About this Research Topic
The connection between mental health disorders and their treatments is still a key focus for research. The multifaceted applications offered by computational modelling can be used to develop and enhance diagnostic tools, personalize treatment plans, and increase our understanding of biological, chemical, and psychological processes in the treatment of mental disorders.
This Research Topic aims to explore the integration of computational modelling in advancing psychiatric care, with a focus on approaches predicting therapy outcomes, enhancing treatment options, examining underlying neural mechanisms of pharmacological and psychedelics (such as psilocybin and LSD) which are emerging as promising treatment candidates.
We welcome papers utilizing computational models of behavior (including reinforcement learning and Bayesian models), non-invasive measures of brain activity including neuroimaging and electroencephalography (EEG), network analysis, theory-driven models, and deep learning models. By advancing computational modelling in psychiatry, we aim to foster research that bridges biological, psychological, and behavioral insights for improved diagnosis, personalized treatment, and better patient outcomes.
Topics of interest include (but are not limited to):
- Neurobiological Effects of Pharmacological and Psychedelic Therapies: Simulations of brain network dynamics, examining neural connectivity and plasticity to predict the effects of pharmacological treatments, including psychedelics like psilocybin and LSD.
- Novel Diagnostic and Tracking Tools: Exploration of emerging technologies, such as gait analysis, eye tracking, virtual reality, and facial recognition, as objective tools for diagnosing and tracking mental disorders.
- Behavioral and Psychological Modelling: Development of models for decision-making processes, reinforcement learning, and Bayesian analysis for understanding patient behavior, guiding treatment decisions, and predicting therapeutic outcomes.
- Noninvasive Brain Activity Measurement: use of neuroimaging techniques (such as fMRI, PET) for brain mapping of activity under therapeutic interventions, application of EEG to capture real-time dynamics and development of comprehensive model of brain activity using combined techniques.
- Musculoskeletal Conditions and Psychological Health: Assessing new approaches to evaluate the impact of musculoskeletal conditions like arthritis, osteoporosis, and chronic pain on psychological health outcomes, particularly in the older population.
- Data- and Theory-Driven Models: Development of new algorithms and computational techniques to model treatment mechanisms and risk factors, combining data-driven and theoretical approaches to improve mental health care.
- Precision Psychiatry through Computational Modelling: Applications of computational models to enhance diagnostic accuracy and tailor psychiatric treatments to individual needs, fostering a more personalized approach.
This Research Topic aims to explore the integration of computational modelling in advancing psychiatric care, with a focus on approaches predicting therapy outcomes, enhancing treatment options, examining underlying neural mechanisms of pharmacological and psychedelics (such as psilocybin and LSD) which are emerging as promising treatment candidates.
We welcome papers utilizing computational models of behavior (including reinforcement learning and Bayesian models), non-invasive measures of brain activity including neuroimaging and electroencephalography (EEG), network analysis, theory-driven models, and deep learning models. By advancing computational modelling in psychiatry, we aim to foster research that bridges biological, psychological, and behavioral insights for improved diagnosis, personalized treatment, and better patient outcomes.
Topics of interest include (but are not limited to):
- Neurobiological Effects of Pharmacological and Psychedelic Therapies: Simulations of brain network dynamics, examining neural connectivity and plasticity to predict the effects of pharmacological treatments, including psychedelics like psilocybin and LSD.
- Novel Diagnostic and Tracking Tools: Exploration of emerging technologies, such as gait analysis, eye tracking, virtual reality, and facial recognition, as objective tools for diagnosing and tracking mental disorders.
- Behavioral and Psychological Modelling: Development of models for decision-making processes, reinforcement learning, and Bayesian analysis for understanding patient behavior, guiding treatment decisions, and predicting therapeutic outcomes.
- Noninvasive Brain Activity Measurement: use of neuroimaging techniques (such as fMRI, PET) for brain mapping of activity under therapeutic interventions, application of EEG to capture real-time dynamics and development of comprehensive model of brain activity using combined techniques.
- Musculoskeletal Conditions and Psychological Health: Assessing new approaches to evaluate the impact of musculoskeletal conditions like arthritis, osteoporosis, and chronic pain on psychological health outcomes, particularly in the older population.
- Data- and Theory-Driven Models: Development of new algorithms and computational techniques to model treatment mechanisms and risk factors, combining data-driven and theoretical approaches to improve mental health care.
- Precision Psychiatry through Computational Modelling: Applications of computational models to enhance diagnostic accuracy and tailor psychiatric treatments to individual needs, fostering a more personalized approach.
Keywords: computational modelling, precision psychiatry, diagnosis, psychiatric, treatment
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
