Publications by authors named "Karen Ambrosen"

Background: While several risk factors for schizophrenia have been identified, their individual impacts are rather small. The relative independent and cumulative impacts of multiple risk factors on disease risk and age of onset warrant further investigation.

Study Design: We conducted a register-based case-control study including all individuals receiving a schizophrenia spectrum disorder in Denmark from 1973 to 2018 ( = 29,142), and a healthy control sample matched 5:1 on age, sex, and parental socioeconomic status ( = 136,387).

View Article and Find Full Text PDF

Cognitive impairments are core features in individuals across the psychosis continuum and predict functional outcomes. Nevertheless, substantial variability in cognitive functioning within diagnostic groups, along with considerable overlap with healthy controls, hampers the translation of research findings into personalized treatment planning. Aligned with precision medicine, we employed a data driven machine learning method, self-organizing maps, to conduct transdiagnostic clustering based on cognitive functions in a sample comprising 228 healthy controls, 200 individuals at ultra-high risk for psychosis, and 98 antipsychotic-naïve patients with first-episode psychosis.

View Article and Find Full Text PDF

Patients with schizophrenia exhibit structural and functional dysconnectivity but the relationship to the well-documented cognitive impairments is less clear. This study investigates associations between structural and functional connectivity and executive functions in antipsychotic-naïve patients experiencing schizophrenia. Sixty-four patients with schizophrenia and 95 matched controls underwent cognitive testing, diffusion weighted imaging and resting state functional magnetic resonance imaging.

View Article and Find Full Text PDF

Background: Facial expressions are a core aspect of non-verbal communication. Reduced emotional expressiveness of the face is a common negative symptom of schizophrenia, however, quantifying negative symptoms can be clinically challenging and involves a considerable element of rater subjectivity. We used computer vision to investigate if (i) automated assessment of facial expressions captures negative as well as positive and general symptom domains, and (ii) if automated assessments are associated with treatment response in initially antipsychotic-naïve patients with first-episode psychosis.

View Article and Find Full Text PDF

Psychotic disorders have been linked to immune-system abnormalities, increased inflammatory markers, and subtle neuroinflammation. Studies further suggest a dysfunctional blood brain barrier (BBB). The endothelial Glycocalyx (GLX) functions as a protective layer in the BBB, and GLX shedding leads to BBB dysfunction.

View Article and Find Full Text PDF

The impact of psychological and physical health on quality of life (QoL) in patients with early psychosis remain relatively unexplored. We evaluated the predictive value of psychopathological and metabolic parameters on QoL in antipsychotic-naïve patients with first-episode psychosis before and after initial antipsychotic treatment. At baseline, 125 patients underwent assessments of psychopathology, prevalence of metabolic syndrome (MetS), and QoL.

View Article and Find Full Text PDF
Article Synopsis
  • The dysconnectivity hypothesis of schizophrenia is supported by research, but previous findings on white matter (WM) changes in patients are inconsistent due to various confounding factors like MRI processing and clinical differences.* -
  • A study involving 86 first-episode, antipsychotic-naïve patients and 112 controls used refined methods to analyze WM using diffusion MRI and fixel-based analysis (FBA), focusing on fiber density and cross-section in relation to symptoms.* -
  • Results showed reduced fiber density in specific brain areas correlated with various psychotic symptoms, highlighting distinct relationships between WM microstructure and both psychosis-specific and anxio-depressive symptoms, suggesting a more detailed approach in future research.*
View Article and Find Full Text PDF

Schizophrenia is associated with aberrations in the Default Mode Network (DMN), but the clinical implications remain unclear. We applied data-driven, unsupervised machine learning based on resting-state electroencephalography (rsEEG) functional connectivity within the DMN to cluster antipsychotic-naïve patients with first-episode schizophrenia. The identified clusters were investigated with respect to psychopathological profile and cognitive deficits.

View Article and Find Full Text PDF

Background: Brain structural alterations and cognitive dysfunction are independent predictors for poor clinical outcome in schizophrenia, and the associations between these domains remains unclear. We employed a novel, multiblock partial least squares correlation (MB-PLS-C) technique and investigated multivariate cortico-cognitive patterns in patients with treatment-resistant schizophrenia (TRS) and matched healthy controls (HC).

Method: Forty-one TRS patients (age 38.

View Article and Find Full Text PDF

Background: Disturbances in presynaptic dopamine activity and levels of GABA (gamma-aminobutyric acid) and glutamate plus glutamine collectively may have a role in the pathophysiology of psychosis, although separately they are poor diagnostic markers. We tested whether these neurotransmitters in combination improve the distinction of antipsychotic-naïve patients with first-episode psychosis from healthy control subjects.

Methods: We included 23 patients (mean age 22.

View Article and Find Full Text PDF

Modern diffusion and functional magnetic resonance imaging (dMRI/fMRI) provide non-invasive high-resolution images from which multi-layered networks of whole-brain structural and functional connectivity can be derived. Unfortunately, the lack of observed correspondence between the connectivity profiles of the two modalities challenges the understanding of the relationship between the functional and structural connectome. Rather than focusing on correspondence at the level of connections we presently investigate correspondence in terms of modular organization according to shared canonical processing units.

View Article and Find Full Text PDF

Background: Validated clinical prediction models of short-term remission in psychosis are lacking. Our aim was to develop a clinical prediction model aimed at predicting 4-6-week remission following a first episode of psychosis.

Method: Baseline clinical data from the Athens First Episode Research Study was used to develop a Support Vector Machine prediction model of 4-week symptom remission in first-episode psychosis patients using repeated nested cross-validation.

View Article and Find Full Text PDF

Objective: Psychosis spectrum disorders are associated with cerebral changes, but the prognostic value and clinical utility of these findings are unclear. Here, we applied a multivariate statistical model to examine the predictive accuracy of global white matter fractional anisotropy (FA) for transition to psychosis in individuals at ultra-high risk for psychosis (UHR).

Methods: 110 UHR individuals underwent 3 Tesla diffusion-weighted imaging and clinical assessments at baseline, and after 6 and 12 months.

View Article and Find Full Text PDF

The organization of the human brain remains elusive, yet is of great importance to the mechanisms of integrative brain function. At the macroscale, its structural and functional interpretation is conventionally assessed at the level of cortical units. However, the definition and validation of such cortical parcellations are problematic due to the absence of a true gold standard.

View Article and Find Full Text PDF

The reproducibility of machine-learning analyses in computational psychiatry is a growing concern. In a multimodal neuropsychiatric dataset of antipsychotic-naïve, first-episode schizophrenia patients, we discuss a workflow aimed at reducing bias and overfitting by invoking simulated data in the design process and analysis in two independent machine-learning approaches, one based on a single algorithm and the other incorporating an ensemble of algorithms. We aimed to (1) classify patients from controls to establish the framework, (2) predict short- and long-term treatment response, and (3) validate the methodological framework.

View Article and Find Full Text PDF

Diffusion-weighted magnetic resonance imaging (DW-MRI) tractography is a non-invasive tool to probe neural connections and the structure of the white matter. It has been applied successfully in studies of neurological disorders and normal connectivity. Recent work has revealed that tractography produces a high incidence of false-positive connections, often from "bottleneck" white matter configurations.

View Article and Find Full Text PDF

Evaluation of the structural connectivity (SC) of the brain based on tractography has mainly focused on the choice of diffusion model, tractography algorithm, and their respective parameter settings. Here, we systematically validate SC derived from a post mortem monkey brain, while varying key acquisition parameters such as the b-value, gradient angular resolution and image resolution. As gold standard we use the connectivity matrix obtained invasively with histological tracers by Markov et al.

View Article and Find Full Text PDF