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1. Cortical growth from infancy to adolescence in preterm and term-born children.

Author

Kelly, Claire, Thompson, Deanne, Adamson, Chris, Ball, Gareth, Dhollander, Thijs, Beare, Richard, Matthews, Lillian, Alexander, Bonnie, Cheong, Jeanie, Doyle, Lex, Anderson, Peter, and Inder, Terrie

Subjects
longitudinal, magnetic resonance imaging, neurodevelopment, very preterm birth, Infant, Child, Infant, Newborn, Humans, Male, Child, Preschool, Female, Adolescent, Premature Birth, Longitudinal Studies, Cognition, Gestational Age, Magnetic Resonance Imaging, Brain
Abstract

Early life experiences can exert a significant influence on cortical and cognitive development. Very preterm birth exposes infants to several adverse environmental factors during hospital admission, which affect cortical architecture. However, the subsequent consequence of very preterm birth on cortical growth from infancy to adolescence has never been defined; despite knowledge of critical periods during childhood for establishment of cortical networks. Our aims were to: chart typical longitudinal cortical development and sex differences in cortical development from birth to adolescence in healthy term-born children; estimate differences in cortical development between children born at term and very preterm; and estimate differences in cortical development between children with normal and impaired cognition in adolescence. This longitudinal cohort study included children born at term (≥37 weeks gestation) and very preterm (

Published
2024

2. Low-Resource Named Entity Recognition: Can One-vs-All AUC Maximization Help?

Author

Nguyen, Ngoc Dang, Tan, Wei, Du, Lan, Buntine, Wray, Beare, Richard, and Chen, Changyou

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

Named entity recognition (NER), a task that identifies and categorizes named entities such as persons or organizations from text, is traditionally framed as a multi-class classification problem. However, this approach often overlooks the issues of imbalanced label distributions, particularly in low-resource settings, which is common in certain NER contexts, like biomedical NER (bioNER). To address these issues, we propose an innovative reformulation of the multi-class problem as a one-vs-all (OVA) learning problem and introduce a loss function based on the area under the receiver operating characteristic curve (AUC). To enhance the efficiency of our OVA-based approach, we propose two training strategies: one groups labels with similar linguistic characteristics, and another employs meta-learning. The superiority of our approach is confirmed by its performance, which surpasses traditional NER learning in varying NER settings., Comment: 6 pages, 3 figures, ICDM 2023

Published
2023

6. Robust Educational Dialogue Act Classifiers with Low-Resource and Imbalanced Datasets

Author

Lin, Jionghao, Tan, Wei, Nguyen, Ngoc Dang, Lang, David, Du, Lan, Buntine, Wray, Beare, Richard, Chen, Guanliang, and Gasevic, Dragan

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract

Dialogue acts (DAs) can represent conversational actions of tutors or students that take place during tutoring dialogues. Automating the identification of DAs in tutoring dialogues is significant to the design of dialogue-based intelligent tutoring systems. Many prior studies employ machine learning models to classify DAs in tutoring dialogues and invest much effort to optimize the classification accuracy by using limited amounts of training data (i.e., low-resource data scenario). However, beyond the classification accuracy, the robustness of the classifier is also important, which can reflect the capability of the classifier on learning the patterns from different class distributions. We note that many prior studies on classifying educational DAs employ cross entropy (CE) loss to optimize DA classifiers on low-resource data with imbalanced DA distribution. The DA classifiers in these studies tend to prioritize accuracy on the majority class at the expense of the minority class which might not be robust to the data with imbalanced ratios of different DA classes. To optimize the robustness of classifiers on imbalanced class distributions, we propose to optimize the performance of the DA classifier by maximizing the area under the ROC curve (AUC) score (i.e., AUC maximization). Through extensive experiments, our study provides evidence that (i) by maximizing AUC in the training process, the DA classifier achieves significant performance improvement compared to the CE approach under low-resource data, and (ii) AUC maximization approaches can improve the robustness of the DA classifier under different class imbalance ratios., Comment: 12 pages full paper, The 24th International Conference on Artificial Intelligence in Education, AIED 2023 Educational Dialogue Act Classification, Model Robustness, Low-Resource Data, Imbalanced Data, Large Language Models

Published
2023

7. AUC Maximization for Low-Resource Named Entity Recognition

Author

Nguyen, Ngoc Dang, Tan, Wei, Buntine, Wray, Beare, Richard, Chen, Changyou, and Du, Lan

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

Current work in named entity recognition (NER) uses either cross entropy (CE) or conditional random fields (CRF) as the objective/loss functions to optimize the underlying NER model. Both of these traditional objective functions for the NER problem generally produce adequate performance when the data distribution is balanced and there are sufficient annotated training examples. But since NER is inherently an imbalanced tagging problem, the model performance under the low-resource settings could suffer using these standard objective functions. Based on recent advances in area under the ROC curve (AUC) maximization, we propose to optimize the NER model by maximizing the AUC score. We give evidence that by simply combining two binary-classifiers that maximize the AUC score, significant performance improvement over traditional loss functions is achieved under low-resource NER settings. We also conduct extensive experiments to demonstrate the advantages of our method under the low-resource and highly-imbalanced data distribution settings. To the best of our knowledge, this is the first work that brings AUC maximization to the NER setting. Furthermore, we show that our method is agnostic to different types of NER embeddings, models and domains. The code to replicate this work will be provided upon request., Comment: 10 pages, 4 figures, AAAI 2023

Published
2022

8. Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios

Author

Nguyen, Ngoc Dang, Du, Lan, Buntine, Wray, Chen, Changyou, and Beare, Richard

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

Domain adaptation is an effective solution to data scarcity in low-resource scenarios. However, when applied to token-level tasks such as bioNER, domain adaptation methods often suffer from the challenging linguistic characteristics that clinical narratives possess, which leads to unsatisfactory performance. In this paper, we present a simple yet effective hardness-guided domain adaptation (HGDA) framework for bioNER tasks that can effectively leverage the domain hardness information to improve the adaptability of the learnt model in low-resource scenarios. Experimental results on biomedical datasets show that our model can achieve significant performance improvement over the recently published state-of-the-art (SOTA) MetaNER model

Published
2022

9. Atypical Brain Aging and Its Association With Working Memory Performance in Major Depressive Disorder

Author

Adamson, Chris, Adler, Sophie, Alexander-Bloch, Aaron F., Anagnostou, Evdokia, Anderson, Kevin M., Areces-Gonzalez, Ariosky, Astle, Duncan E., Auyeung, Bonnie, Ayub, Muhammad, Bae, Jong Bin, Ball, Gareth, Baron-Cohen, Simon, Beare, Richard, Bedford, Saashi A., Benegal, Vivek, Bethlehem, Richard A.I., Beyer, Frauke, Blangero, John, Cábez, Manuel Blesa, Boardman, James P., Borzage, Matthew, Bosch-Bayard, Jorge F., Bourke, Niall, Bullmore, Edward T., Calhoun, Vince D., Chakravarty, Mallar M., Chen, Christina, Chertavian, Casey, Chetelat, Gaël, Chong, Yap S., Corvin, Aiden, Costantino, Manuela, Courchesne, Eric, Crivello, Fabrice, Cropley, Vanessa L., Crosbie, Jennifer, Crossley, Nicolas, Delarue, Marion, Delorme, Richard, Desrivieres, Sylvane, Devenyi, Gabriel, Di Biase, Maria A., Dolan, Ray, Donald, Kirsten A., Donohoe, Gary, Dorfschmidt, Lena, Dunlop, Katharine, Edwards, Anthony D., Elison, Jed T., Ellis, Cameron T., Elman, Jeremy A., Eyler, Lisa, Fair, Damien A., Fletcher, Paul C., Fonagy, Peter, Franz, Carol E., Galan-Garcia, Lidice, Gholipour, Ali, Giedd, Jay, Gilmore, John H., Glahn, David C., Goodyer, Ian M., Grant, P.E., Groenewold, Nynke A., Gudapati, Shreya, Gunning, Faith M., Gur, Raquel E., Gur, Ruben C., Hammill, Christopher F., Hansson, Oskar, Hedden, Trey, Heinz, Andreas, Henson, Richard N., Heuer, Katja, Hoare, Jacqueline, Holla, Bharath, Holmes, Avram J., Huang, Hao, Ipser, Jonathan, Jack, Clifford R., Jr., Jackowski, Andrea P., Jia, Tianye, Jones, David T., Jones, Peter B., Kahn, Rene S., Karlsson, Hasse, Karlsson, Linnea, Kawashima, Ryuta, Kelley, Elizabeth A., Kern, Silke, Kim, Ki-Woong, Kitzbichler, Manfred G., Kremen, William S., Lalonde, François, Landeau, Brigitte, Lerch, Jason, Lewis, John D., Li, Jiao, Liao, Wei, Liston, Conor, Lombardo, Michael V., Lv, Jinglei, Mallard, Travis T., Marcelis, Machteld, Mathias, Samuel R., Mazoyer, Bernard, McGuire, Philip, Meaney, Michael J., Mechelli, Andrea, Misic, Bratislav, Morgan, Sarah E., Mothersill, David, Ortinau, Cynthia, Ossenkoppele, Rik, Ouyang, Minhui, Palaniyappan, Lena, Paly, Leo, Pan, Pedro M., Pantelis, Christos, Park, Min Tae M., Paus, Tomas, Pausova, Zdenka, Paz-Linares, Deirel, Binette, Alexa Pichet, Pierce, Karen, Qian, Xing, Qiu, Anqi, Raznahan, Armin, Rittman, Timothy, Rodrigue, Amanda, Rollins, Caitlin K., Romero-Garcia, Rafael, Ronan, Lisa, Rosenberg, Monica D., Rowitch, David H., Salum, Giovanni A., Satterthwaite, Theodore D., Schaare, H. Lina, Schabdach, Jenna, Schachar, Russell J., Schöll, Michael, Schultz, Aaron P., Seidlitz, Jakob, Sharp, David, Shinohara, Russell T., Skoog, Ingmar, Smyser, Christopher D., Sperling, Reisa A., Stein, Dan J., Stolicyn, Aleks, Suckling, John, Sullivan, Gemma, Thyreau, Benjamin, Toro, Roberto, Traut, Nicolas, Tsvetanov, Kamen A., Turk-Browne, Nicholas B., Tuulari, Jetro J., Tzourio, Christophe, Vachon-Presseau, Étienne, Valdes-Sosa, Mitchell J., Valdes-Sosa, Pedro A., Valk, Sofie L., van Amelsvoort, Therese, Vandekar, Simon N., Vasung, Lana, Vértes, Petra E., Victoria, Lindsay W., Villeneuve, Sylvia, Villringer, Arno, Vogel, Jacob W., Wagstyl, Konrad, Wang, Yin-Shan S., Warfield, Simon K., Warrier, Varun, Westman, Eric, Westwater, Margaret L., Whalley, Heather C., White, Simon R., Witte, A. Veronica, Yang, Ning, Yeo, B.T. Thomas, Yun, Hyuk Jin, Zalesky, Andrew, Zar, Heather J., Zettergren, Anna, Zhou, Juan H., Ziauddeen, Hisham, Zimmerman, Dabriel, Zugman, Andre, Zuo, Xi-Nian N., Ho, Natalie C.W., Nogovitsyn, Nikita, Metzak, Paul, Ballester, Pedro L., Hassel, Stefanie, Rotzinger, Susan, Poppenk, Jordan, Lam, Raymond W., Taylor, Valerie H., Milev, Roumen, Frey, Benicio N., Harkness, Kate L., Addington, Jean, and Kennedy, Sidney H.

Published
2024
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15. Evolution of the Stellar Mass Function and Infrared Luminosity Function of Galaxies since $z = 1.2$

Author

Beare, Richard, Brown, Michael, Pimbblet, Kevin, and Taylor, Edward

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

We measured evolution of the $K$-band luminosity function and stellar mass function for red and blue galaxies at $z<1.2$ using a sample of 353 594 $I<24$ galaxies in 8.26 square degrees of Bo\"otes. We addressed several sources of systematic and random error in measurements of total galaxy light, photometric redshift and absolute magnitude. We have found that the $K$-band luminosity density for both red and blue galaxies increased by a factor of 1.2 from $z\sim1.1$ to $z\sim0.3$, while the most luminous red (blue) galaxies decreased in luminosity by 0.19 (0.33) mag or $\times0.83 (0.74)$. These results are consistent with $z<0.2$ studies while our large sample size and area result in smaller Poisson and cosmic variance uncertainties than most $z >0.4$ luminosity and mass function measurements. Using an evolving relation for $K$-band mass to light ratios as a function of $(B-V)$ color, we found a slowly decreasing rate of growth in red galaxy stellar mass density of $\times2.3$ from $z\sim1.1$ to $z\sim0.3$, indicating a slowly decreasing rate of migration from the blue cloud to the red sequence. Unlike some studies of the stellar mass function, we find that massive red galaxies grow by a factor of $\times1.7$ from $z\sim1.1$ to $z\sim0.3$, with the rate of growth due to mergers decreasing with time. These results are comparable with measurements of merger rates and clustering, and they are also consistent with the red galaxy stellar mass growth implied by comparing $K$-band luminosity evolution with the fading of passive stellar population models., Comment: 27 pages, 27 figures, accepted for publication in the Astrophysical Journal

Published
2019
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19. An Introduction to Software Tools, Data, and Services for Geospatial Analysis of Stroke Services

Author

Padgham, Mark, Boeing, Geoff, Cooley, David, Tierney, Nicholas, Sumner, Michael, Phan, Thanh G, and Beare, Richard

Subjects
openstreetmap, R, Python, GIS, geospatial, spatial analysis, big data, network science, accessibility, transportation, land use, health, medicine, stroke, catchment, transport, urban planning, mapping, street network
Abstract

Background: There is interest in the use of geospatial data for development of acute stroke services given the importance of timely access to acute reperfusion therapy. This paper aims to introduce clinicians and citizen scientists to the possibilities offered by open source software (R and Python) for analyzing geospatial data. It is hoped that this introduction would stimulate interest in the field as well as generate ideas for improving stroke services. Method: Instructions on installation of libraries for R and Python, source code, and links to census data are provided in a notebook format to enhance experience with running this software. This code illustrates different aspects of using geospatial analysis: 1) creation of choropleth (thematic) map which depicts estimate of stroke cases per post code; 2) use of map to help define service regions for rehabilitation after stroke. Results: choropleth map showing estimate of stroke per post codes and service boundary map for rehabilitation after stroke. Conclusions: the examples in this article illustrate the use of a range of components that underpin geospatial analysis. By providing an accessible introduction to these areas, clinicians and researchers can write code to answer clinically relevant questions on topics such as service delivery and service demand.

Published
2019

20. Type 2 diabetes mellitus, brain atrophy, and cognitive decline.

Author

Moran, Chris, Beare, Richard, Wang, Wei, Callisaya, Michele, Srikanth, Velandai, and Alzheimer's Disease Neuroimaging Initiative (ADNI)

Subjects
Alzheimer's Disease Neuroimaging Initiative, Brain, Cerebral Cortex, Humans, Alzheimer Disease, Diabetes Mellitus, Type 2, Atrophy, Blood Glucose, Magnetic Resonance Imaging, Organ Size, Factor Analysis, Statistical, Longitudinal Studies, Aged, Aged, 80 and over, Female, Male, Cognitive Dysfunction, Aging, Alzheimer's Disease, Dementia, Behavioral and Social Science, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Neurodegenerative, Diabetes, Clinical Research, Neurosciences, Brain Disorders, Acquired Cognitive Impairment, 2.1 Biological and endogenous factors, Aetiology, Neurological, Clinical Sciences, Cognitive Sciences, Neurology & Neurosurgery
Abstract

ObjectiveTo study longitudinal relationships between type 2 diabetes mellitus (T2DM), cortical thickness, and cognitive function in older people with normal cognition, mild cognitive impairment, and Alzheimer disease (AD).MethodsThe sample was derived from the Alzheimer's Disease Neuroimaging Initiative cohort who underwent brain MRI and cognitive tests annually for 5 years. Presence of T2DM was based on fasting blood glucose ≥7.0mml/L or the use of glucose-lowering agents. We used latent growth curve modeling to explore longitudinal relationships between T2DM, cortical thickness, and cognitive function, adjusting for relevant covariates and testing for interactions.ResultsThere were 124 people with T2DM (mean age 75.5 years, SD 6.2) and 693 without T2DM (mean age 75.1 years, SD 6.9) with at least 1 MRI available. AD and lower cortical thickness at study entry was associated with a lower chance of having a MRI available at each follow-up phase (all p < 0.001). T2DM was associated with lower baseline cortical thickness (p = 0.01). We found no direct effect of T2DM on decline in cortical thickness or cognitive function, but there was an indirect pathway linking T2DM and cognitive decline via baseline cortical thickness (β = -0.17, p = 0.022). There was an interaction between T2DM and education whereby the negative effect of T2DM on baseline cortical thickness was reduced in those with greater education (β = 0.34, p = 0.037). These associations changed minimally when adjusted for baseline cognitive diagnosis.ConclusionsIn an older cohort with low cerebrovascular disease burden, T2DM contributes to cognitive decline via neurodegeneration. Prior brain and cognitive reserve may protect against this effect.

Published
2019

30. Interactions between age, sex and visceral adipose tissue on brain ageing.

Author

Moran, Chris, Herson, Jarin, Than, Stephanie, Collyer, Taya, Beare, Richard, Syed, Sarah, and Srikanth, Velandai

Subjects
MAGNETIC resonance imaging, BODY composition, WHITE matter (Nerve tissue), PHYSICAL activity, REGRESSION analysis, MIDDLE age
Abstract

Aim: To examine the associations between visceral adipose tissue (VAT) and brain structural measures at midlife and explore how these associations may be affected by age, sex and cardiometabolic factors. Methods: We used abdominal and brain magnetic resonance imaging data from a population‐based cohort of people at midlife in the UK Biobank. Regression modelling was applied to study associations of VAT volume with total brain volume (TBV), grey matter volume (GMV), white matter volume, white matter hyperintensity volume (WMHV) and total hippocampal volume (THV), and whether these associations were altered by age, sex or cardiometabolic factors. Results: Complete data were available for 17 377 participants (mean age 63 years, standard deviation = 12, 53% female). Greater VAT was associated with lower TBV, GMV and THV (P <.001). We found an interaction between VAT and sex on TBV (P <.001), such that the negative association of VAT with TBV was greater in men (β = −2.89, 95% confidence interval [CI] −2.32 to −10.15) than in women (β = −1.32, 95% CI −0.49 to −3.14), with similar findings for GMV. We also found an interaction between VAT and age (but not sex) on WMHV (P <.001). The addition of other cardiometabolic factors or measures of physical activity resulted in little change to the models. Conclusions: VAT volume is associated with poorer brain health in midlife and this relationship is greatest in men and those at younger ages. [ABSTRACT FROM AUTHOR]

Published
2024
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31. Development of an implementation strategy for routine collection of generic patient reported outcome measures: a qualitative study in multidisciplinary community rehabilitation.

Author

Jolliffe, Laura, Andrew, Nadine E., Srikanth, Velandai, Beare, Richard, Noeske, Kate E., and Snowdon, David A.

Subjects
COMMUNITY health services, NURSES, QUALITATIVE research, EXECUTIVES, SOCIAL workers, PSYCHOLOGISTS, REHABILITATION, QUESTIONNAIRES, STATISTICAL sampling, INTERVIEWING, BEHAVIOR, DESCRIPTIVE statistics, ALLIED health personnel, THEMATIC analysis, ATTITUDES of medical personnel, RESEARCH, CONCEPTUAL structures, RESEARCH methodology, HEALTH outcome assessment, HEALTH care teams, TIME, PATIENTS' attitudes
Abstract

Purpose: To explore staff perceptions of barriers and enablers towards implementing the EQ-5D-5L in community rehabilitation, and develop a theory-informed implementation approach for routine administration of generic patient-reported outcome measures (PROMs) using implementation science frameworks. Materials and methods: A qualitative study was conducted at three sites. Multidisciplinary rehabilitation staff completed individual semi-structured interviews, which were transcribed and coded against the Theoretical Domains Framework (TDF). We identified and selected potentially effective behaviour change techniques using the Behavior Change Wheel. Hypothetical strategies were operationalised. Results: Twenty-one interviews were conducted, and four themes emerged: (1) The Impact of PROMs on patient centered-care; (2) Considerations for validity of PROMs; (3) Service-level impact of embedding PROMs; (4) Practical issues of embedding PROMs within the service. Barriers and enablers were mapped to seven of the TDF domains; relating most to clinicians' "belief about consequences", "reinforcement", and "environmental context and resources". Five hypothetical strategies were developed to overcome identified barriers and strengthen enablers. Key behaviour change techniques underpinning the strategies include: restructuring the physical environment, incentivisation, persuasion and education, enablement, and, social support. Conclusions: Our implementation approach highlights the importance of automating processes, engaging site champions, routinely reporting, and using PROM data to inform service provision. IMPLICATIONS FOR REHABILITATION: Implementation of patient reported outcome measures within multidiscipline rehabilitation settings are likely optimised by establishing infrastructure support e.g., information technology systems to automate the process and minimise manual aspects of data collection. Engaging site champions may be an important enabler for the routine collection of patient reported outcome measures Providing feedback to clinicians on aggregated results of patient reported outcome measures are likely to motivate and encourage routine collection. [ABSTRACT FROM AUTHOR]

Published
2024
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32. Normative cortical activation patterns in children distinguishing one or two tactile sensory inputs points: An functional MRI study.

Author

Donne, Jack H., Beare, Richard, Powell, Jennifer A., Fahey, Michael C., and Williams, Cylie M.

Subjects
*FUNCTIONAL magnetic resonance imaging, *PREFRONTAL cortex, *PARIETAL lobe, *CHILD patients, *CHILD development, *STIMULUS & response (Psychology)
Abstract

This study aims to determine if children follow the same cortical activation patterns as adults when responding to two‐point discrimination sensory stimuli during functional magnetic resonance imaging (fMRI). We recruited 15 children with typical development, aged between 8 and 16 years. These participants were asked to complete a two‐point discrimination test (task block) while undergoing an fMRI scan. In the two‐point stimulus test, strong activation compared to baseline was observed in the left and right frontal orbital cortex. The inferior parietal lobe also exhibited activity, particularly on the right side, with activation in both the supramarginal and angular gyri. Further activity was evident in the left supramarginal gyrus and the left paracingulate gyrus. This novel study on fMRI two‐point discrimination testing in children shows the activation patterns in key brain regions involved in somatosensory discrimination and decision making. Practical Applications: This study provides crucial normative data on cortical activation patterns during two‐point discrimination testing in typically developing children. Such data serve as a valuable reference point for future research investigating sensory processing and cognitive function in pediatric populations. Researchers can use these findings to establish benchmarks and compare cortical activation patterns in children with neurodevelopmental conditions. This information is essential for exploring pathology, which may affect tactile sensation techniques and developing targeted interventions tailored to individual needs in pediatric treatment. Additionally, it lays the groundwork for further exploration into the neural mechanisms underpinning somatosensory discrimination, potentially leading to advancements in understanding and treating sensory‐related disorders in children. [ABSTRACT FROM AUTHOR]

Published
2024
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34. Primary stroke prevention worldwide: translating evidence into action

Author

Abanto, Carlos, Abera, Semaw Ferede, Addissie, Adamu, Adebayo, Oluwadamilola, Adeleye, Amos Olufemi, Adilbekov, Yerzhan, Adilbekova, Bibigul, Adoukonou, Thierry Armel, Aguiar de Sousa, Diana, Ajagbe, Temitope, Akhmetzhanova, Zauresh, Akpalu, Albert, Álvarez Ahlgren, Jhon, Ameriso, Sebastián, Andonova, Silva, Awoniyi, Foloruso Emmanuel, Bakhiet, Moiz, Barboza, Miguel, Basri, Hamidon, Bath, Philip, Bello, Olamide, Bereczki, Dániel, Beretta, Simone, Berkowitz, Aaron, Bernabé-Ortiz, Antonio, Bernhardt, Julie, Berzina, Guna, Bisharyan, Mher, Bovet, Pascal, Budincevic, Hrvoje, Cadilhac, Dominique, Caso, Valeria, Chen, Christopher, Chin, Jerome, Chwojnicki, Kamil, Conforto, Adriana, Cruz, Vitor Tedim, D'Amelio, Marco, Danielyan, Kristine, Davis, Stephen, Demarin, Vida, Dempsey, Robert, Dichgans, Martin, Dokova, Klara, Donnan, Geoffrey, Elkind, Mitchell S., Endres, Matthias, Fischer, Urs, Gankpé, Fortuné, Gaye Saavedra, Andrés, Gil, Artyom, Giroud, Maurice, Gnedovskaya, Elena, Hachinski, Vladimir, Hafdi, Melanie, Hamadeh, Randah, Hamzat, T. Kolapo, Hankey, Graeme, Heldner, Mirjam, Ibrahim, Etedal Ahmed, Ibrahim, Norlinah Mohamed, Inoue, Manabu, Jee, Sungju, Jeng, Jiann-Shing, Kalkonde, Yogesh, Kamenova, Saltanat, Karaszewski, Bartosz, Kelly, Peter, Khan, Taskeen, Kiechl, Stefan, Kondybayeva, Aida, Kõrv, Janika, Kravchenko, Michael, Krishnamurthi, Rita V., Kruja, Jera, Lakkhanaloet, Mongkol, Langhorne, Peter, Lavados, Pablo M., Law, Zhe Kang, Lawal, Abisola, Lazo-Porras, Maria, Lebedynets, Dmytro, Lee, Tsong-Hai, Leung, Thomas, Liebeskind, David S., Lindsay, Patrice, López-Jaramillo, Patricio, Lotufo, Paulo Andrade, Machline-Carrion, Julia, Makanjuola, Akintomiwa, Markus, Hugh Stephen, Marquez-Romero, Juan Manuel, Medina, Marco, Medukhanova, Sabina, Mehndiratta, Man Mohan, Merkin, Alexandr, Mirrakhimov, Erkin, Mohl, Stephanie, Moscoso-Porras, Miguel, Müller-Stierlin, Annabel, Murphy, Sean, Musa, Kamarul Imran, Nasreldein, Ahmed, Nogueira, Raul Gomes, Nolte, Christian, Noubiap, Jean Jacques, Novarro-Escudero, Nelson, Ogun, Yomi, Oguntoye, Richard Ayobami, Oraby, Mohammed Ibrahim, Osundina, Morenike, Ovbiagele, Bruce, Orken, Dilek Necioglu, Ozdemir, Atilla Özcan, Ozturk, Serefnur, Paccot, Melanie, Phromjai, Jurairat, Piradov, Piradov, Platz, Thomas, Potpara, Tatjana, Ranta, Annemarei, Rathore, Farooq, Richard, Edo, Sacco, Ralph L., Sahathevan, Ramesh, Santos Carquín, Irving, Saposnik, Gustavo, Sarfo, Fred Stephen, Sharma, Mike, Sheth, Kevin, Shobhana, A., Suwanwela, Nijasri, Svyato, Irina, Sylaja, P.N., Tao, Xuanchen, Thakur, Kiran T., Toni, Danilo, Topcuoglu, Mehmet Akif, Torales, Julio, Towfighi, Amytis, Truelsen, Thomas Clement, Tsiskaridze, Alexander, Tulloch-Reid, Marshall, Useche, Nicolás, Vanacker, Peter, Vassilopoulou, Sophia, Vukorepa, Gorana, Vuletic, Vladimira, Wahab, Kolawole W., Wang, Wenzhi, Wijeratne, Tissa, Wolfe, Charles, Yifru, Yared Mamushet, Yock-Corrales, Adriana, Yonemoto, Naohiro, Yperzeele, Laetitia, Zhang, Puhong, Owolabi, Mayowa O, Thrift, Amanda G, Mahal, Ajay, Ishida, Marie, Martins, Sheila, Johnson, Walter D, Pandian, Jeyaraj, Abd-Allah, Foad, Yaria, Joseph, Phan, Hoang T, Roth, Greg, Gall, Seana L, Beare, Richard, Phan, Thanh G, Mikulik, Robert, Akinyemi, Rufus O, Norrving, Bo, Brainin, Michael, and Feigin, Valery L

Published
2022
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35. Atypical brain aging and its association with working memory performance in major depressive disorder

Author

Ho, Natalie C.W., primary, Bethlehem, Richard AI., additional, Seidlitz, Jakob, additional, Nogovitsyn, Nikita, additional, Metzak, Paul, additional, Ballester, Pedro L., additional, Hassel, Stefanie, additional, Rotzinger, Susan, additional, Poppenk, Jordan, additional, Lam, Raymond W., additional, Taylor, Valerie H., additional, Milev, Roumen, additional, Adamson, Chris, additional, Adler, Sophie, additional, Alexander-Bloch, Aaron F., additional, Anagnostou, Evdokia, additional, Anderson, Kevin M., additional, Areces-Gonzalez, Ariosky, additional, Astle, Duncan E., additional, Auyeung, Bonnie, additional, Ayub, Muhammad, additional, Bae, Jong Bin, additional, Ball, Gareth, additional, Baron-Cohen, Simon, additional, Beare, Richard, additional, Bedford, Saashi A., additional, Benegal, Vivek, additional, Bethlehem, Richard A.I., additional, Beyer, Frauke, additional, Blangero, John, additional, Blesa Cábez, Manuel, additional, Boardman, James P., additional, Borzage, Matthew, additional, Bosch-Bayard, Jorge F., additional, Bourke, Niall, additional, Bullmore, Edward T., additional, Calhoun, Vince D., additional, Chakravarty, Mallar M., additional, Chen, Christina, additional, Chertavian, Casey, additional, Chetelat, Gaël, additional, Chong, Yap S., additional, Corvin, Aiden, additional, Costantino, Manuela, additional, Courchesne, Eric, additional, Crivello, Fabrice, additional, Cropley, Vanessa L., additional, Crosbie, Jennifer, additional, Crossley, Nicolas, additional, Delarue, Marion, additional, Delorme, Richard, additional, Desrivieres, Sylvane, additional, Devenyi, Gabriel, additional, Di Biase, Maria A., additional, Dolan, Ray, additional, Donald, Kirsten A., additional, Donohoe, Gary, additional, Dorfschmidt, Lena, additional, Dunlop, Katharine, additional, Edwards, Anthony D., additional, Elison, Jed T., additional, Ellis, Cameron T., additional, Elman, Jeremy A., additional, Eyler, Lisa, additional, Fair, Damien A., additional, Fletcher, Paul C., additional, Fonagy, Peter, additional, Franz, Carol E., additional, Galan-Garcia, Lidice, additional, Gholipour, Ali, additional, Giedd, Jay, additional, Gilmore, John H., additional, Glahn, David C., additional, Goodyer, Ian M., additional, Grant, P.E., additional, Groenewold, Nynke A., additional, Gudapati, Shreya, additional, Gunning, Faith M., additional, Gur, Raquel E., additional, Gur, Ruben C., additional, Hammill, Christopher F., additional, Hansson, Oskar, additional, Hedden, Trey, additional, Heinz, Andreas, additional, Henson, Richard N., additional, Heuer, Katja, additional, Hoare, Jacqueline, additional, Holla, Bharath, additional, Holmes, Avram J., additional, Huang, Hao, additional, Ipser, Jonathan, additional, Jack, Clifford R., additional, Jackowski, Andrea P., additional, Jia, Tianye, additional, Jones, David T., additional, Jones, Peter B., additional, Kahn, Rene S., additional, Karlsson, Hasse, additional, Karlsson, Linnea, additional, Kawashima, Ryuta, additional, Kelley, Elizabeth A., additional, Kern, Silke, additional, Kim, Ki-Woong, additional, Kitzbichler, Manfred G., additional, Kremen, William S., additional, Lalonde, François, additional, Landeau, Brigitte, additional, Lerch, Jason, additional, Lewis, John D., additional, Li, Jiao, additional, Liao, Wei, additional, Liston, Conor, additional, Lombardo, Michael V., additional, Lv, Jinglei, additional, Mallard, Travis T., additional, Marcelis, Machteld, additional, Mathias, Samuel R., additional, Mazoyer, Bernard, additional, McGuire, Philip, additional, Meaney, Michael J., additional, Mechelli, Andrea, additional, Misic, Bratislav, additional, Morgan, Sarah E., additional, Mothersill, David, additional, Ortinau, Cynthia, additional, Ossenkoppele, Rik, additional, Ouyang, Minhui, additional, Palaniyappan, Lena, additional, Paly, Leo, additional, Pan, Pedro M., additional, Pantelis, Christos, additional, Park, Min Tae M., additional, Paus, Tomas, additional, Pausova, Zdenka, additional, Paz-Linares, Deirel, additional, Pichet Binette, Alexa, additional, Pierce, Karen, additional, Qian, Xing, additional, Qiu, Anqi, additional, Raznahan, Armin, additional, Rittman, Timothy, additional, Rodrigue, Amanda, additional, Rollins, Caitlin K., additional, Romero-Garcia, Rafael, additional, Ronan, Lisa, additional, Rosenberg, Monica D., additional, Rowitch, David H., additional, Salum, Giovanni A., additional, Satterthwaite, Theodore D., additional, Schaare, H. Lina, additional, Schabdach, Jenna, additional, Schachar, Russell J., additional, Schöll, Michael, additional, Schultz, Aaron P., additional, Sharp, David, additional, Shinohara, Russell T., additional, Skoog, Ingmar, additional, Smyser, Christopher D., additional, Sperling, Reisa A., additional, Stein, Dan J., additional, Stolicyn, Aleks, additional, Suckling, John, additional, Sullivan, Gemma, additional, Thyreau, Benjamin, additional, Toro, Roberto, additional, Traut, Nicolas, additional, Tsvetanov, Kamen A., additional, Turk-Browne, Nicholas B., additional, Tuulari, Jetro J., additional, Tzourio, Christophe, additional, Vachon-Presseau, Étienne, additional, Valdes-Sosa, Mitchell J., additional, Valdes-Sosa, Pedro A., additional, Valk, Sofie L., additional, van Amelsvoort, Therese, additional, Vandekar, Simon N., additional, Vasung, Lana, additional, Vértes, Petra E., additional, Victoria, Lindsay W., additional, Villeneuve, Sylvia, additional, Villringer, Arno, additional, Vogel, Jacob W., additional, Wagstyl, Konrad, additional, Wang, Yin-Shan S., additional, Warfield, Simon K., additional, Warrier, Varun, additional, Westman, Eric, additional, Westwater, Margaret L., additional, Whalley, Heather C., additional, White, Simon R., additional, Witte, A. Veronica, additional, Yang, Ning, additional, Yeo, B.T. Thomas, additional, Yun, Hyuk Jin, additional, Zalesky, Andrew, additional, Zar, Heather J., additional, Zettergren, Anna, additional, Zhou, Juan H., additional, Ziauddeen, Hisham, additional, Zimmerman, Dabriel, additional, Zugman, Andre, additional, Zuo, Xi-Nian N., additional, Frey, Benicio N., additional, Harkness, Kate L., additional, Addington, Jean, additional, and Kennedy, Sidney H., additional

Published
2024
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36. Abstract 109: Towards Individualised Prediction of Neurodevelopmental Outcomes After Neonatal Stroke: A Brain Lesion Network Mapping Study Leveraging Large-Scale Datasets

Author

Kelly, Claire, primary, Chen, Jian, additional, Beare, Richard, additional, Stojanovski, Belinda, additional, Grunt, Sebastian, additional, Slavova, Nedelina, additional, Pastore-Wapp, Manuela, additional, Steinlin, Maja I, additional, Mackay, Mark T, additional, and Yang, Joseph, additional

Published
2024
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38. The $z < 1.2$ optical luminosity function from a sample of $\sim410 \, 000$ galaxies in bootes

Author

Beare, Richard A., Brown, Michael J. I., Pimbblet, Kevin A., Bian, Fuyan, and Lin, Yen-Ting

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

Using a sample of ~410 000 galaxies to depth I_AB = 24 over 8.26 deg^2 in the Bootes field (~10 times larger than z~1 luminosity function studies in the prior literature), we have accurately measured the evolving B-band luminosity function of red galaxies at z<1.2 and blue galaxies at z<1.0. In addition to the large sample size, we utilise photometry that accounts for the varying angular sizes of galaxies, photometric redshifts verified with spectroscopy, and absolute magnitudes that should have very small random and systematic errors. Our results are consistent with the migration of galaxies from the blue cloud to the red sequence as they cease to form stars, and with downsizing in which more massive and luminous blue galaxies cease star formation earlier than fainter less massive ones. Comparing the observed fading of red galaxies with that to be expected from passive evolution alone, we find that the stellar mass contained within the red galaxy population has increased by a factor of ~3.6 from z~1.1 to z~0.1. The bright end of the red galaxy luminosity function fades with decreasing redshift, the rate of fading increasing from ~0.2 mag per unit redshift at z = 1.0 to ~0.8 at z = 0.2. The overall decrease in luminosity implies that the stellar mass in individual highly luminous red galaxies increased by a factor of ~2.2 from z = 1.1 to z = 0.1., Comment: 21 pages, 21 figures

Published
2015
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46. An accurate new method of calculating absolute magnitudes and K-corrections applied to the Sloan filter set

Author

Beare, Richard, Brown, Michael, and Pimbblet, Kevin

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

We describe an accurate new method for determining absolute magnitudes, and hence also K-corrections, which is simpler than most previous methods, being based on a quadratic function of just one suitably chosen observed color. The method relies on the extensive and accurate new set of 129 empirical galaxy template SEDs from Brown et al. (2014). A key advantage of our method is that we can reliably estimate random errors in computed absolute magnitudes due to galaxy diversity, photometric error and redshift error. We derive K-corrections for the five Sloan Digital Sky Survey filters and provide parameter tables for use by the astronomical community. Using the New York Value-Added Galaxy Catalog we compare our K-corrections with those from kcorrect. Our K-corrections produce absolute magnitudes that are generally in good agreement with kcorrect. Absolute g, r, i, z-band magnitudes differ by less than 0.02 mag, and those in the u-band by ~0.04 mag. The evolution of rest-frame colors as a function of redshift is better behaved using our method, with relatively few galaxies being assigned anomalously red colors and a tight red sequence being observed across the whole 0.0 < z < 0.5 redshift range., Comment: 23 pages, 16 figures

Published
2014
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47. The Clustering and Halo Masses of Star Forming Galaxies at z<1

Author

Dolley, Tim, Brown, Michael J. I., Weiner, Benjamin J., Brodwin, Mark, Kochanek, C. S., Pimbblet, Kevin A., Palamara, David P., Jannuzi, Buell T., Dey, Arjun, Atlee, David W., and Beare, Richard

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

We present clustering measurements and halo masses of star forming galaxies at 0.2 < z < 1.0. After excluding AGN, we construct a sample of 22553 24 {\mu}m sources selected from 8.42 deg^2 of the Spitzer MIPS AGN and Galaxy Evolution Survey of Bo\"otes. Mid-infrared imaging allows us to observe galaxies with the highest star formation rates (SFRs), less biased by dust obscuration afflicting the optical bands. We find that the galaxies with the highest SFRs have optical colors which are redder than typical blue cloud galaxies, with many residing within the green valley. At z > 0.4 our sample is dominated by luminous infrared galaxies (LIRGs, L_TIR > 10^11 Lsun) and is comprised entirely of LIRGs and ultra-luminous infrared galaxies (ULIRGs, L_TIR > 10^12 Lsun) at z > 0.6. We observe weak clustering of r_0 = 3-6 Mpc/h for almost all of our star forming samples. We find that the clustering and halo mass depend on L_TIR at all redshifts, where galaxies with higher L_TIR (hence higher SFRs) have stronger clustering. Galaxies with the highest SFRs at each redshift typically reside within dark matter halos of M_halo ~ 10^12.9 Msun/h. This is consistent with a transitional halo mass, above which star formation is largely truncated, although we cannot exclude that ULIRGs reside within higher mass halos. By modeling the clustering evolution of halos, we connect our star forming galaxy samples to their local descendants. Most star forming galaxies at z < 1.0 are the progenitors of L < 2.5L* blue galaxies in the local universe, but star forming galaxies with the highest SFRs (L_TIR >10^11.7 Lsun) at 0.6

Published
2014
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48. Type of anesthesia for cancer resection surgery: No differential impact on cancer recurrence in mouse models of breast cancer

Author

Dubowitz, Julia, primary, Ziegler, Alexandra I., additional, Beare, Richard, additional, Jost-Brinkmann, Fabian, additional, Walker, Adam K., additional, Gillis, Ryan D., additional, Chang, Aeson, additional, Chung, Ni-Chun, additional, Martin, Olga A., additional, Hollande, Frédéric, additional, Riedel, Bernhard, additional, and Sloan, Erica K., additional

Published
2023
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49. White Matter Lesion Progression

Author

Hofer, Edith, Cavalieri, Margherita, Bis, Joshua C, DeCarli, Charles, Fornage, Myriam, Sigurdsson, Sigurdur, Srikanth, Velandai, Trompet, Stella, Verhaaren, Benjamin FJ, Wolf, Christiane, Yang, Qiong, Adams, Hieab HH, Amouyel, Philippe, Beiser, Alexa, Buckley, Brendan M, Callisaya, Michele, Chauhan, Ganesh, de Craen, Anton JM, Dufouil, Carole, van Duijn, Cornelia M, Ford, Ian, Freudenberger, Paul, Gottesman, Rebecca F, Gudnason, Vilmundur, Heiss, Gerardo, Hofman, Albert, Lumley, Thomas, Martinez, Oliver, Mazoyer, Bernard, Moran, Chris, Niessen, Wiro J, Phan, Thanh, Psaty, Bruce M, Satizabal, Claudia L, Sattar, Naveed, Schilling, Sabrina, Shibata, Dean K, Slagboom, P Eline, Smith, Albert, Stott, David J, Taylor, Kent D, Thomson, Russell, Töglhofer, Anna M, Tzourio, Christophe, van Buchem, Mark, Wang, Jing, Westendorp, Rudi GJ, Windham, B Gwen, Vernooij, Meike W, Zijdenbos, Alex, Beare, Richard, Debette, Stéphanie, Ikram, M Arfan, Jukema, J Wouter, Launer, Lenore J, Longstreth, WT, Mosley, Thomas H, Seshadri, Sudha, Schmidt, Helena, and Schmidt, Reinhold

Subjects
Aging, Clinical Research, Genetics, Human Genome, Prevention, Cardiovascular, Aetiology, 2.1 Biological and endogenous factors, Adult, Aged, Cohort Studies, Disease Progression, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Leukoencephalopathies, Male, Middle Aged, Prospective Studies, White Matter, Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium, aging, biological factors, cerebral small vessel diseases, magnetic resonance imaging, white matter lesions, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Neurosciences, Neurology & Neurosurgery
Abstract

Background and purposeWhite matter lesion (WML) progression on magnetic resonance imaging is related to cognitive decline and stroke, but its determinants besides baseline WML burden are largely unknown. Here, we estimated heritability of WML progression, and sought common genetic variants associated with WML progression in elderly participants from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium.MethodsHeritability of WML progression was calculated in the Framingham Heart Study. The genome-wide association study included 7773 elderly participants from 10 cohorts. To assess the relative contribution of genetic factors to progression of WML, we compared in 7 cohorts risk models including demographics, vascular risk factors plus single-nucleotide polymorphisms that have been shown to be associated cross-sectionally with WML in the current and previous association studies.ResultsA total of 1085 subjects showed WML progression. The heritability estimate for WML progression was low at 6.5%, and no single-nucleotide polymorphisms achieved genome-wide significance (P

Published
2015

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