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2 results on '"Kristine Ulrichsen"'

1. Functional brain network modeling in sub-acute stroke patients and healthy controls during rest and continuous attentive tracking

Author

Erlend S. Dørum, Tobias Kaufmann, Dag Alnæs, Geneviève Richard, Knut K. Kolskår, Andreas Engvig, Anne-Marthe Sanders, Kristine Ulrichsen, Hege Ihle-Hansen, Jan Egil Nordvik, and Lars T. Westlye

Subjects
Cerebral stroke, fMRI, Brain network connectivity, Machine learning, Behavioral neuroscience, Cognitive neuroscience, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

A cerebral stroke is characterized by compromised brain function due to an interruption in cerebrovascular blood supply. Although stroke incurs focal damage determined by the vascular territory affected, clinical symptoms commonly involve multiple functions and cognitive faculties that are insufficiently explained by the focal damage alone. Functional connectivity (FC) refers to the synchronous activity between spatially remote brain regions organized in a network of interconnected brain regions. Functional magnetic resonance imaging (fMRI) has advanced this system-level understanding of brain function, elucidating the complexity of stroke outcomes, as well as providing information useful for prognostic and rehabilitation purposes.We tested for differences in brain network connectivity between a group of patients with minor ischemic strokes in sub-acute phase (n = 44) and matched controls (n = 100). As neural network configuration is dependent on cognitive effort, we obtained fMRI data during rest and two load levels of a multiple object tracking (MOT) task. Network nodes and time-series were estimated using independent component analysis (ICA) and dual regression, with network edges defined as the partial temporal correlations between node pairs. The full set of edgewise FC went into a cross-validated regularized linear discriminant analysis (rLDA) to classify groups and cognitive load.MOT task performance and cognitive tests revealed no significant group differences. While multivariate machine learning revealed high sensitivity to experimental condition, with classification accuracies between rest and attentive tracking approaching 100%, group classification was at chance level, with negligible differences between conditions. Repeated measures ANOVA showed significantly stronger synchronization between a temporal node and a sensorimotor node in patients across conditions. Overall, the results revealed high sensitivity of FC indices to task conditions, and suggest relatively small brain network-level disturbances after clinically mild strokes.

Published
2020
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2. Brain age prediction reveals aberrant brain white matter in schizophrenia and bipolar disorder: A multi-sample diffusion tensor imaging study

Author

Siren Tønnesen, Tobias Kaufmann, Ann-Marie G. de Lange, Geneviève Richard, Nhat Trung Doan, Dag Alnæs, Dennis van der Meer, Jaroslav Rokicki, Torgeir Moberget, Ivan I. Maximov, Ingrid Agartz, Sofie R. Aminoff, Dani Beck, Deanna M. Barch, Justyna Beresniewicz, Simon Cervenka, Helena Fatouros-Bergman, Alexander R. Craven, Lena Flyckt, Tiril P. Gurholt, Unn K. Haukvik, Kenneth Hugdahl, Erik Johnsen, Erik G. Jönsson, Knut K. Kolskår, Rune Andreas Kroken, Trine V. Lagerberg, Else-Marie Løberg, Jan Egil Nordvik, Anne-Marthe Sanders, Kristine Ulrichsen, Ole A. Andreassen, Lars T. Westlye, L. Farde, L. Flyckt, G. Engberg, S. Erhardt, H. Fatouros-Bergman, S. Cervenka, L. Schwieler, F. Piehl, I. Agartz, K. Collste, P. Victorsson, A. Malmqvist, M. Hedberg, F. Orhan, C. Sellgren, Psychiatrie & Neuropsychologie, and RS: MHeNs - R2 - Mental Health

Subjects
medicine.medical_specialty, Psychosis, Bipolar Disorder, Cognitive Neuroscience, Audiology, 050105 experimental psychology, Psykiatri, White matter, MOVEMENT, 03 medical and health sciences, 0302 clinical medicine, DEFICITS, Fractional anisotropy, Machine learning, Medicine, Humans, 0501 psychology and cognitive sciences, Radiology, Nuclear Medicine and imaging, Generalizability theory, Bipolar disorder, METAANALYSIS, Biological Psychiatry, GRAY, Psychiatry, ABNORMALITIES, business.industry, 05 social sciences, Linear model, Brain, Brain age, medicine.disease, White Matter, CORPUS-CALLOSUM, INTEGRITY, medicine.anatomical_structure, Diffusion Tensor Imaging, Schizophrenia, DTI, ONSET, Bipolar, PATTERNS, Neurology (clinical), MICROSTRUCTURE, MYELINATION, business, 030217 neurology & neurosurgery, Diffusion MRI
Abstract

BACKGROUND: Schizophrenia (SZ) and bipolar disorder (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, patients with BD, and healthy control (HC) subjects across 10 cohorts.METHODS: We trained 6 cross-validated models using different combinations of DTI data from 927 HC subjects (18-94 years of age) and applied the models to the test sets including 648 patients with SZ (18-66 years of age), 185 patients with BD (18-64 years of age), and 990 HC subjects (17-68 years of age), estimating the brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results.RESULTS: Tenfold cross-validation revealed high accuracy for all models. Compared with HC subjects, the model including all feature sets significantly overestimated the age of patients with SZ (Cohen's d = -20.29) and patients with BD (Cohen's d = 0.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy-based models showed larger group differences than the models based on other DTI-derived metrics.CONCLUSIONS: Brain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners.

Published
2020

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