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Ole A. Andreassen

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28 papers

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YNIMG Journal 2025 Journal Article

White matter microstructure links with brain, bodily and genetic attributes in adolescence, mid- and late life

Max Korbmacher
Mario Tranfa
Giuseppe Pontillo
Dennis van der Meer
Meng-Yun Wang
Ole A. Andreassen
Lars T. Westlye
Ivan I. Maximov

Advanced diffusion magnetic resonance imaging (dMRI) allows one to probe and assess brain white matter (WM) organisation and microstructure in vivo. Various dMRI models with different theoretical and practical assumptions have been developed, representing partly overlapping characteristics of the underlying brain biology with potentially complementary value in the cognitive and clinical neurosciences. To which degree the different dMRI metrics relate to clinically relevant geno- and phenotypes is still debated. Hence, we investigate how tract-based and whole WM skeleton parameters from different dMRI approaches associate with clinically relevant and white matter-related phenotypes (sex, age, pulse pressure (PP), body-mass-index (BMI), brain asymmetry) and genetic markers in the UK Biobank (UKB, n=52,140) and the Adolescent Brain Cognitive Development (ABCD) Study (n=5,844). In general, none of the imaging approaches could explain all examined phenotypes, though the approaches were overall similar in explaining variability of the examined phenotypes. Nevertheless, particular diffusion parameters of the used dMRI approaches stood out in explaining some important phenotypes known to correlate with general human health outcomes. A multi-compartment Bayesian dMRI approach provided the strongest WM associations with age, and together with diffusion tensor imaging, the largest accuracy for sex-classifications. We find a similar pattern of metric and tract-dependent asymmetries across datasets, with stronger asymmetries in ABCD data. The magnitude of WM associations with polygenic scores as well as PP depended more on the sample, and likely age, than dMRI metrics. However, kurtosis was most indicative of BMI and potentially of bipolar disorder polygenic scores. We conclude that WM microstructure is differentially associated with clinically relevant pheno- and genotypes at different points in life.

YNICL Journal 2022 Journal Article

Aberrant default mode connectivity in adolescents with early-onset psychosis: A resting state fMRI study

Eva Hilland
Cecilie Johannessen
Rune Jonassen
Dag Alnæs
Kjetil N. Jørgensen
Claudia Barth
Dimitrios Andreou
Stener Nerland

Abnormal default mode network (DMN) connectivity has been found in schizophrenia and other psychotic disorders. However, there are limited studies on early onset psychosis (EOP), and their results show lack of agreement. Here, we investigated within-network DMN connectivity in EOP compared to healthy controls (HC), and its relationship to clinical characteristics. A sample of 68 adolescent patients with EOP (mean age 16.53 ± 1.12 [SD] years, females 66%) and 95 HC (mean age 16.24 ± 1.50 [SD], females 60%) from two Scandinavian cohorts underwent resting state functional magnetic resonance imaging (rsfMRI). A group independent component analysis (ICA) was performed to identify the DMN across all participants. Dual regression was used to estimate spatial maps reflecting each participant's DMN network, which were compared between EOP and HC using voxel-wise general linear models and permutation-based analyses. Subgroup analyses were performed within the patient group, to explore associations between diagnostic subcategories and current use of psychotropic medication in relation to connectivity strength. The analysis revealed significantly reduced DMN connectivity in EOP compared to HC in the posterior cingulate cortex, precuneus, fusiform cortex, putamen, pallidum, amygdala, and insula. The subgroup analysis in the EOP group showed strongest deviations for affective psychosis, followed by other psychotic disorders and schizophrenia. There was no association between DMN connectivity strength and the current use of psychotropic medication. In conclusion, the findings demonstrate weaker DMN connectivity in adolescent patients with EOP compared to healthy peers, and differential effects across diagnostic subcategories, which may inform our understanding of underlying disease mechanisms in EOP.

YNICL Journal 2022 Journal Article

Adipose tissue distribution from body MRI is associated with cross-sectional and longitudinal brain age in adults

Dani Beck
Ann-Marie G. de Lange
Dag Alnæs
Ivan I. Maximov
Mads L. Pedersen
Olof Dahlqvist Leinhard
Jennifer Linge
Rozalyn Simon

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.

YNICL Journal 2022 Journal Article

Associations between abdominal adipose tissue, reproductive span, and brain characteristics in post-menopausal women

Louise S. Schindler
Sivaniya Subramaniapillai
Claudia Barth
Dennis van der Meer
Mads L. Pedersen
Tobias Kaufmann
Ivan I. Maximov
Jennifer Linge

The menopause transition involves changes in oestrogens and adipose tissue distribution, which may influence female brain health post-menopause. Although increased central fat accumulation is linked to risk of cardiometabolic diseases, adipose tissue also serves as the primary biosynthesis site of oestrogens post-menopause. It is unclear whether different types of adipose tissue play diverging roles in female brain health post-menopause, and whether this depends on lifetime oestrogen exposure, which can have lasting effects on the brain and body even after menopause. Using the UK Biobank sample, we investigated associations between brain characteristics and visceral adipose tissue (VAT) and abdominal subcutaneous adipose tissue (ASAT) in 10,251 post-menopausal females, and assessed whether the relationships varied depending on length of reproductive span (age at menarche to age at menopause). To parse the effects of common genetic variation, we computed polygenic scores for reproductive span. The results showed that higher VAT and ASAT were both associated with higher grey and white matter brain age, and greater white matter hyperintensity load. The associations varied positively with reproductive span, indicating more prominent associations between adipose tissue and brain measures in females with a longer reproductive span. The effects were in general small, but could not be fully explained by genetic variation or relevant confounders. Our findings indicate that associations between abdominal adipose tissue and brain health post-menopause may partly depend on individual differences in cumulative oestrogen exposure during reproductive years, emphasising the complexity of neural and endocrine ageing processes in females.

YNIMG Journal 2022 Journal Article

Associations between brain imaging and polygenic scores of mental health and educational attainment in children aged 9–11

Sara Fernandez-Cabello
Dag Alnæs
Dennis van der Meer
Andreas Dahl
Madelene Holm
Rikka Kjelkenes
Ivan I. Maximov
Linn B. Norbom

Psychiatric disorders are highly heritable and polygenic, and many have their peak onset in late childhood and adolescence, a period of tremendous changes. Although the neurodevelopmental antecedents of mental illness are widely acknowledged, research in youth population cohorts is still scarce, preventing our progress towards the early characterization of these disorders. We included 7,124 children (9-11 years old) from the Adolescent Brain and Cognitive Development Study to map the associations of structural and diffusion brain imaging with common genetic variants and polygenic scores for psychiatric disorders and educational attainment. We used principal component analysis to derive imaging components, and calculated their heritability. We then assessed the relationship of imaging components with genetic and clinical psychiatric risk with univariate models and Canonical correlation analysis (CCA). Most imaging components had moderate heritability. Univariate models showed limited evidence and small associations of polygenic scores with brain structure at this age. CCA revealed two significant modes of covariation. The first mode linked higher polygenic scores for educational attainment with less externalizing problems and larger surface area. The second mode related higher polygenic scores for schizophrenia, bipolar disorder, and autism spectrum disorder to higher global cortical thickness, smaller white matter volumes of the fornix and cingulum, larger medial occipital surface area and smaller surface area of lateral and medial temporal regions. While cross-validation suggested limited generalizability, our results highlight the potential of multivariate models to better understand the transdiagnostic and distributed relationships between mental health and brain structure in late childhood.

YNICL Journal 2022 Journal Article

Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms

Martina J. Lund
Dag Alnæs
Ann-Marie G. de Lange
Ole A. Andreassen
Lars T. Westlye
Tobias Kaufmann

OBJECTIVE: Magnetic resonance imaging (MRI) has shown that estimated brain age is deviant from chronological age in various common brain disorders. Brain age estimation could be useful for investigating patterns of brain maturation and integrity, aiding to elucidate brain mechanisms underlying these heterogeneous conditions. Here, we examined functional brain age in two large samples of children and adolescents and its relation to mental health. METHODS: We used resting-state fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC; n = 1126, age range 8-22 years) to estimate functional connectivity between brain networks, and utilized these as features for brain age prediction. We applied the prediction model to 1387 individuals (age range 8-22 years) in the Healthy Brain Network sample (HBN). In addition, we estimated brain age in PNC using a cross-validation framework. Next, we tested for associations between brain age gap and various aspects of psychopathology and cognitive performance. RESULTS: Our model was able to predict age in the independent test samples, with a model performance of r = 0.54 for the HBN test set, supporting consistency in functional connectivity patterns between samples and scanners. Linear models revealed a significant association between brain age gap and psychopathology in PNC, where individuals with a lower estimated brain age, had a higher overall symptom burden. These associations were not replicated in HBN. DISCUSSION: Our findings support the use of brain age prediction from fMRI-based connectivity. While requiring further extensions and validations, the approach may be instrumental for detecting brain phenotypes related to intrinsic connectivity and could assist in characterizing risk in non-typically developing populations.

YNIMG Journal 2022 Journal Article

Deep neural networks learn general and clinically relevant representations of the ageing brain

Esten H. Leonardsen
Han Peng
Tobias Kaufmann
Ingrid Agartz
Ole A. Andreassen
Elisabeth Gulowsen Celius
Thomas Espeseth
Hanne F. Harbo

The discrepancy between chronological age and the apparent age of the brain based on neuroimaging data — the brain age delta — has emerged as a reliable marker of brain health. With an increasing wealth of data, approaches to tackle heterogeneity in data acquisition are vital. To this end, we compiled raw structural magnetic resonance images into one of the largest and most diverse datasets assembled (n=53542), and trained convolutional neural networks (CNNs) to predict age. We achieved state-of-the-art performance on unseen data from unknown scanners (n=2553), and showed that higher brain age delta is associated with diabetes, alcohol intake and smoking. Using transfer learning, the intermediate representations learned by our model complemented and partly outperformed brain age delta in predicting common brain disorders. Our work shows we can achieve generalizable and biologically plausible brain age predictions using CNNs trained on heterogeneous datasets, and transfer them to clinical use cases.

YNIMG Journal 2022 Journal Article

Generalization of cortical MOSTest genome-wide associations within and across samples

Robert J. Loughnan
Alexey A. Shadrin
Oleksandr Frei
Dennis van der Meer
Weiqi Zhao
Clare E. Palmer
Wesley K. Thompson
Carolina Makowski

Genome-Wide Association studies have typically been limited to univariate analysis in which a single outcome measure is tested against millions of variants. Recent work demonstrates that a Multivariate Omnibus Statistic Test (MOSTest) is well powered to discover genomic effects distributed across multiple phenotypes. Applied to cortical brain MRI morphology measures, MOSTest has resulted in a drastic improvement in power to discover loci when compared to established approaches (min-P). One question that arises is how well these discovered loci replicate in independent data. Here we perform 10 times cross validation within 34,973 individuals from UK Biobank for imaging measures of cortical area, thickness and sulcal depth (>1,000 dimensionality for each). By deploying a replication method that aggregates discovered effects distributed across multiple phenotypes, termed PolyVertex Score (MOSTest-PVS), we demonstrate a higher replication yield and comparable replication rate of discovered loci for MOSTest (# replicated loci: 242-496, replication rate: 96-97%) in independent data when compared with the established min-P approach (# replicated loci: 26-55, replication rate: 91-93%). An out-of-sample replication of discovered loci was conducted with a sample of 4,069 individuals from the Adolescent Brain Cognitive Development® (ABCD) study, who are on average 50 years younger than UK Biobank individuals. We observe a higher replication yield and comparable replication rate of MOSTest-PVS compared to min-P. This finding underscores the importance of using well-powered multivariate techniques for both discovery and replication of high dimensional phenotypes in Genome-Wide Association studies.

YNIMG Journal 2021 Journal Article

Multisite reproducibility and test-retest reliability of the T1w/T2w-ratio: A comparison of processing methods

Stener Nerland
Kjetil N. Jørgensen
Wibeke Nordhøy
Ivan I. Maximov
Robin A.B. Bugge
Lars T. Westlye
Ole A. Andreassen
Oliver M. Geier

BACKGROUND: The ratio of T1-weighted (T1w) and T2-weighted (T2w) magnetic resonance imaging (MRI) images is often used as a proxy measure of cortical myelin. However, the T1w/T2w-ratio is based on signal intensities that are inherently non-quantitative and known to be affected by extrinsic factors. To account for this a variety of processing methods have been proposed, but a systematic evaluation of their efficacy is lacking. Given the dependence of the T1w/T2w-ratio on scanner hardware and T1w and T2w protocols, it is important to ensure that processing pipelines perform well also across different sites. METHODS: We assessed a variety of processing methods for computing cortical T1w/T2w-ratio maps, including correction methods for nonlinear field inhomogeneities, local outliers, and partial volume effects as well as intensity normalisation. These were implemented in 33 processing pipelines which were applied to four test-retest datasets, with a total of 170 pairs of T1w and T2w images acquired on four different MRI scanners. We assessed processing pipelines across datasets in terms of their reproducibility of expected regional distributions of cortical myelin, lateral intensity biases, and test-retest reliability regionally and across the cortex. Regional distributions were compared both qualitatively with histology and quantitatively with two reference datasets, YA-BC and YA-B1+, from the Human Connectome Project. RESULTS: Reproducibility of raw T1w/T2w-ratio distributions was overall high with the exception of one dataset. For this dataset, Spearman rank correlations increased from 0.27 to 0.70 after N3 bias correction relative to the YA-BC reference and from -0.04 to 0.66 after N4ITK bias correction relative to the YA-B1+ reference. Partial volume and outlier corrections had only marginal effects on the reproducibility of T1w/T2w-ratio maps and test-retest reliability. Before intensity normalisation, we found large coefficients of variation (CVs) and low intraclass correlation coefficients (ICCs), with total whole-cortex CV of 10.13% and whole-cortex ICC of 0.58 for the raw T1w/T2w-ratio. Intensity normalisation with WhiteStripe, RAVEL, and Z-Score improved total whole-cortex CVs to 5.91%, 5.68%, and 5.19% respectively, whereas Z-Score and Least Squares improved whole-cortex ICCs to 0.96 and 0.97 respectively. CONCLUSIONS: In the presence of large intensity nonuniformities, bias field correction is necessary to achieve acceptable correspondence with known distributions of cortical myelin, but it can be detrimental in datasets with less intensity inhomogeneity. Intensity normalisation can improve test-retest reliability and inter-subject comparability. However, both bias field correction and intensity normalisation methods vary greatly in their efficacy and may affect the interpretation of results. The choice of T1w/T2w-ratio processing method must therefore be informed by both scanner and acquisition protocol as well as the given study objective. Our results highlight limitations of the T1w/T2w-ratio, but also suggest concrete ways to enhance its usefulness in future studies.

YNIMG Journal 2021 Journal Article

Vertex-wise multivariate genome-wide association study identifies 780 unique genetic loci associated with cortical morphology

Alexey A. Shadrin
Tobias Kaufmann
Dennis van der Meer
Clare E. Palmer
Carolina Makowski
Robert Loughnan
Terry L. Jernigan
Tyler M. Seibert

Brain morphology has been shown to be highly heritable, yet only a small portion of the heritability is explained by the genetic variants discovered so far. Here we extended the Multivariate Omnibus Statistical Test (MOSTest) and applied it to genome-wide association studies (GWAS) of vertex-wise structural magnetic resonance imaging (MRI) cortical measures from N=35,657 participants in the UK Biobank. We identified 695 loci for cortical surface area and 539 for cortical thickness, in total 780 unique genetic loci associated with cortical morphology robustly replicated in 8,060 children of mixed ethnicity from the Adolescent Brain Cognitive Development (ABCD) Study®. This reflects more than 8-fold increase in genetic discovery at no cost to generalizability compared to the commonly used univariate GWAS methods applied to region of interest (ROI) data. Functional follow up including gene-based analyses implicated 10% of all protein-coding genes and pointed towards pathways involved in neurogenesis and cell differentiation. Power analysis indicated that applying the MOSTest to vertex-wise structural MRI data triples the effective sample size compared to conventional univariate GWAS approaches. The large boost in power obtained with the vertex-wise MOSTest together with pronounced replication rates and highlighted biologically meaningful pathways underscores the advantage of multivariate approaches in the context of highly distributed polygenic architecture of the human brain.

YNIMG Journal 2021 Journal Article

White matter microstructure across the adult lifespan: A mixed longitudinal and cross-sectional study using advanced diffusion models and brain-age prediction

Dani Beck
Ann-Marie G. de Lange
Ivan I. Maximov
Geneviève Richard
Ole A. Andreassen
Jan E. Nordvik
Lars T. Westlye

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.

YNICL Journal 2020 Journal Article

Distinct structural brain circuits indicate mood and apathy profiles in bipolar disorder

Wenhao Jiang
Ole A. Andreassen
Ingrid Agartz
Trine V. Lagerberg
Lars T. Westlye
Vince D. Calhoun
Jessica A. Turner

Bipolar disorder (BD) is a severe manic-depressive illness. Patients with BD have been shown to have gray matter (GM) deficits in prefrontal, frontal, parietal, and temporal regions; however, the relationship between structural effects and clinical profiles has proved elusive when considered on a region by region or voxel by voxel basis. In this study, we applied parallel independent component analysis (pICA) to structural neuroimaging measures and the positive and negative syndrome scale (PANSS) in 110 patients (mean age 34.9 ± 11.65) with bipolar disorder, to examine networks of brain regions that relate to symptom profiles. The pICA revealed two distinct symptom profiles and associated GM concentration alteration circuits. The first PANSS pICA profile mainly involved anxiety, depression and guilty feelings, reflecting mood symptoms. Reduced GM concentration in right temporal regions predicted worse mood symptoms in this profile. The second PANSS pICA profile generally covered blunted affect, emotional withdrawal, passive/apathetic social withdrawal, depression and active social avoidance, exhibiting a withdrawal or apathy dominating component. Lower GM concentration in bilateral parietal and frontal regions showed worse symptom severity in this profile. In summary, a pICA decomposition suggested BD patients showed distinct mood and apathy profiles differing from the original PANSS subscales, relating to distinct brain structural networks.

YNIMG Journal 2020 Journal Article

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

Mathias Valstad
Torgeir Moberget
Daniël Roelfs
Nora B. Slapø
Clara M.F. Timpe
Dani Beck
Geneviève Richard
Linn Sofie Sæther

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.

YNICL Journal 2017 Journal Article

Distinct multivariate brain morphological patterns and their added predictive value with cognitive and polygenic risk scores in mental disorders

Nhat Trung Doan
Tobias Kaufmann
Francesco Bettella
Kjetil Nordbø Jørgensen
Christine Lycke Brandt
Torgeir Moberget
Dag Alnæs
Gwenaëlle Douaud

The brain underpinnings of schizophrenia and bipolar disorders are multidimensional, reflecting complex pathological processes and causal pathways, requiring multivariate techniques to disentangle. Furthermore, little is known about the complementary clinical value of brain structural phenotypes when combined with data on cognitive performance and genetic risk. Using data-driven fusion of cortical thickness, surface area, and gray matter density maps (GMD), we found six biologically meaningful patterns showing strong group effects, including four statistically independent multimodal patterns reflecting co-occurring alterations in thickness and GMD in patients, over and above two other independent patterns of widespread thickness and area reduction. Case-control classification using cognitive scores alone revealed high accuracy, and adding imaging features or polygenic risk scores increased performance, suggesting their complementary predictive value with cognitive scores being the most sensitive features. Multivariate pattern analyses reveal distinct patterns of brain morphology in mental disorders, provide insights on the relative importance between brain structure, cognitive and polygenetic risk score in classification of patients, and demonstrate the importance of multivariate approaches in studying the pathophysiological substrate of these complex disorders.

YNIMG Journal 2017 Journal Article

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

Nhat Trung Doan
Andreas Engvig
Krystal Zaske
Karin Persson
Martina Jonette Lund
Tobias Kaufmann
Aldo Cordova-Palomera
Dag Alnæs

Alzheimer's disease (AD) is a debilitating age-related neurodegenerative disorder. Accurate identification of individuals at risk is complicated as AD shares cognitive and brain features with aging. We applied linked independent component analysis (LICA) on three complementary measures of gray matter structure: cortical thickness, area and gray matter density of 137 AD, 78 mild (MCI) and 38 subjective cognitive impairment patients, and 355 healthy adults aged 18–78 years to identify dissociable multivariate morphological patterns sensitive to age and diagnosis. Using the lasso classifier, we performed group classification and prediction of cognition and age at different age ranges to assess the sensitivity and diagnostic accuracy of the LICA patterns in relation to AD, as well as early and late healthy aging. Three components showed high sensitivity to the diagnosis and cognitive status of AD, with different relationships with age: one reflected an anterior-posterior gradient in thickness and gray matter density and was uniquely related to diagnosis, whereas the other two, reflecting widespread cortical thickness and medial temporal lobe volume, respectively, also correlated significantly with age. Repeating the LICA decomposition and between-subject analysis on ADNI data, including 186 AD, 395 MCI and 220 age-matched healthy controls, revealed largely consistent brain patterns and clinical associations across samples. Classification results showed that multivariate LICA-derived brain characteristics could be used to predict AD and age with high accuracy (area under ROC curve up to 0. 93 for classification of AD from controls). Comparison between classifiers based on feature ranking and feature selection suggests both common and unique feature sets implicated in AD and aging, and provides evidence of distinct age-related differences in early compared to late aging.

YNIMG Journal 2017 Journal Article

ENIGMA and the individual: Predicting factors that affect the brain in 35 countries worldwide

Paul M. Thompson
Ole A. Andreassen
Alejandro Arias-Vasquez
Carrie E. Bearden
Premika S. Boedhoe
Rachel M. Brouwer
Randy L. Buckner
Jan K. Buitelaar

In this review, we discuss recent work by the ENIGMA Consortium (http: //enigma. ini. usc. edu) – a global alliance of over 500 scientists spread across 200 institutions in 35 countries collectively analyzing brain imaging, clinical, and genetic data. Initially formed to detect genetic influences on brain measures, ENIGMA has grown to over 30 working groups studying 12 major brain diseases by pooling and comparing brain data. In some of the largest neuroimaging studies to date – of schizophrenia and major depression – ENIGMA has found replicable disease effects on the brain that are consistent worldwide, as well as factors that modulate disease effects. In partnership with other consortia including ADNI, CHARGE, IMAGEN and others 1 1 Abbreviations: ADNI, Alzheimer's Disease Neuroimaging Initiative (http: //www. adni-info. org); CHARGE, the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (http: //www. chargeconsortium. com); IMAGEN, IMAging GENetics Consortium (http: //www. imagen-europe. com). , ENIGMA's genomic screens – now numbering over 30, 000 MRI scans – have revealed at least 8 genetic loci that affect brain volumes. Downstream of gene findings, ENIGMA has revealed how these individual variants – and genetic variants in general – may affect both the brain and risk for a range of diseases. The ENIGMA consortium is discovering factors that consistently affect brain structure and function that will serve as future predictors linking individual brain scans and genomic data. It is generating vast pools of normative data on brain measures – from tens of thousands of people – that may help detect deviations from normal development or aging in specific groups of subjects. We discuss challenges and opportunities in applying these predictors to individual subjects and new cohorts, as well as lessons we have learned in ENIGMA's efforts so far.

YNIMG Journal 2017 Journal Article

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

Erlend S. Dørum
Tobias Kaufmann
Dag Alnæs
Ole A. Andreassen
Geneviève Richard
Knut K. Kolskår
Jan Egil Nordvik
Lars T. Westlye

Age-related differences in cognitive agility vary greatly between individuals and cognitive functions. This heterogeneity is partly mirrored in individual differences in brain network connectivity as revealed using resting-state functional magnetic resonance imaging (fMRI), suggesting potential imaging biomarkers for age-related cognitive decline. However, although convenient in its simplicity, the resting state is essentially an unconstrained paradigm with minimal experimental control. Here, based on the conception that the magnitude and characteristics of age-related differences in brain connectivity is dependent on cognitive context and effort, we tested the hypothesis that experimentally increasing cognitive load boosts the sensitivity to age and changes the discriminative network configurations. To this end, we obtained fMRI data from younger (n=25, mean age 24. 16±5. 11) and older (n=22, mean age 65. 09±7. 53) healthy adults during rest and two load levels of continuous multiple object tracking (MOT). Brain network nodes and their time-series were estimated using independent component analysis (ICA) and dual regression, and the edges in the brain networks were defined as the regularized partial temporal correlations between each of the node pairs at the individual level. Using machine learning based on a cross-validated regularized linear discriminant analysis (rLDA) we attempted to classify groups and cognitive load from the full set of edge-wise functional connectivity indices. While group classification using resting-state data was highly above chance (approx. 70% accuracy), functional connectivity (FC) obtained during MOT strongly increased classification performance, with 82% accuracy for the young and 95% accuracy for the old group at the highest load level. Further, machine learning revealed stronger differentiation between rest and task in young compared to older individuals, supporting the notion of network dedifferentiation in cognitive aging. Task-modulation in edgewise FC was primarily observed between attention- and sensorimotor networks; with decreased negative correlations between attention- and default mode networks in older adults. These results demonstrate that the magnitude and configuration of age-related differences in brain functional connectivity are partly dependent on cognitive context and load, which emphasizes the importance of assessing brain connectivity differences across a range of cognitive contexts beyond the resting-state.

YNIMG Journal 2017 Journal Article

Task modulations and clinical manifestations in the brain functional connectome in 1615 fMRI datasets

Tobias Kaufmann
Dag Alnæs
Christine Lycke Brandt
Nhat Trung Doan
Karolina Kauppi
Francesco Bettella
Trine V. Lagerberg
Akiah O. Berg

Objective An abundance of experimental studies have motivated a range of models concerning the cognitive underpinnings of severe mental disorders, yet the conception that cognitive and brain dysfunction is confined to specific cognitive domains and contexts has limited ecological validity. Schizophrenia and bipolar spectrum disorders have been conceptualized as disorders of brain connectivity; yet little is known about the pervasiveness across cognitive tasks. Methods To address this outstanding issue of context specificity, we estimated functional network connectivity from fMRI data obtained during five cognitive tasks (0-back, 2-back, go/no-go, recognition of positive faces, negative faces) in 90 patients with schizophrenia spectrum, 97 patients with bipolar spectrum disorder, and 136 healthy controls, including 1615 fMRI datasets in total. We tested for main effects of task and group, and their interactions, and used machine learning to classify task labels and predict cognitive domain scores from brain connectivity. Results Connectivity profiles were positively correlated across tasks, supporting the existence of a core functional connectivity backbone common to all tasks. However, 76. 2% of all network links also showed significant task-related alterations, robust on the single subject level as evidenced by high machine-learning performance when classifying task labels. Independent of such task-specific modulations, 9. 5% of all network links showed significant group effects, particularly including sensory (sensorimotor, visual, auditory) and cognitive (frontoparietal, default-mode, dorsal attention) networks. A lack of group by task interactions revealed that the pathophysiological sensitivity remained across tasks. Such pervasiveness across tasks was further supported by significant predictions of cognitive domain scores from the connectivity backbone obtained across tasks. Conclusions The high accuracies obtained when classifying cognitive tasks support that brain connectivity indices provide sensitive and specific measures of cognitive states. Importantly, we provide evidence that brain network dysfunction in severe mental disorders is not confined to specific cognitive tasks and show that the connectivity backbone common to all tasks is predictive of cognitive domain traits. Such pervasiveness across tasks may support a generalization of pathophysiological models from different domains, thereby reducing their complexity and increasing their ecological validity. Future research incorporating a wider range of cognitive tasks, involving other sensory modalities (auditory, somatosensory, motor) and requirements (learning, perceptual inference, decision making, etc.), is needed to assess if under certain circumstances, context dependent aberrations may evolve. Our results provide further evidence from a large sample that fMRI based functional network connectivity can be used to reveal both, state and trait effects in the connectome.

YNICL Journal 2016 Journal Article

Reduced load-dependent default mode network deactivation across executive tasks in schizophrenia spectrum disorders

Beathe Haatveit
Jimmy Jensen
Dag Alnæs
Tobias Kaufmann
Christine L. Brandt
Christian Thoresen
Ole A. Andreassen
Ingrid Melle

BACKGROUND: Schizophrenia is associated with cognitive impairment and brain network dysconnectivity. Recent efforts have explored brain circuits underlying cognitive dysfunction in schizophrenia and documented altered activation of large-scale brain networks, including the task-positive network (TPN) and the task-negative default mode network (DMN) in response to cognitive demands. However, to what extent TPN and DMN dysfunction reflect overlapping mechanisms and are dependent on cognitive state remain to be determined. METHODS: In the current study, we investigated the recruitment of TPN and DMN using independent component analysis in patients with schizophrenia spectrum disorders (n = 29) and healthy controls (n = 21) during two different executive tasks probing planning/problem-solving and spatial working memory. RESULTS: We found reduced load-dependent DMN deactivation across tasks in patients compared to controls. Furthermore, we observed only moderate associations between the TPN and DMN activation across groups, implying that the two networks reflect partly independent mechanisms. Additionally, whereas TPN activation was associated with task performance in both tasks, no such associations were found for DMN. CONCLUSION: These results support a general load-dependent DMN dysfunction in schizophrenia spectrum disorder across two demanding executive tasks that is not merely an epiphenomenon of cognitive dysfunction.

YNIMG Journal 2016 Journal Article

The brain functional connectome is robustly altered by lack of sleep

Tobias Kaufmann
Torbjørn Elvsåshagen
Dag Alnæs
Nathalia Zak
Per Ø. Pedersen
Linn B. Norbom
Sophia H. Quraishi
Enzo Tagliazucchi

Sleep is a universal phenomenon necessary for maintaining homeostasis and function across a range of organs. Lack of sleep has severe health-related consequences affecting whole-body functioning, yet no other organ is as severely affected as the brain. The neurophysiological mechanisms underlying these deficits are poorly understood. Here, we characterize the dynamic changes in brain connectivity profiles inflicted by sleep deprivation and how they deviate from regular daily variability. To this end, we obtained functional magnetic resonance imaging data from 60 young, adult male participants, scanned in the morning and evening of the same day and again the following morning. 41 participants underwent total sleep deprivation before the third scan, whereas the remainder had another night of regular sleep. Sleep deprivation strongly altered the connectivity of several resting-state networks, including dorsal attention, default mode, and hippocampal networks. Multivariate classification based on connectivity profiles predicted deprivation state with high accuracy, corroborating the robustness of the findings on an individual level. Finally, correlation analysis suggested that morning-to-evening connectivity changes were reverted by sleep (control group)—a pattern which did not occur after deprivation. We conclude that both, a day of waking and a night of sleep deprivation dynamically alter the brain functional connectome.

YNICL Journal 2015 Journal Article

Assessing brain structural associations with working memory related brain patterns in schizophrenia and healthy controls using linked independent component analysis

Christine Lycke Brandt
Nhat Trung Doan
Siren Tønnesen
Ingrid Agartz
Kenneth Hugdahl
Ingrid Melle
Ole A. Andreassen
Lars T. Westlye

Schizophrenia (SZ) is a psychotic disorder with significant cognitive dysfunction. Abnormal brain activation during cognitive processing has been reported, both in task-positive and task-negative networks. Further, structural cortical and subcortical brain abnormalities have been documented, but little is known about how task-related brain activation is associated with brain anatomy in SZ compared to healthy controls (HC). Utilizing linked independent component analysis (LICA), a data-driven multimodal analysis approach, we investigated structure-function associations in a large sample of SZ (n = 96) and HC (n = 142). We tested for associations between task-positive (fronto-parietal) and task-negative (default-mode) brain networks derived from fMRI activation during an n-back working memory task, and brain structural measures of surface area, cortical thickness, and gray matter volume, and to what extent these associations differed in SZ compared to HC. A significant association (p < .05, corrected for multiple comparisons) was found between a component reflecting the task-positive fronto-parietal network and another component reflecting cortical thickness in fronto-temporal brain regions in SZ, indicating increased activation with increased thickness. Other structure-function associations across, between and within groups were generally moderate and significant at a nominal p-level only, with more numerous and stronger associations in SZ compared to HC. These results indicate a complex pattern of moderate associations between brain activation during cognitive processing and brain morphometry, and extend previous findings of fronto-temporal brain abnormalities in SZ by suggesting a coupling between cortical thickness of these brain regions and working memory-related brain activation.

YNIMG Journal 2015 Journal Article

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

Dag Alnæs
Tobias Kaufmann
Geneviève Richard
Eugene P. Duff
Markus H. Sneve
Tor Endestad
Jan Egil Nordvik
Ole A. Andreassen

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.

YNIMG Journal 2015 Journal Article

Comparison of variants of canonical correlation analysis and partial least squares for combined analysis of MRI and genetic data

Claudia Grellmann
Sebastian Bitzer
Jane Neumann
Lars T. Westlye
Ole A. Andreassen
Arno Villringer
Annette Horstmann

The standard analysis approach in neuroimaging genetics studies is the mass-univariate linear modeling (MULM) approach. From a statistical view, however, this approach is disadvantageous, as it is computationally intensive, cannot account for complex multivariate relationships, and has to be corrected for multiple testing. In contrast, multivariate methods offer the opportunity to include combined information from multiple variants to discover meaningful associations between genetic and brain imaging data. We assessed three multivariate techniques, partial least squares correlation (PLSC), sparse canonical correlation analysis (sparse CCA) and Bayesian inter-battery factor analysis (Bayesian IBFA), with respect to their ability to detect multivariate genotype–phenotype associations. Our goal was to systematically compare these three approaches with respect to their performance and to assess their suitability for high-dimensional and multi-collinearly dependent data as is the case in neuroimaging genetics studies. In a series of simulations using both linearly independent and multi-collinear data, we show that sparse CCA and PLSC are suitable even for very high-dimensional collinear imaging data sets. Among those two, the predictive power was higher for sparse CCA when voxel numbers were below 400 times sample size and candidate SNPs were considered. Accordingly, we recommend Sparse CCA for candidate phenotype, candidate SNP studies. When voxel numbers exceeded 500 times sample size, the predictive power was the highest for PLSC. Therefore, PLSC can be considered a promising technique for multivariate modeling of high-dimensional brain–SNP-associations. In contrast, Bayesian IBFA cannot be recommended, since additional post-processing steps were necessary to detect causal relations. To verify the applicability of sparse CCA and PLSC, we applied them to an experimental imaging genetics data set provided for us. Most importantly, application of both methods replicated the findings of this data set.

YNIMG Journal 2015 Journal Article

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

Dag Alnæs
Markus H. Sneve
Geneviève Richard
Kristina C. Skåtun
Tobias Kaufmann
Jan Egil Nordvik
Ole A. Andreassen
Tor Endestad

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

YNICL Journal 2015 Journal Article

Negative symptoms in schizophrenia are associated with aberrant striato-cortical connectivity in a rewarded perceptual decision-making task

Greg E. Reckless
Ole A. Andreassen
Andres Server
Tiril Østefjells
Jimmy Jensen

BACKGROUND: Negative symptoms in schizophrenia have been associated with structural and functional changes in the prefrontal cortex. They often persist after treatment with antipsychotic medication which targets, in particular, the ventral striatum (VS). As schizophrenia has been suggested to arise from dysfunctional connectivity between neural networks, it is possible that residual aberrant striato-cortical connectivity in medicated patients plays a role in enduring negative symptomology. The present study examined the relationship between striato-cortical connectivity and negative symptoms in medicated schizophrenia patients. METHODS: We manipulated motivation in a perceptual decision-making task during functional magnetic resonance imaging. Comparing healthy controls (n = 21) and medicated patients with schizophrenia (n = 18) we investigated how motivation-mediated changes in VS activation affected functional connectivity with the frontal cortex, and how changes in connectivity strength from the neutral to motivated condition related to negative symptom severity. RESULTS: A pattern of aberrant striato-cortical connectivity was observed in the presence of intact VS, but altered left inferior frontal gyrus (IFG) motivation-mediated activation in patients. The more severe the patient's negative symptoms, the less the connectivity strength between the right VS and left IFG changed from the neutral to the motivated condition. Despite aberrant striato-cortical connectivity and altered recruitment of the left IFG among patients, both patients and healthy controls adopted a more liberal response strategy in the motivated compared to the neutral condition. CONCLUSIONS: The present findings suggest that there is a link between dysfunctional striato-cortical connectivity and negative symptom severity, and offer a possible explanation as to why negative symptoms persist after treatment with antipsychotics.

YNIMG Journal 2014 Journal Article

The human amygdala encodes value and space during decision making

Olga Therese Ousdal
Karsten Specht
Andres Server
Ole A. Andreassen
Ray J. Dolan
Jimmy Jensen

Valuable stimuli are invariably localized in space. While our knowledge regarding the neural networks supporting value assignment and comparisons is considerable, we lack a basic understanding of how the human brain integrates motivational and spatial information. The amygdala is a key structure for learning and maintaining the value of sensory stimuli and a recent non-human primate study provided initial evidence that it also acts to integrate value with spatial location, a question we address here in a human setting. We measured haemodynamic responses (fMRI) in amygdala while manipulating the value and spatial configuration of stimuli in a simple stimulus–reward task. Subjects responded significantly faster and showed greater amygdala activation when a reward was dependent on a spatial specific response, compared to when a reward required less spatial specificity. Supplemental analysis supported this spatial specificity by demonstrating that the pattern of amygdala activity varied based on whether subjects responded to a motivational target presented in the ipsilateral or contralateral visual space. Our data show that the human amygdala integrates information about space and value, an integration of likely importance for assigning cognitive resources towards highly valuable stimuli in our environment.

YNIMG Journal 2013 Journal Article

Pathway analysis of genetic markers associated with a functional MRI faces paradigm implicates polymorphisms in calcium responsive pathways

Morten Mattingsdal
Andrew Anand Brown
Srdjan Djurovic
Ida Elken Sønderby
Andres Server
Ingrid Melle
Ingrid Agartz
Eivind Hovig

Several lines of evidence suggest that common polygenic variation influences brain function in humans. Combining high-density genetic markers with brain imaging techniques is constricted by the practicalities of collecting sufficiently large brain imaging samples. Pathway analysis promises to leverage knowledge on function of genes to detect recurring signals of moderate effect. We adapt this approach, exploiting the deep information collected on brain function by fMRI methods, to identify molecular pathways containing genetic variants which influence brain activation during a commonly applied experiment based on a face matching task (n =246) which was developed to study neural processing of faces displaying negative emotions. Genetic markers moderately associated (p <10−4) with whole brain activation phenotypes constructed by applying principal components to contrast maps, were tested for pathway enrichment using permutation based methods. The most significant pathways are related to post NMDA receptor activation events, driven by genetic variants in calcium/calmodulin-dependent protein kinase II (CAMK2G, CAMK2D) and a calcium-regulated nucleotide exchange factor (RASGRF2) in which all are activated by intracellular calcium/calmodulin. The most significant effect of the combined polygenic model were localized to the left inferior frontal gyrus (p =1. 03×10−9), a region primarily involved in semantic processing but also involved in processing negative emotions. These findings suggest that pathway analysis of GWAS results derived from principal component analysis of fMRI data is a promising method, to our knowledge, not previously described.

YNIMG Journal 2012 Journal Article

Effect of relevance on amygdala activation and association with the ventral striatum

Olga Therese Ousdal
Greg E. Reckless
Andres Server
Ole A. Andreassen
Jimmy Jensen

While the amygdala historically has been implicated in emotional stimuli processing, recent data suggest a general role in parceling out the relevance of stimuli, regardless of their emotional properties. Using functional magnetic resonance imaging, we tested the relevance hypothesis by investigating human amygdala responses to emotionally neutral stimuli while manipulating their relevance. The task was operationalized as highly relevant if a subsequent opportunity to respond for a reward depended on response accuracy of the task, and less relevant if the reward opportunity was independent of task performance. A region of interest analysis revealed bilateral amygdala activations in response to the high relevance condition compared to the low relevance condition. An exploratory whole-brain analysis yielded robust similar results in bilateral ventral striatum. A subsequent functional connectivity analysis demonstrated increased connectivity between amygdala and ventral striatum for the highly relevant stimuli compared to the less relevant stimuli. These findings suggest that the amygdala's processing profile goes beyond detection of emotions per se, and directly support the proposed role in relevance detection. In addition, the findings suggest a close relationship between amygdala and ventral striatal activity when processing relevant stimuli. Thus, the results may indicate that human amygdala modulates ventral striatum activity and subsequent behaviors beyond that observed for emotional cues, to encompass a broader range of relevant stimuli.