Biology subjects

Brovelli, A.

Publications and source records attributed to Brovelli, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Functional and Structural Brain Age Gaps as Complementary Predictive Markers for Cognitive Performance

Cognitive decline is characterized by substantial inter-individual variability across the lifespan, even among clinically healthy adults. However, it is unclear how structural and functional brain organization jointly relate to differences in cognitive performance within the typical populations. We considered a multimodal brain age framework by predicting for each subject its age from either structural MRI or resting-state functional MRI, after training on the 1000BRAINS cohort (901 participants, 18-85 years). Following, a structural and a functional brain age gap markers were defined as the deviations with respect to the real chronological age. We then used the markers to characterize the interplay between structure and function in shaping cognitive performances. Our findings reveal that the functional marker is as important to predict global cognitive performance as the structural marker. The most accurate prediction was found for global cognition, executive functions and episodic memory, followed by working memory, attention and language. In particular, the structural marker was contributing to all domains, except for working memory, whereas the functional marker had the highest contribution. Overall, structural and functional brain age models together provide a meaningful subject-level summary of brain information for predicting cognition, supporting a multidimensional view of typical aging.

neuroscience↗

bioRxiv2026

Higher-order interdependencies of the epileptogenic network in presurgical resting-state fMRI

Drug-resistant focal epilepsy (DRFE) is understood as a complex brain network disorder. As a result, conventional pair-wise functional connectivity may fail to capture the higher-order interactions between groups of epileptogenic regions. We applied multivariate information theory to stereotactic electroencephalography-defined networks in retrospective resting-state fMRI analysis to determine whether the overall strength and dominant type of interdependencies between three or more regions, represented by the S-information and O-information respectively, could offer clinically relevant insights prior to surgical intervention in DRFE. We found networks involving the epileptogenic zone to be mostly dominated by re-dundant information-sharing, which was predicted by stronger structural and functional connections between regions. Non-temporal lobe epilepsy patients demonstrated greater redundancy and stronger comparative interdependencies than temporal lobe epilepsy patients, in line with a more complex network architecture. Among non-temporal lobe cases with postoperative follow-up, not seizure-free patients displayed more positive O-information deviation scores than seizure-free patients, suggesting elevated redundancy in networks associated with poorer surgical outcomes. Our findings potentiate the S-information and O-information as complementary higher-order descriptors of the epileptogenic network within personalised presurgical assessments.

neuroscience↗

bioRxiv2026

Sensorimotor encoding of epistemic value during goal-directed causal learning

Understanding the neural and computational mechanisms underlying goal-directed causal learning is a central challenge in both cognitive neuroscience and artificial intelligence. This cognitive function depends on balancing reward maximization with information seeking. Although substantial progress has been made in characterizing the neural basis of reward-driven learning, it remains unclear whether and how intrinsic informational value is represented in the brain and propagated through cortico-cortical interactions. Here, we dissociate information-seeking from reward maximisation during goal-directed causal learning using a novel behavioural paradigm in which participants estimate action-outcome contingencies without extrinsic incentives. Bayesian computational modelling reveals that while random exploration dominates behaviour, expected information gain (EIG) explains a significant proportion of exploratory choices in a subset of sessions. Using magnetoencephalography combined with information-theoretic analyses of high-gamma activity, we show that EIG is encoded in the left sensorimotor cortex at the time of action execution, specifically in dorsal premotor and primary motor cortex, and broadcast to primary somatosensory cortex. These findings are consistent with Active Inference predictions that EIG constitutes a key computational drive for exploratory action selection. However, they challenge the view that epistemic valuation exclusively recruits prefrontal reward circuits, and instead they support an embodied account in which the premotor and sensorimotor system mediates the intrinsic valuation during first-personal interventional causal learning.

neuroscience↗

bioRxiv2026

A spectral partial information decomposition framework for quantifying information about cognitive variables in oscillatory brain networks

Neural oscillations are thought to play a central role in encoding and transmitting cognitive information across large-scale brain networks, yet the relative contributions of phase synchrony and amplitude co-modulations to distributed coding remain unclear. A key obstacle is the absence of tools that can simultaneously quantify task-relevant information in the frequency domain and disentangle its phase and amplitude components across pairwise and higher-order interactions. Here, we introduce a spectral partial information decomposition framework (named NeOPID) for quantifying information about cognitive variables in power and phase contributions, and to quantify redundant and synergistic information in brain relations, from pairwise to higher-order interactions. We validated the approach on Kuramoto and Stuart-Landau oscillator networks, including a whole-brain model constrained by macaque anatomical connectivity. NeOPID accurately recovers ground-truth encoding schemes and reveals that phase relations and amplitude co-modulations act as complementary coding channels with both redundant and synergistic components. NeOPID further extends this decomposition to higher-order functional interactions enabling the characterization of how cognitive information is collectively distributed across multiple oscillatory edges via redundant and synergistic encoding. To illustrate biological applicability, we applied NeOPID to local field potentials (LFPs) recorded from the macaque fronto-parietal network during a working memory task. In this dataset, NeOPID identified beta-band amplitude co-modulations as the primary carrier of stimulus information, and revealed that higher-order phase interactions exhibit both redundant and synergistic structure during the memory delay. These results establish NeOPID as a principled tool for dissecting the informational architecture about cognitive processes of oscillatory brain networks.

neuroscience↗

bioRxiv2026

Reciprocal Fronto-Parietal Interactions Support Motor Anticipation during Sequential Reaching

Motor planning is a highly flexible process that adapts to changing behavioral demands. Frontal and parietal circuits are considered as critical nodes that support planning processes. However, it remains unclear whether the coordination of motor and parietal areas is preserved or adjusted in response to varying behavioral contexts. To address this open question, we trained two rhesus macaques to perform a visually-guided sequential reaching task, in which they could adapt their behavior to varying degrees of target predictability within the sequence. Analysis of eye and hand movements revealed distinct visuomotor strategies that were reflected in corresponding neural activity patterns. During motor anticipation, the direction of the upcoming reach toward predictable targets could be decoded from preparatory neural activity prior to target onset both in motor regions (PMd/M1) and parietal area 7A, whereas during reactive movements directional information emerged only after target onset. Using feature-specific information transfer analysis, we found that information about the upcoming movement direction was transmitted between 7A and PMd/M1 through bidirectional interactions. This pattern of interactions was preserved during motor anticipation and shifted earlier in time with respect to target onset. Our findings thus support a reciprocal fronto-parietal network that flexibly adjusts the timing of preparatory activity as different strategies are employed under varying behavioral constraints. Contrary to classical hierarchical models predicting serial activation across parietal and motor areas, parietal-to-motor interactions did not precede motor-to-parietal interactions, consistent with a heterarchical organization of cortical processing during flexible motor planning.

neuroscience↗

bioRxiv2025

The role of structural heterogeneity in shaping whole-brain dynamics across scales

The brain must rapidly and dynamically recruit the appropriate brain regions to respond to environmental changes and sustain flexible cognition. An extensive body of literature views the ability to spontaneously explore a rich repertoire of activity patterns, even during rest, as a correlate of the capacity to respond rapidly to internal fluctuations and external stimuli. Moreover, brain flexibility at rest successfully indexes pathological conditions such as Parkinsons disease, multiple sclerosis, and amyotrophic lateral sclerosis. However, previous work provides no information about the contribution of each region to the flexibility of brain dynamics. Given the functional and structural heterogeneity of brain regions, we hypothesized their roles in sustaining flexibility to be heterogeneous and related to regional structural properties. To systematically investigate these hypotheses, we define the flexibility gradient (FG), a new measure, which quantifies regional contributions to flexibility. We employed three datasets spanning different recording techniques and a range of spatial and temporal scales: 47 MEG recordings, 11 EEG recordings, and 77 fMRI recordings along with the corresponding tractographies. We found that FG is not homogeneously distributed, with associative and occipital regions contributing the most. This gradient is symmetric across left and right hemispheres, and the structural properties of brain regions correlate with their contribution to flexibility. Our findings are stable across modalities and temporal scales, highlighting that brain flexibility is not only a global dynamical property but also a spatially structured feature shaped by the anatomical organization of the brain.

neuroscience↗

bioRxiv2025

Learning Amidst Noise: The Complementary Roles of Neural Predictive Activity and Representational Changes

The ability to extract structured sensory patterns from a noisy environment is fundamental to cognition, yet how the brain learns complex regularities remains unclear. Using magnetoencephalography during a visuomotor task, we tracked the neural dynamics as humans learned non-adjacent temporal dependencies embedded in noise. We reveal that learning is supported by two temporally dissociable mechanisms. Neural predictive activity emerged rapidly, with stimulus-specific patterns appearing before stimulus onset and preceding measurable behavioral improvements. This is followed by a slower build-up of representational change, characterized by an increased neural pattern similarity between statistically dependent, non-adjacent elements. Both processes are supported by a distributed consortium of networks, with the sensorimotor and dorsal attentional networks playing a central role. These findings suggest that both neural predictive activity and representational changes contribute to learning regularities, revealing a temporal hierarchy in which neural predictive activity precedes behavioral improvement and is followed by neural representational changes, possibly facilitating the gradual consolidation of knowledge into stable neural representations.

neuroscience↗

bioRxiv2025

The Topological Architecture of Brain Identity

Accurately identifying individuals from brain activity--functional fingerprinting--is a powerful tool for understanding individual variability and detecting brain disorders. Most current approaches rely on functional connectivity (FC), which measures pairwise correlations between brain regions. However, FC is limited in capturing the higher-order, multiscale structure of brain organization. Here, we propose a novel fingerprinting method based on homological scaffolds, a topological repre-sentation derived from persistent homology of resting-state fMRI data. Using data from the Human Connectome Project (n = 100), we show that scaffold-based fingerprints achieve near-perfect identification accuracy ([~] 100%), outperforming FC-based methods (90%), and remain robust across preprocessing pipelines, atlas choices, and even with drastically shortened scan durations. Unlike FC, in which fingerprinting features localize within networks, scaffolds derive their discriminative power from inter-network connections, revealing the existence of individual mesoscale organizational signatures. Finally, we show that scaffolds bridge redundancy and synergy by balancing redundant information along high-FC border edges with synergistic interactions across the topological voids they enclose. These findings establish topological scaffolds as a powerful tool for capturing individual variability, revealing that unique signatures of brain organization are encoded in the interplay between mesoscale network integration and information dynamics.

neuroscience↗

bioRxiv2024

Higher-order and distributed synergistic functional interactions encode information gain in goal-directed learning

Goal-directed learning arises from distributed neural circuits including the prefrontal, posterior parietal and temporal cortices. However, the role of cortico-cortical functional interactions remains unclear. To address this question, we integrated information dynamics analysis with magnetoencephalography to investigate the encoding of learning signals through neural interactions. Our findings revealed that information gain (the reduction in uncertainty about the causal relationship between actions and outcomes) is represented over the visual, parietal, lateral prefrontal and ventromedial/orbital prefrontal cortices. Cortico-cortical interactions encoded information gain synergistically at the level of pairwise and higher-order relations, such as triplets and quadruplets. Higher-order synergistic interactions were characterized by long-range relationships centered in the ventromedial and orbitofrontal cortices, which served as key receivers in the broadcast of information gain across cortical circuits. Overall, this study provides evidence that information gain is encoded through synergistic and higher-order functional interactions and is broadcast to prefrontal reward circuits.

neuroscience↗

bioRxiv2024

Directed neural interactions in fMRI: a comparison between Granger Causality and Effective Connectivity

A key challenge for network neuroscience is to understand the role of interactions between brain regions and how they contribute to the encoding and broadcasting of information within cognitive processes. This requires computational tools to infer directional relations between brain regions from neural time series. For fMRI, the most common approaches are based on Granger causality (GC) analysis and effective connectivity (EC) models. Despite their different conceptual framing, GC and EC models for fMRI are based on similar mathematical assumptions, grounded on continuous- and discrete-time linear stochastic models. Based on a mapping between multivariate Ornstein-Uhlenbeck (MOU) and multivariate autoregressive (MVAR) processes, we analytically obtain an approximately quadratic relation between EC and GC, after rescaling the GC to compensate for unequal noise variances of the source and target. Simulations show that these relations can be observed in finite time series only if a large amount of data is available, implying that they may emerge only at a group level in real fMRI experiments. We verified this prediction by systematically comparing EC and GC in a large-scale fMRI data set from the Human Connectome Project. Overall, our findings can provide methodological and interpretational guidance in the usage of GC and EC for brain network reconstruction by clearly elucidating what is common between the two methods, but also their respective specificities and biases.

neuroscience↗

bioRxiv2023

Neural shape mediation analysis

Neural signal shapes convey significant information about their generating processes. In this study, we introduce a data-driven methodology to identify sensory and behaviourally-relevant traces within neural responses. We present a phenomenological model that characterises temporal variations in intracranial EEG using eight interpretable parameters: peak time, peak intensity, initial and final baselines, accumulation and depletion period, and their respective concavities. This model effectively captures subtle signal variations, especially in sensory decision-making tasks. By decomposing the signals in this manner, we then conduct a comprehensive brain mediation analysis on iEEG datas shape, pinpointing regions that mediate behavioural processes. Importantly, we can determine which signal dynamics specifically reflect underlying behavioural processes, enhancing the depth of analysis and critique of their role in behaviour. Preliminary applications on a cohort of epileptic patients reveal that our model explains over a third of the signal variance at the trial level across all brain regions. We identified four key regions--encompassing sensory, associative, frontal, and premotor areas--that mediate the impact of task difficulty on reaction time. Notably, in these regions, it was the depletion period, rather than signal amplitude, that correlated with behavioural speed. This study highlights the potential of our approach in providing detailed insights into the neural mechanisms linking stimuli to behaviour.

neuroscience↗

bioRxiv2023

An information-theoretic quantification of the content of communication between brain regions

Quantifying the amount, content and direction of communication between brain regions is key to understanding brain function. Traditional methods to analyze brain activity based on the Wiener-Granger causality principle quantify the overall information propagated by neural activity between simultaneously recorded brain regions, but do not reveal the information flow about specific features of interest (such as sensory stimuli). Here, we develop a new information theoretic measure termed Feature-specific Information Transfer (FIT), quantifying how much information about a specific feature flows between two regions. FIT merges the Wiener-Granger causality principle with information-content specificity. We first derive FIT and prove analytically its key properties. We then illustrate and test them with simulations of neural activity, demonstrating that FIT identifies, within the total information flowing between regions, the information that is transmitted about specific features. We then analyze three neural datasets obtained with different recording methods, magneto- and electro-encephalography, and spiking activity, to demonstrate the ability of FIT to uncover the content and direction of information flow between brain regions beyond what can be discerned with traditional anaytical methods. FIT can improve our understanding of how brain regions communicate by uncovering previously hidden feature-specific information flow.

neuroscience↗

bioRxiv2023

Neural interactions in the human frontal cortex dissociate reward and punishment learning

How human prefrontal and insular regions interact while maximizing rewards and minimizing punishments is unknown. Capitalizing on human intracranial recordings, we demonstrate that the functional specificity toward reward or punishment learning is better disentangled by interactions compared to local representations. Prefrontal and insular cortices display non-selective neural populations to rewards and punishments. Non-selective responses, however, give rise to context-specific interareal interactions. We identify a reward subsystem with redundant interactions between the orbitofrontal and ventromedial prefrontal cortices, with a driving role of the latter. In addition, we find a punishment subsystem with redundant interactions between the insular and dorsolateral cortices, with a driving role of the insula. Finally, switching between reward and punishment learning is mediated by synergistic interactions between the two subsystems. These results provide a unifying explanation of distributed cortical representations and interactions supporting reward and punishment learning.

neuroscience↗

bioRxiv2022

Decomposing neural circuit function into information processing primitives

Cognitive functions arise from the coordinated activity of neural populations distributed over large-scale brain networks. However, it is challenging to understand and measure how specific aspects of neural dynamics translate into operations of information processing, and, ultimately, cognitive functions. An obstacle is that simple circuit mechanisms-such as self-sustained or propagating activity and nonlinear summation of inputs-do not directly give rise to high-level functions. Nevertheless, they already implement simple transformations of the information carried by neural activity. Here, we propose that distinct neural circuit functions, such as stimulus representation, working memory, or selective attention stem from different combinations and types of low-level manipulations of information, or information processing primitives. To test this hypothesis, we combine approaches from information theory with computational simulations of canonical neural circuits involving one or more interacting brain regions that emulate well-defined cognitive functions. More specifically, we track the dynamics of information emergent from dynamic patterns of neural activity, using suitable quantitative metrics to detect where and when information is actively buffered ("active information storage"), transferred ("information transfer") or non-linearly merged ("information modification"), as possible modes of low-level processing. We find that neuronal subsets maintaining representations in working memory or performing attention-related gain modulation are signaled by their boosted involvement in operations of active information storage or information modification, respectively. Thus, information dynamics metrics, beyond detecting which network units participate in cognitive processing, also promise to specify how and when they do it, i.e., through which type of primitive computation, a capability that may be exploited for the parsing of actual experimental recordings.

neuroscience↗

bioRxiv2021

Group-level inference of information-based measures for the analyses of cognitive brain networks from neurophysiological data

The reproducibility crisis in neuroimaging and in particular in the case of underpowered studies has introduced doubts on our ability to reproduce, replicate and generalize findings. As a response, we have seen the emergence of suggested guidelines and principles for neuroscientists known as Good Scientific Practice for conducting more reliable research. Still, every study remains almost unique in its combination of analytical and statistical approaches. While it is understandable considering the diversity of designs and brain data recording, it also represents a striking point against reproducibility. Here, we propose a non-parametric permutation-based statistical framework, primarily designed for neurophysiological data, in order to perform group-level inferences on non-negative measures of information encompassing metrics from information-theory, machine-learning or measures of distances. The framework supports both fixed- and random-effect models to adapt to inter-individuals and inter-sessions variability. Using numerical simulations, we compared the accuracy in ground-truth retrieving of both group models, such as test- and cluster-wise corrections for multiple comparisons. We then reproduced and extended existing results using both spatially uniform MEG and non-uniform intracranial neurophysiological data. We showed how the framework can be used to extract stereotypical task- and behavior-related effects across the population covering scales from the local level of brain regions, inter-areal functional connectivity to measures summarizing network properties. We also present an open-source Python toolbox called Frites1 that includes the proposed statistical pipeline using information-theoretic metrics such as single-trial functional connectivity estimations for the extraction of cognitive brain networks. Taken together, we believe that this framework deserves careful attention as its robustness and flexibility could be the starting point toward the uniformization of statistical approaches. Graphical abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY HighlightsO_LIGroup-level statistics for extracting neurophysiological cognitive brain networks C_LIO_LICombining non-parametric permutations with measures of information C_LIO_LIFixed- and random-effect models, test- and cluster-wise corrections C_LIO_LIMulti-level inferences, from local regions to inter-areal functional connectivity C_LIO_LIA Python open-source toolbox called Frites includes the proposed statistical methods C_LI

neuroscience↗

bioRxiv2021

Stroke-related alterations in inter-areal communication revealed via Granger causality analysis

Beyond causing local ischemia and cell damage at the site of injury, stroke strongly affects long-range anatomical connections, perturbing the functional organization of brain networks. Several studies reported functional connectivity abnormalities parallelling both behavioral deficits and functional recovery across different cognitive domains. FC alterations suggest that long-range communication in the brain is altered after stroke. However, standard FC analyses cannot reveal the directionality and time scale of inter-areal information transfer. We used resting-state fMRI and covariance-based Granger causality analysis to quantify network-level information transfer and its alteration in stroke. Two main large-scale anomalies were observed in stroke patients. First, inter-hemispheric information transfer was significantly decreased with respect to healthy controls. Second, stroke caused inter-hemispheric asymmetries, as information transfer within the affected hemisphere and from the affected to the intact hemisphere was significantly reduced. Both anomalies were more prominent in resting-state networks related to attention and language, and they correlated with impaired performance in several behavioral domains. Overall, our findings support the hypothesis that stroke provokes asymmetries between the affected and spared hemisphere, with different functional consequences depending on which hemisphere is lesioned.

neuroscience↗

bioRxiv2020

Tensorpac : an open-source Python toolbox for tensor-based Phase-Amplitude Couplingmeasurement in electrophysiological brain signals

Despite being the focus of a thriving field of research, the biological mechanisms that underlie information integration in the brain are not yet fully understood. A theory that has gained a lot of traction in recent years suggests that multi-scale integration is regulated by a hierarchy of mutually interacting neural oscillations. In particular, there is accumulating evidence that phase-amplitude coupling (PAC), a specific form of cross-frequency interaction, plays a key role in numerous cognitive processes. Current research in the field is not only hampered by the absence of a gold standard for PAC analysis, but also by the computational costs of running exhaustive computations on large and high-dimensional electrophysiological brain signals. In addition, various signal properties and analyses parameters can lead to spurious PAC. Here, we present Tensorpac, an open-source Python toolbox dedicated to PAC analysis of neurophysiological data. The advantages of Tensorpac include (1) higher computational efficiency thanks to software design that combines tensor computations and parallel computing, (2) the implementation of all most widely used PAC methods in one package, (3) the statistical analysis of PAC measures, and (4) extended PAC visualization capabilities. Tensorpac is distributed under a BSD-3-Clause license and can be launched on any operating system (Linux, OSX and Windows). It can be installed directly via pip or downloaded from Github (https://github.com/EtienneCmb/tensorpac). By making Tensorpac available, we aim to enhance the reproducibility and quality of PAC research, and provide open tools that will accelerate future method development in neuroscience.

bioinformatics↗

bioRxiv2019

A spiking neural-network model of goal-directed behaviour

In mammals, goal-directed and planning processes support flexible behaviour usable to face new situations or changed conditions that cannot be tackled through more efficient but rigid habitual behaviours. Within the Bayesian modelling approach of brain and behaviour, probabilistic models have been proposed to perform planning as a probabilistic inference. Recently, some models have started to face the important challenge met by this approach: grounding such processes on the computations implemented by brain spiking networks. Here we propose a model of goal-directed behaviour that has a probabilistic interpretation and is centred on a recurrent spiking neural network representing the world model. The model, building on previous proposals on spiking neurons and plasticity rules having a probabilistic interpretation, presents these novelties at the system level: (a) the world model is learnt in parallel with its use for planning, and an arbitration mechanism decides when to exploit the world-model knowledge with planning, or to explore, on the basis of an entropy-based confidence on the world model knowledge; (b) the world model is a hidden Markov model (HMM) able to simulate sequences of states and actions, thus planning selects actions through the same neural generative process used to predict states; (c) the world model learns the hidden causes of observations, and their temporal dependencies, through a biologically plausible unsupervised learning mechanism. The model is tested with a visuomotor learning task and validated by comparing its behaviour with the performance and reaction times of human participants solving the same task. The model represents a further step towards the construction of an autonomous architecture bridging goal-directed behaviour as probabilistic inference to brain-like computations. Author summaryGoal-directed behaviour relies on brain processes supporting planning of actions based on the prediction of their consequences before performing them in the environment. An important computational modelling approach of these processes sees the brain as a probabilistic machine implementing goal-directed processes relying on probability distributions and operations on them. An important challenge for this approach is to explain how these distributions and operations might be grounded on the brain spiking neurons and learning processes. Here we propose a hypothesis of how this might happen by presenting a computational model of goal-directed processes based on artificial spiking neural networks. The model presents three main novelties. First, it can plan even while it is still learning the consequences of actions by deciding if planning or exploring the environment based on how confident it is on its predictions. Second, it is able to think alternative possible actions, and their consequences, by relying on the low-level stochasticity of neurons. Third, it can learn to anticipate the consequences of actions in an autonomous fashion based on experience. Overall, the model represents a novel hypothesis on how goal-directed behaviour might rely on the stochastic spiking processes and plasticity mechanisms of the brain neurons.

animal behavior and cognition↗