Lars T. Westlye

YNIMG Journal 2025 Journal Article

Brain-based gene expression and corresponding behavioural relevance of risk genes for broad antisocial behaviour

• Jaroslav Rokicki
• Megan L. Campbell
• Dennis van der Meer
• Alina I. Sartorius
• Natalia Tesli
• Piotr Jahołkowski
• Alexey Shadrin
• Ole Andreassen

Antisocial behaviour (ASB) involves persistent irresponsible, delinquent activities violating rights and safety of others. A meta-analysis of genome-wide association studies revealed significant genetic associations with ASB, yet their brain expression patterns and behavioural relevance remain unclear. Our investigation of fifteen genes associated with ASB examined their biological role and distribution across tissues, integrating post-mortem brain sample data from the Allen-Human-Brain Atlas and the Genotype-Tissue Expression project. We found that these genes were differentially expressed in the brain, particularly in regions like the cerebellum, putamen, and caudate, and were notably downregulated in the pancreas. Single cell type expression analysis revealed that ASB-associated genes had strong correlations with ductal and endothelial cells in the pancreas, indicating a possible metabolic influence on ASB. Certain genes like NTN1, SMAD5, NCAM2, and CDC42EP3 displayed specificity for cognitive terms including chronic pain, heart rate, and aphasia. These expression patterns aligned with neurocognitive domains related to thinking, and learning, distress, motor skills, as determined by fMRI analysis. This study connects specific brain gene expression with potential genetic and metabolic factors in ASB, offering novel insights into its biological basis and possible interdisciplinary approaches to understanding and addressing aggressive behaviours.

YNICL Journal 2025 Journal Article

Probing Autism and ADHD subtypes using cortical signatures of the T1w/T2w-ratio and morphometry

• Linn B. Norbom
• Bilal Syed
• Rikka Kjelkenes
• Jaroslav Rokicki
• Antoine Beauchamp
• Stener Nerland
• Azadeh Kushki
• Evdokia Anagnostou

Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are neurodevelopmental conditions that share genetic etiology and frequently co-occur. Given this comorbidity and well-established clinical heterogeneity, identifying individuals with similar brain signatures may be valuable for predicting clinical outcomes and tailoring treatment strategies. Cortical myelination is a prominent developmental process, and its disruption is a candidate mechanism for both disorders. Yet, no studies have attempted to identify subtypes using T1w/T2w-ratio, a magnetic resonance imaging (MRI) based proxy for intracortical myelin. Moreover, cortical variability arises from numerous biological pathways, and multimodal approaches can integrate cortical metrics into a single network. We analyzed data from 310 individuals aged 2.6-23.6 years, obtained from the Province of Ontario Neurodevelopmental (POND) Network consisting of individuals diagnosed with ASD (n = 136), ADHD (n = 100), and typically developing (TD) individuals (n = 74). We first tested for differences in T1w/T2w-ratio between diagnostic categories and controls. We then performed unimodal (T1w/T2w-ratio) and multimodal (T1w/T2w-ratio, cortical thickness, and surface area) spectral clustering to identify diagnostic-blind subgroups. Linear models revealed no statistically significant case-control differences in T1w/T2w-ratio. Unimodal clustering mostly isolated single individual- or minority clusters, driven by image quality and intensity outliers. Multimodal clustering suggested three distinct subgroups, which transcended diagnostic boundaries, showing separate cortical patterns but similar clinical and cognitive profiles. T1w/T2w-ratio features were the most relevant for demarcation, followed by surface area. While our analysis revealed no significant case-control differences, multimodal clustering incorporating the T1w/T2w-ratio among cortical features holds promise for identifying biologically similar subsets of individuals with neurodevelopmental conditions.

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.

YNIMG Journal 2023 Journal Article

Corrigendum to “Sleep and sleep deprivation differentially alter white matter microstructure: A mixed model design utilising advanced diffusion modelling”

• Irene Voldsbekk
• Inge Groote
• Nathalia Zak
• Daniël Roelfs
• Oliver Geier
• Paulina Due-Tønnessen
• Lise-Linn Løkken
• Marie Strømstad

YNIMG Journal 2023 Journal Article

Spherical means-based free-water volume fraction from diffusion MRI increases non-linearly with age in the white matter of the healthy human brain

• Tomasz Pieciak
• Guillem París
• Dani Beck
• Ivan I. Maximov
• Antonio Tristán-Vega
• Rodrigo de Luis-García
• Lars T. Westlye
• Santiago Aja-Fernández

The term free-water volume fraction (FWVF) refers to the signal fraction that could be found as the cerebrospinal fluid of the brain, which has been demonstrated as a sensitive measure that correlates with cognitive performance and various neuropathological processes. It can be quantified by properly fitting the isotropic component of the magnetic resonance (MR) signal in diffusion-sensitized sequences. Using N=287 healthy subjects (178F/109M) aged 25-94, this study examines in detail the evolution of the FWVF obtained with the spherical means technique from multi-shell acquisitions in the human brain white matter across the adult lifespan, which has been previously reported to exhibit a positive trend when estimated from single-shell data using the bi-tensor signal representation. We found evidence of a noticeably non-linear gain after the sixth decade of life, with a region-specific variate and varying change rate of the spherical means-based multi-shell FWVF parameter with age, at the same time, a heteroskedastic pattern across the adult lifespan is suggested. On the other hand, the FW corrected diffusion tensor imaging (DTI) leads to a region-dependent flattened age-related evolution of the mean diffusivity (MD) and fractional anisotropy (FA), along with a considerable reduction in their variability, as compared to the studies conducted over the standard (single-component) DTI. This way, our study provides a new perspective on the trajectory-based assessment of the brain and explains the conceivable reason for the variations observed in FA and MD parameters across the lifespan with previous studies under the standard diffusion tensor imaging.

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.

YNICL Journal 2022 Journal Article

Brain disconnectome mapping derived from white matter lesions and serum neurofilament light levels in multiple sclerosis: A longitudinal multicenter study

• Henning H. Rise
• Synne Brune
• Claudia Chien
• Tone Berge
• Steffan D. Bos
• Magí Andorrà
• Irene Pulido Valdeolivas
• Mona K. Beyer

BACKGROUND AND OBJECTIVES: Connectivity-based approaches incorporating the distribution and magnitude of the extended brain network aberrations caused by lesions may offer higher sensitivity for axonal damage in patients with multiple sclerosis (MS) than conventional lesion characteristics. Using individual brain disconnectome mapping, we tested the longitudinal associations between putative imaging-based brain network aberrations and levels of serum neurofilament light chain (NfL) as a neuroaxonal injury biomarker. METHODS: MS patients (n = 312, mean age 42.9 years, 71 % female) and healthy controls (HC) (n = 59, mean age 39.9 years, 78 % female) were prospectively enrolled at four European MS centres, and reassessed after two years (MS, n = 242; HC, n = 30). Post-processing of 3 Tesla (3 T) MRI data was performed at one centre using a harmonized pipeline, and disconnectome maps were calculated using BCBtoolkit based on individual lesion maps. Global disconnectivity (GD) was defined as the average disconnectome probability in each patient's white matter. Serum NfL concentrations were measured by single molecule array (Simoa). Robust linear mixed models (rLMM) with GD or T2-lesion volume (T2LV) as dependent variables, patient as a random factor, serum NfL, age, sex, timepoint for visit, diagnosis, treatment, and center as fixed factors were run. RESULTS: ), and longitudinal changes in NfL (t = -2.29, p = 0.02), but not for sex (t = 0.63, p = 0.53) or treatments (t = 0.80-0.83, p = 0.41-0.42). Voxel-wise analyses revealed significant associations between dysconnectivity in cerebellar and brainstem regions and serum NfL (t = 7.03, p < 0.001). DISCUSSION: In our prospective multi-site MS cohort, rLMMs demonstrated that the extent of global and regional brain disconnectivity is sensitive to a systemic biomarker of axonal damage, serum NfL, in patients with MS. These findings provide a neuroaxonal correlate of advanced disconnectome mapping and provide a platform for further investigations of the functional and potential clinical relevance of brain disconnectome mapping in patients with brain disorders.

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 2021 Journal Article

Frequency drift in MR spectroscopy at 3T

• Steve C.N. Hui
• Mark Mikkelsen
• Helge J. Zöllner
• Vishwadeep Ahluwalia
• Sarael Alcauter
• Laima Baltusis
• Deborah A. Barany
• Laura R. Barlow

PURPOSE: field, especially when gradient intensive sequences are used. The aim of the study was to set a benchmark for typical drift encountered during MR spectroscopy (MRS) to assess the need for real-time field-frequency locking on MRI scanners by comparing field drift data from a large number of sites. METHOD: A standardized protocol was developed for 80 participating sites using 99 3T MR scanners from 3 major vendors. Phantom water signals were acquired before and after an EPI sequence. The protocol consisted of: minimal preparatory imaging; a short pre-fMRI PRESS; a ten-minute fMRI acquisition; and a long post-fMRI PRESS acquisition. Both pre- and post-fMRI PRESS were non-water suppressed. Real-time frequency stabilization/adjustment was switched off when appropriate. Sixty scanners repeated the protocol for a second dataset. In addition, a three-hour post-fMRI MRS acquisition was performed at one site to observe change of gradient temperature and drift rate. Spectral analysis was performed using MATLAB. Frequency drift in pre-fMRI PRESS data were compared with the first 5:20 minutes and the full 30:00 minutes of data after fMRI. Median (interquartile range) drifts were measured and showed in violin plot. Paired t-tests were performed to compare frequency drift pre- and post-fMRI. A simulated in vivo spectrum was generated using FID-A to visualize the effect of the observed frequency drifts. The simulated spectrum was convolved with the frequency trace for the most extreme cases. Impacts of frequency drifts on NAA and GABA were also simulated as a function of linear drift. Data from the repeated protocol were compared with the corresponding first dataset using Pearson's and intraclass correlation coefficients (ICC). RESULTS: Of the data collected from 99 scanners, 4 were excluded due to various reasons. Thus, data from 95 scanners were ultimately analyzed. For the first 5:20 min (64 transients), median (interquartile range) drift was 0.44 (1.29) Hz before fMRI and 0.83 (1.29) Hz after. This increased to 3.15 (4.02) Hz for the full 30 min (360 transients) run. Average drift rates were 0.29 Hz/min before fMRI and 0.43 Hz/min after. Paired t-tests indicated that drift increased after fMRI, as expected (p < 0.05). Simulated spectra convolved with the frequency drift showed that the intensity of the NAA singlet was reduced by up to 26%, 44 % and 18% for GE, Philips and Siemens scanners after fMRI, respectively. ICCs indicated good agreement between datasets acquired on separate days. The single site long acquisition showed drift rate was reduced to 0.03 Hz/min approximately three hours after fMRI. DISCUSSION: This study analyzed frequency drift data from 95 3T MRI scanners. Median levels of drift were relatively low (5-min average under 1 Hz), but the most extreme cases suffered from higher levels of drift. The extent of drift varied across scanners which both linear and nonlinear drifts were observed.

YNICL Journal 2021 Journal Article

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

• Anne-Marthe Sanders
• Geneviève Richard
• Knut Kolskår
• Kristine M. Ulrichsen
• Tobias Kaufmann
• Dag Alnæs
• Dani Beck
• Erlend S. Dørum

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.

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

Sleep and sleep deprivation differentially alter white matter microstructure: A mixed model design utilising advanced diffusion modelling

• Irene Voldsbekk
• Inge Groote
• Nathalia Zak
• Daniël Roelfs
• Oliver Geier
• Paulina Due-Tønnessen
• Lise-Linn Løkken
• Marie Strømstad

Sleep deprivation influences several critical functions, yet how it affects human brain white matter (WM) is not well understood. The aim of the present work was to investigate the effect of 32 hours of sleep deprivation on WM microstructure compared to changes observed in a normal sleep-wake cycle (SWC). To this end, we utilised diffusion weighted imaging (DWI) including the diffusion tensor model, diffusion kurtosis imaging and the spherical mean technique, a novel biophysical diffusion model. 46 healthy adults (23 sleep deprived vs 23 with normal SWC) underwent DWI across four time points (morning, evening, next day morning and next day afternoon, after a total of 32 hours). Linear mixed models revealed significant group × time interaction effects, indicating that sleep deprivation and normal SWC differentially affect WM microstructure. Voxel-wise comparisons showed that these effects spanned large, bilateral WM regions. These findings provide important insight into how sleep deprivation affects the human brain.

YNICL Journal 2021 Journal Article

Structural brain disconnectivity mapping of post-stroke fatigue

• Kristine M. Ulrichsen
• Knut K. Kolskår
• Geneviève Richard
• Dag Alnæs
• Erlend S. Dørum
• Anne-Marthe Sanders
• Sveinung Tornås
• Jennifer Monereo Sánchez

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.

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

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

• Geneviève Richard
• Knut Kolskår
• Kristine M. Ulrichsen
• Tobias Kaufmann
• Dag Alnæs
• Anne-Marthe Sanders
• Erlend S. Dørum
• Jennifer Monereo Sánchez

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.

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.

YNIMG Journal 2020 Journal Article

Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study

• Ann-Marie G. de Lange
• Melis Anatürk
• Sana Suri
• Tobias Kaufmann
• James H. Cole
• Ludovica Griffanti
• Enikő Zsoldos
• Daria E.A. Jensen

Brain age is becoming a widely applied imaging-based biomarker of neural aging and potential proxy for brain integrity and health. We estimated multimodal and modality-specific brain age in the Whitehall II (WHII) MRI cohort using machine learning and imaging-derived measures of gray matter (GM) morphology, white matter microstructure (WM), and resting state functional connectivity (FC). The results showed that the prediction accuracy improved when multiple imaging modalities were included in the model (R2 = 0. 30, 95% CI [0. 24, 0. 36]). The modality-specific GM and WM models showed similar performance (R2 = 0. 22 [0. 16, 0. 27] and R2 = 0. 24 [0. 18, 0. 30], respectively), while the FC model showed the lowest prediction accuracy (R2 = 0. 002 [-0. 005, 0. 008]), indicating that the FC features were less related to chronological age compared to structural measures. Follow-up analyses showed that FC predictions were similarly low in a matched sub-sample from UK Biobank, and although FC predictions were consistently lower than GM predictions, the accuracy improved with increasing sample size and age range. Cardiovascular risk factors, including high blood pressure, alcohol intake, and stroke risk score, were each associated with brain aging in the WHII cohort. Blood pressure showed a stronger association with white matter compared to gray matter, while no differences in the associations of alcohol intake and stroke risk with these modalities were observed. In conclusion, machine-learning based brain age prediction can reduce the dimensionality of neuroimaging data to provide meaningful biomarkers of individual brain aging. However, model performance depends on study-specific characteristics including sample size and age range, which may cause discrepancies in findings across studies.

YNIMG Journal 2019 Journal Article

Cerebral blood flow changes after a day of wake, sleep, and sleep deprivation

• Torbjørn Elvsåshagen
• Henri JMM. Mutsaerts
• Nathalia Zak
• Linn B. Norbom
• Sophia H. Quraishi
• Per Ø. Pedersen
• Ulrik F. Malt
• Lars T. Westlye

Elucidating the neurobiological effects of sleep and wake is an important goal of the neurosciences. Whether and how human cerebral blood flow (CBF) changes during the sleep-wake cycle remain to be clarified. Based on the synaptic homeostasis hypothesis of sleep and wake, we hypothesized that a day of wake and a night of sleep deprivation would be associated with gray matter resting CBF (rCBF) increases and that sleep would be associated with rCBF decreases. Thirty-eight healthy adult males (age 22. 1 ± 2. 5 years) underwent arterial spin labeling perfusion magnetic resonance imaging at three time points: in the morning after a regular night's sleep, the evening of the same day, and the next morning, either after total sleep deprivation (n = 19) or a night of sleep (n = 19). All analyses were adjusted for hematocrit and head motion. rCBF increased from morning to evening and decreased after a night of sleep. These effects were most prominent in bilateral hippocampus, amygdala, thalamus, and in the occipital and sensorimotor cortices. Group × time interaction analyses for evening versus next morning revealed significant interaction in bilateral lateral and medial occipital cortices and in bilateral insula, driven by rCBF increases in the sleep deprived individuals and decreases in the sleepers, respectively. Furthermore, group × time interaction analyses for first morning versus next morning showed significant effects in medial and lateral occipital cortices, in anterior cingulate gyrus, and in the insula, in both hemispheres. These effects were mainly driven by CBF increases from TP1 to TP3 in the sleep deprived individuals. There were no associations between the rCBF changes and sleep characteristics, vigilant attention, or subjective sleepiness that remained significant after adjustments for multiple analyses. Altogether, these results encourage future studies to clarify mechanisms underlying sleep-related rCBF changes.

YNICL Journal 2017 Journal Article

Brain connectivity aberrations in anabolic-androgenic steroid users

• Lars T. Westlye
• Tobias Kaufmann
• Dag Alnæs
• Ingunn R. Hullstein
• Astrid Bjørnebekk

Sustained anabolic-androgenic steroid (AAS) use has adverse behavioral consequences, including aggression, violence and impulsivity. Candidate mechanisms include disruptions of brain networks with high concentrations of androgen receptors and critically involved in emotional and cognitive regulation. Here, we tested the effects of AAS on resting-state functional brain connectivity in the largest sample of AAS-users to date. We collected resting-state functional magnetic resonance imaging (fMRI) data from 151 males engaged in heavy resistance strength training. 50 users tested positive for AAS based on the testosterone to epitestosterone (T/E) ratio and doping substances in urine. 16 previous users and 59 controls tested negative. We estimated brain network nodes and their time-series using ICA and dual regression and defined connectivity matrices as the between-node partial correlations. In line with the emotional and behavioral consequences of AAS, current users exhibited reduced functional connectivity between key nodes involved in emotional and cognitive regulation, in particular reduced connectivity between the amygdala and default-mode network (DMN) and between the dorsal attention network (DAN) and a frontal node encompassing the superior and inferior frontal gyri (SFG/IFG) and the anterior cingulate cortex (ACC), with further reductions as a function of dependency, lifetime exposure, and cycle state (on/off).

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

Evidence for cortical structural plasticity in humans after a day of waking and sleep deprivation

• Torbjørn Elvsåshagen
• Nathalia Zak
• Linn B. Norbom
• Per Ø. Pedersen
• Sophia H. Quraishi
• Atle Bjørnerud
• Dag Alnæs
• Nhat Trung Doan

Sleep is an evolutionarily conserved process required for human health and functioning. Insufficient sleep causes impairments across cognitive domains, and sleep deprivation can have rapid antidepressive effects in mood disorders. However, the neurobiological effects of waking and sleep are not well understood. Recently, animal studies indicated that waking and sleep are associated with substantial cortical structural plasticity. Here, we hypothesized that structural plasticity can be observed after a day of waking and sleep deprivation in the human cerebral cortex. To test this hypothesis, 61 healthy adult males underwent structural magnetic resonance imaging (MRI) at three time points: in the morning after a regular night's sleep, the evening of the same day, and the next morning, either after total sleep deprivation (N=41) or a night of sleep (N=20). We found significantly increased right prefrontal cortical thickness from morning to evening across all participants. In addition, pairwise comparisons in the deprived group between the two morning scans showed significant thinning of mainly bilateral medial parietal cortices after 23h of sleep deprivation, including the precuneus and posterior cingulate cortex. However, there were no significant group (sleep vs. sleep deprived group) by time interactions and we can therefore not rule out that other mechanisms than sleep deprivation per se underlie the bilateral medial parietal cortical thinning observed in the deprived group. Nonetheless, these cortices are thought to subserve wakefulness, are among the brain regions with highest metabolic rate during wake, and are considered some of the most sensitive cortical regions to a variety of insults. Furthermore, greater thinning within the left medial parietal cluster was associated with increased sleepiness after sleep deprivation. Together, these findings add to a growing body of data showing rapid structural plasticity within the human cerebral cortex detectable with MRI. Further studies are needed to clarify whether cortical thinning is one neural substrate of sleepiness after sleep deprivation.

YNICL Journal 2017 Journal Article

Increased default-mode variability is related to reduced task-performance and is evident in adults with ADHD

• Athanasia M. Mowinckel
• Dag Alnæs
• Mads L. Pedersen
• Sigurd Ziegler
• Mats Fredriksen
• Tobias Kaufmann
• Edmund Sonuga-Barke
• Tor Endestad

Insufficient suppression and connectivity of the default mode network (DMN) is a potential mediator of cognitive dysfunctions across various disorders, including attention deficit/hyperactivity disorder (ADHD). However, it remains unclear if alterations in sustained DMN suppression, variability and connectivity during prolonged cognitive engagement are implicated in adult ADHD pathophysiology, and to which degree methylphenidate (MPH) remediates any DMN abnormalities. This randomized, double-blinded, placebo-controlled, cross-over clinical trial of MPH (clinicaltrials.gov/ct2/show/NCT01831622) explored large-scale brain network dynamics in 20 adults with ADHD on and off MPH, compared to 27 healthy controls, while performing a reward based decision-making task. DMN task-related activation, variability, and connectivity were estimated and compared between groups and conditions using independent component analysis, dual regression, and Bayesian linear mixed models. The results show that the DMN exhibited more variable activation patterns in unmedicated patients compared to healthy controls. Group differences in functional connectivity both between and within functional networks were evident. Further, functional connectivity between and within attention and DMN networks was sensitive both to task performance and case-control status. MPH altered within-network connectivity of the DMN and visual networks, but not between-network connectivity or temporal variability. This study thus provides novel fMRI evidence of reduced sustained DMN suppression in adults with ADHD during value-based decision-making, a pattern that was not alleviated by MPH. We infer from multiple analytical approaches further support to the default mode interference hypothesis, in that higher DMN activation variability is evident in adult ADHD and associated with lower task performance.

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.

YNIMG Journal 2013 Journal Article

Brain development and aging: Overlapping and unique patterns of change

• Christian K. Tamnes
• Kristine B. Walhovd
• Anders M. Dale
• Ylva Østby
• Håkon Grydeland
• George Richardson
• Lars T. Westlye
• J. Cooper Roddey

Early-life development is characterized by dramatic changes, impacting lifespan function more than changes in any other period. Developmental origins of neurocognitive late-life functions are acknowledged, but detailed longitudinal magnetic resonance imaging studies of brain maturation and direct comparisons with aging are lacking. To these aims, a novel method was used to measure longitudinal volume changes in development (n=85, 8–22years) and aging (n=142, 60–91years). Developmental reductions exceeded 1% annually in much of the cortex, more than double to that seen in aging, with a posterior-to-anterior gradient. Cortical reductions were greater than the subcortical during development, while the opposite held in aging. The pattern of lateral cortical changes was similar across development and aging, but the pronounced medial temporal reduction in aging was not precast in development. Converging patterns of change in adolescents and elderly, particularly in the medial prefrontal areas, suggest that late developed cortices are especially vulnerable to atrophy in aging. A key question in future research will be to disentangle the neurobiological underpinnings for the differences and the similarities between brain changes in development and aging.

YNIMG Journal 2013 Journal Article

Neuronal correlates of the five factor model (FFM) of human personality: Multimodal imaging in a large healthy sample

• Astrid Bjørnebekk
• Anders M. Fjell
• Kristine B. Walhovd
• Håkon Grydeland
• Svenn Torgersen
• Lars T. Westlye

Advances in neuroimaging techniques have recently provided glimpse into the neurobiology of complex traits of human personality. Whereas some intriguing findings have connected aspects of personality to variations in brain morphology, the relations are complex and our current understanding is incomplete. Therefore, we aimed to provide a comprehensive investigation of brain–personality relations using a multimodal neuroimaging approach in a large sample comprising 265 healthy individuals. The NEO Personality Inventory was used to provide measures of core aspects of human personality, and imaging phenotypes included measures of total and regional brain volumes, regional cortical thickness and arealization, and diffusion tensor imaging indices of white matter (WM) microstructure. Neuroticism was the trait most clearly linked to brain structure. Higher neuroticism including facets reflecting anxiety, depression and vulnerability to stress was associated with smaller total brain volume, widespread decrease in WM microstructure, and smaller frontotemporal surface area. Higher scores on extraversion were associated with thinner inferior frontal gyrus, and conscientiousness was negatively associated with arealization of the temporoparietal junction. No reliable associations between brain structure and agreeableness and openness, respectively, were found. The results provide novel evidence of the associations between brain structure and variations in human personality, and corroborate previous findings of a consistent neuroanatomical basis of negative emotionality.

YNIMG Journal 2012 Journal Article

A multi-modal investigation of behavioral adjustment: Post-error slowing is associated with white matter characteristics

• Anders M. Fjell
• Lars T. Westlye
• Inge K. Amlien
• Kristine B. Walhovd

When people make mistakes in speeded cognitive tasks, their response time on the next trial will typically be slower. This is referred to as post-error slowing (PES), and is important for optimization of performance, but its exact function remains to be decided. However, although PES is relatively stable over time, we have almost no knowledge about how PES is affected by structural brain characteristics. The aim of this study was to test to what extent white matter (WM) macro- and microstructure can account for individual differences in PES. PES was calculated for 255 healthy participants who performed a modified version of the Eriksen flanker task and underwent structural magnetic resonance imaging and diffusion tensor imaging (DTI). PES was positively related to WM volume in the caudal and rostral middle and superior frontal, medial orbitofrontal gyri and pars orbitalis. DTI analyses with tract-based spatial statistics (TBSS) showed that mean diffusivity in the superior longitudinal fasciculus, inferior fronto-occipital fasciculus and anterior thalamic radiation, as well as axial diffusivity in the corpus callosum, was negatively related to PES. Path analysis demonstrated that WM micro- and macrostructure were complementary in accounting for PES. It is concluded that individual differences in WM characteristics can partly explain why some people are better at adjusting their behavior in response to poor performance than others.

YNIMG Journal 2012 Journal Article

Benefits of multi-modal fusion analysis on a large-scale dataset: Life-span patterns of inter-subject variability in cortical morphometry and white matter microstructure

• Adrian R. Groves
• Stephen M. Smith
• Anders M. Fjell
• Christian K. Tamnes
• Kristine B. Walhovd
• Gwenaëlle Douaud
• Mark W. Woolrich
• Lars T. Westlye

Neuroimaging studies have become increasingly multimodal in recent years, with researchers typically acquiring several different types of MRI data and processing them along separate pipelines that provide a set of complementary windows into each subject's brain. However, few attempts have been made to integrate the various modalities in the same analysis. Linked ICA is a robust data fusion model that takes multi-modal data and characterizes inter-subject variability in terms of a set of multi-modal components. This paper examines the types of components found when running Linked ICA on a large magnetic resonance imaging (MRI) morphometric and diffusion tensor imaging (DTI) data set comprising 484 healthy subjects ranging from 8 to 85years of age. We find several strong global features related to age, sex, and intracranial volume; in particular, one component predicts age to a high accuracy (r=0. 95). Most of the remaining components describe spatially localized modes of variability in white or gray matter, with many components including both tissue types. The multimodal components tend to be located in anatomically-related brain areas, suggesting a morphological and possibly functional relationship. The local components show relationships between surface-based cortical thickness and arealization, voxel-based morphometry (VBM), and between three different DTI measures. Further, we report components related to artifacts (e. g. scanner software upgrades) which would be expected in a dataset of this size. Most of the 100 extracted components showed interpretable spatial patterns and were found to be reliable using split-half validation. This work provides novel information about normal inter-subject variability in brain structure, and demonstrates the potential of Linked ICA as a feature-extracting data fusion approach across modalities. This exploratory approach automatically generates models to explain structure in the data, and may prove especially powerful for large-scale studies, where the population variability can be explored in increased detail.

YNIMG Journal 2012 Journal Article

Hippocampal subfield volumes correlate with memory training benefit in subjective memory impairment

• Andreas Engvig
• Anders M. Fjell
• Lars T. Westlye
• Nina V. Skaane
• Øyvind Sundseth
• Kristine B. Walhovd

Although some older adults experiencing memory problems have been shown to benefit from cognitive training, evidence regarding who will improve from this type of intervention is lacking. Automated hippocampal volumetry might be used to foresee treatment outcomes. We hypothesized that larger hippocampal volumes are associated with greater memory performance changes following training, and that effects are selectively related to specific hippocampal subfields. 19 memory clinic outpatients with subjective memory impairment (mean age=60. 9years) underwent MRI-scanning and then followed an eight week training scheme aimed at improving verbal memory. We assessed verbal memory before and after training, and tested whether pretraining hippocampal volumes were related to memory improvements. To delineate regional specificity, we employed a new technique enabling automated volumetry of seven hippocampal subfields — including the cornu ammonis (CA) sectors and the dentate gyrus (DG). The results showed that larger hippocampal volumes before training were related to greater verbal recall improvements. Subfield volumetry revealed specific correlations between memory improvement and pretraining volumes of the left CA2/3 and CA4/DG. Depressive symptoms further gave a unique contribution in predicting gain of the intervention, independent of hippocampal volume. The results indicated that subjects with a stronger depressive symptom load benefited more from the training. A prediction model including baseline CA2/3-volume and depressive symptoms explained 42% of the variation in recall improvement. Our results are the first to suggest that hippocampal subfield volumetry is related to intervention outcomes in older adults experiencing memory problems. Also, previous studies have tended to exclude patients with concomitant depressive symptoms and memory complaints. The present results, however, strengthen the rationale and potential for cognitive intervention in these patients.

YNIMG Journal 2012 Journal Article

Network-specific effects of age and in-scanner subject motion: A resting-state fMRI study of 238 healthy adults

• Athanasia M. Mowinckel
• Thomas Espeseth
• Lars T. Westlye

Cognitive aging is accompanied by a range of structural and functional differences in the brain, even in the absence of neurodegenerative disease. Functional magnetic resonance imaging (fMRI) studies have reported increased bilateral activation during task performance in elderly participants compared to their younger counterparts, particularly in frontal regions. Alterations have also been observed in the functional architecture of the resting brain, suggesting that aging is associated with changes in the organization of the networks of the brain. However, previous studies have largely focused on the default mode network, and little is known about the effects of age on other resting state-networks (RSNs). The aim of the present study was to investigate age-differences in resting state functional connectivity (RSFC) using fMRI data obtained during rest from 238 healthy participants aged 21–80years. Using independent component analysis (ICA) and dual-regression, the results revealed age-related increases in RSFC across a range of RSNs, including task-positive networks in frontal and parietal regions. In contrast, age-related reductions in the default mode network and occipital visual networks were observed. Furthermore, whereas the effects of age on the various RSNs were found independent of age-related decreases in gray matter volume, sex and subject motion, we report strong positive and widespread effects of estimated subject motion on the RSFC across RSNs. The results provide support for the notion of network-specific effects in aging, manifested as increased tonic activation of task-positive networks, supporting higher-order cognitive functions and cognitive control, along with reduced task-negative default mode network and sensory visual networks during rest. The present results also corroborate recent evidence of strong influence of subject motion on estimated functional connectivity measures and strongly suggest that studies using RSFC measures as imaging phenotypes should adjust for individual differences in in-scanner subject motion.

YNIMG Journal 2010 Journal Article

CSF biomarker pathology correlates with a medial temporo-parietal network affected by very mild to moderate Alzheimer's disease but not a fronto-striatal network affected by healthy aging

• Anders M. Fjell
• Inge K. Amlien
• Lars T. Westlye
• Vidar Stenset
• Tormod Fladby
• Anders Skinningsrud
• Dag E. Eilsertsen
• Atle Bjørnerud

It is suggested that reductions in a medial temporo-parietal episodic memory network characterize Alzheimer's disease (AD), while changes in a fronto-striatal executive network characterize healthy aging. In the present study, magnetic resonance imaging (MRI) was used to test this directly. MRI scans of 372 participants from two samples were analyzed: Sample 1 consisted of 96 very mild to moderate AD patients, 93 healthy elderly (HE), and 137 young (HY), all with available MR scans, while Sample 2 consisted of 46 MCI patients, with available MR scans and measures of CSF biomarkers Aβ42 and tau protein. Substantial morphometric reductions of the medial temporo-parietal network were found in AD, while the fronto-striatal network was less affected. Both networks were affected by healthy aging, but the fronto-striatal to a greater degree than the medial temporo-parietal. Further exploratory analyses of 49 cortical and subcortical structures indicated no overlap between predictors of AD vs. HE and predictors of HE vs. HY. CSF biomarker pathology correlated with the medial temporo-parietal but not fronto-striatal network. Likewise, the AD-prone structures from the exploratory analyses were related to CSF biomarkers, while the aging-prone structures were not. It is concluded that the pattern of macrostructural brain changes in very mild to moderate AD can be clearly delineated from that of healthy aging.

YNIMG Journal 2010 Journal Article

Differentiating maturational and aging-related changes of the cerebral cortex by use of thickness and signal intensity

• Lars T. Westlye
• Kristine B. Walhovd
• Anders M. Dale
• Atle Bjørnerud
• Paulina Due-Tønnessen
• Andreas Engvig
• Håkon Grydeland
• Christian K. Tamnes

Cortical thickness decreases from childhood throughout life, as estimated by magnetic resonance imaging (MRI). This monotone trajectory does not reflect the fundamentally different neurobiological processes underlying morphometric changes in development versus aging. We hypothesized that intracortical gray matter (GM) and subjacent white matter (WM) T1-weighted signal intensity would distinguish developmental and age-related changes in the cortex better than thickness. Intracortical GM and subjacent WM signal intensity and cortical thickness was measured across the brain surface in a healthy life span sample (n =429, 8–85years). We also computed the relaxation rate of T2* (R2*) from multiecho sequences and mapped intracortical GM and subjacent WM values to the surface to delineate age-related variability in R2* and to adjust the T1 signal intensity for possible confounds of accumulated iron. While monotone age-related reductions in thickness were found, both intracortical GM and subcortical WM signal intensity showed inverted U patterns with peaks from eight to approximately 30years of age. The spatial pattern of intracortical neurodevelopment followed a posterior–anterior gradient, with earliest maturation of occipital visual cortices and most protracted in superior frontal regions. From 50s and 60s, substantial signal reductions were observed in several regions, including the insula, cingulate, and inferior temporal gyrus. R2* showed similar patterns but peaked much later than the T1-weighted signal intensity measures. The results are presented as animations yielding detailed depictions of the dynamic regional variability in cortical neurodevelopment and aging and demonstrate that cortical thickness and T1-weighted signal intensity are sensitive to different cortical maturational and aging-related processes.

YNIMG Journal 2010 Journal Article

Effects of memory training on cortical thickness in the elderly

• Andreas Engvig
• Anders M. Fjell
• Lars T. Westlye
• Torgeir Moberget
• Øyvind Sundseth
• Vivi Agnete Larsen
• Kristine B. Walhovd

The brain's ability to alter its functional and structural architecture in response to experience and learning has been extensively studied. Mental stimulation might serve as a reserve mechanism in brain aging, but macrostructural brain changes in response to cognitive training have been demonstrated in young participants only. We examined the short-term effects of an intensive memory training program on cognition and brain structure in middle-aged and elderly healthy volunteers. The memory trainers completed an 8-week training regimen aimed at improving verbal source memory utilizing the Method of Loci (MoL), while control participants did not receive any intervention. Both the memory trainers and the controls underwent magnetic resonance imaging (MRI) scans and memory testing pre and post 8weeks of training or no training, respectively. Cortical thickness was automatically measured across the cortical mantle, and data processing and statistical analyses were optimized for reliable detection of longitudinal changes. The results showed that memory training improved source memory performance. Memory trainers also showed regional increases in cortical thickness compared with controls. Furthermore, thickness change in the right fusiform and lateral orbitofrontal cortex correlated positively with improvement in source memory performance, suggesting a possible functional significance of the structural changes. These findings demonstrate that systematic mental exercise may induce short-term structural changes in the aging human brain, indicating structural brain plasticity in elderly. The present study included short-term assessments, and follow-up studies are needed in order to assess whether such training indeed alters the long-term structural trajectories.

YNIMG Journal 2010 Journal Article

When does brain aging accelerate? Dangers of quadratic fits in cross-sectional studies

• Anders. M. Fjell
• Kristine B. Walhovd
• Lars T. Westlye
• Ylva Østby
• Christian K. Tamnes
• Terry L. Jernigan
• Anthony Gamst
• Anders M. Dale

Many brain structures show a complex, non-linear pattern of maturation and age-related change. Often, quadratic models (β 0 + β 1age+ β 2age2 + ε) are used to describe such relationships. Here, we demonstrate that the fitting of quadratic models is substantially affected by seemingly irrelevant factors, such as the age-range sampled. Hippocampal volume was measured in 434 healthy participants between 8 and 85 years of age, and quadratic models were fit to subsets of the sample with different age-ranges. It was found that as the bottom of the age-range increased, the age at which volumes appeared to peak was moved upwards and the estimated decline in the last part of the age-span became larger. Thus, whether children were included or not affected the estimated decline between 60 and 85 years. We conclude that caution should be exerted in inferring age-trajectories from global fit models, e. g. the quadratic model. A nonparametric local smoothing technique (the smoothing spline) was found to be more robust to the effects of different starting ages. The results were replicated in an independent sample of 309 participants.

YNIMG Journal 2009 Journal Article

Increased sensitivity to effects of normal aging and Alzheimer's disease on cortical thickness by adjustment for local variability in gray/white contrast: A multi-sample MRI study

• Lars T. Westlye
• Kristine B. Walhovd
• Anders M. Dale
• Thomas Espeseth
• Ivar Reinvang
• Naftali Raz
• Ingrid Agartz
• Douglas N. Greve

MRI-based estimates of cerebral morphometric properties, e. g. cortical thickness, are pivotal to studies of normal and pathological brain changes. These measures are based on automated or manual segmentation procedures, which utilize the tissue contrast between gray and white matter on T1-weighted MR images. Tissue contrast is unlikely to remain a constant property across groups of different age and health. An important question is therefore how the sensitivity of cortical thickness estimates is influenced by variability in WM/GM contrast. The effect of adjusting for variability in WM/GM contrast on age sensitivity of cortical thickness was tested in 1189 healthy subjects from six different samples, enabling evaluation of consistency of effects within and between sites and scanners. Further, the influence of Alzheimer's disease (AD) diagnosis on cortical thickness with and without correction for contrast was tested in an additional sample of 96 patients. In healthy controls, regional increases in the sensitivity of the cortical thickness measure to age were found after correcting for contrast. Across samples, the strongest effects were observed in frontal, lateral temporal and parietal areas. Controlling for contrast variability also increased the cortical thickness estimates' sensitivity to AD, thus replicating the finding in an independent clinical sample. The results showed increased sensitivity of cortical estimates to AD in areas earlier reported to be compromised in AD, including medial temporal, inferior and superior parietal regions. In sum, the findings indicate that adjusting for contrast can increase the sensitivity of MR morphometry to variables of interest.

YNIMG Journal 2008 Journal Article

The relationship between diffusion tensor imaging and volumetry as measures of white matter properties

• Anders M. Fjell
• Lars T. Westlye
• Doug N. Greve
• Bruce Fischl
• Thomas Benner
• André J.W. van der Kouwe
• David Salat
• Atle Bjørnerud

There is still limited knowledge about the relationship between different structural brain parameters, despite huge progress in analysis of neuroimaging data. The aim of the present study was to test the relationship between fractional anisotropy (FA) from diffusion tensor imaging (DTI) and regional white matter (WM) volume. As WM volume has been shown to develop until middle age, the focus was on changes in WM properties in the age range of 40 to 60 years. 100 participants were scanned with magnetic resonance imaging (MRI). Each hemisphere was parcellated into 35 WM regions, and volume, FA, axial, and radial diffusion in each region were calculated. The relationships between age and the regional measures of FA and WM volume were tested, and then FA and WM volume were correlated, corrected for intracranial volume, age, and sex. WM volume was weakly related to age, while FA correlated negatively with age in 26 of 70 regions, caused by a mix of reduced axial and increased radial diffusion with age. 23 relationships between FA and WM volume were found, with seven being positive and sixteen negative. The positive correlations were mainly caused by increased radial diffusion. It is concluded that FA is more sensitive than volume to changes in WM integrity during middle age, and that FA-age correlations probably are related to reduced amount of myelin with increasing age. Further, FA and WM volume are moderately to weakly related and to a large extent sensitive to different characteristics of WM integrity.