Biology subjects

Principe, A.

Publications and source records attributed to Principe, A..

7 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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bioRxiv2019

Low entropy map of brain oscillatory activity identifies spatially localized events: a new method for automated epilepsy focus prediction

The spatial mapping of localized events in brain activity critically depends on the correct identification of the pattern signatures associated with those events. For instance, in the context of epilepsy research, a number of different electrophysiological patterns have been associated with epileptogenic activity. Motivated by the need to define automated seizure focus detectors, we propose a novel data-driven algorithm for the spatial identification of localized events that is based on the following rationale: the distribution of emerging oscillations during confined events across all recording sites is highly non-uniform and can be mapped using a spatial entropy function. By applying this principle to EEG recording obtained from 67 distinct seizure epochs, our method successfully identified the seizure focus on a group of ten drug-resistant temporal lobe epilepsy patients (average sensitivity: 0.94, average specificity: 0.90) together with its characteristic electrophysiological pattern signature. Cross-validation of the method outputs with postresective information revealed the consistency of our findings in long follow-up seizure-free patients. Overall, our methodology provides a reliable computational procedure that might be used as in both experimental and clinical domains to identify the neural populations undergoing an emerging functional or pathological transition.\n\nConflict of interestsThe authors declare no competing financial interests.

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bioRxiv2018

Human hippocampal theta oscillations reflect sequential dependencies during spatial planning.

Movement-related theta oscillations in rodent hippocampus coordinate forward sweeps of location-specific neural activity that could be used to evaluate spatial trajectories online. This raises the possibility that increases in human hippocampal theta power accompany the evaluation of upcoming spatial choices. To test this hypothesis, we measured neural oscillations during a spatial planning task that closely resembles a perceptual decision-making paradigm. In this task, participants searched visually for the shortest path between a start and goal location in novel mazes that contained multiple choice points, and were subsequently asked to make a spatial decision at one of those choice points. We observed ~4-8 Hz hippocampal/medial temporal lobe theta power increases specific to sequential planning that were negatively correlated with subsequent decision speed, where decision speed was inversely correlated with choice accuracy. These results implicate the hippocampal theta rhythm in decision tree search during planning in novel environments.

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bioRxiv2016

The dynamics of human cognition: increasing global integration coupled with decreasing segregation found using intracortical EEG

Cognitive processing requires the ability to flexibly integrate and process information across large brain networks. More information is needed on how brain networks dynamically reorganize to allow such broad communication across many different brain regions in order to integrate the necessary information. Here, we use intracranial EEG to record neural activity from 12 epileptic patients while they perform three cognitive tasks in order to study how the functional connectivity changes to facilitate communication across the underlying network spanning many different brain regions. At the topological level, this facilitation is characterized by measures of integration and segregation. Across all patients, we found significant increases in integration and decreases in segregation during cognitive processing, especially in the gamma band (50-90 Hz). Accordingly, we also found significantly higher level of global synchronization and functional connectivity during the execution of the cognitive task, again particularly in the gamma band. More importantly, we demonstrate here for the first time that the modulations at the level of functional connectivity facilitating communication across the network were not caused by changes in the level of the underlying oscillations but caused by a rearrangement of the mutual synchronisation between the different nodes as proposed by the \"Communication Through Coherence\" Theory.

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bioRxiv2016

Degenerate time-dependent network dynamics anticipate seizures in human epileptic brain

Epileptic seizures are known to follow specific changes in brain dynamics. While some algorithms can nowadays robustly detect these changes, a clear understanding of the mechanism by which these alterations occur and generate seizures is still lacking. Here, we provide cross-validated evidence that such changes are initiated by an alteration of physiological network state dynamics. Specifically, our analysis of long intracranial EEG recordings from a group of 10 patients identifies a critical phase of a few hours in which time-dependent network states become less variable (\"degenerate\") and is followed by a global functional connectivity reduction before seizure onset. This critical phase is characterized by an abnormal occurrence of highly correlated network instances and is shown to particularly affect the activity of resection regions in patients with validated postsurgical outcome. Our approach characterizes pre-seizure networks dynamics as a cascade of two sequential events providing new insights into seizure prediction and control.

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