Multimodal Brain Data Integration

Invited Speaker: Sophia Ulonska, Dr 

Biomedical Image Informatic Group, VRVis GmbH, Vienna, Austria 

Biography of the speaker: 

Dr Sophia Ulonska is a senior researcher at the nonprofit research center VRVis GmbH, located in Vienna, Austria. She is a mathematician who obtained her PhD in Technical Science in the field of Chemical and Process Engineering at TU Wien by developing algorithms to model, monitor and control E. Coli and CHO fermentation processes. At VRVis, she is doing computational neuroscience, particularly in areas such as multimodal data analysis, neural decoding and evolution. Furthermore, she is working on developing scientific software that allows neuroscientist to interactively explore multimodal brain data in humans, mice and Drosophila melanogaster.  

Description of the general focus of the symposium: 

Modeling and understanding the brain, its development, function, and evolution, present several challenges. To fully comprehend the intricate dynamics of neural architecture across spatial and temporal scales, multimodal integration ranging from neurogenetics to developmental dynamics and functional organization is required. Recent advancements in genetic profiling, brain atlas learning, connectome analysis, and cutting-edge neuroimaging technologies have facilitated multi-scale investigations of brain networks and morphology. This symposium emphasizes computational strategies for integrating such diverse data sources into multimodal brain atlases. It explores how to mine such heterogeneous brain datasets for functional insights across different model organisms, such as fruit flies (Drosophila melanogaster) and mice, as well as human data. This approach can reveal complex organizational principles that link neurogenetics to functional connectivity and network graphs, as well as trace patterns of neurocognitive evolution across species in silico. Such computational methods are increasingly vital because they leverage the expanding universe of multimodal brain data for resource-efficient hypothesis generation, whether for more refined experimental testing or in experimentally inaccessible cases.   

In particular, we want to show how the tool BrainTrawler provides a low-threshold to a multimodal database consisting of transcriptomic, connectivity, and activity data to explore human and mouse brain space. Furthermore, we highlight how the LarvalBrain tool offers unprecedented access and query possibilities for unique data collection of fruit fly larvae across different developmental stages, enabling novel workflows that support behavioral experiments. Furthermore, we present a mouse case study demonstrating how the integration of multiparametric MRI with gene expression data can improve the interpretation of neuroimaging results and aid in identifying brain regions engaged by pharmacological interventions. Finally, we showcase how fusing paleogenomics with publicly available brain data allows for the exploration of the cognitive and socio-affective trait space of archaic brains for the computational reconstruction of brain evolution.   

In summary, we aim to demonstrate and raise awareness of complementary approaches that computationally mine the ever-expanding public multimodal brain databases for the much-needed integrated exploration and understanding of brain function, morphology, and evolution across biological levels and model organisms. 

BrainTrawler: An interactive platform for exploring multimodal data in humans and mice 

Brief description of the talk: 

Sophia Ulonska (VRVis GmbH) will showcase how multimodal brain data are integrated into the data resource Brain Transcriptomic and Connectivity Data (BrainTACO) within the free web-based platform BrainTrawler. She will highlight an innovative and unique method for deriving circuits (neural networks) from brain activity signals in real time, with the capability to mine differentially expressed genes in the entire circuit or its components. This enables multimodal exploration, from single-cell gene expression to connectivity and brain activity across brain initiatives and species in a 3D context in both humans and mice. BrainTrawler aims to support researchers in accelerating data-driven insights and hypothesis generation to understand the brain. In addition, it supports the teaching of brain-gene-function relationships. Furthermore, it is valuable to identify potential target genes in the context of meso-scale brain circuits.