Biology subjects

Gollo, L. L.

Publications and source records attributed to Gollo, L. L..

11 recordsLinked to original sources

bioRxiv2026

Structure-Constrained Intrinsic Timescales Across Tasks

Intrinsic neural timescale (INT) quantifies the persistence of spontaneous neural dynamics and offers a principled metric for characterizing brain-wide temporal organization. Although a hierarchy of INTs has been established during rest, how task engagement reconfigures this organization and how it is constrained by the structural connectome (SC) remain poorly understood. Here, we systematically mapped whole-brain INT using high-resolution fMRI data from the Human Connectome Project during rest and seven tasks spanning working memory, gambling, motor, language, social, relational, and emotion domains. Task engagement induced robust, regionally heterogeneous changes in INT while largely preserving the brain-wide temporal hierarchy across cognitive states. SC-INT coupling remained strong but consistently decreased during tasks, indicating that anatomical architecture continues to constrain INT, although its influence is attenuated under task demands. To investigate these findings mechanistically, we employed a multiscale, whole-brain neuronal-network model, which revealed that INT increase and peak within a broad critical-like regime. Strong SC-INT coupling, as observed empirically, emerged in the subcritical regime, weakened progressively with increasing network excitability, and reversed in the supercritical regime. These results demonstrate that task engagement reconfigures INTs while maintaining their hierarchical organization, suggesting that both resting and task states operate largely within a common subcritical dynamical regime.

neuroscience↗

bioRxiv2025

The Connectome Modulates Critical Brain Dynamics Across Local and Global Scales

Neuronal activity in the brain has been hypothesized to operate near criticality-a dynamical regime poised between order and disorder that maximizes information processing, adaptability, and dynamic range. While criticality has been extensively studied at local scales (within neuronal populations) and at global scales (across interacting brain regions), the interplay between these levels remains poorly understood. Here, we propose a multiscale computational framework that bridges local and global criticality within a single, mechanistic model. At the mesoscopic level, individual brain regions are represented by neural mass models tuned near the transition between asynchronous and synchronous regimes. These regions are then coupled via an empirically derived mouse connectome to investigate how structural connectivity shapes the emergence of large-scale coordination. We show that (i) local near critical dynamics for an isolated brain region can be faithfully reproduced within a mean-field model framework, (ii) local distance to criticality is modulated by long-range coupling, (iii) whole-brain simulations reveal non-linear gradients of timescales and heterogeneous shifts towards/away from local criticality, and (iv) global criticality, manifested in scale-free avalanche distributions and optimal functional connectivity, emerges when local populations are locally tuned near criticality and coupled within an optimal range. These results demonstrate that local and global criticality are dynamically intertwined but not directly aligned, and that their relationship depends on the underlying structural connectivity. Our multiscale modeling framework provides a tractable tool for generating testable hypotheses on how brain criticality co-arises across scales and how it may be modulated in health and disease.

neuroscience↗

bioRxiv2025

Stroke Shifts Brain Dynamics Toward Criticality: Evidence from Intrinsic Neural Timescales

Stroke disrupts brain function beyond focal lesions, altering multiscale temporal dynamics essential for information processing. We investigated intrinsic neural timescales (INT) and other properties of long-range temporal correlations, using longitudinal fMRI data from 15 ischemic stroke patients across six months, and compared them to age-matched controls. Results show that stroke patients exhibited significantly prolonged INT in multiple cortical regions, reflecting slowed temporal dynamics and disrupted hierarchy. These dynamic changes persisted through recovery and were more pronounced in patients with poor outcomes, especially within cognitive control networks. Computational modelling suggested that stroke-induced INT prolongation driven by heightened neuronal excitability reflects a dynamic shift towards criticality. Our findings position long-range temporal correlations and INT as potential biomarkers for monitoring and predicting functional recovery. This framework provides a novel perspective on stroke-induced brain changes and suggests avenues for targeted neurorehabilitation using interventions aiming at restoring intrinsic temporal dynamics.

neuroscience↗

bioRxiv2024

Mapping and Modeling Age-Related Changes in Intrinsic Neural Timescales

Intrinsic timescales of brain regions exhibit heterogeneity, escalating with hierarchical levels, and are crucial for the temporal integration of external stimuli. Normal aging, often associated with cognitive deficits, involves progressive neuronal and synaptic loss, shaping brain structure and dynamics. The impact of these structural changes on temporal integration coding in the aged brain remains largely unknown. To address this gap, we mapped differences in intrinsic timescales and gray matter volume (GMV) within brain regions using magnetic resonance imaging (MRI) in young and elderly adults. We found shorter intrinsic timescales across multiple large-scale functional networks in the elderly cohort, and a significant positive association between intrinsic timescales and GMV. Additionally, age-related decline in performance on a visual discrimination task was linked to a reduction in intrinsic timescales in the cuneus. To explain these age-related changes in GMV and intrinsic timescales, we propose an age-dependent model of spiking neuron networks. In younger subjects, brain regions were modeled in a near-critical branching regime, while in elderly subjects, regions had fewer neurons and synapses, pushing the dynamics toward a more subcritical regime. The empirical results were reproduced by the model: Neuronal networks representing brain regions in young subjects exhibited longer intrinsic timescales due to critical slowing down. These findings reveal how age-related structural brain changes may drive alterations in brain dynamics, offering testable predictions and informing future interventions for cognitive decline.

bioinformatics↗

bioRxiv2020

Detection of cross-frequency coupling between brain areas: an extension of phase-linearity measurement.

The current paper proposes a method to estimate phase to phase cross-frequency coupling between brain areas, applied to broadband signals, without any a priori hypothesis about the frequency of the synchronized components. N:m synchronization is the only form of cross-frequency synchronization that allows the exchange of information at the time resolution of the faster signal, hence likely to play a fundamental role in large-scale coordination of brain activity. The proposed method, named cross-frequency phase linearity measurement (CF-PLM), builds and expands upon the phase linearity measurement, an iso-frequency connectivity metrics previously published by our group. The main idea lies in using the shape of the interferometric spectrum of the two analyzed signals in order to estimate the strength of cross-frequency coupling. Here, we demonstrate that the CF-PLM successfully retrieves the (different) frequencies of the original broad-band signals involved in the connectivity process. Furthermore, if the broadband signal has some frequency components that are synchronized in iso-frequency and some others that are synchronized in cross-frequency, our methodology can successfully disentangle them and describe the behaviour of each frequency component separately. We first provide a theoretical explanation of the metrics. Then, we test the proposed metric on simulated data from coupled oscillators synchronized in iso- and cross-frequency (using both Rossler and Kuramoto oscillator models), and subsequently apply it on real data from brain activity, using source-reconstructed Magnetoencephalography (MEG) data. In the synthetic data, our results show reliable estimates even in the presence of noise and limited sample sizes. In the real signals, components synchronized in cross-frequency are retrieved, together with their oscillation frequencies. All in all, our method is useful to estimate n:m synchronization, based solely on the phase of the signals (independently of the amplitude), and no a-priori hypothesis is available about the expected frequencies. Our method can be exploited to more accurately describe patterns of cross-frequency synchronization and determine the central frequencies involved in the coupling.

neuroscience↗

bioRxiv2020

Dynamical effects of dendritic pruning implicated in aging and neurodegeneration: Towards a measure of neuronal reserve

Aging is a main risk factor for neurodegenerative disorders including Alzheimers disease. It is often accompanied by reduced cognitive functions, gray-matter volume, and dendritic integrity. Although age-related brain structural changes have been observed across multiple scales, their functional implications remain largely unknown. Here we simulate the aging effects on neuronal morphology as dendritic pruning and characterize its dynamical implications. Utilizing a minimal computational modeling approach, we simulate the dynamics of detailed digitally reconstructed pyramidal neurons of humans obtained from the online repository Neuromorpho.org. We show that as aging progressively affects neuronal integrity, neuronal firing rate is reduced, which causes a reduction in energy consumption, energy efficiency, and dynamic range. Pruned neurons require less energy but their function is often impaired, which can explain the diminished ability to distinguish between similar experiences (pattern separation) in older people. Our measures indicate that the resilience of neuronal dynamics is neuron-specific, heterogeneous, and strongly affected by dendritic topology and the centrality of the soma. Based on the emergent neuronal dynamics, we propose to classify the effects of dendritic deterioration, and put forward that soma centrality measures neuronal reserve. Moreover, our findings suggest that increasing dendritic excitability could partially mitigate the dynamical effects of aging.

neuroscience↗

bioRxiv2020

Stochastic synchronization of dynamics on the human connectome

Synchronization is a collective mechanism by which oscillatory networks achieve their functions. Factors driving synchronization include the networks topological and dynamical properties. However, how these factors drive the emergence of synchronization in the presence of potentially disruptive external inputs like stochastic perturbations is not well understood, particularly for real-world systems such as the human brain. Here, we aim to systematically address this problem using a large-scale model of the human brain network (i.e., the human connectome). The results show that the model can produce complex synchronization patterns transitioning between incoherent and coherent states. When nodes in the network are coupled at some critical strength, a counterintuitive phenomenon emerges where the addition of noise increases the synchronization of global and local dynamics, with structural hub nodes benefiting the most. This stochastic synchronization effect is found to be driven by the intrinsic hierarchy of neural timescales of the brain and the heterogeneous complex topology of the connectome. Moreover, the effect coincides with clustering of node phases and node frequencies and strengthening of the functional connectivity of some of the connectomes subnetworks. Overall, the work provides broad theoretical insights into the emergence and mechanisms of stochastic synchronization, highlighting its putative contribution in achieving network integration underpinning brain function.

neuroscience↗

bioRxiv2019

Extensive functional repertoire underpins complex behaviours: insights from Parkinson’s disease

Rapid reconfigurations of brain activity support efficient neuronal communication and flexible behaviour. Suboptimal brain dynamics impair this adaptability, possibly leading to functional deficiencies. We hypothesize that impaired flexibility in brain activity can lead to motor and cognitive symptoms of Parkinsons disease (PD). To test this hypothesis, we studied the functional repertoire - the number of distinct configurations of neural activity - using source-reconstructed magnetoencephalography in PD patients and controls. We found stereotyped brain dynamics and reduced flexibility in PD. The intensity of this reduction was proportional to symptoms severity, which can be explained by beta-band hyper-synchronization. Moreover, the basal ganglia were prominently involved in the abnormal patterns of brain activity. Our findings support the hypotheses that: symptoms in PD reflect impaired brain flexibility, this impairment preferentially involves the basal ganglia, and beta-band hypersynchronization is associated with reduced brain flexibility. These findings highlight the importance of extensive functional repertoires for behaviour and motor.

neuroscience↗

bioRxiv2019

Spatially resolved dendritic integration: Towards a functional classification of neurons

The vast tree-like dendritic structure of neurons allows them to receive and integrate input from many neurons. A wide variety of neuronal morphologies exist, however, their role in dendritic integration, and how it shapes the response of the neuron, is not yet fully understood. Here, we study the evolution and interactions of dendritic spikes in excitable neurons with complex real branch structures. We focus on dozens of digitally reconstructed illustrative neurons from the online repository NeuroMorpho.org, which contains over 100,000 neurons. Yet, our methods can be promptly extended to any other neuron. This approach allows us to estimate and map specific and heterogeneous patterns of activity observed across extensive dendritic trees with thousands of compartments. We propose a classification of neurons based on the location of the soma (centrality) and the number of branches connected to the soma. These are key topological factors in determining the neurons energy consumption, firing rate, and the dynamic range, which quantifies the range in synaptic input rate that can be reliably encoded by the neurons firing rate. Moreover, we find that bifurcations, the structural building blocks of complex dendrites, play a major role in increasing the dynamic range of neurons. Our results provide a better understanding of the effects of neuronal morphology in the diversity of neuronal dynamics and function.

neuroscience↗

bioRxiv2018

Metastable brain waves

Traveling patterns of neuronal activity - brain waves - have been observed across a breadth of neuronal recordings, states of awareness, and species, but their emergence in the human brain lacks a firm understanding. Here, we analyze the complex nonlinear dynamics that emerge from modeling large-scale spontaneous neural activity on a whole-brain network derived from human tractography. We find a rich array of three-dimensional wave patterns, including traveling waves, spiral waves, sources, and sinks. These patterns are metastable, such that multiple spatiotemporal wave patterns are visited in sequence. Transitions between states correspond to reconfigurations of underlying phase flows, characterized by nonlinear instabilities. These metastable dynamics accord with empirical data from multiple imaging modalities, including electrical waves in cortical tissue, sequential spatiotemporal patterns in resting-state MEG data, and large-scale waves in human electrocorticography. By moving the study of functional networks from a spatially static to an inherently dynamic (wave-like) frame, our work unifies apparently diverse phenomena across functional neuroimaging modalities and makes specific predictions for further experimentation.

neuroscience↗

bioRxiv2017

Neural decoding of visual stimuli varies with fluctuations in global network efficiency

Functional magnetic resonance imaging (fMRI) studies have shown that neural activity fluctuates spontaneously between different states of global synchronization over a timescale of several seconds. Such fluctuations generate transient states of high and low correlation across distributed cortical areas. It has been hypothesized that such fluctuations in global efficiency might alter patterns of activity in local neuronal populations elicited by changes in incoming sensory stimuli. To test this prediction, we used a linear decoder to discriminate patterns of neural activity elicited by face and motion stimuli presented periodically while participants underwent time-resolved fMRI. As predicted, decoding was reliably higher during states of high global efficiency than during states of low efficiency, and this difference was evident across both visual and non-visual cortical regions. The results indicate that slow fluctuations in global network efficiency are associated with variations in the pattern of activity across widespread cortical regions responsible for representing distinct categories of visual stimulus. More broadly, the findings highlight the importance of understanding the impact of global fluctuations in functional connectivity on specialised, stimulus driven neural processes.

neuroscience↗