20,171 results on '"Thompson, Paul"'
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2. Back Cover
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Thompson, Paul and Plummer, Ken
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- 2021
3. Index
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Thompson, Paul and Plummer, Ken
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4. Chapter 8 Conclusion: what can we learn?
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Thompson, Paul and Plummer, Ken
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5. Chapter 9 Epilogue
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Thompson, Paul and Plummer, Ken
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6. Chapter 7 Social divisions: class, gender, ethnicity - and more
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Thompson, Paul and Plummer, Ken
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- 2021
7. Biographical summaries
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Thompson, Paul and Plummer, Ken
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8. Further reading
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Thompson, Paul and Plummer, Ken
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9. Notes
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Thompson, Paul and Plummer, Ken
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10. Chapter 5 Fighting or mixing: quantitative and qualitative research
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Thompson, Paul and Plummer, Ken
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11. Voices 7 Reflections for the future
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Thompson, Paul and Plummer, Ken
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12. Voices 5 Into the field
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Thompson, Paul and Plummer, Ken
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13. Chapter 6 Fieldwork: making methods
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Thompson, Paul and Plummer, Ken
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14. Voices 6 On the margins
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Thompson, Paul and Plummer, Ken
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15. Voices 4 Old and new trends
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Thompson, Paul and Plummer, Ken
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16. Chapter 4 Organising: creating research worlds
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Thompson, Paul and Plummer, Ken
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17. Voices 3 Old boundaries, new thoughts
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Thompson, Paul and Plummer, Ken
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18. Chapter 3 Contexts: Empire, politics and culture
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Thompson, Paul and Plummer, Ken
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- 2021
19. Voices 1 Moments of discovery
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Thompson, Paul and Plummer, Ken
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- 2021
20. Voices 2 Beginnings
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Thompson, Paul and Plummer, Ken
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- 2021
21. Chapter 2 Life stories: biography and creativity
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Thompson, Paul and Plummer, Ken
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- 2021
22. Chapter 1 Introduction: the pioneers of social research study
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Thompson, Paul and Plummer, Ken
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- 2021
23. Finding and using the pioneers' interviews
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Thompson, Paul and Plummer, Ken
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- 2021
24. Acknowledgments
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Thompson, Paul and Plummer, Ken
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- 2021
25. List of abbreviations
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Thompson, Paul and Plummer, Ken
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- 2021
26. Title page, Copyright information
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Thompson, Paul and Plummer, Ken
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- 2021
27. Front Cover
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Thompson, Paul and Plummer, Ken
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- 2021
28. Table of contents
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Thompson, Paul and Plummer, Ken
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- 2021
29. Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization
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Hoang, Bao, Pang, Yijiang, Liang, Siqi, Zhan, Liang, Thompson, Paul, and Zhou, Jiayu
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Computer Science - Machine Learning - Abstract
Independent and identically distributed (i.i.d.) data is essential to many data analysis and modeling techniques. In the medical domain, collecting data from multiple sites or institutions is a common strategy that guarantees sufficient clinical diversity, determined by the decentralized nature of medical data. However, data from various sites are easily biased by the local environment or facilities, thereby violating the i.i.d. rule. A common strategy is to harmonize the site bias while retaining important biological information. The ComBat is among the most popular harmonization approaches and has recently been extended to handle distributed sites. However, when faced with situations involving newly joined sites in training or evaluating data from unknown/unseen sites, ComBat lacks compatibility and requires retraining with data from all the sites. The retraining leads to significant computational and logistic overhead that is usually prohibitive. In this work, we develop a novel Cluster ComBat harmonization algorithm, which leverages cluster patterns of the data in different sites and greatly advances the usability of ComBat harmonization. We use extensive simulation and real medical imaging data from ADNI to demonstrate the superiority of the proposed approach. Our codes are provided in https://github.com/illidanlab/distributed-cluster-harmonization., Comment: 11 pages, 7 figures, accepted to KDD2024-ADS
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- 2024
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30. Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation
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Tang, Haoteng, Liu, Guodong, Dai, Siyuan, Ye, Kai, Zhao, Kun, Wang, Wenlu, Yang, Carl, He, Lifang, Leow, Alex, Thompson, Paul, Huang, Heng, and Zhan, Liang
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
The MRI-derived brain network serves as a pivotal instrument in elucidating both the structural and functional aspects of the brain, encompassing the ramifications of diseases and developmental processes. However, prevailing methodologies, often focusing on synchronous BOLD signals from functional MRI (fMRI), may not capture directional influences among brain regions and rarely tackle temporal functional dynamics. In this study, we first construct the brain-effective network via the dynamic causal model. Subsequently, we introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE). This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks via an ordinary differential equation (ODE) model, which characterizes spatial-temporal brain dynamics. Our framework is validated on several clinical phenotype prediction tasks using two independent publicly available datasets (HCP and OASIS). The experimental results clearly demonstrate the advantages of our model compared to several state-of-the-art methods.
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- 2024
31. "A walker's approach [...] is a phenomenological one": W.G. Sebald and the Instant.
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Thompson, Paul
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- 2020
32. Brain‐age prediction: Systematic evaluation of site effects, and sample age range and size
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Yu, Yuetong, Cui, Hao‐Qi, Haas, Shalaila S, New, Faye, Sanford, Nicole, Yu, Kevin, Zhan, Denghuang, Yang, Guoyuan, Gao, Jia‐Hong, Wei, Dongtao, Qiu, Jiang, Banaj, Nerisa, Boomsma, Dorret I, Breier, Alan, Brodaty, Henry, Buckner, Randy L, Buitelaar, Jan K, Cannon, Dara M, Caseras, Xavier, Clark, Vincent P, Conrod, Patricia J, Crivello, Fabrice, Crone, Eveline A, Dannlowski, Udo, Davey, Christopher G, de Haan, Lieuwe, de Zubicaray, Greig I, Di Giorgio, Annabella, Fisch, Lukas, Fisher, Simon E, Franke, Barbara, Glahn, David C, Grotegerd, Dominik, Gruber, Oliver, Gur, Raquel E, Gur, Ruben C, Hahn, Tim, Harrison, Ben J, Hatton, Sean, Hickie, Ian B, Pol, Hilleke E Hulshoff, Jamieson, Alec J, Jernigan, Terry L, Jiang, Jiyang, Kalnin, Andrew J, Kang, Sim, Kochan, Nicole A, Kraus, Anna, Lagopoulos, Jim, Lazaro, Luisa, McDonald, Brenna C, McDonald, Colm, McMahon, Katie L, Mwangi, Benson, Piras, Fabrizio, Rodriguez‐Cruces, Raul, Royer, Jessica, Sachdev, Perminder S, Satterthwaite, Theodore D, Saykin, Andrew J, Schumann, Gunter, Sevaggi, Pierluigi, Smoller, Jordan W, Soares, Jair C, Spalletta, Gianfranco, Tamnes, Christian K, Trollor, Julian N, Ent, Dennis Van't, Vecchio, Daniela, Walter, Henrik, Wang, Yang, Weber, Bernd, Wen, Wei, Wierenga, Lara M, Williams, Steven CR, Wu, Mon‐Ju, Zunta‐Soares, Giovana B, Bernhardt, Boris, Thompson, Paul, Frangou, Sophia, Ge, Ruiyang, and Group, ENIGMA‐Lifespan Working
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Biological Psychology ,Psychology ,Clinical Research ,Neurosciences ,Aging ,Neurological ,Mental health ,Humans ,Adolescent ,Female ,Aged ,Adult ,Child ,Young Adult ,Male ,Brain ,Aged ,80 and over ,Child ,Preschool ,Middle Aged ,Magnetic Resonance Imaging ,Neuroimaging ,Sample Size ,ENIGMA‐Lifespan Working Group ,benchmarking ,brain aging ,brainAGE ,Cognitive Sciences ,Experimental Psychology ,Biological psychology ,Cognitive and computational psychology - Abstract
Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.
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- 2024
33. Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity.
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McWhinney, Sean, Hlinka, Jaroslav, Bakstein, Eduard, Dietze, Lorielle, Corkum, Emily, Abé, Christoph, Alda, Martin, Alexander, Nina, Benedetti, Francesco, Berk, Michael, Bøen, Erlend, Bonnekoh, Linda, Boye, Birgitte, Brosch, Katharina, Canales-Rodríguez, Erick, Cannon, Dara, Dannlowski, Udo, Demro, Caroline, Diaz-Zuluaga, Ana, Elvsåshagen, Torbjørn, Eyler, Lisa, Fortea, Lydia, Fullerton, Janice, Goltermann, Janik, Gotlib, Ian, Grotegerd, Dominik, Haarman, Bartholomeus, Hahn, Tim, Howells, Fleur, Jamalabadi, Hamidreza, Jansen, Andreas, Kircher, Tilo, Klahn, Anna, Kuplicki, Rayus, Lahud, Elijah, Landén, Mikael, Leehr, Elisabeth, Lopez-Jaramillo, Carlos, Mackey, Scott, Malt, Ulrik, Martyn, Fiona, Mazza, Elena, McDonald, Colm, McPhilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Melloni, Elisa, Mitchell, Philip, Nabulsi, Leila, Nenadić, Igor, Nitsch, Robert, Opel, Nils, Ophoff, Roel, Ortuño, Maria, Overs, Bronwyn, Pineda-Zapata, Julian, Pomarol-Clotet, Edith, Radua, Joaquim, Repple, Jonathan, Roberts, Gloria, Rodriguez-Cano, Elena, Sacchet, Matthew, Salvador, Raymond, Savitz, Jonathan, Scheffler, Freda, Schofield, Peter, Schürmeyer, Navid, Shen, Chen, Sim, Kang, Sponheim, Scott, Stein, Dan, Stein, Frederike, Straube, Benjamin, Suo, Chao, Temmingh, Henk, Teutenberg, Lea, Thomas-Odenthal, Florian, Thomopoulos, Sophia, Urosevic, Snezana, Usemann, Paula, van Haren, Neeltje, Vargas, Cristian, Vieta, Eduard, Vilajosana, Enric, Vreeker, Annabel, Winter, Nils, Yatham, Lakshmi, Thompson, Paul, Andreassen, Ole, Ching, Christopher, and Hajek, Tomas
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MRI ,bipolar disorder ,body mass index ,obesity ,principal component analysis ,psychiatry ,Humans ,Bipolar Disorder ,Principal Component Analysis ,Adult ,Female ,Male ,Magnetic Resonance Imaging ,Middle Aged ,Obesity ,Schizophrenia ,Cerebral Cortex ,Cluster Analysis ,Young Adult ,Brain - Abstract
Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.
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- 2024
34. Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk.
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Zhu, Yinghan, Maikusa, Norihide, Radua, Joaquim, Sämann, Philipp, Fusar-Poli, Paolo, Agartz, Ingrid, Andreassen, Ole, Bachman, Peter, Baeza, Inmaculada, Chen, Xiaogang, Choi, Sunah, Corcoran, Cheryl, Ebdrup, Bjørn, Fortea, Adriana, Garani, Ranjini, Glenthøj, Birte, Glenthøj, Louise, Haas, Shalaila, Hamilton, Holly, Hayes, Rebecca, He, Ying, Heekeren, Karsten, Kasai, Kiyoto, Katagiri, Naoyuki, Kim, Minah, Kristensen, Tina, Kwon, Jun, Lawrie, Stephen, Lebedeva, Irina, Lee, Jimmy, Loewy, Rachel, Mathalon, Daniel, McGuire, Philip, Mizrahi, Romina, Mizuno, Masafumi, Møller, Paul, Nemoto, Takahiro, Nordholm, Dorte, Omelchenko, Maria, Raghava, Jayachandra, Røssberg, Jan, Rössler, Wulf, Salisbury, Dean, Sasabayashi, Daiki, Smigielski, Lukasz, Sugranyes, Gisela, Takahashi, Tsutomu, Tamnes, Christian, Tang, Jinsong, Theodoridou, Anastasia, Tomyshev, Alexander, Uhlhaas, Peter, Værnes, Tor, van Amelsvoort, Therese, Waltz, James, Westlye, Lars, Zhou, Juan, Thompson, Paul, Hernaus, Dennis, Jalbrzikowski, Maria, and Koike, Shinsuke
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Humans ,Psychotic Disorders ,Magnetic Resonance Imaging ,Male ,Female ,Brain ,Neuroimaging ,Adult ,Young Adult ,Machine Learning ,Adolescent ,Prodromal Symptoms - Abstract
Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.
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- 2024
35. ConspEmoLLM: Conspiracy Theory Detection Using an Emotion-Based Large Language Model
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Liu, Zhiwei, Liu, Boyang, Thompson, Paul, Yang, Kailai, and Ananiadou, Sophia
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Computer Science - Computation and Language - Abstract
The internet has brought both benefits and harms to society. A prime example of the latter is misinformation, including conspiracy theories, which flood the web. Recent advances in natural language processing, particularly the emergence of large language models (LLMs), have improved the prospects of accurate misinformation detection. However, most LLM-based approaches to conspiracy theory detection focus only on binary classification and fail to account for the important relationship between misinformation and affective features (i.e., sentiment and emotions). Driven by a comprehensive analysis of conspiracy text that reveals its distinctive affective features, we propose ConspEmoLLM, the first open-source LLM that integrates affective information and is able to perform diverse tasks relating to conspiracy theories. These tasks include not only conspiracy theory detection, but also classification of theory type and detection of related discussion (e.g., opinions towards theories). ConspEmoLLM is fine-tuned based on an emotion-oriented LLM using our novel ConDID dataset, which includes five tasks to support LLM instruction tuning and evaluation. We demonstrate that when applied to these tasks, ConspEmoLLM largely outperforms several open-source general domain LLMs and ChatGPT, as well as an LLM that has been fine-tuned using ConDID, but which does not use affective features. This project will be released on https://github.com/lzw108/ConspEmoLLM/., Comment: Work in progress
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- 2024
36. Synthesizing study-specific controls using generative models on open access datasets for harmonized multi-study analyses
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Gadewar, Shruti P., Zhu, Alyssa H., Gari, Iyad Ba, Somu, Sunanda, Thomopoulos, Sophia I., Thompson, Paul M., Nir, Talia M., and Jahanshad, Neda
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Quantitative Biology - Quantitative Methods - Abstract
Neuroimaging consortia can enhance reliability and generalizability of findings by pooling data across studies to achieve larger sample sizes. To adjust for site and MRI protocol effects, imaging datasets are often harmonized based on healthy controls. When data from a control group were not collected, statistical harmonization options are limited as patient characteristics and acquisition-related variables may be confounded. Here, in a multi-study neuroimaging analysis of Alzheimer's patients and controls, we tested whether it is possible to generate synthetic control MRIs. For one case-control study, we used a generative adversarial model for style-based harmonization to generate site-specific controls. Downstream feature extraction, statistical harmonization and group-level multi-study case-control and case-only analyses were performed twice, using either true or synthetic controls. All effect sizes using synthetic controls overlapped with those based on true study controls. This line of work may facilitate wider inclusion of case-only studies in multi-study consortia.
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- 2024
37. McDermott as a Colleague
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Thompson, Paul B.
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- 2020
38. Using rare genetic mutations to revisit structural brain asymmetry.
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Kopal, Jakub, Kumar, Kuldeep, Shafighi, Kimia, Saltoun, Karin, Modenato, Claudia, Moreau, Clara, Huguet, Guillaume, Jean-Louis, Martineau, Martin, Charles-Olivier, Saci, Zohra, Younis, Nadine, Douard, Elise, Jizi, Khadije, Beauchamp-Chatel, Alexis, Kushan, Leila, Silva, Ana, van den Bree, Marianne, Linden, David, Owen, Michael, Hall, Jeremy, Lippé, Sarah, Draganski, Bogdan, Sønderby, Ida, Andreassen, Ole, Glahn, David, Thompson, Paul, Zatorre, Robert, Jacquemont, Sébastien, Bzdok, Danilo, and Bearden, Carrie
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Humans ,DNA Copy Number Variations ,Genome-Wide Association Study ,Functional Laterality ,Brain Mapping ,Brain ,Magnetic Resonance Imaging - Abstract
Asymmetry between the left and right hemisphere is a key feature of brain organization. Hemispheric functional specialization underlies some of the most advanced human-defining cognitive operations, such as articulated language, perspective taking, or rapid detection of facial cues. Yet, genetic investigations into brain asymmetry have mostly relied on common variants, which typically exert small effects on brain-related phenotypes. Here, we leverage rare genomic deletions and duplications to study how genetic alterations reverberate in human brain and behavior. We designed a pattern-learning approach to dissect the impact of eight high-effect-size copy number variations (CNVs) on brain asymmetry in a multi-site cohort of 552 CNV carriers and 290 non-carriers. Isolated multivariate brain asymmetry patterns spotlighted regions typically thought to subserve lateralized functions, including language, hearing, as well as visual, face and word recognition. Planum temporale asymmetry emerged as especially susceptible to deletions and duplications of specific gene sets. Targeted analysis of common variants through genome-wide association study (GWAS) consolidated partly diverging genetic influences on the right versus left planum temporale structure. In conclusion, our gene-brain-behavior data fusion highlights the consequences of genetically controlled brain lateralization on uniquely human cognitive capacities.
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- 2024
39. ENIGMAs simple seven: Recommendations to enhance the reproducibility of resting-state fMRI in traumatic brain injury.
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Esopenko, Carrie, de Souza, Nicola, Dominguez D, Juan, Newsome, Mary, Dobryakova, Ekaterina, Cwiek, Andrew, Mullin, Hollie, Kim, Nicholas, Mayer, Andrew, Adamson, Maheen, Bickart, Kevin, Breedlove, Katherine, Dennis, Emily, Disner, Seth, Haswell, Courtney, Hodges, Cooper, Hoskinson, Kristen, Johnson, Paula, Königs, Marsh, Li, Lucia, Liebel, Spencer, Livny, Abigail, Morey, Rajendra, Muir, Alexandra, Olsen, Alexander, Razi, Adeel, Su, Matthew, Tate, David, Velez, Carmen, Wilde, Elisabeth, Zielinski, Brandon, Thompson, Paul, Hillary, Frank, Caeyenberghs, Karen, Imms, Phoebe, Irimia, Andrei, and Monti, Martin
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Functional MRI ,Functional connectivity ,Lesions ,Reproducibility ,Resting state fMRI ,Traumatic brain injury - Abstract
Resting state functional magnetic resonance imaging (rsfMRI) provides researchers and clinicians with a powerful tool to examine functional connectivity across large-scale brain networks, with ever-increasing applications to the study of neurological disorders, such as traumatic brain injury (TBI). While rsfMRI holds unparalleled promise in systems neurosciences, its acquisition and analytical methodology across research groups is variable, resulting in a literature that is challenging to integrate and interpret. The focus of this narrative review is to address the primary methodological issues including investigator decision points in the application of rsfMRI to study the consequences of TBI. As part of the ENIGMA Brain Injury working group, we have collaborated to identify a minimum set of recommendations that are designed to produce results that are reliable, harmonizable, and reproducible for the TBI imaging research community. Part one of this review provides the results of a literature search of current rsfMRI studies of TBI, highlighting key design considerations and data processing pipelines. Part two outlines seven data acquisition, processing, and analysis recommendations with the goal of maximizing study reliability and between-site comparability, while preserving investigator autonomy. Part three summarizes new directions and opportunities for future rsfMRI studies in TBI patients. The goal is to galvanize the TBI community to gain consensus for a set of rigorous and reproducible methods, and to increase analytical transparency and data sharing to address the reproducibility crisis in the field.
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- 2024
40. Normative modelling of brain morphometry across the lifespan with CentileBrain: algorithm benchmarking and model optimisation.
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Ge, Ruiyang, Yu, Yuetong, Qi, Yi, Fan, Yu-Nan, Chen, Shiyu, Gao, Chuntong, Haas, Shalaila, New, Faye, Boomsma, Dorret, Brodaty, Henry, Brouwer, Rachel, Buckner, Randy, Caseras, Xavier, Crivello, Fabrice, Crone, Eveline, Erk, Susanne, Fisher, Simon, Franke, Barbara, Glahn, David, Dannlowski, Udo, Grotegerd, Dominik, Gruber, Oliver, Hulshoff Pol, Hilleke, Schumann, Gunter, Tamnes, Christian, Walter, Henrik, Wierenga, Lara, Jahanshad, Neda, Thompson, Paul, and Frangou, Sophia
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Humans ,Male ,Female ,Longevity ,Benchmarking ,Brain ,Models ,Statistical ,Algorithms - Abstract
The value of normative models in research and clinical practice relies on their robustness and a systematic comparison of different modelling algorithms and parameters; however, this has not been done to date. We aimed to identify the optimal approach for normative modelling of brain morphometric data through systematic empirical benchmarking, by quantifying the accuracy of different algorithms and identifying parameters that optimised model performance. We developed this framework with regional morphometric data from 37 407 healthy individuals (53% female and 47% male; aged 3-90 years) from 87 datasets from Europe, Australia, the USA, South Africa, and east Asia following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The multivariate fractional polynomial regression (MFPR) emerged as the preferred algorithm, optimised with non-linear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3000 study participants. This model can inform about the biological and behavioural implications of deviations from typical age-related neuroanatomical changes and support future study designs. The model and scripts described here are freely available through CentileBrain.
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- 2024
41. Cortical microstructural associations with CSF amyloid and pTau
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Nir, Talia M, Villalón-Reina, Julio E, Salminen, Lauren E, Haddad, Elizabeth, Zheng, Hong, Thomopoulos, Sophia I, Jack, Clifford R, Weiner, Michael W, Thompson, Paul M, and Jahanshad, Neda
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Biomedical and Clinical Sciences ,Biological Psychology ,Clinical and Health Psychology ,Clinical Sciences ,Psychology ,Brain Disorders ,Behavioral and Social Science ,Clinical Research ,Basic Behavioral and Social Science ,Acquired Cognitive Impairment ,Aging ,Dementia ,Neurodegenerative ,Alzheimer's Disease ,Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD) ,Neurosciences ,Biomedical Imaging ,2.1 Biological and endogenous factors ,Aetiology ,Neurological ,Aged ,Aged ,80 and over ,Female ,Humans ,Male ,Alzheimer Disease ,Amyloid ,Amyloid beta-Peptides ,Biomarkers ,Brain ,Cerebral Cortex ,Cognitive Dysfunction ,Diffusion Magnetic Resonance Imaging ,Gray Matter ,Neuroimaging ,Peptide Fragments ,tau Proteins ,Middle Aged ,Alzheimer’s Disease Neuroimaging Initiative ,Biological Sciences ,Medical and Health Sciences ,Psychology and Cognitive Sciences ,Psychiatry ,Clinical sciences ,Biological psychology ,Clinical and health psychology - Abstract
Diffusion MRI (dMRI) can be used to probe microstructural properties of brain tissue and holds great promise as a means to non-invasively map Alzheimer's disease (AD) pathology. Few studies have evaluated multi-shell dMRI models such as neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator (MAP)-MRI in cortical gray matter where many of the earliest histopathological changes occur in AD. Here, we investigated the relationship between CSF pTau181 and Aβ1-42 burden and regional cortical NODDI and MAP-MRI indices in 46 cognitively unimpaired individuals, 18 with mild cognitive impairment, and two with dementia (mean age: 71.8 ± 6.2 years) from the Alzheimer's Disease Neuroimaging Initiative. We compared findings to more conventional cortical thickness measures. Lower CSF Aβ1-42 and higher pTau181 were associated with cortical dMRI measures reflecting less hindered or restricted diffusion and greater diffusivity. Cortical dMRI measures, but not cortical thickness measures, were more widely associated with Aβ1-42 than pTau181 and better distinguished Aβ+ from Aβ- participants than pTau+ from pTau- participants. dMRI associations mediated the relationship between CSF markers and delayed logical memory performance, commonly impaired in early AD. dMRI metrics sensitive to early AD pathogenesis and microstructural damage may be better measures of subtle neurodegeneration in comparison to standard cortical thickness and help to elucidate mechanisms underlying cognitive decline.
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- 2024
42. DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features
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Belov, Vladimir, Erwin-Grabner, Tracy, Zeng, Ling-Li, Ching, Christopher R. K., Aleman, Andre, Amod, Alyssa R., Basgoze, Zeynep, Benedetti, Francesco, Besteher, Bianca, Brosch, Katharina, Bülow, Robin, Colle, Romain, Connolly, Colm G., Corruble, Emmanuelle, Couvy-Duchesne, Baptiste, Cullen, Kathryn, Dannlowski, Udo, Davey, Christopher G., Dols, Annemiek, Ernsting, Jan, Evans, Jennifer W., Fisch, Lukas, Fuentes-Claramonte, Paola, Gonul, Ali Saffet, Gotlib, Ian H., Grabe, Hans J., Groenewold, Nynke A., Grotegerd, Dominik, Hahn, Tim, Hamilton, J. Paul, Han, Laura K. M., Harrison, Ben J, Ho, Tiffany C., Jahanshad, Neda, Jamieson, Alec J., Karuk, Andriana, Kircher, Tilo, Klimes-Dougan, Bonnie, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Leenings, Ramona, Li, Meng, Linden, David E. J., MacMaster, Frank P., Mehler, David M. A., Meinert, Susanne, Melloni, Elisa, Mueller, Bryon A., Mwangi, Benson, Nenadić, Igor, Ojha, Amar, Okamoto, Yasumasa, Oudega, Mardien L., Penninx, Brenda W. J. H., Poletti, Sara, Pomarol-Clotet, Edith, Portella, Maria J., Pozzi, Elena, Radua, Joaquim, Rodríguez-Cano, Elena, Sacchet, Matthew D., Salvador, Raymond, Schrantee, Anouk, Sim, Kang, Soares, Jair C., Solanes, Aleix, Stein, Dan J., Stein, Frederike, Stolicyn, Aleks, Thomopoulos, Sophia I., Toenders, Yara J., Uyar-Demir, Aslihan, Vieta, Eduard, Vives-Gilabert, Yolanda, Völzke, Henry, Walter, Martin, Whalley, Heather C., Whittle, Sarah, Winter, Nils, Wittfeld, Katharina, Wright, Margaret J., Wu, Mon-Ju, Yang, Tony T., Zarate, Carlos, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., and Goya-Maldonado, Roberto
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Quantitative Biology - Quantitative Methods ,Computer Science - Machine Learning ,Quantitative Biology - Neurons and Cognition - Abstract
Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing an extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible.
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- 2023
43. Emotion Detection for Misinformation: A Review
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Liu, Zhiwei, Zhang, Tianlin, Yang, Kailai, Thompson, Paul, Yu, Zeping, and Ananiadou, Sophia
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Computer Science - Computation and Language - Abstract
With the advent of social media, an increasing number of netizens are sharing and reading posts and news online. However, the huge volumes of misinformation (e.g., fake news and rumors) that flood the internet can adversely affect people's lives, and have resulted in the emergence of rumor and fake news detection as a hot research topic. The emotions and sentiments of netizens, as expressed in social media posts and news, constitute important factors that can help to distinguish fake news from genuine news and to understand the spread of rumors. This article comprehensively reviews emotion-based methods for misinformation detection. We begin by explaining the strong links between emotions and misinformation. We subsequently provide a detailed analysis of a range of misinformation detection methods that employ a variety of emotion, sentiment and stance-based features, and describe their strengths and weaknesses. Finally, we discuss a number of ongoing challenges in emotion-based misinformation detection based on large language models and suggest future research directions, including data collection (multi-platform, multilingual), annotation, benchmark, multimodality, and interpretability., Comment: 30 pages, 11 figures
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- 2023
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44. Prevalence of Tendon Rupture and Tendinopathies Among Patients with Atherosclerotic Cardiovascular Disease Derived From United States Administrative Claims Data
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Gillard, Kristin K., Bloedon, LeAnne, Grady-Benson, John C., Edwards, Alison, Fahy, Sean, Sasiela, William J., Louie, Michael J., and Thompson, Paul D.
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- 2024
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45. Impact of COVID-19 on Ethnically Minoritised Carers in UK’s Care Home Settings: a Systematic Scoping Review
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Thompson, Paul Wesley
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- 2024
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46. Implementation Determinants and Outcomes of a Technology-Enabled Service Targeting Suicide Risk in High Schools: Mixed Methods Study
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Adrian, Molly, Coifman, Jessica, Pullmann, Michael D, Blossom, Jennifer B, Chandler, Casey, Coppersmith, Glen, Thompson, Paul, and Lyon, Aaron R
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Psychology ,BF1-990 - Abstract
BackgroundTechnology-enabled services (TESs), which integrate human service and digital components, are popular strategies to increase the reach and impact of mental health interventions, but large-scale implementation of TESs has lagged behind their potential. ObjectiveThis study applied a mixed qualitative and quantitative approach to gather input from multiple key user groups (students and educators) and to understand the factors that support successful implementation (implementation determinants) and implementation outcomes of a TES for universal screening, ongoing monitoring, and support for suicide risk management in the school setting. MethodsA total of 111 students in the 9th to 12th grade completed measures regarding implementation outcomes (acceptability, feasibility, and appropriateness) via an open-ended survey. A total of 9 school personnel (school-based mental health clinicians, nurses, and administrators) completed laboratory-based usability testing of a dashboard tracking the suicide risk of students, quantitative measures, and qualitative interviews to understand key implementation outcomes and determinants. School personnel were presented with a series of scenarios and common tasks focused on the basic features and functions of the dashboard. Directed content analysis based on the Consolidated Framework for Implementation Research was used to extract multilevel determinants (ie, the barriers or facilitators at the levels of the outer setting, inner setting, individuals, intervention, and implementation process) related to positive implementation outcomes of the TES. ResultsOverarching themes related to implementation determinants and outcomes suggest that both student and school personnel users view TESs for suicide prevention as moderately feasible and acceptable based on the Acceptability of Intervention Measure and Feasibility of Intervention Measure and as needing improvements in usability based on the System Usability Scale. Qualitative results suggest that students and school personnel view passive data collection based on social media data as a relative advantage to the current system; however, the findings indicate that the TES and the school setting need to address issues of privacy, integration into existing workflows and communication patterns, and options for individualization for student-centered care. ConclusionsInnovative suicide prevention strategies that rely on passive data collection in the school context are a promising and appealing idea. Usability testing identified key issues for revision to facilitate widespread implementation.
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- 2020
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47. CARE4Kids Study: Endophenotypes of Persistent Post-Concussive Symptoms in Adolescents: Study Rationale and Protocol.
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Giza, Christopher, Gioia, Gerard, Cook, Lawrence, Asarnow, Robert, Snyder, Aliyah, Babikian, Talin, Thompson, Paul, Bazarian, Jeffery, Whitlow, Christopher, Miles, Christopher, Otallah, Scott, Kamins, Joshua, Didehbani, Nyaz, Rosenbaum, Philip, Chrisman, Sara, Vaughan, Christopher, Cullum, Munro, Popoli, David, Choe, Meeryo, Gill, Jessica, Dennis, Emily, Donald, Christine, and Rivara, Frederick
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MRI ,adolescent ,autonomic nervous system ,biomarker ,blood ,endophenotype ,persistent post-concussion symptoms ,Humans ,Adolescent ,Child ,Post-Concussion Syndrome ,Endophenotypes ,Brain Concussion - Abstract
Treatment of youth concussion during the acute phase continues to evolve, and this has led to the emergence of guidelines to direct care. While symptoms after concussion typically resolve in 14-28 days, a portion (∼20%) of adolescents endorse persistent post-concussive symptoms (PPCS) beyond normal resolution. This report outlines a study implemented in response to the National Institute of Neurological Diseases and Stroke call for the development and initial clinical validation of objective biological measures to predict risk of PPCS in adolescents. We describe our plans for recruitment of a Development cohort of 11- to 17-year-old youth with concussion, and collection of autonomic, neurocognitive, biofluid, and imaging biomarkers. The most promising of these measures will then be validated in a separate Validation cohort of youth with concussion, and a final, clinically useful algorithm will be developed and disseminated. Upon completion of this study, we will have generated a battery of measures predictive of high risk for PPCS, which will allow for identification and testing of interventions to prevent PPCS in the most high-risk youth.
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- 2024
48. A global multicohort study to map subcortical brain development and cognition in infancy and early childhood.
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Alex, Ann, Aguate, Fernando, Botteron, Kelly, Buss, Claudia, Chong, Yap-Seng, Dager, Stephen, Donald, Kirsten, Entringer, Sonja, Fair, Damien, Fortier, Marielle, Gaab, Nadine, Gilmore, John, Girault, Jessica, Graham, Alice, Groenewold, Nynke, Hazlett, Heather, Lin, Weili, Meaney, Michael, Piven, Joseph, Qiu, Anqi, Rasmussen, Jerod, Roos, Annerine, Schultz, Robert, Skeide, Michael, Stein, Dan, Styner, Martin, Thompson, Paul, Turesky, Ted, Wadhwa, Pathik, Zar, Heather, Zöllei, Lilla, de Los Campos, Gustavo, and Knickmeyer, Rebecca
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Male ,Female ,Humans ,Infant ,Newborn ,Child ,Preschool ,Child ,Premature Birth ,Cognition ,Brain ,Neuroimaging ,Magnetic Resonance Imaging - Abstract
The human brain grows quickly during infancy and early childhood, but factors influencing brain maturation in this period remain poorly understood. To address this gap, we harmonized data from eight diverse cohorts, creating one of the largest pediatric neuroimaging datasets to date focused on birth to 6 years of age. We mapped the developmental trajectory of intracranial and subcortical volumes in ∼2,000 children and studied how sociodemographic factors and adverse birth outcomes influence brain structure and cognition. The amygdala was the first subcortical volume to mature, whereas the thalamus exhibited protracted development. Males had larger brain volumes than females, and children born preterm or with low birthweight showed catch-up growth with age. Socioeconomic factors exerted region- and time-specific effects. Regarding cognition, males scored lower than females; preterm birth affected all developmental areas tested, and socioeconomic factors affected visual reception and receptive language. Brain-cognition correlations revealed region-specific associations.
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- 2024
49. Source-based morphometry reveals structural brain pattern abnormalities in 22q11.2 deletion syndrome.
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Ge, Ruiyang, Ching, Christopher, Bassett, Anne, Kushan, Leila, Antshel, Kevin, van Amelsvoort, Therese, Bakker, Geor, Butcher, Nancy, Campbell, Linda, Chow, Eva, Craig, Michael, Crossley, Nicolas, Cunningham, Adam, Daly, Eileen, Doherty, Joanne, Durdle, Courtney, Emanuel, Beverly, Fiksinski, Ania, Forsyth, Jennifer, Fremont, Wanda, Goodrich-Hunsaker, Naomi, Gudbrandsen, Maria, Gur, Raquel, Jalbrzikowski, Maria, Kates, Wendy, Lin, Amy, Linden, David, McCabe, Kathryn, McDonald-McGinn, Donna, Moss, Hayley, Murphy, Declan, Murphy, Kieran, Owen, Michael, Villalon-Reina, Julio, Repetto, Gabriela, Roalf, David, Ruparel, Kosha, Schmitt, J, Schuite-Koops, Sanne, Angkustsiri, Kathleen, Sun, Daqiang, Vajdi, Ariana, van den Bree, Marianne, Vorstman, Jacob, Thompson, Paul, Vila-Rodriguez, Fidel, and Bearden, Carrie
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22q11 deletion syndrome ,gray matter volume ,magnetic resonnance imaging ,source-based morphometry ,Female ,Humans ,Adolescent ,Male ,DiGeorge Syndrome ,Magnetic Resonance Imaging ,Brain ,Psychotic Disorders ,Gray Matter - Abstract
22q11.2 deletion syndrome (22q11DS) is the most frequently occurring microdeletion in humans. It is associated with a significant impact on brain structure, including prominent reductions in gray matter volume (GMV), and neuropsychiatric manifestations, including cognitive impairment and psychosis. It is unclear whether GMV alterations in 22q11DS occur according to distinct structural patterns. Then, 783 participants (470 with 22q11DS: 51% females, mean age [SD] 18.2 [9.2]; and 313 typically developing [TD] controls: 46% females, mean age 18.0 [8.6]) from 13 datasets were included in the present study. We segmented structural T1-weighted brain MRI scans and extracted GMV images, which were then utilized in a novel source-based morphometry (SBM) pipeline (SS-Detect) to generate structural brain patterns (SBPs) that capture co-varying GMV. We investigated the impact of the 22q11.2 deletion, deletion size, intelligence quotient, and psychosis on the SBPs. Seventeen GMV-SBPs were derived, which provided spatial patterns of GMV covariance associated with a quantitative metric (i.e., loading score) for analysis. Patterns of topographically widespread differences in GMV covariance, including the cerebellum, discriminated individuals with 22q11DS from healthy controls. The spatial extents of the SBPs that revealed disparities between individuals with 22q11DS and controls were consistent with the findings of the univariate voxel-based morphometry analysis. Larger deletion size was associated with significantly lower GMV in frontal and occipital SBPs; however, history of psychosis did not show a strong relationship with these covariance patterns. 22q11DS is associated with distinct structural abnormalities captured by topographical GMV covariance patterns that include the cerebellum. Findings indicate that structural anomalies in 22q11DS manifest in a nonrandom manner and in distinct covarying anatomical patterns, rather than a diffuse global process. These SBP abnormalities converge with previously reported cortical surface area abnormalities, suggesting disturbances of early neurodevelopment as the most likely underlying mechanism.
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- 2024
50. Tackling the dimensions in imaging genetics with CLUB-PLS
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Altmann, Andre, Aguila, Ana C Lawry, Jahanshad, Neda, Thompson, Paul M, and Lorenzi, Marco
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Quantitative Biology - Genomics ,Computer Science - Machine Learning ,Electrical Engineering and Systems Science - Image and Video Processing ,Quantitative Biology - Quantitative Methods - Abstract
A major challenge in imaging genetics and similar fields is to link high-dimensional data in one domain, e.g., genetic data, to high dimensional data in a second domain, e.g., brain imaging data. The standard approach in the area are mass univariate analyses across genetic factors and imaging phenotypes. That entails executing one genome-wide association study (GWAS) for each pre-defined imaging measure. Although this approach has been tremendously successful, one shortcoming is that phenotypes must be pre-defined. Consequently, effects that are not confined to pre-selected regions of interest or that reflect larger brain-wide patterns can easily be missed. In this work we introduce a Partial Least Squares (PLS)-based framework, which we term Cluster-Bootstrap PLS (CLUB-PLS), that can work with large input dimensions in both domains as well as with large sample sizes. One key factor of the framework is to use cluster bootstrap to provide robust statistics for single input features in both domains. We applied CLUB-PLS to investigating the genetic basis of surface area and cortical thickness in a sample of 33,000 subjects from the UK Biobank. We found 107 genome-wide significant locus-phenotype pairs that are linked to 386 different genes. We found that a vast majority of these loci could be technically validated at a high rate: using classic GWAS or Genome-Wide Inferred Statistics (GWIS) we found that 85 locus-phenotype pairs exceeded the genome-wide suggestive (P<1e-05) threshold., Comment: 12 pages, 4 Figures, 2 Tables
- Published
- 2023
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