Biology subjects

Valente, G.

Publications and source records attributed to Valente, G..

11 recordsLinked to original sources

bioRxiv2026

Theta-Phase Targeted Encoding Gates Memory Sensitivity

Theta phase has been proposed to play an important role in memory. It may gate encoding and retrieval, such that certain phases are more favorable for memory processing than others, or become part of the memory representation, such that reinstating the encoding phase benefits retrieval. To test and distinguish these competing accounts, we used a closed-loop EEG system, allowing us to control the phase at which items were presented at encoding and retrieval. In an associative memory task, visual targets were presented at predefined phases of the ongoing mid-frontal theta rhythm, thereby pairing individual memory items with specific encoding phases. During retrieval, targets appeared either at the same phase as during encoding or at an alternative phase. We show that theta phase during encoding predicted subsequent memory sensitivity, while finding no evidence that retrieval phase nor the match between encoding and retrieval phases influenced performance. Therefore, our findings are consistent with a gating account, in which the rhythmic state at the moment of encoding influences the efficacy of memory formation.

neuroscience↗

bioRxiv2026

Distributed representational encoding of food attributes in ventral visual cortex

Despite substantial progress in understanding how visual features of food are processed in the brain, it remains unclear how subjective food properties, such as perceived palatability, perceived caloric content, and perceived health value, are reflected in neural representational structure after accounting for visual similarity. Using functional MRI and representational similarity analysis (RSA), we examined visual, subjective, and categorical food representations in 25 healthy young women viewing 96 food images during an orthogonal color-discrimination task. Univariate analyses revealed reliable activation differences between high- and low-calorie foods in visual and ventral temporal cortex, with additional effects in orbitofrontal regions. Surface-based parcel-wise RSA and hypothesis-driven ROI analyses showed that visual models explained representational structure most strongly in early visual cortex and extended into occipitotemporal regions. Food-related models showed smaller but reliable effects in lateral and ventral occipitotemporal cortex: perceived calorie, perceived health, and objective calorie category were all associated with neural representational geometry in LOTC and VOTC. However, partial RSA and commonality analyses indicated that these effects were largely shared rather than separable. Perceived-calorie structure remained reliable after controlling for visual models, but not after controlling for perceived health. Palatability did not show reliable positive representational correspondence. These findings suggest that food-related information is reflected within occipitotemporal visual representational spaces during incidental viewing, but not as an isolated scalar calorie code. Instead, calorie-related effects appear to reflect a broader food-property axis on which energy density, perceived health, category structure, and learned food knowledge covary.

neuroscience↗

bioRxiv2025

SC-Framework: A robust and FAIR semi-interactive environment for single-cell resolution datasets

The accelerated development of single-cell technologies has profoundly impacted the field of biological research, facilitating unparalleled insights into cellular heterogeneity. However, this progress has also produced new computational challenges in the field of bioinformatics: single-cell datasets are increasingly high-dimensional, multimodal, and large-scale, while analysis workflows often remain fragmented, data type specific, ad hoc, and difficult to reproduce. The prevailing methodologies are dependent on a combination of public tools, which hinders the reproducibility of results, limits scalability, and complicates the efforts to establish benchmarks. The necessity for a higher-level, unified framework for single-cell data analysis is paramount to address these inherent limitations. Here, we introduce the SC-Framework, providing the integration of standardized data structures, declarative workflows and standardized computational backends in a containerized environment, enabling analysts to focus on biological interpretation rather than technical overhead. SC-Framework is available at GitHub (https://github.com/loosolab/SC-Framework).

bioinformatics↗

bioRxiv2024

Navigating the Maze: Identifying Potential Pitfalls in Attention State Classification from fMRI Brain Patterns

Multi-voxel pattern analysis (MVPA) is a powerful technique to decode brain states from functional magnetic resonance imaging (fMRI) activity patterns. In neurofeedback (NF) applications, it has been used to perform real-time classification of brain activity patterns, establishing a closed-loop system that provides immediate feedback to the participants, enabling them to learn to control a complex mental state. However, MVPA has many potential limitations when applied to fMRI datasets (especially in real-time analysis) arising from small effect sizes, small number of training samples, high dimensionality of the data and, more generally, design choices. All these factors might produce inaccurate classification results. In this work, we followed a previous NF paradigm for sustained attention training. Participants were presented with composite images superimposing faces and scenes. They were instructed to focus on one class (either face or scene) for an extended period. A logistic regression classifier was trained to determine whether participants were adequately focusing on the instructed category based on their fMRI data. We analysed the classification outputs of the no-feedback training runs using various classifier settings, including whole brain data and different masking approaches, combined with different methods for the computation of single-trial fMRI responses. Furthermore, a ventricle mask was used as a control condition for the classification task, and simulations were carried out to assess the influence of the class order on the classification performances. We found inflation of the decoding accuracy for several common design choices and confounders. In particular, motion artefacts and low frequency drifts coupled with the task timing might have artificially increased the accuracy scores. Furthermore, the simulations revealed that fixed order in the presentation of experimental conditions resulted in further inflation of the classification accuracies especially in GLM-based and average-based trial estimate methods. We discuss the drawbacks of applying MVPA using the analysed sustained attention paradigm and provide insights for future improvements.

neuroscience↗

bioRxiv2024

Bridging Auditory Perception and Natural Language Processing with Semantically informed Deep Neural Networks

Sound recognition is effortless for humans but poses a significant chal-lenge for artificial hearing systems. Deep neural networks (DNNs), especially convolutional neural networks (CNNs), have recently sur-passed traditional machine learning in sound classification. However, current DNNs map sounds to labels using binary categorical variables, neglecting the semantic relations between labels. Cognitive neuroscience research suggests that human listeners exploit such semantic informa-tion besides acoustic cues. Hence, our hypothesis is that incorporating semantic information improves DNNs sound recognition performance, emulating human behavior. In our approach, sound recognition is framed as a regression problem, with CNNs trained to map spec-trograms to continuous semantic representations from NLP models (Word2Vec, BERT, and CLAP text encoder). Two DNN types were trained: semDNN with continuous embeddings and catDNN with cat-egorical labels, both with a dataset extracted from a collection of 388,211 sounds enriched with semantic descriptions. Evaluations across four external datasets, confirmed the superiority of semantic labeling from semDNN compared to catDNN, preserving higher-level relations. Importantly, an analysis of human similarity ratings for natural sounds, showed that semDNN approximated human listener behavior better than catDNN, other DNNs, and NLP models. Our work contributes to understanding the role of semantics in sound recognition, bridging the gap between artificial systems and human auditory perception.

neuroscience↗

bioRxiv2024

Unbiased estimation of the coefficient of determination in linear models: an application to fMRI encoding model comparison

Neuroscientific investigation has greatly benefited from the combination of functional Magnetic Resonance Imaging (fMRI) with linearized encoding, which allows to validate and compare computational models of neural activity based on neuroimaging data. In linearized encoding, a multidimensional feature space, usually obtained from a computational model applied to the stimuli, is related to the measured brain activity. This is often done by mapping such space to a dataset (training data, or in-sample), and validating the mapping on a separate dataset (test data, or out-of-sample), to avoid overfitting. When comparing models, the one with the highest explained variance on the test data, as indicated by the coefficient of determination (R2), is the one that better reflects the neural computations performed by the brain. An implicit assumption underlying this procedure is that the out-of-sample R2 is an unbiased estimator of the explanatory power of a computational model in the population of stimuli, and can therefore be safely used to compare models. In this work, we show that this is not the case, as the out-of-sample R2 has a negative bias, related to the amount of overfitting in the training data. This phenomenon has dramatic implications for model comparison when models of different dimensionalities are compared. To this aim, we develop an analytical framework that allows us to evaluate and correct biases in both in- and out-of-sample R2, with and without L2 regularization. Our proposed approach yields unbiased estimators of the population R2, thus enabling a valid model comparison. We validate it through illustrative simulations and with an application to a large public fMRI dataset.

neuroscience↗

bioRxiv2024

Evaluating the effect of denoising submillimeter auditory fMRI data with NORDIC

Functional magnetic resonance imaging (fMRI) has emerged as an essential tool for exploring human brain function. Submillimeter fMRI, in particular, has emerged as a tool to study mesoscopic computations. The inherently low signal-to-noise ratio (SNR) at submillimeter resolutions warrants the use of denoising approaches tailored at reducing thermal noise - the dominant contributing noise component in high resolution fMRI. NORDIC PCA is one of such approaches, and has been benchmarked against other approaches in several applications. Here, we investigate the effects that two versions of NORDIC denoising have on auditory submillimeter data. As investigating auditory functional responses poses unique challenges, we anticipated that the benefit of this technique would be especially pronounced. Our results show that NORDIC denoising improves the detection sensitivity and the reliability of estimates in submillimeter auditory fMRI data. These effects can be explained by the reduction of the noise-induced signal variability. However, we also observed a reduction in the average response amplitude (percent signal), which may suggest that a small amount of signal was also removed. We conclude that, while evaluating the effects of the signal reduction induced by NORDIC may be necessary for each application, using NORDIC in high resolution auditory fMRI studies may be advantageous because of the large reduction in variability of the estimated responses.

neuroscience↗

bioRxiv2019

A novel approach for combining task-dependent gamma with alpha and beta power modulation for enhanced identification of eloquent cortical areas using ECoG in patients with medical-refractory epilepsy

Electrical stimulation mapping (ESM) is the gold standard for identification of \"eloquent\" areas prior to resection of epileptogenic tissue, however, it is time consuming and may cause side effects, especially stimulation-induced seizures and after-discharges. Broadband gamma activity (55 - 200 Hz) recorded with subdural electrocorticography (ECoG) during cognitive tasks has been proposed as an attractive tool for mapping cortical areas with specific function but until now has not proven definitive clinical value. Fewer studies have addressed whether the alpha (8 - 12 Hz) and beta (15 - 25 Hz) band activity could also be used to improve eloquent cortex identification. We compared alpha, beta and broadband gamma activity, and their combination for the identification of eloquent cortical areas defined by ESM. Ten patients participated in a delayed-match-to-sample task, where syllable sounds were matched to visually presented letters and responses given by keyboard. We used a generalized linear model (GLM) approach to find the optimal weighting of low frequency bands and broadband gamma power to predict the ESM categories. Broadband gamma activity increased more in eloquent areas than in non-eloquent areas and this difference had a diagnostic ability (area under (AU) the receiving operating characteristic curve - AUROC) of [~]70%. Both alpha and beta power decreased more in eloquent areas. Alpha power had lower AUROC than broadband gamma while beta had similar AUROC. AUROC was enhanced by the combination of alpha and broadband gamma (3% improvement) and by the combination of beta and broadband gamma (7% improvement) over the use of broadband gamma alone. Further analysis showed that the relative performance of broadband gamma and low frequency bands depended on multiple factors including the time period of the cognitive task, the location of the electrodes and the patients attention to the stimulus. However, the combination of beta band and broadband gamma always gave the best performance. We show how ECoG power modulation from cognitive testing periods can be used to map the probability of eloquence by ESM and how this probability can be used as an aid for optimal ESM planning. We conclude that low frequency power during cognitive testing can contribute to the identification of eloquent areas in patients with focal refractory epilepsy improving its precision but does not replace the need of ESM.\n\nHighlightsO_LIGamma, alpha and beta band activity has significant diagnostic ability to identify ESM defined eloquent cortical areas.\nC_LIO_LIWe present a novel method to combine gamma and low frequency activity for enhanced identification.\nC_LIO_LIWe quantify how identification is dependent on analysis time window, cortical function, and patients attentional engagement.\nC_LIO_LIWith further development, this approach may offer an alternative to ESM mapping with reduced burden for patients.\nC_LI

neuroscience↗

bioRxiv2019

Transfer learning of deep neural network representations for fMRI decoding

BackgroundDeep neural networks have revolutionised machine learning, with unparalleled performance in object classification. However, in brain imaging (e.g. fMRI), the direct application of Convolutional Neural Networks (CNN) to decoding subject states or perception from imaging data seems impractical given the scarcity of available data. New methodIn this work we propose a robust method to transfer information from deep learning (DL) features to brain fMRI data with the goal of decoding. By adopting Reduced Rank Regression with Ridge Regularisation we establish a multivariate link between imaging data and the fully connected layer (fc7) of a CNN. We exploit the reconstructed fc7 features by performing an object image classification task on two datasets: one of the largest fMRI databases, taken from different scanners from more than two hundred subjects watching different movie clips, and another with fMRI data taken while watching static images, ResultsThe fc7 features could be significantly reconstructed from the imaging data, and led to significant decoding performance. Comparison with existing methodsThe decoding based on reconstructed fc7 outperformed the decoding based on imaging data alone. ConclusionIn this work we show how to improve fMRI-based decoding benefiting from the mapping between functional data and CNN features. The potential advantage of the proposed method is twofold: the extraction of stimuli representations by means of an automatic procedure (unsupervised) and the embedding of high-dimensional neuroimaging data onto a space designed for visual object discrimination, leading to a more manageable space from dimensionality point of view.

neuroscience↗

bioRxiv2018

Methods for computing the maximum performance of computational models of fMRI responses

Computational neuroimaging methods aim to predict brain responses (measured e.g. with functional magnetic resonance imaging [fMRI]) on the basis of stimulus features obtained through computational models. The accuracy of such prediction is used as an indicator of how well the model describes the computations underlying the brain function that is being considered. However, the prediction accuracy is bounded by the proportion of the variance of the brain response which is related to the measurement noise and not with the stimuli (or cognitive functions). The bound to the performance of a computational model to the prediction of brain responses has been referred to as the noise ceiling. In previous neuroimaging applications two methods have been proposed for estimating the noise ceiling based on either a split-half procedure or Monte Carlo simulations. These methods make different assumptions over the nature of the effects underlying the data, and, importantly, their relation has not been clarified yet. Here, we use a two-level generative framework to formally describe the partition between the variance of measurement noise and the stimulus related variance. In this framework we derive an analytical form for the noise ceiling that does not require computationally expensive simulations or a splitting procedure that reduce the amount of data. We describe the relation between the newly introduced noise ceiling estimator and the previous methods for variable levels of measurements noise using simulated data. Additionally, as the relation to the noise ceiling is used to make conclusions on the validity of a model with respect to others, we evaluate the effect the interplay between regularization (often used to estimate model fits to the data when the number of computational features in the model is large) and model complexity on the performance with respect to the noise ceiling. Finally, we show the differences between the methods on real fMRI data acquired at 7 Tesla. We demonstrate that while the split half estimator provides a pessimistic estimate of the noise ceiling due to the small amount of data available in conventional fMRI datasets, the parametric nature of the Monte Carlo estimator results in overly optimistic estimates. For this reason, for real data, we propose a robust procedure to the estimation of the noise ceiling based on bootstraps.\n\nAuthor SummaryEncoding computational models in brain responses measured with fMRI allows testing the algorithmic representations carried out by the neural population within voxels. The accuracy of a model in predicting new responses is used as a measure of the brain validity of this model, but the result of this analysis is determined not only by how precisely the model describes the responses but also by the quality of the data. In this article, we validate existing approaches to estimate the best possible accuracy that any computational model can achieve conditioned to the amount of measurement noise that is present in the experimental data (i.e. the noise ceiling). Additionally we introduce a close form estimation of the noise ceiling that does not require computationally or data expensive procedures. All the methods are compared using simulated and real fMRI data. We draw conclusions over the impact of regularisation procedures and model complexity and make practical recommendations on how to report the results of computational models in neuroimaging.

neuroscience↗

bioRxiv2018

Somatosensory imagery induces topographically specific activation patterns instrumental to fMRI-based Brain Computer Interfacing

Real-time functional magnetic resonance imaging (fMRI) is a promising non-invasive method for brain computer interfaces (BCIs). BCIs translate brain activity into signals that allow communication with the outside world. Visual and motor imagery are often used as information-encoding strategies, but can be challenging if not grounded in recent experience in these modalities, e.g. in patients with locked-in-syndrome (LIS). In contrast, somatosensory imagery might constitute a more suitable information-encoding strategy as somatosensory function is often very robust. Somatosensory imagery has been shown to activate somatotopic cortex, but it has been unclear so far whether it can be reliably detected on a single-trial level and successfully classified according to specific somatosensory imagery content.\n\nUsing ultra-high field 7-T fMRI, we show reliable and high-accuracy single-trial decoding of left-foot vs. right-hand somatosensory imagery. Correspondingly, higher decoding accuracies were associated with greater spatial separation of hand and foot decoding-weight patterns in primary somatosensory cortex (S1). Exploiting these novel neuroscientific insights, we developed - and provide a proof of concept for - basic BCI communication by showing that binary (yes/no) answers encoded by somatosensory imagery can be decoded with high accuracy not only offline but also in real-time.\n\nThis study demonstrates that body part-specific somatosensory imagery differentially activates somatosensory cortex in a topographically specific manner; evidence which was surprisingly still lacking in the literature. It is also offers a promising novel somatosensory imagery based fMRI-BCI control strategy, with particularly high potential for visually and motor-impaired patients. The strategy could also be transferred to lower MRI field strengths and to mobile functional near-infrared spectroscopy. Finally, given that communication BCIs provide the BCI user with a form of feedback based on their brain signals and can thus be considered as a specific form of neurofeedback, and that repeated use of a BCI has been shown to enhance underlying representations, we expect that the current BCI could also offer an interesting new approach for somatosensory rehabilitation training in the context of stroke and phantom limb pain.

neuroscience↗