Publications by authors named "Anne-Marthe Sanders"

18 Publications

  • Page 1 of 1

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

TVA-based modeling of short-term memory capacity, speed of processing and perceptual threshold in chronic stroke patients undergoing cognitive training: case-control differences, reliability, and associations with cognitive performance.

PeerJ 2020 28;8:e9948. Epub 2020 Oct 28.

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

Attentional deficits following stroke are common and pervasive, and are important predictors for functional recovery. Attentional functions comprise a set of specific cognitive processes allowing to attend, filter and select among a continuous stream of stimuli. These mechanisms are fundamental for more complex cognitive functions such as learning, planning and cognitive control, all crucial for daily functioning. The distributed functional neuroanatomy of these processes is a likely explanation for the high prevalence of attentional impairments following stroke, and underscores the importance of a clinical implementation of computational approaches allowing for sensitive and specific modeling of attentional sub-processes. The Theory of Visual Attention (TVA) offers a theoretical, computational, neuronal and practical framework to assess the efficiency of visual selection performance and parallel processing of multiple objects. Here, in order to assess the sensitivity and reliability of TVA parameters reflecting short-term memory capacity (), processing speed () and perceptual threshold ( ), we used a whole-report paradigm in a cross-sectional case-control comparison and across six repeated assessments over the course of a three-week computerized cognitive training (CCT) intervention in chronic stroke patients (> 6 months since hospital admission, NIHSS ≤ 7 at hospital discharge). Cross-sectional group comparisons documented lower short-term memory capacity, lower processing speed and higher perceptual threshold in patients ( = 70) compared to age-matched healthy controls ( = 140). Further, longitudinal analyses in stroke patients during the course of CCT ( = 54) revealed high reliability of the TVA parameters, and higher processing speed at baseline was associated with larger cognitive improvement after the intervention. The results support the feasibility, reliability and sensitivity of TVA-based assessment of attentional functions in chronic stroke patients.
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http://dx.doi.org/10.7717/peerj.9948DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7602688PMC
October 2020

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

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

Correction: Dose response of the 16p11.2 distal copy number variant on intracranial volume and basal ganglia.

Mol Psychiatry 2020 Mar;25(3):692-695

Department of Psychiatry and Mental Health, Anzio Road, 7925, Cape Town, South Africa.

Prior to and following the publication of this article the authors noted that the complete list of authors was not included in the main article and was only present in Supplementary Table 1. The author list in the original article has now been updated to include all authors, and Supplementary Table 1 has been removed. All other supplementary files have now been updated accordingly. Furthermore, in Table 1 of this Article, the replication cohort for the row Close relative in data set, n (%) was incorrect. All values have now been corrected to 0(0%). The publishers would like to apologise for this error and the inconvenience it may have caused.
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http://dx.doi.org/10.1038/s41380-019-0358-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7608381PMC
March 2020

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

Dose response of the 16p11.2 distal copy number variant on intracranial volume and basal ganglia.

Mol Psychiatry 2020 03 3;25(3):584-602. Epub 2018 Oct 3.

Department of Psychiatry and Mental Health, Anzio Road, 7925, Cape Town, South Africa.

Carriers of large recurrent copy number variants (CNVs) have a higher risk of developing neurodevelopmental disorders. The 16p11.2 distal CNV predisposes carriers to e.g., autism spectrum disorder and schizophrenia. We compared subcortical brain volumes of 12 16p11.2 distal deletion and 12 duplication carriers to 6882 non-carriers from the large-scale brain Magnetic Resonance Imaging collaboration, ENIGMA-CNV. After stringent CNV calling procedures, and standardized FreeSurfer image analysis, we found negative dose-response associations with copy number on intracranial volume and on regional caudate, pallidum and putamen volumes (β = -0.71 to -1.37; P < 0.0005). In an independent sample, consistent results were obtained, with significant effects in the pallidum (β = -0.95, P = 0.0042). The two data sets combined showed significant negative dose-response for the accumbens, caudate, pallidum, putamen and ICV (P = 0.0032, 8.9 × 10, 1.7 × 10, 3.5 × 10 and 1.0 × 10, respectively). Full scale IQ was lower in both deletion and duplication carriers compared to non-carriers. This is the first brain MRI study of the impact of the 16p11.2 distal CNV, and we demonstrate a specific effect on subcortical brain structures, suggesting a neuropathological pattern underlying the neurodevelopmental syndromes.
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http://dx.doi.org/10.1038/s41380-018-0118-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7042770PMC
March 2020

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

Virtual Reality Training for Upper Extremity in Subacute Stroke (VIRTUES): A multicenter RCT.

Neurology 2017 Dec 15;89(24):2413-2421. Epub 2017 Nov 15.

From the University of Bergen (I.B., J.S.S.), Norway; Department of Clinical Medicine (I.B.), University of Aarhus, Denmark; Haukeland University Hospital (J.S.S., H.H.); Competence Center for Clinical Research (J.A.), Haukeland University Hospital, Bergen; Sunnaas Rehabilitation Hospital (F.B., A.-M.S.), Nesoddtangen; Department of Clinical Medicine (F.B.), University of Oslo, Norway; Hammel Neurorehabilitation Center and University Research Clinic (H.P.); Neurorehabilitation Skive (L.Q.K.), Hammel Neurorehabilitation Center and University Research Clinic, Skive, Denmark; Jessa Hospitals (M.M., L.T.), Herk-de-Stad; and KU Leuven (G.V.), Belgium.

Objective: To compare the effectiveness of upper extremity virtual reality rehabilitation training (VR) to time-matched conventional training (CT) in the subacute phase after stroke.

Methods: In this randomized, controlled, single-blind phase III multicenter trial, 120 participants with upper extremity motor impairment within 12 weeks after stroke were consecutively included at 5 rehabilitation institutions. Participants were randomized to either VR or CT as an adjunct to standard rehabilitation and stratified according to mild to moderate or severe hand paresis, defined as ≥20 degrees wrist and 10 degrees finger extension or less, respectively. The training comprised a minimum of sixteen 60-minute sessions over 4 weeks. The primary outcome measure was the Action Research Arm Test (ARAT); secondary outcome measures were the Box and Blocks Test and Functional Independence Measure. Patients were assessed at baseline, after intervention, and at the 3-month follow-up.

Results: Mean time from stroke onset for the VR group was 35 (SD 21) days and for the CT group was 34 (SD 19) days. There were no between-group differences for any of the outcome measures. Improvement of upper extremity motor function assessed with ARAT was similar at the postintervention ( = 0.714) and follow-up ( = 0.777) assessments. Patients in VR improved 12 (SD 11) points from baseline to the postintervention assessment and 17 (SD 13) points from baseline to follow-up, while patients in CT improved 13 (SD 10) and 17 (SD 13) points, respectively. Improvement was also similar for our subgroup analysis with mild to moderate and severe upper extremity paresis.

Conclusions: Additional upper extremity VR training was not superior but equally as effective as additional CT in the subacute phase after stroke. VR may constitute a motivating training alternative as a supplement to standard rehabilitation.

Clinicaltrialsgov Identifier: NCT02079103.

Classification Of Evidence: This study provides Class I evidence that for patients with upper extremity motor impairment after stroke, compared to conventional training, VR training did not lead to significant differences in upper extremity function improvement.
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http://dx.doi.org/10.1212/WNL.0000000000004744DOI Listing
December 2017

Virtual reality training for upper extremity in subacute stroke (VIRTUES): study protocol for a randomized controlled multicenter trial.

BMC Neurol 2014 Sep 28;14:186. Epub 2014 Sep 28.

Background: Novel virtual reality rehabilitation systems provide the potential to increase intensity and offer challenging and motivating tasks. The efficacy of virtual reality systems to improve arm motor function early after stroke has not been demonstrated yet in sufficiently powered studies. The objective of the study is to investigate whether VR training as an adjunct to conventional therapy is more effective in improving arm motor function in the subacute phase after stroke than dose-matched conventional training, to assess patient and therapist satisfaction when working with novel virtual reality training and to calculate cost-effectiveness in terms of resources required to regain some degree of dexterity.

Methods/design: Randomized controlled observer-blind trial.

Discussion: Virtual reality systems are promising tools for rehabilitation of arm motor function after stroke. Their introduction in combination with traditional physical and occupational therapy may enhance recovery after stroke, and at the same time demand little personnel resources to increase training intensity. The VIRTUES trial will provide further evidence of VR-based treatment strategies to clinicians, patients and health economists.

Trial Registration: ClinicalTrials.gov NCT02079103.
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http://dx.doi.org/10.1186/s12883-014-0186-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4180981PMC
September 2014
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