Topic: neurological disorders

This talk aims to highlight the immense potential of Artificial Intelligence (AI) in advancing the field of psychology and cognitive neuroscience. Through the integration of machine learning algorithms, big data analytics, and neuroimaging techniques, AI has the potential to revolutionize the way we study human cognition and brain characteristics. In this talk, I will highlight our latest scientific advancements in utilizing AI to gain deeper insights into variations in cognitive performance across the lifespan and along the continuum from healthy to pathological functioning. The presentation

With this webinar series, the ALBA Disability & Accessibility Working Group aims to bring down the ivory tower of ableism among the brain research community, one extraordinary neuroscientist at a time. These webinars give a platform to scientists with disabilities across the globe and neuroscience disciplines, while reflecting on how to promote inclusive working environments and accessibility to research. For this 2nd episode, Prof. Philip Haydon (Tufts University School of Medicine, Boston, USA) will talk about his research and experience. Prof. Philip runs an active laboratory researching a

EEG has several applications in neuroscience, neural engineering, and diagnosis of neurological disorders. It records the electrical activity of the brain tissues resulting from the interaction of distinct neuronal populations. Various methods have been developed for the automatic classification of EEG signals. These algorithms operate by acquiring unique and non-redundant features from EEG data. However, a large portion of the proposed algorithms usually use temporal features while ignoring the dense spatial network structure generating the EEG. We propose a classification pipeline that utili

Broadly, the Mauro Costa-Mattioli laboratory (The MCM Lab) encompasses two complementary lines of research. The first one, more traditional but very important, aims at unraveling the molecular mechanisms underlying memory formation (e.g., using state-of-the-art molecular and cell-specific genetic approaches). Learning and memory disorders can strike the brain during development (e.g., Autism Spectrum Disorders and Down Syndrome), as well as during adulthood (e.g., Alzheimer’s disease). We are interested in understanding the specific circuits and molecular pathways that are primarily targeted i

Sex differences in brain structure and function are of substantial scientific interest because of sex-related susceptibility to psychiatric and neurological disorders. Neuroinflammation is a common denominator of many of these diseases and thus microglia as the brain´s immunocompetent and instrumental cells has come into focus in sex specific studies. We and others show that male microglia are more frequent in specific brain areas and appear to have a higher potential to respond to stimuli, whereas female microglia seem to acquire a more “protective” phenotype.

Advancing therapies for neurological disorders relies on developing efficient, personalized whole-brain models that can capture mechanistic insights and pathological features. Current challenges in creating such computational models arise from managing a considerable number of adjustable parameters. The conventional manual exploration of this parameter space is a serious limitation for personalizing models to individual subject data [1]. This work aims to address this by approaching the problem as an optimization challenge in the context of developing brain models capable of characterizing in