Biology subjects

Varona, P.

Publications and source records attributed to Varona, P..

7 recordsLinked to original sources

bioRxiv2026

ExSEnt for explainable dementia detection: disentangling temporal and amplitude-driven complexity boosts EEG-based classification

Early detection of dementia enables timely intervention and better care planning. Electroencephalography, being accessible and noninvasive, offers a practical avenue for monitoring pathological alterations in neural activity. Classical biomarkers like the theta-to-alpha power ratio (TAR) along with complexity measures are common methods that are usually evaluated and used for dementia detection. In this study, we aimed to assess the discriminative ability of a novel entropy-based family of measures, Extrema-Segmented Entropy (ExSEnt), summarized by multiple robust statistics per subject for dementia, along with classical measures, and evaluate the incremental value of these metrics. We analyzed an EEG dataset comprising healthy controls, individuals with Alzheimers disease, or frontotemporal dementia. Following preprocessing and group-level analyses of independent components, we focused on source-space activity from the prefrontal cortex and visual association cortices--regions implicated in early disease. From these sources, we computed complexity metrics: Sample Entropy, Katz Fractal Dimension, Higuchi Fractal Dimension, and Hurst exponent and ExSEnt metrics along with TAR and band-limited power at delta, beta, low and high gamma bands. Using stability-based selection with elastic net logistic models, we identified a reliable set of discriminative features and quantified their cross-subject robustness. This framework isolates interpretable and trustworthy source-local biomarkers from single-region time series. We observed that the alpha/theta temporal entropy measures (ExSEnt) are selected as the most reliably informative metrics in the left prefrontal cortex, yielding a classification performance comparable to what was recently reported with high-dimensional deep learning methods for this dataset, with a simple logistic regression model on a single brain source.

neuroscience↗

bioRxiv2024

Mu and beta power effects of fast response trait double dissociate during precue and movement execution in the sensorimotor cortex

A better understanding of the neural and muscular mechanisms underlying motor responses is essential for advancing neurorehabilitation protocols, brain-computer interfaces (BCI), feature engineering for biosignal classification algorithms, and identifying biomarkers of disease and performance enhancement strategies. In this study, we examined the neuromuscular dynamics of healthy individuals during a sequential finger-pinching task, focusing on the relationships between cortical oscillations and muscle activity in simultaneous electroencephalography (EEG) and electromyography (EMG) recordings. We contrasted two pairs of subsets of the dataset based on the latency of EMG onset: an across-subjects trait-based comparison and a within-subjects state-based comparison. Trait-based analyses showed that fast responders had higher baseline beta power, indicating stronger motor inhibition and efficient resetting of motor networks, and greater mu desynchronization during movement, reflecting enhanced motor cortex activation. Visual association areas also displayed more pronounced changes in different phases of the task in subjects with lower latency. Fast responders exhibited lower baseline EMG activity and stronger EMG power during movement initiation, showing effective motor inhibition and rapid muscle activation. State-based analyses revealed no significant EEG differences between fast and slow trials, while EMG differences were only detected after movement onset. These results highlight that fast response trait is related to electro-physiological differences at specific frequency bands and task phases, offering insights for enhancing motor function in rehabilitation, biomarker identification and BCI applications. HighlightsO_LIWe analyzed simultaneous EEG and EMG recordings during a sequential finger movement task C_LIO_LIWe identified neural correlates of response latency trait, but not state C_LIO_LIFast responders show higher baseline beta power, indicating stronger motor inhibition C_LIO_LILower latency trait was linked to a sharper postcue sensorimotor mu power decrease C_LIO_LIFast responders have lower pre-movement EMG and higher EMG onset power C_LI

neuroscience↗

bioRxiv2020

Modeling the variability of the electromotor commandsystem of pulse mormyrids

Mormyrids, a family of weakly electric fish, use electric pulses for communication and for extracting information from the environment (active electroreception). The electromotor system controls the timing of pulse generation. Ethological studies have described several sequences of pulse intervals (SPIs) which are related to distinct behaviors (e.g. mating or exploratory behaviors). Accelerations, scallops, rasps, and cessations are four SPI patterns reported in these fish, each showing characteristic temporal structures and large variability. This paper presents a computational model of the electromotor command circuit that reproduces SPI patterns as a function of the inputs to the model while keeping the same internal network configuration. The topology of the model is based on a simplified representation of the network with four neuron clusters (nuclei). An initial ad hoc tuned configuration (S-T) was built to reproduce nucleus characteristics and network topology as described by detailed morphological and electrophysiological studies. Then, a genetic algorithm (GA) was developed and applied to automatically tune the synaptic parameters of the model connectivity. Two different configurations obtained from the GA are presented here: one optimized to a set of synthetic examples of SPI patterns (S-GA) and another configuration adjusted to patterns recorded from freely-behaving Gnathonemus Petersii specimens (R-GA). Robustness analyses to input variability were performed to discard overfitting and assess validity. Results show that the set of SPI patterns are consistently reproduced, both for synthetic data and for recorded data. This model can be used as a tool to test novel hypotheses regarding temporal structure in electrogeneration.

neuroscience↗

bioRxiv2019

Temporal discrimination from the interaction between dynamic synapses and intrinsic subthreshold oscillations

The interaction between synaptic and intrinsic dynamics can efficiently shape neuronal input-output relationships in response to temporally structured spike trains. We use a neuron model with subthreshold oscillations receiving inputs through a synapse with short-term depression and facilitation to show that the combination of intrinsic subthreshold and synaptic dynamics leads to channel-specific nontrivial responses and recognition of specific temporal structures. We employ the Generalized Integrate-and-Fire (GIF) model, which can be subjected to analytical characterization. We map the temporal structure of spike input trains to the type of spike response, and show how the emergence of nontrivial input-output preferences is modulated by intrinsic and synaptic parameters in a synergistic manner. We demonstrate that these temporal input discrimination properties are robust to noise and to variations in synaptic strength, suggesting that they likely contribute to neuronal computation in biological circuits. Furthermore, we also illustrate the presence of these input-output relationships in conductance-based models.\n\nAuthor summaryNeuronal subthreshold oscillations underlie key aspects of information processing in single neuron and network dynamics. Dynamic synapses provide a channel-specific temporal modulation of input information. We combine a neuron model that displays subthreshold oscillations and a dynamic synapse to analytically assess their interplay in processing trains of spike-mediated synaptic currents. Our results show that the co-action of intrinsic and synaptic dynamics builds nontrivial input-output relationships, which are resistant to noise and to changes in synaptic strength. The discrimination of a precise temporal structure of the input signal is shaped as a function of the joint interaction of intrinsic oscillations and synaptic dynamics. This interaction can result in channel-specific recognition of precise temporal patterns, hence greatly expanding the flexibility and complexity in information processing achievable by individual neurons with respect to temporal discrimination mechanisms based on intrinsic neuronal dynamics alone.

neuroscience↗

bioRxiv2018

RTHybrid: a standardized and open-source real-time software model library for experimental neuroscience

Closed-loop technologies provide novel ways of online observation, control and bidirectional interaction with the nervous system, which help to study complex non-linear and partially observable neural dynamics. These protocols are often difficult to implement due to the temporal precision required when interacting with biological components, which in many cases can only be achieved using real-time technology. In this paper we introduce RTHybrid (www.github.com/GNB-UAM/RTHybrid), a free and open-source software that includes a neuron and synapse model library to build hybrid circuits with living neurons in a wide variety of experimental contexts. In an effort to encourage the standardization of real-time software technology in neuroscience research, we compared different open-source real-time operating system patches, RTAI, Xenomai 3 and Preempt-RT, according to their performance and usability. RTHybrid has been developed to run over Linux operating systems supporting both Xenomai 3 and Preempt-RT real-time patches, and thus allowing an easy implementation in any laboratory. We report a set of validation tests and latency benchmarks for the construction of hybrid circuits using this library. With this work we want to promote the dissemination of standardized, user-friendly and open-source software tools developed for open- and closed-loop experimental neuroscience.

neuroscience↗

bioRxiv2018

Automatic adaptation of model neurons and connections to build hybrid circuits with living networks

Hybrid circuits built by creating mono- or bi-directional interactions among living cells and model neurons and synapses are an effective way to study neuron, synaptic and neural network dynamics. However, hybrid circuit technology has been largely underused in the context of neuroscience studies mainly because of the inherent difficulty in implementing and tuning this type of interactions. In this paper, we present a set of algorithms for the automatic adaptation of model neurons and connections in the creation of hybrid circuits with living neural networks. The algorithms perform model time and amplitude scaling, drift compensation, goal-driven synaptic and model tuning/calibration and also automatic parameter mapping. These algorithms have been implemented in RTHybrid, an open-source library that works with hard real-time constraints. We provide validation examples by building hybrid circuits in a central pattern generator. The results of the validation experiments show that the proposed dynamic adaptation facilitates building hybrid circuits and closed-loop communication among living and artificial model neurons and connections. Furthermore contributes to characterize system dynamics, achieve control, automate experimental protocols and extend the lifespan of the preparations.

neuroscience↗

bioRxiv2018

Robust dynamical invariants in sequential neural activity

By studying different sources of temporal variability in central pattern generator circuits, in this paper we unveil distinct aspects of the instantaneous balance between flexibility and robustness in sequential dynamics -a property that characterizes many systems that display neural rhythms. The level of irregularity and coordination was characterized using intrinsic time references and intervals in long recordings of the pyloric central pattern generator. The analysis demonstrated strong robustness of transient dynamics in keeping not only the activation sequences but also specific cycle-by-cycle temporal relationships in the form of dynamical invariants. The rich dynamics of neurons and connections balance flexibility and coordination to readily negotiate the interactions between neurons and produce the resultant rhythm. In particular, two dynamical invariants were identified between time intervals that build the sequence, existing even outside steady states. We suggest that invariant temporal sequence relationships could be present in other networks, including those related to brain rhythms, and underlie rhythm programming and functionality.

neuroscience↗