Biology subjects

Allegra, M.

Publications and source records attributed to Allegra, M..

16 recordsLinked to original sources

bioRxiv2026

Towards a scalable effective connectivity estimation in fMRI

Characterizing directed connections between brain regions is fundamental for understanding the brains network organization and how it supports cognitive processes. However, a reliable methodology allowing assessment of effective connectivity (EC) at the whole-brain level and across high-resolution parcellations has remained elusive. This limitation reflects a trade-off between model detail and scalability. Mechanistic approaches such as Dynamic Causal Modeling (DCM) provide a principled description of directed interactions but become impractical for large parcellations, whereas scalable methods based on linear-system inversion often fail to reliably recover key features of the underlying effective connectivity. Here, using data from the Human Connectome Project S1200 release, we show that the multivariate Ornstein-Uhlenbeck (mOU) approach for EC estimation can be adapted to yield EC estimates that are in broad agreement with those of an advanced DCM, at least when the spatial variability of the hemodynamic response falls within the physiological range characteristic of the healthy adult brain. Furthermore, thanks to its low computational cost, mOU can be easily scaled up to large neuroimaging cohorts and high-resolution parcellations. High-resolution, mOU-based EC outperforms functional connectivity for single-subject connectome fingerprinting. In addition, it enables a high-resolution characterization of the brains hierarchical organization and modular architecture by identifying directed interactions within and between communities and revealing driver and receiver regions. Overall, our methodology provides a reliable and scalable EC estimation pipeline of wide applicability to cognitive and network neuroscience.

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bioRxiv2026

A Whole-Brain Dynamical Framework Linking Resting-State Activity to TMS-Evoked Responses

A major challenge in systems neuroscience is understanding how external perturbations interact with ongoing brain activity. Transcranial magnetic stimulation (TMS), increasingly used in both basic and clinical neuroscience and often combined with electroencephalography (EEG), provides a unique opportunity to probe this interaction. However, how intrinsic dynamics constrain the propagation of TMS-evoked activity remains poorly understood. In particular, effective connectivity (EC)--capturing directed, state-dependent interactions between brain regions--is thought to critically shape perturbational spread, yet remains difficult to estimate at the whole-brain EEG level. Here we introduce an analytically tractable, generative whole-brain model that links spontaneous EEG activity to cortical responses under perturbation. By deriving a closed-form expression for the models cross-spectral density, we directly fit empirical resting-state EEG spectra and infer biophysically interpretable local dynamical parameters without time-domain simulations. We then estimate stimulation-site-specific EC using only a small fraction of the TMS-EEG trials. The resulting model accurately predicts the spatiotemporal structure of TMS-evoked potentials (TEPs) in unseen trials. Moreover, even without subject-specific refitting, group-level EC templates capture canonical site-specific propagation motifs underlying single-subject early TMS responses. Together, our results establish an analytical framework for individualized whole-brain modeling of TMS-EEG with potential applicability to model-based neuromodulation.

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bioRxiv2026

Perinatal ischemic stroke impairs motor and cognitive development, muscle structure, and gut microbiota composition, with functional improvement following fecal microbiota transplantation in a mouse model

Perinatal ischemic stroke is an early developmental brain injury caused by obstruction of cerebral blood vessels and is a leading cause of cerebral palsy and cognitive disability in survivors. However, progress in understanding its impact on the brain and other organ systems, as well as in developing effective therapies, remains limited, in part due to the scarcity of relevant preclinical models. Here, we induced ischemic stroke via middle cerebral artery occlusion in perinatal mice and investigated its effects within and beyond the brain across development into adulthood. We found that perinatal stroke disrupted fine motor development and impaired memory. In addition, it induced structural alterations in skeletal muscle and significant changes in gut microbiota composition. Notably, gut-targeted intervention using fecal microbiota transplantation improved fine motor function. Our findings demonstrate, for the first time, the multisystem developmental impact of perinatal stroke, extending beyond the brain, and identify gut microbiota modulation as a promising and potentially safe therapeutic strategy to improve motor outcomes after stroke.

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bioRxiv2025

Exploring neural manifolds across a wide range of intrinsic dimensions

Recent technical breakthroughs have enabled a rapid surge in the number of neurons that can be simultaneously recorded, calling for the development of robust methods to investigate neural activity at a population level. In this context, it is becoming increasingly important to characterize the neural activity manifold, the set of configurations visited by the network within the space defined by the instantaneous firing rates of all neurons. The intrinsic dimension (ID) of the manifold is a key parameter allowing to relate neural trajectories with the ongoing network computations. While several studies suggested that the ID may be typically low in neural manifolds, contrasting findings have disputed this statement, leading to a wide debate. Part of the disagreement may stem from the lack of a shared and robust methodology to measure the ID. In the case of curvature, linear methods tend to overestimate the ID; in the case of undersampling, nonlinear methods tend underestimate it. Here we show that adapting the full correlation integral (FCI) method yields an estimator that is robust to both curvature and undersampling. We tested our metric on artificial data, including neural trajectories generated recurrent neural networks (RNNs) performing simple tasks and a benchmark dataset consisting of non-linearly embedded high-dimensional data. Our methodology provides a reliable and versatile tool for the analysis of neural geometry.

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bioRxiv2025

Structural Connectome Dimension Shapes Brain Dynamics in Health and Disease

The structural connectome serves as the foundation for neural information signaling, playing a primary constraint on brain functionality. Yet, its influence on emergent dynamical properties is not fully understood. Generally, a key measure of a systems structural impact on dynamical phenomena is its dimension. By tracking the temporal evolution of diffusive perturbations, we estimate a scale-dependent measure of dimension of empirical connectomes. At the local scale, it is highly heterogeneous and follows a gradient from sensory-motor to high-cognitive areas. At the global scale, it encapsulates mesoscale topological information related to the balance between segregation and integration. Furthermore, by comparing connectomes from stroke patients, we find that dimension captures the local effects of lesions and, at a global level, is linked to impaired critical patterns and decreased cognitive performance. Overall, the dimension of the connectome may serve as a powerful descriptor for bridging the gap between structure and function in the human brain.

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bioRxiv2025

Task engagement differentially drives hippocampal and neocortical neural codes

Sensory inputs are progressively transformed into internal representations of the environment along the cortical hierarchy. How does the behavioral relevance of these inputs affect this encoding? Using two-photon calcium imaging in mice navigating virtual-reality environments, we found that visual cortex maintained high sensory discrimination regardless of behavioural engagement, whereas the hippocampal dentate gyrus required active navigation for effective discrimination. These findings suggest that sensory cortices act as general-purpose sensory discriminators, while the hippocampus filters information based on task relevance.

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bioRxiv2025

Spontaneous Dynamics Predict the Effects of Targeted Intervention in Hippocampal Neuronal Cultures

Microstimulation is a powerful tool for causal interrogation of neural circuits and for therapeutic neuromodulation. However, predicting network-level responses to focal perturbations remains a major challenge. To address this problem in a tractable manner, we combine experiments on networks of hippocampal neurons cultured on high-density multielectrode arrays with spiking network model simulations. To characterize spontaneous and stimulation-evoked network dynamics, we employ a combination of direct electrophysiological readouts and information-theoretic measures. We find that single-channel stimulation reliably evokes a small set of site-specific, stereotyped network activity patterns. Remarkably, effective connectivity inferred from spontaneous activity captures the spatial organization of perturbation responses, enabling reliable ranking of stimulation-evoked effects across the network. Our spiking network model reproduces these observations and reveals the interplay between short-term synaptic depression and distance-dependent excitatory and inhibitory connectivity in shaping both spontaneous and evoked interactions at different effective scales. Spontaneous activity involves local structural routes, while perturbation-evoked responses engage comparatively longer, polysynaptic pathways. By unifying in silico modeling with experimental measurements, this work links spontaneous network structure to stimulation-evoked dynamics, suggesting that spontaneous effective connectivity may serve as a tractable proxy for stimulation targeting in recurrent circuits, with potential implications for the rational design of neuromodulatory interventions.

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bioRxiv2024

Switching patterns of cortical-subcortical interaction in the human brain

Resting-state fMRI studies show that functional connectivity (FC) undergoes rapid fluctuations. Although the underlying neural mechanisms are poorly understood, a recent contribution analyzing stroke patients suggested that FC fluctuations involve a dynamic reconfiguration of cortico-subcortical interactions. Here, we analyze cortical-subcortical dynamic FC in a large cohort of healthy subjects (Human Connectome Project data base). Our analysis confirms that FC shifts are synchronized in cortex and subcortex. Two core subcortical clusters comprising, respectively, limbic regions (hippocampus and amygdala) and subcortical nuclei (thalamus and basal ganglia) change their connectivity pattern with cortical regions. We consistently identify two recurring FC patterns (states). In state 1, limbic regions couple with the default mode network, in state 2 with sensorimotor networks. An opposite pattern is observed for thalamus/basal ganglia. Our findings hint at a general relevance of cortico-subcortical interactions in the generation of whole-brain spontaneous FC patterns.

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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.

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bioRxiv2024

Generative whole-brain dynamics models from healthy subjects predict functional alterations in stroke at the level of individual patients

Computational whole-brain models describe the resting activity of each brain region based on a local model, inter-regional functional interactions, and a structural connectome that specifies the strength of inter-regional connections. Strokes damage the healthy structural connectome that forms the backbone of these models and produce large alterations in inter-regional functional interactions. These interactions are typically measured by correlating the timeseries of activity between two brain regions, so-called resting functional connectivity. We show that adding information about the structural disconnections produced by a patients lesion to a whole-brain model previously trained on structural and functional data from a large cohort of healthy subjects predicts the resting functional connectivity of the patient about as well as fitting the model directly to the patients data. Furthermore, the model dynamics reproduce functional connectivity-based measures that are typically abnormal in stroke patients as well as measures that specifically isolate these abnormalities. Therefore, although whole-brain models typically involve a large number of free parameters, the results show that even after fixing those parameters, the model reproduces results from a population very different than the population on which the model was trained. In addition to validating the model, these results show that the model mechanistically captures relationships between the anatomical structure and functional activity of the human brain.

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bioRxiv2023

Controlling target brain regions by optimal selection of input nodes

The network control theory framework holds great potential to inform neurostimulation experiments aimed at inducing desired activity states in the brain. However, the current applicability of the framework is limited by inaccurate modeling of brain dynamics, and an overly ambitious focus on whole-brain activity control. In this work, we leverage recent progress in linear modeling of brain dynamics (effective connectivity) and we exploit the concept of target controllability to focus on the control of a single region or a small subnetwork of nodes. We discuss when control may be possible with a reasonably low energy cost and few stimulation loci, and give general predictions on where to stimulate depending on the subset of regions one wishes to control. Importantly, using the robustly asymmetric effective connectome instead of the symmetric structural connectome (as in previous research), we highlight the fundamentally different roles in- and out-hubs have in the control problem, and the relevance of inhibitory connections. The large degree of inter-individual variation in the effective connectome implies that the control problem is best formulated at the individual level, but we discuss to what extent group results may still prove useful.

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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

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bioRxiv2021

Inhibitory control of synaptic signals preceding motor action in mouse frontal cortex

Preparatory activity in the frontal cortex preceding movement onset is thought to represent a neuronal signature of motor planning. However, how excitatory and inhibitory synaptic inputs to frontal neurons are integrated during movement preparation remains unclear. Here we address this question by performing in vivo whole-cell patch-clamp recordings in the secondary motor cortex (MOs) of head-fixed mice moving on a treadmill. We find that both superficial and deep principal neurons show slowly increasing (~10 s) membrane potential and spike rate ramps preceding the onset of spontaneous, self-paced running periods. By contrast, in animals trained to perform a goal-directed task, both membrane potential and spike ramps are characterized by larger amplitudes and accelerated kinetics during preparation of goal-driven movement. To determine the role of local inhibitory neurons in shaping these task-dependent preparatory signals, we chemogenetically suppressed the activity of specific interneuron subpopulations in untrained animals. Inactivation of parvalbumin-positive (PV+) interneurons leads to depolarized membrane potential ramps with increased amplitudes during preparation of movement, while inactivation of somatostatin-positive (SOM+) interneurons abolishes membrane potential ramps. A computational model of the local MOs circuit shows that SOM+-mediated inhibition of PV+ interneurons in conjunction with recurrent connectivity among the principal neurons can reproduce slow ramping signals, while plasticity of excitatory synapses on SOM+ interneurons can explain the acceleration of these signals in trained animals. Together, our data reveal that local inhibitory neurons play distinct roles in controlling task-dependent preparatory ramping signals when MOs neurons integrate external inputs during motor planning. HighlightsO_LIPrincipal neurons in MOs show slow preparatory membrane potential and firing rate ramps preceding the onset of spontaneous, self-paced running periods. C_LIO_LIIn animals trained to perform a goal-directed task, both membrane potential and spike ramps are faster and larger in amplitude. C_LIO_LIInactivation of PV+ interneurons disinhibits MOs principal neurons and increases the amplitude of membrane potential ramps, while inactivation of SOM+ interneurons abolishes membrane potential ramps. C_LIO_LIOur modeling results suggest that the concerted action of external inputs and local inactivation shapes task-dependent preparatory motor signals in MOs neurons. C_LI

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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.

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bioRxiv2019

The hippocampus as a perceptual map: neuronal and behavioral discrimination during memory encoding

The hippocampus is thought to encode similar events as distinct memory representations that are used for behavioral decisions. Where and how this "pattern separation" function is accomplished in the hippocampal circuit, and how it relates to behavior, is still unclear. Here we perform in vivo 2-photon Ca2+ imaging from hippocampal subregions of head-fixed mice performing a virtual-reality spatial discrimination task. We find that population activity in the input region of the hippocampus, the dentate gyrus, robustly discriminates small changes in environments, whereas spatial discrimination in CA1 reflects the behavioral performance of the animals and depends on the degree of differences between environments. Our results demonstrate that the dentate gyrus amplifies small differences in its inputs, while downstream hippocampal circuits will act as the final arbiter on this decorrelated information, thereby producing a "perceptual map" that will guide behaviour.

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bioRxiv2018

Brain network dynamics during spontaneous strategy shifts and incremental task optimization

With practice, humans may improve their performance in a task by either optimizing a known strategy or discovering a novel, potentially more fruitful strategy. How does the brain support these two fundamental abilities? In the present experiment, subjects performed a simple perceptual decision-making task. They could either use and progressively optimize an instructed strategy based on stimulus position, or spontaneously devise and then use a new strategy based on stimulus color. We investigated how local and long-range BOLD coherence behave during these two types of strategy learning by applying a recently developed unsupervised fMRI analysis technique that was specifically designed to probe the presence of transient correlations. Converging evidence showed that the posterior portion of the default network, i.e. the precuneus and the angular gyrus bilaterally, has a central role in the optimization of the current strategy: these regions encoded the relevant spatial information, increased the level of local coherence and the strength of connectivity with other relevant regions in the brain (e.g. visual cortex, dorsal attention network). This increase was proportional to the task optimization achieved by subjects, as measured by the reduction of reaction times, and was transiently disrupted when subjects were forced to change strategy. By contrast, the anterior portion of the default network (i.e. medial prefrontal cortex) together with rostral portion of the fronto-parietal network showed an increase in local coherence and connectivity only in subjects that would at some point spontaneously choose the new strategy. Overall, our findings shed light on the dynamic interactions between regions related with attention and with cognitive control, underlying the balance between strategy exploration and exploitation. Results suggest that the default network, far from being \"shut-down\" during task performance, has a pivotal role in the background exploration and monitoring of potential alternative courses of action.

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