Biology subjects

Battaglia, D.

Publications and source records attributed to Battaglia, D..

18 recordsLinked to original sources

bioRxiv2026

Claustral pathway coordinates distributed cortical dynamics with hippocampal output during sleep to promote memory consolidation

The claustrum is a broadly connected subcortical structure proposed to coordinate distributed cortical activity. Claustral neurons are recruited during synchronized brain states and contribute to sleep-dependent memory consolidation, primarily through effects on cortical dynamics. Yet the claustrum also innervates the subicular complex, a major hippocampal output node, raising the possibility that it may regulate both cortical state and hippocampo-cortical dialogue during sleep. Here, we identify a projection-defined population of claustral neurons targeting the subicular complex, CLAsc, that is preferentially recruited during slow-wave sleep and tracks cortical and subicular sleep dynamics. Optogenetic activation of CLAsc neurons during post-learning sleep enhanced spatial memory consolidation without detectable changes in ripple occurrence or slow-oscillation-spindle coupling. Instead, CLAsc activation imposed a stereotyped cortical-subicular motif: a brief gamma-rich Up state followed by a coordinated Down state across prefrontal, retrosplenial and subicular regions. Within this gamma-rich Up state, interareal coherence increased, gamma bursts became synchronized, and lagged directed interactions were transiently reorganized, including an enhanced prefrontal-to-subicular component. Together, these findings identify the claustrum as a state-dependent coordinator that transforms ongoing cortical and hippocampal-output activity into coordinated network transitions during sleep, providing a circuit mechanism through which distributed brain states may support memory consolidation.

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bioRxiv2025

Coordinated multilaminar dynamics underlie multiplexed computation in motor cortex

Functional multiplexing is a signature of higher-order brain regions, supported by single-unit mixed selectivity and the recurrent circuitry of the six-layered cortical microcircuit, which allows columns to retain incoming information and integrate it into stable latent representations. It remains unclear whether multiplexing arises from specialized processing within individual layers or from the collective coordination of multiple layers. Here, we analyzed laminar recordings from macaque motor cortex during a delayed match-to-sample task. We identified laminarly distributed, behaviorally specific subspaces that encoded distinct task variables. When the behavioral meaning of a variable was preserved, the same coding subspace was reused across time; when sample information was transformed into a match, the sample subspace was recycled to instead encode stimulus validity. In contrast, identical sensory variables -such as target direction-engaged distinct subspaces when their computational role differed, e.g. during visual encoding versus motor preparation. Subtle variations in laminar weights gave rise to multiple coexistent subspaces, enabling multiplexed representations at the columnar scale. Beyond their coexistence, we tracked the temporal evolution of individual coding subspaces. Task timing paced their geometric reorganization, with task-relevant information propagating along structured trajectories as the laminar center of mass shifted between superficial and deep layers. These laminar trajectories were consistent across sites, though propagation patterns varied, while overall population activity lacked structured dynamics, leaving coherent laminar information to emerge atop a background of unspecific fluctuations. HighlightsO_LICoordinated multilaminar activity encodes task-relevant information in motor cortex C_LIO_LIDifferent layer combinations span multiplexed coding spaces C_LIO_LICoding spaces for task condition, cue and direction are flexibly repurposed over time C_LIO_LITask timing paces the geometric reorganization of coding spaces C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/690660v2_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@12158d4org.highwire.dtl.DTLVardef@4ee7b3org.highwire.dtl.DTLVardef@4dd46corg.highwire.dtl.DTLVardef@1b533f3_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2024

Compensating functional connectivity changesdue to structural connectivity damage viamodifications of local dynamics

Neurological pathologies as e.g. Alzheimers Disease or Multiple Sclerosis are often associated to neurodegenerative processes affecting the strength and the transmission speed of long-range inter-regional fiber tracts. Such degradation of Structural Connectivity impacts on large-scale brain dynamics and the associated Functional Connectivity, eventually perturbing network computations and cognitive performance. Functional Connectivity however is not bound to merely mirror Structural Connectivity, but rather reflects the complex coordinated dynamics of many regions. Here, using analytical characterizations of toy models and computational simulations connectome-base whole-brain models, we predict that suitable modulations of regional dynamics could precisely compensate for the effects of structural degradation, as if the original Structural Connectivity strengths and speeds of conduction were effectively restored. The required dynamical changes are widespread and aspecific (i.e. they do not need to be restricted to specific regions) so that they could be potentially implemented via neuromodulation or pharmacological therapy, globally shifting regional excitability and/or excitation/inhibition balance. Computational modelling and theory thus suggest that, in the future therapeutic interventions could be designed to "repair brain dynamics" rather than structure to boost functional connectivity without having to block or revert neurodegenerative processes. AUTHOR SUMMARYNeurological disorders affect Structural Connectivity, i.e. the wiring infrastructure interlinking distributed brain regions. Here we propose that the resulting disruptions in Functional Connectivity, i.e. inter-regional coordination and information sharing, could be compensated by modifying local dynamics so to effectively emulate the restoration of Structural Connectivity (but through a suitable "software patch" rather than by repairing the "hardware"). For simple toy models involving a few regions we can achieve an analytical understanding of how structural and dynamical changes jointly control Functional Connectivity. We then show that the concept of "effective connectome change" via modulation of dynamics robustly extend also to simulation of large-scale models embedding realistic whole-brain connectivity. We thus forecast that novel therapeutic strategies could be devised, targeting dynamics rather than neurodegenerative mechanisms.

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bioRxiv2024

States of functional connectivity flow and their multiplex dynamics in human epilepsy and postictal aphasia

We present a methodological framework for analysing multi-frequency dynamic functional connectivity (dFC) in electrophysiological recordings. The approach characterises not only the magnitude of network reconfiguration over time, but also whether these changes are spatially random or, instead, spatially organised in ways that drive a slower reconfiguration of modular structure. We define a generative null model of multi-scale connectivity fluctuations that differ in their degree of spatiotemporal organisation, and we describe dFC flows through the joint assessment of (i) instantaneous reconfiguration speed and (ii) the extent and quality of ongoing modular reorganisation. Different combinations of these features delineate distinct "flow styles", ranging from more liquid to more frozen dynamics. As a case study, we apply this framework to SEEG recordings from epileptic patients. We identify transitions between dynamic "allegiance states", whose flow styles closely mirror those of the null model. Seizure onset is associated with a pronounced slowing of dFC-speed, while a specific post-ictal regime combines low speed with highly frozen allegiance, and aligns most strongly with clinician-annotated aphasia. These pilot results suggest that temporal multiplex network analyses can reveal transient, frequency-specific network regimes linked to symptom expression and offers a generalisable tool for dissecting fast network dynamics in intracranial recordings. Author SummaryCognitive functions rely on the brains capacity to continually reorganize interactions among neuronal populations. Capturing this flexibility requires describing not only average functional network structure but also how it evolves over time. Using time- and frequency-resolved coherence from intracranial recordings in epilepsy patients, we model dynamic functional connectivity as a temporal multiplex network spanning multiple frequency bands. We introduce a frame-work that distinguishes different "styles" of network reconfiguration--faster or slower, and more structured or more random.

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bioRxiv2023

Phase-dependent stimulation response is shaped by the brain's transient collective dynamics.

Exogenous stimulation is a promising tool for investigating and altering cognitive processes in the brain, with potential clinical applications. Following experimental observations, we hypothesise that the effect of stimulation crucially depends on the endogenous dynamics of the brain. Our study explores how local and global dynamical properties, like the stimulation phase of regional oscillatory activity and the transient network states, modulate the effect of single pulse stimulation in a large-scale network. Our findings demonstrate that the effect of stimulation strongly depends on the interplay between stimulated phase, transient network state, and brain region. Importantly, we show that stimulation is not only state-dependent but can also induce global state switching. Lastly, predicting the effect of stimulation by using machine learning shows that state-aware measures can increase the performance by up to 40%. Our results suggest that a fine characterisation of the complex brain dynamics in experimental setups is essential for improving the reliability of exogenous stimulation.

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bioRxiv2023

Interdependence patterns of multi-frequency oscillations predict visuomotor behavior

We show that sensorimotor behavior can be reliably predicted from single-trial EEG oscillations fluctuating in a coordinated manner across brain regions, frequency bands and movement time epochs. We define high-dimensional oscillatory portraits to capture the interdependence between basic oscillatory elements, quantifying oscillations occurring in single-trials at specific frequencies, locations and time epochs. We find that the general structure of the element-interdependence networks (effective connectivity) remains stable across task conditions, reflecting an intrinsic coordination architecture and responds to changes in task constraints by subtle but consistently distinct topological reorganizations. Trial categories are reliably and significantly better separated using oscillatory portraits, than from the information contained in individual oscillatory elements, suggesting an inter-element coordination-based encoding. Furthermore, single-trial oscillatory portrait fluctuations are predictive of fine trial-to-trial variations in movement kinematics. Remarkably, movement accuracy appears to be reflected in the capacity of the oscillatory coordination architecture to flexibly update as an effect of movement-error integration.

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bioRxiv2022

Hippocampal gamma oscillations form complex ensembles modulated by behavior and learning

The hippocampus and the entorhinal cortex display a rich oscillatory activity, believed to support neural information processing in key cognitive functions1. In the hippocampal region CA1, a "slow gamma" rhythm (30-80 Hz) generated in CA3 would support memory retrieval whereas a "medium gamma" rhythm (60-120 Hz) generated in the entorhinal cortex would support memory encoding2,3. However, descriptions involving discrete gamma sub-bands can only partially account for the haphazard diversity of oscillatory behaviors observed in individual recordings during spatial navigation behavior. Here, we stress that transient gamma oscillatory episodes at any frequency or phase relative to the ongoing theta (4-12 Hz) rhythm can be recorded at any layer within CA1. Eventually, the commonly reported averages are dominated by a minority of very strong power events overshadowing gamma heterogeneity. Nevertheless, we show that such gamma diversity can be naturally explained by a simple mechanistic model, and that behavior-related information (position within a maze) can be decoded from most individual gamma events, despite their low power and erratic-like nature. Our results indicate that behavior specifically shapes ensembles of irregular hippocampal gamma oscillations, in a way which evolves with learning, depends on the hippocampal layer and is hard to reconcile with the hypothesis of rigid, narrowly tuned gamma sub-bands. Beyond randomness, the pervasive gamma diversity may thus reflect complexity at the "fringe-of-synchrony"4 likely functional but invisible to classic average-based analyses.

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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.

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bioRxiv2021

Early memory deficits and extensive brain network disorganization in the AppNL-F/MAPT double knock-in mouse model of familial Alzheimer's disease.

A critical challenge in current research on AD is to clarify the relationship between early neuropathology and network dysfunction associated to the emergence of subtle memory alterations which announce disease onset. In the present work, the new generation AppNL-F/MAPT double knock in (dKI) model was used to evaluate early stages of AD. The initial step of tau pathology was restricted to the perirhinal-entorhinal region, sparing the hippocampus. This discrete neuropathological sign was associated with deficits in the object-place associative memory, one of the earliest recognition memories affected in individuals at risk for developing AD. Analyses of task-dependent c-Fos activation was carried out in 22 brain regions across the medial prefrontal cortex, claustrum, retrosplenial cortex, and medial temporal lobe. Initial hyperactivity was detected in the entorhinal cortex and the claustrum of dKI mice. The retention phase was associated to reduced network efficiency especially across cingulate cortical regions, which may be caused by a disruption of information flow through the retrosplenial cortex. Moreover, the relationship between network global efficiency and memory performance in the WT could predict memory loss in the dKI, further linking reduced network efficiency to memory dysfunction. Our results suggest that early perirhinal-entorhinal pathology is associated with local hyperactivity which spreads towards connected regions such as the claustrum, the medial prefrontal cortex and ultimately the key retrosplenial hub which is needed to relay information flow from frontal to temporal lobes. The similarity between our findings and those reported in the earliest stages of AD suggests that the AppNL-F/MAPT dKI model has a high potential for generating key information on the initial stage of the disease.

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bioRxiv2021

Structured Modulations of Ongoing Variability by Task and Development

In carefully designed experiments, cognitive scientists interpret the mean event-related potentials (ERP) in terms of cognitive operations. However, the huge signal variability from one trial to the next, questions the representability of such mean events. We explored here whether this variability is an unwanted noise, or an informative part of the neural response. We took advantage of the rapid changes in the visual system during human infancy and analyzed the variability of visual responses to central and lateralized faces in 2-to 6-month-old infants and adults using high-density electroencephalography (EEG). We observed that neural trajectories of individual trials always remain very far from ERP components, only moderately bending their direction with a substantial temporal jitter across trials. However, single trial trajectories displayed characteristic patterns of acceleration and deceleration when approaching ERP components, as if they were under the active influence of steering forces causing transient attraction and stabilization. These dynamic events could only partly be accounted for by induced microstate transitions or phase reset phenomena. Furthermore, these structured modulations of response variability, both between and within trials, had a rich sequential organization, which, in infants, was modulated by the task difficulty. Our approaches to characterize Event Related Variability (ERV) expand and reinterpret classic ERP analyses, making them compliant with pervasive neural variability and providing a more faithful description of neural events following stimulus presentation.

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bioRxiv2021

Cerebellar connectivity maps embody individual adaptive behavior

From planification to execution, cerebellar microcircuits encode different features of skilled movements. However, it is unknown whether cerebellar synaptic connectivity maps encode movement features in a motor context specific manner. Here we investigated the spatial organization of excitatory synaptic connectivity in mice cerebellar cortex in different locomotor contexts: during development and in normal, trained or altered locomotor conditions. We combined optical, electrophysiological and graph modelling approaches to describe synaptic connectivity between granule cells (GCs) and Purkinje cells (PCs). Synaptic map maturation during development revealed a critical period in juvenile animals before the establishment of a stereotyped functional organization in adults. However, different locomotor conditions lead to specific GC-PC connectivity maps in PCs. Ultimately, we demonstrated that the variability in connectivity maps directly accounts for individual specific behavioral features of mice locomotion, suggesting that GC-PC networks encode a general motor context as well as individual specific internal models underlying motor adaptation.

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bioRxiv2021

Disordered information processing dynamics in experimental epilepsy

Neurological disorders share common high-level alterations, such as cognitive deficits, anxiety, and depression. This raises the possibility of fundamental alterations in the way information conveyed by neural firing is maintained and dispatched in the diseased brain. Using experimental epilepsy as a model of neurological disorder we tested the hypothesis of altered information processing, analyzing how neurons in the hippocampus and the entorhinal cortex store and exchange information during slow and theta oscillations. We equate the storage and sharing of information to low level, or primitive, information processing at the algorithmic level, the theoretical intermediate level between structure and function. We find that these low-level processes are organized into substates during brain states marked by theta and slow oscillations. Their internal composition and organization through time are disrupted in epilepsy, loosing brain state-specificity, and shifting towards a regime of disorder in a brain region dependent manner. We propose that the alteration of information processing at an algorithmic level may be a mechanism behind the emergent and widespread co-morbidities associated with epilepsy, and perhaps other disorders.

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bioRxiv2020

Cortical ignition dynamics is tightly linked to the core organisation of the human connectome

The capability of cortical regions to flexibly sustain an "ignited" state of activity has been discussed in relation to conscious perception or hierarchical information processing. Here, we investigate how the intrinsic propensity of different regions to get ignited is determined by the specific topological organisation of the structural connectome. More specifically, we simulated the resting-state dynamics of mean-field whole-brain models and assessed how dynamic multi-stability and ignition differ between a reference model embedding a realistic human connectome, and alternative models based on a variety of randomised connectome ensembles. We found that the strength of global excitation needed to first trigger ignition in a subset of regions is substantially smaller for the model embedding the empirical human connectome. Furthermore, when increasing the strength of excitation, the propagation of ignition outside of this initial core -which is able to self-sustain its high activity- is way more gradual than for any of the randomised connectomes, allowing for graded control of the number of ignited regions. We explain both these assets in terms of the exceptional weighed core-shell organisation of the empirical connectome, speculating that this topology of human structural connectivity may be attuned to support an enhanced ignition dynamic. Author summaryThe activity of the cortex in mammals constantly fluctuates in relation to cognitive tasks, but also during rest. The ability of brain regions to display ignition, a fast transition from low to high activity is central for the emergence of conscious perception and decision making. Here, using a biophysically inspired model of cortical activity, we show how the structural organization of human cortex supports and constrains the rise of this ignited dynamics in spontaneous cortical activity. We found that the weighted core-shell organization of the human connectome allows for a uniquely graded ignition. This graded ignition implies a smooth control of the ignition in cortical areas tuned by the global excitability. The smooth control cannot be replicated by surrogate connectomes, even though they conserve key local or global network properties. Indeed, the first trigger of ignition in the human cortex has the lowest global excitability and corresponds with the strongest interconnected areas, the ignition core. Finally, we suggest developmental and evolutionary constraints of the mesoscale organization that support this enhanced ignition dynamics in cortical activity.

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bioRxiv2020

Modular slowing of resting-state dynamic Functional Connectivity as a marker of cognitive dysfunction induced by sleep deprivation

Dynamic Functional Connectivity (dFC) in the resting state (rs) is considered as a correlate of cognitive processing. Describing dFC as a flow across morphing connectivity configurations, our notion of dFC speed quantifies the rate at which FC networks evolve in time. Here we probe the hypothesis that variations of rs dFC speed and cognitive performance are selectively interrelated within specific functional subnetworks. In particular, we focus on Sleep Deprivation (SD) as a reversible model of cognitive dysfunction. We found that whole-brain level (global) dFC speed significantly slows down after 24h of SD. However, the reduction in global dFC speed does not correlate with variations of cognitive performance in individual tasks, which are subtle and highly heterogeneous. On the contrary, we found strong correlations between performance variations in individual tasks -including Rapid Visual Processing (RVP, assessing sustained visual attention)- and dFC speed quantified at the level of functional subnetworks of interest. Providing a compromise between classic static FC (no time) and global dFC (no space), modular dFC speed analyses allow quantifying a different speed of dFC reconfiguration independently for sub-networks overseeing different tasks. Importantly, we found that RVP performance robustly correlates with the modular dFC speed of a characteristic frontoparietal module. HighlightsO_LISleep Deprivation (SD) slows down the random walk in FC space implemented by Dynamic Functional Connectivity (dFC) at rest. C_LIO_LIWhole-brain level slowing of dFC speed does not selectively correlate with fine and taskspecific changes in performance C_LIO_LIWe quantify dFC speed separately for different link-based modules coordinated by distinct regional "meta-hubs" C_LIO_LIModular dFC speed variations capture subtle and task-specific variations of cognitive performance induced by SD. C_LI Author summaryWe interpreted dynamic Functional Connectivity (dFC) as a random walk in the space of possible FC networks performed with a quantifiable "speed". Here, we analyze a fMRI dataset in which subjects are scanned and cognitively tested both before and after Sleep Deprivation (SD), used as a reversible model of cognitive dysfunction. While global dFC speed slows down after a sleepless night, it is not a sufficiently sensitive metric to correlate with fine and specific cognitive performance changes. To boost the capacity of dFC speed analyses to account for fine and specific cognitive decline, we introduce the notion of modular dFC speed. Capitalizing on an edge-centric measure of functional connectivity, which we call Meta-Connectivity, we isolate subgraphs of FC describing relatively independent random walks (dFC modules) and controlled by distinct "puppet masters" (meta-hubs). We then find that variations of the random walk speed of distinct dFC modules now selectively correlate with SD-induced variations of performance in the different tasks. This is in agreement with the fact that different subsystems - distributed but functionally distinct- oversee different tasks. The high sensitivity of modular dFC analyses bear promise of future applications to the early detection and longitudinal characterization of pathologies such as Alzheimers disease.

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bioRxiv2020

Completion and augmentation of connectomic datasets in dementia and Alzheimer's Disease using Virtual Patient Cohorts

Large neuroimaging datasets, including information about structural (SC) and functional connectivity (FC), play an increasingly important role in clinical research, where they guide the design of algorithms for automated stratification, diagnosis or prediction. A major obstacle is, however, the problem of missing features (e.g., lack of concurrent DTI SC and resting-state fMRI FC measurements for many of the subjects). We propose here to address the missing connectivity features problem by introducing strategies based on computational whole-brain network modeling. Using two datasets, the ADNI dataset and a healthy aging dataset, for proof-of-concept, we demonstrate the feasibility of virtual data completion (i.e., inferring "virtual FC" from empirical SC or "virtual SC" from empirical FC), by using self-consistent simulations of linear and nonlinear brain network models. Furthermore, by performing machine learning classification (to separate age classes or control from patient subjects) we show that algorithms trained on virtual connectomes achieve discrimination performance comparable to when trained on actual empirical data; similarly, algorithms trained on virtual connectomes can be used to successfully classify novel empirical connectomes. Completion algorithms can be combined and reiterated to generate realistic surrogate connectivity matrices in arbitrarily large number, opening the way to the generation of virtual connectomic datasets with network connectivity information comparable to the one of the original data. Significance statementPersonalized information on anatomical connectivity ("structural connectivity", SC) or coordinated resting state activation patterns ("functional connectivity, FC) is a source of powerful neuromarkers to detect and track the development of neurodegenerative diseases. However, there are often "gaps" in the available information, with only SC (or FC) being known but not FC (or SC). Exploiting whole-brain modelling, we show that gap in databases can be filled by inferring the other connectome through computational simulations. The generated virtual connectomic data carry information analogous to the one of empirical connectomes, so that machine learning algorithms can be trained on them. This opens the way to the release in the future of cohorts of "virtual patients", complementing traditional datasets in data-driven predictive medicine.

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bioRxiv2020

Dynamic core-periphery structure of information sharing networks in entorhinal cortex and hippocampus

Neural computation is associated with the emergence, reconfiguration and dissolution of cell assemblies in the context of varying oscillatory states. Here, we describe the complex spatio-temporal dynamics of cell assemblies through temporal network formalism. We use a sliding window approach to extract sequences of networks of information sharing among single units in hippocampus and enthorinal cortex during anesthesia and study how global and node-wise functional connectivity properties evolve along time and as a function of changing global brain state (theta vs slow-wave oscillations). First, we find that information sharing networks display, at any time, a core-periphery structure in which an integrated core of more tightly functionally interconnected units link to more loosely connected network leaves. However the units participating to the core or to the periphery substantially change across time-windows, with units entering and leaving the core in a smooth way. Second, we find that discrete network states can be defined on top of this continuously ongoing liquid core-periphery reorganization. Switching between network states results in a more abrupt modification of the units belonging to the core and is only loosely linked to transitions between global oscillatory states. Third, we characterize different styles of temporal connectivity that cells can exhibit within each state of the sharing network. While inhibitory cells tend to be central, we show that, otherwise, anatomical localization only poorly influences the patterns of temporal connectivity of the different cells. Furthermore, cells can change temporal connectivity style when the network changes state. Altogether, these findings reveal that the sharing of information mediated by the intrinsic dynamics of hippocampal and enthorinal cortex cell assemblies have a rich spatiotemporal structure, which could not have been identified by more conventional time- or state-averaged analyses of functional connectivity. AUTHOR SUMMARYIt is generally thought that computations performed by local brain circuits rely on complex neural processes, associated to the flexible waxing and waning of cell assemblies, i.e. ensemble of cells firing in tight synchrony. Although cell assembly formation is inherently and unavoidably dynamical, it is still common to find studies in which essentially "static" approaches are used to characterize this process. In the present study, we adopt instead a temporal network approach. Avoiding usual time averaging procedures, we reveal that hub neurons are not hardwired but that cells vary smoothly their degree of integration within the assembly core. Furthermore, our temporal network framework enables the definition of alternative possible styles of "hubness". Some cells may share information with a multitude of other units but only in an intermittent manner, as "activists" in a flash mob. In contrast, some other cells may share information in a steadier manner, as resolute "lobbyists". Finally, by avoiding averages over pre-imposed states, we show that within each global oscillatory state a rich switching dynamics can take place between a repertoire of many available network states. We thus show that the temporal network framework provides a natural and effective language to rigorously describe the rich spatiotemporal patterns of information sharing instantiated by cell assembly evolution.

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bioRxiv2019

Computing Hubs in the Hippocampus and Cortex

Neural computation, which relies on the active storage and sharing of information, occurs within large neuron networks in the highly dynamic context of varying brain states. Whether such functions are performed by specific subsets of neurons and whether they occur in specific dynamical regimes remain poorly understood. Using high density recordings in the hippocampus, medial entorhinal and medial prefrontal cortex of the rat, we identify computing microstates, or discreet epochs, in which specific computing hub neurons perform well defined storage and sharing operations in a brain state-dependent manner. We retrieve a multiplicity of distinct computing microstates within each global brain state, such as REM and nonREM sleep. Half of recorded neurons act as computing hubs in at least one microstate, suggesting that functional roles are not firmly hardwired but dynamically reassigned at the second timescale. We identify sequences of microstates whose temporal organization is dynamic and stands between order and disorder. We propose that global brain states constrain the language of neuronal computations by regulating the syntactic complexity of these microstate sequences.

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bioRxiv2017

Functional Connectivity Dynamics of the Resting State across the Human Adult Lifespan

Functional Connectivity (FC) during resting-state or task conditions is not fixed but inherently dynamic. Yet, there is no consensus on whether fluctuations in FC may resemble isolated transitions between discrete FC states rather than continuous changes. This quarrel hampers advancing the study of dynamic FC. This is unfortunate as the structure of fluctuations in FC can certainly provide more information about developmental changes, aging, and progression of pathologies. We merge the two perspectives and consider dynamic FC as an ongoing network reconfiguration, including a stochastic exploration of the space of possible steady FC states. The statistical properties of this random walk deviate both from a purely "order-driven" dynamics, in which the mean FC is preserved, and from a purely "randomness-driven" scenario, in which fluctuations of FC remain uncorrelated over time. Instead, dynamic FC has a complex structure endowed with long-range sequential correlations that give rise to transient slowing and acceleration epochs in the continuous flow of reconfiguration. Our analysis for fMRI data in healthy elderly revealed that dynamic FC tends to slow down and becomes less complex as well as more random with increasing age. These effects appear to be strongly associated with age-related changes in behavioural and cognitive performance. HighlightsO_LIDynamic Functional Connectivity (dFC) at rest and during cognitive task performs a "complex" (anomalous) random walk. C_LIO_LISpeed of dFC slows down with aging. C_LIO_LIResting dFC replaces complexity by randomness with aging. C_LIO_LITask performance correlates with the speed and complexity of dFC. C_LI

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