Biology subjects

Bonifazi, P.

Publications and source records attributed to Bonifazi, P..

14 recordsLinked to original sources

bioRxiv2026

Bridging Higher-Order Information Theory and Neuroimaging: A Voxel-Wise O-Information Framework

The brains functional organization has been extensively studied through pairwise connectivity analyses. While these approaches have provided important insights into brain network organization, they fall short in capturing the complexity of high-order functional interactions (HOI). Particularly relevant is the investigation of redundancy and synergy patterns -not addressable with pairwise interactions-, revealing fundamental mechanisms of brain integration and information processing across various cognitive functions and clinical conditions. Conventional neuroimaging software packages are primarily designed for classical (general linear model-like) analyses but lack native support for HOI metrics. To address this gap, this study introduces a novel framework that bridges high-order information theory with conventional neuroimaging analysis pipelines and is subsequently applied to resting-state functional MRI to demonstrate its practical utility. By representing HOI into voxel-level metrics, our approach allows standard neuroimaging analyses to probe complex multivariate dependencies. Moreover, voxel-level group-comparison analyses show age differences linked with reduced redundancy in default mode network interactions. These findings advance our understanding of the complex relationship between multivariate functional interactions, voxel-level neuroimaging, and behavior, highlighting novel analytic strategies to study high-order information processing underlying cognitive function and its alterations in pathological conditions.

neuroscience↗

bioRxiv2025

Brain structural modules associated to functional high-order interactions in the human brain,

The brains modular organization, ranging from microcircuits to large-scale networks, has been extensively studied in terms of its structural and functional properties. Particularly insightful has been the investigation of the coupling between structural connectivity (SC) and functional connectivity (FC), whose analysis has revealed important insights into the brains efficiency and adaptability related to various cognitive functions. Interestingly, links in SC are intrinsically pairwise but this is not the case for FC; and while recent work demonstrates the relevance of the brains high-order interactions (HOI), the coupling of between SC and functional HOI remains unexplored. To address this gap, this study leverages functional MRI and diffusion weighted imaging to delineate the brains modular structure by investigating the coupling between SC and functional HOI. Our results demonstrates that structural networks can be associated with both redundant and synergistic functional interactions. In particular, SC exhibits both positive and negative correlations with redundancy, it shows consistent positive correlations with synergy, indicating that a higher density of structural connections is linked to increased synergistic interactions. These findings advance our understanding of the complex relationship between structural and high-order functional properties, shedding light on the brains architecture underlying its modular organization. Significance StatementUnderstanding the intricate relationship between the brain structural and functional connectivity is instrumental for enabling a richer characterization of cognitive functions. This challenge is complex, since it involves comparing pairwise links representing white-matter fibers with higher-order functional interactions that can involve multiple brain regions sharing information -- indeed, these interactions can be redundant in presence of shared information, or synergistic when novel information arises in the whole subsystem. Here we advance the investigation of the coupling between brain structure and high-order interactions, offering new insights into how structural properties are associated with both redundant and synergistic functional modules. Furthermore, the approach presented here opens new doors to investigate how this coupling between structural connectivity and functional HOI is modulated during development or under diverse brain pathologies.

neuroscience↗

bioRxiv2024

Partial Correlation as a Tool for Mapping Functional-Structural Correspondence in Human Brain Connectivity

Brain structure-function coupling has been studied in health and disease by many different researchers in recent years. Most of the studies have estimated functional connectivity matrices as correlation coefficients between different brain areas, despite well-known disadvantages compared to partial correlation connectivity matrices. Indeed, partial correlation represents a more sensible model for structural connectivity since, under a Gaussian approximation, it accounts only for direct dependencies between brain areas. Motivated by this and following previous results by different authors, we investigate structure-function coupling using partial correlation matrices of functional magnetic resonance imaging (fMRI) brain activity time series under various regularization (a.k.a. noise-cleaning) algorithms. We find that, across different algorithms and conditions, partial correlation provides a higher match with structural connectivity retrieved from Density Weighted Imaging data than standard correlation, and this occurs at both subject and population levels. Importantly, we also show that regularization and thresholding are crucial for this match to emerge. Finally, we assess neuro-genetic associations in relation to structure-function coupling, which presents promising opportunities to further advance research in the field of network neuroscience, particularly concerning brain disorders.

neuroscience↗

bioRxiv2024

Cell lipotypes localization in brain by mass spectrometry imaging

The study investigates brain lipid super-specialisation by defining characteristic spectral lipotypic profiles for the five primary cerebral cell-types. Utilizing a computational approach, the research visualizes the anatomical distribution of these profiles with high spatial resolution in brain tissues. This method unveils cellular stereotypic lipidic signatures within the CNS, providing a new framework for exploring the physiological roles of lipids in diverse cell-types present in brain or in any other tissue.

neuroscience↗

bioRxiv2023

Open datasets and code for multi-scale relations on structure, function and neuro-genetics in the human brain

The human brain is an extremely complex network of structural and functional connections that operate at multiple spatial and temporal scales. Investigating the relationship between these multi-scale connections is critical to advancing our comprehension of brain function and disorders. However, accurately predicting structural connectivity from its functional counterpart remains a challenging pursuit. One of the major impediments is the lack of public repositories that integrate structural and functional networks at diverse resolutions. Addressing this issue, we provide open datasets and code enabling the examination of the correspondence between structural and functional connectivities at different scales. We present a module-level strategy that overcomes region-level approaches in understanding the structure-function correspondence. Moreover, we also provide additional resources focused on neuro-genetic associations of module-level network metrics, which present promising opportunities to further advance research in the field of network neuroscience, particularly concerning brain disorders.

neuroscience↗

bioRxiv2022

Acute inhibition of the STA3 signaling pathway during epileptogenesis prevent GABAergic cells loss and imprinting of epileptic state: an in-vitro proof

Epilepsy, the condition of recurrent unprovoked seizures resulting from a wide variety of causes, is one of the worlds most prominent brain syndrome. Seizures which are an expression of neuronal network dysfunction occur in a positive feedback loop of concomitant factors where seizures generate more seizures, including also neuro-inflammatory responses. Among other pathways involved in inflammatory responses, the JAK/STAT signaling pathway has been proposed to prevent epilepsy. In this work we tested on a model of temporal lobe epilepsy in-vitro, the hypothesis that acute inhibition of STAT3-phosphorylation - during epileptogenesis, can prevent structural damages in the hippocampal circuits, and the imprinting both of neural epileptic activity and inflammatory glial states. We performed calcium imaging of spontaneous circuits dynamics in organotypic hippocampal slices previously exposed to hyper-excitable conditions through the blockage of GABAergic synaptic transmission. Epileptogenic conditions lead to imprinted epileptic dynamics in the circuits in terms of higher frequency of neuronal firing and circuits synchronizations, higher correlated activity in neuronal pairs and decreased complexity in synchronization patterns. Acute inhibition of the STAT3-phosphorylation during epileptogenesis, prevented the imprinting of epileptic activity patterns, general cell loss, GABAergic cells loss and the persistence of inflammatory reactive glial states. This work provides further evidence that inhibiting the STAT3 signaling pathway under epileptogenesis can prevent patho-topological reorganization of neuro-glial circuits.

neuroscience↗

bioRxiv2020

Microglial phagocytosis dysfunction is related to local neuronal activity in a genetic model of epilepsy

Microglial phagocytosis of apoptotic cells is an essential component of the brain regenerative response in neurodegenerative diseases. Phagocytosis is very efficient in physiological conditions, as well as during apoptotic challenge induced by excitotoxicity or inflammation, but is impaired in mouse and human mesial temporal lobe epilepsy (MTLE). Here we extend our studies to a genetic model of progressive myoclonus epilepsy type 1 (EPM1) in mice lacking cystatin B (CSTB), an inhibitor of cysteine proteases involved in lysosomal proteolysis. We first demonstrated that microglial phagocytosis was impaired in the hippocampus in Cstb knock-out (KO) mice when seizures arise and hippocampal atrophy begins, at 1 month of age. To test if this blockage was related to the lack of Cstb in microglia, we used an in vitro model of phagocytosis and siRNAs to acutely reduce Cstb expression but we found no significant effect in the phagocytosis of apoptotic cells. We then tested whether seizures were involved in the phagocytosis impairment, similar to MTLE, and analyzed Cstb KO mice before seizures begin, at postnatal day 14. Here, phagocytosis impairment was restricted to the granule neuron layer but not to the subgranular zone, where there are no active neurons. Furthermore, we observed apoptotic cells (both phagocytosed and not phagocytosed) in Cstb deficient mice at close proximity to active, cFos+ neurons and used mathematical modeling to demonstrate that the physical relationship between apoptotic cells and cFos+ neurons was specific for Cstb KO mice. These results suggest a complex crosstalk between apoptosis, phagocytosis and neuronal activity, hinting that local neuronal activity could be related to phagocytosis dysfunction in Cstb KO mice. Overall, this data suggest that phagocytosis impairment is an early feature of hippocampal damage in epilepsy and opens novel therapeutic approaches for epileptic patients based on targeting microglial phagocytosis.

neuroscience↗

bioRxiv2020

Low variability of dynamical functional connectivity in cerebellar networks

Brain networks can be defined and explored using different types of connectivity. Here, we studied P=48 healthy participants with neuroimaging state-of-the-art techniques and analyzed the relationship between the actual structural connectivity (SC) networks (between 2514 regions of interest covering the entire brain and brainstem) and the dynamical functional connectivity (DFC) among the same regions. To do so, we focused on a combination of two metrics: the first one measures the degree of SC-DFC similarity -i.e. how much functional correlations can be explained by structural pathways- and the second one, the intrinsic variability of DFC networks across time. Overall, we found that cerebellar networks have smaller DFC variability than other networks in the cerebrum. Moreover, our results clearly evidence the internal structure of the cerebellum, which is divided in two differentiated networks, the posterior and anterior parts, the latter also being connected to the brain stem. The mechanism for keeping the DFC variability low in the posterior part of the cerebellum is consistent with another finding, namely, it exhibits the highest SC-DFC similarity among all other sub-networks, i.e. its structure constrains very strongly its dynamics. On the other hand, the anterior part of the cerebellum, which also exhibits a low level of DFC variability, has the lowest SC-DFC similarity, suggesting very different dynamical mechanisms. It is likely that its connections with the brain stem -which regulates sleep cycles, cardiac and respiratory functioning- might have a critical role in DFC variations in the anterior part. A lot is known about cerebellar networks, such as having extremely rich and complex anatomy and functionality, connecting to the brainstem, and cerebral hemispheres, and participating in a large variety of cognitive functions, such as movement control and coordination, executive function, visual-spatial cognition, language processing, and emotional regulation. However, as far as we know, our findings of low variability in the dynamical functional connectivity of cerebellar networks and its possible relation with the above functions, have not been reported so far. Further research is still needed to shed light on these findings.

neuroscience↗

bioRxiv2019

A neuroprosthetic system to restore neuronal communication in modular networks

Recent advances in neurotechnology allow neurological impairments to be treated or reduced by brain machine interfaces and neuroprostheses. To develop energy-efficient and real-time capable devices, neuromorphic computing systems are envisaged as the core of next-generation neurobiohybrid systems for brain repair. We demonstrate here the first exploitation of a neuromorphic prosthesis to restore bidirectional interactions between two neuronal populations, even when one is damaged or completely missing. We used in vitro modular cell cultures to mimic the mutual interaction between neuronal assemblies and created a focal lesion to functionally disconnect the two populations. Then, we employed our neuromorphic prosthesis for two specific applications with future clinical implications: bidirectional bridging to artificially reconnect two disconnected neuronal modules and hybrid bidirectional bridging to replace the activity of one module with a neuromorphic spiking neural network. Our neuroprosthetic system opens up new avenues for the development of novel bioelectrical therapeutics for human applications.

bioengineering↗

bioRxiv2018

The emergence of dynamical instantaneous memory in the spontaneous activity of spatially confined neuronal assemblies in vitro

Understanding the dynamics between communicating cell assemblies is essential for deciphering the neural code and identifying the mechanism underlying memory formation. In this work, in order to unveil possible emergent intrinsic memory phenomena in the communication between cell assemblies, we study the spontaneous dynamics of in vitro spatially confined inter-connected neuronal circuits grown on multi-electrode arrays. The spontaneous dynamics of the global network was characterized by the coupling of the activity independently generated by each circuit. The asymptotic functional connectivity of the network reflected its modular organization. Instantaneous functional connectivity maps on ten seconds epochs, revealed more complex dynamical states with the simultaneous activation of distinct circuits. When looking at the similarity of the generated network events, we observed that spontaneous network events occurring at temporal distances below two dozens of seconds had an average higher similarity compared to randomly played network events. Such a memory phenomenon was not observed in networks where spontaneous events were less frequent and in networks topologically organized as open lines. These results support the hypothesis that dynamical instantaneous memory, characterized by drifting network dynamics with decaying degree of similarity, is an intrinsic property of neuronal networks.

neuroscience↗

bioRxiv2018

Interaction information along lifespan of the resting brain dynamics reveals a major redundant role of the default mode network

Interaction Information generalizes the univariate Shannon Entropy to triplets of variables, allowing the detection of redundant or synergetic interactions in dynamical networks. Here, we calculated interaction information from functional magnetic resonance imaging and asked whether redundancy or synergy vary across brain regions and along lifespan. We found high overlapping between the pattern of high redundancy and the default mode network, and this occurred along lifespan. The pattern of high values of synergy, more heterogeneous and variable along lifespan, was overlapping with different cognitive domains, such as spatial and temporal memory, emotion processing and motor skills. Moreover, the amount of redundancy and synergy seem to be balanced along lifespan, suggesting informational compensatory mechanisms in brain networks.

neuroscience↗

bioRxiv2018

Modeling driver cells in developing neuronal networks

Spontaneous emergence of synchronized population activity is a characteristic feature of developing brain circuits. Recent experiments in the developing neo-cortex showed the existence of driver cells able to impact the synchronization dynamics when single-handedly stimulated. We have developed a spiking network model capable to reproduce the experimental results, thus identifying two classes of driver cells: functional hubs and low functionally connected (LC) neurons. The functional hubs arranged in a clique orchestrated the synchronization build-up, while the LC drivers were lately or not at all recruited in the synchronization process. Notwithstanding, they were able to alter the network state when stimulated by modifying the temporal activation of the functional clique or even its composition. LC drivers can lead either to higher population synchrony or even to the arrest of population dynamics, upon stimulation. Noticeably, some LC driver can display both effects depending on the received stimulus. We show that in the model the presence of inhibitory neurons together with the assumption that younger cells are more excitable and less connected is crucial for the emergence of LC drivers. These results provide a further understanding of the structural-functional mechanisms underlying synchronized firings in developing circuits possibly related to the coordinated activity of cell assemblies in the adult brain.\n\nAuthor SummaryThere is timely interest on the impact of peculiar neurons (driver cells) and of small neuronal sub-networks (cliques) on operational brain dynamics. We first provide experimental data concerning the effect of stimulated driver cells on the bursting activity observable in the developing entorhinal cortex. Secondly, we develop a network model able to fully reproduce the experimental observations. Analogously to the experiments two types of driver cells can be identified: functional hubs and low functionally connected (LC) drivers. We explain the role of hub neurons, arranged in a clique, for the orchestration of the bursting activity in control conditions. Furthermore, we report a new mechanism, which can explain why and how LC drivers emerge in the structural-functional organization of the enthorinal cortex.

neuroscience↗

bioRxiv2017

What the structural-functional connectome reveals about brain aging: The key role of the fronto-striatal-thalamic circuit and the rejuvenating impact of physical activity

Physiological ageing affects brain structure and function impacting its morphology, connectivity and performance. However, at which extent brain-connectivity metrics reflect the age of an individual and whether treatments or lifestyle factors such as physical activity influence the age-connectivity match is still unclear. Here, we assessed the level of physical activity and collected brain images from healthy participants (N=155) ranging from 10 to 80 years to build functional (resting-state) and structural (tractography) connectivity matrices that were combined as connectivity descriptors. Connectivity descriptors were used to compute a maximum likelihood age estimator that was optimized by minimizing the mean absolute error. The connectivity-based estimated age, i.e. the brain-connectome age (BCA), was compared to the chronological age (ChA). Our results were threefold. First, we showed that ageing widely affects the structural-functional connectivity of multiple structures, such as the anterior part of the default mode network, basal ganglia, thalamus, insula, cingulum, hippocampus, parahippocampus, occipital cortex, fusiform, precuneus and temporal pole. Second, our analysis showed that the structure-function connectivity between basal ganglia and thalamus to orbitofrontal and frontal areas make a major contribution to age estimation. Third, we found that high levels of physical activity reduce BCA as compared to ChA, and vice versa, low levels increment it. In conclusion, the BCA model results highlight the impact of physical activity and the key role played by the connectivity between basal ganglia and thalamus to frontal areas on the process of healthy aging. Notably, the same methodology can be generally applied both to evaluate the impact of other factors and therapies on brain ageing, and to identify the structural-functional brain connectivity correlate of other biomarkers than ChA.

neuroscience↗

bioRxiv2017

Progressive alterations in brain connectivity patterns revealed by diffusion-tensor brain networks across severity stages in Alzheimer’s disease

Alzheimers disease (AD) is a chronically progressive neurodegenerative disease highly correlated to aging. Whether AD originates by targeting a localized brain area and propagates to the rest of the brain across disease-severity progression is a question with an unknown answer. Here, we aim to provide an answer to this question at the group-level by looking at differences in diffusion-tensor brain networks. In particular, making use of data from Alzheimer's Disease Neuroimaging Initiative (ADNI), four different groups were defined (all of them matched by age, sex and education level): G1 (N1=36, healthy control subjects, Control), G2 (N2=36, early mild cognitive impairment, EMCI), G3 (N3=36, late mild cognitive impairment, LMCI) and G4 (N4=36, AD). Diffusion-tensor brain networks were compared across three disease stages: stage I 3(Control vs EMCI), stage II (Control vs LMCI) and stage III (Control vs AD). The group comparison was performed using the multivariate distance matrix regression analysis, a technique that was born in genomics and was recently proposed to handle brain functional networks, but here applied to diffusion-tensor data. The results were three-fold: First, no significant differences were found in stage I. Second, significant differences were found in stage II in the connectivity pattern of a subnetwork strongly associated to memory function (including part of the hippocampus, amygdala, entorhinal cortex, fusiform gyrus, inferior and middle temporal gyrus, parahippocampal gyrus and temporal pole). Third, a widespread disconnection across the entire AD brain was found in stage III, affecting more strongly the same memory subnetwork appearing in stage II, plus the other new subnetworks,including the default mode network, medial visual network, frontoparietal regions and striatum. Our results are consistent with a scenario where progressive alterations of connectivity arise as the disease severity increases and provide the brain areas possibly involved in such a degenerative process. Further studies applying the same strategy to longitudinal data are needed to fully confirm this scenario.

neuroscience↗