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1. Overcoming challenges of translating deep-learning models for glioblastoma: the ZGBM consortium

Author

Shuaib, Haris, Barker, Gareth J, Sasieni, Peter, De Vita, Enrico, Chelliah, Alysha, Andrei, Roman, Ashkan, Keyoumars, Beaumont, Erica, Brazil, Lucy, Rowland-Hill, Chris, Lau, Yue Hui, Luis, Aysha, Powell, James, Swampillai, Angela, Tenant, Sean, Thust, Stefanie C, Wastling, Stephen, Young, Tom, and Booth, Thomas C

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Machine Learning
Abstract

Objective: To report imaging protocol and scheduling variance in routine care of glioblastoma patients in order to demonstrate challenges of integrating deep-learning models in glioblastoma care pathways. Additionally, to understand the most common imaging studies and image contrasts to inform the development of potentially robust deep-learning models. Methods: MR imaging data were analysed from a random sample of five patients from the prospective cohort across five participating sites of the ZGBM consortium. Reported clinical and treatment data alongside DICOM header information were analysed to understand treatment pathway imaging schedules. Results: All sites perform all structural imaging at every stage in the pathway except for the presurgical study, where in some sites only contrast-enhanced T1-weighted imaging is performed. Diffusion MRI is the most common non-structural imaging type, performed at every site. Conclusion: The imaging protocol and scheduling varies across the UK, making it challenging to develop machine-learning models that could perform robustly at other centres. Structural imaging is performed most consistently across all centres. Advances in knowledge: Successful translation of deep-learning models will likely be based on structural post-treatment imaging unless there is significant effort made to standardise non-structural or peri-operative imaging protocols and schedules.

Published
2024

2. A self-supervised text-vision framework for automated brain abnormality detection

Author

Wood, David A., Guilhem, Emily, Kafiabadi, Sina, Busaidi, Ayisha Al, Dissanayake, Kishan, Hammam, Ahmed, Mansoor, Nina, Townend, Matthew, Agarwal, Siddharth, Wei, Yiran, Mazumder, Asif, Barker, Gareth J., Sasieni, Peter, Ourselin, Sebastien, Cole, James H., and Booth, Thomas C.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Artificial neural networks trained on large, expert-labelled datasets are considered state-of-the-art for a range of medical image recognition tasks. However, categorically labelled datasets are time-consuming to generate and constrain classification to a pre-defined, fixed set of classes. For neuroradiological applications in particular, this represents a barrier to clinical adoption. To address these challenges, we present a self-supervised text-vision framework that learns to detect clinically relevant abnormalities in brain MRI scans by directly leveraging the rich information contained in accompanying free-text neuroradiology reports. Our training approach consisted of two-steps. First, a dedicated neuroradiological language model - NeuroBERT - was trained to generate fixed-dimensional vector representations of neuroradiology reports (N = 50,523) via domain-specific self-supervised learning tasks. Next, convolutional neural networks (one per MRI sequence) learnt to map individual brain scans to their corresponding text vector representations by optimising a mean square error loss. Once trained, our text-vision framework can be used to detect abnormalities in unreported brain MRI examinations by scoring scans against suitable query sentences (e.g., 'there is an acute stroke', 'there is hydrocephalus' etc.), enabling a range of classification-based applications including automated triage. Potentially, our framework could also serve as a clinical decision support tool, not only by suggesting findings to radiologists and detecting errors in provisional reports, but also by retrieving and displaying examples of pathologies from historical examinations that could be relevant to the current case based on textual descriptors., Comment: Under Review

Published
2024

3. Regional patterns of human cortex development correlate with underlying neurobiology

Author

Lotter, Leon D., Saberi, Amin, Hansen, Justine Y., Misic, Bratislav, Paquola, Casey, 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, Papadopoulos Orfanos, Dimitri, 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., and Dukart, Juergen

Published
2024
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4. Investigating grey matter volumetric trajectories through the lifespan at the individual level

Author

Shi, Runye, Xiang, Shitong, Jia, Tianye, Robbins, Trevor W., Kang, Jujiao, Banaschewski, Tobias, Barker, Gareth J., Bokde, Arun L. W., Desrivières, Sylvane, Flor, Herta, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Brühl, Rüdiger, Martinot, Jean-Luc, Martinot, Marie-Laure Paillère, Artiges, Eric, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Paus, Tomáš, Poustka, Luise, Hohmann, Sarah, Millenet, Sabina, Fröhner, Juliane H., Smolka, Michael N., Vaidya, Nilakshi, Walter, Henrik, Whelan, Robert, Schumann, Gunter, Lin, Xiaolei, Sahakian, Barbara J., and Feng, Jianfeng

Published
2024
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5. The relationship between negative life events and cortical structural connectivity in adolescents

Author

Sibilia, Francesca, Jost-Mousseau, Coline, Banaschewski, Tobias, Barker, Gareth J., Büchel, Christian, Desrivières, Sylvane, Flor, Herta, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Ittermann, Bernd, Martinot, Jean-Luc, Martinot, Marie-Laure Paillère, Artiges, Eric, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Poustka, Luise, Millenet, Sabina, Fröhner, Juliane H., Smolka, Michael N., Walter, Henrik, Whelan, Robert, Schumann, Gunter, and Bokde, Arun L.W.

Published
2024
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7. Association between vmPFC gray matter volume and smoking initiation in adolescents

Author

Xiang, Shitong, Jia, Tianye, Xie, Chao, Cheng, Wei, Chaarani, Bader, Banaschewski, Tobias, Barker, Gareth J., Bokde, Arun L. W., Büchel, Christian, Desrivières, Sylvane, Flor, Herta, Grigis, Antoine, Gowland, Penny A., Brühl, Rüdiger, Martinot, Jean-Luc, Martinot, Marie-Laure Paillère, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Poustka, Luise, Hohmann, Sarah, Fröhner, Juliane H., Smolka, Michael N., Vaidya, Nilakshi, Walter, Henrik, Whelan, Robert, Garavan, Hugh, Schumann, Gunter, Sahakian, Barbara J., Robbins, Trevor W., and Feng, Jianfeng

Published
2023
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8. Genetic variants associated with longitudinal changes in brain structure across the lifespan

Author

Brouwer, Rachel M, Klein, Marieke, Grasby, Katrina L, Schnack, Hugo G, Jahanshad, Neda, Teeuw, Jalmar, Thomopoulos, Sophia I, Sprooten, Emma, Franz, Carol E, Gogtay, Nitin, Kremen, William S, Panizzon, Matthew S, Olde Loohuis, Loes M, Whelan, Christopher D, Aghajani, Moji, Alloza, Clara, Alnæs, Dag, Artiges, Eric, Ayesa-Arriola, Rosa, Barker, Gareth J, Bastin, Mark E, Blok, Elisabet, Bøen, Erlend, Breukelaar, Isabella A, Bright, Joanna K, Buimer, Elizabeth EL, Bülow, Robin, Cannon, Dara M, Ciufolini, Simone, Crossley, Nicolas A, Damatac, Christienne G, Dazzan, Paola, de Mol, Casper L, de Zwarte, Sonja MC, Desrivières, Sylvane, Díaz-Caneja, Covadonga M, Doan, Nhat Trung, Dohm, Katharina, Fröhner, Juliane H, Goltermann, Janik, Grigis, Antoine, Grotegerd, Dominik, Han, Laura KM, Harris, Mathew A, Hartman, Catharina A, Heany, Sarah J, Heindel, Walter, Heslenfeld, Dirk J, Hohmann, Sarah, Ittermann, Bernd, Jansen, Philip R, Janssen, Joost, Jia, Tianye, Jiang, Jiyang, Jockwitz, Christiane, Karali, Temmuz, Keeser, Daniel, Koevoets, Martijn GJC, Lenroot, Rhoshel K, Malchow, Berend, Mandl, René CW, Medel, Vicente, Meinert, Susanne, Morgan, Catherine A, Mühleisen, Thomas W, Nabulsi, Leila, Opel, Nils, de la Foz, Víctor Ortiz-García, Overs, Bronwyn J, Paillère Martinot, Marie-Laure, Redlich, Ronny, Marques, Tiago Reis, Repple, Jonathan, Roberts, Gloria, Roshchupkin, Gennady V, Setiaman, Nikita, Shumskaya, Elena, Stein, Frederike, Sudre, Gustavo, Takahashi, Shun, Thalamuthu, Anbupalam, Tordesillas-Gutiérrez, Diana, van der Lugt, Aad, van Haren, Neeltje EM, Wardlaw, Joanna M, Wen, Wei, Westeneng, Henk-Jan, Wittfeld, Katharina, Zhu, Alyssa H, Zugman, Andre, Armstrong, Nicola J, Bonfiglio, Gaia, Bralten, Janita, Dalvie, Shareefa, Davies, Gail, Di Forti, Marta, Ding, Linda, Donohoe, Gary, Forstner, Andreas J, and Gonzalez-Peñas, Javier

Subjects
Biological Psychology, Psychology, Genetics, Biomedical Imaging, Mental Health, Biotechnology, Human Genome, Prevention, Aging, Brain Disorders, Neurosciences, 2.1 Biological and endogenous factors, Aetiology, Mental health, Neurological, Brain, Genome-Wide Association Study, Humans, Longevity, Magnetic Resonance Imaging, IMAGEN Consortium, Cognitive Sciences, Neurology & Neurosurgery, Biological psychology
Abstract

Human brain structure changes throughout the lifespan. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental and neurodegenerative diseases. In this study, we identified common genetic variants that affect rates of brain growth or atrophy in what is, to our knowledge, the first genome-wide association meta-analysis of changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 15,640 individuals were used to compute rates of change for 15 brain structures. The most robustly identified genes GPR139, DACH1 and APOE are associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive functioning, insomnia, height, body mass index and smoking. Gene set findings implicate both early brain development and neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help to determine biological pathways underlying optimal and dysfunctional brain development and aging.

Published
2022

9. Automated triaging of head MRI examinations using convolutional neural networks

Author

Wood, David A., Kafiabadi, Sina, Busaidi, Ayisha Al, Guilhem, Emily, Montvila, Antanas, Agarwal, Siddharth, Lynch, Jeremy, Townend, Matthew, Barker, Gareth, Ourselin, Sebastien, Cole, James H., and Booth, Thomas C.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

The growing demand for head magnetic resonance imaging (MRI) examinations, along with a global shortage of radiologists, has led to an increase in the time taken to report head MRI scans around the world. For many neurological conditions, this delay can result in increased morbidity and mortality. An automated triaging tool could reduce reporting times for abnormal examinations by identifying abnormalities at the time of imaging and prioritizing the reporting of these scans. In this work, we present a convolutional neural network for detecting clinically-relevant abnormalities in $\text{T}_2$-weighted head MRI scans. Using a validated neuroradiology report classifier, we generated a labelled dataset of 43,754 scans from two large UK hospitals for model training, and demonstrate accurate classification (area under the receiver operating curve (AUC) = 0.943) on a test set of 800 scans labelled by a team of neuroradiologists. Importantly, when trained on scans from only a single hospital the model generalized to scans from the other hospital ($\Delta$AUC $\leq$ 0.02). A simulation study demonstrated that our model would reduce the mean reporting time for abnormal examinations from 28 days to 14 days and from 9 days to 5 days at the two hospitals, demonstrating feasibility for use in a clinical triage environment., Comment: Accepted as an oral presentation at Medical Imaging with Deep Learning (MIDL) 2021

Published
2021

10. A shared neural basis underlying psychiatric comorbidity

Author

Xie, Chao, Xiang, Shitong, Shen, Chun, Peng, Xuerui, Kang, Jujiao, Li, Yuzhu, Cheng, Wei, He, Shiqi, Banaschewski, Tobias, Barker, Gareth J., Bokde, Arun L. W., Bromberg, Uli, Büchel, Christian, 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, Fröhner, Juliane H., Smolka, Michael N., Walter, Henrik, Whelan, Robert, Sahakian, Barbara J., Robbins, Trevor W., Schumann, Gunter, Jia, Tianye, and Feng, Jianfeng

Published
2023
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11. Reward Processing in Novelty Seekers: A Transdiagnostic Psychiatric Imaging Biomarker.

Author

Qi, Shile, Schumann, Gunter, Bustillo, Juan, Turner, Jessica A, Jiang, Rongtao, Zhi, Dongmei, Fu, Zening, Mayer, Andrew R, Vergara, Victor M, Silva, Rogers F, Iraji, Armin, Chen, Jiayu, Damaraju, Eswar, Ma, Xiaohong, Yang, Xiao, Stevens, Michael, Mathalon, Daniel H, Ford, Judith M, Voyvodic, James, Mueller, Bryon A, Belger, Aysenil, Potkin, Steven G, Preda, Adrian, Zhuo, Chuanjun, Xu, Yong, Chu, Congying, Banaschewski, Tobias, Barker, Gareth J, Bokde, Arun LW, Quinlan, Erin Burke, Desrivières, Sylvane, Flor, Herta, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Martinot, Jean-Luc, Paillère Martinot, Marie-Laure, Artiges, Eric, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Paus, Tomáš, Poustka, Luise, Hohmann, Sarah, Fröhner, Juliane H, Smolka, Michael N, Walter, Henrik, Whelan, Robert, Calhoun, Vince D, Sui, Jing, and IMAGEN Consortium

Subjects
IMAGEN Consortium, Humans, Magnetic Resonance Imaging, Reward, Attention Deficit Disorder with Hyperactivity, Depressive Disorder, Major, Adolescent, Adult, Young Adult, Biomarkers, ADHD, Attention-deficit/hyperactivity disorder, MDD, Major depressive disorders, Novelty seeking, Reward processing, Schizophrenia, Substance use, Prevention, Serious Mental Illness, Pediatric, Mental Health, Neurosciences, Brain Disorders, Substance Misuse, Behavioral and Social Science, Depression, Alcoholism, Alcohol Use and Health, Clinical Research, 2.1 Biological and endogenous factors, Aetiology, Mental health, Good Health and Well Being, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

BackgroundDysfunctional reward processing is implicated in multiple mental disorders. Novelty seeking (NS) assesses preference for seeking novel experiences, which is linked to sensitivity to reward environmental cues.MethodsA subset of 14-year-old adolescents (IMAGEN) with the top 20% ranked high-NS scores was used to identify high-NS-associated multimodal components by supervised fusion. These features were then used to longitudinally predict five different risk scales for the same and unseen subjects (an independent dataset of subjects at 19 years of age that was not used in predictive modeling training at 14 years of age) (within IMAGEN, n ≈1100) and even for the corresponding symptom scores of five types of patient cohorts (non-IMAGEN), including drinking (n = 313), smoking (n = 104), attention-deficit/hyperactivity disorder (n = 320), major depressive disorder (n = 81), and schizophrenia (n = 147), as well as to classify different patient groups with diagnostic labels.ResultsMultimodal biomarkers, including the prefrontal cortex, striatum, amygdala, and hippocampus, associated with high NS in 14-year-old adolescents were identified. The prediction models built on these features are able to longitudinally predict five different risk scales, including alcohol drinking, smoking, hyperactivity, depression, and psychosis for the same and unseen 19-year-old adolescents and even predict the corresponding symptom scores of five types of patient cohorts. Furthermore, the identified reward-related multimodal features can classify among attention-deficit/hyperactivity disorder, major depressive disorder, and schizophrenia with an accuracy of 87.2%.ConclusionsAdolescents with higher NS scores can be used to reveal brain alterations in the reward-related system, implicating potential higher risk for subsequent development of multiple disorders. The identified high-NS-associated multimodal reward-related signatures may serve as a transdiagnostic neuroimaging biomarker to predict disease risks or severity.

Published
2021

12. Labelling imaging datasets on the basis of neuroradiology reports: a validation study

Author

Wood, David A., Kafiabadi, Sina, Busaidi, Aisha Al, Guilhem, Emily, Lynch, Jeremy, Townend, Matthew, Montvila, Antanas, Siddiqui, Juveria, Gadapa, Naveen, Benger, Matthew, Barker, Gareth, Ourselin, Sebastian, Cole, James H., and Booth, Thomas C.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Natural language processing (NLP) shows promise as a means to automate the labelling of hospital-scale neuroradiology magnetic resonance imaging (MRI) datasets for computer vision applications. To date, however, there has been no thorough investigation into the validity of this approach, including determining the accuracy of report labels compared to image labels as well as examining the performance of non-specialist labellers. In this work, we draw on the experience of a team of neuroradiologists who labelled over 5000 MRI neuroradiology reports as part of a project to build a dedicated deep learning-based neuroradiology report classifier. We show that, in our experience, assigning binary labels (i.e. normal vs abnormal) to images from reports alone is highly accurate. In contrast to the binary labels, however, the accuracy of more granular labelling is dependent on the category, and we highlight reasons for this discrepancy. We also show that downstream model performance is reduced when labelling of training reports is performed by a non-specialist. To allow other researchers to accelerate their research, we make our refined abnormality definitions and labelling rules available, as well as our easy-to-use radiology report labelling app which helps streamline this process.

Published
2020

13. Automated Labelling using an Attention model for Radiology reports of MRI scans (ALARM)

Author

Wood, David A., Lynch, Jeremy, Kafiabadi, Sina, Guilhem, Emily, Busaidi, Aisha Al, Montvila, Antanas, Varsavsky, Thomas, Siddiqui, Juveria, Gadapa, Naveen, Townend, Matthew, Kiik, Martin, Patel, Keena, Barker, Gareth, Ourselin, Sebastian, Cole, James H., and Booth, Thomas C.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Labelling large datasets for training high-capacity neural networks is a major obstacle to the development of deep learning-based medical imaging applications. Here we present a transformer-based network for magnetic resonance imaging (MRI) radiology report classification which automates this task by assigning image labels on the basis of free-text expert radiology reports. Our model's performance is comparable to that of an expert radiologist, and better than that of an expert physician, demonstrating the feasibility of this approach. We make code available online for researchers to label their own MRI datasets for medical imaging applications.

Published
2020

15. Author Correction: A shared neural basis underlying psychiatric comorbidity

Author

Xie, Chao, Xiang, Shitong, Shen, Chun, Peng, Xuerui, Kang, Jujiao, Li, Yuzhu, Cheng, Wei, He, Shiqi, Bobou, Marina, Broulidakis, M. John, van Noort, Betteke Maria, Zhang, Zuo, Robinson, Lauren, Vaidya, Nilakshi, Winterer, Jeanne, Zhang, Yuning, King, Sinead, Banaschewski, Tobias, Barker, Gareth J., Bokde, Arun L. W., Bromberg, Uli, Büchel, Christian, Flor, Herta, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Ittermann, Bernd, Lemaître, Hervé, Martinot, Jean-Luc, Martinot, Marie-Laure Paillère, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Paus, Tomáš, Poustka, Luise, Fröhner, Juliane H., Schmidt, Ulrike, Sinclair, Julia, Smolka, Michael N., Stringaris, Argyris, Walter, Henrik, Whelan, Robert, Desrivières, Sylvane, Sahakian, Barbara J., Robbins, Trevor W., Schumann, Gunter, Jia, Tianye, and Feng, Jianfeng

Published
2023
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16. Impaired verbal memory function is related to anterior cingulate glutamate levels in schizophrenia: findings from the STRATA study

Author

Griffiths, Kira, Egerton, Alice, Millgate, Edward, Anton, Adriana, Barker, Gareth J., Deakin, Bill, Drake, Richard, Eliasson, Emma, Gregory, Catherine J., Howes, Oliver D., Kravariti, Eugenia, Lawrie, Stephen M., Lewis, Shôn, Lythgoe, David J., Murphy, Anna, McGuire, Philip, Semple, Scott, Stockton-Powdrell, Charlotte, Walters, James T. R., Williams, Stephen R., and MacCabe, James H.

Published
2022
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21. CREB Transcription in the Medial Prefrontal Cortex Regulates the Formation of Long-Term Associative Recognition Memory

Author

Barker, Gareth Robert, Wong, Liang Fong, Uney, James B., and Warburton, Elizabeth C.

Abstract

The medial prefrontal cortex (mPFC) is known to be critical for specific forms of long-term recognition memory, however the cellular mechanisms in the mPFC that underpin memory maintenance have not been well characterized. This study examined the importance of phosphorylation of cAMP responsive element binding protein (CREB) in the mPFC for different forms of long-term recognition memory in the rat. Adenoviral transduction of the mPFC with a dominant-negative inhibitor of CREB impaired object-in-place memory following a 6 or 24 h retention delay, but no impairment was observed following delays of 5 min or 3 h. Long-term object temporal order memory and spatial temporal order memory was also impaired. In contrast, there were no impairments in novel object recognition or object location memory. These results establish, for the first time, the importance of CREB phosphorylation within the mPFC for memory of associative and temporal information crucial to recognition.

Published
2020
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22. Optimising brain age estimation through transfer learning: A suite of pre‐trained foundation models for improved performance and generalisability in a clinical setting

Author

Wood, David A., primary, Townend, Matthew, additional, Guilhem, Emily, additional, Kafiabadi, Sina, additional, Hammam, Ahmed, additional, Wei, Yiran, additional, Al Busaidi, Ayisha, additional, Mazumder, Asif, additional, Sasieni, Peter, additional, Barker, Gareth J., additional, Ourselin, Sebastien, additional, Cole, James H., additional, and Booth, Thomas C., additional

Published
2024
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23. Human subcortical brain asymmetries in 15,847 people worldwide reveal effects of age and sex.

Author

Guadalupe, Tulio, Mathias, Samuel R, vanErp, Theo GM, Whelan, Christopher D, Zwiers, Marcel P, Abe, Yoshinari, Abramovic, Lucija, Agartz, Ingrid, Andreassen, Ole A, Arias-Vásquez, Alejandro, Aribisala, Benjamin S, Armstrong, Nicola J, Arolt, Volker, Artiges, Eric, Ayesa-Arriola, Rosa, Baboyan, Vatche G, Banaschewski, Tobias, Barker, Gareth, Bastin, Mark E, Baune, Bernhard T, Blangero, John, Bokde, Arun LW, Boedhoe, Premika SW, Bose, Anushree, Brem, Silvia, Brodaty, Henry, Bromberg, Uli, Brooks, Samantha, Büchel, Christian, Buitelaar, Jan, Calhoun, Vince D, Cannon, Dara M, Cattrell, Anna, Cheng, Yuqi, Conrod, Patricia J, Conzelmann, Annette, Corvin, Aiden, Crespo-Facorro, Benedicto, Crivello, Fabrice, Dannlowski, Udo, de Zubicaray, Greig I, de Zwarte, Sonja MC, Deary, Ian J, Desrivières, Sylvane, Doan, Nhat Trung, Donohoe, Gary, Dørum, Erlend S, Ehrlich, Stefan, Espeseth, Thomas, Fernández, Guillén, Flor, Herta, Fouche, Jean-Paul, Frouin, Vincent, Fukunaga, Masaki, Gallinat, Jürgen, Garavan, Hugh, Gill, Michael, Suarez, Andrea Gonzalez, Gowland, Penny, Grabe, Hans J, Grotegerd, Dominik, Gruber, Oliver, Hagenaars, Saskia, Hashimoto, Ryota, Hauser, Tobias U, Heinz, Andreas, Hibar, Derrek P, Hoekstra, Pieter J, Hoogman, Martine, Howells, Fleur M, Hu, Hao, Hulshoff Pol, Hilleke E, Huyser, Chaim, Ittermann, Bernd, Jahanshad, Neda, Jönsson, Erik G, Jurk, Sarah, Kahn, Rene S, Kelly, Sinead, Kraemer, Bernd, Kugel, Harald, Kwon, Jun Soo, Lemaitre, Herve, Lesch, Klaus-Peter, Lochner, Christine, Luciano, Michelle, Marquand, Andre F, Martin, Nicholas G, Martínez-Zalacaín, Ignacio, Martinot, Jean-Luc, Mataix-Cols, David, Mather, Karen, McDonald, Colm, McMahon, Katie L, Medland, Sarah E, Menchón, José M, Morris, Derek W, Mothersill, Omar, Maniega, Susana Munoz, and Mwangi, Benson

Subjects
Brain, Humans, Magnetic Resonance Imaging, Organ Size, Aging, Sex Characteristics, Quantitative Trait, Heritable, Adolescent, Adult, Aged, Middle Aged, Female, Male, Functional Laterality, Meta-Analysis as Topic, Young Adult, Age, Enigma, Handedness, Heritability, Meta-analysis, Sex, Subcortical brain asymmetry, Quantitative Trait, Heritable, Medical and Health Sciences, Psychology and Cognitive Sciences, Experimental Psychology
Abstract

The two hemispheres of the human brain differ functionally and structurally. Despite over a century of research, the extent to which brain asymmetry is influenced by sex, handedness, age, and genetic factors is still controversial. Here we present the largest ever analysis of subcortical brain asymmetries, in a harmonized multi-site study using meta-analysis methods. Volumetric asymmetry of seven subcortical structures was assessed in 15,847 MRI scans from 52 datasets worldwide. There were sex differences in the asymmetry of the globus pallidus and putamen. Heritability estimates, derived from 1170 subjects belonging to 71 extended pedigrees, revealed that additive genetic factors influenced the asymmetry of these two structures and that of the hippocampus and thalamus. Handedness had no detectable effect on subcortical asymmetries, even in this unprecedented sample size, but the asymmetry of the putamen varied with age. Genetic drivers of asymmetry in the hippocampus, thalamus and basal ganglia may affect variability in human cognition, including susceptibility to psychiatric disorders.

Published
2017

24. A letter to the editor:The effects of alcohol use on brain glutamate in first episode psychosis

Author

King, Bridget, Kempton, Matthew J., Broberg, Brian V., Merritt, Kate, Barker, Gareth J., Lythgoe, David J., Perez-Iglesias, Rocio, Baandrup, Lone, Düring, Signe W., Stone, James M., Rostrup, Egill, Sommer, Iris E., Glenthøj, Birte, Kahn, René S., Dazzan, Paola, McGuire, Philip K., Egerton, Alice, King, Bridget, Kempton, Matthew J., Broberg, Brian V., Merritt, Kate, Barker, Gareth J., Lythgoe, David J., Perez-Iglesias, Rocio, Baandrup, Lone, Düring, Signe W., Stone, James M., Rostrup, Egill, Sommer, Iris E., Glenthøj, Birte, Kahn, René S., Dazzan, Paola, McGuire, Philip K., and Egerton, Alice

Abstract

Alcohol use in schizophrenia is associated with poorer outcomes, such as increased suicidality and positive symptoms (Lv et al., 2023). Both alcohol use and psychosis have been independently associated with changes in brain glutamate levels. However, whether alcohol use impacts brain glutamate levels in people with psychosis is unknown. Potentially, overlapping glutamatergic pathophysiology could mean that, patients with schizophrenia are more vulnerable to the effects of alcohol.

Published
2024

25. Nonlinear functional mapping of the human brain

Author

Allgaier, Nicholas, Banaschewski, Tobias, Barker, Gareth, Bokde, Arun L. W., Bongard, Josh C., Bromberg, Uli, Büchel, Christian, Cattrell, Anna, Conrod, Patricia J., Danforth, Christopher M., Desrivières, Sylvane, Dodds, Peter S., Flor, Herta, Frouin, Vincent, Gallinat, Jürgen, Gowland, Penny, Heinz, Andreas, Ittermann, Bernd, Mackey, Scott, Martinot, Jean-Luc, Murphy, Kevin, Nees, Frauke, Papadopoulos-Orfanos, Dimitri, Poustka, Luise, Smolka, Michael N., Walter, Henrik, Whelan, Robert, Schumann, Gunter, Garavan, Hugh, and Consortium, IMAGEN

Subjects
Quantitative Biology - Neurons and Cognition, Computer Science - Neural and Evolutionary Computing
Abstract

The field of neuroimaging has truly become data rich, and novel analytical methods capable of gleaning meaningful information from large stores of imaging data are in high demand. Those methods that might also be applicable on the level of individual subjects, and thus potentially useful clinically, are of special interest. In the present study, we introduce just such a method, called nonlinear functional mapping (NFM), and demonstrate its application in the analysis of resting state fMRI from a 242-subject subset of the IMAGEN project, a European study of adolescents that includes longitudinal phenotypic, behavioral, genetic, and neuroimaging data. NFM employs a computational technique inspired by biological evolution to discover and mathematically characterize interactions among ROI (regions of interest), without making linear or univariate assumptions. We show that statistics of the resulting interaction relationships comport with recent independent work, constituting a preliminary cross-validation. Furthermore, nonlinear terms are ubiquitous in the models generated by NFM, suggesting that some of the interactions characterized here are not discoverable by standard linear methods of analysis. We discuss one such nonlinear interaction in the context of a direct comparison with a procedure involving pairwise correlation, designed to be an analogous linear version of functional mapping. We find another such interaction that suggests a novel distinction in brain function between drinking and non-drinking adolescents: a tighter coupling of ROI associated with emotion, reward, and interoceptive processes such as thirst, among drinkers. Finally, we outline many improvements and extensions of the methodology to reduce computational expense, complement other analytical tools like graph-theoretic analysis, and allow for voxel level NFM to eliminate the necessity of ROI selection., Comment: 21 pages, 12 figures, and 1 table

Published
2015

26. The empirical replicability of task-based fMRI as a function of sample size

Author

Bossier, Han, Roels, Sanne P., Seurinck, Ruth, Banaschewski, Tobias, Barker, Gareth J., Bokde, Arun L.W., Quinlan, Erin Burke, Desrivières, Sylvane, Flor, Herta, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Ittermann, Bernd, Martinot, Jean-Luc, Artiges, Eric, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Poustka, Luise, Fröhner Dipl-Psych, Juliane H., Smolka, Michael N., Walter, Henrik, Whelan, Robert, Schumann, Gunter, and Moerkerke, Beatrijs

Published
2020
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27. Factors affecting the labelling accuracy of brain MRI studies relevant for deep learning abnormality detection

Author

Benger, Matthew, primary, Wood, David A., additional, Kafiabadi, Sina, additional, Al Busaidi, Aisha, additional, Guilhem, Emily, additional, Lynch, Jeremy, additional, Townend, Matthew, additional, Montvila, Antanas, additional, Siddiqui, Juveria, additional, Gadapa, Naveen, additional, Barker, Gareth, additional, Ourselin, Sebastian, additional, Cole, James H., additional, and Booth, Thomas C., additional

Published
2023
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30. Adolescent to young adult longitudinal development of subcortical volumes in two European sites with four waves.

Author

Backhausen, Lea L., Fröhner, Juliane H., Lemaître, Hervé, Artiges, Eric, Martinot, Marie‐Laure Palillère, Herting, Megan M., Sticca, Fabio, Banaschewski, Tobias, Barker, Gareth J., Bokde, Arun L. W., Desrivières, Sylvane, Flor, Herta, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Brühl, Rüdiger, Nees, Frauke, Papadopoulos‐Orfanos, Dimitri, and Poustka, Luise

Subjects
YOUNG adults, ADULT development, TEENAGERS, ADOLESCENT development, TEENAGE boys
Abstract

Adolescent subcortical structural brain development might underlie psychopathological symptoms, which often emerge in adolescence. At the same time, sex differences exist in psychopathology, which might be mirrored in underlying sex differences in structural development. However, previous studies showed inconsistencies in subcortical trajectories and potential sex differences. Therefore, we aimed to investigate the subcortical structural trajectories and their sex differences across adolescence using for the first time a single cohort design, the same quality control procedure, software, and a general additive mixed modeling approach. We investigated two large European sites from ages 14 to 24 with 503 participants and 1408 total scans from France and Germany as part of the IMAGEN project including four waves of data acquisition. We found significantly larger volumes in males versus females in both sites and across all seven subcortical regions. Sex differences in age‐related trajectories were observed across all regions in both sites. Our findings provide further evidence of sex differences in longitudinal adolescent brain development of subcortical regions and thus might eventually support the relationship of underlying brain development and different adolescent psychopathology in boys and girls. [ABSTRACT FROM AUTHOR]

Published
2024
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32. Neurocognitive Analysis of Low-level Arsenic Exposure and Executive Function Mediated by Brain Anomalies Among Children, Adolescents, and Young Adults in India

Author

Vaidya, Nilakshi, Holla, Bharath, Heron, Jon, Sharma, Eesha, Zhang, Yuning, Fernandes, Gwen, Iyengar, Udita, Spiers, Alex, Yadav, Anupa, Das, Surajit, Roy, Sanjit, Ahuja, Chirag K, Barker, Gareth J, Basu, Debasish, Bharath, Rose Dawn, Hickman, Matthew, Jain, Sanjeev, Kalyanram, Kartik, Kartik, Kamakshi, Krishna, Murali, Krishnaveni, Ghattu, Kumaran, Kalyanaraman, Kuriyan, Rebecca, Murthy, Pratima, Papadopoulos Orfanos, Dimitri, Purushottam, Meera, Kurpad, Sunita Simon, Singh, Lenin, Singh, Roshan, Subodh, B N, Toledano, Mireille, Walter, Henrik, Desrivières, Sylvane, Chakrabarti, Amit, Benegal, Vivek, and Schumann, Gunter

Subjects
Male, Cohort Studies, Young Adult, Executive Function, Brain Diseases, Cross-Sectional Studies, Adolescent, Humans, Brain/pathology, Child, Arsenic
Abstract

IMPORTANCE: Arsenic, a contaminant of groundwater and irrigated crops, is a global public health hazard. Exposure to low levels of arsenic through food extends well beyond the areas with high arsenic content in water.OBJECTIVE: To identify cognitive impairments following commonly prevalent low-level arsenic exposure and characterize their underlying brain mechanisms.DESIGN, SETTING, AND PARTICIPANTS: This multicenter population-based cohort study analyzed cross-sectional data of the Indian Consortium on Vulnerability to Externalizing Disorders and Addictions (cVEDA) cohort, recruited between November 4, 2016, and May 4, 2019. Participants aged 6 to 23 years were characterized using deep phenotyping measures of behavior, neuropsychology, psychopathology, brain neuroimaging, and exposure to developmental adversities and environmental neurotoxins. All analyses were performed between June 1, 2020, and December 31, 2021.EXPOSURE: Arsenic levels were measured in urine as an index of exposure.MAIN OUTCOMES AND MEASURES: Executive function measured using the cVEDA neuropsychological battery, gray matter volume (GMV) from T1-weighted magnetic resonance imaging, and functional network connectivity measures from resting state functional magnetic resonance imaging.RESULTS: A total of 1014 participants aged 6 to 23 years (589 male [58.1%]; mean [SD] age, 14.86 [4.79] years) were included from 5 geographic locations. Sparse-partial least squares analysis was used to describe a negative association of arsenic exposure with executive function (r = -0.12 [P = 5.4 × 10-4]), brain structure (r = -0.20 [P = 1.8 × 10-8]), and functional connectivity (within network, r = -0.12 [P = 7.5 × 10-4]; between network, r = -0.23 [P = 1.8 × 10-10]). Alterations in executive function were partially mediated by GMV (b = -0.004 [95% CI, -0.007 to -0.002]) and within-network functional connectivity (b = -0.004 [95% CI, -0.008 to -0.002]). Socioeconomic status and body mass index moderated the association between arsenic and GMV, such that the association was strongest in participants with lower socioeconomic status and body mass index.CONCLUSIONS AND RELEVANCE: The findings of this cross-sectional study suggest that low-level arsenic exposure was associated with alterations in executive functioning and underlying brain correlates. These results indicate potential detrimental consequences of arsenic exposure that are below the currently recommended guidelines and may extend beyond endemic risk areas. Precision medicine approaches to study global mental health vulnerabilities highlight widespread but potentially modifiable risk factors and a mechanistic understanding of the impact of low-level arsenic exposure on brain development.

Published
2023

33. 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

35. Circadian regulation of hippocampal function is disrupted with corticosteroid treatment

Author

Birnie, Matthew T., primary, Claydon, Matthew D. B., additional, Troy, Oliver, additional, Flynn, Benjamin P., additional, Yoshimura, Mitsuhiro, additional, Kershaw, Yvonne M., additional, Zhao, Zidong, additional, Demski-Allen, Rebecca C. R., additional, Barker, Gareth R. I., additional, Warburton, E. Clea, additional, Bortolotto, Zuner A., additional, Lightman, Stafford L., additional, and Conway-Campbell, Becky L., additional

Published
2023
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36. Longitudinal associations between adolescent catch-up sleep, white-matter maturation and internalizing problems

Author

Guldner, Stella, primary, Sarvasmaa, Anna S., additional, Lemaître, Hervé, additional, Massicotte, Jessica, additional, Vulser, Hélène, additional, Miranda, Ruben, additional, Bezivin – Frère, Pauline, additional, Filippi, Irina, additional, Penttilä, Jani, additional, Banaschewski, Tobias, additional, Barker, Gareth J, additional, Bokde, Arun LW, additional, Bromberg, Uli, additional, Büchel, Christian, additional, Conrod, Patricia J, additional, Desrivières, Sylvane, additional, Flor, Herta, additional, Frouin, Vincent, additional, Gallinat, Jürgen, additional, Garavan, Hugh, additional, Gowland, Penny, additional, Heinz, Andreas, additional, Nees, Frauke, additional, Papadopoulos-Orfanos, Dimitri, additional, Smolka, Michael N, additional, Schumann, Gunter, additional, Artiges, Eric, additional, Martinot, Marie-Laure Paillère, additional, and Martinot, Jean-Luc, additional

Published
2023
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38. Neural basis of reward anticipation and its genetic determinants

Author

IMAGEN Consortium, Jia, Tianye, Macare, Christine, Desrivières, Sylvane, Gonzalez, Dante A., Tao, Chenyang, Ji, Xiaoxi, Ruggeri, Barbara, Nees, Frauke, Banaschewski, Tobias, Barker, Gareth J., Bokde, Arun L. W., Bromberg, Uli, Büchel, Christian, Conrod, Patricia J., Dove, Rachel, Frouin, Vincent, Gallinat, Jürgen, Garavan, Hugh, Gowland, Penny A., Heinz, Andreas, Ittermann, Bernd, Lathrop, Mark, Lemaitre, Hervé, Martinot, Jean-Luc, Paus, Tomáš, Pausova, Zdenka, Poline, Jean-Baptiste, Rietschel, Marcella, Robbins, Trevor, Smolka, Michael N., Müller, Christian P., Feng, Jianfeng, Rothenfluh, Adrian, Flor, Herta, and Schumann, Gunter

Published
2016

46. Bayesian causal network modeling suggests adolescent cannabis use accelerates prefrontal cortical thinning

Author

Owens, Max M., Albaugh, Matthew D., Allgaier, Nicholas, Yuan, Dekang, Robert, Gabriel, Cupertino, Renata B., Spechler, Philip A., Juliano, Anthony, Hahn, Sage, Banaschewski, Tobias, Bokde, Arun L.W., Flor, Herta, Grigis, Antoine, Gowland, Penny, Heinz, Andreas, Martinot, Jean Luc, Artiges, Eric, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Lemaitre, Herve, Poustka, Luise, Millenet, Sabina, Smolka, Michael N., Walter, Henrik, Whelan, Robert, Mackey, Scott, Schumann, Gunter, Garavan, Hugh, Barker, Gareth J., and Hohmann, Sarah

Subjects
Cellular and Molecular Neuroscience, Psychiatry and Mental health, Sir Peter Mansfield Imaging Centre (SPMIC), Biological Psychiatry
Abstract

While there is substantial evidence that cannabis use is associated with differences in human brain development, most of this evidence is correlational in nature. Bayesian causal network (BCN) modeling attempts to identify probable causal relationships in correlational data using conditional probabilities to estimate directional associations between a set of interrelated variables. In this study, we employed BCN modeling in 637 adolescents from the IMAGEN study who were cannabis naïve at age 14 to provide evidence that the accelerated prefrontal cortical thinning found previously in adolescent cannabis users by Albaugh et al. [1] is a result of cannabis use causally affecting neurodevelopment. BCNs incorporated data on cannabis use, prefrontal cortical thickness, and other factors related to both brain development and cannabis use, including demographics, psychopathology, childhood adversity, and other substance use. All BCN algorithms strongly suggested a directional relationship from adolescent cannabis use to accelerated cortical thinning. While BCN modeling alone does not prove a causal relationship, these results are consistent with a body of animal and human research suggesting that adolescent cannabis use adversely affects brain development.

Published
2022

47. Automated triaging of head MRI examinations using convolutional neural networks

Author

Wood, David A., Kafiabadi, Sina, Busaidi, Ayisha Al, Guilhem, Emily, Montvila, Antanas, Agarwal, Siddharth, Lynch, Jeremy, Townend, Matthew, Barker, Gareth, Ourselin, Sebastien, Cole, James H., and Booth, Thomas C.

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

The growing demand for head magnetic resonance imaging (MRI) examinations, along with a global shortage of radiologists, has led to an increase in the time taken to report head MRI scans around the world. For many neurological conditions, this delay can result in increased morbidity and mortality. An automated triaging tool could reduce reporting times for abnormal examinations by identifying abnormalities at the time of imaging and prioritizing the reporting of these scans. In this work, we present a convolutional neural network for detecting clinically-relevant abnormalities in $\text{T}_2$-weighted head MRI scans. Using a validated neuroradiology report classifier, we generated a labelled dataset of 43,754 scans from two large UK hospitals for model training, and demonstrate accurate classification (area under the receiver operating curve (AUC) = 0.943) on a test set of 800 scans labelled by a team of neuroradiologists. Importantly, when trained on scans from only a single hospital the model generalized to scans from the other hospital ($\Delta$AUC $\leq$ 0.02). A simulation study demonstrated that our model would reduce the mean reporting time for abnormal examinations from 28 days to 14 days and from 9 days to 5 days at the two hospitals, demonstrating feasibility for use in a clinical triage environment., Comment: Accepted as an oral presentation at Medical Imaging with Deep Learning (MIDL) 2021

Published
2022

48. Erratum:Correction to: 3D APT and NOE CEST-MRI of healthy volunteers and patients with non-enhancing glioma at 3 T (Magma (New York, N.Y.) (2022) 35 1 (63-73))

Author

Wu, Yulun, Wood, Tobias C, Arzanforoosh, Fatemeh, Hernandez-Tamames, Juan A, Barker, Gareth J, Smits, Marion, and Warnert, Esther A H

Abstract

The original version of this article unfortunately contained a mistake. The following author names were incorrectly structured. Tobias C. Wood Gareth J. Barker Juan A. Hernandez-Tamames Esther A. H. Warnert The original article has been corrected.

Published
2022

49. Correction to: 3D APT and NOE CEST-MRI of healthy volunteers and patients with non-enhancing glioma at 3 T (Magnetic Resonance Materials in Physics, Biology and Medicine, (2022), 10.1007/s10334-021-00996-z)

Author

Wu, Yulun, Wood, Tobias C., Arzanforoosh, Fatemeh, Hernandez-Tamames, Juan A., Barker, Gareth J., Smits, Marion, Warnert, Esther A.H., and Radiology & Nuclear Medicine

Abstract

The original version of this article unfortunately contained a mistake. The following author names were incorrectly structured. Tobias C. Wood Gareth J. Barker Juan A. Hernandez-Tamames Esther A. H. Warnert The original article has been corrected.

Published
2022

50. Global urbanicity is associated with brain and behaviour in young people

Author

Xu, Jiayuan, Liu, Xiaoxuan, Li, Qiaojun, Goldblatt, Ran, Qin, Wen, Liu, Feng, Chu, Congying, Luo, Qiang, Ing, Alex, Guo, Lining, Liu, Nana, Liu, Huaigui, Huang, Conghong, Cheng, Jingliang, Wang, Meiyun, Geng, Zuojun, Zhu, Wenzhen, Zhang, Bing, Liao, Weihua, Qiu, Shijun, Zhang, Hui, Xu, Xiaojun, Yu, Yongqiang, Gao, Bo, Han, Tong, Cui, Guangbin, Chen, Feng, Xian, Junfang, Li, Jiance, Zhang, Jing, Zuo, Xi-Nian, Wang, Dawei, Shen, Wen, Miao, Yanwei, Yuan, Fei, Lui, Su, Zhang, Xiaochu, Xu, Kai, Zhang, Longjiang, Ye, Zhaoxiang, Banaschewski, Tobias, Barker, Gareth J., Bokde, Arun L. W., Flor, Herta, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Martinot, Jean-Luc, Artiges, Eric, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Lemaitre, Herve, Poustka, Luise, Robinson, Lauren, Hohmann, Sarah, Smolka, Michael N., Walter, Henrik, Whelan, Robert, Winterer, Jeanne, Patrick, Kevin, Calhoun, Vince, Li, Mulin Jun, Liang, Meng, Gong, Peng, Barker, Edward D., Clinton, Nicholas, Marquand, Andre, Yu, Le, Yu, Chunshui, and Schumann, Gunter

Subjects
Behavioral Neuroscience, Social Psychology, Experimental and Cognitive Psychology
Abstract

Urbanicity is a growing environmental challenge for mental health. Here, we investigate correlations of urbanicity with brain structure and function, neuropsychology and mental illness symptoms in young people from China and Europe (total n = 3,867). We developed a remote-sensing satellite measure (UrbanSat) to quantify population density at any point on Earth. UrbanSat estimates of urbanicity were correlated with brain volume, cortical surface area and brain network connectivity in the medial prefrontal cortex and cerebellum. UrbanSat was also associated with perspective-taking and depression symptoms, and this was mediated by neural variables. Urbanicity effects were greatest when urban exposure occurred in childhood for the cerebellum, and from childhood to adolescence for the prefrontal cortex. As UrbanSat can be generalized to different geographies, it may enable assessments of correlations of urbanicity with mental illness and resilience globally.

Published
2022

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