Research Software Developer (f/m/d): closely collaborate with resident research groups developing custom-tailored software applications for experimental data acquisition and analysis. Data processing is performed on premises using a local high-performance computing (HPC) cluster comprising multiple hardware architectures (x86, IBM Power, GPU). Main responsibilities include development of scientific software applications in Python, administration of on-premise software development platforms (GitLab, SVN, Perforce) and platform-specific code modifications and patch development for existing open-source analysis software. Linux System Administrator (f/m/d): maintain and tune the Linux infrastructure of our on-premise HPC cluster comprising multiple hardware architectures (x86, IBM Power, GPU). Main responsibilities include maintenance and day-to-day operations of HA cluster filesystems, support and troubleshooting covering HPC-related user-questions, optimizing cluster efficiency and performance.

The DFG funded project Learning From Environment Through the Eyes of Children within SPP 2431 New Data Spaces for the Social Sciences, situated at Goethe University Frankfurt, is looking for candidates for two positions: 1 PostDoc position in Psychology and 1 PhD or PostDoc position in Computer Science. The project aims to establish a new mode of data acquisition capturing young children’s first-person experience in naturalistic settings and develop AI systems to characterize the nature and complexity of these experiences. This interdisciplinary project involves collaboration between the psychology and computer science departments, contributing to the SPP programme's goals of establishing a new multimodal data approach in social science studies.

Advanced noninvasive neuroimaging methods provide valuable information on the brain function, but they have obvious pros and cons in terms of temporal and spatial resolution. Functional magnetic resonance imaging (fMRI) using blood-oxygenation-level-dependent (BOLD) effect provides good spatial resolution in the order of millimeters, but has a poor temporal resolution in the order of seconds due to slow hemodynamic responses to neuronal activation, providing indirect information on neuronal activity. In contrast, electroencephalography (EEG) and magnetoencephalography (MEG) provide excellent temporal resolution in the millisecond range, but spatial information is limited to centimeter scales. Therefore, there has been a longstanding demand for noninvasive brain imaging methods capable of detecting neuronal activity at both high temporal and spatial resolution. In this talk, I will introduce a novel approach that enables Direct Imaging of Neuronal Activity (DIANA) using MRI that can dynamically image neuronal spiking activity in milliseconds precision, achieved by data acquisition scheme of rapid 2D line scan synchronized with periodically applied functional stimuli. DIANA was demonstrated through in vivo mouse brain imaging on a 9.4T animal scanner during electrical whisker-pad stimulation. DIANA with milliseconds temporal resolution had high correlations with neuronal spike activities, which could also be applied in capturing the sequential propagation of neuronal activity along the thalamocortical pathway of brain networks. In terms of the contrast mechanism, DIANA was almost unaffected by hemodynamic responses, but was subject to changes in membrane potential-associated tissue relaxation times such as T2 relaxation time. DIANA is expected to break new ground in brain science by providing an in-depth understanding of the hierarchical functional organization of the brain, including the spatiotemporal dynamics of neural networks.

The "mind of the worm", the first electron microscopy-based connectome of C. elegans, was an early sign of where connectomics is headed, followed by a long time of little progress in a field held back by the immense manual effort required for data acquisition and analysis. This changed over the last few years with several technological breakthroughs, which allowed increases in data set sizes by several orders of magnitude. Brain tissue can now be imaged in 3D up to a millimeter in size at nanometer resolution, revealing tissue features from synapses to the mitochondria of all contained cells. These breakthroughs in acquisition technology were paralleled by a revolution in deep-learning segmentation techniques, that equally reduced manual analysis times by several orders of magnitude, to the point where fully automated reconstructions are becoming useful. Taken together, this gives neuroscientists now access to the first wiring diagrams of thousands of automatically reconstructed neurons connected by millions of synapses, just one line of program code away. In this talk, I will cover these developments by describing the past few years' technological breakthroughs and discuss remaining challenges. Finally, I will show the potential of automated connectomics for neuroscience by demonstrating how hypotheses in reinforcement learning can now be tackled through virtual experiments in synaptic wiring diagrams of the songbird basal ganglia.

MRI data quality is crucial to the result. This workshop talks some aspects we need to pay attention during the data acquisition, including FoV/slice brain coverage, synchronization between image acquisition and stimulus presentation, instruction to participant, real time quality monitoring, the usage of physiological data. Prior to the meeting, we are collecting questions for Xiangrui on anything related to mri protocol/parameters: https://www.tricider.com/admin/2YW93TsWZJ3/2DBkJUoE5Ot