Biology subjects

Zucca, R.

Publications and source records attributed to Zucca, R..

6 recordsLinked to original sources

bioRxiv2025

Data-driven identification of functional networks in artificial and biological neural networks

Understanding how the brain represents information is a central challenge in neuroscience and a practical bottleneck for brain-computer interfaces. Existing analytical tools cannot identify neural representations directly from neural activity data. We introduce MultiPEC, a data-driven method that discovers neural representations by quantifying how sets of signals jointly reduce context prediction error. Applied to artificial neural networks, MultiPEC uncovered class-specific subnetworks whose targeted ablation disproportionately impaired performance, demonstrating their causal role in feature recognition. Applied to EEG recordings from 24 participants, MultiPEC revealed functional signatures of auditory and visual processing. Classification analyses showed that MultiPEC captured fine-grained stimulus submodalities (levels of stimulus meaningfulness) more effectively than broad stimulus domains, highlighting the context-sensitive nature of neural representations. Together, these results establish MultiPEC as a scalable approach for identifying data driven (natural) representations in both biological and artificial systems, with potential applications in adaptive neurotechnology, clinical diagnostics and cognitive neuroscience. HighlightsO_LIMultiPEC identifies neural representations by extracting information clusters from neural activity data. C_LIO_LIEEG analyses reveal individualized, condition-specific functional networks across auditory and visual modalities. C_LIO_LIIn convolutional neural networks, MultiPEC uncovers class-specific networks critical for model performance. C_LIO_LIProvides a data-driven, hypothesis-free framework for mapping neural representations in biological and artificial systems. C_LI

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bioRxiv2023

Identifying the seizure onset zone with intracranial EEG: A systematic comparison between power and connectivity-based biomarkers

BackgroundIctal stereo-encephalography (sEEG) biomarkers for seizure onset zone (SOZ) localization can be classified depending on whether they target abnormalities in signal power or functional connectivity between signals, and they may depend on the frequency band and time window at which they are estimated. New methodThis work aimed to compare and optimize the performance of a power and a connectivity-based biomarkers to identify SOZ contacts from ictal sEEG recordings. To do so, we used a previously introduced power-based measure, the normalized mean activation (nMA), which quantifies the ictal average power activation. Similarly, we defined the normalized mean strength (nMS), to quantify the ictal mean functional connectivity of every contact with the rest. The optimal frequency bands and time windows were selected based on optimizing AUC and F2-score. ResultsThe analysis was performed on a dataset of 67 seizures from 10 patients with pharmacoresistant temporal lobe epilepsy. Our results suggest that the power-based biomarker generally performs better for the detection of SOZ than the connectivity-based one. However, an equivalent performance level can be achieved when both biomarkers are independently optimized. Optimal performance was achieved in the beta and lower-gamma range for the power biomarker and in the higher-gamma range for connectivity, both using a 30 s period after seizure onset. ConclusionsThe results of this study highlight the importance of this optimization step over frequency and time windows when comparing different SOZ discrimination biomarkers. This information should be considered when training SOZ classifiers on retrospective patients data for clinical applications.

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bioRxiv2022

Theta oscillations in the human hippocampus normalize the information content of episodic memory

The principles governing the formation of episodic memories from the continuous stream of sensory stimuli are not fully understood. Theoretical models of the hippocampus propose that the representational format of episodic memories comprise oscillations in the theta frequency band (2-8 Hz) that set the time boundaries in which discrete events are bound encoded in the gamma frequency range (>30 Hz). We investigated this temporal segmentation and binding process by analyzing the intracranial EEG (iEEG) of surgically implanted epileptic patients performing a virtual-navigation task. We found a positive correlation between sensory information density encountered by the subject and hippocampal theta-frequency, suggesting that the human hippocampus normalizes the information content of episodic memories relative to the density of sensory information. This interpretation is further supported by the observation that as a marker of mnemonic encoding, i.e. the amount of persistent gamma events, directly correlates with sensory information density, gamma-frequency power and the phase relation between theta and gamma oscillations remain constant. Using a theoretical model of the hippocampus, we build a model that analogously displays a similar normalization of gamma episodes per theta cycle relative to information density by accounting for the physiological signatures of theta-gamma coding through combining fast and slow inhibitory feedback. We propose that this intrinsic normalization mechanism optimizes the trade-off between the discretization and compression of continuous experience relative to the limited capacity of episodic memory. SummaryWe move in continuous time and space, yet we can encode and recall discrete episodes from our past experiences. The neural mechanism behind this discretization is not fully understood. It has been previously observed that rodent locomotion modulates ongoing hippocampal theta rhythms. Thus, raising the question of whether these slow rhythms bind events together during a single oscillatory cycle relative to the movement speed or overall information density. We quantified the effects of increasing locomotion and sensory information in modulating theta oscillations during virtual navigation with intracranial hippocampal activity from human epileptic patients.. We observed hippocampal theta waves increased with higher speed and higher sensory demands, thereby maintaining constant information per oscillatory cycle. These results highlight the role of hippocampal theta oscillations in discretizing ongoing experience relative to the available information and explain how episodic memory integrates a fixed number of items per oscillatory theta cycle irrespective of richness of the external world.

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bioRxiv2021

Theta phase mediates deliberate action switching in human Supplementary Motor Areas

The ability to deliberately overwrite ongoing automatic actions is a necessary feature of adaptive behavior. It has been proposed that the supplementary motor areas (SMAs) operate as a controller that orchestrates the switching between automatic and deliberate processes by inhibiting ongoing behaviors and so facilitating the execution of alternative ones. In addition, previous studies support the involvement of SMAs theta waves (4-9 Hz) in cognitive control. However, the exact role of such oscillatory dynamics and their contribution to the control of action are not fully understood. To investigate the mechanisms by which the SMAs support direct control of deliberate behavior, we recorded intracranial electroencephalography (iEEG) activity in humans performing a motor sequence task. Subjects had to perform a "change of plans" motor task requiring habitual movements to be overwritten at unpredictable moments. We found that SMAs were exclusively active during trials that demand action reprogramming in response to the unexpected cue but were silent during automatic action execution. Importantly, SMAs activity was characterized by a distinct temporal pattern, expressed in a stereotypical phase alignment of theta oscillations. More specifically, single trial motor performance was correlated with the trial contribution to the global inter-trial phase coherence, with higher coherence associated with faster trials. In addition, theta phase modulated the amplitude of gamma oscillations, with higher cross-frequency coupling in faster trials. Our results suggest that within frontal cortical networks, theta oscillations could encode a control signal that promotes the execution of deliberate actions.

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bioRxiv2019

The Degree Distribution of Human Brain Functional Connectivity is Generalized Pareto: A Multi-Scale Analysis

Are degree distributions of human brain functional connectivity networks heavy-tailed? Initial claims based on least-square fitting suggested that brain functional connectivity networks obey power law scaling in their degree distributions. This interpretation has been challenged on methodological grounds. Subsequently, estimators based on maximum-likelihood and non-parametric tests involving surrogate data have been proposed. No clear consensus has emerged as results especially depended on data resolution. To identify the underlying topological distribution of brain functional connectivity calls for a closer examination of the relationship between resolution and statistics of model fitting. In this study, we analyze high-resolution functional magnetic resonance imaging (fMRI) data from the Human Connectome Project to assess its degree distribution across resolutions. We consider resolutions from one thousand to eighty thousand regions of interest (ROIs) and test whether they follow a heavy or short-tailed distribution. We analyze power law, exponential, truncated power law, log-normal, Weibull and generalized Pareto probability distributions. Notably, the Generalized Pareto distribution is of particular interest since it interpolates between heavy-tailed and short-tailed distributions, and it provides a handle on estimating the tails heaviness or shortness directly from the data. Our results show that the statistics support the short-tailed limit of the generalized Pareto distribution, rather than a power law or any other heavy-tailed distribution. Working across resolutions of the data and performing cross-model comparisons, we further establish the overall robustness of the generalized Pareto model in explaining the data. Moreover, we account for earlier ambiguities by showing that down-sampling the data systematically affects statistical results. At lower resolutions models cannot easily be differentiated on statistical grounds while their plausibility consistently increases up to an upper bound. Indeed, more power law distributions are reported at low resolutions (5K) than at higher ones (50K or 80K). However, we show that these positive identifications at low resolutions fail cross-model comparisons and that down-sampling data introduces the risk of detecting spurious heavy-tailed distributions. This dependence of the statistics of degree distributions on sampling resolution has broader implications for neuroinformatic methodology, especially, when several analyses rely on down-sampled data, for instance, due to a choice of anatomical parcellations or measurement technique. Our findings that node degrees of human brain functional networks follow a short-tailed distribution have important implications for claims of brain organization and function. Our findings do not support common simplistic representations of the brain as a generic complex system with optimally efficient architecture and function, modeled with simple growth mechanisms. Instead these findings reflect a more nuanced picture of a biological system that has been shaped by longstanding and pervasive developmental and architectural constraints, including wiring-cost constraints on the centrality architecture of individual nodes.

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bioRxiv2019

A simplified spiking model of grid-cell scale and intrinsic frequencies

The hexagonal tessellation pattern of grid cells scales up progressively along the dorsal-to-ventral axis of the medial entorhinal cortex (MEC) layer II. This scaling gradient has been hypothesized to originate either from inter population synaptic dynamics as postulated by attractor networks, from projected theta frequencies to different axis levels, as in oscillatory models, or from cellular dynamics dependent on hyperpolarization-activated cation currents. To test the hypothesis that intrinsic cellular properties account for the scale gradient as well as the different oscillatory frequencies observed along the dorsal-to-ventral axis, we have modeled and analyzed data from a population of grid cells simulated with spiking neurons interacting through low-dimensional attractor dynamics. To investigate the causal relationship between oscillatory frequencies and grid scale increase, we analyzed the dominant frequencies of the membrane potential for cells with distinct after-spike dynamics. We observed that intrinsic neuronal membrane properties of simulated cells could induce an increase of grid scale when modulated by after-spike reset values. Differences in the membrane potential oscillatory frequency were observed along the simulated dorsal-to-ventral axis, suggesting that, rather than driving to the increase of grid scale as proposed by interference models of grid cells, they are the result of intrinsic cellular properties of neurons at each axis level. Overall, our results suggest that the after-spike dynamics of cation currents may play a major role in determining the grid cells scale and that oscillatory frequencies are a consequence of intrinsic cellular properties that are specific to different levels of the dorsal-to-ventral axis in the MEC layer II.

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