Publications by authors named "Jan Egil Nordvik"

25 Publications

  • Page 1 of 1

Development and Results of an Implementation Plan for High-Intensity Gait Training.

J Neurol Phys Ther 2021 Oct;45(4):282-291

First Oslo team members are as follows: Tonje Barkenæs, Miriam Byhring, Magnus Hågå, Chris Henderson, Mari Klokkerud, Julia Mbalilaki, Stein-Arne Rimehaug, Thomas Tomren, and Karen Vergoossen.

Background And Purpose: High-intensity gait training is recommended in stroke rehabilitation to improve gait speed, walking distance, and balance. However, identifying effective and efficient implementation methods is a challenge for rehabilitation providers. This article describes the development of an implementation plan, presents findings of each implementation phase, and identifies the project's impact on clinicians and the health system.

Methods: Two inpatient rehabilitation facilities, including 9 physical therapists, collaborated with a knowledge translation center to implement this program. We developed an implementation plan using the Knowledge-to-Action Framework and utilized the Consolidated Framework for Implementation Research to identify barriers and select implementation strategies. Using mix-methods research, including surveys and informal discussions, we evaluated current practice, barriers, outcomes, and the sustainability of high-intensity gait training in practice.

Results: A multicomponent implementation plan that targeted barriers was developed. Before implementation, clinicians reported providing several balance, strength training, and gait interventions to improve walking. Barriers to using high-intensity gait training included knowledge, beliefs, adaptability of high-intensity gait training, resources, culture, and others. Twenty-six implementation strategies were selected to target the barriers. Surveys and informal discussions identified significant changes in perceived practice, adoption of high-intensity gait training, and positive impacts on the health system. The 2-year follow-up survey indicated that the new practice was sustained.

Discussion And Conclusions: Using a multicomponent implementation plan that targeted barriers, we successfully implemented high-intensity gait training in clinical practice. Contributors to successful implementation may include the implementation methods, usual care interventions, and clinicians' readiness for this change.Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A352.).
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1097/NPT.0000000000000364DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8423140PMC
October 2021

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.nicl.2021.102767DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8329542PMC
September 2021

Psychometric properties of the PROMIS-57 questionnaire, Norwegian version.

Qual Life Res 2021 Jun 18. Epub 2021 Jun 18.

University of Oslo, Oslo, Norway.

Purpose: The aims of this cross-sectional study were to explore reliability and validity of the Norwegian version of the Patient-Reported Outcome Measurement System-Profile 57 (PROMIS-57) questionnaire in a general population sample, n = 408, and to examine Item Response properties and factor structure.

Methods: Reliability measures were obtained from factor analysis and item response theory (IRT) methods. Correlations between PROMIS-57 and RAND-36-item health survey (RAND36) were examined for concurrent and discriminant validity. Factor structure and IRT assumptions were examined with factor analysis methods. IRT Item and model fit and graphic plots were inspected, and differential item functioning (DIF) for language, age, gender, and education level were examined.

Results: PROMIS-57 demonstrated excellent reliability and satisfactory concurrent and discriminant validity. Factor structure of seven domains was supported. IRT assumptions were met for unidimensionality, local independence, monotonicity, and invariance with no DIF of consequence for language or age groups. Estimated common variance (ECV) per domain and confirmatory factor analysis (CFA) model fit supported unidimensionality for all seven domains. The GRM IRT Model demonstrates acceptable model fit.

Conclusions: The psychometric properties and factor structure of Norwegian PROMIS-57 were satisfactory. Hence, the 57-item questionnaire along with PROMIS-29, and the corresponding 8 and 4 item short forms for physical function, anxiety, depression, fatigue, sleep disturbance, social participation ability and pain interference, are considered suitable for use in research and clinical care in Norwegian populations. Further studies on longitudinal reliability and sensitivity in patient populations and for Norwegian item calibration and/or reference scores are needed.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s11136-021-02906-1DOI Listing
June 2021

Evidence for Reduced Long-Term Potentiation-Like Visual Cortical Plasticity in Schizophrenia and Bipolar Disorder.

Schizophr Bull 2021 May 8. Epub 2021 May 8.

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

Several lines of research suggest that impairments in long-term potentiation (LTP)-like synaptic plasticity might be a key pathophysiological mechanism in schizophrenia (SZ) and bipolar disorder type I (BDI) and II (BDII). Using modulations of visually evoked potentials (VEP) of the electroencephalogram, impaired LTP-like visual cortical plasticity has been implicated in patients with BDII, while there has been conflicting evidence in SZ, a lack of research in BDI, and mixed results regarding associations with symptom severity, mood states, and medication. We measured the VEP of patients with SZ spectrum disorders (n = 31), BDI (n = 34), BDII (n = 33), and other BD spectrum disorders (n = 2), and age-matched healthy control (HC) participants (n = 200) before and after prolonged visual stimulation. Compared to HCs, modulation of VEP component N1b, but not C1 or P1, was impaired both in patients within the SZ spectrum (χ 2 = 35.1, P = 3.1 × 10-9) and BD spectrum (χ 2 = 7.0, P = 8.2 × 10-3), including BDI (χ 2 = 6.4, P = .012), but not BDII (χ 2 = 2.2, P = .14). N1b modulation was also more severely impaired in SZ spectrum than BD spectrum patients (χ 2 = 14.2, P = 1.7 × 10-4). N1b modulation was not significantly associated with Positive and Negative Syndrome Scale (PANSS) negative or positive symptoms scores, number of psychotic episodes, Montgomery and Åsberg Depression Rating Scale (MADRS) scores, or Young Mania Rating Scale (YMRS) scores after multiple comparison correction, although a nominal association was observed between N1b modulation and PANSS negative symptoms scores among SZ spectrum patients. These results suggest that LTP-like plasticity is impaired in SZ and BD. Adding to previous genetic, pharmacological, and electrophysiological evidence, these results implicate aberrant synaptic plasticity as a mechanism underlying SZ and BD.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/schbul/sbab049DOI Listing
May 2021

Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders.

Hum Brain Mapp 2021 04 19;42(6):1714-1726. Epub 2020 Dec 19.

Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.

The deviation between chronological age and age predicted using brain MRI is a putative marker of overall brain health. Age prediction based on structural MRI data shows high accuracy in common brain disorders. However, brain aging is complex and heterogenous, both in terms of individual differences and the underlying biological processes. Here, we implemented a multimodal model to estimate brain age using different combinations of cortical area, thickness and sub-cortical volumes, cortical and subcortical T1/T2-weighted ratios, and cerebral blood flow (CBF) based on arterial spin labeling. For each of the 11 models we assessed the age prediction accuracy in healthy controls (HC, n = 750) and compared the obtained brain age gaps (BAGs) between age-matched subsets of HC and patients with Alzheimer's disease (AD, n = 54), mild (MCI, n = 90) and subjective (SCI, n = 56) cognitive impairment, schizophrenia spectrum (SZ, n = 159) and bipolar disorder (BD, n = 135). We found highest age prediction accuracy in HC when integrating all modalities. Furthermore, two-group case-control classifications revealed highest accuracy for AD using global T1-weighted BAG, while MCI, SCI, BD and SZ showed strongest effects in CBF-based BAGs. Combining multiple MRI modalities improves brain age prediction and reveals distinct deviations in patients with psychiatric and neurological disorders. The multimodal BAG was most accurate in predicting age in HC, while group differences between patients and HC were often larger for BAGs based on single modalities. These findings indicate that multidimensional neuroimaging of patients may provide a brain-based mapping of overlapping and distinct pathophysiology in common disorders.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/hbm.25323DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7978139PMC
April 2021

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

Heliyon 2020 Sep 15;6(9):e04854. Epub 2020 Sep 15.

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

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.heliyon.2020.e04854DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7501434PMC
September 2020

Brain Age Prediction Reveals Aberrant Brain White Matter in Schizophrenia and Bipolar Disorder: A Multisample Diffusion Tensor Imaging Study.

Biol Psychiatry Cogn Neurosci Neuroimaging 2020 12 8;5(12):1095-1103. Epub 2020 Jul 8.

Catosenteret Rehabilitation Center, Son, Norway.

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 = -0.29) 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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.bpsc.2020.06.014DOI Listing
December 2020

Experience-dependent modulation of the visual evoked potential: Testing effect sizes, retention over time, and associations with age in 415 healthy individuals.

Neuroimage 2020 12 20;223:117302. Epub 2020 Aug 20.

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

Experience-dependent modulation of the visual evoked potential (VEP) is a promising proxy measure of synaptic plasticity in the cerebral cortex. However, existing studies are limited by small to moderate sample sizes as well as by considerable variability in how VEP modulation is quantified. In the present study, we used a large sample (n = 415) of healthy volunteers to compare different quantifications of VEP modulation with regards to effect sizes and retention of the modulation effect over time. We observed significant modulation for VEP components C1 (Cohen's d = 0.53), P1 (d = 0.66), N1 (d=-0.27), N1b (d=-0.66), but not P2 (d = 0.08), and in three clusters of total power modulation, 2-4 min after 2 Hz prolonged visual stimulation. For components N1 (d=-0.21) and N1b (d=-0.38), as well for the total power clusters, this effect was retained after 54-56 min, by which time also the P2 component had gained modulation (d = 0.54). Moderate to high correlations (0.39≤ρ≤0.69) between modulation at different postintervention blocks revealed a relatively high temporal stability in the modulation effect for each VEP component. However, different VEP components also showed markedly different temporal retention patterns. Finally, participant age correlated negatively with C1 (χ=30.4), and positively with P1 modulation (χ=13.4), whereas P2 modulation was larger for female participants (χ=15.4). There were no effects of either age or sex on N1 and N1b potentiation. These results provide strong support for VEP modulation, and especially N1b modulation, as a robust measure of synaptic plasticity, but underscore the need to differentiate between components, and to control for demographic confounders.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.neuroimage.2020.117302DOI Listing
December 2020

The genetic architecture of human brainstem structures and their involvement in common brain disorders.

Nat Commun 2020 08 11;11(1):4016. Epub 2020 Aug 11.

Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway.

Brainstem regions support vital bodily functions, yet their genetic architectures and involvement in common brain disorders remain understudied. Here, using imaging-genetics data from a discovery sample of 27,034 individuals, we identify 45 brainstem-associated genetic loci, including the first linked to midbrain, pons, and medulla oblongata volumes, and map them to 305 genes. In a replication sample of 7432 participants most of the loci show the same effect direction and are significant at a nominal threshold. We detect genetic overlap between brainstem volumes and eight psychiatric and neurological disorders. In additional clinical data from 5062 individuals with common brain disorders and 11,257 healthy controls, we observe differential volume alterations in schizophrenia, bipolar disorder, multiple sclerosis, mild cognitive impairment, dementia, and Parkinson's disease, supporting the relevance of brainstem regions and their genetic architectures in common brain disorders.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41467-020-17376-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7421944PMC
August 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.
View Article and Find Full Text PDF

Download full-text PDF

Source
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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41593-019-0471-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6823048PMC
October 2019

A quality indicator set for use in rehabilitation team care of people with rheumatic and musculoskeletal diseases; development and pilot testing.

BMC Health Serv Res 2019 Apr 29;19(1):265. Epub 2019 Apr 29.

National Advisory Unit on Rehabilitation in Rheumatology, Diakonhjemmet Hospital, P.O. Box 23, 0319, Oslo, Norway.

Background: Systems for monitoring effectiveness and quality of rehabilitation services across health care levels are needed. The purpose of this study was to develop and pilot test a quality indicator set for rehabilitation of rheumatic and musculoskeletal diseases.

Methods: The set was developed according to the Rand/UCLA Appropriateness Method, which integrates evidence review, in-person multidisciplinary expert panel meetings and repeated anonymous ratings for consensus building. The quality indicators were pilot-tested for overall face validity and feasibility in 15 specialist and 14 primary care rehabilitation units. Pass rates (percentages of "yes") of the indicators were recorded in telephone interviews with 29 unit managers (structure indicators), and 164 patients (process and outcome indicators). Time use and participants' numeric rating of face validity (0-10, 10 = high validity) were recorded.

Results: Nineteen structure, 12 process and five outcome indicators were developed and piloted. Mean (range) sum pass rates for the structure, process and outcome indicators were 59%(84%), 66%(100%) and 84%(100%), respectively. Mean (range) face validity score for managers/patients was 8.3 (8)/7.9 (9), and mean answering time was 6.0/5.5 min. The final indicator set consists of 19 structure, 11 process and three outcome indicators.

Conclusion: To our knowledge this is the first quality indicator set developed for rehabilitation of rheumatic and musculoskeletal diseases. Good overall face validity and a feasible format indicate a set suitable for monitoring quality in rehabilitation. The variation in pass rates between centers indicates a potential for quality improvement in rheumatic and musculoskeletal rehabilitation in Norway.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12913-019-4091-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6489243PMC
April 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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.7717/peerj.5908DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6276592PMC
November 2018

The effects of multidisciplinary psychosocial interventions on adult cancer patients: a systematic review and meta-analysis.

Disabil Rehabil 2020 04 29;42(8):1062-1070. Epub 2018 Nov 29.

Regional Knowledge Translation Center, Southern-Eastern Norway Regional Health Authority, Sunnaas Rehabilitation Hospital, Oslo, Norway.

To summarize evidence on the effects of multidisciplinary psychosocial rehabilitation interventions for adult cancer patients on fatigue, quality of life, participation, coping, and self-efficacy. We searched MEDLINE, Embase, PyscINFO, PEDro, OT Seeker, Sociological Abstracts, CINAHL, and Cochrane CENTRAL for randomized controlled trials. Two reviewers selected articles independently. Thirty-one articles were included and four meta-analyses were conducted. The results of one meta-analysis was statistically significant when comparing multidisciplinary psychosocial interventions to standard care on fatigue among breast cancer patients (standardized mean differences [SMD] 0.30 (95% confidence interval [CI] 0.04, 0.56)) at 2-6 months follow-up. However, no significant results were revealed on health-related quality of life among breast cancer (SMD 0.38 (95% CI -0.40, 1.16)), prostate cancer (SMD 0.06 (95% CI -0.18, 0.29)), and patients with different cancer diagnoses (SMD 0.06 (95% CI -0.14, 0.25)) at follow-up. One study reported on effects of interventions on participation, and four studied the outcomes of coping and self-efficacy. Multidisciplinary psychosocial interventions may decrease fatigue among breast cancer patients. There is an urgent need for rigorous designed trials in cancer rehabilitation, preferably on fatigue, participation, and coping or self-efficacy. The interventions need to be thoroughly described.Implications for rehabilitationMultidisciplinary psychosocial interventions may reduce fatigue among breast cancer patients.The effects of multidisciplinary psychosocial interventions among cancer patients on health-related quality of life, participation, and coping are unclear.Urgent need for a systemic approach to the development and conduction of multidisciplinary psychosocial interventions, ideally based on guidelines for complex interventions.Need of larger and more rigorously conducted randomized controlled trials investigating the effects of these rehabilitation interventions on fatigue, participation and coping.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1080/09638288.2018.1515265DOI Listing
April 2020

Multiple object tracking and pupillometry reveal deficits in both selective and intensive attention in unilateral spatial neglect.

J Clin Exp Neuropsychol 2019 04 14;41(3):270-289. Epub 2018 Nov 14.

b Department of Psychology , University of Oslo , Oslo , Norway.

Introduction: Unilateral spatial neglect is typically associated with a spatial attention deficit, as neglect patients fail to respond to objects in their contralesional hemispace. However, growing evidence suggests that also nonspatial attention impairments (e.g., arousal) play a role and influences the recovery from this syndrome.

Method: Nonspatial and spatial attentional functions were assessed in 13 right-hemisphere stroke patients with neglect, 13 right-hemisphere stroke patients without neglect, and 26 healthy control participants, by investigating pupillary responses and performance on a multiple object tracking task (MOT)-that is, a dynamic task of divided attention where cognitive load can be manipulated precisely. The task was alternately presented in the left and right hemispace to assess spatial attention functioning.

Results: Results revealed smaller pupillary dilations in both patient groups than in controls, suggesting reduced attentional resources or arousal, and while patients without neglect and controls revealed significant effects of cognitive load on their pupillary responses, neglect patients did not. Both MOT and visual search (VS) tasks revealed spatial symptoms of neglect, while MOT performance measures additionally indicated reduced cognitive functioning in the ipsilateral hemispace. Moreover, the MOT task revealed severely reduced divided attention in neglect patients, as they only managed to track one target in the contralesional hemispace and occasionally two targets at the time in the ipsilesional hemispace.

Conclusion: Our results suggest that a stroke may lead to reduced attentional resources. Furthermore, as neglect patients showed no indications in their pupillary responses that they were able to regulate the allocation of resources in accordance with the varying task demands, it appears they additionally had impaired mechanisms for adjusting arousal levels. Our findings suggest that neglect involves nonspatial as well as spatial attention impairments, as also ipsilesional performance was reduced in this group.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1080/13803395.2018.1536735DOI Listing
April 2019

A longitudinal study of computerized cognitive training in stroke patients - effects on cognitive function and white matter.

Top Stroke Rehabil 2018 05 25;25(4):241-247. Epub 2018 Feb 25.

b Research Group for Lifespan Changes in Brain and Cognition, Department of Psychology , University of Oslo , Oslo , Norway.

Background Computerized cognitive training is suggested to enhance attention and working memory functioning following stroke, but effects on brain and behavior are not sufficiently studied and longitudinal studies assessing brain and behavior relationships are scarce. Objective The study objectives were to investigate relations between neuropsychological performance post-stroke and white matter microstructure measures derived from diffusion tensor imaging (DTI), including changes after 6 weeks of working memory training. Methods In this experimental training study, 26 stroke patients underwent DTI and neuropsychological tests at 3 time points - before and after a passive phase of 6 weeks, and again after 6 weeks of working memory training (Cogmed QM). Fractional anisotropy (FA) was extracted from stroke-free brain areas to assess the white matter microstructure. Twenty-two participants completed the majority of training (≥18/25 sessions) and were entered into longitudinal analyses. Results Significant correlations between FA and baseline cognitive functions were observed (r = 0.58, p = 0.004), however, no evidence was found of generally improved cognitive functions following training or of changes in white matter microstructure. Conclusions While white matter microstructure related to baseline cognitive function in stroke patients, the study revealed no effect on cognitive functions or microstructural changes in white matter in relation to computerized working memory training.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1080/10749357.2018.1443570DOI Listing
May 2018

A large, open source dataset of stroke anatomical brain images and manual lesion segmentations.

Sci Data 2018 02 20;5:180011. Epub 2018 Feb 20.

Child Mind Institute, New York, New York 10022, USA.

Stroke is the leading cause of adult disability worldwide, with up to two-thirds of individuals experiencing long-term disabilities. Large-scale neuroimaging studies have shown promise in identifying robust biomarkers (e.g., measures of brain structure) of long-term stroke recovery following rehabilitation. However, analyzing large rehabilitation-related datasets is problematic due to barriers in accurate stroke lesion segmentation. Manually-traced lesions are currently the gold standard for lesion segmentation on T1-weighted MRIs, but are labor intensive and require anatomical expertise. While algorithms have been developed to automate this process, the results often lack accuracy. Newer algorithms that employ machine-learning techniques are promising, yet these require large training datasets to optimize performance. Here we present ATLAS (Anatomical Tracings of Lesions After Stroke), an open-source dataset of 304 T1-weighted MRIs with manually segmented lesions and metadata. This large, diverse dataset can be used to train and test lesion segmentation algorithms and provides a standardized dataset for comparing the performance of different segmentation methods. We hope ATLAS release 1.1 will be a useful resource to assess and improve the accuracy of current lesion segmentation methods.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/sdata.2018.11DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5819480PMC
February 2018

Distinguishing early and late brain aging from the Alzheimer's disease spectrum: consistent morphological patterns across independent samples.

Neuroimage 2017 09 27;158:282-295. Epub 2017 Jun 27.

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

Alzheimer's disease (AD) is a debilitating age-related neurodegenerative disorder. Accurate identification of individuals at risk is complicated as AD shares cognitive and brain features with aging. We applied linked independent component analysis (LICA) on three complementary measures of gray matter structure: cortical thickness, area and gray matter density of 137 AD, 78 mild (MCI) and 38 subjective cognitive impairment patients, and 355 healthy adults aged 18-78 years to identify dissociable multivariate morphological patterns sensitive to age and diagnosis. Using the lasso classifier, we performed group classification and prediction of cognition and age at different age ranges to assess the sensitivity and diagnostic accuracy of the LICA patterns in relation to AD, as well as early and late healthy aging. Three components showed high sensitivity to the diagnosis and cognitive status of AD, with different relationships with age: one reflected an anterior-posterior gradient in thickness and gray matter density and was uniquely related to diagnosis, whereas the other two, reflecting widespread cortical thickness and medial temporal lobe volume, respectively, also correlated significantly with age. Repeating the LICA decomposition and between-subject analysis on ADNI data, including 186 AD, 395 MCI and 220 age-matched healthy controls, revealed largely consistent brain patterns and clinical associations across samples. Classification results showed that multivariate LICA-derived brain characteristics could be used to predict AD and age with high accuracy (area under ROC curve up to 0.93 for classification of AD from controls). Comparison between classifiers based on feature ranking and feature selection suggests both common and unique feature sets implicated in AD and aging, and provides evidence of distinct age-related differences in early compared to late aging.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.neuroimage.2017.06.070DOI Listing
September 2017

Binocular rivalry after right-hemisphere stroke: Effects of attention impairment on perceptual dominance patterns.

Brain Cogn 2017 10 27;117:84-96. Epub 2017 Jun 27.

Department of Psychology, University of Oslo, Oslo, Norway.

Binocular rivalry is when perception fluctuates while the stimuli, consisting of different images presented to each eye, remain unchanged. The fluctuation rate and predominance ratio of these images are regarded as information source for understanding properties of consciousness and perception. We administered a binocular rivalry task to 26 right-hemisphere stroke patients and 26 healthy control participants, using stimuli such as simple Gabor anaglyphs. Each single Gabor image was of unequal spatial frequency compared to its counterpart, allowing assessment of the effect of relative spatial frequency on rivalry predominance. Results revealed that patients had significantly decreased alternation rate compared to healthy controls, with severity of patients' attention impairment predicting alternation rates. The patient group had higher predominance ratio for high compared to low relative spatial frequency stimuli consistent with the hypothesis that damage to the right hemisphere may disrupt processing of relatively low spatial frequencies. Degree of attention impairment also predicted the effect of relative spatial frequencies. Lastly, both groups showed increased predominance rates in the right eye compared to the left eye. This right eye dominance was more pronounced in patients than controls, suggesting that right hemisphere stroke may additionally affect eye predominance ratios.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.bandc.2017.06.007DOI Listing
October 2017

Increased sensitivity to age-related differences in brain functional connectivity during continuous multiple object tracking compared to resting-state.

Neuroimage 2017 03 20;148:364-372. Epub 2017 Jan 20.

NORMENT, KG Jebsen Centre for Psychosis Research, 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:

Age-related differences in cognitive agility vary greatly between individuals and cognitive functions. This heterogeneity is partly mirrored in individual differences in brain network connectivity as revealed using resting-state functional magnetic resonance imaging (fMRI), suggesting potential imaging biomarkers for age-related cognitive decline. However, although convenient in its simplicity, the resting state is essentially an unconstrained paradigm with minimal experimental control. Here, based on the conception that the magnitude and characteristics of age-related differences in brain connectivity is dependent on cognitive context and effort, we tested the hypothesis that experimentally increasing cognitive load boosts the sensitivity to age and changes the discriminative network configurations. To this end, we obtained fMRI data from younger (n=25, mean age 24.16±5.11) and older (n=22, mean age 65.09±7.53) healthy adults during rest and two load levels of continuous multiple object tracking (MOT). Brain network nodes and their time-series were estimated using independent component analysis (ICA) and dual regression, and the edges in the brain networks were defined as the regularized partial temporal correlations between each of the node pairs at the individual level. Using machine learning based on a cross-validated regularized linear discriminant analysis (rLDA) we attempted to classify groups and cognitive load from the full set of edge-wise functional connectivity indices. While group classification using resting-state data was highly above chance (approx. 70% accuracy), functional connectivity (FC) obtained during MOT strongly increased classification performance, with 82% accuracy for the young and 95% accuracy for the old group at the highest load level. Further, machine learning revealed stronger differentiation between rest and task in young compared to older individuals, supporting the notion of network dedifferentiation in cognitive aging. Task-modulation in edgewise FC was primarily observed between attention- and sensorimotor networks; with decreased negative correlations between attention- and default mode networks in older adults. These results demonstrate that the magnitude and configuration of age-related differences in brain functional connectivity are partly dependent on cognitive context and load, which emphasizes the importance of assessing brain connectivity differences across a range of cognitive contexts beyond the resting-state.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.neuroimage.2017.01.048DOI Listing
March 2017

Age-related differences in brain network activation and co-activation during multiple object tracking.

Brain Behav 2016 11 7;6(11):e00533. Epub 2016 Sep 7.

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; Department of Psychology University of Oslo Oslo Norway.

Introduction: Multiple object tracking (MOT) is a powerful paradigm for measuring sustained attention. Although previous fMRI studies have delineated the brain activation patterns associated with tracking and documented reduced tracking performance in aging, age-related effects on brain activation during MOT have not been characterized. In particular, it is unclear if the task-related activation of different brain networks is correlated, and also if this coordination between activations within brain networks shows differential effects of age.

Methods: We obtained fMRI data during MOT at two load conditions from a group of younger ( = 25, mean age = 24.4 ± 5.1 years) and older ( = 21, mean age = 64.7 ± 7.4 years) healthy adults. Using a combination of voxel-wise and independent component analysis, we investigated age-related differences in the brain network activation. In order to explore to which degree activation of the various brain networks reflect unique and common mechanisms, we assessed the correlations between the brain networks' activations.

Results: Behavioral performance revealed an age-related reduction in MOT accuracy. Voxel and brain network level analyses converged on decreased load-dependent activations of the dorsal attention network (DAN) and decreased load-dependent deactivations of the default mode networks (DMN) in the old group. Lastly, we found stronger correlations in the task-related activations within DAN and within DMN components for younger adults, and stronger correlations between DAN and DMN components for older adults.

Conclusion: Using MOT as means for measuring attentional performance, we have demonstrated an age-related attentional decline. Network-level analysis revealed age-related alterations in network recruitment consisting of diminished activations of DAN and diminished deactivations of DMN in older relative to younger adults. We found stronger correlations within DMN and within DAN components for younger adults and stronger correlations between DAN and DMN components for older adults, indicating age-related alterations in the coordinated network-level activation during attentional processing.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/brb3.533DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5102637PMC
November 2016

Functional connectivity indicates differential roles for the intraparietal sulcus and the superior parietal lobule in multiple object tracking.

Neuroimage 2015 Dec 21;123:129-37. Epub 2015 Aug 21.

Department of Psychology, University of Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Norway.

Attentive tracking requires sustained object-based attention, rather than passive vigilance or rapid attentional shifts to brief events. Several theories of tracking suggest a mechanism of indexing objects that allows for attentional resources to be directed toward the moving targets. Imaging studies have shown that cortical areas belonging to the dorsal frontoparietal attention network increase BOLD-signal during multiple object tracking (MOT). Among these areas, some studies have assigned IPS a particular role in object indexing, but the neuroimaging evidence has been sparse. In the present study, we tested participants on a continuous version of the MOT task in order to investigate how cortical areas engage in functional networks during attentional tracking. Specifically, we analyzed the data using eigenvector centrality mapping (ECM) analysis, which provides estimates of individual voxels' connectedness with hub-like parts of the functional network. The results obtained using permutation based voxel-wise statistics support the proposed role for the IPS in object indexing as this region displayed increased centrality during tracking as well as increased functional connectivity with both prefrontal and visual perceptual cortices. In contrast, the opposite pattern was observed for the SPL, with decreasing centrality, as well as reduced functional connectivity with the visual and frontal cortices, in agreement with a hypothesized role for SPL in attentional shifts. These findings provide novel evidence that IPS and SPL serve different functional roles during MOT, while at the same time being highly engaged during tracking as measured by BOLD-signal changes.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.neuroimage.2015.08.029DOI Listing
December 2015

Attentional load modulates large-scale functional brain connectivity beyond the core attention networks.

Neuroimage 2015 Apr 13;109:260-72. Epub 2015 Jan 13.

Department of Psychology, University of Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Norway. Electronic address:

In line with the notion of a continuously active and dynamic brain, functional networks identified during rest correspond with those revealed by task-fMRI. Characterizing the dynamic cross-talk between these network nodes is key to understanding the successful implementation of effortful cognitive processing in healthy individuals and its breakdown in a variety of conditions involving aberrant brain biology and cognitive dysfunction. We employed advanced network modeling on fMRI data collected during a task involving sustained attentive tracking of objects at two load levels and during rest. Using multivariate techniques, we demonstrate that attentional load levels can be significantly discriminated, and from a resting-state condition, the accuracy approaches 100%, by means of estimates of between-node functional connectivity. Several network edges were modulated during task engagement: The dorsal attention network increased connectivity with a visual node, while decreasing connectivity with motor and sensory nodes. Also, we observed a decoupling between left and right hemisphere dorsal visual streams. These results support the notion of dynamic network reconfigurations based on attentional effort. No simple correspondence between node signal amplitude change and node connectivity modulations was found, thus network modeling provides novel information beyond what is revealed by conventional task-fMRI analysis. The current decoding of attentional states confirms that edge connectivity contains highly predictive information about the mental state of the individual, and the approach shows promise for the utilization in clinical contexts.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.neuroimage.2015.01.026DOI Listing
April 2015
-->