A5017272558- Functional Brain Connectivity Studies
- Mental Health Research Topics
- Schizophrenia research and treatment
- Advanced Neuroimaging Techniques and Applications
- Diet and metabolism studies
- Mental Health and Psychiatry
- Treatment of Major Depression
- Psychosomatic Disorders and Their Treatments
- Health, Environment, Cognitive Aging
- EEG and Brain-Computer Interfaces
- Machine Learning in Healthcare
- Nutritional Studies and Diet
- Adipose Tissue and Metabolism
- Genetic Associations and Epidemiology
- Neural and Behavioral Psychology Studies
Ludwig-Maximilians-Universität München
2020-2024
Max Planck Institute of Psychiatry
2022-2024
Gold Skin Care Center
2024
LMU Klinikum
2024
Abstract Using machine learning, we recently decomposed the neuroanatomical heterogeneity of established schizophrenia to discover two volumetric subgroups—a ‘lower brain volume’ subgroup (SG1) and an ‘higher striatal (SG2) with otherwise normal structure. In this study, investigated whether MRI signatures these subgroups were also already present at time first-episode psychosis (FEP) they related clinical presentation remission over 1-, 3-, 5-years. We included 572 FEP 424 healthy controls...
Symptom heterogeneity characterizes psychotic disorders and hinders the delineation of underlying biomarkers. Here, we identify symptom-based subtypes recent-onset psychosis (ROP) patients from multi-center PRONIA (Personalized Prognostic Tools for Early Psychosis Management) database explore their multimodal biological functional signatures. We clustered N = 328 ROP based on maximum factor scores in an exploratory analysis
Abstract Background Electroencephalography (EEG) is a noninvasive, cost-effective, and robust tool, which directly measures in vivo neuronal mass activity with high temporal resolution. Combined state-of-the-art machine learning (ML) techniques, EEG recordings could potentially yield silico biomarkers of severe mental disorders. Hypothesis Pathological physiological aging processes influence the electrophysiological signatures schizophrenia (SCZ) major depressive disorder (MDD). Study Design...
<title>Abstract</title> Understanding the neurobiological underpinnings of weight gain could reduce excess mortality and improve long-term trajectories psychiatric disorders. We used support-vector machines whole-brain voxel-wise grey matter volume to generate validate a BMI predictor in healthy individuals (N = 1504) applied it with schizophrenia (SCZ,N 146), clinical high-risk states for psychosis (CHR,N 213) recent-onset depression (ROD,N 200). computed BMIgap...