Biology subjects

Castelo-Branco, M.

Publications and source records attributed to Castelo-Branco, M..

25 records · Page 2Linked to original sources

bioRxiv2022

A two-stage framework for neural processing of biological motion

It remains to be understood how biological motion is hierarchically computed, from discrimination of local "life motion" animacy to global dynamic body perception. Here, we addressed this functional separation of the correlates of the perception of life motion, defined as characteristic for the local motion of parts of living beings, from perception of global motion of a body. We hypothesized that life motion processing can be isolated, by using a single dot motion perceptual decision paradigm featuring the biomechanical details of local realistic motion of a single joint. To ensure that we were indeed tackling processing of biological motion properties we used a discrimination instead of detection task. We discovered using representation similarity analysis that two key early dorsal and two ventral stream regions (visual motion selective hMT+ and V3A, extrastriate body area EBA and a region within fusiform gyrus FFG) showed robust and separable signals related to encoding of life motion and global motion. These signals reflected two independent processing stages, as revealed by representation dissimilarity analysis and deconvolution of fMRI responses to each motion pattern. This study showed that higher level pSTS encodes both classes of biological motion in a similar way, revealing a higher-level integrative stage, reflecting scale independent biological motion perception. Our results reveal a two-stage framework for neural computation of biological motion, with an independent contribution of dorsal and ventral regions for the initial stage.

neuroscience↗

bioRxiv2021

Slow fluctuations in ongoing brain activity decrease in amplitude with ageing yet their impact on task-related evoked responses is dissociable from behaviour

In humans, ageing is characterized by decreased brain signal variability and increased behavioural variability. To understand how reduced brain variability segregates with increased behavioural variability, we investigated the association between reaction time variability, evoked brain responses and ongoing brain signal dynamics, in young (N = 36) and older adults (N = 39). We studied the electroencephalogram (EEG) and pupil size fluctuations to characterize the cortical and arousal responses elicited by a cued go/no-go task. Evoked responses were strongly modulated by slow (< 2 Hz) fluctuations of the ongoing signals, which presented reduced power in the older participants. Although variability of the evoked responses was lower in the older participants, once we adjusted for the effect of the ongoing signal fluctuations, evoked responses were equally variable in both groups. Moreover, the modulation of the evoked responses caused by the ongoing signal fluctuations had no impact on reaction time, thereby explaining why although ongoing brain signal variability is decreased in older individuals, behavioural variability is not. Finally, we showed that adjusting for the effect of the ongoing signal was critical to unmask the link between neural responses and behaviour.

neuroscience↗

bioRxiv2021

Modulation of early level EEG signatures by distributed facial emotion cues

Face perception plays an important role in our daily social interactions, as it is essential to recognize emotions. The N170 Event Related Potential (ERP) component has been widely identified as a major face-sensitive neuronal marker. However, despite extensive investigations conducted to examine this electroencephalographic pattern, there is yet no agreement regarding its sensitivity to the content of facial expressions. Here, we aim to clarify the EEG signatures of the recognition of facial expressions by investigating ERP components that we hypothesize to be associated with this cognitive process. We asked the question whether the recognition of facial expressions is encoded by the N170 as weel as at the level of P100 and P250. In order to test this hypothesis, we analysed differences in amplitudes and latencies for the three ERPs, in a sample of 20 participants. A visual paradigm requiring explicit recognition of happy, sad and neutral faces was used. The facial cues were explicitly controlled to vary only regarding mouth and eye components. We found that non neutral emotion expressions elicit a response difference in the amplitude of N170 and P250. In contrast with the P100, there by excluding a role for low level factors. Our study brings new light to the controversy whether emotional face expressions modulate early visual response components, which have been often analysed apart. The results support the tenet that neutral and emotional faces evoke distinct N170 patterns, but go further by revealing that this is also true for P250, unlike the P100.

neuroscience↗

bioRxiv2021

Optimizing EEG source reconstruction with concurrent fMRI-derived spatial priors

Reconstructing EEG sources involves a complex pipeline, with the inverse problem being the most challenging. Multiple inversion algorithms are being continuously developed, aiming to tackle the non-uniqueness of this problem, which has been shown to be partially circumvented by also including fMRI-derived spatial priors in the inverse models. However, only task activation maps and resting-state networks (RSNs) have been explored so far, overlooking the recent, but already accepted, notion that brain networks exhibit dynamic functional connectivity (dFC) fluctuations. Moreover, there is no consensus regarding the inversion algorithm of choice, nor a systematic comparison between different sets of spatial priors. Using simultaneous EEG-fMRI data, here we compared four different inversion algorithms (MN, LORETA, EBB and MSP) under a Bayesian framework, each with three different sets of priors consisting of: 1) those specific to the algorithm (S1); 2) S1 plus fMRI task activation maps and RSNs (S2); and 3) S2 plus network modules of task-related dFC states estimated from the dFC fluctuations (S3). The quality of the reconstructed EEG sources was quantified in terms of model-based metrics, namely the free-energy and variance explained of the inversion models, and the overlap/proportion of brain regions known to be involved in the visual perception tasks that the participants were submitted to, and RSN templates, with/within EEG source components. Model-based metrics suggested that model parsimony is preferred, with the combination MSP+S1 exhibiting the best performance. However, optimal overlap/proportion values were found using EBB+S2 or MSP+S3, respectively, indicating that fMRI spatial priors, including dFC state modules, are crucial for the EEG source components to reflect neuronal activity of interest. Our results pave the way towards a more informative selection of the optimal EEG source reconstruction approach, which may be crucial in future studies.

neuroscience↗

bioRxiv2020

Linked deterioration of early visual perception, function and structure in healthy human aging

Low-level visual perception deteriorates during healthy aging. We hypothesized that age-related retinal and cortical structure deteriorations affect perception through specific disruptions of neural function. We measured perceptual visual acuity in fifty healthy adults aged 20-80 years. We then measured these participants early visual field map (V1, V2 and V3) functional population receptive field (pRF) sizes and structural surface areas using fMRI, and their retinal structure using high-definition optical coherence tomography. With increasing age visual acuity decreased, pRF sizes increased, visual field maps surface areas decreased, and retinal thickness decreased. Among these measures, only functional pRF sizes predicted perceptual visual acuity. PRF sizes were in turn predicted by cortical structure only (surface areas), which were only predicted by retinal structure (thickness). We propose that age-related retinal structural deterioration disrupts cortical structure, thereby disrupting cortical functional neural interactions that normally sharpen visual position selectivity: the resulting functional disruption underlies age-related perceptual deterioration.

neuroscience↗

bioRxiv2020

Dopaminergic Gene Dosage in Autism versus Developmental Delay: From Complex Networks to Machine Learning approaches

The neural basis of behavioural changes in Autism Spectrum Disorders (ASD) remains a controversial issue. One factor contributing to this challenge is the phenotypic heterogeneity observed in ASD, which suggests that several different system disruptions may contribute to diverse patterns of impairment between and within study samples. Here, we took a retrospective approach, using SFARI data to study ASD by focusing on participants with genetic imbalances targeting the dopaminergic system. Using complex network analysis, we investigated the relations between participants, Gene Ontology (GO) and gene dosage related to dopaminergic neurotransmission from a polygenic point of view. We converted network analysis into a machine learning binary classification problem to differentiate ASD diagnosed participants from DD (developmental delay) diagnosed participants. Using 1846 participants to train a Random Forest algorithm, our best classifier achieved on average a diagnosis predicting accuracy of 85.18% (sd 1.11%) on a test sample of 790 participants using gene dosage features. In addition, we observed that if the classifier uses GO features it was also able to infer a correct response based on the previous examples because it is tied to a set of biological process, molecular functions and cellular components relevant to the problem. This yields a less variable and more compact set of features when comparing with gene dosage classifiers. Other facets of knowledge-based systems approaches addressing ASD through network analysis and machine learning, providing an interesting avenue of research for the future, are presented through the study. Lay SummaryThere are important issues in the differential diagnosis of Autism Spectrum Disorders. Gene dosage effects may be important in this context. In this work, we studied genetic alterations related to dopamine processes that could impact brain development and function of 2636 participants. On average, from a genetic sample we were able to correctly separate autism from developmental delay with an accuracy of 85%.

genetics↗

bioRxiv2020

The boundaries of State-Space Granger Causality Analysis applied to BOLD simulated data: a comparative modelling and simulation approach

BackgroundThe analysis of connectivity has become a fundamental tool in human neuroscience. Granger Causality Mapping is a data-driven method that uses Granger Causality (GC) to assess the existence and direction of influence between signals, based on temporal precedence of information. More recently, a theory of Granger causality has been developed for state-space (SS-GC) processes, but little is known about its statistical validation and application on functional magnetic resonance imaging (fMRI) data. New MethodWe implemented a new heuristic, focusing on the application of SS-GC with a distinct statistical validation technique - Time Reversed Testing - to generative synthetic models and compare it to classical multivariate computational frameworks. We also test a range of experimental parameters, including block structure, sampling frequency, noise and system mean pairwise correlation, using a statistical framework of binary classification. ResultsWe found that SS-GC with time reversed testing outperforms other frameworks. The results validate the application of SS-GC to generative models. When estimating reliable causal relations, SS-GC returns promising results, especially when considering synthetic data with an high impact of noise and sampling rate. ConclusionsSS-GC with time reversed testing offers a possible framework for future analysis of fMRI data in the context of data-driven causality analysis. HighlightsO_LIState-Space GC was combined with a statistical validation step, using a Time Reversed Testing. C_LIO_LIThis novel heuristic overpowers classical GC, when applied to generative models. C_LIO_LICorrectly identified connections between variables increase with the increase of number of blocks and number of points per block. C_LIO_LISNR and subsampling have a significant impact on the results. C_LI

neuroscience↗