RCIF at WashU

Quick links

Citing the RCIF

In order to participate in future funding opportunities, we need you, the users, to cite the RCIF in your publications:

Computations were performed using the facilities of the Washington University Research Computing and Informatics Facility (RCIF). The RCIF has received funding from NIH S10 program grants: 1S10OD025200-01A1 and 1S10OD030477-01.

top

Journal Articles

2024

  1. Jin X, Hao Y, Hilliard J, et al. A quality assurance framework for routine monitoring of deep learning cardiac substructure computed tomography segmentation models in radiotherapy. Med Phys. 2024; 51: 2741–2758. https://doi.org/10.1002/mp.16846
  2. K. Bhalla, J.M. Luna, A. Tabassum, D.L. Bennett, and A. Gastounioti, Transfer learning versus engineered features in predicting response to neoadjuvant therapy with MRI in breast cancer patients. 2024 IEEE International Symposium on Biomedical Imaging, May 27-30, 2024, Athens, Greece.
  3. S. Das Gupta, K. Getz, J. Hernandez Lopez, D. L. Bennett, A. Toriola, and A. Gastounioti. Beyond mammographic density: an unsupervised machine learning approach to identify effects of early adulthood adiposity on breast parenchymal tissue patterns in premenopausal women. SPIE Medical Imaging, San Diego, CA, USA, February 18-22, 2024.
  4. K. Anant, J. Hernandez Lopez, S. Das Gupta, D. L. Bennett, and A. Gastounioti. Breast density assessment via deep learning: Head-to-head model comparisons in full-field digital mammograms and synthetic mammograms. SPIE Medical Imaging, San Diego, CA, USA, February 18-22, 2024.
  5. Earnest T, Bani A, Ha SM, Hobbs DA, Kothapalli D, Yang B, Lee JJ, Benzinger TLS, Gordon BA, Sotiras A; Alzheimer’s Disease Neuroimaging Initiative. Data-driven decomposition and staging of flortaucipir uptake in Alzheimer’s disease. Alzheimers Dement. 2024 Apr 29. doi: 10.1002/alz.13769. Epub ahead of print. PMID: 38683905.

2023

  1. Zhang Z, Liu J, Yang D, Kamilov US, Hugo GD. Deep learning-based motion compensation for four-dimensional cone-beam computed tomography (4D-CBCT) reconstruction. Med Phys. 2023; 50: 808–820. https://doi.org/10.1002/mp.16103
  2. J. Hernandez Lopez, Z. Sun, S. Shoushtari, U. Kamilov, D.L. Bennett, and A. Gastounioti. Long-term breast cancer risk prediction in Black women: external validation of a mammography-driven AI model. 2023 San Antonio Breast Cancer Symposium, San Antonio, TX, December 5-9, 2023.
  3. S. Das Gupta, K. Getz, J. Hernandez Lopez, D. L. Bennett, A. Toriola, and A. Gastounioti. Early adulthood adiposity, attained adiposity and breast parenchymal complexity in premenopausal women. 109th Scientific Assembly and Annual Meeting of the Radiological Society of North America, McCormick Place, Chicago, IL, USA, November 26 - 30, 2023.
  4. K. Anant, J. Hernandez Lopez, S. Das Gupta, D. L. Bennett, and A. Gastounioti. Artificial-intelligence-driven breast density assessment in the transition from full-field digital mammograms to digital breast tomosynthesis. AACR Special Conference: Advances in Breast Cancer Research, October 19-22, 2023.
  5. Li R, Zheng J, Zayed MA, Saffitz JE, Woodard PK, Jha AK. Carotid atherosclerotic plaque segmentation in multi-weighted MRI using a two-stage neural network: advantages of training with high-resolution imaging and histology. Front Cardiovasc Med. 2023 May 24;10:1127653. doi: 10.3389/fcvm.2023.1127653. PMID: 37293278; PMCID: PMC10244753.
  6. Bani, A., Ha, S.M., Xiao, P., Earnest, T., Lee, J., Sotiras, A. Scalable Orthonormal Projective NMF via Diversified Stochastic Optimization. In: Frangi, A., de Bruijne, M., Wassermann, D., Navab, N. (eds) Information Processing in Medical Imaging. IPMI 2023. Lecture Notes in Computer Science, vol 13939. Springer, Cham. https://doi.org/10.1007/978-3-031-34048-2_38
  7. Qiu, P., Chakrabarty, S., Nguyen, P., Ghosh, S.S., Sotiras, A. QCResUNet: Joint Subject-Level and Voxel-Level Prediction of Segmentation Quality. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14223. Springer, Cham. https://doi.org/10.1007/978-3-031-43901-8_17
  8. Jirsaraie RJ, Kaufmann T, Bashyam V, Erus G, Luby JL, Westlye LT, Davatzikos C, Barch DM, Sotiras A. Benchmarking the generalizability of brain age models: Challenges posed by scanner variance and prediction bias. Hum Brain Mapp. 2023 Feb 15;44(3):1118-1128. doi: 10.1002/hbm.26144. Epub 2022 Nov 8. PMID: 36346213; PMCID: PMC9875922.
  9. Soumyendu Sekhar Ghosh, Rajat Dhar, Daniel S. Marcus, and Aristeidis Sotiras “Siam-VAE: a hybrid deep learning based anomaly detection framework for automated quality control of head CT scans”, Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124650X (7 April 2023); https://doi.org/10.1117/12.2654464
  10. Jin Yang, Daniel S. Marcus, and Aristeidis Sotiras “Abdominal CT pancreas segmentation using multi-scale convolution with aggregated transformations”, Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124651R (10 April 2023); https://doi.org/10.1117/12.2651702
  11. Pan Xiao, Xiaobing Yu, Aaron Mintz, Jieqi Wang, Mahati Mokkarala, Vamsi R. Narra, Daniel S. Marcus, Andrew J. Bierhals, and Aristeidis Sotiras “A generative-discriminative deep learning approach to classify radiology reports based on the presence of follow up recommendations”, Proc. SPIE 12469, Medical Imaging 2023: Imaging Informatics for Healthcare, Research, and Applications, 124690P (10 April 2023); https://doi.org/10.1117/12.2651950
  12. Satrajit Chakrabarty, Pamela LaMontagne, Joshua Shimony, Daniel S. Marcus, and Aristeidis Sotiras “Non-invasive classification of IDH mutation status of gliomas from multi-modal MRI using a 3D convolutional neural network”, Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124650W (7 April 2023); https://doi.org/10.1117/12.2651391
  13. Sanders, A. F. P., Kandala, S., Harms, M. P., Marek, S., Bookheimer, S. Y., Buckner, R. L., Dapretto, M., Somerville, L. H., Thomas, K. M, Van Essen, D. C., Yacoub, E., & Barch, D. M. Age-related differences in resting state functional connectivity. Cerebral Cortex, 33, 6928-6942. PMCID: PMC10233258
  14. Pine JG, Paul SE, Johnson E, Bogdan R, Kandala S, Barch DM. Polygenic Risk for Schizophrenia, Major Depression, and Post-traumatic Stress Disorder and Hippocampal Subregion Volumes in Middle Childhood. Behav Genet. 2023 May;53(3):279-291. doi: 10.1007/s10519-023-10134-1. Epub 2023 Jan 31. PMID: 36720770; PMCID: PMC10875985.
  15. Jirsaraie, R. J., Kaufmann, T., Bashyam, V., Erus, G., Luby, J. L., Westlye, L. T., Davatzikos, C., Barch, D. M., & Sotiras, A. Benchmarking the generalizability of the brain age models using diverse pediatric samples. Human Brain Mapping, 44, 1118-1128. PMCID: PMC9875922
  16. Herzberg, M. P., Hennefield, L., Luking, K. R., Sanders, A. F. P., Vogel, A. C., Kandala, S., Tillman, R., Luby, J. L., & Barch, D. M. Family income moderates the relationship between adverse experiences in childhood and putamen volume. Developmental Neurobiology, 83, 28-39. PMCID: PMC10038819
  17. Zhang, W., Gorelik, A. J., Wang, Q., Norton, S. A., Hershey, T., Agrawal, A., Bijsterbosch, J. D., & Bogdan, R. Associations between COVID-19 and putative markers of neuroinflammation: A diffusion basis spectrum imaging study. Brain, Behavior, & Immunity - Health, 100722. https://doi.org/10.1016/j.bbih.2023.100722
  18. Bijsterbosch, J. D., Farahibozorg, S.-R., Glasser, M. F., Van Essen, D., Snyder, L. H., Woolrich, M. W., & Smith, S. M. Evaluating functional brain organization in individuals and identifying contributions to network overlap. Imaging Neuroscience, 1, 1–19. https://doi.org/10.1162/imag_a_00046
  19. Hannon, K., Balogh, L., Lenzini, P., Bijsterbosch, J.D. Comparing data-driven subtypes of depression informed by clinical and neuroimaging data: A Registered Report. Biological Psychiatry: Global Open Science.
  20. Lenzini, P., Earnest, T., Ha, S.M., Bani, A., Sotiras, A., & Bijsterbosch, J. Morphological versus functional network organization: a comparison between structural covariance networks and probabilistic functional modes. MICCAI workshop on Machine Learning in Clinical Neuroimaging
  21. Zhang, W., Singh, S. P., Clement, A., Calfee, R. P., Bijsterbosch, J. D., & Cheng, A. L. Improvements in Physical Function and Pain Interference and Changes in Mental Health Among Patients Seeking Musculoskeletal Care. JAMA Network Open, 6(6), e2320520. https://doi.org/10.1001/jamanetworkopen.2023.20520
  22. Easley, T., Chen, R., Hannon, K., Dutt, R., & Bijsterbosch, J. Population modeling with machine learning can enhance measures of mental health - Open-data replication. Neuroimage: Reports, 3(2), 100163. https://doi.org/10.1016/j.ynirp.2023.100163
  23. Zhang W, Rutlin J, Eisenstein SA, Wang Y, Barch DM, Hershey T, Bogdan R, Bijsterbosch JD. Neuroinflammation in the Amygdala Is Associated With Recent Depressive Symptoms. Biol Psychiatry Cogn Neurosci Neuroimaging. 2023 Sep;8(9):967-975. doi: 10.1016/j.bpsc.2023.04.011. Epub 2023 May 9. PMID: 37164312.
  24. Luckett PH, Olufawo M, Lamichhane B, Park KY, Dierker D, Verastegui GT, Yang P, Kim AH, Chheda MG, Snyder AZ, Shimony JS, Leuthardt EC. Predicting survival in glioblastoma with multimodal neuroimaging and machine learning. J Neurooncol. 2023 Sep;164(2):309-320. doi: 10.1007/s11060-023-04439-8. Epub 2023 Sep 5. PMID: 37668941; PMCID: PMC10522528
  25. Rashid B, Glasser MF, Nichols T, Van Essen D, Juttukonda MR, Schwab NA, Greve DN, Yacoub E, Lovely A, Terpstra M, Harms MP, Bookheimer SY, Ances BM, Salat DH, Arnold SE. Cardiovascular and metabolic health is associated with functional brain connectivity in middle-aged and older adults: Results from the Human Connectome Project-Aging study. Neuroimage. 2023 Aug 1;276:120192. doi: 10.1016/j.neuroimage.2023.120192. Epub 2023 May 27. PMID: 37247763; PMCID: PMC10330931. https://doi.org/10.1016/j.neuroimage.2023.120192

2022

  1. Zhang, W., Paul, S. E., Winkler, A., Bogdan, R., & Bijsterbosch, J. D. (2022). Shared brain and genetic architectures between mental health and physical activity. Translational Psychiatry, 12(1), 1–12. https://doi.org/10.1038/s41398-022-02172-w
  2. Dutt, R. K., Hannon, K., Easley, T. O., Griffis, J. C., Zhang, W., & Bijsterbosch, J. D. (2022). Mental health in the UK Biobank: A roadmap to self‐report measures and neuroimaging correlates. Human Brain Mapping. https://doi.org/10.1002/hbm.25690
  3. McKay, N. S.; Dincer, A.; Mehrotra, V.; Aschenbrenner, A. J.; Balota, D. A.; Hornbeck, R. C.; Hassenst, J.; Morris, J. C.; Benzinger, T. L.S.; Gordon, B, A., Beta-amyloid moderates the relationship between cortical thickness and attentional control in middle- and older-aged adults. Neurobiology of Aging 2022, 112, 181-190.
  4. Hawks, Z. W.; Todorov, A.; Marrus, N., Nishino, T.; Talovic, M.; Nebel, M. B.; Girault, J. B.; Davis, S.; Marek, S.; Seitzman, B.A.; Eggebrecht ,A. T.; Elison, J.; Dager, S.; Mosconi, M. W.; Tychsen, L.; Snyder, A. Z.; Botteron, K.; Estes, A.; Evans, A.; Gerig, G.; Hazlett, H.C.; McKinstry, R.C.; Pandey, J.; Schultz, R.; Styner, M.; Wolff, J. J.; Zwaigenbaum, L.; Markson, L.; Petersen, S. E.; Constantino, J. N.; White, D. A.; Piven, J.; Pruett J.; for The IBIS Network, A prospective evaluation of infant cerebellar-cerebral functional connectivity in relation to behavioral development in autism. Biological Psychiatry Global Open Science 2022, XX, yy-zz.
  5. Millar, P. R.; Luckett, P. H.; Gordon, B. A.; Benzinger, T. L. S.; Schindler, S. E.; Fagan, A. M.; Cruchaga, C.; Bateman, R.; Allegri, R.; Jucker, M.; Lee, J. H.; Mori, H.; Salloway, S. P.; Yakuchev, I.; Morris, J. C.; Ances, B. M.; Dominantly Inherited Alzheimer Network (DIAN), Predicting brain age from functional connectivity in symptomatic and preclinical Alzheimer disease. NeuroImage 2022, 257, 119228.

    2021

  6. Sadler, B.; Wilborn, J.; Antunes, L.; Kuensting, T.; Hale, A. T.; Gannon, S. R.; McCall, K.; Cruchaga, C.; Harms, M.; Voisin, N.; Reymond, A.; Cappuccio, G.; Brunetti-Pierri, N.; Tartaglia, M.; Niceta, M.; Leoni, C.; Zampino, G.; Ashley-Koch, A.; Urbizu, A.; Garrett, M. E.; Soldano, K.; Macaya, A.; Conrad, D.; Strahle, J.; Dobbs, M. B.; Turner, T. N.; Shannon, C. N.; Brockmeyer, D.; Limbrick, D. D.; Gurnett, C. A.; Haller, G., Rare and de novo coding variants in chromodomain genes in Chiari I malformation. The American Journal of Human Genetics 2021, 108, 100-114.
  7. Quiggle, A.; Charng, W. L.; Antunes, L.; Nikolov, M.; Bledsoe, X.; Hecht, J. T.; Dobbs, M. B.; Gurnett, C. A., Whole exome sequencing in individuals with idiopathic clubfoot reveals a recurrent filamin B (FLNB) deletion. Clinical Orthopaedics and Related Research 2021, 480, 421-430.
  8. Freund, M. C; Bugg, J. M.; Braver, T. S., A representational similarity analysis of cognitive control during color-word stroop. Journal of Neuroscience 2021, 41, 7388-7402.
  9. Brier, M. R.; Snyder, A. Z.; Tanenbaum, A.; Rudick, R. A.; Fisher, E.; Jones, S.; Shimony, J. S.; Cross, A. H.; Benzinger, T. L. S.; Naismith, R. T., Quantitative signal properties from standardized MRIs correlate with multiple sclerosis disability. Annals of Clinical and TranslationalNeurology 2021, 8, 1096-1109.
  10. Luckett, P. H.; McCullough, A.; Gordon, B. A.; Strain, J.; Flores, S.; Dincer, A.; McCarthy, J.; Kuffner, T.; Stern, A.; Meeker, K. L.; Berman, S. B.; Chhatwal, J. P.; Cruchaga, C.; Fagan, A. M.; Farlow, M. R.; Fox, N. C.; Jucker, M.; Levin, J.; Masters, C. L.; Mori, H.; Noble, J. M.; Salloway, S.; Schofield, P. R.; Brickman, A. M.; Brooks, W. S.; Cash, D. M.; Fulham, M. J.; Ghetti, B.; Jack, C. R.; Vöglein, J.; Klunk, W.; Koeppe, R.; Oh, H.; Su, Y.; Weiner, M.; Wang, Q.; Swisher, L.; Marcus, D.; Koudelis, D.; Joseph-Mathurin, N.; Cash, L.; Hornbeck, R.; Xiong, C.; Perrin, R. J.; Karch, C. M.; Hassenstab, J.; McDade, E.; Morris, J. C.; Benzinger, T. L.S.; Bateman, R. J.; Ances, B. M.; for the Dominantly Inherited Alzheimer Network (DIAN), Modeling autosomal dominant Alzheimer’s disease with machine learning. Alzheimer’s Dement 2021, 17, 1005-1016.
  11. Matlock, M. K.; Hoffman, M.; Dang, N. L.; Folmsbee, D. L.; Langkamp, L. A.; Hutchison, G. R.; Kumar, N.; Sarullo, K.; Swamidass, S. J., Deep learning coordinate-free quantum chemistry. The Journal of Physical Chemistry A 2021, 125, 8978-8986.
  12. Millar, P. R.; Ances, B. M.; Gordon, B, A.; Benzinger, T. L.S.; Morris, J. C.; Balota, D. A., Evaluating cognitive relationships with resting-state and task-driven blood oxygen level-dependent variability. Journal of Cognitive Neuroscience 2021, 33, 279-302.
  13. Barnette, D. A.; Schleiff, M. A.; Datta, A.; Flynn, N.; Swamidass, J.; Miller, G. P., Meloxicam methyl group determines enzyme specificity for thiazole bioactivation compared to sudoxicam. Toxicology Letters 2021, 338, 10-20.
  14. Flynn, N.R.; Ward, M.D.; Schleiff, M.A.; Laurin, C.M.C.; Farmer, R.; Conway, S.J.; Boysen, G.; Swamidass, S.J.; Miller, G.P., Bioactivation of isoxazole-containing bromodomain and extra-terminal domain (BET) inhibitors. Metabolites 2021, 11, 390.
  15. Schleiff, M. A.; Payakachat, S.; Schleiff, B. M.; Swamidass, S. J.; Boysen, G.; Miller, G.P., Impacts of diphenylamine NSAID halogenation on bioactivation risks. Toxicology 2021, 458, 152832.
  16. Liu, Z.; Mhlanga, J. C.; Laforest, R.; Derenoncourt, P. R.; Siegel, B. A.; Jha, A. K., A Bayesian approach to tissue-fraction estimation for oncological PET segmentation. Physics in Medicine & Biology 2021, 66, 124002.
  17. Hughes, T. B.; Flynn, N.; Dang, N. L.; Swamidass. S. J., Modeling the bioactivation and subsequent reactivity of drugs. Chemical Research in Toxicology 2021, 34, 584-600.
  18. Liu, Z.; Moon, H. S.; Laforest, R.; Perlmutter, J. S.; Norris, S. A.; Jha, A. K., Fully automated 3D segmentation of dopamine transporter SPECT images using an estimation based approach. arXiv preprint arXiv:2101.06729.

    2020

  19. Edwards, R. L.; Heueck, I.; Lee, S. G.; Shah, I. T.; Miller, J. J.; Jezewski, A. J.; Mikati, M. O.; Wang, X.; Brothers, R. C.; Heidel, K. M.; Osbourn, D. M.; Burnham, C. D.; Alvarez, S.; Fritz, S. A.; Dowd, C. S.; Jez, J. M.; Odom John, A. R., Potent, specific MEPicides for treatment of zoonotic staphylococci. PLOS Pathogens 2020, 16, e1007806.
  20. Datta, A.; Matlock, M. A.; Dang, N. L.; Moulin, T.; Woeltje, K. F.; Yanik, E. L.; Swamidass, S. J., “Black box” to “conversational” machine learning: ondansetron reduces risk of hospital-acquired venous thromboembolism. IEEE Journal of Biomedical and Health Informatics 2020, 6, 2204-2214.
  21. Sarullo, K.; Matlock, M. K.; Swamidass, S. J., Site-level bioactivity of small-molecules from deep-learned representations of quantum chemistry. The Journal of Physical Chemistry A 2020, 124, 9194-9202.
  22. Baradaran-Ghahfarokhi, M.; Reynoso, F.; Darafsheh, A.; Sun, B.; Prusator, M. T.; Mutic, S.; Zhao, T., A Monte Carlo based analytic model of the in-room neutron ambient dose equivalent for a Mevion gantry-mounted passively scattered proton system. Journal of Radiological Protection 2020, 40, 980-996.
  23. Lu, J.; Mazidi, H.; Ding, T.; Zhang, O.; Lew, M. D., Single-molecule 3D orientation imaging reveals nanoscale compositional heterogeneity in lipid membranes. Angewandte Chemie International Edition 2020, 59, 17572-17579.
  24. Flynn, N. R.; Dang, N. L.; Ward, M. D.; Swamidass, S. J., XenoNet: inference and likelihood of intermediate metabolite formation. Journal of Chemical Information and Modeling 2020, 60, 3431-3449.
  25. Baradaran‐Ghahfarokhi, M.; Reynoso, F.; Sun, B.; Darafsheh, A.; Prusator, M. T.; Mutic, S.; Zhao, T., A Monte Carlo‐based analytic model of neutron dose equivalent for a mevion gantry‐mounted passively scattered proton system for craniospinal irradiation. Medical Physics 2020, 47, 4509-4521.
  26. Dincer, A.; Gordon, B. A.; Hari-Raj, A.; Keefe, S. J.; Flores, S.; McKay, N. S.; Paulick, A. M.; Lewis, K. E. S.; Feldman, R. L.; Hornbeck, R. C.; Allegri, R.; Ances, B. M.; Berman, S. B.; Brickman, A. M.; Brooks, W. S.; Cash, D. M; Chhatwal, J. P.; Farlow, M. R.; la Fougère, C.; Fox, N. C.; Fulham, M. J.; Jack, C. R.; Joseph-Mathurin, N.; Karch, C. M.; Lee, A.; Levin, J.; Masters, C. L.; McDade, E. M.; Oh, H.; Perrin, R. J.; Raji, C.; Salloway, S. P.; Schofield, P. R.; Su, Y.; Villemagne, V. L.; Wang, Q.; Weiner, M. W.; Xiong, C.; Yakushev, I.; Morris, J. C.; Bateman, R. J.; Benzinger, T. L. S., Comparing cortical signatures of atrophy between late-onset and autosomal dominant Alzheimer disease. NeuroImage: Clinical 2020, 28, 102491.
  27. Hughes, T. B.; Dang, N. L.; Kumar, A.; Flynn, N. R; Swamidass, S. J., Metabolic forest: predicting the diverse structures of drug metabolites. Journal of Chemical Information and Modeling 2020, 60, 4702-4716.
  28. Sadler, B.; Haller, G.; Antunes, L.; Nikolov, M.; Amarillo, I.; Coe, B.; Dobbs, M. B.; Gurnett, C. A., Rare and de novo duplications containing SHOX in clubfoot. Journal of Medical Genetics 2020, 57, 851–857.
  29. Millar, P. R.; Ances, B. M.; Gordon, B. A.; Benzinger, T. L. S.; Fagan, A. M.; Morris, J. C.; Balota, D. A., Evaluating resting-state BOLD variability in relation to biomarkers of preclinical Alzheimer’s disease. Neurobiology of Aging 2020, 96, 233-245.
  30. Zhang, H.; Sharma, G.; Dhawan, S.; Dhanraj, D.; Li, Z.; Biswas, P., Comparison of discrete, discrete-sectional, modal and moment models for aerosol dynamics simulations. Aerosol Science and Technology 2020, 54, 739-760.
  31. Barnette, D. A.; Schleiff, M. A.; Osborn, L. R.; Flynn, N.; Matlock, M.; Swamidass, S. J.; Miller, G. P., Dual mechanisms suppress meloxicam bioactivation relative to sudoxicam. Toxicology 2020, 440, 152478.
  32. Vermunt, L.; Dicks, E.; Wang, G.; Dincer, A.; Flores, S.; Keefe, S. J.; Berman, S. B.; Cash, D. M.; Chhatwal, J. P.; Cruchaga, C.; Fox, N. C.; Ghetti, B.; Graff-Radford, N. R.; Hassenstab, J.; Karch, C. M.; Laske, C.; Levin, J.; Masters, C. L.; McDade, E.; Mori, H.; Morris, J. C.; Noble, J. M.; Perrin, R. J.; Schofield, P. R.; Xiong, C.; Scheltens, P.; Visser, P. J.; Bateman, R. J.; Benzinger, T. L. S.; Tijms, B. M.; Gordon, B. A.; Dominantly Inherited Alzheimer Network (DIAN), Single-subject grey matter network trajectories over the disease course of autosomal dominant Alzheimer’s disease. Brain Communications 2020, 2, fcaa102.
  33. Millar, P. R.; Petersen, S. E.; Ances, B. M.; Gordon, B. A.; Benzinger, T. L. S.; Morris, J. C.; Balota, D. A., Evaluating the sensitivity of resting-state BOLD variability to age and cognition after controlling for motion and cardiovascular influences: a network-based approach. Cerebral Cortex 2020, 30, 5686–5701.
  34. Dang, N. L.; Matlock, M. K.; Hughes, T. B.; Swamidass, S. J., The metabolic rainbow: deep learning phase I metabolism in five colors. Journal of Chemical Information and Modeling 2020, 60, 1146-1164.

    2019

  35. Sadler, B.; Haller, G.; Antunes, L.; Bledsoe, X.; Morcuende, J.; Giampietro, P.; Raggio, C.; Miller,N.; Kidane, Y.; Wise, C. A.; Amarillo, I.; Walton, N.; Seeley, M.; Johnson, D.; Jenkins, C.; Jenkins, T.; Oetjens, M.; Tong, R. S.; Druley, T. E.; Dobbs, M. B.; Gurnett, C. A., Distal chromosome 16p11.2 duplications containing SH2B1 in patients with scoliosis. Journal of Medical Genetics 2019, 0, 1-7.
  36. Gordon, B. A.; Blazey, T. M.; Christensen, J.; Dincer, A.; Flores, S.; Keefe, S.; Chen, C.; Su, Y.; McDade, E. M.; Wang, G.; Li, Y.; Hassenstab, J.; Aschenbrenner, A.; Hornbeck, R.; Jack, Jr., C. R.; Ances, B. M.; Berman, S. B.; Brosch, J. R.; Galasko, D.; Gauthier, S.; Lah, J. L.; Masellis, M.; van Dyck, C. H.; Mintun, M. A.; Klein, G.; Ristic, S.; Cairns, N. J.; Marcus, D. S.; Xiong, C.; Holtzman, D. M.; Raichle, M. E.; Morris, J. C.; Bateman, R. J.; Benzinger, T. L. S., Tau PET in autosomal dominant Alzheimer’s disease: relationship with cognition, dementia and other biomarkers. Brian 2019, 0, 1-14.
  37. Barnette, D. A.; Davis, M. A.; Flynn, N. R.; Pidugu, A. S.; Swamidass, S. J.; Miller, G. P., Comprehensive kinetic and modeling analyses revealed CYP2C9 and 3A4 determine terbinafine metabolic clearance and bioactivation, Biochemical Pharmacology 2019, 170, 113661.
  38. Matlock, M. K.; Tambe, A.; Elliott-Higgins, J.; Hines, R. N.; Miller, G. P.; Swamidass, S. J., A time-embedding network models the ontogeny of 23 hepatic drug metabolizing enzymes. Chemical Research in Toxicology 2019, 32, 1707-1721.
  39. Davis, M. A.; Barnette, D. A.; Flynn, N. R.; Pidugu, A. S.; Swamidass, S. J.; Boysen, G.; Miller, G. P., Chemical Research in Toxicology 2019, 32, 1151-1164.
  40. Saliba, E. P.; Barnes, A. B., Fast electron paramagnetic resonance magic angle spinning simulations using analytical powder averaging techniques. The Journal of Chemical Physics 2019, 151, 114107.
  41. Lerman-Sinkoff, D. B.; Kandala, S.; Calhoun, V. D.; Barch, D. M.; Mamah, D. T., Transdiagnostic multimodal neuroimaging in psychosis: structural, resting-state, and task magnetic resonance imaging correlates of cognitive control. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2019, 4, 870-880.
  42. Wang, J.; Agrawala, A.; Flores, K., Are hints about glass forming ability hidden in the liquid structure? Acta Materialia 2019, 171, 163-169.

    2018

  43. Gordon, B. A.; Blazey, T. M.; Su, Y.; Hari-Raj, A.; Dincer, A.; Flores, S.; Christensen, J.; McDade, E.; Wang, G.; Xiong, C.; Cairns, N. J.; Hassenstab, J.; Marcus, D. S.; Fagan, A. M.; Jack Jr, C. R.; Hornbeck, R. C.; Paumier, K. L.; Ances, B. M.; Berman, S. B.; Brickman, A. M.; Cash, D. M.; Chhatwal, J. P.; Correia, S.; Förster, S.; Fox, N. C; Christian la Fougère, N. R. G.; Levin, J.; Masters, C. L.; Rossor, M. N.; Salloway, S.; Saykin, A. J.; Schofield, P. R.; Thompson, P. M.; Weiner, M. M.; Holtzman, D. M.; Raichle, M. E.;Morris, J. C.; Bateman, R. J.; Benzinger, T. L. S., Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer’s disease: a longitudinal study. The Lancet Neurology 2018, 17, 241-250.
  44. Dang, N. L.; Hughes, T. B.; Miller, G. P.; Swamidass, S. J., Computationally assessing the bioactivation of drugs by N-Dealkylation. Chemical Research in Toxicology 2018, 31, 68-80.
  45. Matlock, M. K.; Dang, N. L.; Swamidass, S. J., Learning a local-variable model of aromatic and conjugated systems. ACS Central Science 2018, 4, 52-62.
  46. Ramasubramanian, S.; Rudy, Y., The structural basis of IKs ion-channel activation: mechanistic insights from molecular simulations. Biophysical Journal 2018, 114, 2584-2594.
  47. Matthews, T. P.; Poudel, J.; Li, L.; Wang, L. V.; Anastasio, M. A., Parameterized joint reconstruction of the initial pressure and sound speed distributions for photoacoustic computed tomography. SIAM Journal on Imaging Science 2018, 11, 1560-1588.
  48. Gordon, B. A.; Cullough, A.; Mishra, S.; Blazey, T. M.; Su, Y.; Christensen, J.; Dincer, A.; Jackson, K.; Hornbeck, R. C.; Morris, J. C.; Ances, B. M.; Benzinger, T. L. S., Cross-sectional and longitudinal atrophy is preferentially associated with tau rather than amyloid β positron emission tomography pathology. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring 2018, 10, 245-252.
  49. Haller, G.; McCall, K.; Jenkitkasemwong, S.; Sadler, B.; Antunes, L.; Nikolov, M.; Whittle, J.; Upshaw, Z.; Shin, J.; Baschal, E.; Cruchaga, C.; Harms, M.; Raggio, C.; Morcuende, J. A.; Giampietro, P.; Miller, N. H.; Wise, C. A.; Gray, R. S.; Solnica-Krezel, L.; Knutson, M.; Dobbs, M. B.; Gurnett, C. A., A missense variant in SLC39A8 is associated with severe idiopathic scoliosis. Nature Communications 2018, 9, 4171.
  50. Xu, J.; Rudy, Y., Effects of β-subunit on gating of a potassium ion channel: Molecular simulations of cardiac IKs activation. Journal of Molecular and Cellular Cardiology 2018, 124, 35-44.
  51. Dang, N. L.; Hughes, T. B.; Miller, G. P.; Swamidass, S. J., Computationally assessing the bioactivation of drugs by N-dealkylation. Chemical Research in Toxicology 2018, 31, 68-80.
  52. Mishra, S.; Blazey, T. M.; Holtzman, D. M.; Cruchaga, C.; Su, Y.; Morris, J. C.; Benzinger, T. L. S.; Gordon, B. A., Longitudinal brain imaging in preclinical Alzheimer disease: impact of APOE ε4 genotype. Brian 2018, 141, 1828-1839.
  53. Barnette, D. A.; Davis, M. A.; Dang, N. L.; Pidugu, A. S.; Hughes, T.; Swamidass, S. J.; Boysen, G.; Miller, G. P., Lamisil (terbinafine) toxicity: Determining pathways to bioactivation through computational and experimental approaches. Biochemical Pharmacology 2018, 156, 10-21.
  54. Matlock, M. K.; Hughes, T. B.; Dahlin, J. L.; Swamidass, S. J., Modeling small-molecule reactivity identifies promiscuous bioactive compounds. Journal of Chemical Information and Modeling 2018, 58, 1483-1500.
  55. Ramasubramanian, S.; Rudy, Y., The structural basis of IKs ion-channel activation: mechanistic insights from molecular simulations. Biophysical Journal 2018, 114, 2584-2594.

    2017

  56. Hughes, T. B.; Swamidass, S. J., Deep learning to predict the formation of quinone species in drug metabolism. Chemical Research in Toxicology 2017, 30, 642-656.
  57. Kay, K. N.; Yeatman, J. D., Bottom-up and top-down computations in word- and face-selective cortex. eLife 2017, 6, e22341.
  58. Kreisch, C. D.; O’Sullivan, J. A.; Arvidson, R. E.; Politte, D. V.; He, L.; Stein, N. T.; Finkel, J.; Guinness, E. A.; Wolff, M. J.; Lapotre, M. G. A., Regularization of Mars reconnaissance orbiter CRISM along-track oversampled hyperspectral imaging observations of Mars. ICARUS 2017, 282, 136-151.
  59. Lerman-Sinkoff, D. B.; Sui, J.; Rachakonda, S.; Kandala, S.; Calhoune, V. D.; Barchf, D. M., Multimodal neural correlates of cognitive control in the Human Connectome Project. NeuroImage 2017, 163, 41-54.
  60. Thind, A. S.; Huang, X.; Sun, J.; Mishra, R., First-principles prediction of a stable hexagonal phase of CH3NH3PbI3. Chemistry of Materials 2017, 29, 6003-6011.
  61. Fristoe, T. S.; Iwaniuk, A. N.; Botero, C. A., Big brains stabilize populations and facilitate colonization of variable habitats in birds. Nature Ecology & Evolution 2017, 1, 1706–1715.
  62. Ohlemacher, S. I.; Giblin, D. E.; André d’Avignon, D.; Stapleton, A. E.; Trautner, B. W.; Henderson, J. P., Enterobacteria secrete an inhibitor of Pseudomonas virulence during clinical bacteriuria. The Journal of Clinical Investigation 2017, 127, 4018-4030.
  63. Matthews, T. P.; Anastasio, M. A., Joint reconstruction of the initial pressure and speed of sound distributions from combined photoacoustic and ultrasound tomography measurements. Inverse Problems 2017, 33, 124002.
  64. Simms, C. L.; Yan, L. L.; Zaher, H. S., Ribosome collision is critical for quality control during No-Go decay. Molecular Cell 2017, 68, 361-373.
  65. Matthews, T. P.; Wang, K.; Li, C.; Duric, N.; Anastasio, M. A., Regularized dual averaging image reconstruction for full-wave ultrasound computed tomography. IEEE Transactions on Ultrasonics. Ferroelectrics, and Frequency Control 2017, 64, 811-825.
  66. Dang, N. L.; Hughes, T. B.; Miller, G. P.; Swamidass, S. J., Computational approach to structural alerts: furans, phenols, nitroaromatics, and thiophenes. Chemical Research in Toxicology 2017, 30, 1046–1059.

    2016

  67. Dang, N. L.; Hughes, T. B.; Krishnamurthy, V.; Swamidass, S. J., A simple model predicts UGT-mediated metabolism. Bioinformatics 2016, 32, 3183-3189.
  68. Etzel, J. A.; Valchev, N.; Gazzola, V.; Keysers, C., Is brain activity during action observation modulated by the perceived fairness of the actor? Plos One 2016, 11, e0145350.
  69. Etzel, J. A.; Cole, M. W.; Zacks, J. M.; Kay, K. N.; Braver, T. S., Reward motivation enhances task coding in frontoparietal cortex. Cerebral Cortex 2016, 26, 1647-1659.
  70. Gao, Z. N.; Myung, Y.; Huang, X.; Kanjolia, R.; Park, J.; Mishra, R.; Banerjee, P., Doping mechanism in transparent, conducting Tantalum doped ZnO films deposited using atomic layer deposition. Advanced Materials Interfaces 2016, 3, 1600496.
  71. Haller, G.; Alvarado, D.; McCall, K.; Yang, P.; Cruchaga, C.; Harms, M.; Goate, A.; Willing, M.; Morcuende, J. A.; Baschal, E.; Miller, N. H.; Wise, C.; Dobbs, M. B.; Gurnett, C. A., A polygenic burden of rare variants across extracellular matrix genes among individuals with adolescent idiopathic scoliosis. Human Molecular Genetics 2016, 25, 202-209.
  72. Huang, X.; Huang, S.; Biswas, P.; Mishra, R., Band gap insensitivity to large chemical pressures in ternary bismuth iodides for photovoltaic applications. Journal of Physical Chemistry C 2016, 120, 28924-28932.
  73. Lerman-Sinkoff, D. B.; Barch, D. M., Network community structure alterations in adult schizophrenia: identification and localization of alterations. Neuroimage-Clinical 2016, 10, 96-106.
  74. Ranjbar, H.; Wen, J.; Mathews, A. J.; Komarov, S.; Wang, Q.; Li, K.; O’Sullivan, J. A.; Tai, Y. C., A simultaneous beta and coincidence-gamma imaging system for plant leaves. Physics in Medicine and Biology 2016, 61, 3572-3595.
  75. Sloutsky, R.; Naegle, K. M., High-resolution identification of specificity determining positions in the LacI protein family using ensembles of sub-sampled alignments. Plos One 2016, 11, e0162579.
  76. Sotiropoulos, S. N.; Hernandez-Fernandez, M.; Vu, A. T.; Andersson, J. L.; Moeller, S.; Yacoub, E.; Lenglet, C.; Ugurbil, K.; Behrens, T. E. J.; Jbabdi, S., Fusion in diffusion MRI for improved fibre orientation estimation: An application to the 3T and 7T data of the Human Connectome Project. Neuroimage 2016, 134, 396-409.
  77. Hughes, T. B.; Dang, N. L; Miller, G. P.; Swamidass, S. J., Modeling reactivity to biological macromolecules with a deep multitask network. Acs Central Science 2016, 2, 529–537.

    2015

  78. Haller, G.; Alvarado, D. M.; Willing, M. C.; Braverman, A. C.; Bridwell, K. H.; Kelly, M.; Lenke, L. G.; Luhmann, S. J.; Gurnett, C. A.; Dobbs, M. B., Genetic risk for aortic aneurysm in adolescent idiopathic scoliosis. Journal of Bone and Joint Surgery-American Volume 2015, 97A , 1411-1417.
  79. Hammann, B. A.; Ma, Z. L.; Wentz, K. M.; Kamunde-Devonish, M. K.; Johnson, D. W.; Hayes, S. E., Structural study by solid-state Ga-71 NMR of thin film transistor precursors. Dalton Transactions 2015, 44, 17652-17659.
  80. Hoff, D. E. M.; Albert, B. J.; Saliba, E. P.; Scott, F. J.; Choi, E. J.; Mardini, M.; Barnes, A. B., Frequency swept microwaves for hyperfine decoupling and time domain dynamic nuclear polarization. Solid State Nuclear Magnetic Resonance 2015, 72, 79-89.
  81. Holehouse, A. S.; Garai, K.; Lyle, N.; Vitalis, A.; Pappu, R. V., Quantitative assessments of the distinct contributions of polypeptide backbone amides versus side chain groups to chain expansion via chemical denaturation. Journal of the American Chemical Society 2015, 137, 2984-2995.
  82. Hughes, T. B.; Miller, G. P.; Swamidass, S. J., Modeling epoxidation of drug-like molecules with a deep machine learning network. Acs Central Science 2015, 1, 168-180.
  83. Hughes, T. B.; Miller, G. P.; Swamidass, S. J., Site of reactivity models predict molecular reactivity of diverse chemicals with glutathione. Chemical Research in Toxicology 2015, 28, 797-809.
  84. Kay, K. N.; Weiner, K. S.; Grill-Spector, K., Attention reduces spatial uncertainty in human ventral temporal cortex. Current Biology 2015, 25, 595-600.
  85. Koh, E. I.; Hung, C. S.; Parker, K. S.; Crowley, J. R.; Giblin, D. E.; Henderson, J. P., Metal selectivity by the virulence-associated yersiniabactin metallophore system. Metallomics 2015, 7, 1011-1022.
  86. Mathews, A. J.; Li, K.; Komarov, S.; Wang, Q.; Ravindranath, B.; O’Sullivan, J. A.; Tai, Y. C., A generalized reconstruction framework for unconventional PET systems. Medical Physics 2015, 42, 4591-4609.

    2014

  87. Han, K.; Mac Donald, C. L.; Johnson, A. M.; Barnes, Y.; Wierzechowski, L.; Zonies, D.; Oh, J.; Flaherty, S.; Fang, R.; Raichle, M. E.; Brody, D. L., Disrupted modular organization of resting-state cortical functional connectivity in US military personnel following concussive ‘mild’ blast-related traumatic brain injury. Neuroimage 2014, 84, 76-96.
  88. Ravindranath, B.; Wen, J.; Mathews, A.; Tomov, D.; Catherall, D.; Li, K.; Wang, Q.; Komarov, S.; O’Sullivan, J.; Tai, Y. C., Performance evaluation of Microinsert II – A submillimeter resolution small animal PET scanner. Journal of Nuclear Medicine 2014, 55, 2143.
  89. Wang, Q.; Mathews, A. J.; Li, K.; Wen, J.; Komarov, S.; O’Sullivan, J. A.; Tai, Y. C., A dedicated high-resolution PET imager for plant sciences. Physics in Medicine and Biology 2014, 59, 5613-5629.

    2013

  90. Benzinger, T. L. S.; Blazey, T.; Jack, C. R.; Koeppe, R. A.; Su, Y.; Xiong, C. J.; Raichle, M. E.; Snyder, A. Z.; Ances, B. M.; Bateman, R. J.; Cairns, N. J.; Fagan, A. M.; Goate, A.; Marcus, D. S.; Aisen, P. S.; Christensen, J. J.; Ercole, L.; Hornbeck, R. C.; Farrar, A. M.; Aldea, P.; Jasielec, M. S.; Owen, C. J.; Xie, X. Y.; Mayeux, R.; Brickman, A.; McDade, E.; Klunk, W.; Mathis, C. A.; Ringman, J.; Thompson, P. M.; Ghetti, B.; Saykin, A. J.; Sperling, R. A.; Johnson, K. A.; Salloway, S.; Correia, S.; Schofield, P. R.; Masters, C. L.; Rowe, C.; Villemagne, V. L.; Martins, R.; Ourselin, S.; Rossor, M. N.; Fox, N. C.; Cash, D. M.; Weiner, M. W.; Holtzman, D. M.; Buckles, V. D.; Moulder, K.; Morris, J. C., Regional variability of imaging biomarkers in autosomal dominant Alzheimer’s disease. Proceedings of the National Academy of Sciences of the United States of America 2013, 110, E4502-E4509.
  91. Chen, K. L.; Bloch, C. D.; Hill, P. M.; Klein, E. E., Evaluation of neutron dose equivalent from the Mevion S250 proton accelerator: measurements and calculations. Physics in Medicine and Biology 2013, 58, 8709-8723.
  92. Larson-Prior, L. J.; Oostenveld, R.; Della Penna, S.; Michalareas, G.; Prior, F.; Babajani-Feremi, A.; Schoffelen, J. M.; Marzetti, L.; de Pasquale, F.; Di Pompeo, F.; Stout, J.; Woolrich, M.; Luo, Q.; Bucholz, R.; Fries, P.; Pizzella, V.; Romani, G. L.; Corbetta, M.; Snyder, A. Z.; Consortium, W. U.-M. H., Adding dynamics to the Human Connectome Project with MEG. Neuroimage 2013, 80, 190-201.
  93. Marcus, D. S.; Harms, M. P.; Snyder, A. Z.; Jenkinson, M.; Wilson, J. A.; Glasser, M. F.; Barch, D. M.; Archie, K. A.; Burgess, G. C.; Ramaratnam, M.; Hodge, M.; Horton, W.; Herrick, R.; Olsen, T.; McKay, M.; House, M.; Hileman, M.; Reid, E.; Harwell, J.; Coalson, T.; Schindler, J.; Elam, J. S.; Curtiss, S. W.; Van Essen, D. C.; Consortium, W. U.-M. H., Human Connectome Project informatics: Quality control, database services, and data visualization. Neuroimage 2013, 80, 202-219.
  94. Gordon, B. A.; Blazey, T.; Benzinger, T. L. S.; Head, D., Effects of aging and Alzheimer’s disease along the longitudinal axis of the hippocampus. Journal of Alzheimer’s Disease 2013, 37, 41-50.
  95. Hill, P. M.; Klein, E. E.; Bloch, C., Optimizing field patching in passively scattered proton therapy with the use of beam current modulation. Physics in Medicine and Biology 2013, 58, 5527-5539
  96. Karzon, R. K.; Hullar, T. E., Audiologic and vestibular findings in Wolfram syndrome. Ear and Hearing 2013, 34, 809-812.
  97. Kovacs, S. A.; Bricker, W. P.; Niedzwiedzki, D. M.; Colletti, P. F.; Lo, C. S., Computational determination of the pigment binding motif in the chlorosome protein a of green sulfur bacteria. Photosynthesis Research 2013, 118, 231-247.
  98. Marshall, B. A.; Permutt, M. A.; Paciorkowski, A. R.; Hoekel, J.; Karzon, R.; Wasson, J.; Viehover, A.; White, N. H.; Shimony, J. S.; Manwaring, L.; Austin, P.; Hullar, T. E.; Hershey, T.; Washington University Wolfram Study Group, Phenotypic characteristics of early Wolfram syndrome. Orphanet Journal of Rare Diseases 2013, 8, 64.
  99. Mathews, A. J.; Komarov, S.; Wu, H. Y.; O’Sullivan, J. A.; Tai, Y. C., Improving PET imaging for breast cancer using virtual pinhole PET half-ring insert. Physics in Medicine and Biology 2013, 58, 6407-6427.
  100. Sotiropoulos, S. N.; Jbabdi, S.; Xu, J. Q.; Andersson, J. L.; Moeller, S.; Auerbach, E. J.; Glasser, M. F.; Hernandez, M.; Sapiro, G.; Jenkinson, M.; Feinberg, D. A.; Yacoub, E.; Lenglet, C.; Van Essen, D. C.; Ugurbil, K.; Behrens, T. E. J.; Consortium, W. U.-M. H., Advances in diffusion MRI acquisition and processing in the Human Connectome Project. Neuroimage 2013, 80, 125-143.
  101. Sotiropoulos, S. N.; Jbabdi, S.; Andersson, J. L.; Woolrich, M. W.; Ugurbil, K.; Behrens, T. E. J., RubiX: combining spatial resolutions for Bayesian inference of crossing fibers in diffusion MRI. IEEE Transactions on Medical Imaging 2013, 32, 969-982.

    2012

  102. Hershey, T.; Lugar, H. M.; Shimony, J. S.; Rutlin, J.; Koller, J. M.; Perantie, D. C.; Paciorkowski, A. R.; Eisenstein, S. A.; Permutt, M. A.; Washington University Wolfram Study Group, Early Brain vulnerability in Wolfram Syndrome. Plos One 2012, 7, e40604.
  103. Nguyen, C.; Foster, E. R.; Paciorkowski, A. R.; Viehoever, A.; Considine, C.; Bondurant, A.; Marshall, B. A.; Hershey, T.; Washington University Wolfram Study Group, Reliability and validity of the Wolfram Unified Rating Scale (WURS). Orphanet Journal of Rare Diseases 2012, 7, 89.
  104. Pickett, K. A.; Duncan, R. P.; Paciorkowski, A. R.; Permutt, M. A.; Marshall, B.; Hershey, T.; Earhart, G. M.; Washington University Wolfram Study Group, Balance impairment in individuals with Wolfram Syndrome. Gait & Posture 2012, 36, 619-624.
  105. Pickett, K. A.; Duncan, R. P.; Hoekel, J.; Marshall, B.; Hershey, T.; Earhart, G. M.; Washington University Wolfram Study Group, Early presentation of gait impairment in Wolfram Syndrome. Orphanet Journal of Rare Diseases 2012, 7, 92.
  106. Tai, Y. C.; Mathews, A.; Komarov, S.; Wu, H.; Ravindranath, B.; Wen, J.; O’Sullivan, J.; Dehdashti, F., Initial human study of virtual-pinhole PET technology for head and neck cancer imaging. Journal of Nuclear Medicine 2012, 53, 552.

    2011

  107. Cole, M. W.; Etzel, J. A.; Zacks, J. M.; Schneider, W.; Braver, T. S., Rapid transfer of abstract rules to novel contexts in human lateral prefrontal cortex. Frontiers in Human Neuroscience 2011, 5, 142.
  108. Rilling, J. K; Glasser, M. F; Jbabdi, S; Andersson, J; Preuss, T. M., Continuity, divergence, and the evolution of brain language pathways. Frontiers in Evolution Neuroscience 2011, 3, 11.
  109. Glasser, M. F.; Van Essen, D. C., Mapping Human cortical areas in vivo based on Myelin content as revealed by T1- and T2-weighted MRI. Journal of Neuroscience 2011, 31, 11597-11616.
  110. Marcus, D. S.; Harwell, J.; Olsen, T.; Hodge, M.; Glasser, M. F.; Prior, F.; Jenkinson, M.; Laumann, T.; Curtiss, S. W.; Van Essen, D. C., Informatics and data mining tools and strategies for the Human Connectome Project. Neuroimage 2013, 80, 202-219.

top

Conference Papers

2020

  1. Rahman, M. A.; Laforest, R.; Jha, A. K., “A list-mode OSEM-based attenuation and scatter compensation method for SPECT,” 2020 IEEE 17th International Symposium on Biomedical Imaging, Iowa City, IA, USA, 2020, 646-650.

    2019

  2. Matlock, M. K.; Datta, A.; Dang, N. L.; Jiang, K.; Swamidass, S. J., “Deep learning long-range information in undirected graphs with wave networks,” 2019 International Joint Conference on Neural Networks, Budapest, Hungary, 2019, 1-8.

    2017

  3. Matthews, T. P.; Wang, K.; Li, C.; Duric, N.; Anastasio, M. A., Image reconstruction for ultrasound computed tomography by use of the regularized dual averaging method. Proceedings of SPIE, 2017, 10139, 101390P.
  4. Matthews, T. P.; Anastasio, M. A., Joint reconstruction of the sound speed and initial pressure distributions for ultrasound computed tomography and photoacoustic computed tomography. Proceedings of SPIE, 2017, 10139, 101390B.

    2014

  5. Gardner, J. R.; Kusner, M. J.; Xu, Z.; Weinberger, K. Q.; Cunningham, J. P., Bayesian optimization with inequality constraints. Proceedings of the 31st International Conference on Machine Learning, ICML 2014, 937-945.
  6. Kusner, M.; Tyree, S.; Weinberger, K. Q.; Agrawal, K., Stochastic neighbor compression. Proceedings of the 31st International Conference on Machine Learning, ICML 2014, 622-630.

    2013

  7. Bloch, C.; Hill, P. M; Chen, K. L; Saito, A.; Klein, E. E., Startup of the Kling Center for proton therapy. AIP Conference Proceedings 2013, 1525, 314.

top

Books

2012

  1. Etzel, J.A.; Cole, M. W.; Braver, T. S., Looking outside the searchlight. In machine learning and interpretation in neuroimaging; lecture notes in computer science, volume 7263; Langs, G., Rish, I.; Grosse-Wentrup, M.; Murphy, B., Ed.; Springer: Berlin, Heidelberg, 2012, 26-33.

top

Dissertations

2021

  1. Hawks, Z. W. Testing candidate cerebellar presymptomatic biomarkers for autism spectrum disorder. Ph.D. Dissertation, Washington University in St. Louis, MO, 2021. 2020
  2. Miller, P. R. Evaluating bold variability as a potential biomarker of age-and ad-related differences. Ph.D. Dissertation, Washington University in St. Louis, MO, 2020.
  3. Lerman-Sinkoff, D. B. Transdiagnostic multimodal neural correlates of psychosis dimensions. Ph.D. Dissertation, Washington University in St. Louis, MO, 2020.
  4. Miller, J. Prodrug activation in Staphylococci and the implications for antimicrobial development. Ph.D. Dissertation, Washington University in St. Louis, MO, 2020.
  5. Barnette, D. Determining metabolic pathways to liver toxicity for multiple drug classes through integrated computational and experimental techniques. Ph.D. Dissertation, University of Arkansas for Medical Sciences, AK, 2020.

    2019

  6. Yi, Y. N. Machine learning and empirical asset pricing. Ph.D. Dissertation, Washington University in St. Louis, MO, 2019.
  7. Saliba, E. P. Electron decoupling with chirped microwave pulses for magic angle spinning dynamic nuclear polarization nuclear magnetic resonance spectroscopy. Ph.D. Dissertation, Washington University in St. Louis, MO, 2019.

    2014

  8. Matthews, A. J. A four-dimensional image reconstruction framework for PET under arbitrary geometries. Ph.D. Dissertation, Washington University in St. Louis, MO, 2014.

    2011

  9. Gaona, C. Nonuniform power changes and spatial, temporal and spectral diversity in high gamma band (>60 Hz) signals in human electrocorticography. Ph.D. Dissertation, Washington University in St. Louis, MO, 2011.

top