Biology subjects

Bonetti, L.

Publications and source records attributed to Bonetti, L..

At least 19 recordsLinked to original sources

bioRxiv2026

Structured recurrence of latent neural processes predicts memory recognition performance

Understanding cognition requires explaining not only which neural processes are engaged, but how their relationships evolve through time. We tested whether the recurrent organization of low-dimensional whole-brain dynamics relates to auditory recognition behaviour beyond activation magnitude. After a brief learning phase, 65 participants underwent magnetoencephalography (MEG) while recognizing three-, five-, and seven-sound auditory sequences, presented unchanged or with a single early or late tone alteration. Broadband Brain Network Estimation via Source Separation (BROAD-NESS) identified broadband latent networks from source-reconstructed activity and represented their joint activation as trajectories through a multidimensional state space; recurrence quantification analysis characterized their temporal organization. Memorized melodies elicited more recurrent states and longer, more deterministic and persistent recurrence structures, together with lower divergence, than changed melodies. Across participants, structured recurrence was consistently associated with higher accuracy and faster responses, whereas recurrence frequency showed no consistent behavioural association. After adjustment for condition, melody length, their interaction, and matched root-mean-square activation amplitude, recurrence measures increased marginal R2 by 0.273. Nested participant-wise cross-validation reduced held-out prediction error by 11.81% (95% CI: 4.01%-17.65%). These findings support a view of cognition in which performance depends critically on how low-dimensional neural processes jointly organize and evolve over time, beyond how strongly they are activated.

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bioRxiv2026

Ageing reshapes the resting-state network landscape of the human brain

Ageing alters brain rhythms and large-scale functional organisation, but whether these changes reflect isolated effects or a coordinated reconfiguration remains unclear. We applied Frequency-resolved Network Estimation via Source Separation (FREQ-NESS) to source-reconstructed resting-state MEG from 164 healthy younger and older adults across two independent datasets. FREQ-NESS revealed that ageing reshaped the frequency-resolved organisation of whole-brain networks. Older adults showed a slower alpha-network peak and a flatter decay of network prominence from alpha into beta frequencies, together with changes in entropy across the spectrum. These spectral changes were accompanied by shifts in the spatial expression of dominant networks along the lateral, posterior-anterior, and inferior-superior spatial axes. Rather than following a uniform anatomical shift, the direction of age differences varied across frequencies. Together, these findings show that healthy ageing is associated with a reshaped configuration of endogenous brain networks, characterised not simply by a uniform loss of network organisation, but by selective reconfiguration of the prominence and spatiotemporal expression of whole-brain activity across frequencies.

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bioRxiv2026

Memory encoding begins with spontaneous neural reactivation

Long-term memory relies on neural reactivation mechanisms that support consolidation and retrieval. However, it remains unknown whether these mechanisms also operate during encoding, the initial stage of memory formation. Using a decoding classifier, we analyzed magnetoencephalography (MEG) data from 67 participants as they actively encoded or passively listened to brief melodies. We found that the classifier generalized to the rest period immediately following encoding, revealing spontaneous neural reactivation and stable patterns after stimulus offset. This extends prior evidence of neural replay, which is typically observed during sleep and rest, by demonstrating reactivation during encoding, within the task itself. Decoding accuracy during encoding was correlated with recognition performance in subsequent tests, reinforcing its role in memory, with the largest differences between low- and high-recognition performance groups emerging 10 days after encoding. Whole-brain network analysis identified two primary systems: an auditory network (primary auditory cortex, insula, superior temporal gyrus) showing few condition differences, and a memory/associative network (hippocampus, parahippocampal gyrus, cingulate cortex) driving the neural reactivation underlying successful encoding. Together, these results demonstrate that spontaneous neural reactivation supports memory encoding even before formal consolidation.

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bioRxiv2026

Brain dynamics of memory encoding for simple versus complex musical sequences

Memory encoding is the foundational process by which the brain transforms sensory input into lasting representations. While the neural mechanisms of auditory memory have been extensively studied, how musical complexity modulates the neural activity during memory encoding remains poorly understood. Here, we used magnetoencephalography (MEG) to investigate the encoding of simple (tonal) versus complex (atonal) musical melodies in 67 participants. Behaviorally, the latter melodies were consistently rated as more complex and associated with lower recognition accuracy across three testing sessions (same day, one day later, and ten days after the encoding task). At the neural level, source-localized analyses revealed distinct spatiotemporal dynamics: simple melodies elicited stronger activity in auditory cortices (left and right Heschl's gyrus) and cingulate regions (medial and anterior cingulate gyrus), while complex melodies recruited the left hippocampus more extensively across multiple tones. These findings demonstrate that musical complexity shapes neural encoding processes from the outset, with tonal sequences benefiting from efficient sensory processing and atonal sequences requiring greater memory-related recruitment. Our study provides novel insights into how the human brain encodes complex auditory information, providing a framework for understanding the neural basis of memory formation for temporally structured stimuli.

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bioRxiv2026

Gaming Addiction Transmission: Interpersonal Neural Pathways Revealed by HYPER-NESS

Gaming addiction may be contagious through social interaction. In team competitive video games, individuals of varying addiction levels are often paired. This raises the question of whether, and through what mechanisms, one individuals addiction level is affected by other players. Methodologically, addressing this question requires understanding both what happens between brains and within individual brains during a gaming session. To this end, we used HYPER-NESS (Hyper Brain Network Estimation via Source Separation), a novel analytical framework to decompose and weight the distinct contributions of inter-brain and intra-brain processes to social brain networks. We applied this framework to a hyperscanning fNIRS dataset where dyads were scanned while playing a video game together against experimenters. We found that inter-brain and intra-brain contributions to the social brain networks were differentially associated with changes in game reward and social reward sensitivity, depending on the addiction level of ones partner. Granger causality analysis of both social and individual brain networks revealed influences from the high-addiction partner to low-addiction partner. Furthermore, significant cross-frequency coupling was found selectively between low-addiction players, supporting the idea that this form of inter-brain interactions underpins the joint processing of task-relevant rewards. To our knowledge, these findings provide the first neural-level account for the hypothesis that gaming addiction propagates though dyadic social interaction.

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bioRxiv2026

Efficient ageing: Simulated lesion of the structural connectome reveals optimised decline in the healthy ageing brain

Healthy ageing is associated with widespread white-matter change and altered connectome organisation, yet the link between local microstructural decline and whole-brain network communication remains unclear. Here we combined tract-based spatial statistics (TBSS) with probabilistic tractography and graph analysis to quantify the connectome-level consequences of age-sensitive white-matter hotspots. In two independent diffusion MRI datasets of healthy young and older adults (n = 144 total; Dataset 1: 77 participants; Dataset 2: 67 participants), we first identified age-sensitive fractional anisotropy clusters and then used them as constraints in a cross-dataset simulated-lesion framework. This allowed us to estimate how strongly each structural connection depended on age-sensitive tissue and to test the resulting network effects against matched-mass null lesions. Across both datasets, ageing hotspot lesioning reduced global efficiency, but consistently less than expected under the null model, indicating a less-than-random disruption of network integration. Age-sensitive tissue was disproportionately embedded in the brains integrative backbone: proportional degree loss was strongest in high-degree nodes, rich-club connections showed the greatest hotspot dependence, and nodewise losses were concentrated in frontal, cingulate and subcortical association systems, whereas posterior sensory and temporo-limbic regions were relatively spared. These findings suggest that healthy ageing reflects a selective and constrained reconfiguration of structural connectivity rather than simply pointing to uniform decline.

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bioRxiv2026

Network reconfiguration preserves prediction error signallingin the aging brain

Cognitive aging is widely associated with a progressive weakening of predictive brain mechanisms. This view is supported by decades of electrophysiological studies reporting attenuated mismatch responses in older adults. Yet the literature remains inconsistent, suggesting that aging may not uniformly attenuate predictive processing. One possibility is that multiple predictive subsystems operate concurrently but have rarely been disentangled. Here we address this question by separating whole-brain networks underlying predictive processing in source-reconstructed magnetoencephalography (MEG) data from 77 younger and older adults performing the auditory local-global paradigm. Network decomposition revealed three temporally overlapping predictive subsystems with distinct functional profiles. Aging exerted selective effects across these networks. Sensory prediction error responses were enhanced within a network linking auditory cortices with medial cingulate regions, whereas responses associated with reorientation of attention and contextual pattern processing were attenuated in older adults. The level of multivariate recurrency across these networks was preserved with aging, while the processing of sensory violations induced more recurrency and less divergence relative to contextual violations in both groups. These findings challenge the prevailing view that predictive processing simply declines with age. Instead, aging redistributes predictive resources across distinct neural systems, amplifying sensory-based processes while weakening more cognitively demanding predictive mechanisms.

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bioRxiv2026

Ibogaine is associated with reorganization of high-beta brain networks in veterans with post-traumatic stress disorder

Post-traumatic stress disorder (PTSD) is a debilitating condition that affects millions of veterans. A recent observational study in 30 veterans showed that a single dose of the atypical psychedelic ibogaine can be highly effective at treating PTSD up to twelve months later. Although a prior study demonstrated that ibogaine transiently alters electroencephalography (EEG) power in various frequency bands, the long-term, network-level neural mechanisms targeted by ibogaine are unclear. Here, we investigated whether ibogaine-related clinical improvements are associated with the reorganization of certain brain networks. We applied a novel framework, FREQuency-resolved brain Network Estimation via Source Separation (FREQ-NESS), to identify frequency-specific brain networks in resting-state EEG data acquired at baseline, three to four days after treatment (immediate-post), and one month after ibogaine. At both the immediate-post and one month-post timepoints, high-beta (24 and 25 Hz) networks shifted away from frontal areas and towards posterior regions, an effect that was replicated in an independent EEG dataset on ibogaine treatment for opioid use disorder. This posterior shift was significantly correlated with improvements in PTSD symptoms at both timepoints. Neural field modeling demonstrated that these posterior high-beta shifts are associated with increases in corticocortical, but not corticothalamic, connectivity. Our results are consistent with prior evidence implicating aberrant frontal beta-band activity in PTSD. Overall, we demonstrate that the reconfiguration of high-beta brain networks could be a robust biomarker for ibogaines therapeutic effects.

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bioRxiv2026

Beyond where: When and how brain stimulation drives state transitions

Brain stimulation is increasingly used to treat neurological and psychiatric conditions, but stimulation sites and timing are typically chosen based on a priori assumptions from prior studies, not individualised mechanisms. Here, we investigate why some brain regions respond more to stimulation, when they respond best, and whether optimal targets for brain-state transitions are defined by functional anatomy (e.g. an auditory region in a resting-to-listening task transition), by system dynamics, or a combination of both. To do so, we build subject-specific whole-brain Hopf models fitted to MEG data, separating signals into five canonical frequency bands. We find that regions with a smaller oscillation radius and greater temporal variability respond more strongly to stimulation. Moreover, responsiveness was highly dependent on state and frequency band, with slower frequencies exhibiting phase-driven responsiveness, while faster ones depending more on network synchrony. Importantly, optimal nodes for transitioning between brain states are not defined by functional labels but rather by their dynamical regime. These results emphasise the value of mechanistic, personalised models for identifying when and where to stimulate the brain, and may help inform more precise brain-stimulation strategies.

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bioRxiv2025

Imbalanced Brain Connectome: A Novel Link between Functional Somatic Disorders, Multiple Chemical Sensitivity, and Post-COVID patients

This study aimed to investigate and assess shared patterns of structural brain connectivity across three clinical populations. Despite significant advancements in neuroimaging, structural connectivity alterations have been underexplored in functional somatic disorders (FSDs), multiple chemical sensitivity (MCS), and post-COVID-19 conditions (PC). Clinical observations suggest that olfactory symptoms may represent a missing link between these three patient groups. Using diffusion-weighted MRI and voxel-based probabilistic tractography, structural connectivity was analysed in 57 females (MCS: 16, FSD: 15, PC: 11, and 15 matched healthy controls). Independent-sample t-tests (with FDR correction) and permutation testing were applied to assess group-level differences, particularly in inter- and intra-hemispheric connectivity. A marked decrease in inter-hemispheric connectivity was observed in patients (70.9%) compared with controls (29.1%; p < 0.001). The finding was consistent across all diagnostic groups. These alterations remained stable following reanalysis and aggregation, reliably distinguishing patients from controls. No significant differences were found in intra-hemispheric connectivity across the patient groups. Additionally, out of 12 predefined interregional connections, ten showed significant alterations in at least one patient group. The findings reveal robust and consistent reduced inter-hemispheric connectivity in patients with MCS, FSD, and PC, highlighting a potentially shared neurobiological signature among the patient groups.

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bioRxiv2025

Brain Dynamics of Mental Manipulation

Humans effortlessly juggle their internal thoughts, but the neuronal dynamics that support mental manipulation are largely unknown. Leveraging the high spatiotemporal fidelity of intracranial recordings in humans (N = 30), we provide evidence that mental sound manipulation involves the inhibition of sensory cortex and the coordinated engagement of memory and control networks. This modulation manifests in two ways. First, there is a shift in the balance between faster (> 30 Hz) and slower (< 30 Hz) dynamics in primary and secondary auditory areas, suggesting a decrease in local excitability. Second, there is a distributed increase in oscillatory synchrony (6-10 Hz), which predicts imagery vividness and task performance. This evidence points to a key role of local excitability and inter-areal synchrony in the manipulation of thought.

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bioRxiv2025

Shared and modality-specific brain networks underlying predictive coding of temporal sequences

Predictive coding posits that the brain continuously generates and updates internal models to anticipate incoming sensory input. While auditory and visual modalities have been studied independently in this context, direct comparisons using matched paradigms are scarce. Here, we employed magnetoencephalography (MEG) to investigate how the brain of 83 participants encodes and consciously recognises temporally unfolding sequences that acquire Gestalt-like structure over time, a feature rarely addressed in cross-modal research. Participants memorised matched auditory and visual sequences with coherent temporal structure and later identified whether test sequences were familiar or novel. Multivariate decoding revealed robust discrimination between the brain mechanisms underlying encoding and recognition of memorised and novel sequences, with sustained temporal generalisation in the auditory domain and time-specific responses in the visual domain. Using the BROAD-NESS pipeline, we identified modality-specific and supramodal brain networks. Auditory memory engaged auditory cortex, cingulate gyrus, and hippocampus, whereas visual memory involved orbitofrontal cortex and visual areas. Notably, both modalities recruited a shared network including hippocampus and medial cingulate during recognition. These findings provide compelling evidence for distinct and shared predictive learning mechanisms across sensory systems, advancing our understanding of how the brain integrates and evaluates temporally structured, Gestalt-like information.

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bioRxiv2025

FREQ-NESS reveals age-related differences in frequency-resolved brain networks during auditory recognition and resting state

Understanding how brain networks operate across different frequencies during cognitive tasks, and how these dynamics change with age, remains a central challenge in cognitive neuroscience. While previous studies have focused on resting-state activity and passive listening, less is known about frequency-specific brain dynamics during event-related tasks that require active memory engagement. In this study, we extend the recently developed FREQ-NESS analytical pipeline by adapting it to event-related task and resting state source-reconstructed magnetoencephalography (MEG) data from 70 healthy participants. This method quantified the variance explained by frequency-specific brain networks, their spatial organization, and associated time-resolved power estimates. We found significant effects of age, condition, and their interaction in the variance explained by leading components at 1.07 Hz, 2.86 Hz, and 10.00 Hz. Younger adults exhibited stronger peaks at 1.07 and 2.86 Hz during the task and a more pronounced 10.00 Hz peak at rest, whereas older adults showed the opposite pattern. Time-frequency analysis revealed age- and condition-dependent desynchronization in the alpha and beta bands (7.10-22.90 Hz). These findings demonstrate the effectiveness of the adapted FREQ-NESS pipeline for event-related tasks and highlight the importance of frequency-resolved network analysis for characterizing age-related changes in active auditory memory processing.

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bioRxiv2025

Neural signatures of predictive coding underlying the acquisition of incidental sensory associations

A longstanding question in cognitive science is whether the human brain learns sensory regularities that are irrelevant to ongoing behavior, a phenomenon known as incidental associative learning. Here, we provide evidence at the single subject level that humans indeed acquired such incidental associations and reveal their neural signatures by combining Electroencephalography (EEG), multivariate decoding and computational modeling. We found robust encoding of incidental predictions and prediction errors underlying associative learning, consistent with predictive coding theories. Prediction errors were modulated by epistemic uncertainty, incidental associations persisted in memory over days and generalized beyond stimulus-stimulus pairings to encompass broader sensory events, including the absence of stimulation. Together, our results challenge traditional assumptions that associative learning requires behavioral relevance or physical co-occurrence and point to uncertainty minimization about environmental state transitions as a general objective of brain function. This intrinsic drive may reflect a fundamental computational principle supporting the formation of internal predictive models that guide perception, learning, and adaptive behavior.

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bioRxiv2025

LSD reconfigures the frequency-specific network landscape of the human brain

Lysergic acid diethylamide (LSD) and other psychedelic substances profoundly alter human consciousness. While several studies have demonstrated changes in brain function and connectivity associated with psychedelics, we still have a limited understanding of how LSD reshapes brain networks operating across different frequency bands. In this study, we applied the recently developed FREQ-NESS method to MEG data from 14 healthy participants who received LSD under four conditions: eyes-closed with or without music and eyes-open with or without a video stimulus. LSD significantly restructures canonical networks in the alpha and beta bands. Relative to broadband brain activity, it enhances the prominence of high alpha (12.1, 13.3 Hz) across all experimental conditions and high beta (25.3 Hz) in three conditions. Conversely, LSD decreases the prominence of low beta (18.1, 19.3 Hz) in both Open and Closed conditions and low alpha (8.5 Hz) in the latter. In addition, LSD substantially alters the spatial distributions or topographies of the networks. Under LSD, the low alpha (8.5 Hz) network shifts anteriorly toward the motor cortex, while high alpha (12.1, 13.3 Hz) becomes more localized to the visual cortex. Low beta (18.1, 19.3 Hz) expands over the temporal and occipital cortices, whereas high beta (25.3, 26.5 Hz) topographies remain unchanged. Our findings provide critical insights into the specific frequencies and spatial networks in which LSD modulates brain connectivity, adding nuance to prevailing theories about network disintegration under psychedelics. Significance statementPsychedelic substances such as lysergic acid diethylamide (LSD) profoundly alter perception and cognition, yet their effect on brain activity across different frequency bands remains unclear. Using FREQ-NESS applied to MEG data, we show that LSD reorganizes brain networks by enhancing high-frequency alpha and beta rhythms while suppressing lower-frequency counterparts. These changes occur across various experimental conditions and shift the spatial distribution of brain activity, particularly in motor and visual regions. Our findings suggest that LSD modulates brain connectivity in a frequency- and region-specific manner, offering new insights into how psychedelics alter consciousness. This work advances our understanding of LSDs neural effects, which may be relevant for therapeutic applications and models of brain function.

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bioRxiv2024

The neural mechanisms of concept formation over time in music

The interplay between abstraction, learning and memory is crucial for humans to interact with the environment. Abstraction in relation to concept formation is typically studied with Gestalt stimuli varying in their physical properties while maintaining abstract internal relations. The role of temporal integration in recognising abstract concepts has been, however, overlooked even in a sensory domain relying on temporal item succession such as audition. Here, we investigated the neural mechanisms underlying abstract concept generalisation over time using a musical adaptation of the old/new recognition paradigm. During magnetoencephalography (MEG) recordings, 108 participants categorised five-tone sequences as previously learned or novel or as musical or not. The sequences varied in pace, ranging from very fast (125 ms per sound) to very slow (5000 ms per sound). On the behavioural level, sequence duration exerted a substantial impact on the recognition and musicality judgments of the novel sequences. The differences in neural responses between memorised and novel sequences especially involved regions in the auditory cortex, medial cingulate gyrus and medial temporal lobe and were most pronounced at the highest musical pace, diminishing proportionally as the pace decreased. These findings contribute to our understanding of how the brain generalises and derives meaning from abstract concepts that unfold over time learned through the auditory system.

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bioRxiv2024

BROADband brain Network Estimation via Source Separation (BROAD-NESS)

Auditory memory enables the recognition of sound sequences by integrating sensory input with memory traces and predictive mechanisms. While predictive coding has been proposed as a key framework for this process, the large-scale brain networks supporting it remain poorly understood. Using BROADband brain Network Estimation via Source Separation (BROAD-NESS), we provide three fundamental insights into the neural organisation of auditory memory and predictive coding. First, auditory cortices participate in two distinct, orthogonal whole-brain networks, expanding on the conventional view focused on forward and backward information flow between single brain regions. One network involves the medial cingulate gyrus, while the other integrates prefrontal and hippocampal regions, inferior temporal cortex, and insula. Second, we present evidence for a dual-stream mechanism in auditory memory recognition, which both parallels and diverges from the well-established dual-stream hypothesis in vision. Third, predictive coding in conscious auditory memory is supported by large-scale networks generating confirmed predictions and prediction errors. While previous studies examined predictive coding in isolated regions or pairwise connections, our findings reveal how whole-brain networks coordinate these processes, highlighting fine-grained spatial gradients and distinct temporal dynamics. These findings enhance our understanding of auditory perception, memory, and prediction, as well as their underlying basis in whole-brain dynamical networks.

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bioRxiv2024

Thalamo-frontal connectivity patterns in Tourette Syndrome: Insights from combined intracranial DBS and EEG recordings

Thalamic deep brain stimulation (DBS) has shown clinical improvement for patients with treatment-refractory Tourette Syndrome (TS). Advancing DBS for TS requires identifying reliable electrophysiological markers. Recognising TS as a network disorder, we investigated thalamo-cortical oscillatory connectivity by combining local field potential (LFP) recordings from the DBS thalamic target region using the PerceptTM PC neurostimulator with high-density EEG in eight male TS patients (aged 27 to 38) while stimulation was off. We identified a spatially and spectrally distinct oscillatory network connecting the medial thalamus and frontal regions in the alpha band (8-12 Hz), with connectivity strength negatively correlated with TS symptom severity. Moreover, reduced thalamo-frontal alpha connectivity before tic onset, localised in sensorimotor regions and the inferior parietal cortex, suggests its direct role in tic generation. Importantly, associations with symptoms and pre-tic dynamics were specific to connectivity patterns and not evident in the pure power spectra. These findings underscore the importance of investigating electrophysiological oscillatory connectivity to characterise pathological network connections in TS, potentially guiding stimulation-based interventions and future research on closed-loop DBS for TS.

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