Biology subjects

Baroni, F.

Publications and source records attributed to Baroni, F..

6 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↗

bioRxiv2024

TFAP2C and HNRNPK control mTOR cell metabolism and prion propagation

Heterogeneous Nuclear Ribonucleoprotein K (hnRNP K) is a limiting factor for prion propagation. However, little is known about the function of hnRNP K except that it is essential to cell survival. Here, we performed a synthetic-viability CRISPR ablation screen to identify epistatic interactors of HNRNPK. We found that deletion of Transcription Factor AP-2{gamma} (TFAP2C) suppressed the death of hnRNP K-depleted LN-229 and U-251 MG cells, whereas its overexpression hypersensitized cells to hnRNP K loss. HNRNPK ablation decreased cellular ATP, downregulated genes related to lipid and glucose metabolism, and enhanced autophagy. Co-occurrent deletion of TFAP2C reversed these effects, restoring transcriptional balance and alleviating energy deficiency. We linked HNRNPK and TFAP2C interaction to mTOR signaling, observing that HNRNPK ablation inhibited mTORC1 activity through downregulation of mTOR and Rptor, while TFAP2C overexpression enhanced mTORC1 downstream functions. In prion-infected cells, TFAP2C activation reduced prion levels and countered the increased prion propagation caused by HNRNPK suppression. Short-term pharmacological inhibition of mTOR also elevated prion levels and partially mimicked the effects of HNRNPK silencing. Our study identifies TFAP2C as a genetic interactor of HNRNPK, implicates their roles in mTOR metabolic regulation, and establishes a causative link between these activities and prion propagation.

cell biology↗

bioRxiv2024

Synchrony, oscillations, and phase relationships in collective neuronal activity: a highly comparative overview of methods

Neuronal activity is organized in collective patterns that are critical for information coding, generation, and communication between brain areas. These patterns are often described in terms of synchrony, oscillations, and phase relationships. Many methods have been proposed for the quantification of these collective states of dynamic neuronal organization. However, it is difficult to determine which method is best suited for which experimental setting and research question. This choice is further complicated by the fact that most methods are sensitive to a combination of synchrony, oscillations, and other factors; in addition, some of them display systematic biases that can complicate their interpretation. To address these challenges, we adopt a highly comparative approach, whereby spike trains are represented by a diverse library of measures. This enables unsupervised or supervised classification in the space of measures, or in that of spike trains. We compile a battery of 122 measures of synchrony, oscillations, and phase relationships, complemented with 9 measures of spiking intensity and variability. We first apply them to sets of synthetic spike trains with known statistical properties, and show that all measures are confounded by extraneous factors such as firing rate or population frequency, but to different extents. Then, we analyze spike trains recorded in different species-rat, mouse, and monkey-and brain areas- primary sensory cortices and hippocampus-and show that our highly comparative approach provides a high-dimensional quantification of collective network activity that can be leveraged for both unsupervised and supervised classification of firing patterns. Overall, the highly comparative approach provides a detailed description of the empirical properties of multineuron spike train analysis methods, including practical guidelines for their use in experimental settings, and advances our understanding of neuronal coordination and coding. Author summaryCognition and brain-body regulation rely on collective patterns of neural activity, which are typically described in terms of synchrony, oscillations and phase relationships. Many methods have been proposed for measuring these properties, and selecting the most appropriate method for a given research question can be a daunting task. To address this issue, we assembled a broad range of statistical measures and tested them on both synthetic and biological spike trains. Our analyses indicate that there is not an overall "best" measure, and inform on the relative advantages and drawbacks of a broad range of measures with respect to several criteria of interest for their empirical application, including their modulation by firing rate or spike failures, population frequency, sequentialness and rhythmicity, as well as their bias and precision resulting from finite time window length and number of neurons. Our results provide a comprehensive picture of the range of available methods for the quantification of collective patterns of neural activity, enabling researchers to make better informed decisions and avoid interpretational pitfalls.

neuroscience↗

bioRxiv2019

Converging intracortical signatures of two separated processing timescales in human early auditory cortex.

Neural oscillations in auditory cortex are argued to support parsing and representing speech constituents at their corresponding temporal scales. Yet, how incoming sensory information interacts with ongoing spontaneous brain activity, what features of the neuronal microcircuitry underlie spontaneous and stimulus-evoked spectral fingerprints, and what these fingerprints entail for stimulus encoding, remain largely open questions. We used a combination of human invasive electrophysiology, computational modeling and decoding techniques to assess the information encoding properties of brain activity and to relate them to a plausible underlying neuronal microarchitecture. We analyzed intracortical auditory EEG activity from 10 patients while they were listening to short sentences. Pre-stimulus neural activity in early auditory cortical regions often exhibited power spectra with a shoulder in the delta range and a small bump in the beta range. Speech decreased power in the beta range, and increased power in the delta-theta and gamma ranges. Using multivariate machine learning techniques, we assessed the spectral profile of information content for two aspects of speech processing: detection and discrimination. We obtained better phase than power information decoding, and a bimodal spectral profile of information content with better decoding at low (delta-theta) and high (gamma) frequencies than at intermediate (beta) frequencies. These experimental data were reproduced by a simple rate model made of two subnetworks with different timescales, each composed of coupled excitatory and inhibitory units, and connected via a negative feedback loop. Modeling and experimental results were similar in terms of pre-stimulus spectral profile (except for the iEEG beta bump), spectral modulations with speech, and spectral profile of information content. Altogether, we provide converging evidence from both univariate spectral analysis and decoding approaches for a dual timescale processing infrastructure in human auditory cortex, and show that it is consistent with the dynamics of a simple rate model.\n\nAuthor summaryLike most animal vocalizations, speech results from a pseudo-rhythmic process that reflects the convergence of motor and auditory neural substrates and the natural resonance properties of the vocal apparatus towards efficient communication. Here, we leverage the excellent temporal and spatial resolution of intracranial EEG to demonstrate that neural activity in human early auditory cortical areas during speech perception exhibits a dual-scale spectral profile of power changes, with speech increasing power in low (delta-theta) and high (gamma - high-gamma) frequency ranges, while decreasing power in intermediate (alpha-beta) frequencies. Single-trial multivariate decoding also resulted in a bimodal spectral profile of information content, with better decoding at low and high frequencies than at intermediate ones. From both spectral and informational perspectives, these patterns are consistent with the activity of a relatively simple computational model comprising two reciprocally connected excitatory/inhibitory sub-networks operating at different (low and high) timescales. By combining experimental, decoding and modeling approaches, we provide consistent evidence for the existence, information coding value and underlying neuronal architecture of dual timescale processing in human auditory cortex.

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↗