Four Story Slam presentations led by Priyanka Rao-Ruiz:
Colette Moses
EMBO Postdoctoral Fellow at the department of Evolutionary Neurogenomics Laboratory, Molecular Neuroscience at Swammerdam Institute for Life Sciences
Duplicated genes and neurodegenerative disease risk: The impact of individual differences
The human neocortex underwent a dramatic expansion during recent evolution, occurring at the same time as rapid acquisition of novel, human-specific (HS) genetic material in many regions of the human genome via segmental duplications. Several highly duplicated genome regions contain HS genes that may serve critical neurological functions. However, their repetitive nature also makes them prone to genetic recombination, and deletions and duplications at these loci have been linked to neurodevelopmental disorders. I will discuss the gene duplication events that occurred in one particular region of our genome, and how structural variation between individuals at this locus affects our predisposition to developmental disorders and neurodegenerative diseases. This work has the potential to provide clues towards future clinical interventions for these serious conditions.
Fernando Nobrega Santos
Research Associate at the Multiscale Division of the Department of Anatomy and Neurosciences at VUmc, and Research Fellow at the Institute for Advanced Studies at UvA
Emergence of High Order Hubs in the Human Connectome
In this work, we propose a multivariate signal processing pipeline for building high-order networks -beyond pairwise connectivity - from resting state fMRI signals and explore the consequences for our understanding of the human brain. We investigated high-order hubs in the human brain by searching for three-point interactions. We found that well-known integration and segregation patterns emerged spontaneously from the high-order hubs and can be considered emergent high-order properties of functional brain networks. For instance, one of the high order hubs is consistent with the sensory-motor system and emerges as correlate of gate speed in healthy controls. We believe that our work introduces a promising heuristic route for hyper-graph representation of high-order brain activity and opens exciting avenues for further research in network neuroscience.
Gianina Cristian
PhD candidate, Department of Child Psychiatry, Amsterdam University Medical Center
Machine Learning and Network Integration of Clinical, Cognitive and EEG Autism Spectrum Disorder Characteristics for Personalized Clinical Decision
Promising treatment developments for Autism Spectrum Disorder (ASD) have failed to establish effectiveness owing to inter- and intraindividual variability across biological and clinical dimensions. This calls for the development of personalized interventions by unifying and relating individual dimensions to a) inform individualized treatment choice and b) uncover mechanisms, the targeting of which results in individually desirable outcomes.
I will highlight how novel analytical methods and techniques can facilitate these objectives. First, using a machine learning model derived from a clinical symptomatology and EEG characteristics we predicted core behavioral improvement in future patients with 80%-92% average accuracy. I will further show how we are currently implementing network models to develop mechanistic hypotheses based on ways in which individual biological and clinical dimensions relate to each other. Together, these approaches will help to get beyond the current deadlock in treatment development based upon historical diagnostic concepts.
Floor Loonstra
MD PhD Candidate at the MS Center Amsterdam at Amsterdam UMC
Project Y: the search for clues explaining phenotype variability in MS
To study phenotypic variability in multiple sclerosis patients, well-defined unbiased cohort studies are necessary. The most common and probably most important confounding factor when studying disease phenotype in MS is age. Therefore, we initiated a cohort study of MS patients and healthy controls of the same birth year (1966), called Project Y. In project Y, a cross-sectional population based study, age cannot be a confounder. I will discuss the total number of identified cases in our cohort (n = 452), the clinical characteristics and some of the results of our sub research questions, regarding brain atrophy on MRI, blood biomarkers and exposure to environmental factors early in life. With Project Y, we hope to gain novel insights into the etiology of MS that explain individual disease patterns, which may lead to individual treatments.
