A review paper on deep learning in neuroimaging data analysis
The paper entitled Deep learning in neuroimaging data analysis: Applications, challenges, and solutions by Lev Kiar Avberšek and Grega Repovš has been published in Frontiers in Neuroimaging. The paper provides an overview of the use of deep learning as a form of machine learning in the analysis of neuroimaging data.
A new paper on dynamic and static functional connectivity
We are pleased to announce that a new paper has been published in Network Neuroscience entitled Static and dynamic fMRI-derived functional connectomes represent largely similar information. The paper was co-authored with Alan Anticevic and John D. Murray from Yale University. The paper provides valuable insights into the comparison and interpretation
May
18
2023
Grega Repovš on the therapeutic effects of psychedelics
In an article in N1 Slovenia, you can read about research on the therapeutic effects of psychedelics in the treatment of mental disorders. One of the interviewees in the article is the head of our lab, Prof. Dr. Grega Repovš.
In collaboration with the Anticevic Lab at Yale School of Medicine, we have developed the Quantitative Neuroimaging Environment & Toolbox (QuNex). This toolbox integrates several packages to support a flexible and extensible framework for data organization, preprocessing, quality assurance, and various analyses across neuroimaging modalities. A detailed description of the
