Biology subjects

Direito, B.

Publications and source records attributed to Direito, B..

9 recordsLinked to original sources

bioRxiv2026

All signals considered: Data quality partially explains inter-individual task differences in a large, open fNIRS dataset

Significance: High inter-subject variability and limited reproducibility in functional near-infrared spectroscopy (fNIRS) research may partly reflect global systemic physiology and signal quality differences, possibly distorting task-evoked hemodynamic responses. Aim: We investigate how signal quality relates to inter-subject variability in motor-task fNIRS responses and introduce a large, open, multi-task, near whole-head fNIRS dataset with extensive peripheral physiology and short-channel recordings. Approach: Fifty-seven participants completed resting-state, motor action, motor imagery, emotion recognition, visual, and auditory tasks during fNIRS recording. Peripheral measures included pulse oximetry, heart rate, blood oxygen saturation, respiration, room temperature, galvanic skin response, electrocardiogram, and electromyography. Signal quality was assessed using the scalp coupling index (SCI), coefficient of variation (CV), signal-to-noise ratio (SNR) and a spectral measure here coined the coupling SNR (cSNR). Results: Quality metrics were weakly to moderately correlated, except SNR and CV, which showed the expected inverse relationship. All quality metrics were significantly related to channel length and associated with task-related activation estimates. Group-level analyses validated activation in expected task-related regions. Conclusions: The assessed metrics capture complementary features of fNIRS signal quality and may help explain individual activation differences. The dataset provides a comprehensive, open resource enabling future evaluation of physiological correction methods and confound mitigation.

neuroscience↗

bioRxiv2026

A Neurofeedback therapy of facial expression recognition in Autism shifts connectivity to higher levels within the third visual pathway in relation to clinical improvements

The mechanistic role of the third visual pathway in autism spectrum disorder (ASD) remains unknown. We previously developed a neurofeedback therapy for autism targeting the posterior superior temporal sulcus (pSTS), a region in this network that underlies the perception and imagery of emotional facial expressions, resulting in improvements in adaptive behavior and recognition of fear in facial expressions. Here, we investigated the impact of this 5-session therapy on the functional connectivity of that core region of the third visual pathway. We found evidence for a profound reorganization of this network with treatment-induced decreases in connectivity between low-level visual areas, the pSTS, and the posterior occipital face area (OFA), and increased connectivity with higher-level visual regions (fusiform face area - FFA), right middle STS (mSTS), and parietal cortex. These changes, suggesting the restoration of connectivity in regions known to be underconnected in ASD, such as mSTS and pSTS, and in a set of regions belonging to the temporoparietal junction and the ventral attention network, which are known to be involved in broader aspects of social cognition, were positively associated with clinical improvements. The demonstration of treatment response associated with network reconfiguration paves the way for multicentric trials to probe this observed reorganization as a treatment target.

neuroscience↗

bioRxiv2026

Beyond Regional Activations: Structural Connectivity Message-Passing Shallow Neural Networks for Brain Decoding

Brain decoding from fMRI data using artificial neural networks traditionally operates at the regional level, identifying which brain areas activate during tasks but ignoring how these regions interact through structural networks. While Graph Neural Networks can capture connectivity, they require prohibitively large datasets for typical neuroscience studies. We introduce a message-passing mechanism that allows a shallow neural network to incorporate structural connectivity, enabling network-level interpretation from limited data. Using motor task data from 30 Human Connectome Project subjects, we evaluate seven structural connectivity matrices derived from deterministic and probabilistic tractography. Our approach achieves 83.0% classification accuracy while revealing functional network organization. We demonstrate that sparser, anatomy-driven connectivity matrices outperform dense alternatives, and that normalizing for network size improves model performance. Critically, our method is capable of exposing structural pathways contributing towards classification, distinguishing between complete network recruitment and selective regional activation. This approach bridges the gap between high-performance brain decoding and biological fidelity of the model, enhancing neuroscientific understanding, with implications for analyzing network dysfunctions in neurological disorders such as Alzheimers disease (AD), attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder, mild cognitive impairment (MCI), and schizophrenia.

neuroscience↗

bioRxiv2026

Structurally Restricted Message-Passing within Shallow Architectures for Explainable Network-Level Brain Decoding on Small Cohorts

The application of artificial intelligence (AI) to functional magnetic resonance imaging (fMRI) has gained increasing attention due to its ability to model complex, high-dimensional brain data and capture nonlinear patterns of neural activity. However, deep learning architectures, such as Graph Neural Networks (GNNs), typically require large sample sizes to achieve stable convergence, limiting their applicability in neuroimaging contexts where data are often scarce. This challenge highlights the need for compact, data-efficient models that maintain predictive performance and interpretability. Shallow neural networks (SNNs) have demonstrated robustness in low-sample settings but commonly rely on region-level features that treat brain areas independently, overlooking the brains intrinsically network-based organization. To address this limitation, we propose a structurally constrained message-passing framework that integrates diffusion tensor imaging (DTI)-derived structural connectivity with region-level fMRI signals within a shallow architecture. This approach enables network-level modeling while preserving the stability and data efficiency of SNNs. The method is evaluated on 30 subjects performing a Theory of Mind (ToM) task from the Human Connectome Project Young Adult dataset. A baseline SNN achieved global accuracies of 88.2% (fully connected), 80.0% (pruned), and 84.7% (retrained), while the proposed model achieved 87.1%, 77.6%, and 84.7%, respectively. Although structural constraints led to a more pronounced performance decrease after pruning, retraining restored accuracy to baseline levels, demonstrating that biological constraints can be incorporated without compromising predictive validity. Model interpretability was assessed using SHAP (Shapley Additive Explanations). While the baseline model primarily identified isolated regions as key contributors, the proposed framework revealed distributed, structurally coherent networks as the main drivers of classification. These networks showed correspondence with established ToM regions, including the temporo-parietal junction, superior temporal sulcus, and inferior frontal gyrus. Importantly, the findings suggest that groups of moderately informative regions can collectively form highly relevant subnetworks. Overall, the proposed framework achieves competitive performance in a limited dataset while incorporating graph-inspired message passing into a shallow architecture. Its explainability provides insight into how structurally constrained networks support stimulus-driven responses in ToM and demonstrates potential for investigating network dysfunction in disorders such as Alzheimers disease, ADHD, autism spectrum disorder, bipolar disorder, mild cognitive impairment, and schizophrenia.

bioinformatics↗

bioRxiv2025

Neural correlates of emotional responses to self-selected music: evidence from multivariate pattern analysis

Music is a uniquely powerful stimulus for evoking complex and deeply felt emotions. While previous research has identified neural correlates of music-evoked emotional responses, less is known about how these felt emotions are represented in the brain, particularly when elicited by familiar, personally meaningful music. Here, we used a personalized fMRI paradigm in which participants (N = 20) each selected musical excerpts corresponding to the nine emotion categories defined by the Geneva Emotional Music Scale. These self-selected excerpts were presented during functional MRI scanning. We first examined the neural correlates of music-evoked emotion by comparing brain activity during music listening to that during exposure to white noise. The maps were consistent with previous research, highlighting clusters in sensory and limbic regions. We then used multivoxel pattern analysis to decode emotion categories from whole-brain activation patterns. The results revealed that music-evoked emotions could be reliably discriminated based on distributed neural activity, with consistent involvement of the superior temporal gyrus, supplementary motor area, amygdala, and cerebellum, among other auditory, motor, and interoceptive regions. These findings provide new insight into the neural encoding of musical emotions and highlight the value of personalized, music-based paradigms for research in auditory and affective neuroscience.

neuroscience↗

bioRxiv2025

Music as a real-time fMRI neurofeedback interface for modulating interhemispheric connectivity: effects on mood and recruitment of the putamen and insula

Music is a universal language that transcends cultures and is deeply rooted in human evolutionary history. Its creation and appreciation recruit the limbic and reward systems, leading to the evocation of emotions ranging from happiness and sadness to tenderness and grief. Here, we investigate the potential of music as an interventional tool in a novel neurofeedback connectivity-based experiment. This study proposes a musical interface for real-time fMRI neurofeedback that is adaptable to diverse experimental paradigms, namely the ones aiming at improving mood and other affective dimensions. Using a previously developed motor imagery connectivity-based approach, we evaluate its feasibility and efficacy by comparing the modulation of bilateral premotor cortex (PMC) activity during functional runs with real versus sham (random) feedback in 22 healthy adults. We also assess its performance against a visual feedback interface. The experiment involves a 50-minute MRI session, including anatomical scans, a PMC functional localizer run, and four neurofeedback runs (two with active feedback and two with sham feedback). Pre- and post-session questionnaires assess the neurobehavioral impact on mood, musical background (as a potential predictor of NF success), and subjective feedback experiences. During neurofeedback, participants perform motor imagery of finger-tapping, with feedback delivered as a dynamic, pre-validated chord progression that evolves or regresses based on the functional connectivity between left and right PMC. We found that our implementation of music-based feedback was successful, with participants managing to modulate their own connectivity using the proposed interface. The modulation performance was similar for active and sham NF runs, possibly due to the power of music to boost neuromodulation, but the network recruitment was stronger for active NF, including in the insula, putamen, and target ROIs. Behaviorally, we found a decrease in tension and an improvement in the overall mood of the participants after the session. When comparing our results to previous NF data with a visual interface, we found stronger brain activations, in particular in NF-relevant regions such as the insula and the putamen. This work shows that it is possible to directly modulate interhemispheric connectivity using a rt-fMRI musical interface with direct effects on mood and recruitment of saliency and learning networks.

neuroscience↗

bioRxiv2024

Development and assessment of a new multichannel electrocutaneous device for non-invasive somatosensory stimulation for magnetic resonance applications

Electrocutaneous stimulation (ES) relies on the application of an electrical current flowing through the surface of the skin, eliciting a tactile percept. It can be applied for somatosensory mapping approaches at functional magnetic resonance imaging (fMRI) to obtain somatotopic maps illustrating the spatial patterns reflecting the functional organization of the primary somatosensory cortex (S1). However, its accessibility remains constrained, particularly in applications requiring multiple stimulation channels. Furthermore, the magnetic resonance (MR) environment poses several limitations in this regard. This study presents a prototype of a multichannel electrocutaneous stimulation device designed for somatosensory stimulation of the upper limbs of human participants in an MR environment in an inexpensive, safe, customizable, controlled, reproducible, and automated way. Our current-controlled, voltage-limited, stimulation device comprises 20 stimulation channels that can be individually configured to deliver various non-simultaneous combinations of personalized electrical pulses, depending on the subject, stimulation site, and stimulation paradigm. It can deliver a predefined electrical stimulus during fMRI acquisition, synchronized with the stimulation task design and triggered upon initiation of the acquisition sequence. Regarding device assessment, we conducted tests using an electrical circuit equivalent to the impedance of the human body and the electrode-skin interface to validate its feasibility. Then, we evaluated user acceptability by testing the device in human participants. Considering the stringent conditions of the MR environment, we performed a comprehensive set of safety and compatibility evaluations using a phantom. Lastly, we acquired structural and functional MR data from a participant during a somatosensory stimulation experiment to validate brain activity elicited by electric stimulation with our device. These assessments confirmed the devices safety in fMRI studies and its ability to elicit brain activity in the expected brain areas. The scope of application of our device includes fMRI studies focused on somatosensory mapping and brain-computer interfaces related to somatosensory feedback.

neuroscience↗

bioRxiv2024

Decoding Music-Evoked Valence and Arousal: Unraveling the Neural Correlates of Naturalistic Music Characteristics through fMRI

Music can convey basic emotions, such as joy and sadness, and more complex ones, such as tenderness or nostalgia. Its effects on emotion regulation and reward have attracted much attention in cognitive and affective neuroscience. Understanding the neural correlates of music-evoked emotions may guide the development of neurorehabilitation interventions based on music. Here, we used fMRI to examine the relationship between the classification of music excerpts regarding perceived valence and arousal and their neural correlates. Twenty participants were scanned while listening to 96 musical excerpts, which were classified beforehand into four categories as a function of valence (positive vs. negative) and arousal (high vs. low). Differences in valence and arousal modulated activity in cortical regions, most noticeably the music-specific subregions of the auditory cortex, but also in the thalamus and regions of the reward network such as the amygdala. Using multivoxel pattern analysis, we created a computational model able to decode the valence and arousal of the music excerpts significantly above chance. We further explored how a set of musical features relate to brain activity in valence-, arousal-, reward-, and auditory-related ROIs. The results emphasize the differential involvement of musical features in the brain, notably expressive features such as Vibrato and Tonal and Spectral dissonance in valence, arousal, and reward brain networks, while a broader set of features modulate sensory auditory networks. Using ecologically valid music stimuli, we contribute to the definition of the neural substrates of music listening and evoked emotions. Moreover, the definition of the musical features that modulate specific brain networks paves the way to developing novel music-based neurorehabilitation strategies.

neuroscience↗

bioRxiv2020

The boundaries of State-Space Granger Causality Analysis applied to BOLD simulated data: a comparative modelling and simulation approach

BackgroundThe analysis of connectivity has become a fundamental tool in human neuroscience. Granger Causality Mapping is a data-driven method that uses Granger Causality (GC) to assess the existence and direction of influence between signals, based on temporal precedence of information. More recently, a theory of Granger causality has been developed for state-space (SS-GC) processes, but little is known about its statistical validation and application on functional magnetic resonance imaging (fMRI) data. New MethodWe implemented a new heuristic, focusing on the application of SS-GC with a distinct statistical validation technique - Time Reversed Testing - to generative synthetic models and compare it to classical multivariate computational frameworks. We also test a range of experimental parameters, including block structure, sampling frequency, noise and system mean pairwise correlation, using a statistical framework of binary classification. ResultsWe found that SS-GC with time reversed testing outperforms other frameworks. The results validate the application of SS-GC to generative models. When estimating reliable causal relations, SS-GC returns promising results, especially when considering synthetic data with an high impact of noise and sampling rate. ConclusionsSS-GC with time reversed testing offers a possible framework for future analysis of fMRI data in the context of data-driven causality analysis. HighlightsO_LIState-Space GC was combined with a statistical validation step, using a Time Reversed Testing. C_LIO_LIThis novel heuristic overpowers classical GC, when applied to generative models. C_LIO_LICorrectly identified connections between variables increase with the increase of number of blocks and number of points per block. C_LIO_LISNR and subsampling have a significant impact on the results. C_LI

neuroscience↗