Publications by authors named "Kristine M Ulrichsen"

12 Publications

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Linking objective measures of physical activity and capability with brain structure in healthy community dwelling older adults.

Neuroimage Clin 2021 24;31:102767. Epub 2021 Jul 24.

NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway; KG Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway.

Maintaining high levels of daily activity and physical capability have been proposed as important constituents to promote healthy brain and cognitive aging. Studies investigating the associations between brain health and physical activity in late life have, however, mainly been based on self-reported data or measures designed for clinical populations. In the current study, we examined cross-sectional associations between physical activity, recorded by an ankle-positioned accelerometer for seven days, physical capability (grip strength, postural control, and walking speed), and neuroimaging based surrogate markers of brain health in 122 healthy older adults aged 65-88 years. We used a multimodal brain imaging approach offering complementary structural MRI based indicators of brain health: global white matter fractional anisotropy (FA) and mean diffusivity (MD) based on diffusion tensor imaging, and subcortical and global brain age based on brain morphology inferred from T1-weighted MRI data. In addition, based on the results from the main analysis, follow-up regression analysis was performed to test for association between the volume of key subcortical regions of interest (hippocampus, caudate, thalamus and cerebellum) and daily steps, and a follow-up voxelwise analysis to test for associations between walking speed and FA across the white matter Tract-Based Spatial Statistics (TBSS) skeleton. The analyses revealed a significant association between global FA and walking speed, indicating higher white matter integrity in people with higher pace. Voxelwise analysis supported widespread significant associations. We also found a significant interaction between sex and subcortical brain age on number of daily steps, indicating younger-appearing brains in more physically active women, with no significant associations among men. These results provide insight into the intricate associations between different measures of brain and physical health in old age, and corroborate established public health advice promoting physical activity.
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http://dx.doi.org/10.1016/j.nicl.2021.102767DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8329542PMC
September 2021

Structural brain disconnectivity mapping of post-stroke fatigue.

Neuroimage Clin 2021 22;30:102635. Epub 2021 Mar 22.

NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Norway. Electronic address:

Stroke patients commonly suffer from post stroke fatigue (PSF). Despite a general consensus that brain perturbations constitute a precipitating event in the multifactorial etiology of PSF, the specific predictive value of conventional lesion characteristics such as size and localization remains unclear. The current study represents a novel approach to assess the neural correlates of PSF in chronic stroke patients. While previous research has focused primarily on lesion location or size, with mixed or inconclusive results, we targeted the extended structural network implicated by the lesion, and evaluated the added explanatory value of a structural disconnectivity approach with regards to the brain correlates of PSF. To this end, we estimated individual structural brain disconnectome maps in 84 S survivors in the chronic phase (≥3 months post stroke) using information about lesion location and normative white matter pathways obtained from 170 healthy individuals. PSF was measured by the Fatigue Severity Scale (FSS). Voxel wise analyses using non-parametric permutation-based inference were conducted on disconnectome maps to estimate regional effects of disconnectivity. Associations between PSF and global disconnectivity and clinical lesion characteristics were tested by linear models, and we estimated Bayes factor to quantify the evidence for the null and alternative hypotheses, respectively. The results revealed no significant associations between PSF and disconnectome measures or lesion characteristics, with moderate evidence in favor of the null hypothesis. These results suggest that symptoms of post-stroke fatigue among chronic stroke patients are not simply explained by lesion characteristics or the extent and distribution of structural brain disconnectome, and are discussed in light of methodological considerations.
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http://dx.doi.org/10.1016/j.nicl.2021.102635DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8044723PMC
July 2021

Reliability, sensitivity, and predictive value of fMRI during multiple object tracking as a marker of cognitive training gain in combination with tDCS in stroke survivors.

Hum Brain Mapp 2021 03 20;42(4):1167-1181. Epub 2020 Nov 20.

NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.

Computerized cognitive training (CCT) combined with transcranial direct current stimulation (tDCS) has showed some promise in alleviating cognitive impairments in patients with brain disorders, but the robustness and possible mechanisms are unclear. In this prospective double-blind randomized clinical trial, we investigated the feasibility and effectiveness of combining CCT and tDCS, and tested the predictive value of and training-related changes in fMRI-based brain activation during attentive performance (multiple object tracking) obtained at inclusion, before initiating training, and after the three-weeks intervention in chronic stroke patients (>6 months since hospital admission). Patients were randomized to one of two groups, receiving CCT and either (a) tDCS targeting left dorsolateral prefrontal cortex (1 mA), or (b) sham tDCS, with 40s active stimulation (1 mA) before fade out of the current. Of note, 77 patients were enrolled in the study, 54 completed the cognitive training, and 48 completed all training and MRI sessions. We found significant improvement in performance across all trained tasks, but no additional gain of tDCS. fMRI-based brain activation showed high reliability, and higher cognitive performance was associated with increased tracking-related activation in the dorsal attention network and default mode network as well as anterior cingulate after compared to before the intervention. We found no significant associations between cognitive gain and brain activation measured before training or in the difference in activation after intervention. Combined, these results show significant training effects on trained cognitive tasks in stroke survivors, with no clear evidence of additional gain of concurrent tDCS.
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http://dx.doi.org/10.1002/hbm.25284DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7856645PMC
March 2021

Dissecting the cognitive phenotype of post-stroke fatigue using computerized assessment and computational modeling of sustained attention.

Eur J Neurosci 2020 10 11;52(7):3828-3845. Epub 2020 Jul 11.

NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.

Post-stroke fatigue (PSF) is prevalent among stroke patients, but its mechanisms are poorly understood. Many patients with PSF experience cognitive difficulties, but studies aiming to identify cognitive correlates of PSF have been largely inconclusive. With the aim of characterizing the relationship between subjective fatigue and attentional function, we collected behavioral data using the attention network test (ANT) and self-reported fatigue scores using the fatigue severity scale (FSS) from 53 stroke patients. In order to evaluate the utility and added value of computational modeling for delineating specific underpinnings of response time (RT) distributions, we fitted a hierarchical drift diffusion model (hDDM) to the ANT data. Results revealed a relationship between fatigue and RT distributions. Specifically, there was a positive interaction between FSS score and elapsed time on RT. Group analyses suggested that patients without PSF increased speed during the course of the session, while patients with PSF did not. In line with the conventional analyses based on observed RT, the best fitting hDD model identified an interaction between elapsed time and fatigue on non-decision time, suggesting an increase in time needed for stimulus encoding and response execution rather than cognitive information processing and evidence accumulation. These novel results demonstrate the significance of considering the sustained nature of effort when defining the cognitive phenotype of PSF, intuitively indicating that the cognitive phenotype of fatigue entails an increased vulnerability to sustained effort, and suggest that the use of computational approaches offers a further characterization of specific processes underlying behavioral differences.
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http://dx.doi.org/10.1111/ejn.14861DOI Listing
October 2020

Genetic control of variability in subcortical and intracranial volumes.

Mol Psychiatry 2020 Feb 11. Epub 2020 Feb 11.

NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.

Sensitivity to external demands is essential for adaptation to dynamic environments, but comes at the cost of increased risk of adverse outcomes when facing poor environmental conditions. Here, we apply a novel methodology to perform genome-wide association analysis of mean and variance in ten key brain features (accumbens, amygdala, caudate, hippocampus, pallidum, putamen, thalamus, intracranial volume, cortical surface area, and cortical thickness), integrating genetic and neuroanatomical data from a large lifespan sample (n = 25,575 individuals; 8-89 years, mean age 51.9 years). We identify genetic loci associated with phenotypic variability in thalamus volume and cortical thickness. The variance-controlling loci involved genes with a documented role in brain and mental health and were not associated with the mean anatomical volumes. This proof-of-principle of the hypothesis of a genetic regulation of brain volume variability contributes to establishing the genetic basis of phenotypic variance (i.e., heritability), allows identifying different degrees of brain robustness across individuals, and opens new research avenues in the search for mechanisms controlling brain and mental health.
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http://dx.doi.org/10.1038/s41380-020-0664-1DOI Listing
February 2020

Brain age prediction in stroke patients: Highly reliable but limited sensitivity to cognitive performance and response to cognitive training.

Neuroimage Clin 2020 30;25:102159. Epub 2019 Dec 30.

NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway. Electronic address:

Cognitive deficits are important predictors for outcome, independence and quality of life after stroke, but often remain unnoticed and unattended because other impairments are more evident. Computerized cognitive training (CCT) is among the candidate interventions that may alleviate cognitive difficulties, but the evidence supporting its feasibility and effectiveness is scarce, partly due to the lack of tools for outcome prediction and monitoring. Magnetic resonance imaging (MRI) provides candidate markers for disease monitoring and outcome prediction. By integrating information not only about lesion extent and localization, but also regarding the integrity of the unaffected parts of the brain, advanced MRI provides relevant information for developing better prediction models in order to tailor cognitive intervention for patients, especially in a chronic phase. Using brain age prediction based on MRI based brain morphometry and machine learning, we tested the hypotheses that stroke patients with a younger-appearing brain relative to their chronological age perform better on cognitive tests and benefit more from cognitive training compared to patients with an older-appearing brain. In this randomized double-blind study, 54 patients who suffered mild stroke (>6 months since hospital admission, NIHSS≤7 at hospital discharge) underwent 3-weeks CCT and MRI before and after the intervention. In addition, patients were randomized to one of two groups receiving either active or sham transcranial direct current stimulation (tDCS). We tested for main effects of brain age gap (estimated age - chronological age) on cognitive performance, and associations between brain age gap and task improvement. Finally, we tested if longitudinal changes in brain age gap during the intervention were sensitive to treatment response. Briefly, our results suggest that longitudinal brain age prediction based on automated brain morphometry is feasible and reliable in stroke patients. However, no significant association between brain age and both performance and response to cognitive training were found.
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http://dx.doi.org/10.1016/j.nicl.2019.102159DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6953960PMC
January 2021

Common brain disorders are associated with heritable patterns of apparent aging of the brain.

Nat Neurosci 2019 10 24;22(10):1617-1623. Epub 2019 Sep 24.

Centre for Psychiatry Research, Department of Clinical Neuroscience Karolinska Institutet & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden.

Common risk factors for psychiatric and other brain disorders are likely to converge on biological pathways influencing the development and maintenance of brain structure and function across life. Using structural MRI data from 45,615 individuals aged 3-96 years, we demonstrate distinct patterns of apparent brain aging in several brain disorders and reveal genetic pleiotropy between apparent brain aging in healthy individuals and common brain disorders.
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http://dx.doi.org/10.1038/s41593-019-0471-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6823048PMC
October 2019

Assessing distinct patterns of cognitive aging using tissue-specific brain age prediction based on diffusion tensor imaging and brain morphometry.

PeerJ 2018 30;6:e5908. Epub 2018 Nov 30.

NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.

Multimodal imaging enables sensitive measures of the architecture and integrity of the human brain, but the high-dimensional nature of advanced brain imaging features poses inherent challenges for the analyses and interpretations. Multivariate age prediction reduces the dimensionality to one biologically informative summary measure with potential for assessing deviations from normal lifespan trajectories. A number of studies documented remarkably accurate age prediction, but the differential age trajectories and the cognitive sensitivity of distinct brain tissue classes have yet to be adequately characterized. Exploring differential brain age models driven by tissue-specific classifiers provides a hitherto unexplored opportunity to disentangle independent sources of heterogeneity in brain biology. We trained machine-learning models to estimate brain age using various combinations of FreeSurfer based morphometry and diffusion tensor imaging based indices of white matter microstructure in 612 healthy controls aged 18-87 years. To compare the tissue-specific brain ages and their cognitive sensitivity, we applied each of the 11 models in an independent and cognitively well-characterized sample ( = 265, 20-88 years). Correlations between true and estimated age and mean absolute error (MAE) in our test sample were highest for the most comprehensive brain morphometry ( = 0.83, CI:0.78-0.86, MAE = 6.76 years) and white matter microstructure ( = 0.79, CI:0.74-0.83, MAE = 7.28 years) models, confirming sensitivity and generalizability. The deviance from the chronological age were sensitive to performance on several cognitive tests for various models, including spatial Stroop and symbol coding, indicating poorer performance in individuals with an over-estimated age. Tissue-specific brain age models provide sensitive measures of brain integrity, with implications for the study of a range of brain disorders.
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http://dx.doi.org/10.7717/peerj.5908DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6276592PMC
November 2018

Brain scans from 21,297 individuals reveal the genetic architecture of hippocampal subfield volumes.

Mol Psychiatry 2020 11 2;25(11):3053-3065. Epub 2018 Oct 2.

NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.

The hippocampus is a heterogeneous structure, comprising histologically distinguishable subfields. These subfields are differentially involved in memory consolidation, spatial navigation and pattern separation, complex functions often impaired in individuals with brain disorders characterized by reduced hippocampal volume, including Alzheimer's disease (AD) and schizophrenia. Given the structural and functional heterogeneity of the hippocampal formation, we sought to characterize the subfields' genetic architecture. T1-weighted brain scans (n = 21,297, 16 cohorts) were processed with the hippocampal subfields algorithm in FreeSurfer v6.0. We ran a genome-wide association analysis on each subfield, co-varying for whole hippocampal volume. We further calculated the single-nucleotide polymorphism (SNP)-based heritability of 12 subfields, as well as their genetic correlation with each other, with other structural brain features and with AD and schizophrenia. All outcome measures were corrected for age, sex and intracranial volume. We found 15 unique genome-wide significant loci across six subfields, of which eight had not been previously linked to the hippocampus. Top SNPs were mapped to genes associated with neuronal differentiation, locomotor behaviour, schizophrenia and AD. The volumes of all the subfields were estimated to be heritable (h2 from 0.14 to 0.27, all p < 1 × 10) and clustered together based on their genetic correlations compared with other structural brain features. There was also evidence of genetic overlap of subicular subfield volumes with schizophrenia. We conclude that hippocampal subfields have partly distinct genetic determinants associated with specific biological processes and traits. Taking into account this specificity may increase our understanding of hippocampal neurobiology and associated pathologies.
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http://dx.doi.org/10.1038/s41380-018-0262-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6445783PMC
November 2020

Key Brain Network Nodes Show Differential Cognitive Relevance and Developmental Trajectories during Childhood and Adolescence.

eNeuro 2018 Jul-Aug;5(4). Epub 2018 Jul 11.

NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, 0450 Oslo Norway.

Human adolescence is a period of rapid changes in cognition and goal-directed behavior, and it constitutes a major transitional phase towards adulthood. One of the mechanisms suggested to underlie the protracted maturation of functional brain networks, is the increased network integration and segregation enhancing neural efficiency. Importantly, the increasing coordinated network interplay throughout development is mediated through functional hubs, which are highly connected brain areas suggested to be pivotal nodes for the regulation of neural activity. To elucidate brain hub development during childhood and adolescence, we estimated voxel-wise eigenvector centrality (EC) using functional magnetic resonance imaging (fMRI) data from two different psychological contexts (resting state and a working memory task), in a large cross-sectional sample ( = 754) spanning the age from 8 to 22 years, and decomposed the maps using independent component analysis (ICA). Our results reveal significant age-related centrality differences in cingulo-opercular, visual, and sensorimotor network nodes during both rest and task performance, suggesting that common neurodevelopmental processes manifest across different mental states. Supporting the functional significance of these developmental patterns, the centrality of the cingulo-opercular node was positively associated with task performance. These findings provide evidence for protracted maturation of hub properties in specific nodes of the brain connectome during the course of childhood and adolescence and suggest that cingulo-opercular centrality is a key factor supporting neurocognitive development.
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http://dx.doi.org/10.1523/ENEURO.0092-18.2018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6071203PMC
February 2019

Clinical Utility of Mindfulness Training in the Treatment of Fatigue After Stroke, Traumatic Brain Injury and Multiple Sclerosis: A Systematic Literature Review and Meta-analysis.

Front Psychol 2016 23;7:912. Epub 2016 Jun 23.

Sunnaas Rehabilitation Hospital HT, Nesodden Norway.

Background: Fatigue is a common symptom following neurological illnesses and injuries, and is rated as one of the most debilitating sequela in conditions such as stroke, traumatic brain injury (TBI), and multiple sclerosis (MS). Yet effective treatments are lacking, suggesting a pressing need for a better understanding of its etiology and mechanisms that may alleviate the symptoms. Recently mindfulness-based interventions have demonstrated promising results for fatigue symptom relief.

Objective: Investigate the efficacy of mindfulness-based interventions for fatigue across neurological conditions and acquired brain injuries.

Materials And Methods: Systematic literature searches were conducted in PubMed, Medline, Web of Science, and PsycINFO. We included randomized controlled trials applying mindfulness-based interventions in patients with neurological conditions or acquired brain injuries. Four studies (N = 257) were retained for meta-analysis. The studies included patients diagnosed with MS, TBI, and stroke.

Results: The estimated effect size for the total sample was -0.37 (95% CI: -0.58, -0.17).

Conclusion: The results indicate that mindfulness-based interventions may relieve fatigue in neurological conditions such as stroke, TBI, and MS. However, the effect size is moderate, and further research is needed in order to determine the effect and improve our understanding of how mindfulness-based interventions affect fatigue symptom perception in patients with neurological conditions.
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http://dx.doi.org/10.3389/fpsyg.2016.00912DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4917545PMC
July 2016
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