifests as ion-channel degeneracy, in which multiple combinations of intrinsic conductances yield comparable electrophysiological phenotypes. MethodologyWe used a population-based, data-driven modelling framework to generate large ensembles of biophysically detailed CA1 pyramidal neuron models constrained by somatic electrophysiological features extracted from patch-clamp recordings in acute slices from early-birth rats. 10 reconstructed morphologies were incorporated, and model populations were analyzed using parameter correlation analysis, principal component analysis, and generalization tests to assess robustness, degeneracy, and morphology dependence of intrinsic properties. ConclusionsAcross the model population, similar somatic firing behaviours emerged from widely different combinations of intrinsic parameters, demonstrating robust two-level ion channel degeneracy both within and across morphologies. Each morphology occupied a distinct region of parameter space, indicating morphology-specific compensatory effects, while weak pairwise parameter correlations suggested distributed compensation rather than tight parameter dependencies. Even with a fixed morphology, multiple parameter subspaces supported comparable electrophysiological phenotypes. Generalization across morphologies was structure-dependent and non-reciprocal, with successful parameter similarity occurring preferentially between structurally similar neurons. Interestingly, to accurately simulate spike-frequency adaptation, it was important to retain some kinetic properties of the ion channel models as free parameters during optimization. Together, these findings show that dendrite morphology shapes the valid parameter space, and similar electrophysiology of CA1 pyramidal neurons arises from the interplay between structural variability and ion-channel diversity. This work highlights the importance of population-based modelling for capturing biological variability and provides insights into how neuronal robustness might be maintained despite substantial heterogeneity, and offers a scalable pipeline for generating biophysically realistic CA1 neuron populations for use in network simulations. Author summaryNeurons must reliably process information even though their internal components, such as ion channels and cellular shape, can vary widely from cell to cell. How stable behaviour emerges from such variability is a fundamental question in neuroscience. In this study, we explored this problem using detailed computer models of early-birth rat hippocampal CA1 pyramidal neurons, a cell type that plays a central role in learning and memory. Instead of building a single "average" neuron model, we created large populations of models that all reproduced key experimental recordings but differed in their internal parameters. We found that neurons with different shapes and different combinations of ion channels could nevertheless generate similar electrical activity. This phenomenon, known as ion channel degeneracy, allows neurons to remain functional despite biological variability or perturbations. Our results show that neuronal shape strongly influences which parameter combinations are viable, but that multiple solutions exist even for the same morphology. The population of models we provide offers a resource for future studies of early-birth CA1 pyramidal cell function and dysfunction.

Transcendent thinking (TT) is an enduring affective and cognitive process characterized by abstract meaning-making, moral reflection, self-referential integration, and strong emotional engagement. Despite growing interest in its developmental and affective significance, the intrinsic neural dynamics that predict individual differences in disposition to TT remain poorly understood. Most prior work has relied on linear functional connectivity measures, which may be insufficient to capture the nonlinear and multiscale nature of brain dynamics underlying higher-order affective dispositions like TT. Here, we introduce a nonlinear functional brain network (FBN) framework based on multiscale entropy (MSE) to investigate whether intrinsic resting-state nonlinear brain dynamics predict disposition to TT in adolescents. Functional connectivity was defined as inter-regional similarity in MSE profiles derived from resting-state fMRI, yielding weighted networks that capture scale-dependent dynamical correspondence rather than linear synchrony. Graph-theoretical, spectral, and information-theoretic measures were computed and evaluated against signal-level and network-level null models. Predictive performance was assessed using machine-learning models and compared with conventional time series-based FBNs. Global intelligence (IQ) was examined as a control cognitive variable. MSE-based network features, particularly spectral energy and Shannon entropy, showed significant associations with TT and enabled reliable prediction of individual differences, whereas time series-based network measures failed to predict TT. No network measures reliably predicted IQ. Overall, these results indicate that intrinsic nonlinear brain dynamics carry predictive information about affective dispositions, rather than domainspecific or network-localized cognitive abilities such as IQ. This work demonstrates that nonlinear, multiscale network representations of resting-state brain activity provide a principled and predictive framework for modeling individual differences in enduring affective dispositions.

Human-dog interaction is widely used to alleviate stress, yet the accompanying cortical and autonomic dynamics during acute stress and recovery remain incompletely characterized. In this study, 70 adult dog owners completed a standardized stress protocol while prefrontal cortex activity was continuously monitored with functional near-infrared spectroscopy (fNIRS), alongside subjective stress and salivary cortisol measures. Participants then underwent a recovery phase involving interaction with a companion dog, manipulating contact type (direct in person vs. indirect video conferencing), and familiarity (own vs. unfamiliar dog). Stress responses were quantified through heart rate (HR), heart rate variability (HRV), low- and high-frequency spectral power (LF, HF, and LF/HF), and prefrontal functional connectivity (FC) based on maximum cross-correlation coefficients between fNIRS channels. As expected, HR, HRV-derived indices, and FC increased from baseline to the stress phase, confirming robust engagement of autonomic and prefrontal networks. During the recovery phase, all dog interaction conditions demonstrated reductions in HR, LF/HF ratio, and FC toward or below baseline, consistent with physiological and neural stress recovery; direct interaction was associated with particularly pronounced parasympathetic enhancement and a drop in FC that fell significantly below baseline in some cases. Across groups, HRV, LF/HF, and FC were the most consistent predictors of subjective stress ratings, whereas associations with cortisol were limited. These findings suggest that human-dog interaction promotes coordinated autonomic and prefrontal recovery from acute stress, and that fNIRS-derived metrics might provide a marker of stress modulation that can distinguish high-cognitive load and low cognitive demand states beyond traditional stress indices.

Objective: Skin sympathetic nerve activity (SKNA) has emerged as a promising non-invasive surrogate measure of sympathetic drive, but its relevant physiological characteristics remain ill-defined. This observational study aims to investigate its regulatory patterns during rest and Valsalva maneuver (VM) in healthy participants. Method: Using a two-layer strategy integrating signal analysis and physiological modelling, we analyzed data recorded from 41 subjects performing repeated VMs. The observational layer includes time-domain feature comparisons using linear mixed-effect models, and time-varying spectral coherence analysis. The mechanistic layer proposes a mathematical model to investigate whether baroreflex and respiratory modulation are sufficient to reproduce the observed HR and average SKNA (aSKNA) dynamics. Main Results: Mean integrated SKNA (iSKNA) showed more significant change than HRV for VM induced effects. We also found mean iSKNA increase during VM varies with BMI and sex. The coherence analysis indicated that iSKNA strongly synchronized with EDR under resting conditions. The proposed model successfully reproduced main characteristics of aSKNA dynamics, yielding a high median Pearson correlation coefficient of 0.80 ([Q1, Q3] = [0.60, 0.91]). In contrast, HR dynamics were only partially captured, with a median PCC of 0.37 ([Q1, Q3] = [0.16, 0.55]). These results likely suggest SKNA provides a more direct representation of sympathetic burst dynamics during VM in healthy subjects. Significance: This study provides convergent evidence that SKNA reflects known autonomic regulatory influences in healthy subjects. These findings strengthen the physiological interpretability of SKNA while clarifying its appropriate use as a practical biomarker of sympathetic function.

Anatomically and biophysically detailed models of neurons have been widely used to study information processing in these cells. Most studies focused on understanding specific phenomena, while more general models that aim to capture various cellular processes simultaneously remain rare even though such models are required to predict neuronal behavior under more complex, natural conditions. In this study, we aimed to develop a detailed, data-driven, general-purpose biophysical model of hippocampal CA1 pyramidal neurons. We leveraged extensive morphological, biophysical and physiological data available for this cell type, and established a systematic workflow for model construction and validation that relies on our recently developed software tools. The model is based on a high-quality morphological reconstruction and includes a diverse curated set of ion channel models. After incorporating the available constraints on the distribution of ion channels, the remaining free parameters were optimized using the Neuroptimus tool to fit a variety of electrophysiological features extracted from somatic whole-cell recordings. Validation using HippoUnit confirmed the models ability to replicate key electrophysiological features, including somatic voltage responses to current input, the attenuation of synaptic potentials and backpropagating action potentials, and nonlinear synaptic integration in oblique dendrites. Our model also included active dendritic spines, modeled either explicitly or by merging their biophysical mechanisms into those of the parent dendrite. We found that many aspects of neuronal behavior were unaffected by the level of detail in modeling spines, but modeling nonlinear synaptic integration accurately required the explicit modeling of spines. Our data-driven model of CA1 pyramidal cells matching diverse experimental constraints is a general tool for the investigation of the activity and plasticity of these cells and can also be a reliable component of detailed models of the hippocampal network. Our systematic approach to building and validating general-purpose models should apply to other cell types as well. Author SummaryThe brain processes information through the activity of billions of individual neurons. To understand how these cells work, scientists build detailed computer models that reproduce their electrical behavior. These models make it possible to explore situations that are difficult or impossible to test experimentally. However, many existing neuron models were designed to explain only a few specific phenomena, which limits their usefulness in more complex settings. In this study, we developed a comprehensive computer model of a hippocampal CA1 pyramidal neuron, a cell type that plays a central role in learning and memory. We built the model using extensive experimental data and applied automated methods to ensure that it reproduces a broad range of observed neuronal behaviors. We also examined how small structures called dendritic spines--tiny protrusions where most synaptic communication occurs--affect how neurons combine incoming signals. We found that even simplified models without individual spines can capture many aspects of neuronal activity, but understanding more complex forms of signal integration requires modeling spines explicitly. Our work also supports the development of more realistic simulations of brain circuits.

Fraud in the health landscape is an aggravating issue, with far-reaching consequences burdening the financial stability of the health industry and threatening the quality of medical care. It results from vulnerabilities within the current healthcare framework that are exploited by the fraudsters in their favor. In spite of many developed models that aim to detect fraudulent patterns in insurance claims, the accuracy of such models frequently suffers as a result of the imbalance issue of the Medicare dataset and irrelevant features. This study ventures to improve detection performance and accuracy by employing a deep learning model along with data sampling and feature selection techniques. Comparative analysis among different combinations is conducted to determine their efficacy to enhance the accuracy of the fraud detection model. Hence, the suggested model clearly demonstrates that a combination of myriad data sampling and feature selection techniques is helping to improve accuracy and performance. The accuracy was thus 95.4%, with negligible evidence of overfitting detected using both Chi-square and Synthetic Minority Over-sampling (SMOTE) techniques. Ultimately, the study findings underscore the significance of employing combined techniques instead of using only the baseline deep learning model for better performance in detecting Medicare insurance fraud.

The impact of cellular activities on large-scale brain dynamics is thought to determine brain functioning and disease, yet the causal relationships of neural mechanisms across scales remain unclear. Recently, the cerebellum has been reported to affect whole-brain dynamics during sensorimotor integration. To disclose the underlying mechanisms, we have developed a multiscale digital brain co-simulator, in which a spiking neural network of the olivo-cerebellar microcircuit is embedded in a mouse virtual brain and wired with other nodes using an atlas-based long-range connectome. Parameters and bi-directional interfaces between the spiking olivo-cerebellar network and other rate-coded modules were tuned to match experimental data of primary sensory and motor cortex (M1 and S1) power spectral densities and neuronal spiking rates. Then, the role of the cerebellar circuitry on sensorimotor integration was analyzed by lesioning critical circuit connections in silico. Simulations showed that spike processing within the cerebellar circuit is key to explaining the gamma-band coherence between M1 and S1 during sensorimotor integration. These results provide a mechanistic explanation of how the cerebellum promotes the formation of sensorimotor contingencies in relevant cortical modules as the basis of its critical role in sensorimotor prediction. On a broader perspective, this modelling approach opens new perspectives for the multiscale investigation of brain physiological and pathological states in relation to specific cellular and microcircuit properties.

Traditional psychiatric assessments for depression are often hindered by subjective bias and patient recall in-accuracy. This paper presents a multimodal passive Brain-Computer Interface (pBCI) designed for the objective screening of depressive traits through the end-to-end decoding of neural dynamics. We implemented a hybrid EEG-fNIRS framework to capture synchronized electro-hemodynamic responses during an emotional working memory (EWM) task. To classify sub-clinical depressive tendencies based on BDI-II scores, we utilized SincShallowNet, a deep learning architecture optimized for raw signal processing via learnable Sinc-filters. Our results demonstrate that the pBCI achieves peak performance in the auditory modality, with the integration of EEG and low-pass filtered fNIRS (0.15 Hz) yielding a balanced accuracy of 90.9% and an F1-score of 0.867. By isolating purely endogenous neural markers during the EWM maintenance phase, the system provides a robust "silent observer" for mental state monitoring. These findings validate the potential of multimodal pBCIs as high-precision, data-driven tools for early-stage depression screening, offering a scalable alternative to traditional clinical interviews and a foundation for longitudinal mental health monitoring.

A mean field model (MFM) is a mesoscopic description of neuronal population dynamics that can reduce the complexity of neural microcircuits into equations preserving key functional properties. The generation of a MFM is a complex mathematical process that starts with the incorporation of single neuron input/output relationships and local connectivity. Once neuron electroresponsiveness and synaptic properties are defined, in principle, the process can be automatized. Here we develop a tool for automatic MFM derivation from biophysically grounded spiking networks (Auto-MFM) by performing micro-to-mesoscale parameter remapping, estimating input/output relationships specific for different neuronal populations (i.e., transfer functions), and optimizing transfer function parameters. Auto-MFM was tested using a spiking cerebellar circuit as a generative model. The cerebellar MFM derived with Auto-MFM accurately reproduced cerebellar population dynamics of the corresponding spiking network, matching mean and time-varying firing rates across a wide range of stimulation patterns. Auto-MFM allowed us to model and explore physiological and pathological circuit variants; indeed, it was used to map ataxia-related structural connectivity alterations of the cerebellar network, in which Purkinje cells with simplified dendritic structure altered the cerebellar connectivity. Furthermore, Auto-MFM was used to create a library of cerebellar MFMs by sweeping the level of the excitatory conductance at mossy fiber - granule cell synapse, which is altered in several neuropathologies. Auto-MFM is thus proving a flexible and powerful tool to generate region-specific MFMs of healthy and pathological brain networks to be embedded in brain digital models.

Generalization is a fundamental criterion for evaluating learning effectiveness, a domain where biological intelligence excels yet artificial intelligence continues to face challenges. In biological learning and memory, the well-documented spacing effect shows that appropriately spaced intervals between learning trials can significantly improve behavioral performance. While multiple theories have been proposed to explain its underlying mechanisms, one compelling hypothesis is that spaced training promotes integration of input and innate variations, thereby enhancing generalization to novel but related scenarios. Here we examine this hypothesis by introducing a bio-inspired spacing effect into artificial neural networks, integrating input and innate variations across spaced intervals at the neuronal, synaptic, and network levels. These spaced ensemble strategies yield significant performance gains across various benchmark datasets and network architectures. Biological experiments on Drosophila further validate the complementary effect of appropriate variations and spaced intervals in improving generalization, which together reveal a convergent computational principle shared by biological learning and machine learning.