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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. The genetic architecture of the human hypothalamus and its involvement in neuropsychiatric behaviours and disorders

Author

Chen, Shi-Dong, You, Jia, Zhang, Wei, Wu, Bang-Sheng, Ge, Yi-Jun, Xiang, Shi-Tong, Du, Jing, Kuo, Kevin, 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, Lemaitre, Herve, Paus, Tomáš, Poustka, Luise, Hohmann, Sarah, Millenet, Sabina, Baeuchl, Christian, Smolka, Michael N., Vaidya, Nilakshi, Walter, Henrik, Whelan, Robert, Schumann, Gunter, Feng, Jian-Feng, Dong, Qiang, Cheng, Wei, and Yu, Jin-Tai

Published
2024
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4. Whole-brain gray matter maturation trajectories associated with autistic traits from adolescence to early adulthood

Author

Gros, Guillaume, Miranda Marcos, Ruben, Latrille, Anthony, Saitovitch, Ana, Gollier-Briant, Fanny, Fossati, Philippe, Schmidt, Liane, Banaschewski, Tobias, Barker, Gareth J., Bokde, Arun L. W., Desrivières, Sylvane, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Brühl, Rüdiger, Martinot, Jean-Luc, Paillère Martinot, Marie-Laure, Artiges, Eric, Nees, Frauke, Papadopoulos Orfanos, Dimitri, Poustka, Luise, Hohmann, Sarah, Holz, Nathalie, Fröhner, Juliane H., Smolka, Michael N., Vaidya, Nilakshi, Walter, Henrik, Whelan, Robert, Schumann, Gunter, Lemaitre, Hervé, and Vulser, Hélène

Published
2024
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5. Covariation of preadult environmental exposures, adult brain imaging phenotypes, and adult personality traits

Author

Xue, Kaizhong, Gao, Bo, Chen, Feng, Wang, Meiyun, Cheng, Jingliang, Zhang, Bing, Zhu, Wenzhen, Qiu, Shijun, Geng, Zuojun, Zhang, Xiaochu, Cui, Guangbin, Yu, Yongqiang, Zhang, Quan, Liao, Weihua, Zhang, Hui, Xu, Xiaojun, Han, Tong, Qin, Wen, Liu, Feng, Liang, Meng, Guo, Lining, Xu, Qiang, Xu, Jiayuan, Fu, Jilian, Zhang, Peng, Li, Wei, Shi, Dapeng, Wang, Caihong, Lui, Su, Yan, Zhihan, Zhang, Jing, Li, Jiance, Wang, Dawei, Xian, Junfang, Xu, Kai, Zuo, Xi-Nian, Zhang, Longjiang, Ye, Zhaoxiang, 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, Lemaitre, Herve, Poustka, Luise, Hohmann, Sarah, Holz, Nathalie, Fröhner, Juliane H., Smolka, Michael N., Vaidya, Nilakshi, Walter, Henrik, Whelan, Robert, Shen, Wen, Miao, Yanwei, and Yu, Chunshui

Published
2023
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6. 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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8. 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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9. 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

10. 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

11. 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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12. 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

13. Federated Analysis in COINSTAC Reveals Functional Network Connectivity and Spectral Links to Smoking and Alcohol Consumption in Nearly 2,000 Adolescent Brains

Author

Gazula, Harshvardhan, Rootes-Murdy, Kelly, Holla, Bharath, Basodi, Sunitha, Zhang, Zuo, Verner, Eric, Kelly, Ross, Murthy, Pratima, Chakrabarti, Amit, Basu, Debasish, Bhagyalakshmi Nanjayya, Subodh, Lenin Singh, Rajkumar, Lourembam Singh, Roshan, Kalyanram, Kartik, Kartik, Kamakshi, Kalyanaraman, Kumaran, Ghattu, Krishnaveni, Kuriyan, Rebecca, Kurpad, Sunita Simon, Barker, Gareth J, Bharath, Rose Dawn, Desrivieres, Sylvane, Purushottam, Meera, Orfanos, Dimitri Papadopoulos, Sharma, Eesha, Hickman, Matthew, Toledano, Mireille, Vaidya, Nilakshi, Banaschewski, Tobias, Bokde, Arun L. W., Flor, Herta, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Brühl, Rüdiger, Martinot, Jean-Luc, Paillére Martinot, Marie-Laure, Artiges, Eric, Nees, Frauke, Paus, Tomás, Poustka, Luise, Fröhner, Juliane H., Robinson, Lauren, Smolka, Michael N., Walter, Henrik, Winterer, Jeanne, Whelan, Robert, Turner, Jessica A., Sarwate, Anand D., Plis, Sergey M., Benegal, Vivek, Schumann, Gunter, and Calhoun, Vince D.

Published
2023
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14. 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

15. 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

18. Growth trajectories for executive and social cognitive abilities in an Indian population sample: Impact of demographic and psychosocial determinants

Author

Sharma, Eesha, Ravi, G.S., Kumar, Keshav, Thennarasu, Kandavel, Heron, Jon, Hickman, Matthew, Vaidya, Nilakshi, Holla, Bharath, Rangaswamy, Madhavi, Mehta, Urvakhsh Meherwan, Krishna, Murali, Chakrabarti, Amit, Basu, Debashish, Nanjayya, Subodh Bhagyalakshmi, Singh, Rajkumar Lenin, Lourembam, Roshan, Kumaran, Kalyanaraman, Kuriyan, Rebecca, Kurpad, Sunita Simon, Kartik, Kamakshi, Kalyanram, Kartik, Desrivieres, Sylvane, Barker, Gareth, Orfanos, Dimitri Papadopoulos, Toledano, Mireille, Purushottam, Meera, Bharath, Rose Dawn, Murthy, Pratima, Jain, Sanjeev, Schumann, Gunter, and Benegal, Vivek

Published
2023
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19. Orbitofrontal control of conduct problems? Evidence from healthy adolescents processing negative facial affect

Author

Böttinger, Boris William, Baumeister, Sarah, Millenet, Sabina, Barker, Gareth J., 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, Artiges, Eric, Orfanos, Dimitri Papadopoulos, Paus, Tomáš, Poustka, Luise, Fröhner, Juliane H., Smolka, Michael N., Walter, Henrik, Whelan, Robert, Schumann, Gunter, Banaschewski, Tobias, Brandeis, Daniel, and Nees, Frauke

Published
2022
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21. Reward Versus Nonreward Sensitivity of the Medial Versus Lateral Orbitofrontal Cortex Relates to the Severity of Depressive Symptoms

Author

Artiges, Eric, Aydin, Semiha, Banaschewski, Tobias, Barbot, Alexis, Barker, Gareth, Becker, Andreas, Bezivin-Frere, Pauline, Biondo, Francesca, Bokde, Arun, Büchel, Christian, Chu, Congying, Conrod, Patricia, Daedelow, Laura, Dalley, Jeffrey, Desrivieres, Sylvane, Dooley, Eoin, Filippi, Irina, Fillmer, Ariane, Flor, Herta, Fröhner, Juliane, Frouin, Vincent, Garavan, Hugh, Gowland, Penny, Grimmer, Yvonne, Heinz, Andreas, Hohmann, Sarah, Ihlenfeld, Albrecht, Ing, Alex, Isensee, Corinna, Ittermann, Bernd, Jia, Tianye, Lemaitre, Hervé, Lethbridge, Emma, Martinot, Jean-Luc, Millenet, Sabina, Miller, Sarah, Miranda, Ruben, Nees, Frauke, Paillere, Marie-Laure, Papadopoulos, Dimitri, Paus, Tomáš, Pausova, Zdenka, Pentilla, Jani, Poline, Jean-Baptiste, Poustka, Luise, Burke, Erin, Rapp, Michael, Robbins, Trevor, Robert, Gabriel, Rogers, John, Ruggeri, Barbara, Schumann, Gunter, Smolka, Michael, Stringaris, Argyris, van Noort, Betteke, Walter, Henrik, Whelan, Robert, Simon, Roux, Williams, Steve, Zhang, Yuning, Xie, Chao, Rolls, Edmund T., Robbins, Trevor W., Sahakian, Barbara J., Zhang, Jie, Liu, Zhaowen, Cheng, Wei, Luo, Qiang, Zac Lo, Chun-Yi, Wang, He, Barker, Gareth J., Bokde, Arun L.W., Quinlan, Erin Burke, Desrivières, Sylvane, Grigis, Antoine, Paillère Martinot, Marie-Laure, Orfanos, Dimitri Papadopoulos, Fröhner, Juliane H., Smolka, Michael N., and Feng, Jianfeng

Published
2021
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22. Automated Brain Abnormality Detection using a Self-Supervised Text-Vision Framework

Author

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

Published
2024
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23. Unravelling robust brain-behavior links of depressive complaints through granular network models for understanding heterogeneity

Author

Freichel, René, primary, Lenartowicz, Agatha, additional, Douw, Linda, additional, Kruschwitz, Johann D., additional, Banaschewski, Tobias, additional, Barker, Gareth J., additional, Bokde, Arun L.W., additional, Desrivières, Sylvane, additional, Flor, Herta, additional, Grigis, Antoine, additional, Garavan, Hugh, 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, Holz, Nathalie, additional, Baeuchl, Christian, additional, Smolka, Michael N., additional, Vaidya, Nilakshi, additional, Whelan, Robert, additional, Frouin, Vincent, additional, Schumann, Gunter, additional, Walter, Henrik, additional, and Blanken, Tessa F., additional

Published
2024
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24. 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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25. A Developmental Perspective on Facets of Impulsivity and Brain Activity Correlates From Adolescence to Adulthood

Author

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, Paillère Martinot, Marie-Laure, Artiges, Eric, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Lemaitre, Herve, Paus, Tomáš, Poustka, Luise, Hohmann, Sarah, Millenet, Sabina, Fröhner, Juliane H., Robinson, Lauren, Smolka, Michael N., Walter, Henrik, Winterer, Jeanne M., Whelan, Robert, Schumann, Gunter, Kaiser, Anna, Holz, Nathalie E., Baumeister, Sarah, Ittermann, Bernd, Schwarz, Emanuel, and Brandeis, Daniel

Published
2022
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26. Brain Signatures During Reward Anticipation Predict Persistent Attention-Deficit/Hyperactivity Disorder Symptoms

Author

Jia, Tianye, 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, Paillère Martinot, Marie-Laure, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Paus, Tomáš, Poustka, Luise, Fröhner, Juliane H., Smolka, Michael N., Walter, Henrik, Whelan, Robert, Schumann, Gunter, Chen, Di, Cheng, Wei, Cao, Miao, Gowland, Penny A., Robbins, T.W., Sahakian, Barbara J., and Feng, Jianfeng

Published
2022
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27. 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, Brühl, Rüdiger, Martinot, Jean-Luc, Artiges, Eric, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Lemaitre, Herve, Paus, Tomáš, Poustka, Luise, Robinson, Lauren, Hohmann, Sarah, Fröhner, Juliane H., 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

Published
2022
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30. 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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35. 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, and Poustka, Luise

Subjects
NEUROTRANSMITTER receptors, NEURAL development, CELL populations, NEUROGLIA, AGING, ADOLESCENCE
Abstract

Human brain morphology undergoes complex changes over the lifespan. Despite recent progress in tracking brain development via normative models, current knowledge of underlying biological mechanisms is highly limited. We demonstrate that human cortical thickness development and aging trajectories unfold along patterns of molecular and cellular brain organization, traceable from population-level to individual developmental trajectories. During childhood and adolescence, cortex-wide spatial distributions of dopaminergic receptors, inhibitory neurons, glial cell populations, and brain-metabolic features explain up to 50% of the variance associated with a lifespan model of regional cortical thickness trajectories. In contrast, modeled cortical thickness change patterns during adulthood are best explained by cholinergic and glutamatergic neurotransmitter receptor and transporter distributions. These relationships are supported by developmental gene expression trajectories and translate to individual longitudinal data from over 8000 adolescents, explaining up to 59% of developmental change at cohort- and 18% at single-subject level. Integrating neurobiological brain atlases with normative modeling and population neuroimaging provides a biologically meaningful path to understand brain development and aging in living humans. The neurobiology of human brain development and aging is hard to study in vivo. The authors report on distinct spatial associations between brain morphology and cellular as well as molecular brain properties throughout neurodevelopment and aging. [ABSTRACT FROM AUTHOR]

Published
2024
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37. 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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40. A letter to the editor: The effects of alcohol use on brain glutamate in first episode psychosis

Author

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

Published
2024
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41. 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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42. Machine learning models for diagnosis and risk prediction in eating disorders, depression, and alcohol use disorder

Author

Desrivières, Sylvane, primary, Zhang, Zuo, additional, Robinson, Lauren, additional, Whelan, Robert, additional, Jollans, Lee, additional, Wang, Zijian, additional, Nees, Frauke, additional, Chu, Congying, additional, Bobou, Marina, additional, Du, Dongping, additional, Cristea, Ilinca, additional, Banaschewski, Tobias, additional, Barker, Gareth, additional, Bokde, Arun, additional, Grigis, Antoine, additional, Garavan, Hugh, additional, Heinz, Andreas, additional, Bruhl, Rudiger, additional, Martinot, Jean-Luc, additional, Martinot, Marie-Laure Paillère, additional, Artiges, Eric, additional, Orfanos, Dimitri Papadopoulos, additional, Poustka, Luise, additional, Hohmann, Sarah, additional, Millenet, Sabina, additional, Fröhner, Juliane, additional, Smolka, Michael, additional, Vaidya, Nilakshi, additional, Walter, Henrik, additional, Winterer, Jeanne, additional, Broulidakis, M., additional, van Noort, Betteke, additional, Stringaris, Argyris, additional, Penttilä, Jani, additional, Grimmer, Yvonne, additional, Insensee, Corinna, additional, Becker, Andreas, additional, Zhang, Yuning, additional, King, Sinead, additional, Sinclair, Julia, additional, Schumann, Gunter, additional, and Schmidt, Ulrike, additional

Published
2024
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43. Reward Processing in Novelty Seekers: A Transdiagnostic Psychiatric Imaging Biomarker

Author

Schumann, Gunter, Banaschewski, Tobias, Barker, Gareth J., Bokde, Arun L.W., 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, Qi, Shile, 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, Calhoun, Vince D., and Sui, Jing

Published
2021
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44. Decentralized Multisite VBM Analysis During Adolescence Shows Structural Changes Linked to Age, Body Mass Index, and Smoking: a COINSTAC Analysis

Author

Gazula, Harshvardhan, Holla, Bharath, Zhang, Zuo, Xu, Jiayuan, Verner, Eric, Kelly, Ross, Jain, Sanjeev, Bharath, Rose Dawn, Barker, Gareth J., Basu, Debasish, Chakrabarti, Amit, Kalyanram, Kartik, Kumaran, Kalyanaraman, Singh, Lenin, Kuriyan, Rebecca, Murthy, Pratima, Benega, Vivek, Plis, Sergey M., Sarwate, Anand D., Turner, Jessica A., Schumann, Gunter, and Calhoun, Vince D.

Published
2021
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46. A Modest Increase in 11C-PK11195-Positron Emission Tomography TSPO Binding in Depression Is Not Associated With Serum C-Reactive Protein or Body Mass Index

Author

Wlazly, Dominika, Dickinson, Amber, Foster, Andy, Knight, Clare, Leckey, Claire, Morgan, Paul, Morgan, Angharad, O'Hagan, Caroline, Touchard, Samuel, Khan, Shahid, Murphy, Phil, Parker, Christine, Patel, Jai, Richardson, Jill, Acton, Paul, Austin, Nigel, Bhattacharya, Anindya, Carruthers, Nick, de Boer, Peter, Drevets, Wayne, Isaac, John, Jones, Declan, Kemp, John, Kolb, Hartmuth, Nye, Jeff, Wittenberg, Gayle, Barker, Gareth, Bogdanova, Anna, Byrom, Heidi, Cash, Diana, Cattaneo, Annamaria, Enache, Daniela, Gee, Tony, Hastings, Caitlin, Kose, Melisa, Lombardo, Giulia, Mariani, Nicole, McLaughlin, Anna, Mondelli, Valeria, Nettis, Maria, Nikkheslat, Naghmeh, Pariante, Carmine, Randall, Karen, Schubert, Julia, Sforzini, Luca, Sheridan, Hannah, Simmons, Camilla, Singh, Nisha, Turkheimer, Federico, Van Loo, Vicky, Veronese, Mattia, Rodriguez, Marta Vicente, Wood, Toby, Worrell, Courtney, Zajkowska, Zuzanna, Campbell, Brian, Egebjerg, Jan, Eriksson, Hans, Gastambide, Francois, Adams, Karen Husted, Jeggo, Ross, Moeller, Thomas, Nelson, Bob, Plath, Niels, Thomsen, Christian, Pederson, Jan Torleif, Zorn, Stevin, Deith, Catherine, Farmer, Scott, McClean, John, McPherson, Andrew, Penandes, Nagore, Scouller, Paul, Sutherland, Murray, Attenburrow, Mary Jane, Benjamin, Jithen, Jones, Helen, Mada, Fran, Oladejo, Akintayo, Smith, Katy, Balice-Gordon, Rita, Binneman, Brendon, Duerr, James, Fullerton, Terence, Goli, Veeru, Hughes, Zoe, Piro, Justin, Samad, Tarek, Sporn, Jonathan, Hoskins, Liz, Kohn, Charmaine, Wilcock, Lauren, Aigbirhio, Franklin, Bhatti, Junaid, Bullmore, Ed, Chamberlain, Sam, Correia, Marta, Crofts, Anna, Fryer, Tim, Graves, Martin, Hatton, Alex, Kitzbichler, Manfred, Lynall, Mary-Ellen, Maurice, Christina, O'Donnell, Ciara, Pointon, Linda, St George Hyslop, Peter, Turner, Lorinda, Vertes, Petra, Widmer, Barry, Williams, Guy, Cavanagh, Jonathan, McColl, Alison, Shaw, Robin, Boddeke, Erik, Baird, Alison, Clare, Stuart, Cowen, Phil, Huang, I-Shu (Dante), Hurley, Sam, Lovestone, Simon, Nevado-Holgado, Alejo, Ribe, Elena, Vyas, Anviti, Winchester, Laura, Cleal, Madeleine, Gomez-Nicola, Diego, Mancuso, Renzo, Perry, Hugh, Cercignani, Mara, Clarke, Charlotte, Colasanti, Alessandro, Harrison, Neil, Murray, Rosemary, O'Connor, Jason, Mount, Howard, Schubert, Julia J., Fryer, Tim D., Manavaki, Roido, Kitzbichler, Manfred G., Nettis, Maria A., Pariante, Carmine M., Bullmore, Edward T., and Turkheimer, Federico E.

Published
2021
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47. Substance Use Initiation, Particularly Alcohol, in Drug-Naive Adolescents: Possible Predictors and Consequences From a Large Cohort Naturalistic Study

Author

Rapp, Michael, Artiges, Eric, Schneider, Sophia, Paus, Tomas, Barbot, Alexis, Barker, Gareth, Bokde, Arun, Vetter, Nora, Büchel, Christian, Cattrell, Anna, Constant, Patrick, Gowland, Penny, Crombag, Hans, Dalley, Jeffrey, Decideur, Benjamin, Spranger, Tade, Ripley, Tamzin, Heym, Nadja, Flor, Herta, Sommer, Wolfgang, Fuchs, Birgit, Gallinat, Jürgen, Garavan, Hugh, Spanagel, Rainer, Kaviani, Mehri, Heinrichs, Bert, Andreas Heinz, Subramaniam, Naresh, Jia, Tianye, Ihlenfeld, Albrecht, Ireland, James, Ittermann, Bernd, Conrod, Patricia, Banaschewski, Tobias, Jones, Jennifer, Klaassen, Arno, Lalanne, Christophe, Lanzerath, Dirk, Lawrence, Claire, Lemaitre, Hervé, Desrivieres, Sylvane, Mallik, Catherine, Mann, Karl, Mar, Adam, Martinez-Medina, Lourdes, Martinot, Jean-Luc, Mennigen, Eva, Mesquita de Carvahlo, Fabiana, Schwartz, Yannick, Bruehl, Ruediger, Müller, Kathrin, Nees, Frauke, Nymberg, Charlotte, Lathrop, Mark, Robbins, Trevor, Pausova, Zdenka, Pentilla, Jani, Biondo, Francesca, Poline, Jean-Baptiste, Poustka, Luise, Millenet, Sabina, Smolka, Michael, Fröhner, Juliane, Struve, Maren, Williams, Steve, Hübner, Thomas, Bromberg, Uli, Aydin, Semiha, Rogers, John, Romanowski, Alexander, Schmäl, Christine, Schmidt, Dirk, Ripke, Stephan, Arroyo, Mercedes, Schubert, Florian, Pena-Oliver, Yolanda, Fauth-Bühler, Mira, Mignon, Xavier, Whelan, Robert, Speiser, Claudia, Fadai, Tahmine, Stephens, Dai, Ströhle, Andreas, Paillere, Marie-Laure, Strache, Nicole, Theobald, David, Jurk, Sarah, Vulser, Helene, Miranda, Ruben, Yacubilin, Juliana, Frouin, Vincent, Genauck, Alexander, Parchetka, Caroline, Gemmeke, Isabel, Kruschwitz, Johann, WeiB, Katharina, Walter, Henrik, Feng, Jianfeng, Papadopoulos, Dimitri, Filippi, Irina, Ing, Alex, Ruggeri, Barbara, Xu, Bing, Macare, Christine, Chu, Congying, Hanratty, Eanna, Burke Quinlan, Erin, Robert, Gabriel, Schumann, Gunter, Yu, Tao, Ziesch, Veronika, Stedman, Alicia, Ivanov, Iliyan, Parvaz, Muhammad A., Velthorst, Eva, Shaik, Riaz B., Sandin, Sven, Gan, Gabriela, Spechler, Philip, Albaugh, Matthew D., Chaarani, Bader, Mackey, Scott, Bokde, Arun L.W., Quinlan, Erin Burke, Desrivières, Sylvane, Grigis, Antoine, Heinz, Andreas, Paillère Martinot, Marie-Laure, Lemaitre, Herve, Orfanos, Dimitri Papadopoulos, Paus, Tomáš, Hohmann, Sarah, Fröhner, Juliane H., and Smolka, Michael N.

Published
2021
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49. 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

50. 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

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