Biology subjects

Coletta, L.

Publications and source records attributed to Coletta, L..

13 recordsLinked to original sources

bioRxiv2026

Endogenous oxytocin reconfigures brainwide network dynamics toward salience-related states

Oxytocin (OXT) can exert diverse effects across social, affective and motivational domains, yet its impact on brain function at the systems level remains unclear. Here, we combine chemogenetics with fMRI and electrophysiology in mice to investigate how endogenous OXT reshapes intrinsic large-scale brain network dynamics. Using mice constitutively expressing hM3Dq DREADD receptors in OXT-releasing hypothalamic neurons, we show that chemogenetic stimulation of OXT neurons elicits widespread increases in cerebral blood volume, with prominent effects in fronto-striatal regions and dorsal hippocampal areas. This broad functional response is accompanied by a network-specific reconfiguration of brain-wide functional connectivity, characterized by increased coupling between key components of the rodent social brain, and negative functional coupling between hippocampal and fronto-cortical regions. In vivo electrophysiology under matched conditions shows concordant changes in slow (<1 Hz) and delta-band coherence across frontalthalamic and fronto-hippocampal circuits, revealing a plausible neural correlate of the observed connectivity effects. Notably, these changes are accompanied by a marked reconfiguration of the temporal architecture of fMRI network activity, with increased occupancy of co-activation states engaging saliencerelated regions and a concomitant suppression of sensory-dominated states. Together, these findings identify dynamic network-state reconfiguration as a systems-level mechanism through which endogenous OXT can tune functional communication across social-affective circuits.

neuroscience↗

bioRxiv2026

Cortical excitability inversely modulates fMRI connectivity via low-frequency neuronal coupling

The neural mechanisms supporting fMRI connectivity are poorly understood. Leveraging an aggregated analysis of novel and existing chemogenetic manipulations and electrophysiological recordings in the mouse medial prefrontal cortex (PFC), we show that local cortical excitability inversely modulates large-scale fMRI connectivity. Specifically, we find that bidirectional chemogenetic manipulations of cortical excitability produce opposite effects on fMRI connectivity, resulting in fMRI hypoconnectivity when excitability is enhanced, and fMRI hyperconnectivity when excitability is suppressed, despite corresponding increases or decreases in local neuronal firing. Notably, while each chemogenetic manipulation produces a distinct profile of interareal electrophysiological coherence, only low-frequency (< 4Hz) coherence predicts the direction and magnitude of the ensuing fMRI connectivity changes. Biophysical network modelling shows that the observed electrophysiological coherence profiles can arise from the interaction between direct inter-area communication changes mediated by local firing-rate variations, and shared larger-scale low-frequency covariation of ongoing neuronal activity. Together, our results reveal an inverse relationship between regional cortical excitability and large-scale fMRI connectivity, and indicate that fMRI connectivity is primarily supported by low frequency (<4 Hz) electrophysiological coupling. These findings open new avenues for modeling and interpreting fMRI connectivity in health, and in response to pathological or exogenous perturbations linked to cortical excitability.

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bioRxiv2025

Developmental excitation-inhibition imbalance permanently reprograms autism-relevant social brain circuits

An influential theory proposes that an imbalance between excitation and inhibition (E:I) plays a central role in the etiology of autism and related developmental disorders. However, controversy exists as to whether this imbalance is a direct causal mechanism for autism, or a compensatory response to other primary etiological factors. Using chemogenetic manipulations in neonatal mice, we show that a transient E:I imbalance during development is sufficient to permanently reprogram autism-relevant social brain circuits. Chemogenetically manipulated mice exhibit lifelong impairments in sociability, persistent dysregulation of multiple autism-risk synaptic genes, and sustained cortical hyperexcitability in adulthood. Importantly, these social impairments are robustly rescued by pharmacological inhibition of neuronal excitability. Developmental E:I imbalance also disrupts functional connectivity in social brain regions enriched for transcriptionally dysregulated genes, suggesting a convergence of transcriptional and circuit-level pathology. Finally, multivariate modelling shows that behavioral dysfunction in chemogenetically manipulated animals closely associates with disrupted connectivity between prefrontal and mesolimbic dopaminergic regions. Collectively, our findings reconcile conflicting theories in the field and point to activity-dependent transcriptional remodeling as a foundational mechanism by which transient E:I imbalance during development can cause lasting, autism-relevant circuit dysfunction.

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bioRxiv2025

Supervised white matter bundle segmentation in glioma patients with transfer learning

In clinical settings, the virtual dissection of white matter tracts represents an informative source of information for monitoring neurological conditions or to support the planning of a treatment. The automation of this task using data-driven approaches, and in particular deep learning models, provides promising evidence of good accuracy when carried out on healthy individuals. However, the lack of large clinical datasets and the profound differences between healthy and clinical populations hinder the translation of these results to patients. Here, we investigated for the first time the effectiveness of transfer learning in adapting a deep learning architecture trained on a healthy population to glioma patients. Importantly, we provided the first thorough characterization of domain shift and its complexity, distinguishing systematic (i.e. measurement and pre-processing related) from tumor specific components. Our results suggest that (i) models trained on a large normative healthy population have a significant performance drop when the inference is carried out on patients; (ii) transfer learning can be an effective strategy to overcome the shortage of clinical data and to manage the systematic shift; (iii) fine tuning of the learning model cannot accommodate large white matter deformations induced by the tumor. The results were coherent across the five white matter bundles and three different input modalities tested, highlighting their robustness and generalizability. Our work provides valuable insights for advancing automated white matter segmentation in clinical populations and enhancing clinical transfer learning applications. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=72 SRC="FIGDIR/small/659452v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@1c2bbbcorg.highwire.dtl.DTLVardef@1b3555eorg.highwire.dtl.DTLVardef@a9bb4dorg.highwire.dtl.DTLVardef@147dc5e_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIWhite matter bundle segmentation is essential for planning glioma tumor resection. C_LIO_LIWe trained a deep learning model on a healthy dataset to segment white matter bundles. C_LIO_LIWe observed a sub-optimal performance on glioma patients due to domain shift. C_LIO_LITransfer learning mitigated domain shift between healthy and clinical populations. C_LIO_LIWe highlighted domain shift domains solved/not solved by transfer learning. C_LI

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bioRxiv2023

Modular subgraphs in large-scale connectomes underpin spontaneous co-fluctuation "events" in mouse and human brains

Previous studies have adopted an edge-centric framework to study fine-scale dynamics in human fMRI. To date, however, no studies have applied this same framework to data collected from model organisms. Here, we analyze structural and functional imaging data from lightly anesthetized mice through an edge-centric lens. We find evidence of "bursty" dynamics and events - brief periods of high-amplitude network connectivity. Further, we show that on a per-frame basis events best explain static FC and can be divided into a series of hierarchically-related clusters. The co-fluctuation patterns associated with each centroid link distinct anatomical areas and largely adhere to the boundaries of algorithmically detected functional brain systems. We then investigate the anatomical connectivity undergirding high-amplitude co-fluctuation patterns. We find that events induce modular bipartitions of the anatomical network of inter-areal axonal projections. Finally, we replicate these same findings in a human imaging dataset. In summary, this report recapitulates in a model organism many of the same phenomena observed in previously edge-centric analyses of human imaging data. However, unlike human subjects, the murine nervous system is amenable to invasive experimental perturbations. Thus, this study sets the stage for future investigation into the causal origins of fine-scale brain dynamics and high-amplitude co-fluctuations. Moreover, the cross-species consistency of the reported findings enhances the likelihood of future translation.

neuroscience↗

bioRxiv2023

Macroscale coupling between structural and effective connectivity in the mouse brain

How the emergent functional connectivity (FC) relates to the underlying anatomy (structural connectivity, SC) is one of the biggest questions of modern neuroscience. At the macro-scale level, no one-to-one correspondence between structural and functional links seems to exist. And we posit that to better understand their coupling, two key aspects should be taken into account: the directionality of the structural connectome and the limitations of describing network functions in terms of FC. Here, we employed an accurate directed SC of the mouse brain obtained by means of viral tracers, and related it with single-subject effective connectivity (EC) matrices computed by applying a recently developed DCM to whole-brain resting-state fMRI data. We analyzed how SC deviates from EC and quantified their couplings by conditioning both on the strongest SC links and EC links. We found that when conditioning on the strongest EC links, the obtained coupling follows the unimodal-transmodal functional hierarchy. Whereas the reverse is not true, as there are strong SC links within high-order cortical areas with no corresponding strong EC links. This mismatch is even more clear across networks. Only the connections within sensory motor networks align both in terms of effective and structural strength.

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bioRxiv2022

Redefining the connectome: A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity

The macroscale connectome is the network of physical, white-matter tracts between brain areas. The connections are generally weighted and their values interpreted as measures of communication efficacy. In most applications, weights are either assigned based on imaging features-e.g. diffusion parameters-or inferred using statistical models. In reality, the ground-truth weights are unknown, motivating the exploration of alternative edge weighting schemes. Here, we explore a multi-modal (combining diffusion and functional MRI data) regression-based, explanatory model that endows reconstructed fiber tracts with directed and signed weights. Benchmarking this method on Human Connectome Project data, we find that the model fits observed data well, outperforming a suite of null models. The estimated weights are subject-specific and highly reliable, even when fit using relatively few training samples. Next, we analyze the resulting network using graph-theoretic tools from network neuroscience, revealing bilaterally symmetric communities that span cerebral hemispheres. These communities exhibit a clear mapping onto known functional systems. We also study the shortest paths structure of this network, discovering that almost every edge participates in at least one shortest path. We also find evidence of robust asymmetries in edge weights, that the network reconfigures in response to naturalistic stimuli, and that estimated edge weights differ with age. In summary, we offer a simple framework for weighting connectome data, demonstrating both its ease of implementation while benchmarking its utility for typical connectome analyses, including graph theoretic modeling and brain-behavior associations.

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bioRxiv2022

A model of the mouse cortex with attractor dynamics explains the structure and emergence of rsfMRI co-activation patterns

Resting state fMRI activity in mammals exhibits rich dynamics on a fast, frame-by-frame timescale of seconds, including the robust emergence of recurring fMRI co-activation patterns (CAPs). To understand how such dynamics emerges from the underlying anatomical cortico-cortical connectivity, we developed a whole-cortex model of resting-state fMRI activity in the mouse. Our model implemented neural input-output nonlinearities and excitatory-inhibitory interactions within cortical regions, as well as directed anatomical connectivity between regions inferred from the Allen mouse brain atlas. We found that, even if the model parameters were fitted to explain static properties of fMRI activity on the timescale of minutes, the model generated rich frame-by-frame attractor dynamics, with multiple stationary and oscillatory attractors. Guided by these theoretical predictions, we found that empirical mouse fMRI time series exhibited analogous signatures of attractor dynamics, and that model attractors recapitulated the topographical organization of empirical fMRI CAPs. The model established key relationships between attractor dynamics, CAPs and features of the directed cortico-cortical intra- and inter-hemispheric anatomical connectivity. Specifically, we found that neglecting fiber directionality severely affected the number of models attractors and their ability to explain CAPs. Furthermore, modifying inter-hemispheric anatomical connectivity strength by decreasing or increasing it from the value of real mouse anatomical data, resulted in fewer attractors generated by cortico-cortical interactions and reduced non-homotopic features of the attractors generated by the model, which were important for better predicting empirical CAPs. These results offer novel theoretical insight into the dynamic organization of resting state fMRI in the mouse brain and suggest that the frame-wise BOLD activity captured by CAPs reflects an emerging property of cortical dynamics resulting from directed cortico-cortical interactions. Author summaryWhole-brain activity at rest transitions on a timescale of seconds between stereotyped co-activation patterns (CAPs), each with a distinct spatial profile of co-activation across different brain regions. CAPs have been robustly reported across datasets and mammalian species, including humans and mice. However, the significance and origin of these patterns remain unknown. Here we studied the origin of CAPs using a computational model of the whole cortex based on real-world directed measurements of mouse cortical anatomical connectivity. We found that we could explain the formation and topography of CAPs in terms of attractors (that is, states the network tends to converge to) that reflect the information in the anatomical connections between cortical regions. Attractors and CAPs are significantly influenced by the directionality of connectivity (available in mice but not in humans) and the strength of inter-hemispheric coupling. Together, our findings provide a novel possible explanation of the mechanisms of CAP generation which relates to cortico-cortical connectivity, and suggest that CAPs are at least in part an emergent property of cortico-cortical interactions which can be generated also in absence of the action of arousing modulatory nuclei.

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bioRxiv2021

Unique spatiotemporal fMRI dynamics in the awake mouse brain

Human imaging studies have shown that spontaneous brain activity exhibits stereotypic spatiotemporal reorganization in awake, conscious conditions with respect to minimally conscious states. However, whether and how this phenomenon can be generalized to lower mammalian species, remains unclear. Leveraging a robust protocol for resting-state fMRI (rsfMRI) mapping in non-anesthetized, head-fixed mice, we investigated functional network topography and dynamic structure of spontaneous brain activity in wakeful animals. We found that rsfMRI networks in the awake state, while anatomically comparable to those observed under anesthesia, are topologically configured to maximize interregional communication, departing from the underlying community structure of the mouse axonal connectome. We further report that rsfMRI activity in wakeful animals exhibits unique spatiotemporal dynamics characterized by a state-dependent, dominant occurrence of coactivation patterns encompassing a prominent participation of arousal-related forebrain nuclei, and functional anti-coordination between visual-auditory and polymodal cortical areas. We finally show that rsfMRI dynamics in awake mice exhibits a stereotypical temporal structure, in which state-dominant coactivation patterns are configured as network attractors. These findings suggest that spontaneous brain activity in awake mice is critically shaped by state-specific involvement of basal forebrain arousal systems, and document that its dynamic structure recapitulates distinctive, evolutionarily-relevant principles that are predictive of conscious states in higher mammalian species.

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bioRxiv2020

Cortical silencing results in paradoxical fMRI overconnectivity

fMRI-based measurements of functional connectivity are commonly interpreted as an index of anatomical coupling and direct interareal communication. However, causal testing of this hypothesis has been lacking. Here we combine neural silencing, resting-state fMRI and in vivo electrophysiology to causally probe how inactivation of a cortical region affects brain-wide functional coupling. We find that chronic silencing of the prefrontal cortex (PFC) via overexpression of a potassium channel paradoxically increases rsfMRI connectivity between the silenced area and its thalamo-cortical terminals. Acute chemogenetic silencing of the PFC reproduces analogous patterns of overconnectivity, an effect associated with over-synchronous fMRI coupling between polymodal thalamic regions and widespread cortical districts. Notably, multielectrode recordings revealed that chemogenetic inactivation of the PFC attenuates gamma activity and increases delta power in the silenced area, resulting in robustly increased delta band coherence between functionally overconnected regions. The observation of enhanced rsfMRI coupling between chemogenetically silenced areas challenges prevailing interpretations of functional connectivity as a monotonic index of direct axonal communication, and points at a critical contribution of slow rhythm generators to the establishment of brain-wide functional coupling.

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bioRxiv2020

Regional, layer, and cell-class specific connectivity of the mouse default mode network

The evolutionarily conserved default mode network (DMN) is characterized by temporally correlated activity between brain regions during resting states. The DMN has emerged as a selectively vulnerable network in multiple disorders, so understanding its anatomical composition will provide fundamental insight into how its function is impacted by disease. Reproducible rodent analogs of the human DMN offer an opportunity to investigate the underlying brain regions and structural connectivity (SC) with high spatial and cell type resolution. Here, we performed systematic analyses using mouse resting state functional magnetic resonance imaging to identify the DMN and whole brain axonal tracing data, co-registered to the 3D Allen Mouse Common Coordinate Framework reference atlas. We identified the specific, predominantly cortical, brain regions comprising the mouse DMN and report preferential SC between these regions. Next, at the cell class level, we report that cortical layer (L) 2/3 neurons in DMN regions project almost exclusively to other DMN regions, whereas L5 neurons project to targets both in and out of the DMN. We then test the hypothesis that in- and out-DMN projection patterns originate from distinct L5 neuron sub-classes using an intersectional viral tracing strategy to label all the axons from neurons defined by a single target. In the ventral retrosplenial cortex, a core DMN region, we found two L5 projection types related to the DMN and mapped them to unique transcriptomically-defined cell types. Together, our results provide a multi-scale description of the anatomical correlates of the mouse DMN.

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bioRxiv2020

Network structure of the mouse brain connectome with voxel resolution

Fine-grained descriptions of brain connectivity are fundamental for understanding how neural information is processed and relayed across spatial scales. Prior investigations of the mouse brain connectome have employed discrete anatomical parcellations, limiting spatial resolution and potentially concealing network attributes critical to the organization of the mammalian connectome. Here we provide a voxel-level description of the network and hierarchical structure of the directed mouse connectome, unconstrained by regional partitioning. We show that integrative hub regions can be directionally segregated into neural sinks and sources, defining a hierarchical axis. We describe a set of structural communities that spatially reconstitute previously described fMRI networks of the mouse brain, and document that neuromodulatory nuclei are strategically wired as critical orchestrators of inter-modular and network communicability. Notably, like in primates, the directed mouse connectome is organized along two superimposed cortical gradients reflecting unimodal-transmodal functional processing and a modality-specific sensorimotor axis. These structural features can be related to patterns of intralaminar connectivity and to the spatial topography of dynamic fMRI brain states, respectively. Together, our results reveal a high-resolution structural scaffold linking mesoscale connectome topography to its macroscale functional organization, and create opportunities for identifying targets of interventions to modulate brain function in a physiologically-accessible species.

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bioRxiv2019

Common functional networks in the mouse brain revealed by multi-centre resting-state fMRI analysis

Preclinical applications of resting-state functional magnetic resonance imaging (rsfMRI) offer the possibility to non-invasively probe whole-brain network dynamics and to investigate the determinants of altered network signatures observed in human studies. Mouse rsfMRI has been increasingly adopted by numerous laboratories world-wide. Here we describe a multi-centre comparison of 17 mouse rsfMRI datasets via a common image processing and analysis pipeline. Despite prominent cross-laboratory differences in equipment and imaging procedures, we report the reproducible identification of several large-scale resting-state networks (RSN), including a murine default-mode network, in the majority of datasets. A combination of factors was associated with enhanced reproducibility in functional connectivity parameter estimation, including animal handling procedures and equipment performance. Our work describes a set of representative RSNs in the mouse brain and highlights key experimental parameters that can critically guide the design and analysis of future rodent rsfMRI investigations.

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