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  • Brandt, Christine Lycke, et al. (creator_code:aut_t)
  • Cognitive effort and schizophrenia modulate large-scale functional brain connectivity
  • 2015
  • record:In_t: Schizophrenia Bulletin. - 0586-7614 .- 1745-1701. ; 41:6, s. 1360-1369
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • Schizophrenia (SZ) is characterized by cognitive dysfunction and disorganized thought, in addition to hallucinations and delusions, and is regarded a disorder of brain connectivity. Recent efforts have been made to characterize the underlying brain network organization and interactions. However, to which degree connectivity alterations in SZ vary across different levels of cognitive effort is unknown. Utilizing independent component analysis (ICA) and methods for delineating functional connectivity measures from functional magnetic resonance imaging (fMRI) data, we investigated the effects of cognitive effort, SZ and their interactions on between-network functional connectivity during 2 levels of cognitive load in a large and well-characterized sample of SZ patients (n = 99) and healthy individuals (n = 143). Cognitive load influenced a majority of the functional connections, including but not limited to fronto-parietal and default-mode networks, reflecting both decreases and increases in between-network synchronization. Reduced connectivity in SZ was identified in 2 large-scale functional connections across load conditions, with a particular involvement of an insular network. The results document an important role of interactions between insular, default-mode, and visual networks in SZ pathophysiology. The interplay between brain networks was robustly modulated by cognitive effort, but the reduced functional connectivity in SZ, primarily related to an insular network, was independent of cognitive load, indicating a relatively general brain network-level dysfunction.
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  • Lycke Brandt, Christine, et al. (creator_code:aut_t)
  • Working memory networks and activation patterns in schizophrenia and bipolar disorder : comparison with healthy controls
  • 2014
  • record:In_t: British Journal of Psychiatry. - 0007-1250 .- 1472-1465. ; 204:4, s. 290-298
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • BACKGROUND: Schizophrenia and bipolar disorder are severe mental disorders with overlapping genetic and clinical characteristics, including cognitive impairments. An important question is whether these disorders also have overlapping neuronal deficits.AIMS: To determine whether large-scale brain networks associated with working memory, as measured with functional magnetic resonance imaging (fMRI), are the same in both schizophrenia and bipolar disorder, and how they differ from those in healthy individuals.METHOD: Patients with schizophrenia (n = 100) and bipolar disorder (n = 100) and a healthy control group (n = 100) performed a 2-back working memory task while fMRI data were acquired. The imaging data were analysed using independent component analysis to extract large-scale networks of task-related activations.RESULTS: Similar working memory networks were activated in all groups. However, in three out of nine networks related to the experimental task there was a graded response difference in fMRI signal amplitudes, where patients with schizophrenia showed greater activation than those with bipolar disorder, who in turn showed more activation than healthy controls. Secondary analysis of the patient groups showed that these activation patterns were associated with history of psychosis and current elevated mood in bipolar disorder.CONCLUSIONS: The same brain networks were related to working memory in schizophrenia, bipolar disorder and controls. However, some key networks showed a graded hyperactivation in the two patient groups, in line with a continuum of neuronal abnormalities across psychotic disorders.
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  • Schwarz, E, et al. (creator_code:aut_t)
  • Reproducible grey matter patterns index a multivariate, global alteration of brain structure in schizophrenia and bipolar disorder
  • 2019
  • record:In_t: Translational psychiatry. - : Springer Science and Business Media LLC. - 2158-3188. ; 9:1, s. 12-
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • Schizophrenia is a severe mental disorder characterized by numerous subtle changes in brain structure and function. Machine learning allows exploring the utility of combining structural and functional brain magnetic resonance imaging (MRI) measures for diagnostic application, but this approach has been hampered by sample size limitations and lack of differential diagnostic data. Here, we performed a multi-site machine learning analysis to explore brain structural patterns of T1 MRI data in 2668 individuals with schizophrenia, bipolar disorder or attention-deficit/ hyperactivity disorder, and healthy controls. We found reproducible changes of structural parameters in schizophrenia that yielded a classification accuracy of up to 76% and provided discrimination from ADHD, through it lacked specificity against bipolar disorder. The observed changes largely indexed distributed grey matter alterations that could be represented through a combination of several global brain-structural parameters. This multi-site machine learning study identified a brain-structural signature that could reproducibly differentiate schizophrenia patients from controls, but lacked specificity against bipolar disorder. While this currently limits the clinical utility of the identified signature, the present study highlights that the underlying alterations index substantial global grey matter changes in psychotic disorders, reflecting the biological similarity of these conditions, and provide a roadmap for future exploration of brain structural alterations in psychiatric patients.
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