Biology subjects

Sayal, A.

Publications and source records attributed to Sayal, A..

7 recordsLinked to original sources

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↗

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↗

bioRxiv2023

A human cortical adaptive mutual inhibition circuit underlying competition for perceptual decision and repetition suppression reversal

A model based on inhibitory coupling has been proposed to explain perceptual oscillations. This adapting reciprocal inhibition model postulates that it is the strength of inhibitory coupling that determines the fate of competition between percepts. Here, we used an fMRI-based adaptation technique to reveal the influence of neighboring neuronal populations, such as reciprocal inhibition, in motion-selective hMT+/V5. If reciprocal inhibition exists in this region, the following predictions should hold: 1. stimulus-driven response would not simply decrease, as predicted by simple repetition-suppression of neuronal populations, but instead increase due to the activity from adjacent populations; 2. perceptual decision involving competing representations, should reflect decreased reciprocal inhibition by adaptation; 3. neural activity for the competing percept should also later on increase upon adaptation. Our results confirm these three predictions, showing that a model of perceptual decision based on adapting reciprocal inhibition holds true. Finally, they also show that the well-known repetition suppression phenomenon can be reversed by this mechanism. Significance StatementfMRI-based adaptation has been developed as a tool to identify functional selectivity in the human brain. This is based on the notion that stimulus-selective adaptation leads to direct response suppression. In this study, we go a step further by showing that adaptation can also reveal the influence of neighboring neuronal populations. Our data reveals neural evidence for a disinhibition effect as a result of the adaptation of adjacent populations, which is in line with the adapting reciprocal inhibition model. Reciprocal inhibition can, thus, be tracked in the human brain using fMRI, adding to the understanding of human multistable perception and the neural coding of visual information. Moreover, our results also provide a mechanism for reversal of repetition suppression.

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↗