Biology subjects

Castelo-Branco, M.

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

3 recordsLinked to original sources

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↗