Our research is centered around the physics and engineering of human behavior. Our objective is to achieve a comprehensive quantitative characterization of human behavior, focusing on memory processes, emotions, and psychological well-being, and to discern whether and to what extent these dynamics can be quantified, predicted, and controlled. 

We believe that significant breakthroughs in understanding human brain and behavior’s complexity will arise from a shift in focus—from a reductive decomposition of the behavior to an emphasis on the principles governing its operation as an integrated complex system. 

Computational, thus, we primarily employ techniques from complexity science and machine learning, including dynamical systems theory, network control theory, and recurrent neural networks. To train and test our models, we utilize cross-sectional and longitudinal behavioral and psychological data, such as thought trajectories in mind-wandering experiments and symptom trajectories of patients with major depression. Additionally, we incorporate neuroimaging data (fMRI, DTI, T1, EEG) and neural stimulation techniques (ECT, tACS, etc.)

1. Machine Learning 
Data-driven Inference 

The inherent challenge is to observe a minor fraction of the trajectory of the select measurable variables and extrapolate inferences about the entire trajectory of all pertinent state variables. 

2. Control 
of Complex Systems

The control of human behaviors goes beyond merely satisfying a controllability property. It involves strategically steering the system to prevent reaching tipping points or to reduce the impact of failures. 

3. Complex System Models of Major Depression 

We systematically apply the technologies developed in our research to inform neuropsychiatric decision making and intervention policies

4. Memory Processes & Spontaneous Thoughts

Few cognitive functions are as distinctly human as the capacity for spontaneous thought: we believe understanding the thought processes is a key to treatment of mental disorders and improving AI. 

Additional Resources

Here, we share some of the most inspiring papers, talks, and datasets that we find of interest.

A unique piece on how psychiatric disorders can be conceptualised as networks of evolving and reinforcing symptoms

A seminal work on informing neurosciences and especially neuroimaging by the theory of control

An inspiring paper on the significance of formal theory in psychological research

One of the best papers to understand how we could inform mathematical models of brain and behavior

Mathematical modeling in terms of equations is a critical step in understanding a phenomenon. This paper discusses a data-driven approach to derive those equations in any context.



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Prof. Dr. Hamidreza Jamalabadi

Philipps-Universität Marburg
Klinik für Psychiatrie und Psychotherapie
Rudolf-Bultmann-Straße 8
35039 Marburg

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