Delineating white matter phenotypes of psychomotor functioning in large-scale cohorts of healthy individuals and patients with psychiatric disorders across the life span

• Applicants: Professor Dr. Dusan Hirjak; Professor Dr. Klaus Maier-Hein
• Subject Area: Biological Psychiatry
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 536508040

Aberrant psychomotor functioning in terms of muscular hand weakness, sedentary behavior, psychomotor slowing, apathy, anxiety and cognitive impairment is a core clinical feature with transdiagnostic importance. Objectively-measured psychomotor abnormalities may provide useful, noninvasive, inexpensive, and quickly administered indicators of overall health-related motor, affective and cognitive impairment in healthy people and psychiatric patients. However, large-scale neuroimaging studies that could shed light on the role of white matter (WM) tracts in psychomotor functioning of healthy individuals and patients with psychiatric disorders such as anxiety disorders, major depressive disorders, schizophrenia spectrum disorders (SSD), mild cognitive impairment (MCI) and Alzheimer’s dementia (AD) are still missing. Further, it is still unknown how WM alterations modulate the relationship between the three psychomotor domains (motor, affective, and cognitive) in healthy individuals and psychiatric patients across the life span. The proposed bi-centric research project will fill these gaps by investigating associations between different measures of psychomotor functioning and microstructural WM alterations in over 2400 healthy individuals and more than 1500 psychiatric patients across the life span using data from two large consortia (Human Connectome Project (HCP) and Alzheimer’s Disease Neuroimaging Initiative (ADNI)) as well as from our own working groups, which will be analyzed using state of the art computational image analysis approaches for diffusion-weighted magnetic resonance imaging (dMRI) data, such as tractometry, radiomics and deep learning. To this end we will delineate a comprehensive WM phenotype based on radiomics features and deep learning that can be used for multivariate taxometric analyses to identify neurobiologically distinct psychiatric psychomotor biotypes that do not respect clinical diagnosis boundaries according to ICD-10, ICD-11 or DSM-5. Overall, this study integrates multivariate data from different large-scale populations and represents a step toward better understanding of the psychomotor domain in healthy individuals and psychiatric patients across the life span, which is a mandatory prerequisite for collaboration with national and international researchers. By leveraging the newest technological developments in the areas of dMRI-based WM research and medical data analysis to reach these goals, the proposed study will also field-test these technologies and examine their diagnostic value for psychomotor abnormalities across and beyond different psychiatric entities across the life span and help to create new framework conditions for modern neuroscience research.

• DFG Programme: Research Grants
• Co-Investigator: Dr. Peter Neher