Biology subjects

Matsulevits, A.

Publications and source records attributed to Matsulevits, A..

3 recordsLinked to original sources

bioRxiv2024

Benchmarking Stroke Outcome Prediction through Comprehensive Data Analysis - NeuralCup 2023

Stroke remains a leading cause of mortality and long-term disability worldwide, with variable recovery trajectories posing substantial challenges in anticipating post-event care and rehabilitation planning. To address these challenges, we established the NeuralCup consortium to benchmark predictive models of stroke outcome through a collaborative, data-driven approach. This study presents findings from 15 international teams who used a comprehensive dataset including clinical and imaging data, to identify and compare predictors of motor, cognitive, and emotional outcomes one year post-stroke. Our analyses integrated traditional statistical approaches and novel machine learning algorithms to uncover optimal recipes for predicting each domain. The differences in these optimal recipes reflect distinct brain mechanisms in response to different tasks. Key predictors across all domains included infarct characteristics, T1-weighted MRI sequences, and demographic factors. Additionally, integrating FLAIR imaging and white matter tract analysis significantly improved the prediction of cognitive and motor outcomes, respectively. These findings support a multifaceted approach to stroke outcome prediction, underscoring the potential of collaborative data science to develop personalized care strategies that enhance recovery and quality of life for stroke survivors. To encourage further model development and validation, we provide access to the training dataset at http://neuralcup.bcblab.com

neuroscience↗

bioRxiv2023

Priming using Human and Chimpanzee Expressions of Emotion Biases Attention toward Positive Emotions

Perceiving and correctly interpreting emotional expressions is one of the most important abilities for social animals communication. It determines the majority of social interactions, group dynamics, and cooperation, being highly relevant for an individuals survival. Core mechanisms of this ability have been hypothesized to be shared across closely related species with phylogenetic similarities. Here, we explored homologies in human processing of different species facial expressions using eye-tracking. Introducing a prime-target paradigm, we tested the influences on human attention elicited by priming with differently valenced emotional stimuli depicting human and chimpanzee faces. We demonstrated an attention shift towards the conspecific (human) target picture that was congruent with the valence depicted in the primer picture. We did not find this effect with heterospecific (chimpanzee) primers and ruled out that this was due to participants interpreting them incorrectly. Implications about the involvement of related emotion-processing mechanisms for human and chimpanzee facial expressions, are discussed. Systematic cross-species-investigations of emotional expressions are needed to unravel how emotion representation mechanisms can extend to process other species faces. Through such studies, we address the gap of a shared evolutionary ancestry between humans and other animals to ultimately answer the question of "Where do emotions come from?".

evolutionary biology↗

bioRxiv2023

Deep Learning disconnectomes to accelerate and improve long-term predictions for post-stroke symptoms

Deep learning as a truly transformative force is revolutionizing a wide range of fields, making a significant difference in medical imaging, where recent advancements have yielded some truly remarkable outcomes. In a connected brain, maps of white matter damage -- otherwise known as disconnectomes -- are essential for capturing the effects of focal lesions. However, the current tools for obtaining such information are prohibitively slow and not admitted for clinical usage. Here, we have explored the potential of deep-learning models to accurately generate disconnectomes in a population of stroke survivors. We trained a 3D U-Net algorithm to produce deep-disconnectomes from binary lesion masks. This artificial neural network was able to capture most information obtained in conventional disconnectomes, i.e., statistical maps filtering normative white-matter networks, but output a deep-disconnectome 170 times faster - compared to disconnectome computation with the state-of-the-art BCBToolkit software. Moreover, the deep-disconnectomes were challenged to predict cognitive and behavioral outcomes one-year post-stroke. In an additional cohort of N=139 stroke survivors, N=86 neuropsychological scores were predicted from deep-disconnectomes achieving, on average, 85.2% of accuracy and R2= 0.208. The deep-disconnectomes predictivity power outperformed the conventional disconnectome predictions for clinical scores. In summary, we have achieved a significant milestone for clinical neuroimaging by accelerating and ameliorating the creation of disconnectome maps using deep learning. By integrating deep learning into the management of stroke, one of the most prevailing catalysts for acquired disabilities, we deepen our understanding of its impact on the brain. This novel approach may offer potential avenues for acute intervention, ultimately enhancing patients overall quality of life.

neuroscience↗