Ann-Marie G. de Lange

Research Fellow

Email a.m.g.d.lange@psykologi.uio.no

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I am a neuroscientist and clinical psychologist specialising in population neuroscience and applied machine learning. My research combines neuroimaging (MRI), clinical, genetic, and psychosocial data to study brain health and disease risk, with a particular focus on women’s brain and mental health across the lifespan.

In my research group FemiLab, we use computational modelling in clinical and population-based cohorts to study sex-specific patterns in brain structure, cognition, and health outcomes. Our work focuses on key female transition phases; adolescence, pregnancy, menopause, and ageing, as well as sex differences in risk factors and disease trajectories. We are actively engaged in science communication and advocacy for inclusive, data-driven approaches to brain health research. Our work has been supported by competitive grants from funders such as the Swiss National Science Foundation and the Research Council of Norway.

My background includes a professional degree in clinical psychology and a PhD in neuroimaging and brain plasticity from the University of Oslo. Following a postdoctoral fellowship at the University of Oxford, I established FemiLab, formerly based at Lausanne University Hospital (CHUV) in Switzerland. I currently hold a Lecturer position (Teaching & Research) at Queen Mary University of London, with honorary affiliations at the Department of Psychiatry, University of Oxford, and the Department of Psychology, University of Oslo.

Gurholt, Tiril Pedersen; Beck, Dani; Voldsbekk, Irene; Parker, Nadine; Askeland-Gjerde, Daniel E. & Lange, Ann-Marie Glasø de [Show all 13 contributors for this article] (2026). Linking obesity with white matter microstructure highlights the importance of brainstem tracts and sex differences. Brain Communications. 8(1), p. 1–15. doi: 10.1093/braincomms/fcag026. Full text in Research Archive Show summary
While obesity (body mass index ≥ 30) has been consistently associated with white matter diffusion magnetic resonance imaging (MRI) phenotypes, the contributions of common obesity phenotypes on various diffusion metrics, and the moderating effects of sex and age, require further clarification. This study aims to elucidate these body–brain connections to enhance our understanding of the comorbid link between obesity and body anthropometrics and the brain using a large-scale dataset. We analysed cross-sectional data from 40 040 participants from the UK Biobank (52.2% female; ages 44–83 years) using multiple linear regression to evaluate how obesity and body anthropometrics relate to regional white matter diffusion tensor imaging metrics (fractional anisotropy, axial diffusivity, radial diffusivity, mean diffusivity). We also examined interactions with age and sex. Our analyses revealed significant associations between individual obesity phenotypes (i.e. obesity and body anthropometrics) and diffusion tensor imaging metrics of small effects, with partial correlation coefficient |r| effect sizes ranging from 0.02 to 0.20 for most regions of interest with largest effects in brainstem tracts. We observed more widespread sex-by-obesity phenotypes than age-by-obesity phenotypes interaction effects on diffusion tensor imaging metrics. Our results link obesity and body anthropometrics with white matter phenotypes and suggests that shared body fat-related pathways link physical and brain health that may vary based on sex and age. Understanding these body–brain relationships, and the role of age and sex, could enhance the development and evaluation of targeted, personalized, treatment strategies for brain disorders that co-occur with obesity, although further longitudinal and intervention studies are needed to map the causal dynamics of these associations.
Luzum, Geske; Aamodt, Eva B.; Allore, Heather; Alnæs, Dag; Beyer, Mona K. & Lange, Ann-Marie G. de [Show all 11 contributors for this article] (2026). Longitudinal Associations Between Physical Activity Behavior and Structural Brain MRI Features After Stroke: A Sub-Study From the Nor-COAST Project. Neurorehabilitation and Neural Repair. ISSN 1545-9683. doi: 10.1177/15459683251399125. Full text in Research Archive Show summary
Background Post-stroke physical activity (PA) behavior may partly explain inter-individual differences in cortical and sub-cortical brain volumes and brain age estimates. Objective To investigate longitudinal associations of post-stroke PA behavior with structural brain MRI features. Methods: Data were from a multicenter longitudinal cohort study. PA estimates were based on accelerometer measurements. Separate linear mixed models assessed average daily step count at 18 and 36 months, and longitudinal PA trajectory groups as measured at 3, 18, and 36 months after stroke, as primary and secondary exposures. Dependent variables included brain age gap (BAG), representing the discrepancy between brain MRI predicted age and chronological age, and MRI-based cortical, hippocampal, and thalamic volumes at 18- and 36 months post-stroke. Models accounted for age, sex, education, stroke severity, intracranial volume, and MRI scanner. Results: We included 146 participants (age, mean [SD]: 70.3 [11.1]; 45.7% female) with predominantly mild strokes. Every +1000 steps/day were associated with −1.15 (95% CI: −1.76 to −0.53) lower BAG, 2.63 mL (95% CI: 0.31-5.00) larger cortical volume, and 0.07 mL (95% CI: 0.03-0.11) larger hippocampal volume. The association between step/day and thalamic volume was curvilinear, with the largest volumes observed at 4700 steps/day. Out of 4 PA trajectory groups, participants in the most active group had −7.44 years (95% CI: −2.86 to −12.01) lower BAG and 0.90 mL (95% CI: 1.48-0.33) larger thalamic volumes than the least active group. Conclusions: Higher PA levels post-stroke were associated with larger brain volumes and younger-appearing brains.
Oppenheimer, Hannah; Shadrin, Alexey; Andersen, Jonas Østerhaug; Schindler, Louise S.; Crestol, Arielle & Andreassen, Ole [Show all 10 contributors for this article] (2025). Investigating Shared Cardiovascular Factors and Genetic Overlap of Pregnancy-Related Disorders, Major Depressive Disorder, and Alzheimer’s Disease. Biological Psychiatry. ISSN 0006-3223. doi: 10.1016/j.biopsych.2025.09.018. Full text in Research Archive
Oppenheimer, Hannah; Meer, Dennis van der; Schindler, Louise S.; Crestol, Arielle; Shadrin, Alexey & Andreassen, Ole [Show all 9 contributors for this article] (2025). No causal links between estradiol and female’s brain and mental health using Mendelian randomization. Nature Communications. 16(1). doi: 10.1038/s41467-025-65878-7. Full text in Research Archive
Askeland-Gjerde, Daniel E.; Westlye, Lars Tjelta; Andersson, Patrik; Korbmacher, Max; Lange, Ann-Marie Glasø de & Meer, Dennis van der [Show all 10 contributors for this article] (2025). Mediation Analyses Link Cardiometabolic Factors and Liver Fat With White Matter Hyperintensities and Cognitive Performance: A UK Biobank Study. Biological Psychiatry: Global Open Science. 5(4). doi: 10.1016/j.bpsgos.2025.100488. Full text in Research Archive Show summary
Background Liver fat is associated with cardiometabolic disease, cerebrovascular disease, and dementia. Cerebrovascular disease, most often cerebral small vessel disease, identified by magnetic resonance imaging as white matter hyperintensities (WMHs) often contributes to dementia. However, liver fat’s role in the relationship between cardiometabolic risk, WMHs, and cognitive performance is unclear. Methods In the UK Biobank cohort (N = 32,461, 52.6% female; mean age 64.2 ± 7.7 years; n = 23,354 in the cognitive performance subsample), we used linear regression to investigate associations between cardiometabolic factors measured at baseline and liver fat, WMHs, and cognitive performance measured at follow-up, which was 9.3 ± 2.0 years later on average. We used structural equation modeling to investigate whether liver fat mediated associations between cardiometabolic factors and WMHs and whether WMHs mediated associations between liver fat and cognitive performance. Results Nearly all cardiometabolic factors were significantly associated with liver fat (|r| range = 0.03–0.41, p = 3.4 × 10−8 to 0) and WMHs (|r| = 0.04–0.15, p = 5.8 × 10−13 to 7.0 × 10−159) in regression models. Liver fat was associated with WMHs (r = 0.11, p = 4.3 × 10−82) and cognitive performance (r = −0.03, p = 1.6 × 10−7). Liver fat mediated the associations between cardiometabolic factors and WMHs (|βmediation| = 0.003–0.027, pmediation = 1.9 × 10−8 to 0), and WMHs mediated the associations between liver fat and cognitive performance (βmediation = −0.01, pmediation = 0). Conclusions Our findings indicate that liver fat mediates associations between cardiometabolic factors and WMHs and that WMHs mediate the association between liver fat and cognitive performance. This suggests that liver fat may be important for understanding the effects of cardiometabolic factors on cerebrovascular disease and cognitive function. Experimental studies are warranted to determine relevant targets for preventing vascular-driven cognitive impairment.
Beck, Dani; Whitmore, Lucy; MacSweeney, Niamh; Brieant, Alexis; Karl, Valerie & Lange, Ann-Marie Glasø de [Show all 9 contributors for this article] (2024). Dimensions of Early-Life Adversity Are Differentially Associated With Patterns of Delayed and Accelerated Brain Maturation. Biological Psychiatry. ISSN 0006-3223. 97(1), p. 64–72. doi: 10.1016/j.biopsych.2024.07.019. Full text in Research Archive
Crestol, Arielle; Lange, Ann-Marie Glasø de; Schindler, Louise S; Subramaniapillai, Sivaniya; Nerland, Stener & Oppenheimer, Hannah [Show all 11 contributors for this article] (2024). Linking menopause-related factors, history of depression, APOE ε4, and proxies of biological aging in the UK biobank cohort. Hormones and Behavior. ISSN 0018-506X. 164. doi: 10.1016/j.yhbeh.2024.105596. Full text in Research Archive Show summary
In a subset of females, postmenopausal status has been linked to accelerated aging and neurological decline. A complex interplay between reproductive-related factors, mental disorders, and genetics may influence brain function and accelerate the rate of aging in the postmenopausal phase. Using multiple regressions corrected for age, in this preregistered study we investigated the associations between menopause-related factors (i.e., menopausal status, menopause type, age at menopause, and reproductive span) and proxies of cellular aging (leukocyte telomere length, LTL) and brain aging (white and gray matter brain age gap, BAG) in 13,780 females from the UK Biobank (age range 39–82). We then determined how these proxies of aging were associated with each other, and evaluated the effects of menopause-related factors, history of depression (= lifetime broad depression), and APOE ε4 genotype on BAG and LTL, examining both additive and interactive relationships. We found that postmenopausal status and older age at natural menopause were linked to longer LTL and lower BAG. Surgical menopause and longer natural reproductive span were also associated with longer LTL. BAG and LTL were not significantly associated with each other. The greatest variance in each proxy of biological aging was most consistently explained by models with the addition of both lifetime broad depression and APOE ε4 genotype. Overall, this study demonstrates a complex interplay between menopause-related factors, lifetime broad depression, APOE ε4 genotype, and proxies of biological aging. However, results are potentially influenced by a disproportionate number of healthier participants among postmenopausal females. Future longitudinal studies incorporating heterogeneous samples are an essential step towards advancing female health.
Beck, Dani; Lange, Ann-Marie Glasø de; Gurholt, Tiril Pedersen; Voldsbekk, Irene; Maximov, Ivan & Subramaniapillai, Sivaniya [Show all 19 contributors for this article] (2024). Dissecting unique and common variance across body and brain health indicators using age prediction. Human Brain Mapping. ISSN 1065-9471. 45(6). doi: 10.1002/hbm.26685. Full text in Research Archive Show summary
Ageing is a heterogeneous multisystem process involving different rates of decline in physiological integrity across biological systems. The current study dissects the unique and common variance across body and brain health indicators and parses inter-individual heterogeneity in the multisystem ageing process. Using machine-learning regression models on the UK Biobank data set (N = 32,593, age range 44.6–82.3, mean age 64.1 years), we first estimated tissue-specific brain age for white and gray matter based on diffusion and T1-weighted magnetic resonance imaging (MRI) data, respectively. Next, bodily health traits, including cardiometabolic, anthropometric, and body composition measures of adipose and muscle tissue from bioimpedance and body MRI, were combined to predict ‘body age’. The results showed that the body age model demonstrated comparable age prediction accuracy to models trained solely on brain MRI data. The correlation between body age and brain age predictions was 0.62 for the T1 and 0.64 for the diffusion-based model, indicating a degree of unique variance in brain and bodily ageing processes. Bayesian multilevel modelling carried out to quantify the associations between health traits and predicted age discrepancies showed that higher systolic blood pressure and higher muscle-fat infiltration were related to older-appearing body age compared to brain age. Conversely, higher hand-grip strength and muscle volume were related to a younger-appearing body age. Our findings corroborate the common notion of a close connection between somatic and brain health. However, they also suggest that health traits may differentially influence age predictions beyond what is captured by the brain imaging data, potentially contributing to heterogeneous ageing rates across biological systems and individuals.
Sartorius, Alina Marie; Rokicki, Jaroslav; Birkeland, Siri; Bettella, Francesco; Barth, Claudia & Lange, Ann-Marie Glasø de [Show all 17 contributors for this article] (2024). An evolutionary timeline of the oxytocin signaling pathway. Communications Biology. 7(1). doi: 10.1038/s42003-024-06094-9. Full text in Research Archive Show summary
Oxytocin is a neuropeptide associated with both psychological and somatic processes like parturition and social bonding. Although oxytocin homologs have been identified in many species, the evolutionary timeline of the entire oxytocin signaling gene pathway has yet to be described. Using protein sequence similarity searches, microsynteny, and phylostratigraphy, we assigned the genes supporting the oxytocin pathway to different phylostrata based on when we found they likely arose in evolution. We show that the majority (64%) of genes in the pathway are ‘modern’. Most of the modern genes evolved around the emergence of vertebrates or jawed vertebrates (540 - 530 million years ago, ‘mya’), including OXTR, OXT and CD38. Of those, 45% were under positive selection at some point during vertebrate evolution. We also found that 18% of the genes in the oxytocin pathway are ‘ancient’, meaning their emergence dates back to cellular organisms and opisthokonta (3500–1100 mya). The remaining genes (18%) that evolved after ancient and before modern genes were classified as ‘medium-aged’. Functional analyses revealed that, in humans, medium-aged oxytocin pathway genes are highly expressed in contractile organs, while modern genes in the oxytocin pathway are primarily expressed in the brain and muscle tissue.
Subramaniapillai, Sivaniya; Schindler, Louise S.; Redmond, Paul; Bastin, Mark E.; Wardlaw, Joanna M. & Hernández, Maria Valdés [Show all 23 contributors for this article] (2024). Sex-Dependent Effects of Cardiometabolic Health and APOE4 on Brain Age: A Longitudinal Cohort Study. Neurology. ISSN 0028-3878. 103(6). doi: 10.1212/WNL.0000000000209744. Full text in Research Archive
Barth, Claudia; Galea, Liisa A.M.; Jacobs, Emily G.; Lee, Bonnie H; Westlye, Lars Tjelta & Lange, Ann-Marie Glasø de (2024). Menopausal hormone therapy and the female brain: leveraging neuroimaging and prescription registry data from the UK Biobank cohort. eLIFE. doi: 10.7554/eLife.99538.1. Full text in Research Archive Show summary
Background and Objectives Menopausal hormone therapy (MHT) is generally thought to be neuroprotective, yet results have been inconsistent. Here, we present a comprehensive study of MHT use and brain characteristics in middle-to older aged females from the UK Biobank, assessing detailed MHT data, APOE ε4 genotype, and tissue-specific gray (GM) and white matter (WM) brain age gap (BAG), as well as hippocampal and white matter hyperintensity (WMH) volumes. Methods A total of 19,846 females with magnetic resonance imaging data were included (current-users = 1,153, 60.1 ± 6.8 years; past-users = 6,681, 67.5 ± 6.2 years; never-users = 12,012, mean age 61.6 ± 7.1 years). For a sub-sample (n = 538), MHT prescription data was extracted from primary care records. Brain measures were derived from T1-, T2- and diffusion-weighted images. We fitted regression models to test for associations between the brain measures and MHT variables including user status, age at initiation, dosage and duration, formulation, route of administration, and type (i.e., bioidentical vs synthetic), as well as active ingredient (e.g., estradiol hemihydrate). We further tested for differences in brain measures among MHT users with and without a history of hysterectomy ± bilateral oophorectomy and examined associations by APOE ε4 status. Results We found significantly higher GM and WM BAG (i.e., older brain age relative to chronological age) as well as smaller left and right hippocampus volumes in current MHT users, not past users, compared to never-users. Effects were modest, with the largest effect size indicating a group difference of 0.77 years (∼9 months) for GM BAG. Among MHT users, we found no significant associations between age at MHT initiation and brain measures. Longer duration of use and older age at last use post menopause was associated with higher GM and WM BAG, larger WMH volume, and smaller left and right hippocampal volumes. MHT users with a history of hysterectomy ± bilateral oophorectomy showed lower GM BAG relative to MHT users without such history. Although we found smaller hippocampus volumes in carriers of two APOE ε4 alleles compared to non-carriers, we found no interactions with MHT variables. In the sub-sample with prescription data, we found no significant associations between detailed MHT variables and brain measures after adjusting for multiple comparisons. Discussion Our results indicate that population-level associations between MHT use, and female brain health might vary depending on duration of use and past surgical history. Future research is crucial to establish causality, dissect interactions between menopause-related neurological changes and MHT use, and determine individual-level implications to advance precision medicine in female health care.
Korbmacher, Max; Meer, Dennis van der; Beck, Dani; Lange, Ann-Marie Glasø de; Eikefjord, Eli Nina Hølleland & Lundervold, Arvid [Show all 9 contributors for this article] (2024). Brain asymmetries from mid- to late life and hemispheric brain age. Nature Communications. 15. doi: 10.1038/s41467-024-45282-3. Full text in Research Archive Show summary
The human brain demonstrates structural and functional asymmetries which have implications for ageing and mental and neurological disease development. We used a set of magnetic resonance imaging (MRI) metrics derived from structural and diffusion MRI data in N=48,040 UK Biobank participants to evaluate age-related differences in brain asymmetry. Most regional grey and white matter metrics presented asymmetry, which were higher later in life. Informed by these results, we conducted hemispheric brain age (HBA) predictions from left/right multimodal MRI metrics. HBA was concordant to conventional brain age predictions, using metrics from both hemispheres, but offers a supplemental general marker of brain asymmetry when setting left/right HBA into relationship with each other. In contrast to WM brain asymmetries, left/right discrepancies in HBA are lower at higher ages. Our findings outline various sex-specific differences, particularly important for brain age estimates, and the value of further investigating the role of brain asymmetries in brain ageing and disease development.
Lange, Ann-Marie Glasø de; Leonardsen, Esten Høyland; Barth, Claudia; Schindler, Louise S; Crestol, Arielle & Holm, Madelene Christin [Show all 10 contributors for this article] (2024). Parental status and markers of brain and cellular age: A 3D convolutional network and classification study. Psychoneuroendocrinology. ISSN 0306-4530. 165. doi: 10.1016/j.psyneuen.2024.107040. Full text in Research Archive
Ambikairajah, Ananthan; Khondoker, Mizanur; Morris, Edward; Lange, Ann-Marie Glasø de; Saleh, Rasha N. M. & Minihane, Anne Marie [Show all 7 contributors for this article] (2024). Investigating the synergistic effects of hormone replacement therapy, apolipoprotein E and age on brain health in the UK Biobank. Human Brain Mapping. ISSN 1065-9471. 45(2). doi: 10.1002/hbm.26612. Full text in Research Archive
Korbmacher, Max; Lange, Ann-Marie Glasø de; Meer, Dennis van der; Beck, Dani; Eikefjord, Eli Nina & Lundervold, Arvid [Show all 9 contributors for this article] (2023). Brain‐wide associations between white matter and age highlight the role of fornix microstructure in brain ageing. Human Brain Mapping. ISSN 1065-9471. 44, p. 4101–4119. doi: 10.1002/hbm.26333. Full text in Research Archive Show summary
Unveiling the details of white matter (WM) maturation throughout ageing is a fundamental question for understanding the ageing brain. In an extensive comparison of brain age predictions and age-associations of WM features from different diffusion approaches, we analyzed UK Biobank diffusion magnetic resonance imaging (dMRI) data across midlife and older age (N = 35,749, 44.6–82.8 years of age). Conventional and advanced dMRI approaches were consistent in predicting brain age. WM-age associations indicate a steady microstructure degeneration with increasing age from midlife to older ages. Brain age was estimated best when combining diffusion approaches, showing different aspects of WM contributing to brain age. Fornix was found as the central region for brain age predictions across diffusion approaches in complement to forceps minor as another important region. These regions exhibited a general pattern of positive associations with age for intra axonal water fractions, axial, radial diffusivities, and negative relationships with age for mean diffusivities, fractional anisotropy, kurtosis. We encourage the application of multiple dMRI approaches for detailed insights into WM, and the further investigation of fornix and forceps as potential biomarkers of brain age and ageing.
Trofimova, Olga; Latypova, Adeliya; DiDomenicantonio, Giulia; Lutti, Antoine; Lange, Ann-Marie Glasø de & Kliegel, Matthias [Show all 14 contributors for this article] (2023). Topography of associations between cardiovascular risk factors and myelin loss in the ageing human brain. Communications Biology. 6. doi: 10.1038/s42003-023-04741-1. Full text in Research Archive
Schindler, Louise S; Subramaniapillai, Sivaniya; Ambikairajah, Ananthan; Barth, Claudia; Crestol, Arielle & Voldsbekk, Irene [Show all 15 contributors for this article] (2023). Cardiometabolic health across menopausal years is linked to white matter hyperintensities up to a decade later. Frontiers in Global Women’s Health. 4. doi: 10.3389/fgwh.2023.1320640. Full text in Research Archive Show summary
Introduction: The menopause transition is associated with several cardiometabolic risk factors. Poor cardiometabolic health is further linked to microvascular brain lesions, which can be detected as white matter hyperintensities (WMHs) using T2-FLAIR magnetic resonance imaging (MRI) scans. Females show higher risk for WMHs post-menopause, but it remains unclear whether changes in cardiometabolic risk factors underlie menopause-related increase in brain pathology. Methods: In this study, we assessed whether cross-sectional measures of cardiometabolic health, including body mass index (BMI) and waist-to-hip ratio (WHR), blood lipids, blood pressure, and long-term blood glucose (HbA1c), as well as longitudinal changes in BMI and WHR, differed according to menopausal status at baseline in 9,882 UK Biobank females (age range 40–70 years, n premenopausal = 3,529, n postmenopausal = 6,353). Furthermore, we examined whether these cardiometabolic factors were associated with WMH outcomes at the follow-up assessment, on average 8.78 years after baseline. Results: Postmenopausal females showed higher levels of baseline blood lipids (HDL β = 0.14, p < 0.001, LDL β = 0.20, p < 0.001, triglycerides β = 0.12, p < 0.001) and HbA1c (β = 0.24, p < 0.001) compared to premenopausal women, beyond the effects of age. Over time, BMI increased more in the premenopausal compared to the postmenopausal group (β = −0.08, p < 0.001), while WHR increased to a similar extent in both groups (β = −0.03, p = 0.102). The change in WHR was however driven by increased waist circumference only in the premenopausal group. While the group level changes in BMI and WHR were in general small, these findings point to distinct anthropometric changes in pre- and postmenopausal females over time. Higher baseline measures of BMI, WHR, triglycerides, blood pressure, and HbA1c, as well as longitudinal increases in BMI and WHR, were associated with larger WMH volumes (β range = 0.03–0.13, p ≤ 0.002). HDL showed a significant inverse relationship with WMH volume (β = −0.27, p < 0.001). Discussion: Our findings emphasise the importance of monitoring cardiometabolic risk factors in females from midlife through the menopause transition and into the postmenopausal phase, to ensure improved cerebrovascular outcomes in later years.
Subramaniapillai, Sivaniya; Galea, Liisa A.M.; Einstein, Gillian & Lange, Ann-Marie Glasø de (2023). Sex and gender in health research: Intersectionality matters. Frontiers in Neuroendocrinology (FIN). ISSN 0091-3022. p. 1–6. doi: 10.1016/j.yfrne.2023.101104. Full text in Research Archive
Løchen, Aili Røtterud; Kolskår, Knut-Kristian; Lange, Ann-Marie Glasø de; Sneve, Markus Handal; Haatveit, Beathe & Lagerberg, Trine Vik [Show all 11 contributors for this article] (2023). Visual processing deficits in patients with schizophrenia spectrum and bipolar disorders and associations with psychotic symptoms, and intellectual abilities. Heliyon. 9(2). doi: 10.1016/j.heliyon.2023.e13354. Full text in Research Archive Show summary
Abstract Objective Low-level sensory disruption is hypothesized as a precursor to clinical and cognitive symptoms in severe mental disorders. We compared visual discrimination performance in patients with schizophrenia spectrum disorder or bipolar disorder with healthy controls, and investigated associations with clinical symptoms and IQ. Methods Patients with schizophrenia spectrum disorder (n = 32), bipolar disorder (n = 55) and healthy controls (n = 152) completed a computerized visual discrimination task. Participants responded whether the latter of two consecutive grids had higher or lower spatial frequency, and discrimination thresholds were estimated using an adaptive maximum likelihood procedure. Case-control differences in threshold were assessed using linear regression, F-test and post-hoc pair-wise comparisons. Linear models were used to test for associations between visual discrimination threshold and psychotic symptoms derived from the PANSS and IQ assessed using the Matrix Reasoning and Vocabulary subtests from the Wechsler Abbreviated Scale of Intelligence (WASI). Results Robust regression revealed a significant main effect of diagnosis on discrimination threshold (robust F = 6.76, p = .001). Post-hoc comparisons revealed that patients with a schizophrenia spectrum disorder (mean = 14%, SD = 0.08) had higher thresholds compared to healthy controls (mean = 10.8%, SD = 0.07, β = 0.35, t = 3.4, p = .002), as did patients with bipolar disorder (12.23%, SD = 0.07, β = 0.21, t = 2.42, p = .04). There was no significant difference between bipolar disorder and schizophrenia (β = −0.14, t = −1.2, p = .45). Linear models revealed negative associations between IQ and threshold across all participants when controlling for diagnostic group (β = −0.3, t = −3.43, p = .0007). This association was found within healthy controls (t = −3.72, p = .0003) and patients with bipolar disorder (t = −2.53, p = .015), and no significant group by IQ interaction on threshold (F = 0.044, p = .97). There were no significant associations between PANSS domain scores and discrimination threshold. Conclusion Patients with schizophrenia spectrum or bipolar disorders exhibited higher visual discrimination thresholds than healthy controls, supporting early visual deficits among patients with severe mental illness. Discrimination threshold was negatively associated with IQ among healthy controls and bipolar disorder patients. These findings elucidate perception-related disease mechanisms in severe mental illness, which warrants replication in independent samples.
Leonardsen, Esten Høyland; Vidal-Piñeiro, Didac; Roe, James Michael; Frei, Oleksandr; Shadrin, Alexey & Iakunchykova, Olena [Show all 14 contributors for this article] (2023). Genetic architecture of brain age and its casual relations with brain and mental disorders. Molecular Psychiatry. ISSN 1359-4184. doi: 10.1038/s41380-023-02087-y. Full text in Research Archive Show summary
Abstract The difference between chronological age and the apparent age of the brain estimated from brain imaging data—the brain age gap (BAG)—is widely considered a general indicator of brain health. Converging evidence supports that BAG is sensitive to an array of genetic and nongenetic traits and diseases, yet few studies have examined the genetic architecture and its corresponding causal relationships with common brain disorders. Here, we estimate BAG using state-of-the-art neural networks trained on brain scans from 53,542 individuals (age range 3–95 years). A genome-wide association analysis across 28,104 individuals (40–84 years) from the UK Biobank revealed eight independent genomic regions significantly associated with BAG ( p < 5 × 10 −8 ) implicating neurological, metabolic, and immunological pathways – among which seven are novel. No significant genetic correlations or causal relationships with BAG were found for Parkinson’s disease, major depressive disorder, or schizophrenia, but two-sample Mendelian randomization indicated a causal influence of AD ( p = 7.9 × 10 −4 ) and bipolar disorder ( p = 1.35 × 10 −2 ) on BAG. These results emphasize the polygenic architecture of brain age and provide insights into the causal relationship between selected neurological and neuropsychiatric disorders and BAG.
Anatürk, Melis; Patel, Raihaan; Ebmeier, Klaus P.; Georgiopoulos, Georgios; Newby, Danielle & Topiwala, Anya [Show all 12 contributors for this article] (2023). Development and validation of a dementia risk score in the UK Biobank and Whitehall II cohorts. BMJ Mental Health. 26(1). doi: 10.1136/bmjment-2023-300719. Full text in Research Archive
Pasin, Chloé; Consiglio, Camila R.; Huisman, Jana S.; Lange, Ann-Marie Glasø de; Peckham, Hannah & Vallejo-Yagüe, Enriqueta [Show all 18 contributors for this article] (2023). Sex and gender in infection and immunity: addressing the bottlenecks from basic science to public health and clinical applications. Royal Society Open Science. 10(7). doi: 10.1098/rsos.221628. Full text in Research Archive
Voldsbekk, Irene; Kjelkenes, Rikka; Wolfers, Thomas; Dahl, Andreas; Lund, Martina Jonette & Kaufmann, Tobias [Show all 12 contributors for this article] (2023). Shared pattern of impaired social communication and cognitive ability in the youth brain across diagnostic boundaries. Developmental Cognitive Neuroscience. ISSN 1878-9293. 60. doi: 10.1016/j.dcn.2023.101219. Full text in Research Archive Show summary
Background Abnormalities in brain structure are shared across diagnostic categories. Given the high rate of comorbidity, the interplay of relevant behavioural factors may also cross these classic boundaries. Methods We aimed to detect brain-based dimensions of behavioural factors using canonical correlation and independent component analysis in a clinical youth sample (n = 1732, 64 % male, age: 5–21 years). Results We identified two correlated patterns of brain structure and behavioural factors. The first mode reflected physical and cognitive maturation (r = 0.92, p = .005). The second mode reflected lower cognitive ability, poorer social skills, and psychological difficulties (r = 0.92, p = .006). Elevated scores on the second mode were a common feature across all diagnostic boundaries and linked to the number of comorbid diagnoses independently of age. Critically, this brain pattern predicted normative cognitive deviations in an independent population-based sample (n = 1253, 54 % female, age: 8–21 years), supporting the generalisability and external validity of the reported brain-behaviour relationships. Conclusions These results reveal dimensions of brain-behaviour associations across diagnostic boundaries, highlighting potent disorder-general patterns as the most prominent. In addition to providing biologically informed patterns of relevant behavioural factors for mental illness, this contributes to a growing body of evidence in favour of transdiagnostic approaches to prevention and intervention.
Lindseth, Linn Renate Sjøveian; Lange, Ann-Marie Glasø de; Meer, Dennis van der; Agartz, Ingrid; Westlye, Lars Tjelta & Tamnes, Christian Krog [Show all 7 contributors for this article] (2023). Associations between reproductive history, hormone use, APOE ε4 genotype and cognition in middle- to older-aged women from the UK Biobank. Frontiers in Aging Neuroscience. 14:1014605, p. 1–13. doi: 10.3389/fnagi.2022.1014605. Full text in Research Archive Show summary
Introduction: Relative to men, women are at a higher risk of developing age-related neurocognitive disorders including Alzheimer’s disease. While women’s health has historically been understudied, emerging evidence suggests that reproductive life events such as pregnancy and hormone use may influence women’s cognition later in life. Methods: We investigated the associations between reproductive history, exogenous hormone use, apolipoprotein (APOE) ε4 genotype and cognition in 221,124 middle- to older-aged (mean age 56.2 ± 8.0 years) women from the UK Biobank. Performance on six cognitive tasks was assessed, covering four cognitive domains: episodic visual memory, numeric working memory, processing speed, and executive function. Results: A longer reproductive span, older age at menopause, older age at first and last birth, and use of hormonal contraceptives were positively associated with cognitive performance later in life. Number of live births, hysterectomy without oophorectomy and use of hormone therapy showed mixed findings, with task-specific positive and negative associations. Effect sizes were generally small (Cohen’s d < 0.1). While APOE ε4 genotype was associated with reduced processing speed and executive functioning, in a dose-dependent manner, it did not influence the observed associations between female-specific factors and cognition. Discussion: Our findings support previous evidence of associations between a broad range of female-specific factors and cognition. The positive association between a history of hormonal contraceptive use and cognition later in life showed the largest effect sizes (max. d = 0.1). More research targeting the long-term effects of female-specific factors on cognition and age-related neurocognitive disorders including Alzheimer’s disease is crucial for a better understanding of women’s brain health and to support women’s health care.
Holm, Madelene Christin; Leonardsen, Esten Høyland; Beck, Dani; Dahl, Andreas; Kjelkenes, Rikka & Lange, Ann-Marie Glasø de [Show all 7 contributors for this article] (2023). Linking brain maturation and puberty during early adolescence using longitudinal brain age prediction in the ABCD cohort. Developmental Cognitive Neuroscience. ISSN 1878-9293. 60. doi: 10.1016/j.dcn.2023.101220. Full text in Research Archive Show summary
The temporal characteristics of adolescent neurodevelopment are shaped by a complex interplay of genetic, biological, and environmental factors. Using a large longitudinal dataset of children aged 9–13 from the Adolescent Brain Cognitive Development (ABCD) study we tested the associations between pubertal status and brain maturation. Brain maturation was assessed using brain age prediction based on convolutional neural networks and minimally processed T1-weighted structural MRI data. Brain age prediction provided highly accurate and reliable estimates of individual age, with an overall mean absolute error of 0.7 and 1.4 years at the two timepoints respectively, and an intraclass correlation of 0.65. Linear mixed effects (LME) models accounting for age and sex showed that on average, a one unit increase in pubertal maturational level was associated with a 2.22 months higher brain age across time points (β = 0.10, p < .001). Moreover, annualized change in pubertal development was weakly related to the rate of change in brain age (β = .047, p = 0.04). These results demonstrate a link between sexual development and brain maturation in early adolescence, and provides a basis for further investigations of the complex sociobiological impacts of puberty on life outcomes.
Korbmacher, Max; Gurholt, Tiril Pedersen; Lange, Ann-Marie Glasø de; Meer, Dennis van der; Beck, Dani & Eikefjord, Eli Nina [Show all 10 contributors for this article] (2023). Bio-psycho-social factors’ associations with brain age: a large-scale UK Biobank diffusion study of 35,749 participants. Frontiers in Psychology. 14. doi: 10.3389/fpsyg.2023.1117732. Full text in Research Archive Show summary
Kontrollert for godkjenning av Elise Davis-Keaveny
Barth, Claudia; Crestol, Arielle; Lange, Ann-Marie Glasø de & Galea, Liisa A.M (2023). Sex steroids and the female brain across the lifespan: insights into risk of depression and Alzheimer's disease. The Lancet Diabetes and Endocrinology. ISSN 2213-8587. 11(12), p. 926–941. doi: 10.1016/S2213-8587(23)00224-3. Full text in Research Archive
Meer, Dennis van der; Gurholt, Tiril Pedersen; Sønderby, Ida Elken; Shadrin, Alexey; Hindley, Guy Frederick Lanyon & Rahman, Zillur [Show all 18 contributors for this article] (2022). The link between liver fat and cardiometabolic diseases is highlighted by genome-wide association study of MRI-derived measures of body composition. Communications Biology. 5(1). doi: 10.1038/s42003-022-04237-4. Full text in Research Archive Show summary
Abstract Obesity and associated morbidities, metabolic associated fatty liver disease (MAFLD) included, constitute some of the largest public health threats worldwide. Body composition and related risk factors are known to be heritable and identification of their genetic determinants may aid in the development of better prevention and treatment strategies. Recently, large-scale whole-body MRI data has become available, providing more specific measures of body composition than anthropometrics such as body mass index. Here, we aimed to elucidate the genetic architecture of body composition, by conducting genome-wide association studies (GWAS) of these MRI-derived measures. We ran both univariate and multivariate GWAS on fourteen MRI-derived measurements of adipose and muscle tissue distribution, derived from scans from 33,588 White European UK Biobank participants (mean age of 64.5 years, 51.4% female). Through multivariate analysis, we discovered 100 loci with distributed effects across the body composition measures and 241 significant genes primarily involved in immune system functioning. Liver fat stood out, with a highly discoverable and oligogenic architecture and the strongest genetic associations. Comparison with 21 common cardiometabolic traits revealed both shared and specific genetic influences, with higher mean heritability for the MRI measures (h 2 = .25 vs. .13, p = 1.8x10 −7 ). We found substantial genetic correlations between the body composition measures and a range of cardiometabolic diseases, with the strongest correlation between liver fat and type 2 diabetes (r g = .49, p = 2.7x10 −22 ). These findings show that MRI-derived body composition measures complement conventional body anthropometrics and other biomarkers of cardiometabolic health, highlighting the central role of liver fat, and improving our knowledge of the genetic architecture of body composition and related diseases.
Bråthen, Anne Cecilie Sjøli; Sørensen, Øystein; Lange, Ann-Marie Glasø de; Mowinckel, Athanasia Monika; Fjell, Anders Martin & Walhovd, Kristine B (2022). Cognitive and hippocampal changes weeks and years after memory training. Scientific Reports. 12(1). doi: 10.1038/s41598-022-11636-4. Full text in Research Archive
Rokicki, Jaroslav; Kaufmann, Tobias; Lange, Ann-Marie Glasø de; Meer, Dennis van der; Bahrami, Shahram & Sartorius, Alina [Show all 14 contributors for this article] (2022). Oxytocin receptor expression patterns in the human brain across development. Neuropsychopharmacology. ISSN 0893-133X. doi: 10.1038/s41386-022-01305-5. Full text in Research Archive
Schindler, Louise S; Subramaniapillai, Sivaniya; Barth, Claudia; Meer, Dennis van der; Pedersen, Mads Lund & Kaufmann, Tobias [Show all 18 contributors for this article] (2022). Associations between abdominal adipose tissue, reproductive span, and brain characteristics in post-menopausal women. NeuroImage: Clinical. 36. doi: 10.1016/j.nicl.2022.103239. Full text in Research Archive Show summary
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.
Beck, Dani; Lange, Ann-Marie Glasø de; Alnæs, Dag; Maximov, Ivan; Pedersen, Mads Lund & Leinhard, Olof Dahlqvist [Show all 20 contributors for this article] (2022). Adipose tissue distribution from body MRI is associated with cross-sectional and longitudinal brain age in adults. NeuroImage: Clinical. 33. doi: 10.1016/j.nicl.2022.102949. Full text in Research Archive Show summary
There is an intimate body-brain connection in ageing, and obesity is a key risk factor for poor cardiometabolic health and neurodegenerative conditions. Although research has demonstrated deleterious effects of obesity on brain structure and function, the majority of studies have used conventional measures such as waist-to-hip ratio, waist circumference, and body mass index. While sensitive to gross features of body composition, such global anthropometric features fail to describe regional differences in body fat distribution and composition. The sample consisted of baseline brain magnetic resonance imaging (MRI) acquired from 790 healthy participants aged 18–94 years (mean ± standard deviation (SD) at baseline: 46.8 ± 16.3), and follow-up brain MRI collected from 272 of those individuals (two time-points with 19.7 months interval, on average (min = 9.8, max = 35.6). Of the 790 included participants, cross-sectional body MRI data was available from a subgroup of 286 participants, with age range 19–86 (mean = 57.6, SD = 15.6). Adopting a mixed cross-sectional and longitudinal design, we investigated cross-sectional body magnetic resonance imaging measures of adipose tissue distribution in relation to longitudinal brain structure using MRI-based morphometry (T1) and diffusion tensor imaging (DTI). We estimated tissue-specific brain age at two time points and performed Bayesian multilevel modelling to investigate the associations between adipose measures at follow-up and brain age gap (BAG) – the difference between actual age and the prediction of the brain’s biological age – at baseline and follow-up. We also tested for interactions between BAG and both time and age on each adipose measure. The results showed credible associations between T1-based BAG and liver fat, muscle fat infiltration (MFI), and weight-to-muscle ratio (WMR), indicating older-appearing brains in people with higher measures of adipose tissue. Longitudinal evidence supported interaction effects between time and MFI and WMR on T1-based BAG, indicating accelerated ageing over the course of the study period in people with higher measures of adipose tissue. The results show that specific measures of fat distribution are associated with brain ageing and that different compartments of adipose tissue may be differentially linked with increased brain ageing, with potential to identify key processes involved in age-related transdiagnostic disease processes.
Lange, Ann-Marie Glasø de; Anatürk, Melis; Rokicki, Jaroslav; Han, Laura K.M.; Franke, Katja & Alnæs, Dag [Show all 12 contributors for this article] (2022). Mind the gap: Performance metric evaluation in brain-age prediction. Human Brain Mapping. ISSN 1065-9471. doi: 10.1002/hbm.25837. Full text in Research Archive Show summary
Estimating age based on neuroimaging-derived data has become a popular approach to developing markers for brain integrity and health. While a variety of machine-learning algorithms can provide accurate predictions of age based on brain characteristics, there is significant variation in model accuracy reported across studies. We predicted age in two population-based datasets, and assessed the effects of age range, sample size and age-bias correction on the model performance metrics Pearson's correlation coefficient (r), the coefficient of determination (R2), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results showed that these metrics vary considerably depending on cohort age range; r and R2 values are lower when measured in samples with a narrower age range. RMSE and MAE are also lower in samples with a narrower age range due to smaller errors/brain age delta values when predictions are closer to the mean age of the group. Across subsets with different age ranges, performance metrics improve with increasing sample size. Performance metrics further vary depending on prediction variance as well as mean age difference between training and test sets, and age-bias corrected metrics indicate high accuracy—also for models showing poor initial performance. In conclusion, performance metrics used for evaluating age prediction models depend on cohort and study-specific data characteristics, and cannot be directly compared across different studies. Since age-bias corrected metrics generally indicate high accuracy, even for poorly performing models, inspection of uncorrected model results provides important information about underlying model attributes such as prediction variance.
Aamodt, Eva Birgitte; Alnæs, Dag; Lange, Ann-Marie Glasø de; Aam, Stina; Schellhorn, Till & Saltvedt, Ingvild Tina [Show all 8 contributors for this article] (2022). Longitudinal brain age prediction and cognitive function after stroke. Neurobiology of Aging. ISSN 0197-4580. 122, p. 55–64. doi: 10.1016/j.neurobiolaging.2022.10.007. Full text in Research Archive Show summary
Advanced age is associated with post-stroke cognitive decline. Machine learning based on brain scans can be used to estimate brain age of patients, and the corresponding difference from chronological age, the brain age gap (BAG), has been investigated in a range of clinical conditions, yet not thoroughly in post-stroke neurocognitive disorder (NCD). We aimed to investigate the association between BAG and post-stroke NCD over time. Lower BAG (younger appearing brain compared to chronological age) was found associated with lower risk of post-stroke NCD up to 36 months after stroke, even among those showing no evidence of impairments 3 months after hospital admission. For patients with no NCD at baseline, survival analysis suggested that higher baseline BAG was associated with higher risk of post-stroke NCD at 18 and 36 months. In conclusion, a younger appearing brain is associated with a lower risk of post-stroke NCD.
Subramaniapillai, Sivaniya; Suri, Sana; Barth, Claudia; Maximov, Ivan; Voldsbekk, Irene & Meer, Dennis van der [Show all 13 contributors for this article] (2022). Sex- and age-specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort. Human Brain Mapping. ISSN 1065-9471. 43(12), p. 3759–3774. doi: 10.1002/hbm.25882. Full text in Research Archive Show summary
Cardiometabolic risk (CMR) factors are associated with accelerated brain aging and increased risk for sex-dimorphic illnesses such as Alzheimer's disease (AD). Yet, it is unknown how CMRs interact with sex and apolipoprotein E-ϵ4 (APOE4), a known genetic risk factor for AD, to influence brain age across different life stages. Using age prediction based on multi-shell diffusion-weighted imaging data in 21,308 UK Biobank participants, we investigated whether associations between white matter Brain Age Gap (BAG) and body mass index (BMI), waist-to-hip ratio (WHR), body fat percentage (BF%), and APOE4 status varied (i) between males and females, (ii) according to age at menopause in females, and (iii) across different age groups in males and females. We report sex differences in associations between BAG and all three CMRs, with stronger positive associations among males compared to females. Independent of APOE4 status, higher BAG (older brain age relative to chronological age) was associated with greater BMI, WHR, and BF% in males, whereas in females, higher BAG was associated with greater WHR, but not BMI and BF%. These divergent associations were most prominent within the oldest group of females (66–81 years), where greater BF% was linked to lower BAG. Earlier menopause transition was associated with higher BAG, but no interactions were found with CMRs. In conclusion, the findings point to sex- and age-specific associations between CMRs and brain age. Incorporating sex as a factor of interest in studies addressing CMR may promote sex-specific precision medicine, consequently improving health care for both males and females.
Leonardsen, Esten Høyland; Peng, Han; Kaufmann, Tobias; Agartz, Ingrid; Andreassen, Ole & Celius, Elisabeth Gulowsen [Show all 19 contributors for this article] (2022). Deep neural networks learn general and clinically relevant representations of the ageing brain. NeuroImage. ISSN 1053-8119. 256. doi: 10.1016/j.neuroimage.2022.119210. Full text in Research Archive Show summary
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.
Voldsbekk, Irene; Barth, Claudia; Maximov, Ivan I.; Kaufmann, Tobias; Beck, Dani & Richard, Genevive [Show all 9 contributors for this article] (2021). A history of previous childbirths is linked to women's white matter brain age in midlife and older age. Human Brain Mapping. ISSN 1065-9471. 42(13), p. 1–15. doi: 10.1002/hbm.25553. Full text in Research Archive Show summary
Maternal brain adaptations occur in response to pregnancy, but little is known about how parity impacts white matter and white matter ageing trajectories later in life. Utilising global and regional brain age prediction based on multi-shell diffusion-weighted imaging data, we investigated the association between previous childbirths and white matter brain age in 8,895 women in the UK Biobank cohort (age range = 54–81 years). The results showed that number of previous childbirths was negatively associated with white matter brain age, potentially indicating a protective effect of parity on white matter later in life. Both global white matter and grey matter brain age estimates showed unique contributions to the association with previous childbirths, suggesting partly independent processes. Corpus callosum contributed uniquely to the global white matter association with previous childbirths, and showed a stronger relationship relative to several other tracts. While our findings demonstrate a link between reproductive history and brain white matter characteristics later in life, longitudinal studies are required to establish causality and determine how parity may influence women's white matter trajectories across the lifespan.
Jansen, Michelle G; Griffanti, Ludovica; Mackay, Clare E; Anatürk, Melis; Melazzini, Luca & Lange, Ann-Marie Glasø de [Show all 14 contributors for this article] (2021). Association of cerebral small vessel disease burden with brain structure and cognitive and vascular risk trajectories in mid-to-late life. Journal of Cerebral Blood Flow and Metabolism. ISSN 0271-678X. doi: 10.1177/0271678X211048411. Full text in Research Archive
Hellstrøm, Torgeir; Andelic, Nada; Lange, Ann-Marie Glasø de; Helseth, Eirik; Eiklid, Kristin Louise & Westlye, Lars Tjelta (2021). Apolipoprotein ɛ4 Status and Brain Structure 12 Months after Mild Traumatic Injury: Brain Age Prediction Using Brain Morphometry and Diffusion Tensor Imaging. Journal of Clinical Medicine. 10(3). doi: 10.3390/jcm10030418. Full text in Research Archive Show summary
Background: Apolipoprotein E (APOE) ɛ4 is associated with poor outcome following moderate to severe traumatic brain injury (TBI). There is a lack of studies investigating the influence of APOE ɛ4 on intracranial pathology following mild traumatic brain injury (MTBI). This study explores the association between APOE ɛ4 and MRI measures of brain age prediction, brain morphometry, and diffusion tensor imaging (DTI). Methods: Patients aged 16 to 65 with acute MTBI admitted to the trauma center were included. Multimodal MRI was performed 12 months after injury and associated with APOE ɛ4 status. Corrections for multiple comparisons were done using false discovery rate (FDR). Results: Of included patients, 123 patients had available APOE, volumetric, and DTI data of sufficient quality. There were no differences between APOE ɛ4 carriers (39%) and non-carriers in demographic and clinical data. Age prediction revealed high accuracy both for the DTI-based and the brain morphometry based model. Group comparisons revealed no significant differences in brain-age gap between ɛ4 carriers and non-carriers, and no significant differences in conventional measures of brain morphometry and volumes. Compared to non-carriers, APOE ɛ4 carriers showed lower fractional anisotropy (FA) in the hippocampal part of the cingulum bundle, which did not remain significant after FDR adjustment. Conclusion: APOE ɛ4 carriers might be vulnerable to reduced neuronal integrity in the cingulum. Larger cohort studies are warranted to replicate this finding.
Lund, Martina Jonette; Alnæs, Dag; Lange, Ann-Marie Glasø de; Andreassen, Ole; Westlye, Lars Tjelta & Kaufmann, Tobias (2021). Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms. NeuroImage: Clinical. doi: 10.1016/j.nicl.2021.102921. Full text in Research Archive Show summary
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.
Sanders, Anne-Marthe; Richard, Geneviève; Kolskår, Knut-Kristian; Ulrichsen, Kristine Moe; Kaufmann, Tobias & Alnæs, Dag [Show all 11 contributors for this article] (2021). Linking objective measures of physical activity and capability with brain structure in healthy community dwelling older adults. NeuroImage: Clinical. 31. doi: 10.1016/j.nicl.2021.102767. Full text in Research Archive Show summary
Maintaining high levels of daily activity and physical capability have been proposed as important constituents to promote healthy brain and cognitive aging. Studies investigating the associations between brain health and physical activity in late life have, however, mainly been based on self-reported data or measures designed for clinical populations. In the current study, we examined cross-sectional associations between physical activity, recorded by an ankle-positioned accelerometer for seven days, physical capability (grip strength, postural control, and walking speed), and neuroimaging based surrogate markers of brain health in 122 healthy older adults aged 65–88 years. We used a multimodal brain imaging approach offering complementary structural MRI based indicators of brain health: global white matter fractional anisotropy (FA) and mean diffusivity (MD) based on diffusion tensor imaging, and subcortical and global brain age based on brain morphology inferred from T1-weighted MRI data. In addition, based on the results from the main analysis, follow-up regression analysis was performed to test for association between the volume of key subcortical regions of interest (hippocampus, caudate, thalamus and cerebellum) and daily steps, and a follow-up voxelwise analysis to test for associations between walking speed and FA across the white matter Tract-Based Spatial Statistics (TBSS) skeleton. The analyses revealed a significant association between global FA and walking speed, indicating higher white matter integrity in people with higher pace. Voxelwise analysis supported widespread significant associations. We also found a significant interaction between sex and subcortical brain age on number of daily steps, indicating younger-appearing brains in more physically active women, with no significant associations among men. These results provide insight into the intricate associations between different measures of brain and physical health in old age, and corroborate established public health advice promoting physical activity.
Barth, Claudia; Nerland, Stener; Lange, Ann-Marie Glasø de; Wortinger, Laura Anne; Hilland, Eva & Andreassen, Ole Andreas [Show all 8 contributors for this article] (2021). In Vivo Amygdala Nuclei Volumes in Schizophrenia and Bipolar Disorders. Schizophrenia Bulletin. ISSN 0586-7614. 47(5), p. 1431–1441. doi: 10.1093/schbul/sbaa192. Full text in Research Archive Show summary
Abstract Abnormalities in amygdala volume are well-established in schizophrenia and commonly reported in bipolar disorders. However, the specificity of volumetric differences in individual amygdala nuclei is largely unknown. Patients with schizophrenia disorders (SCZ, N = 452, mean age 30.7 ± 9.2 [SD] years, females 44.4%), bipolar disorders (BP, N = 316, 33.7 ± 11.4, 58.5%), and healthy controls (N = 753, 34.1 ± 9.1, 40.9%) underwent T1-weighted magnetic resonance imaging. Total amygdala, nuclei, and intracranial volume (ICV) were estimated with Freesurfer (v6.0.0). Analysis of covariance and multiple linear regression models, adjusting for age, age2, ICV, and sex, were fitted to examine diagnostic group and subgroup differences in volume, respectively. Bilateral total amygdala and all nuclei volumes, except the medial and central nuclei, were significantly smaller in patients relative to controls. The largest effect sizes were found for the basal nucleus, accessory basal nucleus, and cortico-amygdaloid transition area (partial η2 &gt; 0.02). The diagnostic subgroup analysis showed that reductions in amygdala nuclei volume were most widespread in schizophrenia, with the lateral, cortical, paralaminar, and central nuclei being solely reduced in this disorder. The right accessory basal nucleus was marginally smaller in SCZ relative to BP (t = 2.32, P = .05). Our study is the first to demonstrate distinct patterns of amygdala nuclei volume reductions in a well-powered sample of patients with schizophrenia and bipolar disorders. Volume differences in the basolateral complex (lateral, basal, and accessory basal nuclei), an integral part of the threat processing circuitry, were most prominent in schizophrenia.
Beck, Dani; Lange, Ann-Marie G. de; Pedersen, Mads L.; Alnæs, Dag; Maximov, Ivan & Voldsbekk, Irene [Show all 18 contributors for this article] (2021). Cardiometabolic risk factors associated with brain age and accelerate brain ageing. Human Brain Mapping. ISSN 1065-9471. p. 1–21. doi: 10.1002/hbm.25680. Full text in Research Archive Show summary
The structure and integrity of the ageing brain is interchangeably linked to physical health, and cardiometabolic risk factors (CMRs) are associated with dementia and other brain disorders. In this mixed cross-sectional and longitudinal study (interval mean = 19.7 months), including 790 healthy individuals (mean age = 46.7 years, 53% women), we investigated CMRs and health indicators including anthropometric measures, lifestyle factors, and blood biomarkers in relation to brain structure using MRI-based morphometry and diffusion tensor imaging (DTI). We performed tissue specific brain age prediction using machine learning and performed Bayesian multilevel modeling to assess changes in each CMR over time, their respective association with brain age gap (BAG), and their interaction effects with time and age on the tissue-specific BAGs. The results showed credible associations between DTI-based BAG and blood levels of phosphate and mean cell volume (MCV), and between T1-based BAG and systolic blood pressure, smoking, pulse, and C-reactive protein (CRP), indicating older-appearing brains in people with higher cardiometabolic risk (smoking, higher blood pressure and pulse, low-grade inflammation). Longitudinal evidence supported interactions between both BAGs and waist-to-hip ratio (WHR), and between DTI-based BAG and systolic blood pressure and smoking, indicating accelerated ageing in people with higher cardiometabolic risk (smoking, higher blood pressure, and WHR). The results demonstrate that cardiometabolic risk factors are associated with brain ageing. While randomized controlled trials are needed to establish causality, our results indicate that public health initiatives and treatment strategies targeting modifiable cardiometabolic risk factors may also improve risk trajectories and delay brain ageing.
Lange, Ann-Marie Glasø de; Kaufmann, Tobias; Quintana, Daniel; Winterton, Adriano; Andreassen, Ole Andreas & Westlye, Lars Tjelta [Show all 7 contributors for this article] (2021). Prominent health problems, socioeconomic deprivation, and higher brain age in lonely and isolated individuals: A population-based study. Behavioural Brain Research. ISSN 0166-4328. doi: 10.1016/j.bbr.2021.113510. Full text in Research Archive Show summary
Loneliness is linked to increased risk for Alzheimer's disease, but little is known about factors potentially contributing to adverse brain health in lonely individuals. In this study, we used data from 24,867 UK Biobank participants to investigate risk factors related to loneliness and estimated brain age based on neuroimaging data. The results showed that on average, individuals who self-reported loneliness on a single yes/no item scored higher on neuroticism, depression, social isolation, and socioeconomic deprivation, performed less physical activity, and had higher BMI compared to individuals who did not report loneliness. In line with studies pointing to a genetic overlap of loneliness with neuroticism and depression, permutation feature importance ranked these factors as the most important for classifying lonely vs. not lonely individuals (ROC AUC = 0.83). While strongly linked to loneliness, neuroticism and depression were not associated with brain age estimates. Conversely, objective social isolation showed a main effect on brain age, and individuals reporting both loneliness and social isolation showed higher brain age relative to controls – as part of a prominent risk profile with elevated scores on socioeconomic deprivation and unhealthy lifestyle behaviours, in addition to neuroticism and depression. While longitudinal studies are required to determine causality, this finding may indicate that the combination of social isolation and a genetic predisposition for loneliness involves a risk for adverse brain health. Importantly, the results underline the complexity in associations between loneliness and adverse health outcomes, where observed risks likely depend on a combination of interlinked variables including genetic as well as social, behavioural, physical, and socioeconomic factors.
Maximov, Ivan; Meer, Dennis van der; Lange, Ann-Marie Glasø de; Kaufmann, Tobias; Shadrin, Alexey A. & Frei, Oleksandr [Show all 8 contributors for this article] (2021). Fast qualitY conTrol meThod foR derIved diffUsion Metrics (YTTRIUM) in big data analysis: U.K. Biobank 18,608 example. Human Brain Mapping. ISSN 1065-9471. 42(10). doi: 10.1002/hbm.25424. Full text in Research Archive Show summary
Deriving reliable information about the structural and functional architecture of the brain in vivo is critical for the clinical and basic neurosciences. In the new era of large population-based datasets, when multiple brain imaging modalities and contrasts are combined in order to reveal latent brain structural patterns and associations with genetic, demographic and clinical information, automated and stringent quality control (QC) procedures are important. Diffusion magnetic resonance imaging (dMRI) is a fertile imaging technique for probing and visualising brain tissue microstructure in vivo, and has been included in most standard imaging protocols in large-scale studies. Due to its sensitivity to subject motion and technical artefacts, automated QC procedures prior to scalar diffusion metrics estimation are required in order to minimise the influence of noise and artefacts. However, the QC procedures performed on raw diffusion data cannot guarantee an absence of distorted maps among the derived diffusion metrics. Thus, robust and efficient QC methods for diffusion scalar metrics are needed. Here, we introduce Fast qualitY conTrol meThod foR derIved diffUsion Metrics (YTTRIUM), a computationally efficient QC method utilising structural similarity to evaluate diffusion map quality and mean diffusion metrics. As an example, we applied YTTRIUM in the context of tract-based spatial statistics to assess associations between age and kurtosis imaging and white matter tract integrity maps in U.K. Biobank data (n = 18,608). To assess the influence of outliers on results obtained using machine learning (ML) approaches, we tested the effects of applying YTTRIUM on brain age prediction. We demonstrated that the proposed QC pipeline represents an efficient approach for identifying poor quality datasets and artefacts and increases the accuracy of ML based brain age prediction.
Winterton, Adriano; Bettella, Francesco; Lange, Ann-Marie Glasø de; Haram, Marit; Steen, Nils Eiel & Westlye, Lars Tjelta [Show all 8 contributors for this article] (2021). Oxytocin-pathway polygenic scores for severe mental disorders and metabolic phenotypes in the UK Biobank. Translational Psychiatry. doi: 10.1038/s41398-021-01725-9. Full text in Research Archive Show summary
Abstract Oxytocin is a neuromodulator and hormone that is typically associated with social cognition and behavior. In light of its purported effects on social cognition and behavior, research has investigated its potential as a treatment for psychiatric illnesses characterized by social dysfunction, such as schizophrenia and bipolar disorder. While the results of these trials have been mixed, more recent evidence suggests that the oxytocin system is also linked with cardiometabolic conditions for which individuals with severe mental disorders are at a higher risk for developing. To investigate whether the oxytocin system has a pleiotropic effect on the etiology of severe mental illness and cardiometabolic conditions, we explored oxytocin’s role in the shared genetic liability of schizophrenia, bipolar disorder, type-2 diabetes, and several phenotypes linked with cardiovascular disease and type 2 diabetes risk using a polygenic pathway-specific approach. Analysis of a large sample with about 480,000 individuals (UK Biobank) revealed statistically significant associations across the range of phenotypes analyzed. By comparing these effects to those of polygenic scores calculated from 100 random gene sets, we also demonstrated the specificity of many of these significant results. Altogether, our results suggest that the shared effect of oxytocin-system dysfunction could help partially explain the co-occurrence of social and cardiometabolic dysfunction in severe mental illnesses.
Lange, Ann-Marie Glasø de; Anatürk, Melis; Suri, Sana; Kaufmann, Tobias; Cole, James H. & Griffanti, Ludovica [Show all 13 contributors for this article] (2020). Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study. NeuroImage. ISSN 1053-8119. 222. doi: 10.1016/j.neuroimage.2020.117292. Full text in Research Archive
Lange, Ann-Marie Glasø de; Barth, Claudia; Kaufmann, Tobias; Maximov, Ivan; Meer, Dennis van der & Agartz, Ingrid [Show all 7 contributors for this article] (2020). Women's brain aging: effects of sex-hormone exposure, pregnancies, and genetic risk for Alzheimer's disease. Human Brain Mapping. ISSN 1065-9471. p. 1–10. doi: 10.1002/hbm.25180. Full text in Research Archive Show summary
Sex hormones such as estrogen fluctuate across the female lifespan, with high levels during reproductive years and natural decline during the transition to menopause. Women's exposure to estrogen may influence their heightened risk of Alzheimer's disease (AD) relative to men, but little is known about how it affects normal brain aging. Recent findings from the UK Biobank demonstrate less apparent brain aging in women with a history of multiple childbirths, highlighting a potential link between sex‐hormone exposure and brain aging. We investigated endogenous and exogenous sex‐hormone exposure, genetic risk for AD, and neuroimaging‐derived biomarkers for brain aging in 16,854 middle to older‐aged women. The results showed that as opposed to parity, higher cumulative sex‐hormone exposure was associated with more evident brain aging, indicating that i) high levels of cumulative exposure to sex‐hormones may have adverse effects on the brain, and ii) beneficial effects of pregnancies on the female brain are not solely attributable to modulations in sex‐hormone exposure. In addition, for women using hormonal replacement therapy (HRT), starting treatment earlier was associated with less evident brain aging, but only in women with a genetic risk for AD. Genetic factors may thus contribute to how timing of HRT initiation influences women's brain aging trajectories.
Anatürk, Melis; Kaufmann, Tobias; Cole, James H.; Suri, Sana; Griffanti, Ludovica & Zsoldos, Enikő [Show all 12 contributors for this article] (2020). Prediction of brain age and cognitive age: Quantifying brain and cognitive maintenance in aging. Human Brain Mapping. ISSN 1065-9471. doi: 10.1002/hbm.25316. Full text in Research Archive Show summary
The concept of brain maintenance refers to the preservation of brain integrity in older age, while cognitive reserve refers to the capacity to maintain cognition in the presence of neurodegeneration or aging‐related brain changes. While both mechanisms are thought to contribute to individual differences in cognitive function among older adults, there is currently no “gold standard” for measuring these constructs. Using machine‐learning methods, we estimated brain and cognitive age based on deviations from normative aging patterns in the Whitehall II MRI substudy cohort (N = 537, age range = 60.34–82.76), and tested the degree of correspondence between these constructs, as well as their associations with premorbid IQ, education, and lifestyle trajectories. In line with established literature highlighting IQ as a proxy for cognitive reserve, higher premorbid IQ was linked to lower cognitive age independent of brain age. No strong evidence was found for associations between brain or cognitive age and lifestyle trajectories from midlife to late life based on latent class growth analyses. However, post hoc analyses revealed a relationship between cumulative lifestyle measures and brain age independent of cognitive age. In conclusion, we present a novel approach to characterizing brain and cognitive maintenance in aging, which may be useful for future studies seeking to identify factors that contribute to brain preservation and cognitive reserve mechanisms in older age.
Fjell, Anders Martin; Sederevicius, Donatas; Sneve, Markus Handal; Lange, Ann-Marie Glasø de; Bråthen, Anne Cecilie Sjøli & Idland, Ane-Victoria [Show all 16 contributors for this article] (2020). Self-reported sleep problems are related to amyloid deposition in cortical regions with high HOMER1 gene expression. Cerebral Cortex. ISSN 1047-3211. 30(4), p. 2144–2156. doi: 10.1093/cercor/bhz228. Full text in Research Archive Show summary
Sleep problems are related to the elevated levels of the Alzheimer’s disease (AD) biomarker β-amyloid (Aβ). Hypotheses about the causes of this relationship can be generated from molecular markers of sleep problems identified in rodents. A major marker of sleep deprivation is Homer1a, a neural protein coded by the HOMER1 gene, which has also been implicated in brain Aβ accumulation. Here, we tested whether the relationship between cortical Aβ accumulation and self-reported sleep quality, as well as changes in sleep quality over 3 years, was stronger in cortical regions with high HOMER1 mRNA expression levels. In a sample of 154 cognitively healthy older adults, Aβ correlated with poorer sleep quality cross-sectionally and longitudinally (n = 62), but more strongly in the younger than in older individuals. Effects were mainly found in regions with high expression of HOMER1. The anatomical distribution of the sleep-Aβ relationship followed closely the Aβ accumulation pattern in 69 patients with mild cognitive impairment or AD. Thus, the results indicate that the relationship between sleep problems and Aβ accumulation may involve Homer1 activity in the cortical regions, where harbor Aβ deposits in AD. The findings may advance our understanding of the relationship between sleep problems and AD risk.
Rokicki, Jaroslav; Wolfers, Thomas; Nordhøy, Wibeke; Tesli, Natalia; Quintana, Daniel & Alnæs, Dag [Show all 20 contributors for this article] (2020). Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders. Human Brain Mapping. ISSN 1065-9471. 42(6), p. 1714–1726. doi: 10.1002/hbm.25323. Full text in Research Archive Show summary
The deviation between chronological age and age predicted using brain MRI is a putative marker of overall brain health. Age prediction based on structural MRI data shows high accuracy in common brain disorders. However, brain aging is complex and heterogenous, both in terms of individual differences and the underlying biological processes. Here, we implemented a multimodal model to estimate brain age using different combinations of cortical area, thickness and sub‐cortical volumes, cortical and subcortical T1/T2‐weighted ratios, and cerebral blood flow (CBF) based on arterial spin labeling. For each of the 11 models we assessed the age prediction accuracy in healthy controls (HC, n = 750) and compared the obtained brain age gaps (BAGs) between age‐matched subsets of HC and patients with Alzheimer's disease (AD, n = 54), mild (MCI, n = 90) and subjective (SCI, n = 56) cognitive impairment, schizophrenia spectrum (SZ, n = 159) and bipolar disorder (BD, n = 135). We found highest age prediction accuracy in HC when integrating all modalities. Furthermore, two‐group case–control classifications revealed highest accuracy for AD using global T1‐weighted BAG, while MCI, SCI, BD and SZ showed strongest effects in CBF‐based BAGs. Combining multiple MRI modalities improves brain age prediction and reveals distinct deviations in patients with psychiatric and neurological disorders. The multimodal BAG was most accurate in predicting age in HC, while group differences between patients and HC were often larger for BAGs based on single modalities. These findings indicate that multidimensional neuroimaging of patients may provide a brain‐based mapping of overlapping and distinct pathophysiology in common disorders.
Tønnesen, Siren; Kaufmann, Tobias; Lange, Ann-Marie Glasø de; Richard, Geneviève; Doan, Nhat Trung & Alnæs, Dag [Show all 33 contributors for this article] (2020). Brain age prediction reveals aberrant brain white matter in schizophrenia and bipolar disorder: A multisample diffusion tensor imaging study. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. ISSN 2451-9022. 5(12), p. 1095–1103. doi: 10.1016/j.bpsc.2020.06.014. Full text in Research Archive Show summary
Background Schizophrenia (SZ) and bipolar disorder (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, patients with BD, and healthy control (HC) subjects across 10 cohorts. Methods We trained 6 cross-validated models using different combinations of DTI data from 927 HC subjects (18–94 years of age) and applied the models to the test sets including 648 patients with SZ (18–66 years of age), 185 patients with BD (18–64 years of age), and 990 HC subjects (17–68 years of age), estimating the brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results. Results Tenfold cross-validation revealed high accuracy for all models. Compared with HC subjects, the model including all feature sets significantly overestimated the age of patients with SZ (Cohen’s d = −0.29) and patients with BD (Cohen’s d = 0.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy–based models showed larger group differences than the models based on other DTI-derived metrics. Conclusions Brain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners.
Walhovd, Kristine B; Bråthen, Anne Cecilie Sjøli; Panizzon, Matthew S.; Mowinckel, Athanasia Monika; Sørensen, Øystein & Lange, Ann-Marie Glasø de [Show all 11 contributors for this article] (2020). Within-session verbal learning slope is predictive of lifespan delayed recall, hippocampal volume, and memory training benefit, and is heritable. Scientific Reports. 10, p. 1–13. doi: 10.1038/s41598-020-78225-1. Full text in Research Archive Show summary
Memory performance results from plasticity, the ability to change with experience. We show that benefit from practice over a few trials, learning slope, is predictive of long-term recall and hippocampal volume across a broad age range and a long period of time, relates to memory training benefit, and is heritable. First, in a healthy lifespan sample (n = 1825, age 4–93 years), comprising 3483 occasions of combined magnetic resonance imaging (MRI) scans and memory tests over a period of up to 11 years, learning slope across 5 trials was uniquely related to performance on a delayed free recall test, as well as hippocampal volume, independent from first trial memory or total memory performance across the five learning trials. Second, learning slope was predictive of benefit from memory training across ten weeks in an experimental subsample of adults (n = 155). Finally, in an independent sample of male twins (n = 1240, age 51–50 years), learning slope showed significant heritability. Within-session learning slope may be a useful marker beyond performance per se, being heritable and having unique predictive value for long-term memory function, hippocampal volume and training benefit across the human lifespan.
Beck, Dani; Lange, Ann-Marie Glasø de; Maximov, Ivan; Richard, Geneviève; Andreassen, Ole Andreas & Nordvik, Jan E. [Show all 7 contributors for this article] (2020). White matter microstructure across the adult lifespan: A mixed longitudinal and cross-sectional study using advanced diffusion models and brain-age prediction. NeuroImage. ISSN 1053-8119. 224:117441, p. 1–14. doi: 10.1016/j.neuroimage.2020.117441. Full text in Research Archive Show summary
The macro- and microstructural architecture of human brain white matter undergoes substantial alterations throughout development and ageing. Most of our understanding of the spatial and temporal characteristics of these lifespan adaptations come from magnetic resonance imaging (MRI), including diffusion MRI (dMRI), which enables visualisation and quantification of brain white matter with unprecedented sensitivity and detail. However, with some notable exceptions, previous studies have relied on cross-sectional designs, limited age ranges, and diffusion tensor imaging (DTI) based on conventional single-shell dMRI. In this mixed cross-sectional and longitudinal study (mean interval: 15.2 months) including 702 multi-shell dMRI datasets, we combined complementary dMRI models to investigate age trajectories in healthy individuals aged 18 to 94 years (57.12% women). Using linear mixed effect models and machine learning based brain age prediction, we assessed the age-dependence of diffusion metrics, and compared the age prediction accuracy of six different diffusion models, including diffusion tensor (DTI) and kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), restriction spectrum imaging (RSI), spherical mean technique multi-compartment (SMT-mc), and white matter tract integrity (WMTI). The results showed that the age slopes for conventional DTI metrics (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity [AD], radial diffusivity [RD]) were largely consistent with previous research, and that the highest performing advanced dMRI models showed comparable age prediction accuracy to conventional DTI. Linear mixed effects models and Wilk's theorem analysis showed that the ‘FA fine’ metric of the RSI model and ‘orientation dispersion’ (OD) metric of the NODDI model showed the highest sensitivity to age. The results indicate that advanced diffusion models (DKI, NODDI, RSI, SMT mc, WMTI) provide sensitive measures of age-related microstructural changes of white matter in the brain that complement and extend the contribution of conventional DTI.
Lange, Ann-Marie Glasø de; Barth, Claudia; Kaufmann, Tobias; Anatürk, Melis; Suri, Sana & Ebmeier, Klaus [Show all 7 contributors for this article] (2020). The maternal brain: region-specific patterns of brain aging are traceable decades after childbirth. Human Brain Mapping. ISSN 1065-9471. doi: 10.1002/hbm.25152. Full text in Research Archive Show summary
Pregnancy involves maternal brain adaptations, but little is known about how parity influences women's brain aging trajectories later in life. In this study, we replicated previous findings showing less apparent brain aging in women with a history of childbirths, and identified regional brain aging patterns linked to parity in 19,787 middle‐ and older‐aged women. Using novel applications of brain‐age prediction methods, we found that a higher number of previous childbirths were linked to less apparent brain aging in striatal and limbic regions. The strongest effect was found in the accumbens—a key region in the mesolimbic reward system, which plays an important role in maternal behavior. While only prospective longitudinal studies would be conclusive, our findings indicate that subcortical brain modulations during pregnancy and postpartum may be traceable decades after childbirth.
Barth, Claudia & Lange, Ann-Marie Glasø de (2020). Towards an understanding of women's brain aging: the immunology of pregnancy and menopause. Frontiers in Neuroendocrinology (FIN). ISSN 0091-3022. 58:100850, p. 1–11. doi: 10.1016/j.yfrne.2020.100850. Full text in Research Archive Show summary
Women are at significantly greater risk of developing Alzheimer’s disease and show higher prevalence of autoimmune conditions relative to men. Women’s brain health is historically understudied, and little is therefore known about the mechanisms underlying epidemiological sex differences in neurodegenerative diseases, and how female-specific factors may influence women’s brain health across the lifespan. In this review, we summarize recent studies on the immunology of pregnancy and menopause, emphasizing that these major immunoendocrine transition phases may play a critical part in women’s brain aging trajectories.
Lange, Ann-Marie Glasø de & Cole, James H. (2020). Commentary: Correction procedures in brain-age prediction. NeuroImage: Clinical. doi: 10.1016/j.nicl.2020.102229. Full text in Research Archive
Bråthen, Anne Cecilie Sjøli; Lange, Ann-Marie Glasø de; Fjell, Anders Martin & Walhovd, Kristine B (2020). Risk- and protective factors for memory plasticity in aging. Aging, Neuropsychology and Cognition. ISSN 1382-5585. 28(2), p. 201–217. doi: 10.1080/13825585.2020.1727834. Full text in Research Archive Show summary
Risk and protective factors for cognitive function in aging may affect how much individuals benefit from their environment or life experiences by preserving or improving cognitive abilities. We investigated the relations between such factors and outcome from episodic-memory training in 136 healthy young and older adults. Tested risk factors included carrying the ɛ4 variant of the apolipoprotein E allele (APOE), age, body mass index, blood pressure, and cholesterol. Protective factors included higher levels of education, intelligence quotient (IQ), physical activity, fatty acids, and vitamin D. Average increases in memory performance were seen after training, with ample variation between individuals. Being young, female, and having higher IQ were positive predictors of memory improvement. No other relationships were observed. Similar benefit was observed across APOE allelic variation. This indicates that beyond IQ, age, and sex, known risk -and protective factors of cognitive function in aging were not significantly related to memory plasticity.
Lange, Ann-Marie Glasø de; Kaufmann, Tobias; Meer, Dennis van der; Maglanoc, Luigi Angelo; Alnæs, Dag & Moberget, Torgeir [Show all 9 contributors for this article] (2019). Population-based neuroimaging reveals traces of childbirth in the maternal brain. Proceedings of the National Academy of Sciences of the United States of America (PNAS). ISSN 0027-8424. 116(44), p. 22341–22346. doi: 10.1073/pnas.1910666116. Full text in Research Archive
Lange, Ann-Marie Glasø de; Bråthen, Anne Cecilie Sjøli; Rohani, Darius; Fjell, Anders Martin & Walhovd, Kristine B (2018). The temporal dynamics of brain plasticity in aging. Cerebral Cortex. ISSN 1047-3211. 28(5), p. 1857–1865. doi: 10.1093/cercor/bhy003. Full text in Research Archive Show summary
Cognitive training has been suggested as a possible remediation of decline in brain structure with older age. However, it is unknown whether training effects are transient or enduring, as no studies have examined training-induced plasticity relative to decline in older adults across extended periods with multiple intervention phases. We investigated the temporal dynamics of brain plasticity across periods on and off memory training, hypothesizing that (1) a decline in white matter (WM) microstructure would be observed across the duration of the study and (2) that periods of memory training would moderate the WM microstructural decline. In total, 107 older adults followed a 40-week program, including 2 training periods separated by periods with no intervention. The general decline in WM microstructure observed across the duration of the study was moderated following the training periods, demonstrating that cognitive training may mitigate age-related brain deterioration. The training-related improvements were estimated to subside over time, indicating that continuous training may be a premise for the enduring attenuation of neural decline. Memory improvements were largely maintained after the initial training period, and may thus not rely on continuous training to the same degree as WM microstructure.
Bråthen, Anne Cecilie Sjøli; Lange, Ann-Marie Glasø de; Rohani, Darius; Sneve, Markus Handal; Fjell, Anders Martin & Walhovd, Kristine B (2018). Multimodal cortical and hippocampal prediction of episodic‐memory plasticity in young and older adults. Human Brain Mapping. ISSN 1065-9471. 39(11), p. 4480–4492. doi: 10.1002/hbm.24287. Full text in Research Archive Show summary
Episodic memory can be trained in both early and late adulthood, but there is considerable variation in cognitive improvement across individuals. Which brain characteristics make some individuals benefit more than others? We used a multimodal approach to investigate whether volumetric magnetic resonance imaging (MRI) and resting‐state functional MRI characteristics of the cortex and hippocampus, brain regions involved in episodic‐memory function, were predictive of cognitive improvement after memory training. We hypothesized that these brain characteristics would differentially predict memory improvement in young and older adults, given the vulnerability of cortical regions as well as the hippocampus to healthy aging. Following structural and resting‐state activity magnetic resonance scans, 50 young and 76 older participants completed 10 weeks of strategic episodic‐memory training. Both age groups improved their memory performance, but the young adults more so than the older. Vertex‐wise analyses of cortical volume showed no significant relation to memory benefit. When analyzing the two age groups separately, hippocampal volume was predictive of memory improvement in the group of older participants only. In this age group, the lower resting‐state activity of the hippocampus was also predictive of memory improvement. Both volumetric and resting‐state characteristics of the hippocampus explained unique variance of the improvement in the older participants suggesting that a multimodal imaging approach is valuable for the understanding of mechanisms underlying memory plasticity in aging.
Lange, Ann-Marie Glasø de; Bråthen, Anne Cecilie Sjøli; Rohani, Darius; Grydeland, Håkon; Fjell, Anders Martin & Walhovd, Kristine B (2017). The Effects of Memory Training on Behavioral and Microstructural Plasticity in Young and Older Adults. Human Brain Mapping. ISSN 1065-9471. 38(11), p. 5666–5680. doi: 10.1002/hbm.23756. Full text in Research Archive Show summary
Age differences in human brain plasticity are assumed, but have not been systematically investigated. In this longitudinal study, we investigated changes in white matter (WM) microstructure in response to memory training relative to passive and active control conditions in 183 young and older adults. We hypothesized that (i) only the training group would show improved memory performance and microstructural alterations, (ii) the young adults would show larger memory improvement and a higher degree of microstructural alterations as compared to the older adults, and (iii) changes in memory performance would relate to microstructural alterations. The results showed that memory improvement was specific to the training group, and that both the young and older participants improved their performance. The young group improved their memory to a larger extent compared to the older group. In the older sample, the training group showed less age-related decline in WM microstructure compared to the control groups, in areas overlapping the corpus callosum, the cortico-spinal tract, the cingulum bundle, the superior longitudinal fasciculus, and the anterior thalamic radiation. Less microstructural decline was related to a higher degree of memory improvement. Despite individual adaptation securing sufficient task difficulty, no training-related group differences in microstructure were found in the young adults. The observed divergence of behavioral and microstructural responses to memory training with age is discussed within a supply-demand framework. The results demonstrate that plasticity is preserved into older age, and that microstructural alterations may be part of a neurobiological substrate for behavioral improvements in older adults. This is a submitted version of an article which has been accepted and published in the journal Human Brain Mapping. © Wiley
Walhovd, Kristine B; Westerhausen, Rene; Lange, Ann-Marie Glasø de; Bråthen, Anne Cecilie Sjøli; Grydeland, Håkon & Engvig, Andreas [Show all 7 contributors for this article] (2016). Premises of plasticity - And the loneliness of the medial temporal lobe. NeuroImage. ISSN 1053-8119. 131, p. 48–54. doi: 10.1016/j.neuroimage.2015.10.060. Full text in Research Archive Show summary
In this perspective paper, we examine possible premises of plasticity in the neural substrates underlying cognitive change. We take the special role of the medial temporal lobe as an anchoring point, but also investigate characteristics throughout the cortex. Specifically, we examine the dimensions of evolutionary expansion, heritability, variability of morphometric change, and inter-individual variance in myelination with respect to the plastic potential of different brain regions. We argue that areas showing less evolutionary expansion, lower heritability, greater variability of cortical thickness change through the lifespan, and greater inter-individual differences in intracortical myelin content have a great extent of plasticity. While different regions of the brain show these features to varying extent, analyses converge on the medial temporal lobe including the hippocampi as the target of all these premises. We discuss implications for effects of training on brain structures, and conditions under which plasticity may be evoked.
Lange, Ann-Marie Glasø de; Bråthen, Anne Cecilie Sjøli; Grydeland, Håkon; Sexton, Claire; Johansen-Berg, Heidi & Andersson, Jesper L.R. [Show all 10 contributors for this article] (2016). White-matter integrity as a marker for cognitive plasticity in aging. Neurobiology of Aging. ISSN 0197-4580. 47, p. 74–82. doi: 10.1016/j.neurobiolaging.2016.07.007. Full text in Research Archive Show summary
Age-related differences in white matter (WM) integrity are substantial, but it is unknown whether between-subject variability in WM integrity influences the capacity for cognitive improvement. We investigated the effects of memory training related to active and passive control conditions in older adults and tested whether WM integrity at baseline was predictive of training benefits. We hypothesized that (1) memory improvement would be restricted to the training group, (2) widespread areas would show greater mean diffusivity (MD) and lower fractional anisotropy in older adults relative to young adults, and (3) within these areas, variability in WM microstructure in the older group would be predictive of training gains. The results showed that only the group receiving training improved their memory. Significant age differences in MD and fractional anisotropy were found in widespread areas. Within these areas, voxelwise analyses showed a negative relationship between MD and memory improvement in 3 clusters, indicating that WM integrity could serve as a marker for the ability to adapt in response to cognitive challenges in aging.
Fjell, Anders Martin; Sneve, Markus Handal; Grydeland, Håkon; Storsve, Andreas Berg; Lange, Ann-Marie Glasø de & Amlien, Inge Kasbohm [Show all 8 contributors for this article] (2015). Functional connectivity change across multiple cortical networks relates to episodic memory changes in aging. Neurobiology of Aging. ISSN 0197-4580. 36(12), p. 3255–3268. doi: 10.1016/j.neurobiolaging.2015.08.020. Full text in Research Archive Show summary
A major task of contemporary cognitive neuroscience of aging is to explain why episodic memory declines. Change in resting-state functional connectivity (rsFC) could be a mechanism accounting for reduced function. We addressed this through 3 studies. In study 1, 119 healthy participants (20–83 years) were followed for 3.5 years with verbal recall testing and magnetic resonance imaging. Independent of atrophy, recall change was related to change in rsFC in anatomically widespread areas. Striking age-effects were observed in that a positive relationship between rsFC and memory characterized older participants while a negative relationship was seen among the younger and middle-aged. This suggests that cognitive consequences of rsFC change are not stable across age. In study 2 and 3, the age-dependent differences in rsFC-memory relationship were replicated by use of a simulation model (study 2) and by a cross-sectional experimental recognition memory task (study 3). In conclusion, memory changes were related to altered rsFC in an age-dependent manner, and future research needs to detail the mechanisms behind age-varying relationships.

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Beck, Dani; Whitmore, Lucy; MacSweeney, Niamh; Brieant, Alexis; Lange, Ann-Marie Glasø de & Westlye, Lars Tjelta [Show all 8 contributors for this article] (2024). Dimensions of early life adversity are differentially associated with patterns of delayed and accelerated brain maturation. Full text in Research Archive
Meer, Dennis van der; Beck, Dani; Lange, Ann-Marie Glasø de; Eikefjord, Eli Nina Hølleland; Lundervold, Arvid & Andreassen, Ole A. [Show all 8 contributors for this article] (2024). Brain asymmetries from mid-to late life and hemispheric brain age. Full text in Research Archive
Korbmacher, Max; Gurholt, Tiril Pedersen; Lange, Ann-Marie Glasø de; Meer, Dennis van der; Beck, Dani & Eikefjord, Eli Nina Hølleland [Show all 10 contributors for this article] (2023). Brain age estimates from different white matter microstructure features associate concordantly with bio-psycho-social factors. Full text in Research Archive
Korbmacher, Max; Lange, Ann-Marie Glasø de; Meer, Dennis van der; Beck, Dani; Eikefjord, Eli Nina Hølleland & Lundervold, Arvid [Show all 9 contributors for this article] (2023). Fornix and forceps are key regions of white matter brain age. Full text in Research Archive
Korbmacher, Max; Gurholt, Tiril Pedersen; Lange, Ann-Marie Glasø de; Meer, Dennis van der; Beck, Dani & Eikefjord, Eli Nina Hølleland [Show all 10 contributors for this article] (2023). White matter microstructure is predictive of cardiometabolic health. Full text in Research Archive
Korbmacher, Max; Lange, Ann-Marie Glasø de; Meer, Dennis van der; Beck, Dani; Eikefjord, Eli Nina Hølleland & Lundervold, Arvid [Show all 9 contributors for this article] (2023). Regional grey and white matter asymmetry decreases at higher ages. Full text in Research Archive
Voldsbekk, Irene; Lund, Martina Jonette; Kaufmann, Tobias; Fernandez-Cabello, Sara; Kjelkenes, Rikka & Dahl, Andreas [Show all 9 contributors for this article] (2022). Cross-Diagnostic Patterns of Socio-Cognitive Traits in the Developing Brain of Children and Adolescents. Full text in Research Archive
Lange, Ann-Marie Glasø de; Jacobs, Emily G. & Galea, Liisa A.M (2021). The scientific body of knowledge: Whose body does it serve? A spotlight on women’s brain health. Frontiers in Neuroendocrinology (FIN). ISSN 0091-3022. doi: 10.1016/j.yfrne.2020.100898. Full text in Research Archive
Maximov, Ivan; Meer, Dennis van der; Lange, Ann-Marie Glasø de; Kaufmann, Tobias; Shadrin, Alexey & Frei, Oleksandr [Show all 8 contributors for this article] (2021). YTTRIUM: QC algorithm for the processed diffusion maps in UK Biobank 18608 sample. Full text in Research Archive

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Published Sep. 23, 2019 8:35 AM - Last modified Oct. 4, 2025 12:16 PM

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Brains and minds in transition (BRAINMINT)

Ann-Marie G. de Lange

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