News / Events

Organizing and processing neuroimaging data in a standardized manner is crucial as it significantly enhances data quality, reproducibility, and comparability across studies. By employing standardized methods, researchers can efficiently integrate and analyze data from various sources, facilitating large-scale studies that advance our understanding of the brain. This approach not only reduces human effort and minimizes potential errors, but also ensures that research findings are robust and reliable, fostering trust within the scientific community. Such standardization is pivotal for paving the way for innovative treatments and deeper insights into neurological and psychiatric conditions. This talk will cover software tools that aid in this direction, namely Junifer, a tool for extracting features or markers from MRI data, and Julearn, a tool for machine-learning based predictive analytics. Together, these tools represent a significant stride in the standardization and advancement of neuroimaging data analysis.

Day: 17 April 2024

Time: 5pm – 6.30pm

Related Links

Google Scholar

Research group "Applied Maschine Learning"

The response of many systems to local or global stimulations is jerky and highly variable.  Examples from biology include neuron firing avalanches in the brain, and examples from physics include snow avalanches and earthquakes. In this talk we show how the statistical properties of avalanches can be used to extract information about the dynamics of a system. Similar to earthquakes, neuron firing avalanches in neuronal networks have been hypothesized to operate near a critical point, in the optimally healthy situation. Critical points are characterized by large fluctuations on a wide range of scales. Deviations due to changed conditions, such as diseases, drugs, age-related effects, or too little sleep, can drive a system away from the critical point, thereby changing the variability in the response to stimulation.  The changes in the variability often give additional clues about the underlying dynamics and they allow us to predict the response for a much wider range of conditions. We use tools from statistical physics and the theory of phase transitions to describe the statistics and the dynamics of neuron firing avalanches in the brain. We find that the scaling behavior of the statistics of the experimentally measured data agrees with the predictions of simple models, without the need for any data fitting.  Connections to avalanches in materials, including dynamical effects that can lead to unusually large avalanches, and possible implications for memory and the dynamics of the brain will be discussed.  

Collaborators:   
Tyler Salners, Karina E. Avila, Benjamin Nicholson, Christopher R. Myers, John Beggs, Nir Friedman, Shinya Ito, Braden A. W. Brinkman, Masanori Shimono, R. E. Lee DeVille, and Thomas C. Butler   

Date: 29 November 2023

Time: 5pm-6.30 pm

Related Links

The Grainger College of Engineering

Google Scholar

It is with great pleasure that we welcome Prof. Dr. Andreas Horn as the first speaker in our research group colloquium “MeMoTALK” on September 6^th, 2023. In his lecture, he will talk about how connectomes can guide neuromodulation, and present methods and results of various studies, ranging from healthy subjects to neuropsychiatric diseases, that have investigated connectomic effects of focal neuromodulation. Based on these extensive data, Andreas Horn will show how these findings could be transferred to cross-inform each other and guide connectome-based neuromodulation.

Day: 06 September 2023

Time: 5pm – 6.30pm

Registration: infomemoslapde

Related Links

Network Stimulation Laboratory

Google Scholar

ORCID