Biology subjects

Zamora-Lopez, G.

Publications and source records attributed to Zamora-Lopez, G..

10 recordsLinked to original sources

bioRxiv2026

Whole-brain receptor markers and hub excitabilities determine acute and long-term outcomes in stroke

Following a stroke, changes in receptor systems have been observed that may influence the excitability of affected brain regions and drive recovery trajectories. Translating results from animal studies to patients remains a major challenge, and while imaging modalities have provided a wealth of findings, a mechanistic link between local receptor systems and observed imaging data is still missing. To bridge this gap, we built patient-specific digital twins with their own neuromodulatory profiles: we integrated 19 receptor and transporter density maps into a mechanistic whole-brain model, and estimated their influence on cortical excitability for each stroke patient and how these systems subsequently explain recovery trajectories. We found that distinct receptor profiles modulated excitability in perilesional versus non-perilesional tissue. Favourable acute motor outcomes were most strongly explained by increased inhibition mediated by non-perilesional GABA-A receptors, together with enhanced excitability driven by cannabinoid, histaminergic, and noradrenergic systems. For acute language outcomes, we identified receptor signatures that, together with motor findings, formed two cross-domain clusters linking excitability to either better or poorer performance. Remarkably, structurally highly connected hubs played a key role: their acute excitability explained cognitive and language deficits and predicted language recovery at one year. Together, our results establish a mechanistic link between receptor-specific modulation of cortical excitability and behavioural recovery, highlighting potential targets for pharmacological and non-invasive therapeutic interventions.

neuroscience↗

bioRxiv2026

Distinct brain responses to psilocybin and escitalopram in depression captured by the Fluctuation-Dissipation Theorem

In recent decades, the psychedelic psilocybin has been studied as a potential treatment for major depressive disorder (MDD), offering an alternative to traditional antidepressants. However, the brain changes underlying the clinical effects of different interventions remain unclear. Here, we investigated the effects of psilocybin and a conventional antidepressant, escitalopram, from the double-blind randomised controlled trial (DB-RCT) -NCT03429075- on the brains hierarchical organisation. Using pre- and post-treatment resting-state functional magnetic resonance imaging (fMRI) we built whole-brain models and obtained a generative effective connectivity (GEC) matrix for each patient. Based on the GEC, we measured the level of non-equilibrium brain dynamics by quantifying the deviation from the fluctuation-dissipation theorem (FDT) and performed complementary analysis on brain segregation and asymmetry. Our results showed opposite reconfigurations of the hierarchical non-equilibrium brain dynamics following each treatment. Additionally, baseline measures effectively distinguished responders from non-responders within each treatment. These findings suggest that the deviation of the FDT may serve as a marker for differentiating the effects of psilocybin and escitalopram in MDD treatment, overall, contributing to the understanding of therapeutic mechanisms of depression.

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bioRxiv2024

Emergence and long-term maintenance of modularity in plastic networks of spiking neurons

The modular and hierarchical organization of the brain is believed to support the coexistence of segregated (specialization) and integrated (binding) information processes. A relevant question is yet to understand how such architecture naturally emerges and is sustained over time, given the plastic nature of the brains wiring. Following evidences that the sensory cortices organize into assemblies under selective stimuli, it has been shown that stable neuronal assemblies can emerge due to targeted stimulation, embedding various forms of synaptic plasticity in presence of homeostatic and/or control mechanisms. Here, we show that simple spike-timing-dependent plasticity (STDP) rules, based only on pre- and post-synaptic spike times, can also lead to the stable encoding of memories in the absence of any control mechanism. We develop a model of spiking neurons, trained by stimuli targeting different sub-populations. The model satisfies some biologically plausible features: (i) it contains excitatory and inhibitory neurons with Hebbian and anti-Hebbian STDP; (ii) neither the neuronal activity nor the synaptic weights are frozen after the learning phase. Instead, the neurons are allowed to fire spontaneously while synaptic plasticity remains active. We find that only the combination of two inhibitory STDP sub-populations allows for the formation of stable modules in the network, with each sub-population playing a distinctive role. The Hebbian sub-population controls for the firing activity, while the anti-Hebbian neurons promote pattern selectivity. After the learning phase, the network settles into an asynchronous irregular resting-state. This post-learning activity is associated with spontaneous memory recalls which turn out to be fundamental for the long-term consolidation of the learned memories. Due to its simplicity, the introduced model can represent a test-bed for further investigations on the role played by STDP on memory storing and maintenance. Author summaryOne of the most remarkable qualities of the brain is its capacity to learn and adapt. How the learning process imprints and maintains memories, by shaping the architecture of connectivity among neurons in a constantly changing and dynamic environment, is a major question of neuroscience. Here, we explore the idea that the segregation of inputs received by a neural network, with inputs targeting distinct populations, is a key factor for shaping the architecture of the network. We find that the presence of inhibitory neurons is necessary for the emergence and the long-term maintenance of modularity in spiking neural networks with plasticity. In particular, we show that two different inhibitory sub-populations, one subject to Hebbian and the other to anti-Hebbian plasticity, are required to promote the formation of feedback and feed-forward inhibition circuits controlling memory consolidation. On one side, these inhibitory circuits favour long-term memory consolidation by inducing spontaneous memory recalls in the asynchronous irregular resting phase. On another side, the number of inhibitory neurons control the maximal memory capacity of the considered model.

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bioRxiv2024

A General Framework for Characterizing Optimal Communication in Brain Networks

Communication in brain networks is the foundation of cognitive function and behavior. A multitude of evolutionary pressures, including the minimization of metabolic costs while maximizing communication efficiency, contribute to shaping the structure and dynamics of these networks. However, how communication efficiency is characterized depends on the assumed model of communication dynamics. Traditional models include shortest path signaling, random walker navigation, broadcasting, and diffusive processes. Yet, a general and model-agnostic framework for characterizing optimal neural communication remains to be established. Our study addresses this challenge by assigning communication efficiency through game theory, based on a combination of structural data from human cortical networks with computational models of brain dynamics. We quantified the exact influence exerted by each brain node over every other node using an exhaustive multi-site virtual lesioning scheme, creating optimal influence maps for various models of brain dynamics. These descriptions show how communication patterns unfold in the given brain network if regions maximize their influence over one another. By comparing these influence maps with a large variety of brain communication models, we found that optimal communication most closely resembles a broadcasting model in which regions leverage multiple parallel channels for information dissemination. Moreover, we show that the most influential regions within the cortex are formed by its rich-club. These regions exploit their topological vantage point by broadcasting across numerous pathways, thereby significantly enhancing their effective reach even when the anatomical connections are weak. Our work provides a rigorous and versatile framework for characterizing optimal communication across brain networks and reveals the most influential brain regions and the topological features underlying their optimal communication.

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bioRxiv2023

The global communication architecture of the human brain transcends the subcortical - cortical - cerebellar subdivisions

The white matter is made of anatomical fibres that constitute the highway of long-range connections between different parts of the brain. This network is referred to as the brains structural connectivity and lays the foundation of network interaction between brain areas. When analysing the architectural principles of this global network most studies have mainly focused on cortico-cortical and partly on cortico-subcortical connections. Here we show, for the first time, how the integrated cortical, subcortical, and cerebellar brain areas shape the structural architecture of the whole brain. We find that dense clusters vertically transverse cortical, subcortical, and cerebellar brain areas, which are themselves centralised by a global rich-club consisting similarly of cortical and subcortical brain areas. Notably, the most prominent hubs can be found in subcortical brain regions, and their targeted in-silico lesions proved to be most harmful for global signal propagation. Individually, the cortical, subcortical, and cerebellar sub-networks manifest distinct network features despite some similarities, which underline their unique structural fingerprints. Our results, exposing the heterogeneity of internal organisation across cortex, subcortex, and cerebellum, and the crucial role of the subcortex for the integration of the global anatomical pathways, highlight the need to overcome the prevalent cortex-centric focus towards a global consideration of the structural connectivity.

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bioRxiv2023

Inhibitory neurons control the consolidation of neural assemblies via adaptation to selective stimuli

Brain circuits display modular architecture at different scales of organization. Such neural assemblies are typically associated to functional specialization but the mechanisms leading to their emergence and consolidation still remain elusive. In this paper we investigate the role of inhibition in structuring new neural assemblies driven by the entrainment to various inputs. In particular, we focus on the role of partially synchronized dynamics for the creation and maintenance of structural modules in neural circuits by considering a network of excitatory and inhibitory{theta} -neurons with plastic Hebbian synapses. The learning process consists of an entrainment to temporally alternating stimuli that are applied to separate regions of the network. This entrainment leads to the emergence of modular structures. Contrary to common practice in artificial neural networks - where the acquired weights are typically frozen after the learning session - we allow for synaptic adaptation even after the learning phase. We find that the presence of inhibitory neurons in the network is crucial for the emergence and the post-learning consolidation of the modular structures. Indeed networks made of purely excitatory neurons or of neurons not respecting Dales principle are unable to form or maintain the modular architecture induced by the entrained stimuli. We also demonstrate that the number of inhibitory neurons in the network is directly related to the maximal number of neural assemblies that can be consolidated, supporting the idea that inhibition has a direct impact on the memory capacity of the neural network.

neuroscience↗

bioRxiv2022

Integration and segregation in the brain as a cognitive flexibility during tasks and rest

To flexibly respond to a continuously changing environment, the human brain must be able to flexibly switch amongst many demanding cognitive tasks. The flexibility inside the brain is enabled by integrating and segregating information in large-scale functional networks over time. In this study, we used graph theory metrics prior to clustering to identify two brain states, segregated and integrated, in 100 healthy adults selected from the Human Connectome Project (HCP) dataset at rest and during six cognitive tasks. Furthermore, we explored two-dimensional (2D) latent space revealed by a deep autoencoder. In the latent space, the integrated state occupied less space compared with the segregated state. After binning the latent space, we obtained entropy from the probability for each data point of being in the bin. The integrated state showed lower entropy than the segregated state, and the rest modality showed higher entropy in both states compared with tasks. We also found that modularity and global efficiency are good measures for distinguishing between tasks and rest in both states. Overall, the study shows that integration and segregation are present in rest and in task modalities, while integration serves as information compression and segregation as information specialisation. These characteristics ensure the necessary cognitive flexibility to learn new tasks with deep proficiency.

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bioRxiv2021

Posterior integration and thalamo-frontotemporal broadcasting are impaired in disorders of consciousness

The study of the brains static and dynamical activity is opening a valuable source of assistance for the clinical assessment of patients with disorders of consciousness. For example, glucose uptake and dysfunctional spread of naturalistic and synthetic stimuli has proven useful to characterize hampered consciousness. However, understanding of the mechanisms behind loss of consciousness following brain injury is still missing. Here, we study the propagation of endogenous and in-silico exogenous perturbations in patients with disorders of consciousness, based upon directed and causal interactions estimated from resting-state fMRI. We found that patients with disorders of consciousness suffer decreased capacity for neural propagation and responsiveness to events, and that this can be related to glucose metabolism as measured with [18F]FDG-PET. In particular, we show that loss of consciousness is related to the malfunctioning of two neural circuits: the posterior cortical regions failing to convey information, in conjunction with reduced broadcasting of information from subcortical, temporal, parietal and frontal regions. These results seed light on the mechanisms behind disorders of consciousness, triangulating network function with basic measures of brain integrity and behavior. HighlightsO_LIPropagation of neural events and network responses are disrupted in patients with DoC. C_LIO_LILoss of consciousness is related to the malfunctioning of two neural circuits. C_LIO_LIPosterior cortical regions lack to integrate information in altered consciousness. C_LIO_LIBreakdown of information broadcasting of subcortical cortical areas in DoC. C_LIO_LILoss of network responses in DoC patients is related to glucose metabolism. C_LI

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bioRxiv2019

MOU-EC: model-based whole-brain effective connectivity to extract biomarkers for brain dynamics from fMRI data and study distributed cognition

Neuroimaging techniques are increasingly used to study brain cognition in humans. Beyond their individual activation, the functional associations between brain areas have become a standard proxy to describe how information is distributed across the brain network. Among the many analysis tools available, dynamic models of brain activity have been developed to overcome the limitations of original connectivity measures such as functional connectivity. In particular, much effort has been devoted to the assessment of directional interactions between brain areas from their observed activity. This paper summarizes our recent approach to analyze fMRI data based on our whole-brain effective connectivity referred to as MOU-EC, while discussing the pros and cons of its underlying assumptions with respect to other established approaches. Once tuned, the model provides a connectivity measure that reflects the dynamical state of BOLD activity obtained using fMRI, which can be used to explore the brain cognition. We focus on two important applications. First, as a connectivity measure, MOU-EC can be used to extract biomarkers for task-specific brain coordination, understood as the patterns of areas exchanging information. The multivariate nature of connectivity measures raises several challenges for whole-brain analysis, for which machine-learning tools presents some advantages over statistical testing. Second, we show how to interpret changes in MOU-EC connections in a collective and model-based manner, bridging with network analysis. To illustrate our framework, we use a dataset where subjects were recorded in two conditions, watching a movie and a black screen (referred to as rest). Our framework provides a comprehensive set of tools that open exciting perspectives for the study of distributed cognition, as well as neuropathologies.

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bioRxiv2018

Analysis of brain network dynamics estimated from fMRI data: A new framework based on communicability and flow

Neuroimaging techniques such as MRI have been widely used to explore the associations between brain areas. Structural connectivity (SC) captures the anatomical pathways across the brain and functional connectivity (FC) measures the correlation between the activity of brain regions. These connectivity measures have been much studied using network theory in order to uncover the distributed organization of brain structures, in particular FC for task-specific brain communication. However, the application of network theory to study FC matrices is often "static" despite the dynamic nature of time series obtained from fMRI. The present study aims to overcome this limitation by introducing a network-oriented analysis applied to whole-brain effective connectivity (EC) useful to interpret the brain dynamics. Technically, we tune a multivariate Ornstein-Uhlenbeck (MOU) process to reproduce the statistics of the whole-brain resting-state fMRI signals, which provides estimates for MOU-EC as well as input properties (similar to local excitabilities). The network analysis is then based on the Green function (or network impulse response) that describes the interactions between nodes across time for the estimated dynamics. This model-based approach provides time-dependent graph-like descriptor, named communicability, that characterize the roles that either nodes or connections play in the propagation of activity within the network. They can be used at both global and local levels, and also enables the comparison of estimates from real data with surrogates (e.g. random network or ring lattice). In contrast to classical graph approaches to study SC or FC, our framework stresses the importance of taking the temporal aspect of fMRI signals into account. Our results show a merging of functional communities over time (in which input properties play a role), moving from segregated to global integration of the network activity. Our formalism sets a solid ground for the analysis and interpretation of fMRI data, including task-evoked activity.

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