Predictive coding (PC) describes the cognitive process by which the brain infers information about the external environment by matching incoming sensory input with topdown predictions from internal models of the world. The hippocampus is thought to play a central role in generating predictions from past experiences and transmitting them to sensory cortices through hierarchical feedback signals. However, hippocampal-cortical functional connectivity (FC) during audiovisual associative learning remains poorly characterized. To close this gap, we applied a task-based functional connectivity (t-FC) analysis using Beta-Series Correlation on 7T fMRI data. We compared standard Pearson correlation with a PCA-based partial correlation approach to distinguish mediated associations from direct functional interactions. Preliminary results revealed hierarchical connectivity patterns consistent with PC, highlighting direct hippocampal-cortical and temporal-frontal interactions supporting cross-modal prediction.

Meditation has long been associated with improvements in mental well-being, emotional regulation, and attentional control. However, the diversity of meditative techniques and variability in participant expertise across studies have hindered the systematic identification of their neurophysiological correlates. To address this challenge, we investigated the neurophysiological signatures of distinct meditation types in a relatively homogeneous cohort of 35 highly experienced Tibetan monk practitioners. We focused on concentrative meditation, characterized by sustained attention on a single object, and two analytical practices: Loving-Kindness, centered on cultivating prosocial affect, and Emptiness, involving reflective and deconstructive cognitive processes. EEG, ECG, EDA, and respiration were simultaneously recorded during these practices, which were performed in a fixed-order sequence (Concentrative → Loving-Kindness → Emptiness). Linear mixed-model analyses revealed significant modulations across conditions. Peripheral indices indicated enhanced parasympathetic activity (higher RMSSD and SDNN), reduced respiratory rate, and a progressive rise in tonic EDA across the entire three-phases meditation session. Cortically, EEG analyses showed changes in the gamma-band power during the meditation session. Overall, the pattern suggests that advanced meditative states involve coordinated autonomic–cortical integration, reflecting relaxed vigilance with improved emotional regulation and cognitive flexibility; nevertheless, given the fixed-order design, condition-related differences should be interpreted cautiously due to possible order or carryover effects.

Meditation is a complex cognitive practice associated with significant neurophysiological changes, particularly in long-term practitioners. These individuals represent an ideal human model for investigating neural changes associated to their consistent, frequent, and sustained cognitive engagement. However, in Western societies, long-term practitioners are relatively rare compared to Eastern monastic communities. In this study, we leverage a collaboration with a unique monastic population, the Monks and Geshes of the Tibetan University of Sera Jey in India, to examine the long-term effects of meditation on resting-state effective brain connectivity. Specifically, we hypothesize that different levels of meditation experience modulate intrinsic connections in two resting-state brain networks: the default mode network (DMN) and the salience network (SN). To test this, we apply dynamic causal modeling for EEG to analyze effective connectivity and validate our hypothesis. Our results reveal that long-term meditation practice can alter connectivity within the DMN and SN, with distinct patterns of modulation based on meditator experience. Experienced meditators appear to exhibit enhanced self-referential processing in the DMN and reduced reactivity in the SN, supporting the notion that meditation refines attentional control and internal awareness. These findings provide new insights into the neurophysiological mechanisms underlying long-term meditation and highlight the role of monastic practitioners as an invaluable model for studying experience-related modifications in the human brain.

Abstract Functional connectivity (FC) approaches from resting-state fMRI (rs-fMRI) are amply spread to investigate the cortical organization, yet the brainstem remains relatively underexplored despite its pivotal roles in both physiological and pathological conditions. The highly collinear network, in which the strongly interconnected nodes and the widespread neuromodulatory influences induce indirect or mediated interactions, make the estimation of direct brainstem FC challenging. Standard bivariate methods fail to recover the true network structure in such complex topologies, causing false positive interactions. On the other hand, partial correlation can potentially estimate the direct FC, but multicollinearity issues and collider-induced spurious correlations limit its application in high-dimensional scenarios. Here, we propose a physiologically informed framework in which the conditioning strategy for partial correlation estimation is tailored for the investigation of the brainstem and its direct interactions within the network and with whole-brain regions. Specifically, we employed a PCA-regularized partial correlation (PCA − ρ PC ) approach, where PCA is applied to the brainstem covariates to mitigate multicollinearity and model shared modulatory variance. We show that PCA − ρ PC improves the robustness and interpretability of brainstem FC, yielding sparser and more physiologically plausible connectomes compared with conventional (regularized) approaches. Both simulation and real fMRI data raise the possibility that Pearson’s and PCA-regularized approaches may complement each other in an effort to unravel the pattern of direct vs. indirect effects in highly collinear settings, paving the way for future extensions in a wide range of multivariate neuroimaging applications.

The choroid plexus serves as the primary barrier between the brain's blood and cerebrospinal fluid and mediates neuroimmune function. A subset of individuals with autism spectrum disorder (ASD) may exhibit morphological alterations of the choroid plexus. However, to power larger population analyses, an automated tool capable of accurately segmenting the choroid plexus based on magnetic resonance imaging (MRI) is needed. Automated Segmentation of CHOroid PLEXus (ASCHOPLEX) is a deep learning tool that enables finetuning using new, patient-specific, training data, allowing its usage across cohorts for which the model was not originally trained. We evaluated ASCHOPLEX's generalizability to individuals with ASD by performing finetuning on a local dataset of ASD and control (CON) participants. To assess generalizability, we implemented a probabilistic version of the algorithm, which allowed us to quantify the uncertainty in choroid plexus segmentation and evaluate the model's confidence. ASCHOPLEX generalized well to our local dataset, in which all participants were adults. To further assess its performance, we tested the algorithm on the Autism Brain Imaging Data Exchange (ABIDE) dataset, which includes data from children and adults. While ASCHOPLEX performed well in adults, its accuracy declined in children, suggesting limited generalizability to different age groups without additional finetuning. Our findings show that the incorporation of a probabilistic approach during finetuning can strengthen the use of this deep learning tool by providing confidence metrics which allow assessing model reliability. Overall, our findings demonstrate that ASCHOPLEX can generate accurate choroid plexus segmentations in previously unseen data.

Objective: Infrared Thermography (IRT) has been used to monitor skin temperature variation in a contactless manner, in both clinical medicine and psychophysiology. Here, we introduce a new methodology to obtain information about autonomic correlates related to perspiration, peripheral vasomotility, and respiration from infrared recordings. Methods: Our approach involves a model-based decomposition of facial thermograms using Independent Component Analysis (ICA) and an ad-hoc preprocessing procedure. We tested our approach on 30 healthy volunteers whose psychophysiological state was stimulated as part of an experimental protocol. Results: Within-subject ICA analysis identified three independent components demonstrating correlations with the reference physiological signals. Moreover, a linear combination of independent components effectively predicted each physiological signal, achieving median correlations of 0.9 for electrodermal activity, 0.8 for respiration, and 0.73 for photoplethysmography peaks envelope. In addition, we performed a cross-validated inter-subject analysis, which allows to predict physiological signals from facial thermograms of unseen subjects. Conclusions/Significance: Our findings validate the efficacy of features extracted from both original and thermal-derived signals for differentiating experimental conditions. This outcome emphasizes the sensitivity and promise of our approach, advocating for expanded investigations into thermal imaging within biomedical signal analysis. It underscores its potential for enhancing objective assessments of emotional states.

Although Ecological Momentary Assessment (EMA) and physiological measurements provide a valuable opportunity to evaluate therapeutic interventions in real time, no study has used this approach to assess Dialectical Behavior Therapy (DBT) in autistic adults with high levels of emotion dysregulation (ED). In this study, 26 autistic adults were evaluated before and after participating in a standard 5-month DBT program, using Ecological Momentary Assessment (EMA). The EMA included: (1) twelve evaluations per day over a 7-day period, measuring alexithymia, emotional states, subjective arousal and emotion control; (2) continuous physiological monitoring with a wristband to record heart-rate (HR), heart-rate variability (HRV) and skin conductance levels (SCL). Following DBT, no significant differences were found with respect to negative emotions and higher conflicting emotions, but increased rates of identified emotions, positive emotions and emotion control were found. Baseline autonomic responses remained unchanged, whereas subjective arousal was found to correlate positively with HRV. Overall, these results suggest that participants showed enhanced emotion awareness and emotion regulation capabilities following DBT. Our study adds to previous research showing that DBT is efficient in treating ED in autistic adults, using real-time measurements of subjective and physiological markers collected through EMA. Specifically, alexithymia measures decreased post-DBT while positive emotions and emotion control increased. Randomized controlled trials should consider using these methods to improve the assessment of the impact of DBT in the daily life of autistic individuals with ED and/or suicidal behavior.

Meditation has been long associated with improvements in mental well-being, emotional regulation, and attentional control. Yet, the diversity of meditative techniques and participant expertise has hindered the systematic identification of their neurophysiological correlates supporting these benefits. To address this challenge, we investigated the neurophysiological signatures of concentrative and analytical meditation in 35 experienced Tibetan monk practitioners. EEG, ECG, EDA, and respiration were simultaneously recorded during Concentrative, Loving-Kindness and Emptiness meditations. Linear-mixed-models revealed significant modulations in autonomic and cortical activity across meditations. Peripheral indices indicated enhanced parasympathetic tone, decreases respiratory rate, and gradual increase in EDA – reflecting a state of relaxed-alertness with concurrent vagal engagement and sustained sympathetic arousal. EEG analyses supported this state showing elevated gamma-band power during analytical meditations. These findings suggest that advanced meditative states foster an adaptive integration of autonomic and cortical responses, supporting the emergence of relaxed-vigilance – a psychophysiological condition associated with well-being and cognitive flexibility.

Objective The knowledge on breathing control and central chemoreception, key subcortical functions involved in several neuropathologies, is still mainly based on animal studies. In humans, functional MRI (fMRI) offers the needed spatio-temporal resolution and non-invasiveness, but the lack of specific tools and preprocessing solutions hinders its use in brainstem studies. We hereby propose an original fMRI analysis pipeline aimed at unravelling central chemoreception mechanisms, by integrating acquisition, spatial coregistration, noise removal and a novel data-driven analysis solution to compare network activation levels across tasks or conditions in fMRI. Approach Novel analysis methodologies are integrated with the optimization of known preprocessing approaches to physiological noise correction and brainstem-focused coregistration. We couple independent components of fMRI data, separately estimated from healthy subjects during Free Breathing (FB) and Breath Hold (BH), by means of spatial correlation. We then identify statistically significant differences between BH and FB in CO2-dependent components by means of voxel-wise comparisons of components’ percent signal change. Components were localized using the Brainstem Navigator Atlas for enhancing network interpretability. Main Results Using the pipeline we characterized CO2-related BOLD oscillations within the central control system of breathing. We corroborated the primary chemoreceptive role of medullary raphe in healthy subjects. We observed that BH over-activated ascending sensory-motor projections through the postero-lateral thalamus, descending projections through the putamen, and peripheral sensations entry points in the dorsal medulla. We highlighted the role of latero-dorsal tegmentum in the response to hypercapnia-induced aversive effects. Significance Our method allows to non-invasively locate primary chemoreception and related arousal triggers, characterizing alterations and therefore fostering the identification of therapeutic targets in abnormal breathing. Moreover, the proposed strategy addresses the issues of inter-task comparison among homologous independent sources, of their characterization and interpretation. Its extension could benefit all similarly challenging brainstem-focused studies, including those on Parkinson’s and Alzheimer’s diseases. ### Competing Interest Statement The authors have declared no competing interest.

Body odours (BOs) of individuals in specific emotional states can influence receivers’ responses – referred to as an emotional contagion. To investigate the potential of BOs to enhance the effects of mindfulness practice, this quasi-randomised pilot study tested the hypothesis that participants exposed to emotional BOs during mindfulness meditation would exhibit a steeper decrease in state anxiety symptoms compared to mindfulness alone (clean air control). Ninety-eight women meeting criteria for Social Anxiety Disorder (SAD) received two mindfulness sessions over two consecutive days, while randomly allocated to one of four conditions: fear BO, joy BO, neutral BO or a clean air control group. No odour × time interaction effect was observed, rejecting the primary hypothesis. Although not statistically significant, effect size estimates suggested a greater reduction in state anxiety for the group receiving fear chemosignals (Day 1 Cohen’s d = 0.26, Day 2 Cohen’s d = 0.54) compared to the clean air control group. Moreover, the BO groups perceived the mindfulness practice as significantly more helpful compared to the control group (p = 0.002). Given the sample size limits, a larger Randomized Controlled Trial (RCT) incorporating more mindfulness + BO sessions is recommended to further examine the therapeutic potential of human BOs.

The presence of speckle noise poses challenges in the interpretation of diagnostic medical ultrasound (US) images, particularly in detecting low-contrast structures. Among the traditional despeckling approaches, the Lee filter aims at removing this noise while preserving the useful edges of the image. We propose a method to optimize the design process of the Lee filter for the removal of speckle noise from diagnostic medical ultrasound images. Our hypothesis is that it is possible to improve the performance of the Lee filter by adapting the size of its window to the characteristic size of the speckle noise. To test this possibility, we explored a possible relationship between speckle size and the optimal filter window size. We evaluated a measure of speckle dimension based on the estimation of the image auto-covariance function. Then two image quality indexes (Peak Signal-to-Noise Ratio and Structural Similarity Index) were used to assess filter efficiency at different window sizes. Lastly, we implemented a multinomial logistic regression model to generalize this association. This work sets the stage for the possible application of speckle size assessment in optimizing the design of spatial filters. Furthermore, through the analysis of the speckle size in filtered images, we propose to exploit this characteristic to help define an unsupervised measure of image Quality.

Developing reliable and explainable models is a crucial point to effectively integrate and exploit the potentials offered by Deep Learning (DL) architectures in high-stakes scenarios like healthcare. There are several applications that exploit DL to support Autism Spectrum Disorder (ASD) diagnosis, eventually augmented by explainable AI (XAI) tools to provide hints on the decision-making process implemented. On the other hand, Bayesian Neural Networks (BNNs) can provide, together with their prediction, epistemic uncertainty (uncertainty of the model), a key component to asserting the model's reliability. To date, there are no applications which exploit the advantages offered by BNNs and XAI to support the research of biomarkers in ASD. In the present work, authors first developed a BNN which classifies ASD subjects from resting state functional Magnetic Resonance Imaging (rs-fMRI) data obtained from the Autism Brain Imaging Data Exchange (ABIDE) dataset. A Layerwise Relevance Propagation (LRP) algorithm was then used to estimate the importance of cross-correlation connectivity coefficients in the returned predictions. Finally, a group analysis was performed to highlight functional brain connections that report the highest impact on the model's correct classification of ASD subjects. This work ended up producing a framework which combines a bayesian neural network with a XAI methodology, towards a robustness-centric deep learning approach, applied to the case study of ASD diagnosis.

The positive effects of meditation on human wellbeing are indisputable, ranging from emotion regulation improvement to stress reduction and present-moment awareness enhancement. Changes in brain activity regulate and support these phenomena. However, the heterogeneity of meditation practices and their cultural background, as well as their poor categorization limit the generalization of results to all types of meditation. Here, we took advantage of a collaboration with the very singular and precious community of the Monks and Geshes of the Tibetan University of Sera-Jey in India to study the neural correlates of the two main types of meditation recognized in Tibetan Buddhism, namely concentrative and analytical meditation. Twenty-three meditators with different levels of expertise underwent to an ecological (i.e., within the monastery) EEG acquisition consisting of an analytical and/or concentrative meditation session at “their best,” and with the only constraint of performing a 5-min-long baseline at the beginning of the session. Time-varying power-spectral-density estimates of each session were compared against the baseline (i.e., within session) and between conditions (i.e., analytical vs. concentrative). Our results showed that concentrative meditation elicited more numerous and marked changes in the EEG power compared to analytical meditation, and mainly in the form of an increase in the theta, alpha and beta frequency ranges. Moreover, the full immersion in the Monastery life allowed to share the results and discuss their interpretation with the best scholars of the Monastic University, ensuring the identification of the most expert meditators, as well as to highlight better the differences between the different types of meditation practiced by each of them.

Brugada Syndrome (BrS) is a primary electrical epicardial disease characterized by ST-segment elevation followed by a negative T-wave in the right precordial leads on the surface electrocardiogram (ECG), also known as the ‘type 1’ ECG pattern. The risk stratification of asymptomatic individuals with spontaneous type 1 ECG pattern remains challenging. Clinical and electrocardiographic prognostic markers are known. As none of these predictors alone is highly reliable in terms of arrhythmic prognosis, several multi-factor risk scores have been proposed for this purpose. This article presents a new workflow for processing endocardial signals acquired with high-density RV electro-anatomical mapping (HDEAM) from BrS patients. The workflow, which relies solely on Matlab software, calculates various electrical parameters and creates multi-parametric maps of the right ventricle. The workflow, but it has already been employed in several research studies involving patients carried out by our group, showing its potential positive impact in clinical studies. Here, we will provide a technical description of its functionalities, along with the results obtained on a BrS patient who underwent an endocardial HDEAM.

The development of robust tools for segmenting cellular and sub-cellular neuronal structures lags behind the massive production of high-resolution 3D images of neurons in brain tissue. The challenges are principally related to high neuronal density and low signal-to-noise characteristics in thick samples, as well as the heterogeneity of data acquired with different imaging methods. To address this issue, we design a framework which includes sample preparation for high resolution imaging and image analysis. Specifically, we set up a method for labeling thick samples and develop SENPAI, a scalable algorithm for segmenting neurons at cellular and sub-cellular scales in conventional and super-resolution STimulated Emission Depletion (STED) microscopy images of brain tissues. Further, we propose a validation paradigm for testing segmentation performance when a manual ground-truth may not exhaustively describe neuronal arborization. We show that SENPAI provides accurate multi-scale segmentation, from entire neurons down to spines, outperforming state-of-the-art tools. The framework will empower image processing of complex neuronal circuitries. Tools to segment cellular and sub-cellular neuronal structures can be hindered by high neuronal density and low signal-to-noise in thick samples. Here, the authors present SENPAI, a framework for imaging and segmenting neurons from conventional and super-resolution microscopy of clarified brain tissues.

Humans can decode emotional states from the body odors of the conspecifics and this type of emotional communication is particularly relevant in conditions in which social interactions are impaired, as in depression and social anxiety. The present study aimed to explore how body odors collected in happiness and fearful conditions modulate the subjective ratings, the psychophysiological response and the neural processing of neutral faces in individuals with depressive symptoms, social anxiety symptoms, and healthy controls (N = 22 per group). To this aim, electrocardiogram (ECG) and HD-EEG were recorded continuously. Heart Rate Variability (HRV) was extracted from the ECG as a measure of vagal tone, event-related potentials (ERPs) and event-related spectral perturbations (ERPSs) were extracted from the EEG. The results revealed that the HRV increased during the fear and happiness body odors conditions compared to clean air, but no group differences emerged. For ERPs data, repeated measure ANOVA did not show any significant effects. However, the ERPSs analyses revealed a late increase in delta power and a reduced beta power both at an early and a late stage of stimulus processing in response to the neutral faces presented with the emotional body odors, regardless of the presence of depressive or social anxiety symptoms. The current research offers new insights, demonstrating that emotional chemosignals serve as potent environmental cues. This represents a substantial advancement in comprehending the impact of emotional chemosignals in both individuals with and without affective disorders.

In this study, we present an analysis of the relationship between the linguistic profile of a text and the physiological and acoustic characteristics of the reader to improve the emotion recognition systems. To this aim, we recorded the speech and electrodermal activity (EDA) signals from 33 healthy volunteers reading neutral and affective texts aloud. We used the BioVoice toolbox and cvxEDA algorithm to estimate some of the main speech and EDA features, respectively. The selected texts were analyzed to quantify their lexical, morpho-syntactic, and syntactic properties. Correlation and Support Vector Regression analyses between linguistic and speech and EDA features have shown a significant bidirectional association between the morpho-syntactic structure of the text and both sympathetic markers and voice acoustic properties. Specifically, significant relationships were observed between linguistic properties and certain EDA and speech features commonly used to evaluate human emotional state (e.g., edaSymp, mean tonic, F0). These findings suggest that lexical, morpho-syntactic, and syntactic properties may have a significant impact on an individual’s emotional dynamics.

Ultrasound (US) images suffer from speckle noise, a granular pattern that hampers contrast and resolution, making low-contrast structures critically difficult to identify. Albeit traditional filtering and machine learning approaches can handle this problem, both have limitations: such as the need of (hyper-)parameters fine-tuning or the necessity of data collection and annotation. In our study, we explored an unsupervised image filtering method based on blind denoising, so that we can systematically overcome the need of ground truth annotations. Our approach is based on a noise2noise u-net backbone (N2N) fed by a novel image representation approach. Dubbed Emulated Frequency Compound (EFQ), this study is intended to propose and validate it in the small data regime which is compatible with the typical applicative scenario of US imaging. As our experimental validation shows, the adoption of EFQ for N2N results in a favorable performance with respect to a number of state-of-the-art methods and related baselines.

Difficulties in controlling emotions - a proxy for emotion dysregulation (ED)-and difficulties in expressing feelings in words-'absence of emotion labelling' or alexithymia-co-exist in autism and contribute to elevated levels of impulsive and suicidal behaviour. To date, studies linking the two phenomena have relied on retrospective self-reported measures, lacking support for generalizability to real-life situations. The present study investigated in vivo emotion labelling and its impact on emotion control in 29 autistic adults without intellectual disability (ASC) and 28 neurotypical (NT) individuals of similar age, sex, and educational level. Participants were trained in an Ecological Momentary Assessment (EMA) to label their emotions, the arousal dimension, and their emotion control via smartphone over a one-week period. Findings showed that the ASC group experienced more instances of 'having an emotion that I cannot name' and, when they were able to label their emotions, they reported higher rates of negative and conflicting (simultaneously positive and negative) emotions. In both groups, the absence of emotion labelling, and intense negative emotions were associated with impaired emotion control. However, the association between lack of emotional awareness-'I have no emotion'-and impaired emotion control was only evident in ASC individuals. Our study highlights a nuanced facet of emotional processing in the ASC population. Further research is needed to gain a deeper understanding of the complex relationship between ED and alexithymia in autism.

In vivo brain functional connectivity (FC) analysis plays a pivotal role in studying brain activity in both health and disease. Since the brainstem is strongly involved in fundamental physiological processes as well as in pathological conditions, it is important to characterise both its direct and mediated functional connectivity. However, given a complex brain network topology and their strong neuromodulatory role, brainstem nuclei can act as confounders, chain system elements, and colliders (i.e., mediator regions) within brain networks, making the estimation of direct FC an open challenge. In this work, we propose a partial correlation approach exploiting principal component analysis (PCA) to address the multicollinearity issue and alleviate the effects of mediator regions. Specifically, the partial correlation is implemented to focus on brainstem-to-brainstem and brainstem-to-brain direct connectivity, thus highlighting the contribution of specific brainstem nuclei. The methodology was applied to resting-state fMRI data and compared with commonly used regularization approaches, revealing direct and sparse networks between brainstem nuclei and whole-brain regions.