A5087092094- Functional Brain Connectivity Studies
- Schizophrenia research and treatment
- Mental Health Research Topics
- Advanced Neuroimaging Techniques and Applications
- Tryptophan and brain disorders
- Mental Health and Psychiatry
- Child and Adolescent Psychosocial and Emotional Development
- Neurotransmitter Receptor Influence on Behavior
- Bipolar Disorder and Treatment
- Neural and Behavioral Psychology Studies
- Treatment of Major Depression
- Psychosomatic Disorders and Their Treatments
- Neuroscience and Neuropharmacology Research
- Child Abuse and Trauma
- Cannabis and Cannabinoid Research
- Health, Environment, Cognitive Aging
- Attention Deficit Hyperactivity Disorder
- Machine Learning in Healthcare
- Neuroscience and Music Perception
- Health Systems, Economic Evaluations, Quality of Life
- Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Neurological disorders and treatments
- Genetic Associations and Epidemiology
- Neural dynamics and brain function
- Diet and metabolism studies
University of Cologne
2019-2025
University Hospital Cologne
2019-2025
Ludwig-Maximilians-Universität München
2013-2024
ORCID
2024
Authorised Association Consortium
2024
TH Köln - University of Applied Sciences
2023
Forschungszentrum Jülich
2022-2023
Gold Skin Care Center
2018-2019
LMU Klinikum
2010-2018
King's College London
2010-2015
Social and occupational impairments contribute to the burden of psychosis depression. There is a need for risk stratification tools inform personalized functional-disability preventive strategies individuals in at-risk early phases these illnesses.To determine whether predictors associated with social role functioning can be identified patients clinical high-risk (CHR) states or recent-onset depression (ROD) using clinical, imaging-based, combined machine learning; assess geographic,...
Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining and biological broadening the risk spectrum young depressive syndromes remains unclear.To evaluate whether transition predicted CHR or recent-onset depression (ROD) using multimodal machine learning that optimally integrates neurocognitive data, structural magnetic resonance imaging (sMRI), polygenic scores (PRS) for...
It is widely hoped that statistical models can improve decision-making related to medical treatments. Because of the cost and scarcity outcomes data, this hope typically based on investigators observing a model's success in one or two datasets clinical contexts. We scrutinized optimism by examining how well machine learning model performed across several independent trials antipsychotic medication for schizophrenia. Models predicted patient with high accuracy within trial which was developed...
Abstract This article describes the rationale, aims, and methodology of Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ). is largest international collaboration to date that will develop algorithms predict trajectories outcomes individuals at clinical high risk (CHR) for psychosis advance development use novel pharmacological interventions CHR individuals. We present a description participating research networks data processing analysis coordination center, their processes...
Magnetic resonance imaging-based markers of schizophrenia have been repeatedly shown to separate patients from healthy controls at the single-subject level, but it remains unclear whether these reliably distinguish mood disorders across life span and generalize new as well early stages illnesses. The current study used structural MRI-based multivariate pattern classification (i) identify cross-validate a differential diagnostic signature separating with first-episode recurrent (n = 158)...
Background: People at ultra high risk (UHR) of psychosis have an elevated developing a psychotic disorder, but it is difficult to predict which individuals will make transition frank illness. We investigated whether functional magnetic resonance imaging (fMRI) in conjunction with phonological fluency task presentation could distinguish subjects who subsequently developed from those did not. Methods: Sixty-five (41 UHR and 24 healthy controls) were assessed clinical using fMRI, verbal task....
Identifying distinctive subtypes of schizophrenia could ultimately enhance diagnostic and prognostic accuracy. We aimed to uncover neuroanatomical chronic patients test whether stratification can computer-aided discrimination from control subjects. Unsupervised, data-driven clustering structural MRI (sMRI) data was used identify 2 drawn a US-based open science repository (n = 71) we quantified classification improvements compared controls 74) using supervised machine learning. externally...
Previous studies have shown that structural brain changes are among the best-studied candidate markers for schizophrenia (SZ) along with functional connectivity (FC) alterations of resting-state (RS) patterns. This study aimed to investigate effects clinical and sociodemographic variables on classification by applying multivariate pattern analysis (MVPA) both gray matter (GM) volume FC measures in patients SZ healthy controls (HC). RS magnetic resonance imaging data (sMRI) from 74 HC 71 were...
Identifying psychosis subgroups could improve clinical and research precision. Research has focused on symptom subgroups, but there is a need to consider broader spectrum, disentangle illness trajectories, investigate genetic associations.To detect using data-driven methods examine their courses over 1.5 years polygenic scores for schizophrenia, bipolar disorder, major depression educational achievement.This ongoing multisite, naturalistic, longitudinal (6-month intervals) cohort study began...