Larissa Albantakis, PhD
Assistant Professor (Tenure Track)
Dr. Albantakis’ research is aimed at developing novel computational tools to analyze and model the origins, symptoms, and potential for interventions of mental disorders in a causal, mechanistic manner at the individual and group level.
Larissa Albantakis, PhD is a computational neuroscientist and Assistant Professor of Computational Psychiatry. Her research explores the relationship between causation, complexity, consciousness, and cognition, and their quantitative assessments in neural network models and neurophysiological data from healthy subjects and clinical patient populations.
One persistent issue in addressing psychiatric disorders is that the brain is a complex system with strongly interconnected regions. Even local abnormalities impact the brain’s neural dynamics and functional connectivity at a global scale. Dr. Albantakis’ research aims to develop novel theoretical tools to identify the causal origins of distributed dynamical disfunction in clinical patients with psychiatric disorders and to develop whole-brain models fit to individual patients to predict the impact of neural, pharmacological, and behavioral interventions at the subject level. Here, schizophrenia, a disorder of thought with a diverse set of positive and negative symptoms implicating multiple brain areas and potential neural mechanisms, is a primary focus.
Dr. Albantakis obtained her Diploma (MSc) in Physics from Ludwig-Maximilians University in Munich in 2007, and her PhD in Computational Neuroscience from Universitat Pompeu Fabra in Barcelona in 2011 under the supervision of Gustavo Deco. She has been at the University of Wisconsin-Madison since 2012, where she worked together with Giulio Tononi before starting her own research group in 2022.
- Grasso* M, Albantakis* L, Lang JP, Tononi G (2021) Causal reductionism and causal structures. Nature Neuroscience, 24: 1348-1355. doi: 10.1038/s41593-021-00911-8
- Albantakis L, Marshall W, Hoel E, Tononi G (2019) What Caused What? A Quantitative Account of Actual Causation Using Dynamical Causal Networks. Entropy, 21:459. doi: 10.3390/e21050459
- Albantakis L, Deco G (2009) The encoding of alternatives in multiple-choice decision making. Proc Natl Acad Sci, 106, 10308-10313. doi: 10.1073/pnas.0901621106