Publications by authors named "Grotegerd D"

The detection of norm deviations is fundamental to clinical decision making and impacts our ability to diagnose and treat diseases effectively. Current normative modeling approaches rely on generic comparisons and quantify deviations in relation to the population average. However, generic models interpolate subtle nuances and risk the loss of critical information, thereby compromising effective personalization of health care strategies.

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Disorders across the affective disorders-psychosis spectrum such as major depressive disorder (MDD), bipolar disorder (BD), schizoaffective disorder (SCA), and schizophrenia (SCZ), have overlapping symptomatology and high comorbidity rates with other mental disorders. So far, however, it is largely unclear why some of the patients develop comorbidities. In particular, the specific genetic architecture of comorbidity and its relationship with brain structure remain poorly understood.

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Although specific risk factors for brain alterations in bipolar disorders (BD) are currently unknown, obesity impacts the brain and is highly prevalent in BD. Gray matter correlates of obesity in BD have been well documented, but we know much less about brain white matter abnormalities in people who have both obesity and BD. We obtained body mass index (BMI) and diffusion tensor imaging derived fractional anisotropy (FA) from 22 white matter tracts in 899 individuals with BD, and 1287 control individuals from 20 cohorts in the ENIGMA-BD working group.

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Background: Cognitive deficits are a key source of disability in individuals with major depressive disorder (MDD) and worsen with disease progression. Despite their clinical relevance, the underlying mechanisms of cognitive deficits remain poorly elucidated, hampering effective treatment strategies. Emerging evidence suggests that alterations in white matter microstructure might contribute to cognitive dysfunction in MDD.

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Background: The German multicenter research consortium BipoLife aims to investigate the mechanisms underlying bipolar disorders. It focuses in particular on people at high risk of developing the disorder and young patients in the early stages of the disease. Functional and structural magnetic resonance imaging (MRI) data was collected in all participating centers.

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Deficits in memory performance have been linked to a wide range of neurological and neuropsychiatric conditions. While many studies have assessed the memory impacts of individual conditions, this study considers a broader perspective by evaluating how memory recall is differentially associated with nine common neuropsychiatric conditions using data drawn from 55 international studies, aggregating 15,883 unique participants aged 15-90. The effects of dementia, mild cognitive impairment, Parkinson's disease, traumatic brain injury, stroke, depression, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder on immediate, short-, and long-delay verbal learning and memory (VLM) scores were estimated relative to matched healthy individuals.

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  • This study used machine learning to classify subtypes of schizophrenia by analyzing brain images from over 4,000 patients and healthy individuals through international collaboration.* -
  • Researchers identified two neurostructural subgroups: one with predominant cortical loss and enlarged striatum, and another with significant subcortical loss in areas like the hippocampus and striatum.* -
  • The findings suggest this new imaging-based classification could redefine schizophrenia based on biological similarities, enhancing our understanding and treatment of the disorder.*
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Childhood maltreatment (CM) is associated with increased limbic activity, while social support is linked to decreased limbic activity towards negative stimuli. Our study aimed to explore the interaction of perceived social support with CM, and their combined impact on limbic activity in negative emotion processing. A total of 130 healthy individuals (HC) underwent a negative emotional face processing paradigm.

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Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model.

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Smooth pursuit eye movements are considered a well-established and quantifiable biomarker of sensorimotor function in psychosis research. Identifying psychotic syndromes on an individual level based on neurobiological markers is limited by heterogeneity and requires comprehensive external validation to avoid overestimation of prediction models. Here, we studied quantifiable sensorimotor measures derived from smooth pursuit eye movements in a large sample of psychosis probands (N = 674) and healthy controls (N = 305) using multivariate pattern analysis.

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  • - The ENIGMA Anxiety Working Group studied brain structural differences between individuals with specific phobias and healthy participants, focusing on subtypes of phobias like animal and blood-injection-injury (BII) while examining how these differences relate to symptom severity and age.
  • - A total of 1,452 participants with phobias and 2,991 healthy subjects were analyzed, revealing that those with phobias exhibited smaller subcortical volumes and varying cortical thickness, especially noted in adults rather than youths.
  • - The results indicate that brain alterations in specific phobias are more significant than in other anxiety disorders, revealing distinct neural underpinnings linked to fear processing across different phobia types, highlighting a
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  • - The study explores the connections between dimensional psychopathological syndromes and brain structure/function in patients with Major Depressive Disorder, Bipolar Disorder, Schizoaffective Disorder, and Schizophrenia, analyzing data from 1,038 individuals.
  • - Researchers identified three main psychopathological factors—paranoid-hallucinatory syndrome, mania, and depression—and found significant brain volume and cortical thickness differences linked to the paranoid-hallucinatory syndrome.
  • - Genome-wide association studies revealed significant genetic associations for mania and depression, suggesting a need for more dimensional perspectives in psychiatric classifications to improve understanding and treatment.
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Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures.

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Background: Individuals at risk for bipolar disorder (BD) have a wide range of genetic and non-genetic risk factors, like a positive family history of BD or (sub)threshold affective symptoms. Yet, it is unclear whether these individuals at risk and those diagnosed with BD share similar gray matter brain alterations.

Methods: In 410 male and female participants aged 17-35 years, we compared gray matter volume (3T MRI) between individuals at risk for BD (as assessed using the EPI scale; = 208), patients with a DSM-IV-TR diagnosis of BD ( = 87), and healthy controls ( = 115) using voxel-based morphometry in SPM12/CAT12.

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  • Neuroinflammation may impact brain integrity in both multiple sclerosis (MS) and major depressive disorder (MDD), yet the relationship between imaging markers and neuroinflammatory activity needs further exploration.
  • A study involving 684 participants used advanced MRI to analyze gray-to-white matter contrast (GWc) and revealed that both MS and MDD patients exhibited significant changes in brain connectivity compared to healthy controls.
  • The findings indicated that abnormalities in GWc were linked to increased depressive symptoms across different groups, suggesting shared inflammatory mechanisms between MDD and MS that could influence treatment approaches.
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Reduced processing speed is a core deficit in major depressive disorder (MDD) and has been linked to altered structural brain network connectivity. Ample evidence highlights the involvement of genetic-immunological processes in MDD and specific depressive symptoms. Here, we extended these findings by examining associations between polygenic scores for tumor necrosis factor-α blood levels (TNF-α PGS), structural brain connectivity, and processing speed in a large sample of MDD patients.

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  • Formal thought disorder (FTD) is a key symptom of schizophrenia, but its exact neurobiological causes and correlation with brain volume loss are still unclear, which this study seeks to address using a large cohort of patients and controls.
  • The research focuses on differentiating between positive, negative, and total formal thought disorder while investigating brain structural changes and their cellular bases using virtual histology tools.
  • Findings reveal distinct neural networks for positive and negative FTD, with negative FTD showing preserved orbitofrontal thickness and both FTD types linked to unique cellular fingerprint patterns, advancing our understanding of the disorder.
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  • This study looks at how stressful life events can lead to depression in people who might already be vulnerable to it.
  • They compared brain changes in people with depression to those without depression over two years.
  • They found that healthy people had some brain changes when stressed, but depressed people only showed changes when they had a history of tough childhood experiences and went through another episode of depression.
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Background: Depressive symptoms seem to be interrelated in a complex and self-reinforcing way. To gain a better understanding of this complexity, the inclusion of theoretically relevant constructs (such as risk and protective factors) offers a comprehensive view into the complex mechanisms underlying depression.

Methods: Cross-sectional data from individuals diagnosed with a major depressive disorder (N = 986) and healthy controls (N = 1049) were analyzed.

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Although highly effective on average, exposure-based treatments do not work equally well for all patients with anxiety disorders. The identification of pre-treatment response-predicting patient characteristics may enable patient stratification. Preliminary research highlights the relevance of inhibitory fronto-limbic networks as such.

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  • The study evaluates different normative models for analyzing brain structure data to find the most effective approach for research and clinical applications.
  • They tested eight algorithms using data from over 37,000 healthy individuals across multiple regions and identified multivariate fractional polynomial regression (MFPR) as the best-performing model.
  • The MFPR proved to be highly accurate across various age groups and maintains stability over time, offering valuable insights for understanding brain development and assisting in future research.
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  • * This large-scale study analyzed MRI scans from over 2,000 schizophrenia patients and 2,800 healthy controls to assess brain volume and microstructural integrity, using advanced modeling techniques.
  • * Results showed that aggressive behavior was significantly associated with reduced cortical and white matter volumes, particularly in key brain areas, suggesting a direct neurological link to aggression in schizophrenia patients.
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  • Schizophrenia is characterized by significant changes in brain structure, but it's not clear if these changes relate to the brain's network organization.
  • Researchers analyzed MRI scans from nearly 2,500 people with schizophrenia alongside healthy controls to see how structural changes connect to brain networks.
  • The study found that certain regions in the brain that are crucial for connectivity are more affected in schizophrenia, indicating a link between brain network vulnerability and the disease's impact, with some similarities to bipolar disorder but not major depressive disorder.
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Patients with bipolar disorder (BD) show alterations in both gray matter volume (GMV) and white matter (WM) integrity compared with healthy controls (HC). However, it remains unclear whether the phenotypically distinct BD subtypes (BD-I and BD-II) also exhibit brain structural differences. This study investigated GMV and WM differences between HC, BD-I, and BD-II, along with clinical and genetic associations.

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