Genevieve Richard

• Slapø, Nora Berz; Jørgensen, Kjetil Nordbø; Nerland, Stener; Mørch-Johnsen, Lynn Egeland; Pettersen, Johanne Hagen & Roelfs, Daniel [Show all 23 contributors for this article] (2026). Relationship between N100 amplitude and T1w/T2w-ratio in the auditory cortex in schizophrenia spectrum disorders. Schizophrenia. doi: 10.1038/s41537-025-00715-w. Full text in Research Archive Show summary

  Schizophrenia spectrum disorders (SCZspect) are associated with altered function in the auditory cortex (AC), indicated by lower N100 amplitude of the auditory evoked potential (AEP). Although the neural substrate behind lower N100 amplitude remains elusive, myelination in the AC may play a role. This study compared N100 amplitude and magnetic resonance imaging (MRI) T1 weighted and T2 weighted ratio (T1w/T2w-ratio), as a proxy of myelination, in the primary AC (AC1) and secondary AC (AC2) between SCZspect (n = 33, 48% women) and healthy controls (HC, n = 144, 49% women). We also examined the associations between N100 amplitude and T1w/T2w-ratios across groups. We finally explored N100 amplitude and T1w/T2w-ratios and the N100-T1w/T2w-ratio associations between male and female SCZspect and HC. N100 amplitude was significantly lower in male SCZspect compared to male HC (p = 0.01) and nominally lower in SCZspect compared to HC (p = 0.03). However, T1w/T2w-ratios in AC1/AC2 did not differ between groups, and no association was found between N100 amplitude and T1w/T2w-ratio in either group. These findings suggest that sex-specific effects should be considered in SCZspect neurophysiology research. Our results do not support the hypothesis of an association between lower N100 amplitude and lower T1w/T2w-ratio in the AC1/AC2 in SCZspect. More precise assessments of intracortical myelin are needed to understand the relationship between N100 amplitude and cortical myelination in the AC in SCZspect and in healthy controls.

• Kim, Milin; Sharma, Nitin; Leonardsen, Esten Høyland; Rutherford, Saige; Selbæk, Geir & Persson, Karin Ester Torun [Show all 19 contributors for this article] (2025). Predicting Mental and Neurological Illnesses Based on Cerebellar Normative Features. Biological Psychiatry: Global Open Science. 5(5), p. 1–11. doi: 10.1016/j.bpsgos.2025.100541. Full text in Research Archive
• Tamnes, Christian Krog; Dahl, Andreas; Meer, Dennis van der; Agartz, Ingrid; Alnæs, Dag & Andreassen, Ole [Show all 13 contributors for this article] (2025). Relationships between wellbeing polygenic scores, brain structure, and psychopathology in children. Personality and Individual Differences. ISSN 0191-8869. 246. doi: 10.1016/j.paid.2025.113313. Full text in Research Archive Show summary

  Wellbeing is shaped by an interplay of genetic and environmental factors and is associated with both health and functioning. It remains unclear whether genetic influences on wellbeing are linked to brain structure and, in turn, early-life psychopathology. Here, we investigated associations between wellbeing polygenic scores (PGS), magnetic resonance imaging (MRI)-derived measures of brain structure, and parent-reported measures of child psychopathology in a large cross-sectional sample of children from the Adolescent Brain Cognitive Development (ABCD) Study (n = 8844; 8.9–11.0 years old). Preregistered analyses revealed no significant associations between wellbeing PGS and regional cortical thickness or subcortical volumes and only a small negative association with surface area of the parahippocampal cortex (β = −0.03, p = .002), while an exploratory analysis identified a small positive association with intracranial volume (ICV; β = 0.04, p < .001). Preregistered analyses showed small negative associations between wellbeing PGS and general psychopathology (β = −0.09, p < .001) and both internalizing (β = −0.09, p < .001) and externalizing problems (β = −0.07, p < .001). ICV partially mediated the relationships between wellbeing PGS and psychopathology, accounting for 1.3–3.3 % of these relationships. The findings suggest that while wellbeing PGS have limited associations with regional brain structure in children, they exhibit small protective effects against psychopathology.

• Kennedy, Eamonn; Vadlamani, Shashank; Lindsey, Hannah M.; Lei, Pui-Wa; Jo-Pugh, Mary & Thompson, Paul M. [Show all 143 contributors for this article] (2024). Bridging big data in the ENIGMA consortium to combine non-equivalent cognitive measures. Scientific Reports. 14(1). doi: 10.1038/s41598-024-72968-x. Full text in Research Archive Show summary

  Investigators in neuroscience have turned to Big Data to address replication and reliability issues by increasing sample size. These efforts unveil new questions about how to integrate data across distinct sources and instruments. The goal of this study was to link scores across common auditory verbal learning tasks (AVLTs). This international secondary analysis aggregated multisite raw data for AVLTs across 53 studies totaling 10,505 individuals. Using the ComBat-GAM algorithm, we isolated and removed the component of memory scores associated with site effects while preserving instrumental effects. After adjustment, a continuous item response theory model used multiple memory items of varying difficulty to estimate each individual’s latent verbal learning ability on a single scale. Equivalent raw scores across AVLTs were then found by linking individuals through the ability scale. Harmonization reduced total cross-site score variance by 37% while preserving meaningful memory effects. Age had the largest impact on scores overall (− 11.4%), while race/ethnicity variable was not significant (p > 0.05). The resulting tools were validated on dually administered tests. The conversion tool is available online so researchers and clinicians can convert memory scores across instruments. This work demonstrates that global harmonization initiatives can address reproducibility challenges across the behavioral sciences.

• Kim, Milin; Leonardsen, Esten Høyland; Rutherford, Saige; Selbæk, Geir; Persson, Karin Ester Torun & Steen, Nils Eiel [Show all 15 contributors for this article] (2024). Mapping cerebellar anatomical heterogeneity in mental and neurological illnesses. Nature Mental Health. 2(10), p. 1196–1207. doi: 10.1038/s44220-024-00297-z. Full text in Research Archive Show summary

  The cerebellum is linked to motor coordination, cognitive and affective processing, in addition to a wide range of clinical illnesses. To enable robust quantification of individual cerebellar anatomy relative to population norms, we mapped the normative development and aging of the cerebellum across the lifespan using brain scans of >54,000 participants. We estimated normative models at voxel-wise spatial precision, enabling integration with cerebellar atlases. Applying the normative models in independent samples revealed substantial heterogeneity within five clinical illnesses: autism spectrum disorder, mild cognitive impairment, Alzheimer disease, bipolar disorder, and schizophrenia. Notably, individuals with autism spectrum disorder and mild cognitive impairment exhibited increased positive and negative extreme deviations in cerebellar anatomy, while those with schizophrenia and Alzheimer disease showed predominantly negative deviations. Finally, extreme deviations were associated with cognitive scores. Ou

• Jiang, Yuchao; Luo, Cheng; Wang, Jijun; Palaniyappan, Lena; Chang, Xiao & Xiang, Shitong [Show all 105 contributors for this article] (2024). Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm. Nature Communications. 15. doi: 10.1038/s41467-024-50267-3. Full text in Research Archive Show summary

  Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal ‘trajectory’ of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

• Sanders, Anne-Marthe; Richard, Geneviève; Kolskår, Knut-Kristian; Ulrichsen, Kristine Moe; Alnæs, Dag & Dørum, Erlend Solberg [Show all 10 contributors for this article] (2024). Exploring the associations between physical activity level, cognitive performance, and response to computerized cognitive training among chronic stroke patients. Brain and Behavior. 14(2). doi: 10.1002/brb3.3406. Full text in Research Archive Show summary

  Background: Post-stroke attentional and working memory deficits are common and represent relevant predictors of long-term functional recovery and outcome. The individual responses to cognitive rehabilitation and interventions vary between patients and are influenced by multiple factors. Recently, a link between the level of engagement in physical activities and cognitive rehabilitation has been suggested. However, few previous studies have tested the predictive value of physical activity on cognitive performance and response to cognitive training among chronic stroke patients. There is also a lack of knowledge concerning the prognostic value of index stroke characteristics on physical activity in chronic phase. Method: In this cross-sectional and longitudinal study, including stroke survivors suffering mild-to-moderate strokes (n = 52, mean age = 70 years), we used Bayesian regression to test the association between cognitive performance and response to a 3-week intervention with a commonly used computerized cognitive training (CCT) system and baseline physical activity level measured with International Physical Activity Questionnaire. We also tested the association between physical activity level in chronic phase and stroke characteristics, including stroke severity (National Institutes of Health Stroke Scale), ischemic stroke etiology (Trial of Org 10172 in Acute Stroke Treatment), and stroke location (n = 66, mean age = 68 years). For descriptive purposes, we included 104 sex- and age-matched healthy controls (mean age = 69 years). Results: The analyses revealed anecdotal evidence of a positive association between overall cognitive performance and daily minutes of sedentary behavior, indicating that better cognitive performance was associated with more daily hours of sitting still. We found no support for an association between cognitive performance and response to CCT with activity level. In addition, the analysis showed group differences in sedentary behavior between patients with small-vessel disease (n = 20) and cardioembolism (n = 7), indicating more sedentary behavior in patients with small-vessel disease. There was no further support for a predictive value of index stroke characteristics on physical activity level. Conclusion: The results do not support that baseline physical activity level is a relevant predictor of the overall performance or response to CCT in this sample of chronic stroke patients. Similarly, the analyses revealed little evidence for an association between index stroke characteristics and future activity level in patients surviving mild-to-moderate stroke.

• Kennedy, Eamonn; Liebel, Spencer W.; Lindsey, Hannah M.; Vadlamani, Shashank; Lei, Pui-Wa & Adamson, Maheen M. [Show all 145 contributors for this article] (2024). Verbal Learning and Memory Deficits across Neurological and Neuropsychiatric Disorders: Insights from an ENIGMA Mega Analysis. Brain Sciences. 14(7). doi: 10.3390/brainsci14070669. Full text in Research Archive Show summary

  Deficits in memory performance have been linked to a wide range of neurological and neuropsychiatric conditions. While many studies have assessed the memory impacts of individual conditions, this study considers a broader perspective by evaluating how memory recall is differentially associated with nine common neuropsychiatric conditions using data drawn from 55 international studies, aggregating 15,883 unique participants aged 15–90. The effects of dementia, mild cognitive impairment, Parkinson’s disease, traumatic brain injury, stroke, depression, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder on immediate, short-, and long-delay verbal learning and memory (VLM) scores were estimated relative to matched healthy individuals. Random forest models identified age, years of education, and site as important VLM covariates. A Bayesian harmonization approach was used to isolate and remove site effects. Regression estimated the adjusted association of each clinical group with VLM scores. Memory deficits were strongly associated with dementia and schizophrenia (p < 0.001), while neither depression nor ADHD showed consistent associations with VLM scores (p > 0.05). Differences associated with clinical conditions were larger for longer delayed recall duration items. By comparing VLM across clinical conditions, this study provides a foundation for enhanced diagnostic precision and offers new insights into disease management of comorbid disorders.

• Sanders, Anne-Marthe; Richard, Geneviève; Kolskår, Knut-Kristian; Ulrichsen, Kristine Moe; Alnæs, Dag & Beck, Dani [Show all 14 contributors for this article] (2023). Associations between everyday activities and arterial spin labeling-derived cerebral blood flow: A longitudinal study in community-dwelling elderly volunteers. Human Brain Mapping. ISSN 1065-9471. 44, p. 3377–3393. doi: 10.1002/hbm.26287. Full text in Research Archive Show summary

  Cerebral blood flow (CBF) is critical for brain metabolism and function. Age-related changes in CBF are associated with increased risk of neurocognitive disorders and vascular events such as stroke. Identifying correlates and positive modifiers of age- related changes in CBF before the emergence of incipient clinical decline may inform public health advice and clinical practice. Former research has been inconclusive regarding the association between regular physical activity and CBF, and there is a lack of studies on the association between level of everyday activities and CBF, in older adults. To investigate these relationships, 118 healthy community-dwelling adults (65–89 years) underwent pseudo-continuous arterial spin labeling (ASL) MRI, neurocognitive, physical, and activity assessments at baseline. Eighty-six participants completed a follow-up ASL MRI, on average 506 (SD = 113) days after the baseline scan. Cross-sectional analysis revealed credible evidence for positive associations between time spent on low intensity physical activity and CBF in multiple cortical and subcortical regions, time spent on moderate to vigorous intensity physical activity and accumbens CBF, participation in social activity and CBF in multiple cortical regions, and between reading and thalamic CBF, indicating higher regional CBF in more active adults. Longitudinal analysis revealed anecdotal evidence for an interac- tion between time and baseline level of gardening on occipital and parietal CBF, and baseline reading on pallidum CBF, indicating more change in CBF in adults with lower level of activity. The findings support that malleable lifestyle factors contribute to healthy brain aging, with relevance for public health guidelines.

• Slapø, Nora Berz; Nerland, Stener; Jørgensen, Kjetil Nordbø; Mørch-Johnsen, Lynn Egeland; Pettersen, Johanne Hagen & Roelfs, Daniel [Show all 22 contributors for this article] (2023). Auditory Cortex Thickness Is Associated With N100 Amplitude in Schizophrenia Spectrum Disorders. Schizophrenia Bulletin Open. 4(1). doi: 10.1093/schizbullopen/sgad015. Full text in Research Archive Show summary

  Background and Hypothesis: The auditory cortex (AC) may play a central role in the pathophysiology of schizophrenia and auditory hallucinations (AH). Previous schizophrenia studies report thinner AC and impaired AC function, as indicated by decreased N100 amplitude of the auditory evoked potential. However, whether these structural and functional alterations link to AH in schizophrenia remain poorly understood. Study Design: Patients with a schizophrenia spectrum disorder (SCZspect), including patients with a lifetime experience of AH (AH+), without (AH−), and healthy controls underwent magnetic resonance imaging (39 SCZ spect, 22 AH+, 17 AH−, and 146 HC) and electroencephalography (33 SCZ spect, 17 AH+, 16 AH−, and 144 HC). Cortical thickness of the primary (AC1, Heschl’s gyrus) and secondary (AC2, Heschl’s sulcus, and the planum temporale) AC was compared between SCZ spect and controls and between AH+, AH−, and controls. To examine if the association between AC thickness and N100 amplitude differed between groups, we used regression models with interaction terms. Study Results: N100 amplitude was nominally smaller in SCZ spect (P = .03, d = 0.42) and in AH− (P = .020, d = 0.61), while AC2 was nominally thinner in AH+ (P = .02, d = 0.53) compared with controls. AC1 thickness was positively associated with N100 amplitude in SCZspect (t = 2.56, P = .016) and AH− (t= 3.18, P = .008), while AC2 thickness was positively associated with N100 amplitude in SCZspect (t = 2.37, P = .024) and in AH+ (t = 2.68, P = .019). Conclusions: The novel indings of positive associations between AC thickness and N100 amplitude in SCZ spect, suggest that a common neural substrate may underlie AC thickness and N100 amplitude alterations.

• Slapø, Nora Berz; Jørgensen, Kjetil Nordbø; Elvsåshagen, Torbjørn; Nerland, Stener; Roelfs, Daniel & Valstad, Mathias [Show all 18 contributors for this article] (2023). Relationship between function and structure in the visual cortex in healthy individuals and in patients with severe mental disorders. Psychiatry Research : Neuroimaging. ISSN 0925-4927. 332. doi: 10.1016/j.pscychresns.2023.111633. Full text in Research Archive Show summary

  Patients with schizophrenia spectrum disorders (SCZspect) and bipolar disorders (BD) show impaired function in the primary visual cortex (V1), indicated by altered visual evoked potential (VEP). While the neural substrate for altered VEP in these patients remains elusive, altered V1 structure may play a role. One previous study found a positive relationship between the amplitude of the P100 component of the VEP and V1 surface area, but not V1 thickness, in a small sample of healthy individuals. Here, we aimed to replicate these findings in a larger healthy control (HC) sample (n = 307) and to examine the same relationship in patients with SCZspect (n = 30) or BD (n = 45). We also compared the mean P100 amplitude, V1 surface area and V1 thickness between controls and patients and found no significant group differences. In HC only, we found a significant positive P100-V1 surface area association, while there were no significant P100-V1 thickness relationships in HC, SCZspect or BD. Together, our results confirm previous findings of a positive P100-V1 surface area association in HC, whereas larger patient samples are needed to further clarify the function-structure relationship in V1 in SCZspect and BD.

• Tesli, Natalia; Bell, Christina; Hjell, Gabriela; Fischer-Vieler, Thomas; Maximov, Ivan & Richard, Geneviève [Show all 14 contributors for this article] (2022). The age of violence: Mapping brain age in psychosis and psychopathy. NeuroImage: Clinical. 36. doi: 10.1016/j.nicl.2022.103181. Full text in Research Archive Show summary

  Young chronological age is one of the strongest predictors for antisocial behaviour in the general population and for violent offending in individuals with psychotic disorders. An individual's age can be predicted with high accuracy using neuroimaging and machine-learning. The deviation between predicted and chronological age, i.e., brain age gap (BAG) has been suggested to reflect brain health, likely relating partly to neurodevelopmental and aging-related processes and specific disease mechanisms. Higher BAG has been demonstrated in patients with psychotic disorders. However, little is known about the brain-age in violent offenders with psychosis and the possible associations with psychopathy traits. We estimated brain-age in 782 male individuals using T1-weighted MRI scans. Three machine learning models (random forest, extreme gradient boosting with and without hyper parameter tuning) were first trained and tested on healthy controls (HC, n = 586). The obtained BAGs were compared between HC and age matched violent offenders with psychosis (PSY-V, n = 38), violent offenders without psychosis (NPV, n = 20) and non-violent psychosis patients (PSY-NV, n = 138). We ran additional comparisons between BAG of PSY-V and PSY-NV and associations with Positive and Negative Syndrome Scale (PANSS) total score as a measure of psychosis symptoms. Psychopathy traits in the violence groups were assessed with Psychopathy Checklist-revised (PCL-R) and investigated for associations with BAG. We found significantly higher BAG in PSY-V compared with HC (4.9 years, Cohen's d = 0.87) and in PSY-NV compared with HC (2.7 years, d = 0.41). Total PCL-R scores were negatively associated with BAG in the violence groups (d = 1.17, p < 0.05). Additionally, there was a positive association between psychosis symptoms and BAG in the psychosis groups (d = 1.12, p < 0.05). While the significant BAG differences related to psychosis and not violence suggest larger BAG for psychosis, the negative associations between BAG and psychopathy suggest a complex interplay with psychopathy traits. This proof-of-concept application of brain age prediction in severe mental disorders with a history of violence and psychopathy traits should be tested and replicated in larger samples.

• Nerland, Stener; Stokkan, Therese; Jørgensen, Kjetil Nordbø; Wortinger, Laura Anne; Richard, Geneviève & Beck, Dani [Show all 11 contributors for this article] (2022). A comparison of intracranial volume estimation methods and their cross-sectional and longitudinal associations with age. Human Brain Mapping. ISSN 1065-9471. doi: 10.1002/hbm.25978. Full text in Research Archive Show summary

  Intracranial volume (ICV) is frequently used in volumetric magnetic resonance imaging (MRI) studies, both as a covariate and as a variable of interest. Findings of associations between ICV and age have varied, potentially due to differences in ICV estimation methods. Here, we compared five commonly used ICV estimation methods and their associations with age. T1-weighted cross-sectional MRI data was included for 651 healthy individuals recruited through the NORMENT Centre (mean age = 46.1 years, range = 12.0–85.8 years) and 2410 healthy individuals recruited through the UK Biobank study (UKB, mean age = 63.2 years, range = 47.0–80.3 years), where longitudinal data was also available. ICV was estimated with FreeSurfer (eTIV and sbTIV), SPM12, CAT12, and FSL. We found overall high correlations across ICV estimation method, with the lowest observed correlations between FSL and eTIV (r = .87) and between FSL and CAT12 (r = .89). Widespread proportional bias was found, indicating that the agreement between methods varied as a function of head size. Body weight, age, sex, and mean ICV across methods explained the most variance in the differences between ICV estimation methods, indicating possible confounding for some estimation methods. We found both positive and negative cross-sectional associations with age, depending on dataset and ICV estimation method. Longitudinal ICV reductions were found for all ICV estimation methods, with annual percentage change ranging from −0.293% to −0.416%. This convergence of longitudinal results across ICV estimation methods offers strong evidence for age-related ICV reductions in mid- to late adulthood.

• Kolskår, Knut-Kristian; Ulrichsen, Kristine Moe; Richard, Geneviève; Dørum, Erlend Solberg; Schotten, Michel Thiebaut de & Rokicki, Jaroslav [Show all 12 contributors for this article] (2022). Structural disconnectome mapping of cognitive function in poststroke patients. Brain and Behavior. doi: 10.1002/brb3.2707. Full text in Research Archive Show summary

  Background and purpose Sequalae following stroke represents a significant challenge in current rehabilitation. The location and size of focal lesions are only moderately predictive of the diverse cognitive outcome after stroke. One explanation building on recent work on brain networks proposes that the cognitive consequences of focal lesions are caused by damages to anatomically distributed brain networks supporting cognition rather than specific lesion locations. Methods To investigate the association between poststroke structural disconnectivity and cognitive performance, we estimated individual level whole-brain disconnectivity probability maps based on lesion maps from 102 stroke patients using normative data from healthy controls. Cognitive performance was assessed in the whole sample using Montreal Cognitive Assessment, and a more comprehensive computerized test protocol was performed on a subset (n = 82). Results Multivariate analysis using Partial Least Squares on the disconnectome maps revealed that higher disconnectivity in right insular and frontal operculum, superior temporal gyrus and putamen was associated with poorer MoCA performance, indicating that lesions in regions connected with these brain regions are more likely to cause cognitive impairment. Furthermore, our results indicated that disconnectivity within these clusters was associated with poorer performance across multiple cognitive domains. Conclusions These findings demonstrate that the extent and distribution of structural disconnectivity following stroke are sensitive to cognitive deficits and may provide important clinical information predicting poststroke cognitive sequalae.

• Beck, Dani; Lange, Ann-Marie Glasø de; Alnæs, Dag; Maximov, Ivan; Pedersen, Mads Lund & Leinhard, Olof Dahlqvist [Show all 20 contributors for this article] (2022). Adipose tissue distribution from body MRI is associated with cross-sectional and longitudinal brain age in adults. NeuroImage: Clinical. 33. doi: 10.1016/j.nicl.2022.102949. Full text in Research Archive Show summary

  There is an intimate body-brain connection in ageing, and obesity is a key risk factor for poor cardiometabolic health and neurodegenerative conditions. Although research has demonstrated deleterious effects of obesity on brain structure and function, the majority of studies have used conventional measures such as waist-to-hip ratio, waist circumference, and body mass index. While sensitive to gross features of body composition, such global anthropometric features fail to describe regional differences in body fat distribution and composition. The sample consisted of baseline brain magnetic resonance imaging (MRI) acquired from 790 healthy participants aged 18–94 years (mean ± standard deviation (SD) at baseline: 46.8 ± 16.3), and follow-up brain MRI collected from 272 of those individuals (two time-points with 19.7 months interval, on average (min = 9.8, max = 35.6). Of the 790 included participants, cross-sectional body MRI data was available from a subgroup of 286 participants, with age range 19–86 (mean = 57.6, SD = 15.6). Adopting a mixed cross-sectional and longitudinal design, we investigated cross-sectional body magnetic resonance imaging measures of adipose tissue distribution in relation to longitudinal brain structure using MRI-based morphometry (T1) and diffusion tensor imaging (DTI). We estimated tissue-specific brain age at two time points and performed Bayesian multilevel modelling to investigate the associations between adipose measures at follow-up and brain age gap (BAG) – the difference between actual age and the prediction of the brain’s biological age – at baseline and follow-up. We also tested for interactions between BAG and both time and age on each adipose measure. The results showed credible associations between T1-based BAG and liver fat, muscle fat infiltration (MFI), and weight-to-muscle ratio (WMR), indicating older-appearing brains in people with higher measures of adipose tissue. Longitudinal evidence supported interaction effects between time and MFI and WMR on T1-based BAG, indicating accelerated ageing over the course of the study period in people with higher measures of adipose tissue. The results show that specific measures of fat distribution are associated with brain ageing and that different compartments of adipose tissue may be differentially linked with increased brain ageing, with potential to identify key processes involved in age-related transdiagnostic disease processes.

• Ulrichsen, Kristine Moe; Kolskår, Knut-Kristian; Richard, Geneviève; Pedersen, Mads Lund; Alnæs, Dag & Dørum, Erlend Solberg [Show all 13 contributors for this article] (2022). No add-on effect of tDCS on fatigue and depression in chronic stroke patients: A randomized sham-controlled trial combining tDCS with computerized cognitive training. Brain and Behavior. 12(7). doi: 10.1002/brb3.2643. Full text in Research Archive Show summary

  Background Fatigue and emotional distress rank high among self-reported unmet needs in life after stroke. Transcranial direct current stimulation (tDCS) may have the potential to alleviate these symptoms for some patients, but the acceptability and effects for chronic stroke survivors need to be explored in randomized controlled trials. Methods Using a randomized sham-controlled parallel design, we evaluated whether six sessions of 1 mA tDCS (anodal over F3, cathodal over O2) combined with computerized cognitive training reduced self-reported symptoms of fatigue and depression. Among the 74 chronic stroke patients enrolled at baseline, 54 patients completed the intervention. Measures of fatigue and depression were collected at five time points spanning a 2 months period. Results While symptoms of fatigue and depression were reduced during the course of the intervention, Bayesian analyses provided evidence for no added beneficial effect of tDCS. Less severe baseline symptoms were associated with higher performance improvement in select cognitive tasks, and study withdrawal was higher in patients with more fatigue and younger age. Time-resolved symptom analyses by a network approach suggested higher centrality of fatigue items (except item 1 and 2) than depression items. Conclusion The results reveal no add-on effect of tDCS on fatigue or depression but support the notion of fatigue as a relevant clinical symptom with possible implications for treatment adherence and response.

• Voldsbekk, Irene; Barth, Claudia; Maximov, Ivan I.; Kaufmann, Tobias; Beck, Dani & Richard, Genevive [Show all 9 contributors for this article] (2021). A history of previous childbirths is linked to women's white matter brain age in midlife and older age. Human Brain Mapping. ISSN 1065-9471. 42(13), p. 1–15. doi: 10.1002/hbm.25553. Full text in Research Archive Show summary

  Maternal brain adaptations occur in response to pregnancy, but little is known about how parity impacts white matter and white matter ageing trajectories later in life. Utilising global and regional brain age prediction based on multi-shell diffusion-weighted imaging data, we investigated the association between previous childbirths and white matter brain age in 8,895 women in the UK Biobank cohort (age range = 54–81 years). The results showed that number of previous childbirths was negatively associated with white matter brain age, potentially indicating a protective effect of parity on white matter later in life. Both global white matter and grey matter brain age estimates showed unique contributions to the association with previous childbirths, suggesting partly independent processes. Corpus callosum contributed uniquely to the global white matter association with previous childbirths, and showed a stronger relationship relative to several other tracts. While our findings demonstrate a link between reproductive history and brain white matter characteristics later in life, longitudinal studies are required to establish causality and determine how parity may influence women's white matter trajectories across the lifespan.

• Ulrichsen, Kristine Moe; Kolskår, Knut-Kristian; Richard, Geneviève; Alnæs, Dag; Dørum, Erlend Solberg & Sanders, Anne-Marthe [Show all 13 contributors for this article] (2021). Structural brain disconnectivity mapping of post-stroke fatigue. NeuroImage: Clinical. 30. doi: 10.1016/j.nicl.2021.102635. Full text in Research Archive Show summary

  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.

• Monereo-Sánchez, Jennifer; Luykx, Jurjen J.; Pinzón-Espinosa, Justo; Richard, Geneviève; Motazedi, Ehsan & Westlye, Lars Tjelta [Show all 8 contributors for this article] (2021). Diphtheria And Tetanus Vaccination History Is Associated With Lower Odds of COVID-19 Hospitalization. Frontiers in Immunology. 12, p. 1–7. doi: 10.3389/fimmu.2021.749264. Full text in Research Archive Show summary

  Background COVID-19 is characterized by strikingly large, mostly unexplained, interindividual variation in symptom severity: while some individuals remain nearly asymptomatic, others suffer from severe respiratory failure. Previous vaccinations for other pathogens, in particular tetanus, may partly explain this variation, possibly by readying the immune system. Methods We made use of data on COVID-19 testing from 103,049 participants of the UK Biobank (mean age 71.5 years, 54.2% female), coupled to immunization records of the last ten years. Using logistic regression, covarying for age, sex, respiratory disease diagnosis, and socioeconomic status, we tested whether individuals vaccinated for tetanus, diphtheria or pertussis, differed from individuals that had only received other vaccinations on 1) undergoing a COVID-19 test, 2) being diagnosed with COVID-19, and 3) whether they developed severe COVID-19 symptoms. Results We found that individuals with registered diphtheria or tetanus vaccinations are less likely to develop severe COVID-19 than people who had only received other vaccinations (diphtheria odds ratio (OR)=0.47, p-value=5.3*10 -5 ; tetanus OR=0.52, p-value=1.2*10 -4 ). Discussion These results indicate that a history of diphtheria or tetanus vaccinations is associated with less severe manifestations of COVID-19. These vaccinations may protect against severe COVID-19 symptoms by stimulating the immune system. We note the correlational nature of these results, yet the possibility that these vaccinations may influence the severity of COVID-19 warrants follow-up investigations.

• Valstad, Mathias; Roelfs, Daniel; Slapø, Nora Berz; Timpe, Clara Marie Fides; Rai, Ahsan & Matziorinis, Anna Maria [Show all 24 contributors for this article] (2021). Evidence for Reduced Long-Term Potentiation-Like Visual Cortical Plasticity in Schizophrenia and Bipolar Disorder. Schizophrenia Bulletin. ISSN 0586-7614. 47(6), p. 1751–1760. doi: 10.1093/schbul/sbab049. Full text in Research Archive Show summary

  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.

• Sanders, Anne-Marthe; Richard, Geneviève; Kolskår, Knut-Kristian; Ulrichsen, Kristine Moe; Kaufmann, Tobias & Alnæs, Dag [Show all 11 contributors for this article] (2021). Linking objective measures of physical activity and capability with brain structure in healthy community dwelling older adults. NeuroImage: Clinical. 31. doi: 10.1016/j.nicl.2021.102767. Full text in Research Archive Show summary

  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.

• Beck, Dani; Lange, Ann-Marie G. de; Pedersen, Mads L.; Alnæs, Dag; Maximov, Ivan & Voldsbekk, Irene [Show all 18 contributors for this article] (2021). Cardiometabolic risk factors associated with brain age and accelerate brain ageing. Human Brain Mapping. ISSN 1065-9471. p. 1–21. doi: 10.1002/hbm.25680. Full text in Research Archive Show summary

  The structure and integrity of the ageing brain is interchangeably linked to physical health, and cardiometabolic risk factors (CMRs) are associated with dementia and other brain disorders. In this mixed cross-sectional and longitudinal study (interval mean = 19.7 months), including 790 healthy individuals (mean age = 46.7 years, 53% women), we investigated CMRs and health indicators including anthropometric measures, lifestyle factors, and blood biomarkers in relation to brain structure using MRI-based morphometry and diffusion tensor imaging (DTI). We performed tissue specific brain age prediction using machine learning and performed Bayesian multilevel modeling to assess changes in each CMR over time, their respective association with brain age gap (BAG), and their interaction effects with time and age on the tissue-specific BAGs. The results showed credible associations between DTI-based BAG and blood levels of phosphate and mean cell volume (MCV), and between T1-based BAG and systolic blood pressure, smoking, pulse, and C-reactive protein (CRP), indicating older-appearing brains in people with higher cardiometabolic risk (smoking, higher blood pressure and pulse, low-grade inflammation). Longitudinal evidence supported interactions between both BAGs and waist-to-hip ratio (WHR), and between DTI-based BAG and systolic blood pressure and smoking, indicating accelerated ageing in people with higher cardiometabolic risk (smoking, higher blood pressure, and WHR). The results demonstrate that cardiometabolic risk factors are associated with brain ageing. While randomized controlled trials are needed to establish causality, our results indicate that public health initiatives and treatment strategies targeting modifiable cardiometabolic risk factors may also improve risk trajectories and delay brain ageing.

• Grasby, Katrina L.; Jahanshad, Neda; Painter, Jodie N; Colodro-Conde, Lucía; Bralten, Janita & Hibar, DP [Show all 50 contributors for this article] (2020). The genetic architecture of the human cerebral cortex. Science. ISSN 0036-8075. 367(6484). doi: 10.1126/science.aay6690. Full text in Research Archive Show summary

  INTRODUCTION The cerebral cortex underlies our complex cognitive capabilities. Variations in human cortical surface area and thickness are associated with neurological, psychological, and behavioral traits and can be measured in vivo by magnetic resonance imaging (MRI). Studies in model organisms have identified genes that influence cortical structure, but little is known about common genetic variants that affect human cortical structure. RATIONALE To identify genetic variants associated with human cortical structure at both global and regional levels, we conducted a genome-wide association meta-analysis of brain MRI data from 51,665 individuals across 60 cohorts. We analyzed the surface area and average thickness of the whole cortex and 34 cortical regions with known functional specializations. RESULTS We identified 306 nominally genome-wide significant loci (P < 5 × 10−8) associated with cortical structure in a discovery sample of 33,992 participants of European ancestry. Of the 299 loci for which replication data were available, 241 loci influencing surface area and 14 influencing thickness remained significant after replication, with 199 loci passing multiple testing correction (P < 8.3 × 10−10; 187 influencing surface area and 12 influencing thickness). Common genetic variants explained 34% (SE = 3%) of the variation in total surface area and 26% (SE = 2%) in average thickness; surface area and thickness showed a negative genetic correlation (rG = −0.32, SE = 0.05, P = 6.5 × 10−12), which suggests that genetic influences have opposing effects on surface area and thickness. Bioinformatic analyses showed that total surface area is influenced by genetic variants that alter gene regulatory activity in neural progenitor cells during fetal development. By contrast, average thickness is influenced by active regulatory elements in adult brain samples, which may reflect processes that occur after mid-fetal development, such as myelination, branching, or pruning. When considered together, these results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness. To identify specific genetic influences on individual cortical regions, we controlled for global measures (total surface area or average thickness) in the regional analyses. After multiple testing correction, we identified 175 loci that influence regional surface area and 10 that influence regional thickness. Loci that affect regional surface area cluster near genes involved in the Wnt signaling pathway, which is known to influence areal identity. We observed significant positive genetic correlations and evidence of bidirectional causation of total surface area with both general cognitive functioning and educational attainment. We found additional positive genetic correlations between total surface area and Parkinson’s disease but did not find evidence of causation. Negative genetic correlations were evident between total surface area and insomnia, attention deficit hyperactivity disorder, depressive symptoms, major depressive disorder, and neuroticism. CONCLUSION This large-scale collaborative work enhances our understanding of the genetic architecture of the human cerebral cortex and its regional patterning. The highly polygenic architecture of the cortex suggests that distinct genes are involved in the development of specific cortical areas. Moreover, we find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function.

• Hofer, Edith; Roshchupkin, Gennady V.; Adams, Hieab H H; Knol, Maria J.; Lin, Honghuang & Li, Shuo [Show all 51 contributors for this article] (2020). Genetic correlations and genome-wide associations of cortical structure in general population samples of 22,824 adults. Nature Communications. 11. doi: 10.1038/s41467-020-18367-y. Full text in Research Archive Show summary

  Cortical thickness, surface area and volumes vary with age and cognitive function, and in neurological and psychiatric diseases. Here we report heritability, genetic correlations and genome-wide associations of these cortical measures across the whole cortex, and in 34 anatomically predefined regions. Our discovery sample comprises 22,824 individuals from 20 cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank. We identify genetic heterogeneity between cortical measures and brain regions, and 160 genome-wide significant associations pointing to wnt/β-catenin, TGF-β and sonic hedgehog pathways. There is enrichment for genes involved in anthropometric traits, hindbrain development, vascular and neurodegenerative disease and psychiatric conditions. These data are a rich resource for studies of the biological mechanisms behind cortical development and aging.

• Valstad, Mathias; Moberget, Torgeir; Roelfs, Daniël Thomas Heimen; Slapø, Nora Berz; Timpe, Clara Marie Fides & Beck, Dani [Show all 18 contributors for this article] (2020). Experience-dependent modulation of the visual evoked potential: Testing effect sizes, retention over time, and associations with age in 415 healthy individuals. NeuroImage. ISSN 1053-8119. doi: 10.1016/j.neuroimage.2020.117302. Full text in Research Archive Show summary

  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 (χ2=30.4), and positively with P1 modulation (χ2=13.4), whereas P2 modulation was larger for female participants (χ2=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.

• Richard, Geneviève; Petersen, Anders; Ulrichsen, Kristine Moe; Kolskår, Knut-Kristian; Alnæs, Dag & Sanders, Anne-Marthe [Show all 10 contributors for this article] (2020). 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. 8:e9948, p. 1–21. doi: 10.7717/peerj.9948. Full text in Research Archive Show summary

  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 ( K ), processing speed ( C ) and perceptual threshold ( t 0 ), 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 (&gt; 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 ( n  = 70) compared to age-matched healthy controls ( n  = 140). Further, longitudinal analyses in stroke patients during the course of CCT ( n  = 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.

• Palomera, Aldo Còrdova; Meer, Dennis van der; Kaufmann, Tobias; Bettella, Francesco; Wang, Yunpeng & Alnæs, Dag [Show all 40 contributors for this article] (2020). Genetic control of variability in subcortical and intracranial volumes. Molecular Psychiatry. ISSN 1359-4184. p. 1–8. doi: 10.1038/s41380-020-0664-1. Full text in Research Archive Show summary

  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.

• Ulrichsen, Kristine Moe; Alnæs, Dag; Kolskår, Knut-Kristian; Richard, Geneviève; Sanders, Anne-Marthe & Dørum, Erlend Solberg [Show all 11 contributors for this article] (2020). Dissecting the cognitive phenotype of post-stroke fatigue using computerized assessment and computational modeling of sustained attention. European Journal of Neuroscience. ISSN 0953-816X. 52(7), p. 3828–3845. doi: 10.1111/ejn.14861. Full text in Research Archive Show summary

  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.

• Wierenga, Lara M.; Doucet, Gaëlle E.; Dima, Danai; Agartz, Ingrid; Aghajani, Moji & Akudjedu, Theophilus N. [Show all 50 contributors for this article] (2020). Greater male than female variability in regional brain structure across the lifespan. Human Brain Mapping. ISSN 1065-9471. 43, p. 470–499. doi: 10.1002/hbm.25204. Full text in Research Archive Show summary

  For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta‐Analysis) Consortium presents the largest‐ever mega‐analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1‐90 years old (47% females). We observed significant patterns of greater male than female between‐subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene‐environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex‐specific vulnerability to disorders.

• Dørum, Erlend Solberg; Kaufmann, Tobias; Alnæs, Dag; Richard, Geneviève; Kolskår, Knut-Kristian & Engvig, Andreas [Show all 11 contributors for this article] (2020). Functional brain network modeling in sub-acute stroke patients and healthy controls during rest and continuous attentive tracking. Heliyon. 6:e04854(9), p. 1–11. doi: 10.1016/j.heliyon.2020.e04854. Full text in Research Archive Show summary

  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.

• Liew, Sook-Lei; Zavaliangos-Petropulu, Artemis; Jahanshad, Neda; Lang, Catherine E.; Hayward, Kathryn S. & Lohse, Keith R. [Show all 67 contributors for this article] (2020). The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain - behavior relationships after stroke. Human Brain Mapping. ISSN 1065-9471. p. 1–20. doi: 10.1002/hbm.25015. Full text in Research Archive Show summary

  The goal of the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well‐powered meta‐ and mega‐analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large‐scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided.

• Rokicki, Jaroslav; Wolfers, Thomas; Nordhøy, Wibeke; Tesli, Natalia; Quintana, Daniel & Alnæs, Dag [Show all 20 contributors for this article] (2020). Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders. Human Brain Mapping. ISSN 1065-9471. 42(6), p. 1714–1726. doi: 10.1002/hbm.25323. Full text in Research Archive Show summary

  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.

• Tønnesen, Siren; Kaufmann, Tobias; Lange, Ann-Marie Glasø de; Richard, Geneviève; Doan, Nhat Trung & Alnæs, Dag [Show all 33 contributors for this article] (2020). Brain age prediction reveals aberrant brain white matter in schizophrenia and bipolar disorder: A multisample diffusion tensor imaging study. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. ISSN 2451-9022. 5(12), p. 1095–1103. doi: 10.1016/j.bpsc.2020.06.014. Full text in Research Archive Show summary

  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.

• Kolskår, Knut-Kristian; Richard, Geneviève; Alnæs, Dag; Dørum, Erlend Solberg; Sanders, Anne-Marthe & Ulrichsen, Kristine Moe [Show all 10 contributors for this article] (2020). 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. Human Brain Mapping. ISSN 1065-9471. doi: 10.1002/hbm.25284. Full text in Research Archive Show summary

  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.

• Beck, Dani; Lange, Ann-Marie Glasø de; Maximov, Ivan; Richard, Geneviève; Andreassen, Ole Andreas & Nordvik, Jan E. [Show all 7 contributors for this article] (2020). White matter microstructure across the adult lifespan: A mixed longitudinal and cross-sectional study using advanced diffusion models and brain-age prediction. NeuroImage. ISSN 1053-8119. 224:117441, p. 1–14. doi: 10.1016/j.neuroimage.2020.117441. Full text in Research Archive Show summary

  The macro- and microstructural architecture of human brain white matter undergoes substantial alterations throughout development and ageing. Most of our understanding of the spatial and temporal characteristics of these lifespan adaptations come from magnetic resonance imaging (MRI), including diffusion MRI (dMRI), which enables visualisation and quantification of brain white matter with unprecedented sensitivity and detail. However, with some notable exceptions, previous studies have relied on cross-sectional designs, limited age ranges, and diffusion tensor imaging (DTI) based on conventional single-shell dMRI. In this mixed cross-sectional and longitudinal study (mean interval: 15.2 months) including 702 multi-shell dMRI datasets, we combined complementary dMRI models to investigate age trajectories in healthy individuals aged 18 to 94 years (57.12% women). Using linear mixed effect models and machine learning based brain age prediction, we assessed the age-dependence of diffusion metrics, and compared the age prediction accuracy of six different diffusion models, including diffusion tensor (DTI) and kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), restriction spectrum imaging (RSI), spherical mean technique multi-compartment (SMT-mc), and white matter tract integrity (WMTI). The results showed that the age slopes for conventional DTI metrics (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity [AD], radial diffusivity [RD]) were largely consistent with previous research, and that the highest performing advanced dMRI models showed comparable age prediction accuracy to conventional DTI. Linear mixed effects models and Wilk's theorem analysis showed that the ‘FA fine’ metric of the RSI model and ‘orientation dispersion’ (OD) metric of the NODDI model showed the highest sensitivity to age. The results indicate that advanced diffusion models (DKI, NODDI, RSI, SMT mc, WMTI) provide sensitive measures of age-related microstructural changes of white matter in the brain that complement and extend the contribution of conventional DTI.

• Richard, Geneviève; Kolskår, Knut-Kristian; Ulrichsen, Kristine Moe; Kaufmann, Tobias; Alnæs, Dag & Sanders, Anne-Marthe [Show all 12 contributors for this article] (2019). Brain age prediction in stroke patients: Highly reliable but limited sensitivity to cognitive performance and response to cognitive training. NeuroImage: Clinical. 25, p. 1–11. doi: 10.1016/j.nicl.2019.102159. Full text in Research Archive Show summary

  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.

• Høgestøl, Einar August; Kaufmann, Tobias; Nygaard, Gro Owren; Beyer, Mona K.; Sowa, Piotr & Nordvik, Jan Egil [Show all 11 contributors for this article] (2019). Cross-sectional and longitudinal MRI brain scans reveal accelerated brain aging in multiple sclerosis. Frontiers in Neurology. 10:450, p. 1–9. doi: 10.3389/fneur.2019.00450. Full text in Research Archive Show summary

  Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system. By combining longitudinal MRI-based brain morphometry and brain age estimation using machine learning, we tested the hypothesis that MS patients have higher brain age relative to chronological age than healthy controls (HC) and that longitudinal rate of brain aging in MS patients is associated with clinical course and severity. Seventy-six MS patients [71% females, mean age 34.8 years (range 21–49) at inclusion] were examined with brain MRI at three time points with a mean total follow up period of 4.4 years (±0.4 years). We used additional cross-sectional MRI data from 235 HC for case-control comparison. We applied a machine learning model trained on an independent set of 3,208 HC to estimate individual brain age and to calculate the difference between estimated and chronological age, termed brain age gap (BAG). We also assessed the longitudinal change rate in BAG in individuals with MS. MS patients showed significantly higher BAG (4.4 ± 6.6 years) compared to HC (Cohen's D = 0.69, p = 4.0 × 10−6). Longitudinal estimates of BAG in MS patients showed high reliability and suggested an accelerated rate of brain aging corresponding to an annual increase of 0.41 (SE = 0.15) years compared to chronological aging (p = 0.008). Multiple regression analyses revealed higher rate of brain aging in patients with more brain atrophy (Cohen's D = 0.86, p = 4.3 × 10−15) and increased white matter lesion load (WMLL) (Cohen's D = 0.55, p = 0.015). On average, patients with MS had significantly higher BAG compared to HC. Progressive brain aging in patients with MS was related to brain atrophy and increased WMLL. No significant clinical associations were found in our sample, future studies are warranted on this matter. Brain age estimation is a promising method for evaluation of subtle brain changes in MS, which is important for predicting clinical outcome and guide choice of intervention.

• Kaufmann, Tobias; Meer, Dennis van der; Doan, Nhat Trung; Schwarz, Emanuel; Lund, Martina Jonette & Agartz, Ingrid [Show all 84 contributors for this article] (2019). Common brain disorders are associated with heritable patterns of apparent aging of the brain. Nature Neuroscience. ISSN 1097-6256. 22(10), p. 1617–1623. doi: 10.1038/s41593-019-0471-7. Full text in Research Archive Show summary

  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.

• Richard, Geneviève; Kolskår, Knut-Kristian; Sanders, Anne-Marthe; Kaufmann, Tobias; Petersen, Anders & Doan, Nhat Trung [Show all 13 contributors for this article] (2018). Assessing distinct patterns of cognitive aging using tissue-specific brain age prediction based on diffusion tensor imaging and brain morphometry. PeerJ. doi: 10.7717/peerj.5908. Full text in Research Archive Show summary

  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 indepen- dent 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 ( n = 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 ( r = 0 . 83, CI:0.78–0.86, MAE = 6.76 years) and white matter microstructure ( r = 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.

• Meer, Dennis van der; Rokicki, Jaroslav; Kaufmann, Tobias; Palomera, Aldo Còrdova; Moberget, Torgeir & Alnæs, Dag [Show all 54 contributors for this article] (2018). Brain scans from 21,297 individuals reveal the genetic architecture of hippocampal subfield volumes. Molecular Psychiatry. ISSN 1359-4184. p. 1–13. doi: 10.1038/s41380-018-0262-7. Full text in Research Archive Show summary

  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 × 10–16) 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.

• Kolskår, Knut-Kristian; Alnæs, Dag; Kaufmann, Tobias; Richard, Geneviéve´; Sanders, Anne-Marthe & Ulrichsen, Kristine Moe [Show all 10 contributors for this article] (2018). Key Brain Network Nodes Show Differential Cognitive Relevance and Developmental Trajectories during Childhood and Adolescence. eNeuro. 5(4), p. 1–13. doi: 10.1523/ENEURO.0092-18.2018. Full text in Research Archive Show summary

  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 (n = 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.

• Dørum, Erlend Solberg; Kaufmann, Tobias; Alnæs, Dag; Andreassen, Ole Andreas; Richard, Geneviéve´ & Kolskår, Knut-Kristian [Show all 8 contributors for this article] (2017). Increased sensitivity to age-related differences in brain functional connectivity during continuous multiple object tracking compared to resting-state. NeuroImage. ISSN 1053-8119. 148, p. 364–372. doi: 10.1016/j.neuroimage.2017.01.048. Full text in Research Archive
• Dørum, Erlend Solberg; Alnæs, Dag; Kaufmann, Tobias; Richard, Geneviéve´; Lund, Martina Jonette & Tønnesen, Siren [Show all 15 contributors for this article] (2016). Age-related differences in brain network activation and co-activation during multiple object tracking. Brain and Behavior. 6(11). doi: 10.1002/brb3.533. Full text in Research Archive Show summary

  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 (n = 25, mean age = 24.4 ± 5.1 years) and older (n = 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.

• Ulrichsen, Kristine Moe; Kaufmann, Tobias; Dørum, Erlend Solberg; Kolskår, Knut-Kristian; Richard, Geneviéve´ & Alnæs, Dag [Show all 9 contributors for this article] (2016). 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. Frontiers in Psychology. 7. doi: 10.3389/fpsyg.2016.00912. Full text in Research Archive Show summary

  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

• Alnæs, Dag; Sneve, Markus Handal; Richard, Geneviéve´; Skåtun, Kristina Cecilie; Kaufmann, Tobias & Nordvik, Jan Egil [Show all 10 contributors for this article] (2015). Functional connectivity indicates differential roles for the intraparietal sulcus and the superior parietal lobule in multiple object tracking. NeuroImage. ISSN 1053-8119. 123, p. 129–137. doi: 10.1016/j.neuroimage.2015.08.029. Full text in Research Archive
• Alnæs, Dag; Kaufmann, Tobias; Richard, Geneviéve´; Duff, Eugene; Sneve, Markus Handal & Endestad, Tor [Show all 10 contributors for this article] (2015). Attentional load modulates large-scale functional brain connectivity beyond the core attention networks. NeuroImage. ISSN 1053-8119. 109, p. 260–272. doi: 10.1016/j.neuroimage.2015.01.026. Full text in Research Archive Show summary

  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.

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• Kaufmann, Tobias; Meer, Dennis van der; Doan, Nhat Trung; Schwarz, Emanuel; Lund, Martina Jonette & Agartz, Ingrid [Show all 97 contributors for this article] (2020). Publisher Correction: Common brain disorders are associated with heritable patterns of apparent aging of the brain (Nature Neuroscience, (2019), 22, 10, (1617-1623), 10.1038/s41593-019-0471-7). Nature Neuroscience. ISSN 1097-6256. 23(2), p. 295–295. doi: 10.1038/s41593-019-0553-6. Full text in Research Archive

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