Biology subjects

Fernandez-Rubio, G.

Publications and source records attributed to Fernandez-Rubio, G..

18 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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

bioRxiv2024

Working memory predicts long-term recognition of auditory sequences: Dissociation between confirmed predictions and prediction errors

Memory is a crucial cognitive process involving several subsystems: sensory memory (SM), short-term memory (STM), working memory (WM), and long-term memory (LTM). While each has been extensively studied, the interaction between WM and LTM, particularly in relation to predicting temporal sequences, remains largely unexplored. This study investigates the relationship between WM and LTM, and how these relate to aging and musical training. Using three datasets with a total of 244 healthy volunteers across various age groups, we examined the impact of WM on LTM recognition of novel and previously memorized musical sequences. Our results show that WM abilities are significantly related to recognition of novel sequences, with a more pronounced effect in older compared to younger adults. In contrast, WM did not similarly impact the recognition of memorized sequences, which implies that different cognitive processes are involved in handling prediction errors compared to confirmatory predictions, and that WM contributes to these processes differently. Additionally, our findings confirm that musical training enhances memory performance. Future research should extend our investigation to populations with cognitive impairments and explore the underlying neural substrates.

neuroscience↗

bioRxiv2024

FREQuency-resolved brain Network Estimation via Source Separation (FREQ-NESS)

The brain is a dynamic system whose network organization is often studied by focusing on specific frequency bands or anatomical regions, leading to fragmented insights, or by employing complex and elaborate methods that hinder straightforward interpretations. To address this issue, a new analytical pipeline named FREQuency-resolved Network Estimation via Source Separation (FREQ-NESS) is introduced. This is designed to estimate the activation and spatial configuration of simultaneous brain networks across frequencies by analyzing the frequency-resolved multivariate covariance between whole-brain voxel time series. FREQ-NESS is applied to source-reconstructed magnetoencephalography (MEG) data during resting state and isochronous auditory stimulation. Results reveal simultaneous, frequency-specific brain networks during resting state, such as the default mode, alpha-band, and motor-beta networks. During auditory stimulation, FREQ-NESS detects: (1) emergence of networks attuned to the stimulation frequency, (2) spatial reorganization of existing networks, such as alpha-band networks shifting from occipital to sensorimotor areas, (3) stability of networks unaffected by auditory stimuli. Furthermore, auditory stimulation significantly enhances cross-frequency coupling, with the phase of attuned auditory networks modulating the gamma band amplitude of medial temporal lobe networks. In conclusion, FREQ-NESS effectively maps the brains spatiotemporal dynamics, providing a comprehensive view of brain function by revealing simultaneous, frequency-resolved networks and their interaction.

neuroscience↗

bioRxiv2024

The neurophysiology of healthy and pathological aging: A comprehensive systematic review

As the population of older adults grows, so does the prevalence of neurocognitive disorders such as mild cognitive impairment (MCI) and dementia. While biochemical, genetic, and neuroimaging biomarkers have accelerated early detection and diagnosis, neurophysiological measures are absent from daily medical use. Electroencephalography (EEG) and magnetoencephalography (MEG) are two non-invasive techniques that measure neurophysiological signals in the brain and convey information about signal strength at different frequency bands, event-related activity, signal complexity, and temporal correlation between spatially remote brain regions. Here we conducted a pre-registered, comprehensive systematic review of 942 studies using EEG, MEG, and combined MEG and EEG to characterise the neurophysiology of healthy aging, MCI, and dementia under resting-state and task conditions. To complement our search, we also reviewed 51 past reviews in the field. Relevant features of these papers were extracted to present a detailed overview of the current state of evidence. Overall, neurophysiological measures show great promise as diagnostic tools and could prove invaluable in predicting healthy and pathological aging trajectories. However, to reach this potential in clinical practice, it is crucial to adopt longitudinal designs, standardise methodologies, and identify biomarkers at the individual rather than group level.

neuroscience↗

bioRxiv2024

Multilevel irreversibility reveals higher-order organisation of non-equilibrium interactions in human brain dynamics

Information processing in the human brain can be modelled as a complex dynamical system operating out of equilibrium with multiple regions interacting nonlinearly. Yet, despite extensive study of the global level of non-equilibrium in the brain, quantifying the irreversibility of interactions among brain regions at multiple levels remains an unresolved challenge. Here, we present the Directed Multiplex Visibility Graph Irreversibility framework, a method for analysing neural recordings using network analysis of time-series. Our approach constructs directed multi-layer graphs from multivariate time-series where information about irreversibility can be decoded from the marginal degree distributions across the layers, which each represents a variable. This framework is able to quantify the irreversibility of every interaction in the complex system. Applying the method to magnetoencephalography recordings during a long-term memory recognition task, we quantify the multivariate irreversibility of interactions between brain regions and identify the combinations of regions which showed higher levels of non-equilibrium in their interactions. For individual regions, we find higher irreversibility in cognitive versus sensorial brain regions whilst for pairs, strong relationships are uncovered between cognitive and sensorial pairs in the same hemisphere. For triplets and quadruplets, the most non-equilibrium interactions are between cognitive-sensorial pairs alongside medial regions. Finally, for quintuplets, our analysis finds higher irreversibility when the prefrontal cortex is included in the interaction. Combining these results, we show that multilevel irreversibility offers unique insights into the higher-order, hierarchical organisation of neural dynamics and presents a new perspective on the analysis of brain network dynamics.

biophysics↗

bioRxiv2023

Decoding reveals the neural representation of held and manipulate musical thoughts

Vividly imagining a song or a melody is a skill that many people accomplish with relatively little effort. However, we are only beginning to understand how the brain represents, holds, and manipulates these musical "thoughts". Here, we decoded perceived and imagined melodies from magnetoencephalography (MEG) brain data (N = 71) to characterize their neural representation. We found that, during perception, auditory regions represent the sensory properties of individual sounds. In contrast, a widespread network including fronto-parietal cortex, hippocampus, basal nuclei, and sensorimotor regions hold the melody as an abstract unit during both perception and imagination. Furthermore, the mental manipulation of a melody systematically changes its neural representation, reflecting volitional control of auditory images. Our work sheds light on the nature and dynamics of auditory representations, informing future research on neural decoding of auditory imagination.

neuroscience↗

bioRxiv2023

Challenging age-related decline in brain function: Evidence from fast neuroimaging of musical sequence recognition

Aging is often associated with decline in brain processing power and neural predictive capabilities. To challenge this notion, we used the excellent temporal resolution of magnetoencephalography (MEG) to record the whole-brain activity of 39 older adults (over 60 years old) and 37 young adults (aged 18-25 years) during recognition of previously memorised and novel musical sequences. Our results demonstrate that independent of behavioural measures, older compared to young adults showed increased rapid auditory cortex responses (around 100 and 250 ms after each tone of the sequence) and decreased later responses (around 250 and 350 ms) in hippocampus, ventromedial prefrontal cortex and inferior frontal gyrus. Working memory abilities were associated with stronger brain activity for both young and older adults. Our findings unpick the complexity of the healthy aging brain, showing age-related neural transformations in predictive and memory processes and challenging simplistic notions that non-pathological aging merely diminishes neural predictive capabilities.

neuroscience↗

bioRxiv2022

Hierarchical whole-brain dynamics of auditory predictive coding at the fine-grained milliseconds level

Our brain is constantly extracting, predicting, and recognising key spatiotemporal features of the physical world in order to survive. While neural processing of visuospatial patterns has been extensively studied, the hierarchical brain mechanisms underlying conscious recognition of auditory sequences and the associated prediction errors remain elusive. Using magnetoencephalography (MEG), we studied the brain functioning of 83 participants during recognition of previously memorised musical sequences and systematic variations. The results showed feedforward connections originating from auditory cortices, and extending to the hippocampus, anterior cingulate gyrus, and medial cingulate gyrus. Simultaneously, we observed backward connections operating in the opposite direction. Throughout the sequences, the hippocampus and cingulate gyrus maintained the same hierarchical level, except for the final tone, where the cingulate gyrus assumed the top position within the hierarchy. The evoked responses of memorised sequences and variations engaged the same hierarchical brain network but systematically differed in terms of temporal dynamics, strength, and polarity. Furthermore, induced-response analysis showed that alpha and beta power was stronger for the variations, while gamma power was enhanced for the memorised sequences. This study expands on the predictive coding theory by providing quantitative evidence of hierarchical brain mechanisms during conscious memory and predictive processing of auditory sequences.

neuroscience↗