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103. Neural circuitry underlying sustained attention in healthy adolescents and in ADHD symptomatology

Author

O'Halloran, Laura, Cao, Zhipeng, Ruddy, Kathy, Jollans, Lee, Albaugh, Matthew D., Aleni, Andrea, Potter, Alexandra S., Vahey, Nigel, Banaschewski, Tobias, Hohmann, Sarah, Bokde, Arun L.W., Bromberg, Uli, Büchel, Christian, Quinlan, Erin Burke, Desrivières, Sylvane, Flor, Herta, Frouin, Vincent, Gowland, Penny, Heinz, Andreas, Ittermann, Bernd, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Paus, Tomáš, Smolka, Michael N., Walter, Henrik, Schumann, Gunter, Garavan, Hugh, Kelly, Clare, and Whelan, Robert

Published
2018
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105. White matter microstructure is associated with hyperactive/inattentive symptomatology and polygenic risk for attention-deficit/hyperactivity disorder in a population-based sample of adolescents

Author

Albaugh, Matthew D., Hudziak, James. J., Ing, Alex, Chaarani, Bader, Barker, Edward, Jia, Tianye, Lemaitre, Herve, Watts, Richard, Orr, Catherine, Spechler, Philip A., Lepage, Claude, Fonov, Vladimir, Collins, Louis, Rioux, Pierre, Evans, Alan C., Banaschewski, Tobias, Bokde, Arun L. W., Bromberg, Uli, Büchel, Christian, Quinlan, Erin Burke, Desrivières, Sylvane, Flor, Herta, Frouin, Vincent, Gowland, Penny, Heinz, Andreas, Ittermann, Bernd, Martinot, Jean-Luc, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Paus, Tomáš, Poustka, Luise, Fröhner, Juliane H., Smolka, Michael N., Walter, Henrik, Whelan, Robert, Schumann, Gunter, Garavan, Hugh, and Potter, Alexandra

Published
2019
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108. Brain Networks and Adolescent Alcohol Use

Author

Yip, Sarah W., primary, Lichenstein, Sarah D., additional, Liang, Qinghao, additional, Chaarani, Bader, additional, Dager, Alecia, additional, Pearlson, Godfrey, additional, Banaschewski, Tobias, additional, Bokde, Arun L. W., additional, Desrivières, Sylvane, additional, Flor, Herta, additional, Grigis, Antoine, additional, Gowland, Penny, additional, Heinz, Andreas, additional, Brühl, Rüdiger, additional, Martinot, Jean-Luc, additional, Martinot, Marie-Laure Paillère, additional, Artiges, Eric, additional, Nees, Frauke, additional, Orfanos, Dimitri Papadopoulos, additional, Paus, Tomáš, additional, Poustka, Luise, additional, Hohmann, Sarah, additional, Millenet, Sabina, additional, Fröhner, Juliane H., additional, Smolka, Michael N., additional, Vaidya, Nilakshi, additional, Walter, Henrik, additional, Whelan, Robert, additional, Schumann, Gunter, additional, and Garavan, Hugh, additional

Published
2023
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109. Dietary Tyrosine intake is associated with Locus Coeruleus, attention and grey matter maintenance: an MRI structural study on 398 healthy individuals of the Berlin Aging Study-II

Author

Plini, Emanuele Raffaele Giuliano, primary, Melnychuk, Michael Christopher, additional, Harkin, Andrew, additional, Dahl, Martin J, additional, McAuslan, Matthew, additional, Kühn, Simone, additional, Boyle, Rory, additional, Whelan, Robert, additional, Andrews, Ralph, additional, Duzel, Sandra, additional, Dreweilies, Johanna, additional, Wagner, Gert G, additional, Lindenberger, Ulman, additional, Norman, Kristina, additional, Robertson, Ian Hamilton, additional, and Dockree, Paul Michael, additional

Published
2023
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113. Impact of a Common Genetic Variation Associated With Putamen Volume on Neural Mechanisms of Attention-Deficit/Hyperactivity Disorder

Author

Banaschewski, Tobias, Barker, Gareth, Bokde, Arun L.W., Bromberg, Uli, Büchel, Christian, Quinlan, Erin Burke, Desrivières, Sylvane, Flor, Herta, Frouin, Vincent, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Ittermann, Bernd, Martinot, Jean-Luc, Paillère Martinot, Marie-Laure, Artiges, Eric, Lemaitre, Herve, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Paus, Tomáš, Poustka, Luise, Smolka, Michael N., Vetter, Nora C., Jurk, Sarah, Mennigen, Eva, Walter, Henrik, Whelan, Robert, Schumann, Gunter, Xu, Bing, Jia, Tianye, Macare, Christine, Cattrell, Anna, Conrod, Patricia J., and Gallinat, Jürgen

Published
2017
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114. Predicting change trajectories of neuroticism from baseline brain structure using whole brain analyses and latent growth curve models in adolescents

Author

Kühn, Simone, Mascherek, Anna, Banaschewski, Tobias, Bokde, Arun L. W., Büchel, Christian, Quinlan, Erin Burke, Desrivières, Sylvane, Flor, Herta, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Ittermann, Bernd, Martinot, Jean-Luc, Martinot, Marie-Laure Paillère, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Paus, Tomáš, Poustka, Luise, Millenet, Sabina, Fröhner, Juliane H., Smolka, Michael N., Walter, Henrik, Whelan, Robert, Schumann, Gunter, Lindenberger, Ulman, and Gallinat, Jürgen

Published
2020
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117. Trajectories of cortical structures associated with stress across adolescence: a bivariate latent change score approach

Author

Nweze, Tochukwu, Banaschewski, Tobias, Ajaelu, Cyracius, Okoye, Chukwuemeka, Ezenwa, Michael, Whelan, Robert, Papadopoulos Orfanos, Dimitri, Bokde, Arun LW, Desrivières, Sylvane, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Brühl, Rüdiger, Martinot, Jean-Luc, Martinot, Marie-Laure Paillère, Artiges, Eric, Nees, Frauke, Paus, Tomáš, Poustka, Luise, Hohmann, Sarah, Millenet, Sabina, Fröhner, Juliane H, Smolka, Michael N, Walter, Henrik, Schumann, Gunter, Hanson, Jamie L, IMAGEN Consortium, and Apollo - University of Cambridge Repository

Subjects
Adult, Cerebral Cortex, Adolescent, Sexually Transmitted Diseases, bivariate latent change score model, longitudinal models, Stress, Magnetic Resonance Imaging, cognitive functioning, Young Adult, longitudinal mediation analysis, Cross-Sectional Studies, Humans, cortical development, Longitudinal Studies
Abstract

Funder: Cambridge Trust (University of Cambridge); Id: http://dx.doi.org/10.13039/501100000735, Funder: National Institute for Health Research (NIHR) Biomedical Research Centre at South London, Funder: Maudsley NHS Foundation Trust and King's College London, Funder: Medical Research Foundation; Id: http://dx.doi.org/10.13039/501100009187, Funder: Mission Interministérielle de Lutte‐contre‐les‐Drogues‐et‐les‐Conduites‐Addictives (MILDECA), Funder: Assistance‐Publique‐Hôpitaux‐de‐Paris and INSERM, Funder: Paris Sud University IDEX 2012, Funder: Fédération pour la Recherche sur le Cerveau; Id: http://dx.doi.org/10.13039/501100006424, BACKGROUND: Stress exposure in childhood and adolescence has been linked to reductions in cortical structures and cognitive functioning. However, to date, most of these studies have been cross-sectional, limiting the ability to make long-term inferences, given that most cortical structures continue to develop through adolescence. METHODS: Here, we used a subset of the IMAGEN population cohort sample (N = 502; assessment ages: 14, 19, and 22 years; mean age: 21.945 years; SD = 0.610) to understand longitudinally the long-term interrelations between stress, cortical development, and cognitive functioning. To these ends, we first used a latent change score model to examine four bivariate relations - assessing individual differences in change in the relations between adolescent stress exposure and volume, surface area, and cortical thickness of cortical structures, as well as cognitive outcomes. Second, we probed for indirect neurocognitive effects linking stress to cortical brain structures and cognitive functions using rich longitudinal mediation modeling. RESULTS: Latent change score modeling showed that greater baseline adolescence stress at age 14 predicted a small reduction in the right anterior cingulate volume (Std. β = -.327, p = .042, 95% CI [-0.643, -0.012]) and right anterior cingulate surface area (Std. β = -.274, p = .038, 95% CI [-0.533, -0.015]) across ages 14-22. These effects were very modest in nature and became nonsignificant after correcting for multiple comparisons. Our longitudinal analyses found no evidence of indirect effects in the two neurocognitive pathways linking adolescent stress to brain and cognitive outcomes. CONCLUSION: Findings shed light on the impact of stress on brain reductions, particularly in the prefrontal cortex that have consistently been implicated in the previous cross-sectional studies. However, the magnitude of effects observed in our study is smaller than that has been reported in past cross-sectional work. This suggests that the potential impact of stress during adolescence on brain structures may likely be more modest than previously noted.

Published
2023

122. Dietary Tyrosine Intake Is Associated with Locus Coeruleus Integrity, Attentive Cognitive Control and Grey Matter Maintenance: An MRI Structural Study on 398 Healthy Individuals of the Berlin Aging Study-II

Author

Plini, Emanuele Raffaele Giuliano, primary, Melnychuk, Michael Christopher, additional, Harkin, Andrew, additional, Dahl, Martin J, additional, McAuslam, Matthew, additional, Kühn, Simone, additional, Boyle, Rory Thomas, additional, Whelan, Robert, additional, Andrews, Ralph, additional, Duzel, Sandra, additional, Drewelies, Johanna, additional, Wagner, Gert G, additional, Lindenberger, Ulman, additional, Norman, Kristina, additional, Robertson, Ian Hamilton, additional, and Dockree, Paul Michael, additional

Published
2023
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123. Interpretable automatic detection of incomplete hippocampal inversions using anatomical criteria

Author

Hemforth, Lisa, primary, Cury, Claire, additional, Frouin, Vincent, additional, Desrivières, Sylvane, additional, Grigis, Antoine, additional, Garavan, Hugh, additional, Brühl, Rüdiger, additional, Martinot, Jean-Luc, additional, Paillère Martinot, Marie-Laure, additional, Artiges, Eric, additional, Poustka, Luise, additional, Hohmann, Sarah, additional, Millenet, Sabina, additional, Vaidya, Nilakshi, additional, Walter, Henrik, additional, Whelan, Robert, additional, Schumann, Gunter, additional, Couvy-Duchesne, Baptiste, additional, and Colliot, Olivier, additional

Published
2023
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124. Conduct problems are associated with accelerated thinning of emotion-related cortical regions in a community-based sample of adolescents

Author

Albaugh, Matthew D., primary, Hudziak, James. J., additional, Spechler, Philip A., additional, Chaarani, Bader, additional, Lepage, Claude, additional, Jeon, Seun, additional, Rioux, Pierre, additional, Evans, Alan C., additional, Banaschewski, Tobias, additional, Bokde, Arun L.W., additional, Desrivières, Sylvane, additional, Flor, Herta, additional, Gowland, Penny, additional, Heinz, Andreas, additional, Ittermann, Bernd, additional, Martinot, Jean-Luc, additional, Martinot, Marie-Laure Paillère, additional, Nees, Frauke, additional, Orfanos, Dimitri Papadopoulos, additional, Poustka, Luise, additional, Millenet, Sabina, additional, Fröhner, Juliane H., additional, Smolka, Michael N., additional, Walter, Henrik, additional, Whelan, Robert, additional, Schumann, Gunter, additional, Potter, Alexandra S., additional, and Garavan, Hugh, additional

Published
2023
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126. A biologically informed polygenic score of neuronal plasticity moderates the association between cognitive aptitudes and cortical thickness in adolescents

Author

Navarri, Xavier, primary, Vosberg, Daniel E., additional, Shin, Jean, additional, Richer, Louis, additional, Leonard, Gabriel, additional, Pike, G. Bruce, additional, Banaschewski, Tobias, additional, Bokde, Arun L.W., additional, Desrivières, Sylvane, additional, Flor, Herta, additional, Grigis, Antoine, additional, Garavan, Hugh, additional, Gowland, Penny, additional, Heinz, Andreas, additional, Brühl, Rüdiger, additional, Martinot, Jean-Luc, additional, Martinot, Marie-Laure Paillère, additional, Artiges, Eric, additional, Nees, Frauke, additional, Orfanos, Dimitri Papadopoulos, additional, Poustka, Luise, additional, Hohmann, Sarah, additional, Fröhner, Juliane H., additional, Smolka, Michael N., additional, Vaidya, Nilakshi, additional, Walter, Henrik, additional, Whelan, Robert, additional, Schumann, Gunter, additional, Pausova, Zdenka, additional, and Paus, Tomáš, additional

Published
2023
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128. Drinking motives, personality traits and life stressors-identifying pathways to harmful alcohol use in adolescence using a panel network approach

Author

Freichel, René, Pfirrmann, Janine, Cousjin, Janna, de Jong, Peter, Franken, Ingmar, Banaschewski, Tobias, Bokde, Arun L W, Desrivières, Sylvane, Flor, Herta, Grigis, Antoine, Garavan, Hugh, Heinz, Andreas, Martinot, Jean-Luc, Martinot, Marie-Laure Paillère, Artiges, Eric, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Poustka, Luise, Hohmann, Sarah, Fröhner, Juliane H, Smolka, Michael N, Vaidya, Nilakshi, Whelan, Robert, Schumann, Gunter, Walter, Henrik, Veer, Ilya M, Wiers, Reinout W, Consortium, IMAGEN, Freichel, René, Pfirrmann, Janine, Cousjin, Janna, de Jong, Peter, Franken, Ingmar, Banaschewski, Tobias, Bokde, Arun L W, Desrivières, Sylvane, Flor, Herta, Grigis, Antoine, Garavan, Hugh, Heinz, Andreas, Martinot, Jean-Luc, Martinot, Marie-Laure Paillère, Artiges, Eric, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Poustka, Luise, Hohmann, Sarah, Fröhner, Juliane H, Smolka, Michael N, Vaidya, Nilakshi, Whelan, Robert, Schumann, Gunter, Walter, Henrik, Veer, Ilya M, Wiers, Reinout W, and Consortium, IMAGEN

Abstract

Background and aims: Models of alcohol use risk suggest that drinking motives represent the most proximal risk factors on which more distal factors converge. However, little is known about how distinct risk factors influence each other and alcohol use on different temporal scales (within a given moment versus over time). We aimed to estimate the dynamic associations of distal (personality and life stressors) and proximal (drinking motives) risk factors, and their relationship to alcohol use in adolescence and early adulthood using a novel graphical vector autoregressive (GVAR) panel network approach. Design, setting and cases: We estimated panel networks on data from the IMAGEN study, a longitudinal European cohort study following adolescents across three waves (aged 16, 19 and 22 years). Our sample consisted of 1829 adolescents (51% females) who reported alcohol use on at least one assessment wave. Measurements: Risk factors included personality traits (NEO-FFI: neuroticism, extraversion, openness, agreeableness and conscientiousness; SURPS: impulsivity and sensation-seeking), stressful life events (LEQ: sum scores of stressful life events), and drinking motives [drinking motives questionnaire (DMQ): social, enhancement, conformity, coping anxiety and coping depression]. We assessed alcohol use [alcohol use disorders identification test (AUDIT): quantity and frequency] and alcohol-related problems (AUDIT: related problems). Findings: Within a given moment, social [partial correlation (pcor) = 0.17] and enhancement motives (pcor = 0.15) co-occurred most strongly with drinking quantity and frequency, while coping depression motives (pcor = 0.13), openness (pcor = 0.05) and impulsivity (pcor = 0.09) were related to alcohol-related problems. The temporal network showed no predictive associations between distal risk factors and drinking motives. Social motives (beta = 0.21), previous alcohol use (beta = 0.11) and openness (beta = 0.10) predicted alcohol-related probl

Published
2023

129. Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples

Author

Moguilner, Sebastian, Whelan, Robert, Adams, Hieab, Valcour, Victor, Tagliazucchi, Enzo, Ibáñez, Agustín, Moguilner, Sebastian, Whelan, Robert, Adams, Hieab, Valcour, Victor, Tagliazucchi, Enzo, and Ibáñez, Agustín

Abstract

Background: Dementia's diagnostic protocols are mostly based on standardised neuroimaging data collected in the Global North from homogeneous samples. In other non-stereotypical samples (participants with diverse admixture, genetics, demographics, MRI signals, or cultural origins), classifications of disease are difficult due to demographic and region-specific sample heterogeneities, lower quality scanners, and non-harmonised pipelines. Methods: We implemented a fully automatic computer-vision classifier using deep learning neural networks. A DenseNet was applied on raw (unpreprocessed) data from 3000 participants (behavioural variant frontotemporal dementia-bvFTD, Alzheimer's disease-AD, and healthy controls; both male and female as self-reported by participants). We tested our results in demographically matched and unmatched samples to discard possible biases and performed multiple out-of-sample validations. Findings: Robust classification results across all groups were achieved from standardised 3T neuroimaging data from the Global North, which also generalised to standardised 3T neuroimaging data from Latin America. Moreover, DenseNet also generalised to non-standardised, routine 1.5T clinical images from Latin America. These generalisations were robust in samples with heterogenous MRI recordings and were not confounded by demographics (i.e., were robust in both matched and unmatched samples, and when incorporating demographic variables in a multifeatured model). Model interpretability analysis using occlusion sensitivity evidenced core pathophysiological regions for each disease (mainly the hippocampus in AD, and the insula in bvFTD) demonstrating biological specificity and plausibility. Interpretation: The generalisable approach outlined here could be used in the future to aid clinician decision-making in diverse samples. Funding: The specific funding of this article is provided in the acknowledgements section.

Published
2023

130. A biologically informed polygenic score of neuronal plasticity moderates the association between cognitive aptitudes and cortical thickness in adolescents

Author

Navarri, Xavier, Vosberg, Daniel E., Shin, Jean, Richer, Louis, Leonard, Gabriel, Pike, G. Bruce, Banaschewski, Tobias, Bokde, Arun L.W., Desrivières, Sylvane, Flor, Herta, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Brühl, Rüdiger, Martinot, Jean-Luc, Paillère Martinot, Marie-Laure, Artiges, Eric, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Poustka, Luise, Hohmann, Sarah, Fröhner, Juliane H., Smolka, Michael N., Vaidya, Nilakshi, Walter, Henrik, Whelan, Robert, Schumann, Gunter, Pausova, Zdenka, Paus, Tomáš, Navarri, Xavier, Vosberg, Daniel E., Shin, Jean, Richer, Louis, Leonard, Gabriel, Pike, G. Bruce, Banaschewski, Tobias, Bokde, Arun L.W., Desrivières, Sylvane, Flor, Herta, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Brühl, Rüdiger, Martinot, Jean-Luc, Paillère Martinot, Marie-Laure, Artiges, Eric, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Poustka, Luise, Hohmann, Sarah, Fröhner, Juliane H., Smolka, Michael N., Vaidya, Nilakshi, Walter, Henrik, Whelan, Robert, Schumann, Gunter, Pausova, Zdenka, and Paus, Tomáš

Abstract

Although many studies of the adolescent brain identified positive associations between cognitive abilities and cortical thickness, little is known about mechanisms underlying such brain-behavior relationships. With experience-induced plasticity playing an important role in shaping the cerebral cortex throughout life, it is likely that some of the inter-individual variations in cortical thickness could be explained by genetic variations in relevant molecular processes, as indexed by a polygenic score of neuronal plasticity (PGS-NP). Here, we studied associations between PGS-NP, cognitive abilities, and thickness of the cerebral cortex, estimated from magnetic resonance images, in the Saguenay Youth Study (SYS, 533 females, 496 males: age=15.0 ± 1.8 years of age; cross-sectional), and the IMAGEN Study (566 females, 556 males; between 14 and 19 years; longitudinal). Using Gene Ontology, we first identified 199 genes implicated in neuronal plasticity, which mapped to 155,600 single nucleotide polymorphisms (SNPs). Second, we estimated their effect sizes from an educational attainment meta-GWAS to build a PGS-NP. Third, we examined a possible moderating role of PGS-NP in the relationship between performance intelligence quotient (PIQ), and its subtests, and the thickness of 34 cortical regions. In SYS, we observed a significant interaction between PGS-NP and object assembly vis-à-vis thickness in male adolescents (p = 0.026). A median-split analysis showed that, in males with a ‘high’ PGS-NP, stronger associations between object assembly and thickness were found in regions with larger age-related changes in thickness (r = 0.55, p = 0.00075). Although the interaction between PIQ and PGS-NP was non-significant (p = 0.064), we performed a similar median-split analysis. Again, in the high PGS-NP males, positive associations between PIQ and thickness were observed in regions with larger age-related changes in thickness (r = 0.40, p = 0.018). In the IMAGEN cohort, we did not r

Published
2023
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131. Independent contribution of polygenic risk for schizophrenia and cannabis use in predicting psychotic-like experiences in young adulthood: Testing gene × environment moderation and mediation

Author

Hersenen-Medisch 1, TN groep Adan, Onderzoeksgroep 2, Onderzoek, Brain, Neurogenetica, Elkrief, Laurent, Lin, Bochao, Marchi, Mattia, Afzali, Mohammad H., Banaschewski, Tobias, Bokde, Arun L.W., Quinlan, Erin Burke, Desrivières, Sylvane, Flor, Herta, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Ittermann, Bernd, Martinot, Jean Luc, Martinot, Marie Laure Paillère, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Paus, Tomáš, Poustka, Luise, Hohmann, Sarah, Fröhner, Juliane H., Smolka, Michael N., Walter, Henrik, Whelan, Robert, Schumann, Gunter, Luykx, Jurjen, Boks, Marco P., Conrod, Patricia J., Hersenen-Medisch 1, TN groep Adan, Onderzoeksgroep 2, Onderzoek, Brain, Neurogenetica, Elkrief, Laurent, Lin, Bochao, Marchi, Mattia, Afzali, Mohammad H., Banaschewski, Tobias, Bokde, Arun L.W., Quinlan, Erin Burke, Desrivières, Sylvane, Flor, Herta, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Ittermann, Bernd, Martinot, Jean Luc, Martinot, Marie Laure Paillère, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Paus, Tomáš, Poustka, Luise, Hohmann, Sarah, Fröhner, Juliane H., Smolka, Michael N., Walter, Henrik, Whelan, Robert, Schumann, Gunter, Luykx, Jurjen, Boks, Marco P., and Conrod, Patricia J.

Published
2023

132. A stable and replicable neural signature of lifespan adversity in the adult brain

Author

Holz, Nathalie E; https://orcid.org/0000-0002-6225-9267, Zabihi, Mariam; https://orcid.org/0000-0002-7083-2318, Kia, Seyed Mostafa; https://orcid.org/0000-0002-7128-814X, Monninger, Maximillian, Aggensteiner, Pascal-M; https://orcid.org/0000-0002-1048-9044, Siehl, Sebastian; https://orcid.org/0000-0002-8933-7552, Floris, Dorothea L, Bokde, Arun L W; https://orcid.org/0000-0003-0114-4914, Desrivières, Sylvane; https://orcid.org/0000-0002-9120-7060, Flor, Herta; https://orcid.org/0000-0003-4809-5398, Grigis, Antoine, Garavan, Hugh, Gowland, Penny; https://orcid.org/0000-0002-4900-4817, Heinz, Andreas; https://orcid.org/0000-0001-5405-9065, Brühl, Rüdiger; https://orcid.org/0000-0003-0111-5996, Martinot, Jean-Luc, Martinot, Marie-Laure Paillère; https://orcid.org/0000-0002-0452-2450, Orfanos, Dimitri Papadopoulos; https://orcid.org/0000-0002-1242-8990, Paus, Tomáš, Poustka, Luise; https://orcid.org/0000-0002-7738-4394, Fröhner, Juliane H; https://orcid.org/0000-0002-8493-6396, Smolka, Michael N; https://orcid.org/0000-0001-5398-5569, Vaidya, Nilakshi, Walter, Henrik; https://orcid.org/0000-0002-9403-6121, Whelan, Robert; https://orcid.org/0000-0002-2790-7281, Schumann, Gunter; https://orcid.org/0000-0002-7740-6469, Meyer-Lindenberg, Andreas; https://orcid.org/0000-0001-5619-1123, Brandeis, Daniel, Buitelaar, Jan K; https://orcid.org/0000-0001-8288-7757, Nees, Frauke, et al, Holz, Nathalie E; https://orcid.org/0000-0002-6225-9267, Zabihi, Mariam; https://orcid.org/0000-0002-7083-2318, Kia, Seyed Mostafa; https://orcid.org/0000-0002-7128-814X, Monninger, Maximillian, Aggensteiner, Pascal-M; https://orcid.org/0000-0002-1048-9044, Siehl, Sebastian; https://orcid.org/0000-0002-8933-7552, Floris, Dorothea L, Bokde, Arun L W; https://orcid.org/0000-0003-0114-4914, Desrivières, Sylvane; https://orcid.org/0000-0002-9120-7060, Flor, Herta; https://orcid.org/0000-0003-4809-5398, Grigis, Antoine, Garavan, Hugh, Gowland, Penny; https://orcid.org/0000-0002-4900-4817, Heinz, Andreas; https://orcid.org/0000-0001-5405-9065, Brühl, Rüdiger; https://orcid.org/0000-0003-0111-5996, Martinot, Jean-Luc, Martinot, Marie-Laure Paillère; https://orcid.org/0000-0002-0452-2450, Orfanos, Dimitri Papadopoulos; https://orcid.org/0000-0002-1242-8990, Paus, Tomáš, Poustka, Luise; https://orcid.org/0000-0002-7738-4394, Fröhner, Juliane H; https://orcid.org/0000-0002-8493-6396, Smolka, Michael N; https://orcid.org/0000-0001-5398-5569, Vaidya, Nilakshi, Walter, Henrik; https://orcid.org/0000-0002-9403-6121, Whelan, Robert; https://orcid.org/0000-0002-2790-7281, Schumann, Gunter; https://orcid.org/0000-0002-7740-6469, Meyer-Lindenberg, Andreas; https://orcid.org/0000-0001-5619-1123, Brandeis, Daniel, Buitelaar, Jan K; https://orcid.org/0000-0001-8288-7757, Nees, Frauke, and et al

Abstract

Environmental adversities constitute potent risk factors for psychiatric disorders. Evidence suggests the brain adapts to adversity, possibly in an adversity-type and region-specific manner. However, the long-term effects of adversity on brain structure and the association of individual neurobiological heterogeneity with behavior have yet to be elucidated. Here we estimated normative models of structural brain development based on a lifespan adversity profile in a longitudinal at-risk cohort aged 25 years (n = 169). This revealed widespread morphometric changes in the brain, with partially adversity-specific features. This pattern was replicated at the age of 33 years (n = 114) and in an independent sample at 22 years (n = 115). At the individual level, greater volume contractions relative to the model were predictive of future anxiety. We show a stable neurobiological signature of adversity that persists into adulthood and emphasize the importance of considering individual-level rather than group-level predictions to explain emerging psychopathology.

Published
2023

134. Human cortex development is shaped by molecular and cellular brain systems

Author

Lotter, Leon D., Saberi, Amin, Hansen, Justine Y., Misic, Bratislav, Barker, Gareth J., Bokde, Arun L. W., Desrivières, Sylvane, Flor, Herta, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Brühl, Rüdiger, Martinot, Jean-Luc, Paillère, Marie-Laure, Artiges, Eric, Orfanos, Dimitri Papadopoulos, Paus, Tomáš, Poustka, Luise, Hohmann, Sarah, Fröhner, Juliane H., Smolka, Michael N., Vaidya, Nilakshi, Walter, Henrik, Whelan, Robert, Schumann, Gunter, Nees, Frauke, Banaschewski, Tobias, Eickhoff, Simon B., Dukart, Juergen, and IMAGEN Consortium

Subjects
Article
Abstract

Human brain morphology undergoes complex developmental changes with diverse regional trajectories. Various biological factors influence cortical thickness development, but human data are scarce. Building on methodological advances in neuroimaging of large cohorts, we show that population-based developmental trajectories of cortical thickness unfold along patterns of molecular and cellular brain organization. During childhood and adolescence, distributions of dopaminergic receptors, inhibitory neurons, glial cell populations as well as features of brain metabolism explain up to 50% of variance associated with regional cortical thickness trajectories. Cortical maturation patterns in later life are best explained by distributions of cholinergic and glutamatergic systems. These observations are validated in longitudinal data from over 8,000 adolescents, explaining up to 59% of developmental change at population- and 18% at single-subject level. Integrating multilevel brain atlases with normative modeling and population neuroimaging provides a biologically and clinically meaningful path to understand typical and atypical brain development in living humans.

Published
2023

136. Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations

Author

Moguilner, Sebastian, Baez, Sandra, Hernandez, Hernan, Migeot, Joaquín, Legaz, Agustina, Gonzalez-Gomez, Raul, Farina, Francesca R., Prado, Pavel, Cuadros, Jhosmary, Tagliazucchi, Enzo, Altschuler, Florencia, Maito, Marcelo Adrián, Godoy, María E., Cruzat, Josephine, Valdes-Sosa, Pedro A., Lopera, Francisco, Ochoa-Gómez, John Fredy, Hernandez, Alfredis Gonzalez, Bonilla-Santos, Jasmin, Gonzalez-Montealegre, Rodrigo A., Anghinah, Renato, d’Almeida Manfrinati, Luís E., Fittipaldi, Sol, Medel, Vicente, Olivares, Daniela, Yener, Görsev G., Escudero, Javier, Babiloni, Claudio, Whelan, Robert, Güntekin, Bahar, Yırıkoğulları, Harun, Santamaria-Garcia, Hernando, Lucas, Alberto Fernández, Huepe, David, Di Caterina, Gaetano, Soto-Añari, Marcio, Birba, Agustina, Sainz-Ballesteros, Agustin, Coronel-Oliveros, Carlos, Yigezu, Amanuel, Herrera, Eduar, Abasolo, Daniel, Kilborn, Kerry, Rubido, Nicolás, Clark, Ruaridh A., Herzog, Ruben, Yerlikaya, Deniz, Hu, Kun, Parra, Mario A., Reyes, Pablo, García, Adolfo M., Matallana, Diana L., Avila-Funes, José Alberto, Slachevsky, Andrea, Behrens, María I., Custodio, Nilton, Cardona, Juan F., Barttfeld, Pablo, Brusco, Ignacio L., Bruno, Martín A., Sosa Ortiz, Ana L., Pina-Escudero, Stefanie D., Takada, Leonel T., Resende, Elisa, Possin, Katherine L., de Oliveira, Maira Okada, Lopez-Valdes, Alejandro, Lawlor, Brian, Robertson, Ian H., Kosik, Kenneth S., Duran-Aniotz, Claudia, Valcour, Victor, Yokoyama, Jennifer S., Miller, Bruce, and Ibanez, Agustin

Abstract

Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of diversity (including geographical, socioeconomic, sociodemographic, sex and neurodegeneration) on the brain-age gap is unknown. We analyzed datasets from 5,306 participants across 15 countries (7 Latin American and Caribbean countries (LAC) and 8 non-LAC countries). Based on higher-order interactions, we developed a brain-age gap deep learning architecture for functional magnetic resonance imaging (2,953) and electroencephalography (2,353). The datasets comprised healthy controls and individuals with mild cognitive impairment, Alzheimer disease and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (functional magnetic resonance imaging: mean directional error = 5.60, root mean square error (r.m.s.e.) = 11.91; electroencephalography: mean directional error = 5.34, r.m.s.e. = 9.82) associated with frontoposterior networks compared with non-LAC models. Structural socioeconomic inequality, pollution and health disparities were influential predictors of increased brain-age gaps, especially in LAC (R² = 0.37, F² = 0.59, r.m.s.e. = 6.9). An ascending brain-age gap from healthy controls to mild cognitive impairment to Alzheimer disease was found. In LAC, we observed larger brain-age gaps in females in control and Alzheimer disease groups compared with the respective males. The results were not explained by variations in signal quality, demographics or acquisition methods. These findings provide a quantitative framework capturing the diversity of accelerated brain aging.

Published
2024
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137. Coupled changes between ruminating thoughts and resting-state brain networks during the transition into adulthood

Author

Marchitelli, Rocco, Paillère Martinot, Marie-Laure, Trouvé, Alain, Banaschewski, Tobias, Bokde, Arun L. W., Desrivières, Sylvane, Flor, Herta, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Brühl, Rüdiger, Nees, Frauke, Papadopoulos Orfanos, Dimitri, Paus, Tomáš, Poustka, Luise, Hohmann, Sarah, Holz, Nathalie, Vaidya, Nilakshi, Fröhner, Juliane H., Smolka, Michael N., Walter, Henrik, Whelan, Robert, Schumann, Gunter, Martinot, Jean-Luc, and Artiges, Eric

Abstract

Perseverative negative thoughts, known as rumination, might arise from emotional challenges and preclude mental health when transitioning into adulthood. Due to its multifaceted nature, rumination can take several ruminative response styles, that diverge in manifestations, severity, and mental health outcomes. Still, prospective ruminative phenotypes remain elusive insofar. Longitudinal study designs are ideal for stratifying ruminative response styles, especially with resting-state functional MRI whose setup naturally elicits people’s ruminative traits. Here, we considered self-rated questionnaires on rumination and psychopathology, along with resting-state functional MRI data in 595 individuals assessed at age 18 and 22 from the IMAGEN cohort. We conducted independent component analysis to characterize eight single static resting-state functional networks in each subject and session and furthermore conducted a dynamic analysis, tackling the time variations of functional networks during the entire scanning time. We then investigated their longitudinal mediation role between changes in three ruminative response styles (reflective pondering, brooding, and depressive rumination) and changes in internalizing and co-morbid externalizing symptoms. Four static and two dynamic networks longitudinally differentiated these ruminative styles and showed complemental sensitivity to internalizing and co-morbid externalizing symptoms. Among these networks, the right frontoparietal network covaried with all ruminative styles but did not play any mediation role towards psychopathology. The default mode, the salience, and the limbic networks prospectively stratified these ruminative styles, suggesting that maladaptive ruminative styles are associated with altered corticolimbic function. For static measures, only the salience network played a longitudinal causal role between brooding rumination and internalizing symptoms. Dynamic measures highlighted the default-mode mediation role between the other ruminative styles and co-morbid externalizing symptoms. In conclusion, we identified the ruminative styles’ psychometric and neural outcome specificities, supporting their translation into applied research on young adult mental healthcare.

Published
2024
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140. Pubertal maturation and sex effects on the default-mode network connectivity implicated in mood dysregulation

Author

Ernst, Monique, Benson, Brenda, Artiges, Eric, Gorka, Adam X., Lemaitre, Herve, Lago, Tiffany, Miranda, Ruben, Banaschewski, Tobias, Bokde, Arun L. W., Bromberg, Uli, Brühl, Rüdiger, Büchel, Christian, Cattrell, Anna, Conrod, Patricia, Desrivières, Sylvane, Fadai, Tahmine, Flor, Herta, Grigis, Antoine, Gallinat, Juergen, Garavan, Hugh, Gowland, Penny, Grimmer, Yvonne, Heinz, Andreas, Kappel, Viola, Nees, Frauke, Papadopoulos-Orfanos, Dimitri, Penttilä, Jani, Poustka, Luise, Smolka, Michael N., Stringaris, Argyris, Struve, Maren, van Noort, Betteke M., Walter, Henrik, Whelan, Robert, Schumann, Gunter, Grillon, Christian, Martinot, Marie-Laure Paillère, Martinot, Jean-Luc, and for the IMAGEN Consortium

Published
2019
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147. Genuine high-order interactions in brain networks and neurodegeneration

Author

Herzog, Rubén, primary, Rosas, Fernando E., additional, Whelan, Robert, additional, Fittipaldi, Sol, additional, Santamaria-Garcia, Hernando, additional, Cruzat, Josephine, additional, Birba, Agustina, additional, Moguilner, Sebastian, additional, Tagliazucchi, Enzo, additional, Prado, Pavel, additional, and Ibanez, Agustin, additional

Published
2022
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148. The EuroLaD‐EEG consortium: towards a global EEG platform for dementia, for seeking to reduce the regional impact of dementia

Author

Parra‐Rodriguez, Mario A, primary, Prado, Pavel, additional, Moguilner, Sebastian, additional, Herzog, Ruben A, additional, Birba, Agustina, additional, Santamaría‐García, Hernando A, additional, Tagliazucchi, Enzo, additional, Reyes, Pablo A, additional, Cruzat, Josefina A, additional, García, Adolfo M., additional, Whelan, Robert, additional, Lopera, Francisco, additional, Ochoa, John F, additional, Anghinah, Renato, additional, and Ibáñez, Agustin, additional

Published
2022
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149. Interpretable automatic detection of incomplete hippocampal inversions using anatomical criteria

Author

Hemforth, Lisa, Cury, Claire, Frouin, Vincent, Desrivières, Sylvane, Grigis, Antoine, Garavan, Hugh, Brühl, Rüdiger, Martinot, Jean-Luc, Paillère Martinot, Marie-Laure, Artiges, Eric, Poustka, Luise, Hohmann, Sarah, Millenet, Sabina, Vaidya, Nilakshi, Walter, Henrik, Whelan, Robert, Schumann, Gunter, Couvy-Duchesne, Baptiste, Colliot, Olivier, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Université de Rennes (UR), Neuroimagerie: méthodes et applications (EMPENN), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAL, IMAGE ET LANGAGE (IRISA-D6), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), King‘s College London, University of Vermont [Burlington], Physikalisch-Technische Bundesanstalt [Berlin] (PTB), Ecole Normale Supérieure Paris-Saclay (ENS Paris Saclay), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), EPS Barthélemy Durand [Etampes], University Medical Center Göttingen (UMG), University Hospital Mannheim | Universitätsmedizin Mannheim, Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], Trinity College Dublin, Fudan University [Shanghai], Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), University of Queensland [Brisbane], ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), ANR-12-SAMA-0004,ADODEP,Dépression à l'Adolescence: Structure cérébrale et myélinisation(2012), ANR-19-CE37-0017,GeBra,Approches translationelles, profilage transcriptomique, et imagerie cérébrale: vers des nouveaux biomarqueurs et réseaux biologiques dans la resilience au stress(2019), ANR-18-NEUR-0002,ADORe,TARGETING ADOLESCENT NEUROCOGNITIVE PROCESSES IN DEPRESSION TO PROMOTE INTERVENTION RESPONSE(2018), European Project: 351475,NHMRC::NHMRC Project Grants(2005), European Project: 695313,STRATIFY, European Project: 785907,H2020,HBP SGA2(2018), and European Project: 945539,H2020,H2020-SGA-FETFLAG-HBP-2019,HBP SGA3(2020)

Subjects
Deep Learning 3D, Machine Learning, Deep Learning, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, [SDV.MHEP.AHA]Life Sciences [q-bio]/Human health and pathology/Tissues and Organs [q-bio.TO], Incomplete Hippocampal Inversion, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Hippocampus, MRI, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; Incomplete Hippocampal Inversion (IHI) is an atypical anatomical pattern of the hippocampus that has been associated with several brain disorders (epilepsy, schizophrenia). IHI can be visually detected on coronal T1 weighted MRI images. IHI can be absent, partial or complete (no IHI, partial IHI, IHI). However, visual evaluation can be long and tedious, justifying the need for an automatic method. In this paper, we propose, to the best of our knowledge, the first automatic IHI detection method from T1-weighted MRI. The originality of our approach is that, instead of directly detecting IHI, we propose to predict several anatomical criteria, which each characterize a particular anatomical feature of IHI, and that can ultimately be combined for IHI detection. Such individual criteria have the advantage of providing interpretable anatomical information regarding the morphological aspect of a given hippocampus. We relied on a large population of 2,008 participants from the IMAGEN study. The approach is general and can be used with different machine learning models. In this paper, we explored two different backbone models for the prediction: a linear method (ridge regression) and a deep convolutional neural network. We demonstrated that the interpretable, anatomical based prediction was at least as good as when predicting directly the presence of IHI, while providing interpretable information to the clinician or neuroscientist. This approach may be applied to other diagnostic tasks which can be characterized radiologically by several anatomical features.

Published
2023

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