Eva Hilland - Academia.edu (original) (raw)

Papers by Eva Hilland

Background: Over-the-counter analgesics (OTCA) such as Paracetamol and Ibuprofen are frequently u... more Background: Over-the-counter analgesics (OTCA) such as Paracetamol and Ibuprofen are frequently used by adolescents, and the route of administration and access at home allows unsupervised use. Psychological distress and pain occur simultaneously and are more common among females than among males. There is a dynamic interplay between on-label pain indications and psychological distress, and frequent OTCA use or misuse can exacerbate symptoms. No studies have to date provided an overview of frequent OTCA use in a larger population-based study. The current study used survey data to explore associations between and the relative predictive value of on-label pain indication and measures of psychological distress, together with sex differences for weekly OTCA use. Methods: This study included 349,528 adolescents aged 13-19. The data was collected annually between January 2014 and December 2018 as part of the Norwegian Young Data survey. Performance analysis was conducted to explore the relative roles and associations between on-label pain indication and psychological distress in weekly OTCA use. A mixed-effects logistic regression model was used to explore the unique contributions from four domains of on-label pain indication and psychological distress as measured by a combined measure of anxiety and depression (HSCL-10) and peer-bullying involvement as victims or bullies. Results: Thirty percent of females and thirteen percent of males use OTCA weekly. Headache is the strongest on-label pain predictor of weekly OTCA use, followed by abdominal pain. Depression and anxiety are the strongest psychological predictor of weekly OTCA use, and higher symptom levels and being female increase the strength of this association. Anxiety and depression also predict weekly OTCA use after controlling for physiological pain. Conclusions: Sex, pain and anxiety and depression are inter-correlated and strong predictors of frequent OTCA use. Frequent OTCA use in the context of psychological distress may be a form of self-medication that can exacerbate symptoms and decrease psychosocial function. Longitudinal studies that explore causal trajectories between frequent on-label OTCA use and psychological distress are required. OTCA use among adolescents, and particularly among females, with anxiety and depression should be administered with caution and closely monitored. Background Paracetamol (acetaminophen) and Ibuprofen are available as over-the-counter analgesics (OTCA) and are among the most widely used pharmacological agents of our time. Paracetamol, also known as acetaminophen, is a medication used to treat pain and fever. Acetaminophen is the major metabolite of acetanilide and phenacetin responsible for the analgesic effects [1-3]. Ibuprofen is a nonsteroidal antiin ammatory drug (NSAID) used to reduce fever and to treat pain or in ammation. Ibuprofen works by blocking an enzyme that makes prostaglandin (a hormone-like substance that participates in a variety of body functions), which results in lower levels of prostaglandins in the body [3, 4]. Both OTCAs are on the

bioRxiv (Cold Spring Harbor Laboratory), May 14, 2018

Background: Following treatment, many depressed patients have significant residual symptoms. Howe... more Background: Following treatment, many depressed patients have significant residual symptoms. However, large randomised controlled trials (RCT) in this population are lacking. When Attention bias modification training (ABM) leads to more positive emotional biases, associated changes in clinical symptoms have been reported. A broader and more transparent picture of the true advantage of ABM based on larger and more stringent clinical trials have been requested. The current study evaluates the early effect of two weeks ABM training on blinded clinician-rated and self-reported residual symptoms, and whether changes towards more positive attentional biases (AB) would be associated with symptom reduction. Method: A total of 321 patients with a history of depression were included in a preregistered randomized controlled double-blinded trial. Patients were randomised to an emotional ABM paradigm over fourteen days or a closely matched control condition. Symptoms based on the Hamilton Rating Scale for Depression (HRSD) and Beck Depression Inventory II (BDI-II) were obtained at baseline and after ABM training. Results: ABM training led to significantly greater decrease in clinician-rated symptoms of depression as compared to the control condition. No differences between ABM and placebo were found for self-reported symptoms. ABM induced a change of AB towards relatively more positive stimuli for participants that also showed greater symptom reduction. Conclusion: The current study demonstrates that ABM produces early changes in blinded clinician-rated depressive symptoms and that changes in AB is linked to changes in symptoms. ABM may have practical potential in the treatment of residual depression. Trial registration: ClinicalTrials.gov ID: NCT02658682 (retrospectively registered in January 2016).

Biological Psychiatry, Apr 1, 2020

Cognitive abilities and mental disorders are complex traits sharing a largely unknown neuronal ba... more Cognitive abilities and mental disorders are complex traits sharing a largely unknown neuronal basis and aetiology. Their genetic architectures are highly polygenic and overlapping, which is supported by heterogeneous phenotypic expression and substantial clinical overlap. Brain network analysis provides a non-invasive means of dissecting biological heterogeneity yet its sensitivity, specificity and validity in clinical applications remains a major challenge. We used machine learning on static and dynamic temporal synchronization between all brain network nodes in 10,343 healthy individuals from the UK Biobank to predict (i) cognitive and mental health traits and (ii) their genetic underpinnings. We predicted age and sex to serve as our reference point. The traits of interest included individual level educational attainment and fluid intelligence (cognitive) and dimensional measures of depression, anxiety, and neuroticism (mental health). We predicted polygenic scores for educational attainment, fluid intelligence, depression, anxiety, and different neuroticism traits, in addition to schizophrenia. Beyond high accuracy for age and sex, permutation tests revealed above chance-level prediction accuracy for educational attainment and fluid intelligence. Educational attainment and fluid intelligence were mainly negatively associated with static brain connectivity in frontal and default mode networks, whereas age showed positive correlations with a more widespread pattern. In comparison, prediction accuracy for polygenic scores was at chance level across traits, which may serve as a benchmark for future studies aiming to link genetic factors and fMRI-based brain connectomics.

medRxiv (Cold Spring Harbor Laboratory), Sep 30, 2020

doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by pee... more doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

Cognitive abilities and mental disorders are complex traits sharing a largely unknown neuronal ba... more Cognitive abilities and mental disorders are complex traits sharing a largely unknown neuronal basis and aetiology. Their genetic architectures are highly polygenic and overlapping, which is supported by heterogeneous phenotypic expression and substantial clinical overlap. Brain network analysis provides a non-invasive means of dissecting biological heterogeneity yet its sensitivity, specificity and validity in clinical applications remains a major challenge. We used machine learning on static and dynamic temporal synchronization between all brain network nodes in 10,343 healthy individuals from the UK Biobank to predict (i) cognitive and mental health traits and (ii) their genetic underpinnings. We predicted age and sex to serve as our reference point. The traits of interest included individual level educational attainment and fluid intelligence (cognitive) and dimensional measures of depression, anxiety, and neuroticism (mental health). We predicted polygenic scores for educational attainment, fluid intelligence, depression, anxiety, and different neuroticism traits, in addition to schizophrenia. Beyond high accuracy for age and sex, permutation tests revealed above chance-level prediction accuracy for educational attainment and fluid intelligence. Educational attainment and fluid intelligence were mainly negatively associated with static brain connectivity in frontal and default mode networks, whereas age showed positive correlations with a more widespread pattern. In comparison, prediction accuracy for polygenic scores was at chance level across traits, which may serve as a benchmark for future studies aiming to link genetic factors and fMRI-based brain connectomics.

BMC Public Health, Nov 6, 2021

Background: Over-the-counter analgesics (OTCA) such as Paracetamol and Ibuprofen are frequently u... more Background: Over-the-counter analgesics (OTCA) such as Paracetamol and Ibuprofen are frequently used by adolescents, and the route of administration and access at home allows unsupervised use. Psychological distress and pain occur simultaneously and are more common among females than among males. There is a dynamic interplay between on-label pain indications and psychological distress, and frequent OTCA use or misuse can exacerbate symptoms. No studies have to date provided an overview of frequent OTCA use in a larger population-based study. The current study used survey data to explore associations between and the relative predictive value of on-label pain indication and measures of psychological distress, together with sex differences for weekly OTCA use. Methods: This study included 349,528 adolescents aged 13-19. The data was collected annually between January 2014 and December 2018 as part of the Norwegian Young Data survey. Performance analysis was conducted to explore the relative roles and associations between on-label pain indication and psychological distress in weekly OTCA use. A mixed-effects logistic regression model was used to explore the unique contributions from four domains of on-label pain indication and psychological distress as measured by a combined measure of anxiety and depression (HSCL-10) and peer-bullying involvement as victims or bullies. Results: Thirty percent of females and 13 % of males use OTCA weekly. Headache is the strongest on-label pain predictor of weekly OTCA use, followed by abdominal pain. Depression and anxiety are the strongest psychological predictor of weekly OTCA use, and higher symptom levels and being female increase the strength of this association. Anxiety and depression also predict weekly OTCA use after controlling for physiological pain.

Frontiers in Human Neuroscience, Dec 21, 2018

Alterations in resting state networks (RSNs) are associated with emotional-and attentional contro... more Alterations in resting state networks (RSNs) are associated with emotional-and attentional control difficulties in depressed individuals. Attentional bias modification (ABM) training may lead to more adaptive emotional processing in depression, but little is known about the neural underpinnings associated with ABM. In the current study a sample of 134 previously depressed individuals were randomized into 14 days of computerized ABM-or a closely matched placebo training regime followed by a resting state magnetic resonance imaging (MRI) scan. Using independent component analysis (ICA) we examined within-network connectivity in three major RSN's, the default mode network (DMN), the salience network (SN) and the central executive network (CEN) after 2 weeks of ABM training. We found a significant difference between the training groups within the SN, but no difference within the DMN or CEN. Moreover, a significant symptom improvement was observed in the ABM group after training.

Journal of Psychiatric Research, Jun 1, 2021

A recent meta-analysis has questioned the relevance of attention bias modification (ABM) for depr... more A recent meta-analysis has questioned the relevance of attention bias modification (ABM) for depression outcomes. However, there might be patient characteristics not yet accounted for, that are relevant to the outcome. In the context of personalized treatment, the lack of moderator studies have limited the potential for matching ABM-treatment to individual patient characteristics. Subjects (N = 301) were randomly assigned 1:1 to receive either active or placebo Attention Bias Modification (ABM) twice daily for 14 days in a double-blind design (placebo n = 148; ABM n = 153). The outcome was change in symptoms based on the Hamilton Depression Rating Scale (HDRS). Moderator variables were self-reported depression (Beck Depression Inventory-II; BDI-II), anxiety (Beck Anxiety Inventory; BAI) and attentional bias (AB) assessed at baseline. This trial was registered with ClinicalTrials.gov, number NCT02658682. Only BAI (p for interaction = .01, Bootstrap 95% CI [0.046, 0.337]) moderated the effects of ABM on change in clinician rated depressive symptoms. Interactions were significant for BAI scores ≥8. The relative effect of the intervention increased with the highest symptom load. ABM was not effective in patients with the lowest symptom load. Future research should validate this finding and continue investigating moderators of the ABM-intervention to further enhance personalization of treatment to individual symptom characteristics.

bioRxiv (Cold Spring Harbor Laboratory), Jun 20, 2019

Background: Previous structural and functional neuroimaging studies have implicated distributed b... more Background: Previous structural and functional neuroimaging studies have implicated distributed brain regions and networks in depression. However, there are no robust imaging biomarkers that are specific to depression, which may be due to clinical heterogeneity and neurobiological complexity. A dimensional approach and fusion of imaging modalities may yield a more coherent view of the neuronal correlates of depression. Methods: We used linked independent component analysis to fuse cortical macrostructure (thickness, area, gray matter density), white matter diffusion properties and resting-state fMRI default mode network amplitude in patients with a history of depression (n = 170) and controls (n = 71). We used univariate and machine learning approaches to assess the relationship between age, sex, case-control status, and symptom loads for depression and anxiety with the resulting brain components. Results: Univariate analyses revealed strong associations between age and sex with mainly global but also regional specific brain components, with varying degrees of multimodal involvement. In contrast, there were no significant associations with case-control status, nor symptom loads for depression and anxiety with the brain components, nor any interaction effects with age and sex. Machine learning revealed low model performance for classifying patients from controls and predicting symptom loads for depression and anxiety, but high age prediction accuracy. Conclusion: Multimodal fusion of brain imaging data alone may not be sufficient for dissecting the clinical and neurobiological heterogeneity of depression. Precise clinical stratification and methods for brain phenotyping at the individual level based on large training samples may be needed to parse the neuroanatomy of depression.

bioRxiv (Cold Spring Harbor Laboratory), Sep 16, 2019

The network approach to psychopathology has recently received considerable attention, and is as a... more The network approach to psychopathology has recently received considerable attention, and is as a novel way of conceptualizing mental disorders as causally interacting symptoms. In this study, we modeled a joint network of depression symptoms and depression-related brain structures, using 21 symptoms and five regional brain measures. We used a mixed sample of 268 individuals previously treated for one or more major depressive episodes and never depressed individuals. The network revealed associations between brain structure and unique depressive symptoms, which may clarify relationships regarding symptomatic and biological heterogeneity in depression.

Background Modification of attentional biases (ABM) may lead to more adaptive emotion perception ... more Background Modification of attentional biases (ABM) may lead to more adaptive emotion perception and emotion regulation. Understanding the neural basis of these effects may lead to greater precision for future treatment development. Task-related fMRI following ABM training has so far not been investigated in depression. The main aim of the RCT was to explore differences in brain activity after ABM training in response to emotional stimuli. Methods A total of 134 previously depressed individuals were randomized into 14 days of ABM-or a placebo training followed by an fMRI emotion regulation task. Depression symptoms and subjective ratings of perceived negativity during fMRI was examined between the training groups. Brain activation was explored within predefined areas (SVC) and across the whole brain. Activation in areas associated with changes in attentional biases (AB) and degree of depression was explored. Results The ABM group showed reduced activation within the amygdala and within the anterior cingulate cortex (ACC) when passively viewing negative images compared to the placebo group. No group differences were found within predefined SVC's associated with emotion regulation strategies. Response within the temporal cortices was associated with degree of change in AB and with degree of depressive symptoms in ABM versus placebo. Limitations The findings should be replicated in other samples of depressed patients and in studies using designs that allow analyses of within-group variability from baseline to follow-up. Conclusions ABM training has an effect on brain function within circuitry associated with emotional appraisal and the generation of affective states.

Background: Depression is a complex disorder with large inter-individual variability in symptom p... more Background: Depression is a complex disorder with large inter-individual variability in symptom profiles that often occur alongside symptoms of other psychiatric domains such as anxiety. A dimensional and symptom-based approach may help refine the characterization and classification of depressive and anxiety disorders and thus aid in establishing robust biomarkers. We assess the brain functional connectivity correlates of a symptom-based clustering of individuals using functional brain imaging data. Methods: We assessed symptoms of depression and anxiety using Beck's Depression and Beck's Anxiety inventories in individuals with or without a history of depression, and high dimensional data clustering to form subgroups based on symptom profiles. To assess the biological relevance of this subtyping, we compared functional magnetic resonance imagingbased dynamic and static functional connectivity between subgroups in a subset of the total sample. Results: We identified five subgroups with distinct symptom profiles, cutting across diagnostic boundaries and differing in terms of total severity, symptom patterns and centrality. For instance, inability to relax, fear of the worst, and feelings of guilt were among the most severe symptoms in subgroup 1, 2 and 3, respectively. These subgroups showed evidence of differential static brain connectivity patterns, in particular comprising a frontotemporal network. In contrast, we found no significant associations with clinical sum scores, dynamic functional connectivity or global connectivity measures. Conclusion: Adding to the ongoing pursuit of individual-based treatment, the results show subtyping based on a dimensional conceptualization and unique constellations of anxiety and depression symptoms is supported by distinct brain static functional connectivity patterns.

NeuroImage: Clinical, 2022

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

The Journal of Neuroscience, 2014

The striking homogeneity of cerebellar microanatomy is strongly suggestive of a corresponding uni... more The striking homogeneity of cerebellar microanatomy is strongly suggestive of a corresponding uniformity of function. Consequently, theoretical models of the cerebellum's role in motor control should offer important clues regarding cerebellar contributions to cognition. One such influential theory holds that the cerebellum encodes internal models, neural representations of the context-specific dynamic properties of an object, to facilitate predictive control when manipulating the object. The present study examined whether this theoretical construct can shed light on the contribution of the cerebellum to language processing. We reasoned that the cerebellum might perform a similar coordinative function when the context provided by the initial part of a sentence can be highly predictive of the end of the sentence. Using functional MRI in humans we tested two predictions derived from this hypothesis, building on previous neuroimaging studies of internal models in motor control. Firs...

Associations between scanner and cortical thickness in controls. Results from linear regression a... more Associations between scanner and cortical thickness in controls. Results from linear regression analyses testing for group differences in cortical thickness between controls from scanner 1 and controls from scanner 2. Age was included in the analyses as covariate. Average thickness in each cluster is in mm. (DOCX 14 kb)

Results from GLM whole surface vertex-wise between-group analyses of cortical thickness. Results ... more Results from GLM whole surface vertex-wise between-group analyses of cortical thickness. Results from GLMs testing for group differences in cortical thickness between patients with anorexia nervosa and controls, controlling for scanner, and with simulation-based clusterwise correction for multiple comparisons at p

Figure S1. Flow diagram for enrolment, allocation to active placebo or ABM, follow-up after two w... more Figure S1. Flow diagram for enrolment, allocation to active placebo or ABM, follow-up after two weeks, and analyses in accordance with CONSORT (28). Not meeting inclusion criteria = no MDE according to M.I.N.I. Other reasons (n = 42) = current or former mania and/or hypomania according to M.I.N.I. Ten participants (5 in each group) elected to have their data erased. (DOC 72 kb)

Journal of psychiatric research, 2021

A recent meta-analysis has questioned the relevance of attention bias modification (ABM) for depr... more A recent meta-analysis has questioned the relevance of attention bias modification (ABM) for depression outcomes. However, there might be patient characteristics not yet accounted for, that are relevant to the outcome. In the context of personalized treatment, the lack of moderator studies have limited the potential for matching ABM-treatment to individual patient characteristics. Subjects (N = 301) were randomly assigned 1:1 to receive either active or placebo Attention Bias Modification (ABM) twice daily for 14 days in a double-blind design (placebo n = 148; ABM n = 153). The outcome was change in symptoms based on the Hamilton Depression Rating Scale (HDRS). Moderator variables were self-reported depression (Beck Depression Inventory-II; BDI-II), anxiety (Beck Anxiety Inventory; BAI) and attentional bias (AB) assessed at baseline. This trial was registered with ClinicalTrials.gov, number NCT02658682. Only BAI (p for interaction = .01, Bootstrap 95% CI [0.046, 0.337]) moderated th...

NeuroImage: Clinical, 2022

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

1 Clinical Neuroscience Research Group, Department of Psychology, University of Oslo, Oslo, Norwa... more 1 Clinical Neuroscience Research Group, Department of Psychology, University of Oslo, Oslo, Norway 2 NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway 3 Division of Psychiatry, Diakonhjemmet Hospital, Oslo, Norway 4 Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 5 Department of Psychology, University of Oslo, Oslo, Norway

Background: Over-the-counter analgesics (OTCA) such as Paracetamol and Ibuprofen are frequently u... more Background: Over-the-counter analgesics (OTCA) such as Paracetamol and Ibuprofen are frequently used by adolescents, and the route of administration and access at home allows unsupervised use. Psychological distress and pain occur simultaneously and are more common among females than among males. There is a dynamic interplay between on-label pain indications and psychological distress, and frequent OTCA use or misuse can exacerbate symptoms. No studies have to date provided an overview of frequent OTCA use in a larger population-based study. The current study used survey data to explore associations between and the relative predictive value of on-label pain indication and measures of psychological distress, together with sex differences for weekly OTCA use. Methods: This study included 349,528 adolescents aged 13-19. The data was collected annually between January 2014 and December 2018 as part of the Norwegian Young Data survey. Performance analysis was conducted to explore the relative roles and associations between on-label pain indication and psychological distress in weekly OTCA use. A mixed-effects logistic regression model was used to explore the unique contributions from four domains of on-label pain indication and psychological distress as measured by a combined measure of anxiety and depression (HSCL-10) and peer-bullying involvement as victims or bullies. Results: Thirty percent of females and thirteen percent of males use OTCA weekly. Headache is the strongest on-label pain predictor of weekly OTCA use, followed by abdominal pain. Depression and anxiety are the strongest psychological predictor of weekly OTCA use, and higher symptom levels and being female increase the strength of this association. Anxiety and depression also predict weekly OTCA use after controlling for physiological pain. Conclusions: Sex, pain and anxiety and depression are inter-correlated and strong predictors of frequent OTCA use. Frequent OTCA use in the context of psychological distress may be a form of self-medication that can exacerbate symptoms and decrease psychosocial function. Longitudinal studies that explore causal trajectories between frequent on-label OTCA use and psychological distress are required. OTCA use among adolescents, and particularly among females, with anxiety and depression should be administered with caution and closely monitored. Background Paracetamol (acetaminophen) and Ibuprofen are available as over-the-counter analgesics (OTCA) and are among the most widely used pharmacological agents of our time. Paracetamol, also known as acetaminophen, is a medication used to treat pain and fever. Acetaminophen is the major metabolite of acetanilide and phenacetin responsible for the analgesic effects [1-3]. Ibuprofen is a nonsteroidal antiin ammatory drug (NSAID) used to reduce fever and to treat pain or in ammation. Ibuprofen works by blocking an enzyme that makes prostaglandin (a hormone-like substance that participates in a variety of body functions), which results in lower levels of prostaglandins in the body [3, 4]. Both OTCAs are on the

bioRxiv (Cold Spring Harbor Laboratory), May 14, 2018

Background: Following treatment, many depressed patients have significant residual symptoms. Howe... more Background: Following treatment, many depressed patients have significant residual symptoms. However, large randomised controlled trials (RCT) in this population are lacking. When Attention bias modification training (ABM) leads to more positive emotional biases, associated changes in clinical symptoms have been reported. A broader and more transparent picture of the true advantage of ABM based on larger and more stringent clinical trials have been requested. The current study evaluates the early effect of two weeks ABM training on blinded clinician-rated and self-reported residual symptoms, and whether changes towards more positive attentional biases (AB) would be associated with symptom reduction. Method: A total of 321 patients with a history of depression were included in a preregistered randomized controlled double-blinded trial. Patients were randomised to an emotional ABM paradigm over fourteen days or a closely matched control condition. Symptoms based on the Hamilton Rating Scale for Depression (HRSD) and Beck Depression Inventory II (BDI-II) were obtained at baseline and after ABM training. Results: ABM training led to significantly greater decrease in clinician-rated symptoms of depression as compared to the control condition. No differences between ABM and placebo were found for self-reported symptoms. ABM induced a change of AB towards relatively more positive stimuli for participants that also showed greater symptom reduction. Conclusion: The current study demonstrates that ABM produces early changes in blinded clinician-rated depressive symptoms and that changes in AB is linked to changes in symptoms. ABM may have practical potential in the treatment of residual depression. Trial registration: ClinicalTrials.gov ID: NCT02658682 (retrospectively registered in January 2016).

Biological Psychiatry, Apr 1, 2020

Cognitive abilities and mental disorders are complex traits sharing a largely unknown neuronal ba... more Cognitive abilities and mental disorders are complex traits sharing a largely unknown neuronal basis and aetiology. Their genetic architectures are highly polygenic and overlapping, which is supported by heterogeneous phenotypic expression and substantial clinical overlap. Brain network analysis provides a non-invasive means of dissecting biological heterogeneity yet its sensitivity, specificity and validity in clinical applications remains a major challenge. We used machine learning on static and dynamic temporal synchronization between all brain network nodes in 10,343 healthy individuals from the UK Biobank to predict (i) cognitive and mental health traits and (ii) their genetic underpinnings. We predicted age and sex to serve as our reference point. The traits of interest included individual level educational attainment and fluid intelligence (cognitive) and dimensional measures of depression, anxiety, and neuroticism (mental health). We predicted polygenic scores for educational attainment, fluid intelligence, depression, anxiety, and different neuroticism traits, in addition to schizophrenia. Beyond high accuracy for age and sex, permutation tests revealed above chance-level prediction accuracy for educational attainment and fluid intelligence. Educational attainment and fluid intelligence were mainly negatively associated with static brain connectivity in frontal and default mode networks, whereas age showed positive correlations with a more widespread pattern. In comparison, prediction accuracy for polygenic scores was at chance level across traits, which may serve as a benchmark for future studies aiming to link genetic factors and fMRI-based brain connectomics.

medRxiv (Cold Spring Harbor Laboratory), Sep 30, 2020

doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by pee... more doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

Cognitive abilities and mental disorders are complex traits sharing a largely unknown neuronal ba... more Cognitive abilities and mental disorders are complex traits sharing a largely unknown neuronal basis and aetiology. Their genetic architectures are highly polygenic and overlapping, which is supported by heterogeneous phenotypic expression and substantial clinical overlap. Brain network analysis provides a non-invasive means of dissecting biological heterogeneity yet its sensitivity, specificity and validity in clinical applications remains a major challenge. We used machine learning on static and dynamic temporal synchronization between all brain network nodes in 10,343 healthy individuals from the UK Biobank to predict (i) cognitive and mental health traits and (ii) their genetic underpinnings. We predicted age and sex to serve as our reference point. The traits of interest included individual level educational attainment and fluid intelligence (cognitive) and dimensional measures of depression, anxiety, and neuroticism (mental health). We predicted polygenic scores for educational attainment, fluid intelligence, depression, anxiety, and different neuroticism traits, in addition to schizophrenia. Beyond high accuracy for age and sex, permutation tests revealed above chance-level prediction accuracy for educational attainment and fluid intelligence. Educational attainment and fluid intelligence were mainly negatively associated with static brain connectivity in frontal and default mode networks, whereas age showed positive correlations with a more widespread pattern. In comparison, prediction accuracy for polygenic scores was at chance level across traits, which may serve as a benchmark for future studies aiming to link genetic factors and fMRI-based brain connectomics.

BMC Public Health, Nov 6, 2021

Background: Over-the-counter analgesics (OTCA) such as Paracetamol and Ibuprofen are frequently u... more Background: Over-the-counter analgesics (OTCA) such as Paracetamol and Ibuprofen are frequently used by adolescents, and the route of administration and access at home allows unsupervised use. Psychological distress and pain occur simultaneously and are more common among females than among males. There is a dynamic interplay between on-label pain indications and psychological distress, and frequent OTCA use or misuse can exacerbate symptoms. No studies have to date provided an overview of frequent OTCA use in a larger population-based study. The current study used survey data to explore associations between and the relative predictive value of on-label pain indication and measures of psychological distress, together with sex differences for weekly OTCA use. Methods: This study included 349,528 adolescents aged 13-19. The data was collected annually between January 2014 and December 2018 as part of the Norwegian Young Data survey. Performance analysis was conducted to explore the relative roles and associations between on-label pain indication and psychological distress in weekly OTCA use. A mixed-effects logistic regression model was used to explore the unique contributions from four domains of on-label pain indication and psychological distress as measured by a combined measure of anxiety and depression (HSCL-10) and peer-bullying involvement as victims or bullies. Results: Thirty percent of females and 13 % of males use OTCA weekly. Headache is the strongest on-label pain predictor of weekly OTCA use, followed by abdominal pain. Depression and anxiety are the strongest psychological predictor of weekly OTCA use, and higher symptom levels and being female increase the strength of this association. Anxiety and depression also predict weekly OTCA use after controlling for physiological pain.

Frontiers in Human Neuroscience, Dec 21, 2018

Alterations in resting state networks (RSNs) are associated with emotional-and attentional contro... more Alterations in resting state networks (RSNs) are associated with emotional-and attentional control difficulties in depressed individuals. Attentional bias modification (ABM) training may lead to more adaptive emotional processing in depression, but little is known about the neural underpinnings associated with ABM. In the current study a sample of 134 previously depressed individuals were randomized into 14 days of computerized ABM-or a closely matched placebo training regime followed by a resting state magnetic resonance imaging (MRI) scan. Using independent component analysis (ICA) we examined within-network connectivity in three major RSN's, the default mode network (DMN), the salience network (SN) and the central executive network (CEN) after 2 weeks of ABM training. We found a significant difference between the training groups within the SN, but no difference within the DMN or CEN. Moreover, a significant symptom improvement was observed in the ABM group after training.

Journal of Psychiatric Research, Jun 1, 2021

A recent meta-analysis has questioned the relevance of attention bias modification (ABM) for depr... more A recent meta-analysis has questioned the relevance of attention bias modification (ABM) for depression outcomes. However, there might be patient characteristics not yet accounted for, that are relevant to the outcome. In the context of personalized treatment, the lack of moderator studies have limited the potential for matching ABM-treatment to individual patient characteristics. Subjects (N = 301) were randomly assigned 1:1 to receive either active or placebo Attention Bias Modification (ABM) twice daily for 14 days in a double-blind design (placebo n = 148; ABM n = 153). The outcome was change in symptoms based on the Hamilton Depression Rating Scale (HDRS). Moderator variables were self-reported depression (Beck Depression Inventory-II; BDI-II), anxiety (Beck Anxiety Inventory; BAI) and attentional bias (AB) assessed at baseline. This trial was registered with ClinicalTrials.gov, number NCT02658682. Only BAI (p for interaction = .01, Bootstrap 95% CI [0.046, 0.337]) moderated the effects of ABM on change in clinician rated depressive symptoms. Interactions were significant for BAI scores ≥8. The relative effect of the intervention increased with the highest symptom load. ABM was not effective in patients with the lowest symptom load. Future research should validate this finding and continue investigating moderators of the ABM-intervention to further enhance personalization of treatment to individual symptom characteristics.

bioRxiv (Cold Spring Harbor Laboratory), Jun 20, 2019

Background: Previous structural and functional neuroimaging studies have implicated distributed b... more Background: Previous structural and functional neuroimaging studies have implicated distributed brain regions and networks in depression. However, there are no robust imaging biomarkers that are specific to depression, which may be due to clinical heterogeneity and neurobiological complexity. A dimensional approach and fusion of imaging modalities may yield a more coherent view of the neuronal correlates of depression. Methods: We used linked independent component analysis to fuse cortical macrostructure (thickness, area, gray matter density), white matter diffusion properties and resting-state fMRI default mode network amplitude in patients with a history of depression (n = 170) and controls (n = 71). We used univariate and machine learning approaches to assess the relationship between age, sex, case-control status, and symptom loads for depression and anxiety with the resulting brain components. Results: Univariate analyses revealed strong associations between age and sex with mainly global but also regional specific brain components, with varying degrees of multimodal involvement. In contrast, there were no significant associations with case-control status, nor symptom loads for depression and anxiety with the brain components, nor any interaction effects with age and sex. Machine learning revealed low model performance for classifying patients from controls and predicting symptom loads for depression and anxiety, but high age prediction accuracy. Conclusion: Multimodal fusion of brain imaging data alone may not be sufficient for dissecting the clinical and neurobiological heterogeneity of depression. Precise clinical stratification and methods for brain phenotyping at the individual level based on large training samples may be needed to parse the neuroanatomy of depression.

bioRxiv (Cold Spring Harbor Laboratory), Sep 16, 2019

The network approach to psychopathology has recently received considerable attention, and is as a... more The network approach to psychopathology has recently received considerable attention, and is as a novel way of conceptualizing mental disorders as causally interacting symptoms. In this study, we modeled a joint network of depression symptoms and depression-related brain structures, using 21 symptoms and five regional brain measures. We used a mixed sample of 268 individuals previously treated for one or more major depressive episodes and never depressed individuals. The network revealed associations between brain structure and unique depressive symptoms, which may clarify relationships regarding symptomatic and biological heterogeneity in depression.

Background Modification of attentional biases (ABM) may lead to more adaptive emotion perception ... more Background Modification of attentional biases (ABM) may lead to more adaptive emotion perception and emotion regulation. Understanding the neural basis of these effects may lead to greater precision for future treatment development. Task-related fMRI following ABM training has so far not been investigated in depression. The main aim of the RCT was to explore differences in brain activity after ABM training in response to emotional stimuli. Methods A total of 134 previously depressed individuals were randomized into 14 days of ABM-or a placebo training followed by an fMRI emotion regulation task. Depression symptoms and subjective ratings of perceived negativity during fMRI was examined between the training groups. Brain activation was explored within predefined areas (SVC) and across the whole brain. Activation in areas associated with changes in attentional biases (AB) and degree of depression was explored. Results The ABM group showed reduced activation within the amygdala and within the anterior cingulate cortex (ACC) when passively viewing negative images compared to the placebo group. No group differences were found within predefined SVC's associated with emotion regulation strategies. Response within the temporal cortices was associated with degree of change in AB and with degree of depressive symptoms in ABM versus placebo. Limitations The findings should be replicated in other samples of depressed patients and in studies using designs that allow analyses of within-group variability from baseline to follow-up. Conclusions ABM training has an effect on brain function within circuitry associated with emotional appraisal and the generation of affective states.

Background: Depression is a complex disorder with large inter-individual variability in symptom p... more Background: Depression is a complex disorder with large inter-individual variability in symptom profiles that often occur alongside symptoms of other psychiatric domains such as anxiety. A dimensional and symptom-based approach may help refine the characterization and classification of depressive and anxiety disorders and thus aid in establishing robust biomarkers. We assess the brain functional connectivity correlates of a symptom-based clustering of individuals using functional brain imaging data. Methods: We assessed symptoms of depression and anxiety using Beck's Depression and Beck's Anxiety inventories in individuals with or without a history of depression, and high dimensional data clustering to form subgroups based on symptom profiles. To assess the biological relevance of this subtyping, we compared functional magnetic resonance imagingbased dynamic and static functional connectivity between subgroups in a subset of the total sample. Results: We identified five subgroups with distinct symptom profiles, cutting across diagnostic boundaries and differing in terms of total severity, symptom patterns and centrality. For instance, inability to relax, fear of the worst, and feelings of guilt were among the most severe symptoms in subgroup 1, 2 and 3, respectively. These subgroups showed evidence of differential static brain connectivity patterns, in particular comprising a frontotemporal network. In contrast, we found no significant associations with clinical sum scores, dynamic functional connectivity or global connectivity measures. Conclusion: Adding to the ongoing pursuit of individual-based treatment, the results show subtyping based on a dimensional conceptualization and unique constellations of anxiety and depression symptoms is supported by distinct brain static functional connectivity patterns.

NeuroImage: Clinical, 2022

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

The Journal of Neuroscience, 2014

The striking homogeneity of cerebellar microanatomy is strongly suggestive of a corresponding uni... more The striking homogeneity of cerebellar microanatomy is strongly suggestive of a corresponding uniformity of function. Consequently, theoretical models of the cerebellum's role in motor control should offer important clues regarding cerebellar contributions to cognition. One such influential theory holds that the cerebellum encodes internal models, neural representations of the context-specific dynamic properties of an object, to facilitate predictive control when manipulating the object. The present study examined whether this theoretical construct can shed light on the contribution of the cerebellum to language processing. We reasoned that the cerebellum might perform a similar coordinative function when the context provided by the initial part of a sentence can be highly predictive of the end of the sentence. Using functional MRI in humans we tested two predictions derived from this hypothesis, building on previous neuroimaging studies of internal models in motor control. Firs...

Associations between scanner and cortical thickness in controls. Results from linear regression a... more Associations between scanner and cortical thickness in controls. Results from linear regression analyses testing for group differences in cortical thickness between controls from scanner 1 and controls from scanner 2. Age was included in the analyses as covariate. Average thickness in each cluster is in mm. (DOCX 14 kb)

Results from GLM whole surface vertex-wise between-group analyses of cortical thickness. Results ... more Results from GLM whole surface vertex-wise between-group analyses of cortical thickness. Results from GLMs testing for group differences in cortical thickness between patients with anorexia nervosa and controls, controlling for scanner, and with simulation-based clusterwise correction for multiple comparisons at p

Figure S1. Flow diagram for enrolment, allocation to active placebo or ABM, follow-up after two w... more Figure S1. Flow diagram for enrolment, allocation to active placebo or ABM, follow-up after two weeks, and analyses in accordance with CONSORT (28). Not meeting inclusion criteria = no MDE according to M.I.N.I. Other reasons (n = 42) = current or former mania and/or hypomania according to M.I.N.I. Ten participants (5 in each group) elected to have their data erased. (DOC 72 kb)

Journal of psychiatric research, 2021

A recent meta-analysis has questioned the relevance of attention bias modification (ABM) for depr... more A recent meta-analysis has questioned the relevance of attention bias modification (ABM) for depression outcomes. However, there might be patient characteristics not yet accounted for, that are relevant to the outcome. In the context of personalized treatment, the lack of moderator studies have limited the potential for matching ABM-treatment to individual patient characteristics. Subjects (N = 301) were randomly assigned 1:1 to receive either active or placebo Attention Bias Modification (ABM) twice daily for 14 days in a double-blind design (placebo n = 148; ABM n = 153). The outcome was change in symptoms based on the Hamilton Depression Rating Scale (HDRS). Moderator variables were self-reported depression (Beck Depression Inventory-II; BDI-II), anxiety (Beck Anxiety Inventory; BAI) and attentional bias (AB) assessed at baseline. This trial was registered with ClinicalTrials.gov, number NCT02658682. Only BAI (p for interaction = .01, Bootstrap 95% CI [0.046, 0.337]) moderated th...

NeuroImage: Clinical, 2022

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

1 Clinical Neuroscience Research Group, Department of Psychology, University of Oslo, Oslo, Norwa... more 1 Clinical Neuroscience Research Group, Department of Psychology, University of Oslo, Oslo, Norway 2 NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway 3 Division of Psychiatry, Diakonhjemmet Hospital, Oslo, Norway 4 Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 5 Department of Psychology, University of Oslo, Oslo, Norway