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33,631 results on '"Thompson, A. M."'

1. Identifying dysregulated regions in amyotrophic lateral sclerosis through chromatin accessibility outliers

Author

Çelik, Muhammed Hasan, Gagneur, Julien, Lim, Ryan G, Wu, Jie, Thompson, Leslie M, and Xie, Xiaohui

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Rare Diseases, Human Genome, Neurodegenerative, ALS, Brain Disorders, Neurosciences, Amyotrophic Lateral Sclerosis, Humans, Chromatin, Promoter Regions, Genetic, Algorithms, Gene Expression Regulation, Chromatin Immunoprecipitation Sequencing, Histones, Aberrant Gene Expression, Chromatin Accessibility, Epigenetics, Motor Neuron Disease, Multiomics, Outlier Detection, Post-transcriptional Regulation, Rare Disease, Transcriptional Regulation
Abstract

The high heritability of amyotrophic lateral sclerosis (ALS) contrasts with its low molecular diagnosis rate post-genetic testing, pointing to potential undiscovered genetic factors. To aid the exploration of these factors, we introduced EpiOut, an algorithm to identify chromatin accessibility outliers that are regions exhibiting divergent accessibility from the population baseline in a single or few samples. Annotation of accessible regions with histone chromatin immunoprecipitation sequencing and Hi-C indicates that outliers are concentrated in functional loci, especially among promoters interacting with active enhancers. Across different omics levels, outliers are robustly replicated, and chromatin accessibility outliers are reliable predictors of gene expression outliers and aberrant protein levels. When promoter accessibility does not align with gene expression, our results indicate that molecular aberrations are more likely to be linked to post-transcriptional regulation rather than transcriptional regulation. Our findings demonstrate that the outlier detection paradigm can uncover dysregulated regions in rare diseases. EpiOut is available at github.com/uci-cbcl/EpiOut.

Published
2024

3. Bringing traits back into the equation: A roadmap to understand species redistribution

Author

Comte, Lise, Bertrand, Romain, Diamond, Sarah, Lancaster, Lesley T, Pinsky, Malin L, Scheffers, Brett R, Baecher, J Alex, Bandara, RMWJ, Chen, I‐Ching, Lawlor, Jake A, Moore, Nikki A, Oliveira, Brunno F, Murienne, Jerome, Rolland, Jonathan, Rubenstein, Madeleine A, Sunday, Jennifer, Thompson, Laura M, Villalobos, Fabricio, Weiskopf, Sarah R, and Lenoir, Jonathan

Subjects
Climate Change Impacts and Adaptation, Biological Sciences, Environmental Sciences, Climate Action, Phylogeny, Biodiversity, Climate Change, Geography, Phenotype, climate change, leading edge, mechanism, population dynamics, research bias, species range shift, trailing edge, trait-based approach, trait‐based approach, Ecology, Biological sciences, Earth sciences, Environmental sciences
Abstract

Ecological and evolutionary theories have proposed that species traits should be important in mediating species responses to contemporary climate change; yet, empirical evidence has so far provided mixed evidence for the role of behavioral, life history, or ecological characteristics in facilitating or hindering species range shifts. As such, the utility of trait-based approaches to predict species redistribution under climate change has been called into question. We develop the perspective, supported by evidence, that trait variation, if used carefully can have high potential utility, but that past analyses have in many cases failed to identify an explanatory value for traits by not fully embracing the complexity of species range shifts. First, we discuss the relevant theory linking species traits to range shift processes at the leading (expansion) and trailing (contraction) edges of species distributions and highlight the need to clarify the mechanistic basis of trait-based approaches. Second, we provide a brief overview of range shift-trait studies and identify new opportunities for trait integration that consider range-specific processes and intraspecific variability. Third, we explore the circumstances under which environmental and biotic context dependencies are likely to affect our ability to identify the contribution of species traits to range shift processes. Finally, we propose that revealing the role of traits in shaping species redistribution may likely require accounting for methodological variation arising from the range shift estimation process as well as addressing existing functional, geographical, and phylogenetic biases. We provide a series of considerations for more effectively integrating traits as well as extrinsic and methodological factors into species redistribution research. Together, these analytical approaches promise stronger mechanistic and predictive understanding that can help society mitigate and adapt to the effects of climate change on biodiversity.

Published
2024

4. Effects of a liquefied petroleum gas stove intervention on stillbirth, congenital anomalies and neonatal mortality: A multi-country household air pollution intervention network trial

Author

Younger, Ashley, Ye, Wenlu, Alkon, Abbey, Harknett, Kristen, Kirby, Miles A, Elon, Lisa, Lovvorn, Amy E, Wang, Jiantong, Diaz-Artiga, Anaité, McCracken, John P, Gonzalez, Adly Castañaza, Alarcon, Libny Monroy, Mukeshimana, Alexie, Rosa, Ghislaine, Chiang, Marilu, Balakrishnan, Kalpana, Garg, Sarada S, Pillarisetti, Ajay, Piedrahita, Ricardo, Johnson, Michael A, Craik, Rachel, Papageorghiou, Aris T, Toenjes, Ashley, Williams, Kendra N, Underhill, Lindsay J, Hartinger, Stella M, Nicolaou, Laura, Chang, Howard H, Naeher, Luke P, Rosenthal, Joshua, Checkley, William, Peel, Jennifer L, Clasen, Thomas F, Thompson, Lisa M, and Investigators, Household Air Pollution Intervention Network

Subjects
Reproductive Medicine, Biomedical and Clinical Sciences, Health Sciences, Perinatal Period - Conditions Originating in Perinatal Period, Pediatric, Preterm, Low Birth Weight and Health of the Newborn, Prevention, Health Effects of Household Energy Combustion, Climate-Related Exposures and Conditions, Conditions Affecting the Embryonic and Fetal Periods, Clinical Trials and Supportive Activities, Infant Mortality, Health Effects of Indoor Air Pollution, Clinical Research, Reproductive health and childbirth, Good Health and Well Being, Household Air Pollution Intervention Network (HAPIN) Investigators, Birth outcomes, Congenital anomaly, Cooking fuel, Low- and middle-income countries, Neonatal mortality, Stillbirth, Environmental Sciences
Abstract

Household air pollution (HAP) from cooking with solid fuels used during pregnancy has been associated with adverse pregnancy outcomes. The Household Air Pollution Intervention Network (HAPIN) trial was a randomized controlled trial that assessed the impact of a liquefied petroleum gas (LPG) stove and fuel intervention on health in Guatemala, India, Peru, and Rwanda. Here we investigated the effects of the LPG stove and fuel intervention on stillbirth, congenital anomalies and neonatal mortality and characterized exposure-response relationships between personal exposures to fine particulate matter (PM2.5), black carbon (BC) and carbon monoxide (CO) and these outcomes. Pregnant women (18 to

Published
2024

5. Synthesizing study-specific controls using generative models on open access datasets for harmonized multi-study analyses

Author

Gadewar, Shruti P., Zhu, Alyssa H., Gari, Iyad Ba, Somu, Sunanda, Thomopoulos, Sophia I., Thompson, Paul M., Nir, Talia M., and Jahanshad, Neda

Subjects
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.

Published
2024

6. Experimenting with Large Language Models and vector embeddings in NASA SciX

Author

Blanco-Cuaresma, Sergi, Ciucă, Ioana, Accomazzi, Alberto, Kurtz, Michael J., Henneken, Edwin A., Lockhart, Kelly E., Grezes, Felix, Allen, Thomas, Shapurian, Golnaz, Grant, Carolyn S., Thompson, Donna M., Hostetler, Timothy W., Templeton, Matthew R., Chen, Shinyi, Koch, Jennifer, Jacovich, Taylor, Chivvis, Daniel, Alves, Fernanda de Macedo, Paquin, Jean-Claude, Bartlett, Jennifer, Polimera, Mugdha, and Jarmak, Stephanie

Subjects
Computer Science - Computation and Language, Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Artificial Intelligence
Abstract

Open-source Large Language Models enable projects such as NASA SciX (i.e., NASA ADS) to think out of the box and try alternative approaches for information retrieval and data augmentation, while respecting data copyright and users' privacy. However, when large language models are directly prompted with questions without any context, they are prone to hallucination. At NASA SciX we have developed an experiment where we created semantic vectors for our large collection of abstracts and full-text content, and we designed a prompt system to ask questions using contextual chunks from our system. Based on a non-systematic human evaluation, the experiment shows a lower degree of hallucination and better responses when using Retrieval Augmented Generation. Further exploration is required to design new features and data augmentation processes at NASA SciX that leverages this technology while respecting the high level of trust and quality that the project holds., Comment: To appear in the proceedings of the 33th annual international Astronomical Data Analysis Software & Systems (ADASS XXXIII)

Published
2023

8. The Association between Exposure to Fine Particulate Matter and MRI-Assessed Locus Coeruleus Integrity in the Vietnam Era Twin Study of Aging (VETSA)

Author

Puckett, Olivia K., Fennema-Notestine, Christine, Hagler, Donald J., Jr., Braskie, Meredith N., Chen, Jiu-Chiuan, Finch, Caleb E., Kaufman, Joel D., Petkus, Andrew J., Reynolds, Chandra A., Salminen, Lauren E., Thompson, Paul M., Wang, Xinhui, Kremen, William S., Franz, Carol E., and Elman, Jeremy A.

Subjects
Aging -- Health aspects, Magnetic resonance imaging -- Evaluation, Particles -- Environmental aspects -- Health aspects, Environmental issues, Health, Evaluation, Environmental aspects, Health aspects
Abstract

BACKGROUND: Increased exposure to ambient air pollution, especially fine particulate matter [less than or equal to]2.5 [micro]m ([PM.sub.2.5]) is associated with poorer brain health and increased risk for Alzheimer's disease (AD) and related dementias. The locus coeruleus (LC), located in the brainstem, is one of the earliest regions affected by tau pathology seen in AD. Its diffuse projections throughout the brain include afferents to olfactory areas that are hypothesized conduits of cerebral particle deposition. Additionally, extensive contact of the LC with the cerebrovascular system may present an additional route of exposure to environmental toxicants. OBJECTIVE: Our aim was to investigate if exposure to [PM.sub.2.5] was associated with LC integrity in a nationwide sample of men in early old age, potentially representing one pathway through which air pollution can contribute to increased risk for AD dementia. METHODS: We examined the relationship between [PM.sub.2.5] and in vivo magnetic resonance imaging (MRI) estimates of LC structural integrity indexed by contrast to noise ratio ([LC.sub.CNR]) in 381 men [mean age = 67.3; standard deviation (SD) = 2.6] from the Vietnam Era Twin Study of Aging (VETSA). Exposure to [PM.sub.2.5] was taken as a 3-year average over the most recent period for which data were available (average of 5.6 years prior to the MRI scan). We focused on [LC.sub.CNR] in the rostral-middle portion of LC due to its stronger associations with aging and AD than the caudal LC. Associations between [PM.sub.2.5] exposures and LC integrity were tested using linear mixed effects models adjusted for age, scanner, education, household income, and interval between exposure and MRI. A co-twin control analysis was also performed to investigate whether associations remained after controlling for genetic confounding and rearing environment. RESULTS: Multiple linear regressions revealed a significant association between [PM.sub.2.5] and rostral-middle [LC.sub.CNR] (b = -0.16; p = 0.02), whereby higher exposure to [PM.sub.2.5] was associated with lower [LC.sub.CNR]. A co-twin control analysis found that, within monozygotic pairs, individuals with higher [PM.sub.2.5] exposure showed lower [LC.sub.CNR] (b = - 0.11; p = 0.02), indicating associations were not driven by genetic or shared environmental confounds. There were no associations between [PM.sub.2.5] and caudal [LC.sub.CNR] or hippocampal volume, suggesting a degree of specificity to the rostral-middle portion of the LC. DISCUSSION: Given previous findings that loss of LC integrity is associated with increased accumulation of AD-related amyloid and tau pathology, impacts on LC integrity may represent a potential pathway through which exposure to air pollution increases AD risk. https://doi.org/10.1289/EHP14344, Introduction Neuropathological studies indicate that abnormal tau may first appear in the locus coeruleus (LC) starting early in life, (1,2) with substantial accumulation occurring during the course of Alzheimer's disease [...]

Published
2024
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9. One hundred years of EEG for brain and behaviour research

Author

Mushtaq, Faisal, Welke, Dominik, Gallagher, Anne, Pavlov, Yuri G., Kouara, Layla, Bosch-Bayard, Jorge, van den Bosch, Jasper J. F., Arvaneh, Mahnaz, Bland, Amy R., Chaumon, Maximilien, Borck, Cornelius, He, Xun, Luck, Steven J., Machizawa, Maro G., Pernet, Cyril, Puce, Aina, Segalowitz, Sidney J., Rogers, Christine, Awais, Muhammad, Babiloni, Claudio, Bailey, Neil W., Baillet, Sylvain, Bendall, Robert C. A., Brady, Daniel, Bringas-Vega, Maria L., Busch, Niko A., Calzada-Reyes, Ana, Chatard, Armand, Clayson, Peter E., Cohen, Michael X., Cole, Jonathan, Constant, Martin, Corneyllie, Alexandra, Coyle, Damien, Cruse, Damian, Delis, Ioannis, Delorme, Arnaud, Fair, Damien, Falk, Tiago H., Gamer, Matthias, Ganis, Giorgio, Gloy, Kilian, Gregory, Samantha, Hassall, Cameron D., Hiley, Katherine E., Ivry, Richard B., Jerbi, Karim, Jenkins, Michael, Kaiser, Jakob, Keil, Andreas, Knight, Robert T., Kochen, Silvia, Kotchoubey, Boris, Krigolson, Olave E., Langer, Nicolas, Liesefeld, Heinrich R., Lippé, Sarah, London, Raquel E., MacNamara, Annmarie, Makeig, Scott, Marinovic, Welber, Martínez-Montes, Eduardo, Marzuki, Aleya A., Mathew, Ryan K., Michel, Christoph, Millán, José d. R., Mon-Williams, Mark, Morales-Chacón, Lilia, Naar, Richard, Nilsonne, Gustav, Niso, Guiomar, Nyhus, Erika, Oostenveld, Robert, Paul, Katharina, Paulus, Walter, Pfabigan, Daniela M., Pourtois, Gilles, Rampp, Stefan, Rausch, Manuel, Robbins, Kay, Rossini, Paolo M., Ruzzoli, Manuela, Schmidt, Barbara, Senderecka, Magdalena, Srinivasan, Narayanan, Stegmann, Yannik, Thompson, Paul M., Valdes-Sosa, Mitchell, van der Molen, Melle J. W., Veniero, Domenica, Verona, Edelyn, Voytek, Bradley, Yao, Dezhong, Evans, Alan C., and Valdes-Sosa, Pedro

Published
2024
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11. Epigenetic activation of SOX11 is associated with recurrence and progression of ductal carcinoma in situ to invasive breast cancer

Author

Treekitkarnmongkol, Warapen, Shah, Vandna, Kai, Kazuharu, Katayama, Hiroshi, Wong, Justin, Ladha, Farah A., Nguyen, Tristian, Menegaz, Brian, Lu, Wei, Yang, Fei, Mino, Barbara, Tang, Ximing, Gagea, Mihai, Batra, Harsh, Raso, Maria Gabriela, Wistuba, Ignacio I., Krishnamurthy, Savitri, Pinder, Sarah E., Sawyer, Elinor J., Thompson, Alastair M., and Sen, Subrata

Published
2024
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15. Long-Term Results of Organ Preservation in Patients With Rectal Adenocarcinoma Treated With Total Neoadjuvant Therapy: The Randomized Phase II OPRA Trial

Author

Verheij, Floris S, Omer, Dana M, Williams, Hannah, Lin, Sabrina T, Qin, Li-Xuan, Buckley, James T, Thompson, Hannah M, Yuval, Jonathan B, Kim, Jin K, Dunne, Richard F, Marcet, Jorge, Cataldo, Peter, Polite, Blase, Herzig, Daniel O, Liska, David, Oommen, Samuel, Friel, Charles M, Ternent, Charles, Coveler, Andrew L, Hunt, Steven, Gregory, Anita, Varma, Madhulika G, Bello, Brian L, Carmichael, Joseph C, Krauss, John, Gleisner, Ana, Guillem, José G, Temple, Larissa, Goodman, Karyn A, Segal, Neil H, Cercek, Andrea, Yaeger, Rona, Nash, Garrett M, Widmar, Maria, Wei, Iris H, Pappou, Emmanouil P, Weiser, Martin R, Paty, Philip B, Smith, J Joshua, Wu, Abraham J, Gollub, Marc J, Saltz, Leonard B, and Garcia-Aguilar, Julio

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Cancer, Rare Diseases, Radiation Oncology, Clinical Trials and Supportive Activities, Clinical Research, Orphan Drug, Patient Safety, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, Humans, Adenocarcinoma, Chemoradiotherapy, Neoadjuvant Therapy, Neoplasm Recurrence, Local, Neoplasm Staging, Organ Preservation, Rectal Neoplasms, Treatment Outcome, Oncology & Carcinogenesis, Oncology and carcinogenesis
Abstract

Clinical trials frequently include multiple end points that mature at different times. The initial report, typically based on the primary end point, may be published when key planned co-primary or secondary analyses are not yet available. Clinical Trial Updates provide an opportunity to disseminate additional results from studies, published in JCO or elsewhere, for which the primary end point has already been reported.To assess long-term risk of local tumor regrowth, we report updated organ preservation rate and oncologic outcomes of the OPRA trial (ClinicalTrials.gov identifier: NCT02008656). Patients with stage II/III rectal cancer were randomly assigned to receive induction chemotherapy followed by chemoradiation (INCT-CRT) or chemoradiation followed by consolidation chemotherapy (CRT-CNCT). Patients who achieved a complete or near-complete response after finishing treatment were offered watch-and-wait (WW). Total mesorectal excision (TME) was recommended for those who achieved an incomplete response. The primary end point was disease-free survival (DFS). The secondary end point was TME-free survival. In total, 324 patients were randomly assigned (INCT-CRT, n = 158; CRT-CNCT, n = 166). Median follow-up was 5.1 years. The 5-year DFS rates were 71% (95% CI, 64 to 79) and 69% (95% CI, 62 to 77) for INCT-CRT and CRT-CNCT, respectively (P = .68). TME-free survival was 39% (95% CI, 32 to 48) in the INCT-CRT group and 54% (95% CI, 46 to 62) in the CRT-CNCT group (P = .012). Of 81 patients with regrowth, 94% occurred within 2 years and 99% occurred within 3 years. DFS was similar for patients who underwent TME after restaging (64% [95% CI, 53 to 78]) and patients in WW who underwent TME after regrowth (64% [95% CI, 53 to 78]; P = .94). Updated analysis continues to show long-term organ preservation in half of the patients with rectal cancer treated with total neoadjuvant therapy. In patients who enter WW, most cases of tumor regrowth occur in the first 2 years.

Published
2024

16. Cortical microstructural associations with CSF amyloid and pTau

Author

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

Subjects
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.

Published
2024

17. DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features

Author

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

Subjects
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.

Published
2023

18. Did Private Election Administration Funding Advantage Democrats in 2020?

Author

Lal, Apoorva and Thompson, Daniel M

Subjects
Economics - General Economics
Abstract

Private donors contributed more than $350 million to local election officials to support the administration of the 2020 election. Supporters argue these grants were neutral and necessary to maintain normal election operations during the pandemic, while critics worry these grants mostly went to Democratic strongholds and tilted election outcomes. These concerns have led twenty-four states to restrict private election grants. How much did these grants shape the 2020 presidential election? To answer this question, we collect administrative data on private election administration grants and election outcomes. We then use new advances in synthetic control methods to compare presidential election results and turnout in counties that received grants to counties with identical average presidential election results and turnout before 2020. While counties that favor Democrats were much more likely to apply for a grant, we find that the grants did not have a noticeable effect on the presidential election. Our estimates of the average effect of receiving a grant on Democratic vote share range from 0.02 percentage points to 0.36 percentage points. Our estimates of the average effect of receiving a grant on turnout range from -0.03 percentage points to 0.13 percentage points. Across specifications, our 95% confidence intervals typically include negative effects, and our confidence intervals from all specifications fail to include effects on Democratic vote share larger than 0.58 percentage points and effects on turnout larger than 0.40 percentage points. We characterize the magnitude of our effects by asking how large they are compared to the margin by which Biden won the 2020 election. In simple bench-marking exercises, we find that the effects of the grants were likely too small to have changed the outcome of the 2020 presidential election.

Published
2023

19. Tackling the dimensions in imaging genetics with CLUB-PLS

Author

Altmann, Andre, Aguila, Ana C Lawry, Jahanshad, Neda, Thompson, Paul M, and Lorenzi, Marco

Subjects
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

20. Video and Synthetic MRI Pre-training of 3D Vision Architectures for Neuroimage Analysis

Author

Dhinagar, Nikhil J., Singh, Amit, Ozarkar, Saket, Buwa, Ketaki, Thomopoulos, Sophia I., Owens-Walton, Conor, Laltoo, Emily, Chen, Yao-Liang, Cook, Philip, McMillan, Corey, Tsai, Chih-Chien, Wang, J-J, Wu, Yih-Ru, and Thompson, Paul M.

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

Transfer learning represents a recent paradigm shift in the way we build artificial intelligence (AI) systems. In contrast to training task-specific models, transfer learning involves pre-training deep learning models on a large corpus of data and minimally fine-tuning them for adaptation to specific tasks. Even so, for 3D medical imaging tasks, we do not know if it is best to pre-train models on natural images, medical images, or even synthetically generated MRI scans or video data. To evaluate these alternatives, here we benchmarked vision transformers (ViTs) and convolutional neural networks (CNNs), initialized with varied upstream pre-training approaches. These methods were then adapted to three unique downstream neuroimaging tasks with a range of difficulty: Alzheimer's disease (AD) and Parkinson's disease (PD) classification, "brain age" prediction. Experimental tests led to the following key observations: 1. Pre-training improved performance across all tasks including a boost of 7.4% for AD classification and 4.6% for PD classification for the ViT and 19.1% for PD classification and reduction in brain age prediction error by 1.26 years for CNNs, 2. Pre-training on large-scale video or synthetic MRI data boosted performance of ViTs, 3. CNNs were robust in limited-data settings, and in-domain pretraining enhanced their performances, 4. Pre-training improved generalization to out-of-distribution datasets and sites. Overall, we benchmarked different vision architectures, revealing the value of pre-training them with emerging datasets for model initialization. The resulting pre-trained models can be adapted to a range of downstream neuroimaging tasks, even when training data for the target task is limited.

Published
2023

21. Linking Symptom Inventories using Semantic Textual Similarity

Author

Kennedy, Eamonn, Vadlamani, Shashank, Lindsey, Hannah M, Peterson, Kelly S, OConnor, Kristen Dams, Murray, Kenton, Agarwal, Ronak, Amiri, Houshang H, Andersen, Raeda K, Babikian, Talin, Baron, David A, Bigler, Erin D, Caeyenberghs, Karen, Delano-Wood, Lisa, Disner, Seth G, Dobryakova, Ekaterina, Eapen, Blessen C, Edelstein, Rachel M, Esopenko, Carrie, Genova, Helen M, Geuze, Elbert, Goodrich-Hunsaker, Naomi J, Grafman, Jordan, Haberg, Asta K, Hodges, Cooper B, Hoskinson, Kristen R, Hovenden, Elizabeth S, Irimia, Andrei, Jahanshad, Neda, Jha, Ruchira M, Keleher, Finian, Kenney, Kimbra, Koerte, Inga K, Liebel, Spencer W, Livny, Abigail, Lovstad, Marianne, Martindale, Sarah L, Max, Jeffrey E, Mayer, Andrew R, Meier, Timothy B, Menefee, Deleene S, Mohamed, Abdalla Z, Mondello, Stefania, Monti, Martin M, Morey, Rajendra A, Newcombe, Virginia, Newsome, Mary R, Olsen, Alexander, Pastorek, Nicholas J, Pugh, Mary Jo, Razi, Adeel, Resch, Jacob E, Rowland, Jared A, Russell, Kelly, Ryan, Nicholas P, Scheibel, Randall S, Schmidt, Adam T, Spitz, Gershon, Stephens, Jaclyn A, Tal, Assaf, Talbert, Leah D, Tartaglia, Maria Carmela, Taylor, Brian A, Thomopoulos, Sophia I, Troyanskaya, Maya, Valera, Eve M, van der Horn, Harm Jan, Van Horn, John D, Verma, Ragini, Wade, Benjamin SC, Walker, Willian SC, Ware, Ashley L, Werner Jr, J Kent, Yeates, Keith Owen, Zafonte, Ross D, Zeineh, Michael M, Zielinski, Brandon, Thompson, Paul M, Hillary, Frank G, Tate, David F, Wilde, Elisabeth A, and Dennis, Emily L

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

An extensive library of symptom inventories has been developed over time to measure clinical symptoms, but this variety has led to several long standing issues. Most notably, results drawn from different settings and studies are not comparable, which limits reproducibility. Here, we present an artificial intelligence (AI) approach using semantic textual similarity (STS) to link symptoms and scores across previously incongruous symptom inventories. We tested the ability of four pre-trained STS models to screen thousands of symptom description pairs for related content - a challenging task typically requiring expert panels. Models were tasked to predict symptom severity across four different inventories for 6,607 participants drawn from 16 international data sources. The STS approach achieved 74.8% accuracy across five tasks, outperforming other models tested. This work suggests that incorporating contextual, semantic information can assist expert decision-making processes, yielding gains for both general and disease-specific clinical assessment.

Published
2023

23. Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk

Author

Zhu, Yinghan, Maikusa, Norihide, Radua, Joaquim, Sämann, Philipp G., Fusar-Poli, Paolo, Agartz, Ingrid, Andreassen, Ole A., Bachman, Peter, Baeza, Inmaculada, Chen, Xiaogang, Choi, Sunah, Corcoran, Cheryl M., Ebdrup, Bjørn H., Fortea, Adriana, Garani, Ranjini RG., Glenthøj, Birte Yding, Glenthøj, Louise Birkedal, Haas, Shalaila S., Hamilton, Holly K., Hayes, Rebecca A., He, Ying, Heekeren, Karsten, Kasai, Kiyoto, Katagiri, Naoyuki, Kim, Minah, Kristensen, Tina D., Kwon, Jun Soo, Lawrie, Stephen M., Lebedeva, Irina, Lee, Jimmy, Loewy, Rachel L., Mathalon, Daniel H., McGuire, Philip, Mizrahi, Romina, Mizuno, Masafumi, Møller, Paul, Nemoto, Takahiro, Nordholm, Dorte, Omelchenko, Maria A., Raghava, Jayachandra M., Røssberg, Jan I., Rössler, Wulf, Salisbury, Dean F., Sasabayashi, Daiki, Smigielski, Lukasz, Sugranyes, Gisela, Takahashi, Tsutomu, Tamnes, Christian K., Tang, Jinsong, Theodoridou, Anastasia, Tomyshev, Alexander S., Uhlhaas, Peter J., Værnes, Tor G., van Amelsvoort, Therese A. M. J., Waltz, James A., Westlye, Lars T., Zhou, Juan H., Thompson, Paul M., Hernaus, Dennis, Jalbrzikowski, Maria, and Koike, Shinsuke

Published
2024
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24. Mechanisms, detection and impacts of species redistributions under climate change

Author

Lawlor, Jake A., Comte, Lise, Grenouillet, Gaël, Lenoir, Jonathan, Baecher, J. Alex, Bandara, R.M.W.J., Bertrand, Romain, Chen, I-Ching, Diamond, Sarah E., Lancaster, Lesley T., Moore, Nikki, Murienne, Jerome, Oliveira, Brunno F., Pecl, Gretta T., Pinsky, Malin L., Rolland, Jonathan, Rubenstein, Madeleine, Scheffers, Brett R., Thompson, Laura M., van Amerom, Brit, Villalobos, Fabricio, Weiskopf, Sarah R., and Sunday, Jennifer

Published
2024
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26. White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group

Author

Kim, Bo-Gyeom, Kim, Gakyung, Abe, Yoshinari, Alonso, Pino, Ameis, Stephanie, Anticevic, Alan, Arnold, Paul D., Balachander, Srinivas, Banaj, Nerisa, Bargalló, Nuria, Batistuzzo, Marcelo C., Benedetti, Francesco, Bertolín, Sara, Beucke, Jan Carl, Bollettini, Irene, Brem, Silvia, Brennan, Brian P., Buitelaar, Jan K., Calvo, Rosa, Castelo-Branco, Miguel, Cheng, Yuqi, Chhatkuli, Ritu Bhusal, Ciullo, Valentina, Coelho, Ana, Couto, Beatriz, Dallaspezia, Sara, Ely, Benjamin A., Ferreira, Sónia, Fontaine, Martine, Fouche, Jean-Paul, Grazioplene, Rachael, Gruner, Patricia, Hagen, Kristen, Hansen, Bjarne, Hanna, Gregory L., Hirano, Yoshiyuki, Höxter, Marcelo Q., Hough, Morgan, Hu, Hao, Huyser, Chaim, Ikuta, Toshikazu, Jahanshad, Neda, James, Anthony, Jaspers-Fayer, Fern, Kasprzak, Selina, Kathmann, Norbert, Kaufmann, Christian, Kim, Minah, Koch, Kathrin, Kvale, Gerd, Kwon, Jun Soo, Lazaro, Luisa, Lee, Junhee, Lochner, Christine, Lu, Jin, Manrique, Daniela Rodriguez, Martínez-Zalacaín, Ignacio, Masuda, Yoshitada, Matsumoto, Koji, Maziero, Maria Paula, Menchón, Jose M., Minuzzi, Luciano, Moreira, Pedro Silva, Morgado, Pedro, Narayanaswamy, Janardhanan C., Narumoto, Jin, Ortiz, Ana E., Ota, Junko, Pariente, Jose C., Perriello, Chris, Picó-Pérez, Maria, Pittenger, Christopher, Poletti, Sara, Real, Eva, Reddy, Y. C. Janardhan, van Rooij, Daan, Sakai, Yuki, Sato, João Ricardo, Segalas, Cinto, Shavitt, Roseli G., Shen, Zonglin, Shimizu, Eiji, Shivakumar, Venkataram, Soreni, Noam, Soriano-Mas, Carles, Sousa, Nuno, Sousa, Mafalda Machado, Spalletta, Gianfranco, Stern, Emily R., Stewart, S. Evelyn, Szeszko, Philip R., Thomas, Rajat, Thomopoulos, Sophia I., Vecchio, Daniela, Venkatasubramanian, Ganesan, Vriend, Chris, Walitza, Susanne, Wang, Zhen, Watanabe, Anri, Wolters, Lidewij, Xu, Jian, Yamada, Kei, Yun, Je-Yeon, Zarei, Mojtaba, Zhao, Qing, Zhu, Xi, Thompson, Paul M., Bruin, Willem B., van Wingen, Guido A., Piras, Federica, Piras, Fabrizio, Stein, Dan J., van den Heuvel, Odile A., Simpson, Helen Blair, Marsh, Rachel, and Cha, Jiook

Published
2024
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27. Smaller total and subregional cerebellar volumes in posttraumatic stress disorder: a mega-analysis by the ENIGMA-PGC PTSD workgroup

Author

Huggins, Ashley A., Baird, C. Lexi, Briggs, Melvin, Laskowitz, Sarah, Hussain, Ahmed, Fouda, Samar, Haswell, Courtney, Sun, Delin, Salminen, Lauren E., Jahanshad, Neda, Thomopoulos, Sophia I., Veltman, Dick J., Frijling, Jessie L., Olff, Miranda, van Zuiden, Mirjam, Koch, Saskia B. J., Nawjin, Laura, Wang, Li, Zhu, Ye, Li, Gen, Stein, Dan J., Ipser, Jonathan, Seedat, Soraya, du Plessis, Stefan, van den Heuvel, Leigh L., Suarez-Jimenez, Benjamin, Zhu, Xi, Kim, Yoojean, He, Xiaofu, Zilcha-Mano, Sigal, Lazarov, Amit, Neria, Yuval, Stevens, Jennifer S., Ressler, Kerry J., Jovanovic, Tanja, van Rooij, Sanne J. H., Fani, Negar, Hudson, Anna R., Mueller, Sven C., Sierk, Anika, Manthey, Antje, Walter, Henrik, Daniels, Judith K., Schmahl, Christian, Herzog, Julia I., Říha, Pavel, Rektor, Ivan, Lebois, Lauren A. M., Kaufman, Milissa L., Olson, Elizabeth A., Baker, Justin T., Rosso, Isabelle M., King, Anthony P., Liberzon, Isreal, Angstadt, Mike, Davenport, Nicholas D., Sponheim, Scott R., Disner, Seth G., Straube, Thomas, Hofmann, David, Qi, Rongfeng, Lu, Guang Ming, Baugh, Lee A., Forster, Gina L., Simons, Raluca M., Simons, Jeffrey S., Magnotta, Vincent A., Fercho, Kelene A., Maron-Katz, Adi, Etkin, Amit, Cotton, Andrew S., O’Leary, Erin N., Xie, Hong, Wang, Xin, Quidé, Yann, El-Hage, Wissam, Lissek, Shmuel, Berg, Hannah, Bruce, Steven, Cisler, Josh, Ross, Marisa, Herringa, Ryan J., Grupe, Daniel W., Nitschke, Jack B., Davidson, Richard J., Larson, Christine L., deRoon-Cassini, Terri A., Tomas, Carissa W., Fitzgerald, Jacklynn M., Blackford, Jennifer Urbano, Olatunji, Bunmi O., Kremen, William S., Lyons, Michael J., Franz, Carol E., Gordon, Evan M., May, Geoffrey, Nelson, Steven M., Abdallah, Chadi G., Levy, Ifat, Harpaz-Rotem, Ilan, Krystal, John H., Dennis, Emily L., Tate, David F., Cifu, David X., Walker, William C., Wilde, Elizabeth A., Harding, Ian H., Kerestes, Rebecca, Thompson, Paul M., and Morey, Rajendra

Published
2024
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30. Generalized Core Spanner Inexpressibility via Ehrenfeucht-Fra\'iss\'e Games for FC

Author

Thompson, Sam M. and Freydenberger, Dominik D.

Subjects
Computer Science - Logic in Computer Science, Computer Science - Databases, Computer Science - Formal Languages and Automata Theory
Abstract

Despite considerable research on document spanners, little is known about the expressive power of generalized core spanners. In this paper, we use Ehrenfeucht-Fra\"iss\'e games to obtain general inexpressibility lemmas for the logic FC (a finite-model variant of the theory of concatenation). Applying these lemmas give inexpressibility results for FC that we lift to generalized core spanners. In particular, we give several relations that cannot be selected by generalized core spanners, thus demonstrating the effectiveness of the inexpressibility lemmas. As an immediate consequence, we also gain new insights into the expressive power of core spanners.

Published
2023

32. Towards hyperlocal source identification of pollutants in cities by combining mobile measurements with atmospheric modeling

Author

Lin, John C, Fasoli, Ben, Mitchell, Logan, Bares, Ryan, Hopkins, Francesca, Thompson, Tammy M, and Alvarez, Ramón A

Subjects
Earth Sciences, Atmospheric Sciences, Clinical Research, Sustainable Cities and Communities, Statistics, Environmental Engineering, Meteorology & Atmospheric Sciences, Atmospheric sciences, Climate change science, Environmental engineering
Published
2023

33. Contents

Author

Thompson, Joseph M.

Published
2024

35. Introduction

Author

Thompson, Joseph M.

Published
2024

44. Notes

Author

Thompson, Joseph M.

Published
2024

45. Bibliography

Author

Thompson, Joseph M.

Published
2024

48. Incomplete Multimodal Learning for Complex Brain Disorders Prediction

Author

Shirkavand, Reza, Zhan, Liang, Huang, Heng, Shen, Li, and Thompson, Paul M.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Quantitative Biology - Neurons and Cognition
Abstract

Recent advancements in the acquisition of various brain data sources have created new opportunities for integrating multimodal brain data to assist in early detection of complex brain disorders. However, current data integration approaches typically need a complete set of biomedical data modalities, which may not always be feasible, as some modalities are only available in large-scale research cohorts and are prohibitive to collect in routine clinical practice. Especially in studies of brain diseases, research cohorts may include both neuroimaging data and genetic data, but for practical clinical diagnosis, we often need to make disease predictions only based on neuroimages. As a result, it is desired to design machine learning models which can use all available data (different data could provide complementary information) during training but conduct inference using only the most common data modality. We propose a new incomplete multimodal data integration approach that employs transformers and generative adversarial networks to effectively exploit auxiliary modalities available during training in order to improve the performance of a unimodal model at inference. We apply our new method to predict cognitive degeneration and disease outcomes using the multimodal imaging genetic data from Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Experimental results demonstrate that our approach outperforms the related machine learning and deep learning methods by a significant margin.

Published
2023

49. A Comprehensive Corpus Callosum Segmentation Tool for Detecting Callosal Abnormalities and Genetic Associations from Multi Contrast MRIs

Author

Gadewar, Shruti P., Nourollahimoghadam, Elnaz, Bhatt, Ravi R., Ramesh, Abhinaav, Javid, Shayan, Gari, Iyad Ba, Zhu, Alyssa H., Thomopoulos, Sophia, Thompson, Paul M., and Jahanshad, Neda

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Structural alterations of the midsagittal corpus callosum (midCC) have been associated with a wide range of brain disorders. The midCC is visible on most MRI contrasts and in many acquisitions with a limited field-of-view. Here, we present an automated tool for segmenting and assessing the shape of the midCC from T1w, T2w, and FLAIR images. We train a UNet on images from multiple public datasets to obtain midCC segmentations. A quality control algorithm is also built-in, trained on the midCC shape features. We calculate intraclass correlations (ICC) and average Dice scores in a test-retest dataset to assess segmentation reliability. We test our segmentation on poor quality and partial brain scans. We highlight the biological significance of our extracted features using data from over 40,000 individuals from the UK Biobank; we classify clinically defined shape abnormalities and perform genetic analyses.

Published
2023

50. The James Webb Space Telescope Mission

Author

Gardner, Jonathan P., Mather, John C., Abbott, Randy, Abell, James S., Abernathy, Mark, Abney, Faith E., Abraham, John G., Abraham, Roberto, Abul-Huda, Yasin M., Acton, Scott, Adams, Cynthia K., Adams, Evan, Adler, David S., Adriaensen, Maarten, Aguilar, Jonathan Albert, Ahmed, Mansoor, Ahmed, Nasif S., Ahmed, Tanjira, Albat, Rüdeger, Albert, Loïc, Alberts, Stacey, Aldridge, David, Allen, Mary Marsha, Allen, Shaune S., Altenburg, Martin, Altunc, Serhat, Alvarez, Jose Lorenzo, Álvarez-Márquez, Javier, de Oliveira, Catarina Alves, Ambrose, Leslie L., Anandakrishnan, Satya M., Andersen, Gregory C., Anderson, Harry James, Anderson, Jay, Anderson, Kristen, Anderson, Sara M., Aprea, Julio, Archer, Benita J., Arenberg, Jonathan W., Argyriou, Ioannis, Arribas, Santiago, Artigau, Étienne, Arvai, Amanda Rose, Atcheson, Paul, Atkinson, Charles B., Averbukh, Jesse, Aymergen, Cagatay, Bacinski, John J., Baggett, Wayne E., Bagnasco, Giorgio, Baker, Lynn L., Balzano, Vicki Ann, Banks, Kimberly A., Baran, David A., Barker, Elizabeth A., Barrett, Larry K., Barringer, Bruce O., Barto, Allison, Bast, William, Baudoz, Pierre, Baum, Stefi, Beatty, Thomas G., Beaulieu, Mathilde, Bechtold, Kathryn, Beck, Tracy, Beddard, Megan M., Beichman, Charles, Bellagama, Larry, Bely, Pierre, Berger, Timothy W., Bergeron, Louis E., Darveau-Bernier, Antoine, Bertch, Maria D., Beskow, Charlotte, Betz, Laura E., Biagetti, Carl P., Birkmann, Stephan, Bjorklund, Kurt F., Blackwood, James D., Blazek, Ronald Paul, Blossfeld, Stephen, Bluth, Marcel, Boccaletti, Anthony, Boegner Jr., Martin E., Bohlin, Ralph C., Boia, John Joseph, Böker, Torsten, Bonaventura, N., Bond, Nicholas A., Bosley, Kari Ann, Boucarut, Rene A., Bouchet, Patrice, Bouwman, Jeroen, Bower, Gary, Bowers, Ariel S., Bowers, Charles W., Boyce, Leslye A., Boyer, Christine T., Boyer, Martha L., Boyer, Michael, Boyer, Robert, Bradley, Larry D., Brady, Gregory R., Brandl, Bernhard R., Brannen, Judith L., Breda, David, Bremmer, Harold G., Brennan, David, Bresnahan, Pamela A., Bright, Stacey N., Broiles, Brian J., Bromenschenkel, Asa, Brooks, Brian H., Brooks, Keira J., Brown, Bob, Brown, Bruce, Brown, Thomas M., Bruce, Barry W., Bryson, Jonathan G., Bujanda, Edwin D., Bullock, Blake M., Bunker, A. J., Bureo, Rafael, Burt, Irving J., Bush, James Aaron, Bushouse, Howard A., Bussman, Marie C., Cabaud, Olivier, Cale, Steven, Calhoon, Charles D., Calvani, Humberto, Canipe, Alicia M., Caputo, Francis M., Cara, Mihai, Carey, Larkin, Case, Michael Eli, Cesari, Thaddeus, Cetorelli, Lee D., Chance, Don R., Chandler, Lynn, Chaney, Dave, Chapman, George N., Charlot, S., Chayer, Pierre, Cheezum, Jeffrey I., Chen, Bin, Chen, Christine H., Cherinka, Brian, Chichester, Sarah C., Chilton, Zachary S., Chittiraibalan, Dharini, Clampin, Mark, Clark, Charles R., Clark, Kerry W., Clark, Stephanie M., Claybrooks, Edward E., Cleveland, Keith A., Cohen, Andrew L., Cohen, Lester M., Colón, Knicole D., Coleman, Benee L., Colina, Luis, Comber, Brian J., Comeau, Thomas M., Comer, Thomas, Reis, Alain Conde, Connolly, Dennis C., Conroy, Kyle E., Contos, Adam R., Contreras, James, Cook, Neil J., Cooper, James L., Cooper, Rachel Aviva, Correia, Michael F., Correnti, Matteo, Cossou, Christophe, Costanza, Brian F., Coulais, Alain, Cox, Colin R., Coyle, Ray T., Cracraft, Misty M., Noriega-Crespo, Alberto, Crew, Keith A., Curtis, Gary J., Cusveller, Bianca, Maciel, Cleyciane Da Costa, Dailey, Christopher T., Daugeron, Frédéric, Davidson, Greg S., Davies, James E., Davis, Katherine Anne, Davis, Michael S., Day, Ratna, de Chambure, Daniel, de Jong, Pauline, De Marchi, Guido, Dean, Bruce H., Decker, John E., Delisa, Amy S., Dell, Lawrence C., Dellagatta, Gail, Dembinska, Franciszka, Demosthenes, Sandor, Dencheva, Nadezhda M., Deneu, Philippe, DePriest, William W., Deschenes, Jeremy, Dethienne, Nathalie, Detre, Örs Hunor, Diaz, Rosa Izela, Dicken, Daniel, DiFelice, Audrey S., Dillman, Matthew, Disharoon, Maureen O., van Dishoeck, Ewine F., Dixon, William V., Doggett, Jesse B., Dominguez, Keisha L., Donaldson, Thomas S., Doria-Warner, Cristina M., Santos, Tony Dos, Doty, Heather, Douglas Jr., Robert E., Doyon, René, Dressler, Alan, Driggers, Jennifer, Driggers, Phillip A., Dunn, Jamie L., DuPrie, Kimberly C., Dupuis, Jean, Durning, John, Dutta, Sanghamitra B., Earl, Nicholas M., Eccleston, Paul, Ecobichon, Pascal, Egami, Eiichi, Ehrenwinkler, Ralf, Eisenhamer, Jonathan D., Eisenhower, Michael, Eisenstein, Daniel J., Hamel, Zaky El, Elie, Michelle L., Elliott, James, Elliott, Kyle Wesley, Engesser, Michael, Espinoza, Néstor, Etienne, Odessa, Etxaluze, Mireya, Evans, Leah, Fabreguettes, Luce, Falcolini, Massimo, Falini, Patrick R., Fatig, Curtis, Feeney, Matthew, Feinberg, Lee D., Fels, Raymond, Ferdous, Nazma, Ferguson, Henry C., Ferrarese, Laura, Ferreira, Marie-Héléne, Ferruit, Pierre, Ferry, Malcolm, Filippazzo, Joseph Charles, Firre, Daniel, Fix, Mees, Flagey, Nicolas, Flanagan, Kathryn A., Fleming, Scott W., Florian, Michael, Flynn, James R., Foiadelli, Luca, Fontaine, Mark R., Fontanella, Erin Marie, Forshay, Peter Randolph, Fortner, Elizabeth A., Fox, Ori D., Framarini, Alexandro P., Francisco, John I., Franck, Randy, Franx, Marijn, Franz, David E., Friedman, Scott D., Friend, Katheryn E., Frost, James R., Fu, Henry, Fullerton, Alexander W., Gaillard, Lionel, Galkin, Sergey, Gallagher, Ben, Galyer, Anthony D., Marín, Macarena García, Gardner, Lisa E., Garland, Dennis, Garrett, Bruce Albert, Gasman, Danny, Gáspár, András, Gastaud, René, Gaudreau, Daniel, Gauthier, Peter Timothy, Geers, Vincent, Geithner, Paul H., Gennaro, Mario, Gerber, John, Gereau, John C., Giampaoli, Robert, Giardino, Giovanna, Gibbons, Paul C., Gilbert, Karolina, Gilman, Larry, Girard, Julien H., Giuliano, Mark E., Gkountis, Konstantinos, Glasse, Alistair, Glassmire, Kirk Zachary, Glauser, Adrian Michael, Glazer, Stuart D., Goldberg, Joshua, Golimowski, David A., Gonzaga, Shireen P., Gordon, Karl D., Gordon, Shawn J., Goudfrooij, Paul, Gough, Michael J., Graham, Adrian J., Grau, Christopher M., Green, Joel David, Greene, Gretchen R., Greene, Thomas P., Greenfield, Perry E., Greenhouse, Matthew A., Greve, Thomas R., Greville, Edgar M., Grimaldi, Stefano, Groe, Frank E., Groebner, Andrew, Grumm, David M., Grundy, Timothy, Güdel, Manuel, Guillard, Pierre, Guldalian, John, Gunn, Christopher A., Gurule, Anthony, Gutman, Irvin Meyer, Guy, Paul D., Guyot, Benjamin, Hack, Warren J., Haderlein, Peter, Hagan, James B., Hagedorn, Andria, Hainline, Kevin, Haley, Craig, Hami, Maryam, Hamilton, Forrest Clifford, Hammann, Jeffrey, Hammel, Heidi B., Hanley, Christopher J., Hansen, Carl August, Hardy, Bruce, Harnisch, Bernd, Harr, Michael Hunter, Harris, Pamela, Hart, Jessica Ann, Hartig, George F., Hasan, Hashima, Hashim, Kathleen Marie, Hashimoto, Ryan, Haskins, Sujee J., Hawkins, Robert Edward, Hayden, Brian, Hayden, William L., Healy, Mike, Hecht, Karen, Heeg, Vince J., Hejal, Reem, Helm, Kristopher A., Hengemihle, Nicholas J., Henning, Thomas, Henry, Alaina, Henry, Ronald L., Henshaw, Katherine, Hernandez, Scarlin, Herrington, Donald C., Heske, Astrid, Hesman, Brigette Emily, Hickey, David L., Hilbert, Bryan N., Hines, Dean C., Hinz, Michael R., Hirsch, Michael, Hitcho, Robert S., Hodapp, Klaus, Hodge, Philip E., Hoffman, Melissa, Holfeltz, Sherie T., Holler, Bryan Jason, Hoppa, Jennifer Rose, Horner, Scott, Howard, Joseph M., Howard, Richard J., Huber, Jean M., Hunkeler, Joseph S., Hunter, Alexander, Hunter, David Gavin, Hurd, Spencer W., Hurst, Brendan J., Hutchings, John B., Hylan, Jason E., Ignat, Luminita Ilinca, Illingworth, Garth, Irish, Sandra M., Isaacs III, John C., Jackson Jr., Wallace C., Jaffe, Daniel T., Jahic, Jasmin, Jahromi, Amir, Jakobsen, Peter, James, Bryan, James, John C., James, LeAndrea Rae, Jamieson, William Brian, Jandra, Raymond D., Jayawardhana, Ray, Jedrzejewski, Robert, Jeffers, Basil S., Jensen, Peter, Joanne, Egges, Johns, Alan T., Johnson, Carl A., Johnson, Eric L., Johnson, Patricia, Johnson, Phillip Stephen, Johnson, Thomas K., Johnson, Timothy W., Johnstone, Doug, Jollet, Delphine, Jones, Danny P., Jones, Gregory S., Jones, Olivia C., Jones, Ronald A., Jones, Vicki, Jordan, Ian J., Jordan, Margaret E., Jue, Reginald, Jurkowski, Mark H., Justis, Grant, Justtanont, Kay, Kaleida, Catherine C., Kalirai, Jason S., Kalmanson, Phillip Cabrales, Kaltenegger, Lisa, Kammerer, Jens, Kan, Samuel K., Kanarek, Graham Childs, Kao, Shaw-Hong, Karakla, Diane M., Karl, Hermann, Kassin, Susan A., Kauffman, David D., Kavanagh, Patrick, Kelley, Leigh L., Kelly, Douglas M., Kendrew, Sarah, Kennedy, Herbert V., Kenny, Deborah A., Keski-Kuha, Ritva A., Keyes, Charles D., Khan, Ali, Kidwell, Richard C., Kimble, Randy A., King, James S., King, Richard C., Kinzel, Wayne M., Kirk, Jeffrey R., Kirkpatrick, Marc E., Klaassen, Pamela, Klingemann, Lana, Klintworth, Paul U., Knapp, Bryan Adam, Knight, Scott, Knollenberg, Perry J., Knutsen, Daniel Mark, Koehler, Robert, Koekemoer, Anton M., Kofler, Earl T., Kontson, Vicki L., Kovacs, Aiden Rose, Kozhurina-Platais, Vera, Krause, Oliver, Kriss, Gerard A., Krist, John, Kristoffersen, Monica R., Krogel, Claudia, Krueger, Anthony P., Kulp, Bernard A., Kumari, Nimisha, Kwan, Sandy W., Kyprianou, Mark, Labador, Aurora Gadiano, Labiano, Álvaro, Lafrenière, David, Lagage, Pierre-Olivier, Laidler, Victoria G., Laine, Benoit, Laird, Simon, Lajoie, Charles-Philippe, Lallo, Matthew D., Lam, May Yen, LaMassa, Stephanie Marie, Lambros, Scott D., Lampenfield, Richard Joseph, Lander, Matthew Ed, Langston, James Hutton, Larson, Kirsten, Larson, Melora, LaVerghetta, Robert Joseph, Law, David R., Lawrence, Jon F., Lee, David W., Lee, Janice, Lee, Yat-Ning Paul, Leisenring, Jarron, Leveille, Michael Dunlap, Levenson, Nancy A., Levi, Joshua S., Levine, Marie B., Lewis, Dan, Lewis, Jake, Lewis, Nikole, Libralato, Mattia, Lidon, Norbert, Liebrecht, Paula Louisa, Lightsey, Paul, Lilly, Simon, Lim, Frederick C., Lim, Pey Lian, Ling, Sai-Kwong, Link, Lisa J., Link, Miranda Nicole, Lipinski, Jamie L., Liu, XiaoLi, Lo, Amy S., Lobmeyer, Lynette, Logue, Ryan M., Long, Chris A., Long, Douglas R., Long, Ilana D., Long, Knox S., López-Caniego, Marcos, Lotz, Jennifer M., Love-Pruitt, Jennifer M., Lubskiy, Michael, Luers, Edward B., Luetgens, Robert A., Luevano, Annetta J., Lui, Sarah Marie G. Flores, Lund III, James M., Lundquist, Ray A., Lunine, Jonathan, Lützgendorf, Nora, Lynch, Richard J., MacDonald, Alex J., MacDonald, Kenneth, Macias, Matthew J., Macklis, Keith I., Maghami, Peiman, Maharaja, Rishabh Y., Maiolino, Roberto, Makrygiannis, Konstantinos G., Malla, Sunita Giri, Malumuth, Eliot M., Manjavacas, Elena, Marini, Andrea, Marrione, Amanda, Marston, Anthony, Martel, André R, Martin, Didier, Martin, Peter G., Martinez, Kristin L., Maschmann, Marc, Masci, Gregory L., Masetti, Margaret E., Maszkiewicz, Michael, Matthews, Gary, Matuskey, Jacob E., McBrayer, Glen A., McCarthy, Donald W., McCaughrean, Mark J., McClare, Leslie A., McClare, Michael D., McCloskey, John C., McClurg, Taylore D., McCoy, Martin, McElwain, Michael W., McGregor, Roy D., McGuffey, Douglas B., McKay, Andrew G., McKenzie, William K., McLean, Brian, McMaster, Matthew, McNeil, Warren, De Meester, Wim, Mehalick, Kimberly L., Meixner, Margaret, Meléndez, Marcio, Menzel, Michael P., Menzel, Michael T., Merz, Matthew, Mesterharm, David D., Meyer, Michael R., Meyett, Michele L., Meza, Luis E., Midwinter, Calvin, Milam, Stefanie N., Miller, Jay Todd, Miller, William C., Miskey, Cherie L., Misselt, Karl, Mitchell, Eileen P., Mohan, Martin, Montoya, Emily E., Moran, Michael J., Morishita, Takahiro, Moro-Martín, Amaya, Morrison, Debra L., Morrison, Jane, Morse, Ernie C., Moschos, Michael, Moseley, S. 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Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit., Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figures

Published
2023
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