Luigi Maglanoc

• Hurley, Eimir; Geisler, Benjamin Peter; Lupattelli, Angela; Poblador-Plou, Beatriz; Lassalle, Régis & Jové, Jérémy [Show all 30 contributors for this article] (2024). COVID-19 and pregnancy: A European study on pre- and post-infection medication use. European Journal of Clinical Pharmacology. ISSN 0031-6970. doi: 10.1007/s00228-024-03639-z.
• Patel, Yash; Shin, Jean; Abé, Christoph; Agartz, Ingrid; Alloza, Clara & Alnæs, Dag [Show all 51 contributors for this article] (2022). Virtual Ontogeny of Cortical Growth Preceding Mental Illness. Biological Psychiatry. ISSN 0006-3223. 92(4), p. 299–313. doi: 10.1016/j.biopsych.2022.02.959.
• Sæther, Linn Sofie; Ueland, Thor; Haatveit, Beathe; Maglanoc, Luigi; Szabo, Attila & Djurovic, Srdjan [Show all 14 contributors for this article] (2022). Inflammation and cognition in severe mental illness: patterns of covariation and subgroups. Molecular Psychiatry. ISSN 1359-4184. 8, p. 1284–1292. doi: 10.1038/s41380-022-01924-w. Full text in Research Archive
• Engvig, Andreas; Maglanoc, Luigi; Doan, Nhat Trung & Westlye, Lars Tjelta (2022). Data-driven health deficit assessment improves a frailty index’s prediction of current cognitive status and future conversion to dementia: results from ADNI. GeroScience. ISSN 2509-2715. doi: 10.1007/s11357-022-00669-2. Full text in Research Archive
• Ulrichsen, Kristine Moe; Kolskår, Knut-Kristian; Richard, Geneviève; Pedersen, Mads Lund; Alnæs, Dag & Dørum, Erlend Solberg [Show all 13 contributors for this article] (2022). No add-on effect of tDCS on fatigue and depression in chronic stroke patients: A randomized sham-controlled trial combining tDCS with computerized cognitive training. Brain and Behavior. ISSN 2162-3279. 12(7). doi: 10.1002/brb3.2643. Full text in Research Archive
• Patel, Yash; Parker, Nadine; Shin, Jean; Howard, Derek; French, Leon & Thomopoulos, Sophia I. [Show all 299 contributors for this article] (2020). Virtual Histology of Cortical Thickness and Shared Neurobiology in 6 Psychiatric Disorders. JAMA psychiatry. ISSN 2168-6238. 78(1), p. 47–63. doi: 10.1001/jamapsychiatry.2020.2694.
• Elvsåshagen, Torbjørn; Bahrami, Shahram; van der Meer, Dennis; Agartz, Ingrid; Alnæs, Dag & Barch, Deanna M. [Show all 76 contributors for this article] (2020). The genetic architecture of human brainstem structures and their involvement in common brain disorders. Nature Communications. ISSN 2041-1723. 11. doi: 10.1038/s41467-020-17376-1. Full text in Research Archive Show summary

  Brainstem regions support vital bodily functions, yet their genetic architectures and involvement in common brain disorders remain understudied. Here, using imaging-genetics data from a discovery sample of 27,034 individuals, we identify 45 brainstem-associated genetic loci, including the first linked to midbrain, pons, and medulla oblongata volumes, and map them to 305 genes. In a replication sample of 7432 participants most of the loci show the same effect direction and are significant at a nominal threshold. We detect genetic overlap between brainstem volumes and eight psychiatric and neurological disorders. In additional clinical data from 5062 individuals with common brain disorders and 11,257 healthy controls, we observe differential volume alterations in schizophrenia, bipolar disorder, multiple sclerosis, mild cognitive impairment, dementia, and Parkinson’s disease, supporting the relevance of brainstem regions and their genetic architectures in common brain disorders.

• Hilland, Eva; Landrø, Nils Inge; Kraft, Brage; Tamnes, Christian Krog; Fried, Eiko I. & Maglanoc, Luigi Angelo [Show all 7 contributors for this article] (2020). Exploring the Links between Specific Depression Symptoms and Brain Structure: A Network Study. Psychiatry and Clinical Neurosciences. ISSN 1323-1316. 74, p. 220–221. doi: 10.1111/pcn.12969. Full text in Research Archive Show summary

  Understanding the neural substrates of specific symptoms may provide important information about mechanisms underlying depression vulnerability. A growing body of research under the umbrella term ‘network approach’ has recently received considerable attention[5]; the approach understands and aims to model mental disorders as systems of causally interacting symptoms. So far, network studies have been based on symptoms and environmental factors, ignoring relevant neurobiological factors[6]. Here, we address this knowledge gap by modelling a joint network of depression-related brain structures and individual depression symptoms, using 21 symptoms and five regional brain measures. The sample is a mixed group of individuals that previously have been treated for one or more major depressive episodes (MDE) and never depressed individuals, with the goal to model a continuum of depression severity. Hippocampus was negatively associated with changes in appetite and sadness, and positively associated with loss of interest and irritability. Insula was negatively associated with loss of interest in sex and sadness. Cingulate had a negative association with sadness, and positive associations with crying and worthlessness. Fusiform gyrus had positive associations with crying and irritability.

• Hilland, Eva; Landrø, Nils Inge; Harmer, Catherine; Browning, Michael ; Maglanoc, Luigi Angelo & Jonassen, Rune (2019). Attentional bias modification is associated with fMRI response toward negative stimuli in individuals with residual depression: a randomized controlled trial. Journal of Psychiatry & Neuroscience. ISSN 1180-4882. 45(1), p. 23–33. doi: 10.1503/jpn.180118.
• Maglanoc, Luigi Angelo; Kaufmann, Tobias; van der Meer, Dennis; Marquand, André F.; Wolfers, Thomas & Jonassen, Rune [Show all 10 contributors for this article] (2019). Brain connectome mapping of complex human traits and their polygenic architecture using machine learning. Biological Psychiatry. ISSN 0006-3223. p. 1–10. doi: 10.1016/j.biopsych.2019.10.011. Full text in Research Archive Show summary

  Background Mental disorders and individual characteristics such as intelligence and personality are complex traits sharing a largely unknown neuronal basis. 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 assessing individual characteristics relevant for brain function and mental health and their genetic underpinnings in clinical applications remains a challenge. Methods In a machine learning approach, we predicted individual scores for educational attainment, fluid intelligence and dimensional measures of depression, anxiety and neuroticism using fMRI-based static and dynamic temporal synchronization between large-scale brain network nodes in 10,343 healthy individuals from the UK Biobank. In addition to age and sex to serve as our reference point, we also predicted individual polygenic scores for related phenotypes, and 13 different neuroticism traits and schizophrenia. Results Beyond high accuracy for age and sex, supporting the biological sensitivity of the connectome-based features, permutation tests revealed above chance-level prediction accuracy for trait-level 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 contrast, prediction accuracy was at chance level for depression, anxiety, neuroticism and polygenic scores across traits. Conclusion These novel findings provide a benchmark for future studies linking the genetic architecture of individual and mental health traits with fMRI-based brain connectomics.

• de Lange, Ann-Marie Glasø; Kaufmann, Tobias; van der Meer, Dennis; 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. ISSN 0027-8424. 116(44), p. 22341–22346. doi: 10.1073/pnas.1910666116.
• Maglanoc, Luigi Angelo; Kaufmann, Tobias; Jonassen, Rune; Hilland, Eva; Beck, Dani & Landrø, Nils Inge [Show all 7 contributors for this article] (2019). Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis. Human Brain Mapping. ISSN 1065-9471. doi: 10.1002/hbm.24802. Full text in Research Archive
• Kaufmann, Tobias; van der Meer, Dennis; Doan, Nhat Trung; Schwarz, Emanuel; Lund, Martina Jonette & Agartz, Ingrid [Show all 84 contributors for this article] (2019). Common brain disorders are associated with heritable patterns of apparent aging of the brain. Nature Neuroscience. ISSN 1097-6256. 22(10), p. 1617–1623. doi: 10.1038/s41593-019-0471-7. Full text in Research Archive
• Hilland, Eva; Landrø, Nils Inge; Harmer, Catherine; Browning, Michael; Maglanoc, Luigi Angelo & Jonassen, Rune (2019). Attentional bias modification is associated with fMRI response toward negative stimuli in individuals with residual depression: a randomized controlled trial. Journal of Psychiatry & Neuroscience. ISSN 1180-4882. doi: 10.1503/jpn.180118. Full text in Research Archive
• Maglanoc, Luigi Angelo; Kaufmann, Tobias; van der Meer, Dennis; Marquand, André F.; Wolfers, Thomas & Jonassen, Rune [Show all 10 contributors for this article] (2019). Predicting cognitive and mental health traits and their polygenic architecture using large-scale brain connectomics. Nature Neuroscience. ISSN 1097-6256. doi: 10.1101/609586.
• Jonassen, Rune; Harmer, Catherine; Hilland, Eva; Maglanoc, Luigi Angelo; Kraft, Brage & Browning, Michael [Show all 10 contributors for this article] (2019). Effects of Attentional Bias Modification on residual symptoms in depression: A randomized controlled trial. BMC Psychiatry. ISSN 1471-244X. 19(141). doi: 10.1186/s12888-019-2105-8. Full text in Research Archive
• Hilland, Eva; Landrø, Nils Inge; Harmer, Catherine; Maglanoc, Luigi Angelo & Jonassen, Rune (2018). Within-network connectivity in the salience network after attention bias modification training in residual depression: Report from a preregistered clinical trial. Frontiers in Human Neuroscience. ISSN 1662-5161. 12. doi: 10.3389/fnhum.2018.00508. Full text in Research Archive
• Maglanoc, Luigi Angelo; Landrø, Nils Inge; Jonassen, Rune; Kaufmann, Tobias; Còrdova Palomera, Aldo & Hilland, Eva [Show all 7 contributors for this article] (2018). Data-driven clustering reveals a link between symptoms and functional brain connectivity in depression. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. ISSN 2451-9022. 4(1), p. 16–26. doi: 10.1016/j.bpsc.2018.05.005. Full text in Research Archive Show summary

  BACKGROUND: Depression is a complex disorder with large interindividual 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 of depressive and anxiety disorders and thus aid in establishing robust biomarkers. We use resting-state functional magnetic resonance imaging to assess the brain functional connectivity correlates of a symptom-based clustering of individuals. METHODS: We assessed symptoms using the Beck Depression and Beck Anxiety Inventories in individuals with or without a history of depression (N = 1084) and high-dimensional data clustering to form subgroups based on symptom profiles. We compared dynamic and static functional connectivity between subgroups in a subset of the total sample (n = 252). RESULTS: We identified five subgroups with distinct symptom profiles, which cut across diagnostic boundaries with different 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 subgroups 1, 2, and 3, respectively. The distribution of individuals was 32%, 25%, 22%, 10%, and 11% in subgroups 1 to 5, 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. CONCLUSIONS: Adding to the pursuit of individual-based treatment, subtyping based on a dimensional conceptualization and unique constellations of anxiety and depression symptoms is supported by distinct patterns of static functional connectivity in the brain.

• Fu, Cynthia; Costafreda, Sergi; Sankar, Anjali; Adams, Tracey; Mark, Rasenick & Liu, Peng [Show all 10 contributors for this article] (2015). Multimodal functional and structural neuroimaging investigation of major depressive disorder following treatment with duloxetine. BMC Psychiatry. ISSN 1471-244X. 15. doi: 10.1186/s12888-015-0457-2.

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• Kaufmann, Tobias; van der Meer, Dennis; Doan, Nhat Trung; Schwarz, Emanuel; Lund, Martina Jonette & Agartz, Ingrid [Show all 97 contributors for this article] (2020). Publisher Correction: Common brain disorders are associated with heritable patterns of apparent aging of the brain (Nature Neuroscience, (2019), 22, 10, (1617-1623), 10.1038/s41593-019-0471-7). Nature Neuroscience. ISSN 1097-6256. 23(2). doi: 10.1038/s41593-019-0553-6.
• Hilland, Eva; Landrø, Nils Inge; Harmer, Catherine; Maglanoc, Luigi Angelo; Browning, Michael & Jonassen, Rune (2018). Attentional Bias Modification alters fMRI response towards negative stimuli in depression. .
• Jonassen, Rune; Hilland, Eva; Harmer, Catherine; Berge, Torkil; Maglanoc, Luigi Angelo & Kraft, Brage [Show all 9 contributors for this article] (2017). Attentional Bias Modification reduces symptom severity in major depression: Preliminary analysis from an ongoing longitudinal RCT study. .
• Jonassen, Rune; Hilland, Eva; Harmer, Catherine; Berge, Torkil; Maglanoc, Luigi Angelo & Kraft, Brage [Show all 9 contributors for this article] (2016). Attention bias modification reduces symptom severity in Major Depression. Preliminary analysis from an ongoing longitudinal RCT study.

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