2,700 results on '"Cole, James"'
Search Results
2. Normative Diffusion Autoencoders: Application to Amyotrophic Lateral Sclerosis
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Ijishakin, Ayodeji, Hadjasavilou, Adamos, Abdulaal, Ahmed, Montana-Brown, Nina, Townend, Florence, Spinelli, Edoardo, Fillipi, Massimo, Agosta, Federica, Cole, James, and Malaspina, Andrea
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Predicting survival in Amyotrophic Lateral Sclerosis (ALS) is a challenging task. Magnetic resonance imaging (MRI) data provide in vivo insight into brain health, but the low prevalence of the condition and resultant data scarcity limit training set sizes for prediction models. Survival models are further hindered by the subtle and often highly localised profile of ALS-related neurodegeneration. Normative models present a solution as they increase statistical power by leveraging large healthy cohorts. Separately, diffusion models excel in capturing the semantics embedded within images including subtle signs of accelerated brain ageing, which may help predict survival in ALS. Here, we combine the benefits of generative and normative modelling by introducing the normative diffusion autoencoder framework. To our knowledge, this is the first use of normative modelling within a diffusion autoencoder, as well as the first application of normative modelling to ALS. Our approach outperforms generative and non-generative normative modelling benchmarks in ALS prognostication, demonstrating enhanced predictive accuracy in the context of ALS survival prediction and normative modelling in general.
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- 2024
3. Artificial intelligence for abnormality detection in high volume neuroimaging: a systematic review and meta-analysis
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Agarwal, Siddharth, Wood, David A., Grzeda, Mariusz, Suresh, Chandhini, Din, Munaib, Cole, James, Modat, Marc, and Booth, Thomas C
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Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Purpose: Most studies evaluating artificial intelligence (AI) models that detect abnormalities in neuroimaging are either tested on unrepresentative patient cohorts or are insufficiently well-validated, leading to poor generalisability to real-world tasks. The aim was to determine the diagnostic test accuracy and summarise the evidence supporting the use of AI models performing first-line, high-volume neuroimaging tasks. Methods: Medline, Embase, Cochrane library and Web of Science were searched until September 2021 for studies that temporally or externally validated AI capable of detecting abnormalities in first-line CT or MR neuroimaging. A bivariate random-effects model was used for meta-analysis where appropriate. PROSPERO: CRD42021269563. Results: Only 16 studies were eligible for inclusion. Included studies were not compromised by unrepresentative datasets or inadequate validation methodology. Direct comparison with radiologists was available in 4/16 studies. 15/16 had a high risk of bias. Meta-analysis was only suitable for intracranial haemorrhage detection in CT imaging (10/16 studies), where AI systems had a pooled sensitivity and specificity 0.90 (95% CI 0.85 - 0.94) and 0.90 (95% CI 0.83 - 0.95) respectively. Other AI studies using CT and MRI detected target conditions other than haemorrhage (2/16), or multiple target conditions (4/16). Only 3/16 studies implemented AI in clinical pathways, either for pre-read triage or as post-read discrepancy identifiers. Conclusion: The paucity of eligible studies reflects that most abnormality detection AI studies were not adequately validated in representative clinical cohorts. The few studies describing how abnormality detection AI could impact patients and clinicians did not explore the full ramifications of clinical implementation.
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- 2024
4. A self-supervised text-vision framework for automated brain abnormality detection
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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.
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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
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- 2024
5. Semi-Supervised Diffusion Model for Brain Age Prediction
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Ijishakin, Ayodeji, Martin, Sophie, Townend, Florence, Agosta, Federica, Spinelli, Edoardo Gioele, Basaia, Silvia, Schito, Paride, Falzone, Yuri, Filippi, Massimo, Cole, James, and Malaspina, Andrea
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Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Brain age prediction models have succeeded in predicting clinical outcomes in neurodegenerative diseases, but can struggle with tasks involving faster progressing diseases and low quality data. To enhance their performance, we employ a semi-supervised diffusion model, obtaining a 0.83(p<0.01) correlation between chronological and predicted age on low quality T1w MR images. This was competitive with state-of-the-art non-generative methods. Furthermore, the predictions produced by our model were significantly associated with survival length (r=0.24, p<0.05) in Amyotrophic Lateral Sclerosis. Thus, our approach demonstrates the value of diffusion-based architectures for the task of brain age prediction.
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- 2024
6. Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates
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Liu, Mengting, Lu, Minhua, Kim, Sharon Y., Lee, Hyun Ju, Duffy, Ben A., Yuan, Shiyu, Chai, Yaqiong, Cole, James H., Wu, Xiaotong, Toga, Arthur W., Jahanshad, Neda, Gano, Dawn, Barkovich, Anthony James, Xu, Duan, and Kim, Hosung
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- 2024
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7. Rician likelihood loss for quantitative MRI using self-supervised deep learning
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Parker, Christopher S., Schroder, Anna, Epstein, Sean C., Cole, James, Alexander, Daniel C., and Zhang, Hui
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Computer Science - Machine Learning ,Computer Science - Computational Engineering, Finance, and Science ,Electrical Engineering and Systems Science - Image and Video Processing ,Quantitative Biology - Quantitative Methods ,Statistics - Machine Learning - Abstract
Purpose: Previous quantitative MR imaging studies using self-supervised deep learning have reported biased parameter estimates at low SNR. Such systematic errors arise from the choice of Mean Squared Error (MSE) loss function for network training, which is incompatible with Rician-distributed MR magnitude signals. To address this issue, we introduce the negative log Rician likelihood (NLR) loss. Methods: A numerically stable and accurate implementation of the NLR loss was developed to estimate quantitative parameters of the apparent diffusion coefficient (ADC) model and intra-voxel incoherent motion (IVIM) model. Parameter estimation accuracy, precision and overall error were evaluated in terms of bias, variance and root mean squared error and compared against the MSE loss over a range of SNRs (5 - 30). Results: Networks trained with NLR loss show higher estimation accuracy than MSE for the ADC and IVIM diffusion coefficients as SNR decreases, with minimal loss of precision or total error. At high effective SNR (high SNR and small diffusion coefficients), both losses show comparable accuracy and precision for all parameters of both models. Conclusion: The proposed NLR loss is numerically stable and accurate across the full range of tested SNRs and improves parameter estimation accuracy of diffusion coefficients using self-supervised deep learning. We expect the development to benefit quantitative MR imaging techniques broadly, enabling more accurate parameter estimation from noisy data., Comment: 16 pages, 6 figures
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- 2023
8. Interpretable Alzheimer's Disease Classification Via a Contrastive Diffusion Autoencoder
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Ijishakin, Ayodeji, Abdulaal, Ahmed, Hadjivasiliou, Adamos, Martin, Sophie, and Cole, James
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - Abstract
In visual object classification, humans often justify their choices by comparing objects to prototypical examples within that class. We may therefore increase the interpretability of deep learning models by imbuing them with a similar style of reasoning. In this work, we apply this principle by classifying Alzheimer's Disease based on the similarity of images to training examples within the latent space. We use a contrastive loss combined with a diffusion autoencoder backbone, to produce a semantically meaningful latent space, such that neighbouring latents have similar image-level features. We achieve a classification accuracy comparable to black box approaches on a dataset of 2D MRI images, whilst producing human interpretable model explanations. Therefore, this work stands as a contribution to the pertinent development of accurate and interpretable deep learning within medical imaging.
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- 2023
9. First-Year Students’ Psychological Well-Being and Need for Cognition: Are They Important Predictors of Academic Engagement?
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Cole, James S. and Korkmaz, Ali
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- 2013
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10. Systematic Review of Artificial Intelligence for Abnormality Detection in High-volume Neuroimaging and Subgroup Meta-analysis for Intracranial Hemorrhage Detection
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Agarwal, Siddharth, Wood, David, Grzeda, Mariusz, Suresh, Chandhini, Din, Munaib, Cole, James, Modat, Marc, and Booth, Thomas C
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- 2023
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11. Why the Sprouts of Capitalism Were Delayed in China
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Fang, Xing and Cole, James H.
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- 2011
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12. Investigating Differences Between Low- and High-Stakes Test Performance on a General Education Exam
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Cole, James S. and Osterlind, Steven J.
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- 2008
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13. Brain ageing in schizophrenia: evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium.
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Constantinides, Constantinos, Han, Laura KM, Alloza, Clara, Antonucci, Linda Antonella, Arango, Celso, Ayesa-Arriola, Rosa, Banaj, Nerisa, Bertolino, Alessandro, Borgwardt, Stefan, Bruggemann, Jason, Bustillo, Juan, Bykhovski, Oleg, Calhoun, Vince, Carr, Vaughan, Catts, Stanley, Chung, Young-Chul, Crespo-Facorro, Benedicto, Díaz-Caneja, Covadonga M, Donohoe, Gary, Plessis, Stefan Du, Edmond, Jesse, Ehrlich, Stefan, Emsley, Robin, Eyler, Lisa T, Fuentes-Claramonte, Paola, Georgiadis, Foivos, Green, Melissa, Guerrero-Pedraza, Amalia, Ha, Minji, Hahn, Tim, Henskens, Frans A, Holleran, Laurena, Homan, Stephanie, Homan, Philipp, Jahanshad, Neda, Janssen, Joost, Ji, Ellen, Kaiser, Stefan, Kaleda, Vasily, Kim, Minah, Kim, Woo-Sung, Kirschner, Matthias, Kochunov, Peter, Kwak, Yoo Bin, Kwon, Jun Soo, Lebedeva, Irina, Liu, Jingyu, Mitchie, Patricia, Michielse, Stijn, Mothersill, David, Mowry, Bryan, de la Foz, Víctor Ortiz-García, Pantelis, Christos, Pergola, Giulio, Piras, Fabrizio, Pomarol-Clotet, Edith, Preda, Adrian, Quidé, Yann, Rasser, Paul E, Rootes-Murdy, Kelly, Salvador, Raymond, Sangiuliano, Marina, Sarró, Salvador, Schall, Ulrich, Schmidt, André, Scott, Rodney J, Selvaggi, Pierluigi, Sim, Kang, Skoch, Antonin, Spalletta, Gianfranco, Spaniel, Filip, Thomopoulos, Sophia I, Tomecek, David, Tomyshev, Alexander S, Tordesillas-Gutiérrez, Diana, van Amelsvoort, Therese, Vázquez-Bourgon, Javier, Vecchio, Daniela, Voineskos, Aristotle, Weickert, Cynthia S, Weickert, Thomas, Thompson, Paul M, Schmaal, Lianne, van Erp, Theo GM, Turner, Jessica, Cole, James H, ENIGMA Schizophrenia Consortium, Dima, Danai, and Walton, Esther
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ENIGMA Schizophrenia Consortium ,Brain ,Humans ,Magnetic Resonance Imaging ,Prospective Studies ,Schizophrenia ,Aging ,Adolescent ,Adult ,Aged ,Middle Aged ,Female ,Male ,Young Adult ,Mental Health ,Serious Mental Illness ,Brain Disorders ,Neurosciences ,Biomedical Imaging ,Clinical Research ,Behavioral and Social Science ,Neurological ,Mental health ,Good Health and Well Being ,Biological Sciences ,Medical and Health Sciences ,Psychology and Cognitive Sciences ,Psychiatry - Abstract
Schizophrenia (SZ) is associated with an increased risk of life-long cognitive impairments, age-related chronic disease, and premature mortality. We investigated evidence for advanced brain ageing in adult SZ patients, and whether this was associated with clinical characteristics in a prospective meta-analytic study conducted by the ENIGMA Schizophrenia Working Group. The study included data from 26 cohorts worldwide, with a total of 2803 SZ patients (mean age 34.2 years; range 18-72 years; 67% male) and 2598 healthy controls (mean age 33.8 years, range 18-73 years, 55% male). Brain-predicted age was individually estimated using a model trained on independent data based on 68 measures of cortical thickness and surface area, 7 subcortical volumes, lateral ventricular volumes and total intracranial volume, all derived from T1-weighted brain magnetic resonance imaging (MRI) scans. Deviations from a healthy brain ageing trajectory were assessed by the difference between brain-predicted age and chronological age (brain-predicted age difference [brain-PAD]). On average, SZ patients showed a higher brain-PAD of +3.55 years (95% CI: 2.91, 4.19; I2 = 57.53%) compared to controls, after adjusting for age, sex and site (Cohen's d = 0.48). Among SZ patients, brain-PAD was not associated with specific clinical characteristics (age of onset, duration of illness, symptom severity, or antipsychotic use and dose). This large-scale collaborative study suggests advanced structural brain ageing in SZ. Longitudinal studies of SZ and a range of mental and somatic health outcomes will help to further evaluate the clinical implications of increased brain-PAD and its ability to be influenced by interventions.
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- 2023
14. Distributional Gaussian Processes Layers for Out-of-Distribution Detection
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Popescu, Sebastian G., Sharp, David J., Cole, James H., Kamnitsas, Konstantinos, and Glocker, Ben
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning ,Statistics - Machine Learning - Abstract
Machine learning models deployed on medical imaging tasks must be equipped with out-of-distribution detection capabilities in order to avoid erroneous predictions. It is unsure whether out-of-distribution detection models reliant on deep neural networks are suitable for detecting domain shifts in medical imaging. Gaussian Processes can reliably separate in-distribution data points from out-of-distribution data points via their mathematical construction. Hence, we propose a parameter efficient Bayesian layer for hierarchical convolutional Gaussian Processes that incorporates Gaussian Processes operating in Wasserstein-2 space to reliably propagate uncertainty. This directly replaces convolving Gaussian Processes with a distance-preserving affine operator on distributions. Our experiments on brain tissue-segmentation show that the resulting architecture approaches the performance of well-established deterministic segmentation algorithms (U-Net), which has not been achieved with previous hierarchical Gaussian Processes. Moreover, by applying the same segmentation model to out-of-distribution data (i.e., images with pathology such as brain tumors), we show that our uncertainty estimates result in out-of-distribution detection that outperforms the capabilities of previous Bayesian networks and reconstruction-based approaches that learn normative distributions. To facilitate future work our code is publicly available., Comment: Published in Journal of Machine Learning for Biomedical Imaging: Special Issue: Information Processing in Medical Imaging (IPMI) 2021
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- 2022
15. Toward MR protocol-agnostic, unbiased brain age predicted from clinical-grade MRIs
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Valdes-Hernandez, Pedro A., Laffitte Nodarse, Chavier, Peraza, Julio A., Cole, James H., and Cruz-Almeida, Yenisel
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- 2023
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16. Influence of grain size on α′ Cr precipitation in an isothermally aged Fe-21Cr-5Al alloy
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Arivu, Maalavan, Hoffman, Andrew, Poplawsky, Jonathan, Spinelli, Ian, Dai, Cong, Rebak, Raul B., Cole, James, Islamgaliev, Rinat K, Valiev, Ruslan Z., and Wen, Haiming
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- 2024
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17. USAF Special Operations Forces: Past, Present, and Future
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Cole, James L. Jr.
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United States. Air Force ,Vietnam War, 1959-1975 -- Military aspects ,Special forces (Military science) -- Military aspects ,Air forces -- Military aspects ,Air power -- Military aspects ,Military and naval science - Abstract
SPECIAL Operations constitute a unique facet of airpower which is often discussed but less often understood. Limited resources and constrained budgets invariably relegate Special Operations Forces to a low level [...]
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- 2023
18. Letter from the Editors
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Cole, James, Naquin, Susan, and Rankin, Mary
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- 2011
19. An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modeling
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Hahn, Tim, Ernsting, Jan, Winter, Nils R., Holstein, Vincent, Leenings, Ramona, Beisemann, Marie, Fisch, Lukas, Sarink, Kelvin, Emden, Daniel, Opel, Nils, Redlich, Ronny, Repple, Jonathan, Grotegerd, Dominik, Meinert, Susanne, Hirsch, Jochen G., Niendorf, Thoralf, Endemann, Beate, Bamberg, Fabian, Kröncke, Thomas, Bülow, Robin, Völzke, Henry, von Stackelberg, Oyunbileg, Sowade, Ramona Felizitas, Umutlu, Lale, Schmidt, Börge, Caspers, Svenja, Consortium, German National Cohort Study Center, Kugel, Harald, Kircher, Tilo, Risse, Benjamin, Gaser, Christian, Cole, James H., Dannlowski, Udo, and Berger, Klaus
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Computer Science - Machine Learning ,Quantitative Biology - Populations and Evolution - Abstract
The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk-marker of cross-disorder brain changes, growing into a cornerstone of biological age-research. However, Machine Learning models underlying the field do not consider uncertainty, thereby confounding results with training data density and variability. Also, existing models are commonly based on homogeneous training sets, often not independently validated, and cannot be shared due to data protection issues. Here, we introduce an uncertainty-aware, shareable, and transparent Monte-Carlo Dropout Composite-Quantile-Regression (MCCQR) Neural Network trained on N=10,691 datasets from the German National Cohort. The MCCQR model provides robust, distribution-free uncertainty quantification in high-dimensional neuroimaging data, achieving lower error rates compared to existing models across ten recruitment centers and in three independent validation samples (N=4,004). In two examples, we demonstrate that it prevents spurious associations and increases power to detect accelerated brain-aging. We make the pre-trained model publicly available.
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- 2021
20. Automated triaging of head MRI examinations using convolutional neural networks
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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.
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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
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- 2021
21. Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation
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Popescu, Sebastian G., Sharp, David J., Cole, James H., Kamnitsas, Konstantinos, and Glocker, Ben
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Statistics - Machine Learning ,Computer Science - Machine Learning - Abstract
We propose a parameter efficient Bayesian layer for hierarchical convolutional Gaussian Processes that incorporates Gaussian Processes operating in Wasserstein-2 space to reliably propagate uncertainty. This directly replaces convolving Gaussian Processes with a distance-preserving affine operator on distributions. Our experiments on brain tissue-segmentation show that the resulting architecture approaches the performance of well-established deterministic segmentation algorithms (U-Net), which has never been achieved with previous hierarchical Gaussian Processes. Moreover, by applying the same segmentation model to out-of-distribution data (i.e., images with pathology such as brain tumors), we show that our uncertainty estimates result in out-of-distribution detection that outperforms the capabilities of previous Bayesian networks and reconstruction-based approaches that learn normative distributions.
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- 2021
22. Brain Matters: Exploring Bias in AI for Neuroimaging Research
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Martin, Sophie A., Biondo, Francesca, Cole, James H., Taylor, Beatrice, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wesarg, Stefan, editor, Puyol Antón, Esther, editor, Baxter, John S. H., editor, Erdt, Marius, editor, Drechsler, Klaus, editor, Oyarzun Laura, Cristina, editor, Freiman, Moti, editor, Chen, Yufei, editor, Rekik, Islem, editor, Eagleson, Roy, editor, Feragen, Aasa, editor, King, Andrew P., editor, Cheplygina, Veronika, editor, Ganz-Benjaminsen, Melani, editor, Ferrante, Enzo, editor, Glocker, Ben, editor, Moyer, Daniel, editor, and Petersen, Eikel, editor
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- 2023
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23. Chesowanja (Baringo Basin), Kenya
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Gowlett, John A. J., Cole, James N., Rucina, Stephen M., Beyin, Amanuel, editor, Wright, David K., editor, Wilkins, Jayne, editor, and Olszewski, Deborah I., editor
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- 2023
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24. Kilombe Volcano, Kenya
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Gowlett, John A. J., Cole, James N., Herries, Andy I. R., Hoare, Sally, Stanistreet, Ian G., Rucina, Stephen M., Beyin, Amanuel, editor, Wright, David K., editor, Wilkins, Jayne, editor, and Olszewski, Deborah I., editor
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- 2023
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25. A meta-analysis of structural MRI studies of the brain in systemic lupus erythematosus (SLE)
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Cox, Jennifer G., de Groot, Marius, Cole, James H., Williams, Steven C. R., and Kempton, Matthew J.
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- 2023
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26. Hydrodynamic and thermodynamic analysis of PEGylated human serum albumin
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Correia, John J., Stafford, Walter F., Erlandsen, Heidi, Cole, James L., Premathilaka, Sanduni H., Isailovic, Dragan, and Dignam, John David
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- 2024
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27. Characterization of neural damage and neuroinflammation in Pax6 small-eye mice
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Cole, James D., McDaniel, John A., Nilak, Joelle, Ban, Ashley, Rodriguez, Carlos, Hameed, Zuhaad, Grannonico, Marta, Netland, Peter A., Yang, Hu, Provencio, Ignacio, and Liu, Xiaorong
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- 2024
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28. Smaller spared subcortical nuclei are associated with worse post-stroke sensorimotor outcomes in 28 cohorts worldwide
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Liew, Sook-Lei, Zavaliangos-Petropulu, Artemis, Schweighofer, Nicolas, Jahanshad, Neda, Lang, Catherine E, Lohse, Keith R, Banaj, Nerisa, Barisano, Giuseppe, Baugh, Lee A, Bhattacharya, Anup K, Bigjahan, Bavrina, Borich, Michael R, Boyd, Lara A, Brodtmann, Amy, Buetefisch, Cathrin M, Byblow, Winston D, Cassidy, Jessica M, Charalambous, Charalambos C, Ciullo, Valentina, Conforto, Adriana B, Craddock, Richard C, Dula, Adrienne N, Egorova, Natalia, Feng, Wuwei, Fercho, Kelene A, Gregory, Chris M, Hanlon, Colleen A, Hayward, Kathryn S, Holguin, Jess A, Hordacre, Brenton, Hwang, Darryl H, Kautz, Steven A, Khlif, Mohamed Salah, Kim, Bokkyu, Kim, Hosung, Kuceyeski, Amy, Lo, Bethany, Liu, Jingchun, Lin, David, Lotze, Martin, MacIntosh, Bradley J, Margetis, John L, Mohamed, Feroze B, Nordvik, Jan Egil, Petoe, Matthew A, Piras, Fabrizio, Raju, Sharmila, Ramos-Murguialday, Ander, Revill, Kate P, Roberts, Pamela, Robertson, Andrew D, Schambra, Heidi M, Seo, Na Jin, Shiroishi, Mark S, Soekadar, Surjo R, Spalletta, Gianfranco, Stinear, Cathy M, Suri, Anisha, Tang, Wai Kwong, Thielman, Gregory T, Thijs, Vincent N, Vecchio, Daniela, Ward, Nick S, Westlye, Lars T, Winstein, Carolee J, Wittenberg, George F, Wong, Kristin A, Yu, Chunshui, Wolf, Steven L, Cramer, Steven C, Thompson, Paul M, Baugh, Lee, Gallaguet, Adrià Bermudo, Bhattacharya, Anup, Borich, Michael, Boyd, Lara, Brown, Truman, Buetefisch, Cathrin, Byblow, Winston, Cassidy, Jessica, Charalambous, Charalambos, Cloutier, Alison, Cole, James, Conforto, Adriana, Craddock, Richard, Cramer, Steven, Aguayo, Rosalia Dacosta, DiCarlo, Julie, Dimyan, Michael, Domin, Martin, Donnellly, Miranda, Dula, Adrienne, Edwardson, Matthew, and Ermer, Elsa
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Biological Psychology ,Psychology ,Rehabilitation ,Stroke ,Brain Disorders ,Neurosciences ,Aetiology ,2.1 Biological and endogenous factors ,stroke ,rehabilitation ,sensorimotor behaviour ,MRI ,subcortical volumes ,ENIGMA Stroke Recovery Working Group ,Clinical sciences ,Biological psychology - Abstract
Up to two-thirds of stroke survivors experience persistent sensorimotor impairments. Recovery relies on the integrity of spared brain areas to compensate for damaged tissue. Deep grey matter structures play a critical role in the control and regulation of sensorimotor circuits. The goal of this work is to identify associations between volumes of spared subcortical nuclei and sensorimotor behaviour at different timepoints after stroke. We pooled high-resolution T1-weighted MRI brain scans and behavioural data in 828 individuals with unilateral stroke from 28 cohorts worldwide. Cross-sectional analyses using linear mixed-effects models related post-stroke sensorimotor behaviour to non-lesioned subcortical volumes (Bonferroni-corrected, P
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- 2021
29. Feasibility of brain age predictions from clinical T1-weighted MRIs
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Valdes-Hernandez, Pedro A., Laffitte Nodarse, Chavier, Cole, James H., and Cruz-Almeida, Yenisel
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- 2023
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30. Micro X-ray computed tomography examination of mini plate fuel with hot isostatic pressed aluminum cladding
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Chuirazzi, William, Cordes, Nikolaus L., Jue, Jan-Fong, Johnson, Maxine, Cole, James, and Giglio, Jeffrey
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- 2023
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31. Hierarchical Gaussian Processes with Wasserstein-2 Kernels
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Popescu, Sebastian, Sharp, David, Cole, James, and Glocker, Ben
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Statistics - Machine Learning ,Computer Science - Machine Learning - Abstract
Stacking Gaussian Processes severely diminishes the model's ability to detect outliers, which when combined with non-zero mean functions, further extrapolates low non-parametric variance to low training data density regions. We propose a hybrid kernel inspired from Varifold theory, operating in both Euclidean and Wasserstein space. We posit that directly taking into account the variance in the computation of Wasserstein-2 distances is of key importance towards maintaining outlier status throughout the hierarchy. We show improved performance on medium and large scale datasets and enhanced out-of-distribution detection on both toy and real data.
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- 2020
32. Labelling imaging datasets on the basis of neuroradiology reports: a validation study
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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.
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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.
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- 2020
33. Automated Labelling using an Attention model for Radiology reports of MRI scans (ALARM)
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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.
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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.
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- 2020
34. NEURO-DRAM: a 3D recurrent visual attention model for interpretable neuroimaging classification
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Wood, David, Cole, James, and Booth, Thomas
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Computer Science - Machine Learning ,Statistics - Machine Learning - Abstract
Deep learning is attracting significant interest in the neuroimaging community as a means to diagnose psychiatric and neurological disorders from structural magnetic resonance images. However, there is a tendency amongst researchers to adopt architectures optimized for traditional computer vision tasks, rather than design networks customized for neuroimaging data. We address this by introducing NEURO-DRAM, a 3D recurrent visual attention model tailored for neuroimaging classification. The model comprises an agent which, trained by reinforcement learning, learns to navigate through volumetric images, selectively attending to the most informative regions for a given task. When applied to Alzheimer's disease prediction, NEURODRAM achieves state-of-the-art classification accuracy on an out-of-sample dataset, significantly outperforming a baseline convolutional neural network. When further applied to the task of predicting which patients with mild cognitive impairment will be diagnosed with Alzheimer's disease within two years, the model achieves state-of-the-art accuracy with no additional training. Encouragingly, the agent learns, without explicit instruction, a search policy in agreement with standardized radiological hallmarks of Alzheimer's disease, suggesting a route to automated biomarker discovery for more poorly understood disorders., Comment: Improved network figure
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- 2019
35. Analysis of an Automated Machine Learning Approach in Brain Predictive Modelling: A data-driven approach to Predict Brain Age from Cortical Anatomical Measures
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Dafflon, Jessica, Pinaya, Walter H. L, Turkheimer, Federico, Cole, James H., Leech, Robert, Harris, Mathew A., Cox, Simon R., Whalley, Heather C., McIntosh, Andrew M., and Hellyer, Peter J.
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Quantitative Biology - Neurons and Cognition ,Statistics - Machine Learning - Abstract
The use of machine learning (ML) algorithms has significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, which one is the optimal model to predict the target variable? What are the hyperparameters for such a model? Given the plethora of possible answers to these questions, in the last years, automated machine learning (autoML) has been gaining attention. Here, we apply an autoML library called TPOT which uses a tree-based representation of machine learning pipelines and conducts a genetic-programming based approach to find the model and its hyperparameters that more closely predicts the subject's true age. To explore autoML and evaluate its efficacy within neuroimaging datasets, we chose a problem that has been the focus of previous extensive study: brain age prediction. Without any prior knowledge, TPOT was able to scan through the model space and create pipelines that outperformed the state-of-the-art accuracy for Freesurfer-based models using only thickness and volume information for anatomical structure. In particular, we compared the performance of TPOT (mean accuracy error (MAE): $4.612 \pm .124$ years) and a Relevance Vector Regression (MAE $5.474 \pm .140$ years). TPOT also suggested interesting combinations of models that do not match the current most used models for brain prediction but generalise well to unseen data. AutoML showed promising results as a data-driven approach to find optimal models for neuroimaging applications.
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- 2019
36. Similar neural pathways link psychological stress and brain-age in health and multiple sclerosis
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Schulz, Marc-Andre, Hetzer, Stefan, Eitel, Fabian, Asseyer, Susanna, Meyer-Arndt, Lil, Schmitz-Hübsch, Tanja, Bellmann-Strobl, Judith, Cole, James H., Gold, Stefan M., Paul, Friedemann, Ritter, Kerstin, and Weygandt, Martin
- Published
- 2023
- Full Text
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37. Tensile behavior of diffusion bonded AA6061 - AA6061 with variation in cooling method
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Mehta, Abhishek, Woo, Jeongmin, Giglio, Jeffrey J., Jue, Jan-Fong, Keiser, Dennis D., Jr., Cole, James I., and Sohn, Yongho
- Published
- 2023
- Full Text
- View/download PDF
38. Water extraction from icy lunar simulants using low power microwave heating
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Cole, James D., Lim, Sungwoo, Sargeant, Hannah M., Sheridan, Simon, Anand, Mahesh, and Morse, Andrew
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- 2023
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39. Accelerated Aging after Traumatic Brain Injury: An ENIGMA Multi‐Cohort Mega‐Analysis
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Dennis, Emily L, primary, Vervoordt, Samantha, additional, Adamson, Maheen M, additional, Houshang, Amiri, additional, Bigler, Erin D, additional, Caeyenberghs, Karen, additional, Cole, James H, additional, Dams‐O'Connor, Kristen, additional, Deutscher, Evelyn M, additional, Dobryakova, Ekaterina, additional, Genova, Helen M, additional, Grafman, Jordan H, additional, Håberg, Asta K, additional, Hellstrøm, Torgeir, additional, Irimia, Andrei, additional, Koliatsos, Vassilis E, additional, Lindsey, Hannah M, additional, Livny, Abigail, additional, Menon, David K, additional, Merkley, Tricia L, additional, Mohamed, Abdalla Z, additional, Mondello, Stefania, additional, Monti, Martin M, additional, Newcombe, Virginia FJ, additional, Newsome, Mary R, additional, Ponsford, Jennie, additional, Rabinowitz, Amanda, additional, Smevik, Hanne, additional, Spitz, Gershon, additional, Venkatesan, Umesh M, additional, Westlye, Lars T, additional, Zafonte, Ross, additional, Thompson, Paul M, additional, Wilde, Elisabeth A, additional, Olsen, Alexander, additional, and Hillary, Frank G, additional
- Published
- 2024
- Full Text
- View/download PDF
40. Automated Brain Abnormality Detection using a Self-Supervised Text-Vision Framework
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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
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- 2024
- Full Text
- View/download PDF
41. Education Disrupted: Students Beginning College during the COVID-19 Pandemic
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Kinzie, Jillian and Cole, James
- Abstract
In the second year of the COVID-19 pandemic, students entering college in fall 2021 had an unprecedented culmination to high school and transition to college. This chapter explores the experience of entering college students following these unprecedented circumstances, examining high school disruptions, including changes in the learning environment and its relationship to instructional mode preferences in college, and documenting students' sense of optimism for college, their mental and emotional health, and perceptions of academic difficulty. Results show that the educational impact of the pandemic was not uniform across student groups and will remain an important factor in these students' educational journey.
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- 2022
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42. Warming-induced permafrost thaw exacerbates tundra soil carbon decomposition mediated by microbial community
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Feng, Jiajie, Wang, Cong, Lei, Jiesi, Yang, Yunfeng, Yan, Qingyun, Zhou, Xishu, Tao, Xuanyu, Ning, Daliang, Yuan, Mengting M, Qin, Yujia, Shi, Zhou J, Guo, Xue, He, Zhili, Van Nostrand, Joy D, Wu, Liyou, Bracho-Garillo, Rosvel G, Penton, C Ryan, Cole, James R, Konstantinidis, Konstantinos T, Luo, Yiqi, Schuur, Edward AG, Tiedje, James M, and Zhou, Jizhong
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Biological Sciences ,Ecology ,Carbon ,Carbon Cycle ,Global Warming ,Methane ,Microbiota ,Permafrost ,RNA ,Ribosomal ,16S ,Seasons ,Soil ,Soil Microbiology ,Microbiology ,Medical Microbiology ,Evolutionary biology - Abstract
BackgroundIt is well-known that global warming has effects on high-latitude tundra underlain with permafrost. This leads to a severe concern that decomposition of soil organic carbon (SOC) previously stored in this region, which accounts for about 50% of the world's SOC storage, will cause positive feedback that accelerates climate warming. We have previously shown that short-term warming (1.5 years) stimulates rapid, microbe-mediated decomposition of tundra soil carbon without affecting the composition of the soil microbial community (based on the depth of 42684 sequence reads of 16S rRNA gene amplicons per 3 g of soil sample).ResultsWe show that longer-term (5 years) experimental winter warming at the same site altered microbial communities (p < 0.040). Thaw depth correlated the strongest with community assembly and interaction networks, implying that warming-accelerated tundra thaw fundamentally restructured the microbial communities. Both carbon decomposition and methanogenesis genes increased in relative abundance under warming, and their functional structures strongly correlated (R2 > 0.725, p < 0.001) with ecosystem respiration or CH4 flux.ConclusionsOur results demonstrate that microbial responses associated with carbon cycling could lead to positive feedbacks that accelerate SOC decomposition in tundra regions, which is alarming because SOC loss is unlikely to subside owing to changes in microbial community composition. Video Abstract.
- Published
- 2020
43. Winter warming in Alaska accelerates lignin decomposition contributed by Proteobacteria
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Tao, Xuanyu, Feng, Jiajie, Yang, Yunfeng, Wang, Gangsheng, Tian, Renmao, Fan, Fenliang, Ning, Daliang, Bates, Colin T, Hale, Lauren, Yuan, Mengting M, Wu, Linwei, Gao, Qun, Lei, Jiesi, Schuur, Edward AG, Yu, Julian, Bracho, Rosvel, Luo, Yiqi, Konstantinidis, Konstantinos T, Johnston, Eric R, Cole, James R, Penton, C Ryan, Tiedje, James M, and Zhou, Jizhong
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Biological Sciences ,Ecology ,Climate Action ,Alaska ,Burkholderia ,Climate Change ,Hot Temperature ,Lignin ,Permafrost ,Proteobacteria ,Soil ,Soil Microbiology ,Tundra ,Microbiology ,Medical Microbiology ,Evolutionary biology - Abstract
BackgroundIn a warmer world, microbial decomposition of previously frozen organic carbon (C) is one of the most likely positive climate feedbacks of permafrost regions to the atmosphere. However, mechanistic understanding of microbial mediation on chemically recalcitrant C instability is limited; thus, it is crucial to identify and evaluate active decomposers of chemically recalcitrant C, which is essential for predicting C-cycle feedbacks and their relative strength of influence on climate change. Using stable isotope probing of the active layer of Arctic tundra soils after depleting soil labile C through a 975-day laboratory incubation, the identity of microbial decomposers of lignin and, their responses to warming were revealed.ResultsThe β-Proteobacteria genus Burkholderia accounted for 95.1% of total abundance of potential lignin decomposers. Consistently, Burkholderia isolated from our tundra soils could grow with lignin as the sole C source. A 2.2 °C increase of warming considerably increased total abundance and functional capacities of all potential lignin decomposers. In addition to Burkholderia, α-Proteobacteria capable of lignin decomposition (e.g. Bradyrhizobium and Methylobacterium genera) were stimulated by warming by 82-fold. Those community changes collectively doubled the priming effect, i.e., decomposition of existing C after fresh C input to soil. Consequently, warming aggravates soil C instability, as verified by microbially enabled climate-C modeling.ConclusionsOur findings are alarming, which demonstrate that accelerated C decomposition under warming conditions will make tundra soils a larger biospheric C source than anticipated. Video Abstract.
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- 2020
44. Gene-informed decomposition model predicts lower soil carbon loss due to persistent microbial adaptation to warming.
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Guo, Xue, Gao, Qun, Yuan, Mengting, Wang, Gangsheng, Zhou, Xishu, Feng, Jiajie, Shi, Zhou, Hale, Lauren, Wu, Linwei, Zhou, Aifen, Tian, Renmao, Liu, Feifei, Wu, Bo, Chen, Lijun, Jung, Chang Gyo, Niu, Shuli, Li, Dejun, Xu, Xia, Jiang, Lifen, Escalas, Arthur, Wu, Liyou, He, Zhili, Van Nostrand, Joy D, Ning, Daliang, Liu, Xueduan, Yang, Yunfeng, Schuur, Edward AG, Konstantinidis, Konstantinos T, Cole, James R, Penton, C Ryan, Luo, Yiqi, Tiedje, James M, and Zhou, Jizhong
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Bacteria ,Fungi ,Poaceae ,Plant Roots ,Archaea ,Carbon ,Cellulose ,Soil ,Soil Microbiology ,Acclimatization ,Models ,Genetic ,Hot Temperature ,Metagenome ,Metagenomics ,Global Warming ,Carbon Cycle ,Microbiota ,Grassland ,Environmental DNA ,Models ,Genetic - Abstract
Soil microbial respiration is an important source of uncertainty in projecting future climate and carbon (C) cycle feedbacks. However, its feedbacks to climate warming and underlying microbial mechanisms are still poorly understood. Here we show that the temperature sensitivity of soil microbial respiration (Q10) in a temperate grassland ecosystem persistently decreases by 12.0 ± 3.7% across 7 years of warming. Also, the shifts of microbial communities play critical roles in regulating thermal adaptation of soil respiration. Incorporating microbial functional gene abundance data into a microbially-enabled ecosystem model significantly improves the modeling performance of soil microbial respiration by 5-19%, and reduces model parametric uncertainty by 55-71%. In addition, modeling analyses show that the microbial thermal adaptation can lead to considerably less heterotrophic respiration (11.6 ± 7.5%), and hence less soil C loss. If such microbially mediated dampening effects occur generally across different spatial and temporal scales, the potential positive feedback of soil microbial respiration in response to climate warming may be less than previously predicted.
- Published
- 2020
45. - 6 - Koutroulou Magoula in Phthiotida, Central Greece: A Middle Neolithic Tell Site in Context
- Author
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Hamilakis, Yannis, primary, Kyparissi-Apostolika, Nina, additional, Loughlin, Thomas, additional, Carter, Tristan, additional, Cole, James, additional, Facorellis, Yorgos, additional, Katsarou, Stella, additional, Kaznesi, Aggeliki, additional, Pentedeka, Areti, additional, Tsamis, Vasileios, additional, and Zorzin, Nicolas, additional
- Published
- 2022
- Full Text
- View/download PDF
46. Targeted assemblies of cas1 suggest CRISPR-Cas’s response to soil warming
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Wu, Ruonan, Chai, Benli, Cole, James R, Gunturu, Santosh K, Guo, Xue, Tian, Renmao, Gu, Ji-Dong, Zhou, Jizhong, and Tiedje, James M
- Subjects
Microbiology ,Biological Sciences ,Bioinformatics and Computational Biology ,CRISPR-Cas Systems ,Clustered Regularly Interspaced Short Palindromic Repeats ,Genome ,Oklahoma ,Soil ,Environmental Sciences ,Technology ,Biological sciences ,Environmental sciences - Abstract
There is an increasing interest in the clustered regularly interspaced short palindromic repeats CRISPR-associated protein (CRISPR-Cas) system to reveal potential virus-host dynamics. The universal and most conserved Cas protein, cas1 is an ideal marker to elucidate CRISPR-Cas ecology. We constructed eight Hidden Markov Models (HMMs) and assembled cas1 directly from metagenomes by a targeted-gene assembler, Xander, to improve detection capacity and resolve the diverse CRISPR-Cas systems. The eight HMMs were first validated by recovering all 17 cas1 subtypes from the simulated metagenome generated from 91 prokaryotic genomes across 11 phyla. We challenged the targeted method with 48 metagenomes from a tallgrass prairie in Central Oklahoma recovering 3394 cas1. Among those, 88 were near full length, 5 times more than in de-novo assemblies from the Oklahoma metagenomes. To validate the host assignment by cas1, the targeted-assembled cas1 was mapped to the de-novo assembled contigs. All the phylum assignments of those mapped contigs were assigned independent of CRISPR-Cas genes on the same contigs and consistent with the host taxonomies predicted by the mapped cas1. We then investigated whether 8 years of soil warming altered cas1 prevalence within the communities. A shift in microbial abundances was observed during the year with the biggest temperature differential (mean 4.16 °C above ambient). cas1 prevalence increased and even in the phyla with decreased microbial abundances over the next 3 years, suggesting increasing virus-host interactions in response to soil warming. This targeted method provides an alternative means to effectively mine cas1 from metagenomes and uncover the host communities.
- Published
- 2020
47. Interactive impact of childhood maltreatment, depression, and age on cortical brain structure: mega-analytic findings from a large multi-site cohort
- Author
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Tozzi, Leonardo, Garczarek, Lisa, Janowitz, Deborah, Stein, Dan J, Wittfeld, Katharina, Dobrowolny, Henrik, Lagopoulos, Jim, Hatton, Sean N, Hickie, Ian B, Carballedo, Angela, Brooks, Samantha J, Vuletic, Daniella, Uhlmann, Anne, Veer, Ilya M, Walter, Henrik, Bülow, Robin, Völzke, Henry, Klinger-König, Johanna, Schnell, Knut, Schoepf, Dieter, Grotegerd, Dominik, Opel, Nils, Dannlowski, Udo, Kugel, Harald, Schramm, Elisabeth, Konrad, Carsten, Kircher, Tilo, Jüksel, Dilara, Nenadić, Igor, Krug, Axel, Hahn, Tim, Steinsträter, Olaf, Redlich, Ronny, Zaremba, Dario, Zurowski, Bartosz, Fu, Cynthia HY, Dima, Danai, Cole, James, Grabe, Hans J, Connolly, Colm G, Yang, Tony T, Ho, Tiffany C, LeWinn, Kaja Z, Li, Meng, Groenewold, Nynke A, Salminen, Lauren E, Walter, Martin, Simmons, Alan N, van Erp, Theo GM, Jahanshad, Neda, Baune, Bernhard T, van der Wee, Nic JA, van Tol, Marie-Jose, Penninx, Brenda WJH, Hibar, Derrek P, Thompson, Paul M, Veltman, Dick J, Schmaal, Lianne, and Frodl, Thomas
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Biological Psychology ,Biomedical and Clinical Sciences ,Clinical Sciences ,Psychology ,Pediatric ,Neurosciences ,Clinical Research ,Mental Health ,Brain Disorders ,Depression ,Child Abuse and Neglect Research ,Behavioral and Social Science ,Violence Research ,Aetiology ,2.1 Biological and endogenous factors ,Adolescent ,Adult ,Age Factors ,Aged ,Aged ,80 and over ,Brain Cortical Thickness ,Case-Control Studies ,Cerebral Cortex ,Child ,Child Abuse ,Cohort Studies ,Depressive Disorder ,Major ,Female ,Gyrus Cinguli ,Humans ,Magnetic Resonance Imaging ,Male ,Middle Aged ,Parietal Lobe ,Prefrontal Cortex ,Temporal Lobe ,Young Adult ,Childhood maltreatment ,cortical thickness ,ENIGMA ,major depressive disorder ,‘for the ENIGMA-MDD Consortium’ ,Public Health and Health Services ,Psychiatry ,Clinical sciences ,Biological psychology ,Clinical and health psychology - Abstract
BackgroundChildhood maltreatment (CM) plays an important role in the development of major depressive disorder (MDD). The aim of this study was to examine whether CM severity and type are associated with MDD-related brain alterations, and how they interact with sex and age.MethodsWithin the ENIGMA-MDD network, severity and subtypes of CM using the Childhood Trauma Questionnaire were assessed and structural magnetic resonance imaging data from patients with MDD and healthy controls were analyzed in a mega-analysis comprising a total of 3872 participants aged between 13 and 89 years. Cortical thickness and surface area were extracted at each site using FreeSurfer.ResultsCM severity was associated with reduced cortical thickness in the banks of the superior temporal sulcus and supramarginal gyrus as well as with reduced surface area of the middle temporal lobe. Participants reporting both childhood neglect and abuse had a lower cortical thickness in the inferior parietal lobe, middle temporal lobe, and precuneus compared to participants not exposed to CM. In males only, regardless of diagnosis, CM severity was associated with higher cortical thickness of the rostral anterior cingulate cortex. Finally, a significant interaction between CM and age in predicting thickness was seen across several prefrontal, temporal, and temporo-parietal regions.ConclusionsSeverity and type of CM may impact cortical thickness and surface area. Importantly, CM may influence age-dependent brain maturation, particularly in regions related to the default mode network, perception, and theory of mind.
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- 2020
48. ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.
- Author
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Thompson, Paul M, Jahanshad, Neda, Ching, Christopher RK, Salminen, Lauren E, Thomopoulos, Sophia I, Bright, Joanna, Baune, Bernhard T, Bertolín, Sara, Bralten, Janita, Bruin, Willem B, Bülow, Robin, Chen, Jian, Chye, Yann, Dannlowski, Udo, de Kovel, Carolien GF, Donohoe, Gary, Eyler, Lisa T, Faraone, Stephen V, Favre, Pauline, Filippi, Courtney A, Frodl, Thomas, Garijo, Daniel, Gil, Yolanda, Grabe, Hans J, Grasby, Katrina L, Hajek, Tomas, Han, Laura KM, Hatton, Sean N, Hilbert, Kevin, Ho, Tiffany C, Holleran, Laurena, Homuth, Georg, Hosten, Norbert, Houenou, Josselin, Ivanov, Iliyan, Jia, Tianye, Kelly, Sinead, Klein, Marieke, Kwon, Jun Soo, Laansma, Max A, Leerssen, Jeanne, Lueken, Ulrike, Nunes, Abraham, Neill, Joseph O', Opel, Nils, Piras, Fabrizio, Piras, Federica, Postema, Merel C, Pozzi, Elena, Shatokhina, Natalia, Soriano-Mas, Carles, Spalletta, Gianfranco, Sun, Daqiang, Teumer, Alexander, Tilot, Amanda K, Tozzi, Leonardo, van der Merwe, Celia, Van Someren, Eus JW, van Wingen, Guido A, Völzke, Henry, Walton, Esther, Wang, Lei, Winkler, Anderson M, Wittfeld, Katharina, Wright, Margaret J, Yun, Je-Yeon, Zhang, Guohao, Zhang-James, Yanli, Adhikari, Bhim M, Agartz, Ingrid, Aghajani, Moji, Aleman, André, Althoff, Robert R, Altmann, Andre, Andreassen, Ole A, Baron, David A, Bartnik-Olson, Brenda L, Marie Bas-Hoogendam, Janna, Baskin-Sommers, Arielle R, Bearden, Carrie E, Berner, Laura A, Boedhoe, Premika SW, Brouwer, Rachel M, Buitelaar, Jan K, Caeyenberghs, Karen, Cecil, Charlotte AM, Cohen, Ronald A, Cole, James H, Conrod, Patricia J, De Brito, Stephane A, de Zwarte, Sonja MC, Dennis, Emily L, Desrivieres, Sylvane, Dima, Danai, Ehrlich, Stefan, Esopenko, Carrie, Fairchild, Graeme, Fisher, Simon E, Fouche, Jean-Paul, and Francks, Clyde
- Subjects
ENIGMA Consortium ,Brain ,Humans ,Magnetic Resonance Imaging ,Reproducibility of Results ,Depressive Disorder ,Major ,Neuroimaging ,Neurosciences ,Clinical Research ,Mental Health ,Brain Disorders ,Behavioral and Social Science ,Genetics ,Basic Behavioral and Social Science ,Prevention ,2.1 Biological and endogenous factors ,2.3 Psychological ,social and economic factors ,Mental health ,Neurological ,Clinical Sciences ,Public Health and Health Services ,Psychology - Abstract
This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors.
- Published
- 2020
49. Tundra microbial community taxa and traits predict decomposition parameters of stable, old soil organic carbon.
- Author
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Hale, Lauren, Feng, Wenting, Yin, Huaqun, Guo, Xue, Zhou, Xishu, Bracho, Rosvel, Pegoraro, Elaine, Penton, C Ryan, Wu, Liyou, Cole, James, Konstantinidis, Konstantinos T, Luo, Yiqi, Tiedje, James M, Schuur, Edward AG, and Zhou, Jizhong
- Subjects
Bacteria ,Fungi ,Archaea ,Carbon ,Soil ,Soil Microbiology ,Climate Change ,Microbiota ,Tundra ,Permafrost ,Biological Sciences ,Technology ,Environmental Sciences ,Microbiology - Abstract
The susceptibility of soil organic carbon (SOC) in tundra to microbial decomposition under warmer climate scenarios potentially threatens a massive positive feedback to climate change, but the underlying mechanisms of stable SOC decomposition remain elusive. Herein, Alaskan tundra soils from three depths (a fibric O horizon with litter and course roots, an O horizon with decomposing litter and roots, and a mineral-organic mix, laying just above the permafrost) were incubated. Resulting respiration data were assimilated into a 3-pool model to derive decomposition kinetic parameters for fast, slow, and passive SOC pools. Bacterial, archaeal, and fungal taxa and microbial functional genes were profiled throughout the 3-year incubation. Correlation analyses and a Random Forest approach revealed associations between model parameters and microbial community profiles, taxa, and traits. There were more associations between the microbial community data and the SOC decomposition parameters of slow and passive SOC pools than those of the fast SOC pool. Also, microbial community profiles were better predictors of model parameters in deeper soils, which had higher mineral contents and relatively greater quantities of old SOC than in surface soils. Overall, our analyses revealed the functional potential of microbial communities to decompose tundra SOC through a suite of specialized genes and taxa. These results portray divergent strategies by which microbial communities access SOC pools across varying depths, lending mechanistic insights into the vulnerability of what is considered stable SOC in tundra regions.
- Published
- 2019
50. A quantified comparison of cortical atlases on the basis of trait morphometricity
- Author
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Fürtjes, Anna E., Cole, James H., Couvy-Duchesne, Baptiste, and Ritchie, Stuart J.
- Published
- 2023
- Full Text
- View/download PDF
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