After completing the Bachelor’s and Master’s degree in Neuroscience, I continued with a PhD in Neuroscience at the Department of Nuclear Medicine, Technical University of Munich. Over the last few years, I studied and worked in Germany and abroad at the University of Edinburgh, the University of California San Francisco and Yale University. Now, I am a postdoctoral researcher in the Cognition, Values, Behaviour research group at the LMU Munich. Building on my education spanning from mice to human, I aim for a systems neuroscience research approach. On the macroscopic level, I focus on neuro-cognitive relationships by combining (molecular) neuroimaging and behavioural measurements. I am particularly interested in human learning behaviour and the effect of cognitive offloading when using tools for externalising cognitive processes, such as human-centred AIs.
Areas of interest:
Human-centred AI, Reinforcement Learning, Working Memory, Neurocognitive Networks, Cognitive Offloading
I am working on:
Human vs. machine reinforcement learning
Considering hard- and software differences between human and machine, I investigate the comparability of human and machine reinforcement learning. Here, I incorporate the different cognitive components recruited during reinforcement learning including working memory for comparison.
Reinforcement learning across the senses
Human reinforcement learning behaviour has been studied primarily in sterile lab environments using visual stimuli. However, in real life, we are never exposed to such clean unimodal sensory information. I study human reinforcement learning behaviour across sensory modalities. I am particularly interested in the effects on learning through touch.
I have worked on:
Neurocognitive effects underlying digital visual working memory training
I studied the effects of an eight-week working memory training on markers of neural plasticity and cognitive performance using multimodal neuroimaging (FDG PET/MRI) and behavioural measures.
Neural networks underlying multisensory integration processing
Using graph theoretical network analysis based on task fMRI data, I analysed architectural properties of neural networks underlying olfactory-visual stimulation.
Bois, C., Levita, L., Ripp, I., Owens, D. C. G., Johnstone, E. C., Whalley, H. C., & Lawrie, S. M. (2015). Hip-pocampal, amygdala and nucleus accumbens volume in first-episode schizophrenia patients and individuals at high familial risk: A cross-sectional comparison. Schizophrenia Research, 165(1), 45–51. https://doi.org/10.1016/j.schres.2015.03.024
Bois, C., Levita, L., Ripp, I., Owens, D. C. G., Johnstone, E. C., Whalley, H. C., & Lawrie, S. M. (2016). Lon-gitudinal changes in hippocampal volume in the Edinburgh High Risk Study of Schizophrenia. Schizophrenia Research, 173(3), 146–151. https://doi.org/10.1016/j.schres.2014.12.003
Ripp, I., Zur Nieden, A.-N., Blankenagel, S., Franzmeier, N., Lundström, J. N., & Freiherr, J. (2018). Multi-sensory integration processing during olfactory-visual stimulation-An fMRI graph theoretical network analysis. Human Brain Mapping. https://doi.org/10.1002/hbm.24206
Ripp, I., Savio, A., & Yakushev, I. (2018). Reply: Neurometabolic resting-state networks derived from seed-based functional connectivity analysis. Journal of Nuclear Medicine: Official Publication, Society of Nuclear Medicine. https://doi.org/10.2967/jnumed.118.216150
Emch, M., Ripp, I., Wu, Q., Yakushev, I., & Koch, K. (2019). Neural and Behavioral Effects of an Adaptive Online Verbal Working Memory Training in Healthy Middle-Aged Adults. Frontiers in Aging Neuroscience, 11. https://doi.org/10.3389/fnagi.2019.00300
Ripp, I., Stadhouders, T., Savio, A., Goldhardt, O., Cabello, J., Calhoun, V., Riedl, V., Hedderich, D., Diehl-Schmid, J., Grimmer, T., & Yakushev, I. (2020). Integrity of neurocognitive networks in dementing disorders as measured with simultaneous PET/fMRI. Journal of Nuclear Medicine, jnumed.119.234930. https://doi.org/10.2967/jnumed.119.234930
Ripp, I., Wallenwein, L. A., Wu, Q., Emch, M., Koch, K., Cumming, P., & Yakushev, I. (2021). Working memory task induced neural activation: A simultaneous PET/fMRI study. NeuroImage, 237, 118131. https://doi.org/10.1016/j.neuroimage.2021.118131
Wu, Q., Ripp, I., Emch, M., & Koch, K. (2021). Cortical and subcortical responsiveness to intensive adaptive working memory training: An MRI surface-based analysis. Human Brain Mapping, 42(9), 2907–2920. https://doi.org/10.1002/hbm.25412
Yakushev, I. *, Ripp, I.*, Wang M., Savio, A., Schutte, M., Lizarraga, A., Bogdanovic, B., Diehl-Schmid, J., Hedderich, D., Grimmer, T., Kuangyu, S (2021) Mapping covariance in brain FDG uptake to structural con-nectivity. Eur J Nucl Med Mol Imaging. https://doi.org/10.1007/s00259-021-05590-y
A. Sala *, A. Lizarraga *, Ripp, I.*, P. Cumming, Igor Yakushev (2022). Static versus Functional PET: Making Sense of Metabolic Connectivity, Cerebral Cortex, 2021;, bhab27.1 https://doi.org/10.1093/cercor/bhab271
Ripp I., Wu Q., Wallenwein L., Emch M., Yakushev I., Koch K. (2022). Neuronal efficiency following n-back training task is accompanied by a higher cerebral glucose metabolism. NeuroImage, 253, 119095. https://doi.org/10.1016/j.neuroimage.2022.119095
Ripp, I., Emch, M., Wu, Q. et al. (2022) Adaptive working memory training does not produce transfer effects in cognition and neuroimaging. Transl Psychiatry 12, 512. https://doi.org/10.1038/s41398-022-02272-7
This project is about X.