Search

Your search keyword '"Schroeder, Charles E."' showing total 560 results
Start Over Author "Schroeder, Charles E."
560 results on '"Schroeder, Charles E."'

4. U-net model for brain extraction: Trained on humans for transfer to non-human primates

Author

Wang, Xindi, Li, Xin-Hui, Cho, Jae Wook, Russ, Brian E, Rajamani, Nanditha, Omelchenko, Alisa, Ai, Lei, Korchmaros, Annachiara, Sawiak, Stephen, Benn, R Austin, Garcia-Saldivar, Pamela, Wang, Zheng, Kalin, Ned H, Schroeder, Charles E, Craddock, R Cameron, Fox, Andrew S, Evans, Alan C, Messinger, Adam, Milham, Michael P, and Xu, Ting

Subjects
Biomedical and Clinical Sciences, Health Sciences, Neurosciences, Biomedical Imaging, Adult, Animals, Brain, Datasets as Topic, Feasibility Studies, Female, Humans, Image Processing, Computer-Assisted, Macaca, Magnetic Resonance Imaging, Male, Middle Aged, Models, Theoretical, Neural Networks, Computer, Neuroimaging, Young Adult, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences
Abstract

Brain extraction (a.k.a. skull stripping) is a fundamental step in the neuroimaging pipeline as it can affect the accuracy of downstream preprocess such as image registration, tissue classification, etc. Most brain extraction tools have been designed for and applied to human data and are often challenged by non-human primates (NHP) data. Amongst recent attempts to improve performance on NHP data, deep learning models appear to outperform the traditional tools. However, given the minimal sample size of most NHP studies and notable variations in data quality, the deep learning models are very rarely applied to multi-site samples in NHP imaging. To overcome this challenge, we used a transfer-learning framework that leverages a large human imaging dataset to pretrain a convolutional neural network (i.e. U-Net Model), and then transferred this to NHP data using a small NHP training sample. The resulting transfer-learning model converged faster and achieved more accurate performance than a similar U-Net Model trained exclusively on NHP samples. We improved the generalizability of the model by upgrading the transfer-learned model using additional training datasets from multiple research sites in the Primate Data-Exchange (PRIME-DE) consortium. Our final model outperformed brain extraction routines from popular MRI packages (AFNI, FSL, and FreeSurfer) across a heterogeneous sample from multiple sites in the PRIME-DE with less computational cost (20 s~10 min). We also demonstrated the transfer-learning process enables the macaque model to be updated for use with scans from chimpanzees, marmosets, and other mammals (e.g. pig). Our model, code, and the skull-stripped mask repository of 136 macaque monkeys are publicly available for unrestricted use by the neuroimaging community at https://github.com/HumanBrainED/NHP-BrainExtraction.

Published
2021

9. An Open Resource for Non-human Primate Imaging

Author

Milham, Michael P, Ai, Lei, Koo, Bonhwang, Xu, Ting, Amiez, Céline, Balezeau, Fabien, Baxter, Mark G, Blezer, Erwin LA, Brochier, Thomas, Chen, Aihua, Croxson, Paula L, Damatac, Christienne G, Dehaene, Stanislas, Everling, Stefan, Fair, Damian A, Fleysher, Lazar, Freiwald, Winrich, Froudist-Walsh, Sean, Griffiths, Timothy D, Guedj, Carole, Hadj-Bouziane, Fadila, Hamed, Suliann Ben, Harel, Noam, Hiba, Bassem, Jarraya, Bechir, Jung, Benjamin, Kastner, Sabine, Klink, P Christiaan, Kwok, Sze Chai, Laland, Kevin N, Leopold, David A, Lindenfors, Patrik, Mars, Rogier B, Menon, Ravi S, Messinger, Adam, Meunier, Martine, Mok, Kelvin, Morrison, John H, Nacef, Jennifer, Nagy, Jamie, Rios, Michael Ortiz, Petkov, Christopher I, Pinsk, Mark, Poirier, Colline, Procyk, Emmanuel, Rajimehr, Reza, Reader, Simon M, Roelfsema, Pieter R, Rudko, David A, Rushworth, Matthew FS, Russ, Brian E, Sallet, Jerome, Schmid, Michael Christoph, Schwiedrzik, Caspar M, Seidlitz, Jakob, Sein, Julien, Shmuel, Amir, Sullivan, Elinor L, Ungerleider, Leslie, Thiele, Alexander, Todorov, Orlin S, Tsao, Doris, Wang, Zheng, Wilson, Charles RE, Yacoub, Essa, Ye, Frank Q, Zarco, Wilbert, Zhou, Yong-di, Margulies, Daniel S, and Schroeder, Charles E

Subjects
Biological Psychology, Psychology, Neurosciences, Biomedical Imaging, Animals, Brain, Connectome, Datasets as Topic, Information Dissemination, Magnetic Resonance Imaging, Neuroimaging, Primates, Cognitive Sciences, Neurology & Neurosurgery, Biological psychology
Abstract

Non-human primate neuroimaging is a rapidly growing area of research that promises to transform and scale translational and cross-species comparative neuroscience. Unfortunately, the technological and methodological advances of the past two decades have outpaced the accrual of data, which is particularly challenging given the relatively few centers that have the necessary facilities and capabilities. The PRIMatE Data Exchange (PRIME-DE) addresses this challenge by aggregating independently acquired non-human primate magnetic resonance imaging (MRI) datasets and openly sharing them via the International Neuroimaging Data-sharing Initiative (INDI). Here, we present the rationale, design, and procedures for the PRIME-DE consortium, as well as the initial release, consisting of 25 independent data collections aggregated across 22 sites (total = 217 non-human primates). We also outline the unique pitfalls and challenges that should be considered in the analysis of non-human primate MRI datasets, including providing automated quality assessment of the contributed datasets.

Published
2018

12. Combining brain perturbation and neuroimaging in non-human primates

Author

Klink, P. Christiaan, Aubry, Jean-François, Ferrera, Vincent P., Fox, Andrew S., Froudist-Walsh, Sean, Jarraya, Béchir, Konofagou, Elisa E., Krauzlis, Richard J., Messinger, Adam, Mitchell, Anna S., Ortiz-Rios, Michael, Oya, Hiroyuki, Roberts, Angela C., Roe, Anna Wang, Rushworth, Matthew F.S., Sallet, Jérôme, Schmid, Michael Christoph, Schroeder, Charles E., Tasserie, Jordy, Tsao, Doris Y., Uhrig, Lynn, Vanduffel, Wim, Wilke, Melanie, Kagan, Igor, and Petkov, Christopher I.

Published
2021
Full Text
View/download PDF

27. A new paradigm for investigating real-world social behavior and its neural underpinnings.

Author

Alreja, Arish, Ward, Michael J., Ma, Qianli, Russ, Brian E., Bickel, Stephan, Van Wouwe, Nelleke C., González-Martínez, Jorge A., Neimat, Joseph S., Abel, Taylor J., Bagić, Anto, Parker, Lisa S., Richardson, R. Mark, Schroeder, Charles E., Morency, Louis–Philippe, and Ghuman, Avniel Singh

Subjects
EPILEPSY, EYE tracking, TEMPORAL lobectomy, QUALITY control, TRANSLATIONAL research, SOCIAL interaction, ACQUISITION of data
Abstract

Eye tracking and other behavioral measurements collected from patient-participants in their hospital rooms afford a unique opportunity to study natural behavior for basic and clinical translational research. We describe an immersive social and behavioral paradigm implemented in patients undergoing evaluation for surgical treatment of epilepsy, with electrodes implanted in the brain to determine the source of their seizures. Our studies entail collecting eye tracking with other behavioral and psychophysiological measurements from patient-participants during unscripted behavior, including social interactions with clinical staff, friends, and family in the hospital room. This approach affords a unique opportunity to study the neurobiology of natural social behavior, though it requires carefully addressing distinct logistical, technical, and ethical challenges. Collecting neurophysiological data synchronized to behavioral and psychophysiological measures helps us to study the relationship between behavior and physiology. Combining across these rich data sources while participants eat, read, converse with friends and family, etc., enables clinical-translational research aimed at understanding the participants' disorders and clinician–patient interactions, as well as basic research into natural, real-world behavior. We discuss data acquisition, quality control, annotation, and analysis pipelines that are required for our studies. We also discuss the clinical, logistical, and ethical and privacy considerations critical to working in the hospital setting. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

31. Detecting Spontaneous Neural Oscillation Events in Primate Auditory Cortex

Author

Neymotin, Samuel A., primary, Tal, Idan, additional, Barczak, Annamaria, additional, O’Connell, Monica N., additional, McGinnis, Tammy, additional, Markowitz, Noah, additional, Espinal, Elizabeth, additional, Griffith, Erica, additional, Anwar, Haroon, additional, Dura-Bernal, Salvador, additional, Schroeder, Charles E., additional, Lytton, William W., additional, Jones, Stephanie R., additional, Bickel, Stephan, additional, and Lakatos, Peter, additional

Published
2022
Full Text
View/download PDF

33. Temporal Context in Speech Processing and Attentional Stream Selection: A Behavioral and Neural Perspective

Author

Golumbic, Elana M. Zion, Poeppel, David, and Schroeder, Charles E.

Abstract

The human capacity for processing speech is remarkable, especially given that information in speech unfolds over multiple time scales concurrently. Similarly notable is our ability to filter out of extraneous sounds and focus our attention on one conversation, epitomized by the "Cocktail Party" effect. Yet, the neural mechanisms underlying on-line speech decoding and attentional stream selection are not well understood. We review findings from behavioral and neurophysiological investigations that underscore the importance of the temporal structure of speech for achieving these perceptual feats. We discuss the hypothesis that entrainment of ambient neuronal oscillations to speech's temporal structure, across multiple time-scales, serves to facilitate its decoding and underlies the selection of an attended speech stream over other competing input. In this regard, speech decoding and attentional stream selection are examples of "Active Sensing", emphasizing an interaction between proactive and predictive top-down modulation of neuronal dynamics and bottom-up sensory input. (Contains 2 figures.)

Published
2012
Full Text
View/download PDF

43. A New Paradigm for Investigating Real-World Social Behavior and its Neural Underpinnings

Author

Alreja, Arish, primary, Ward, Michael J., additional, Ma, Qianli, additional, Russ, Brian E., additional, Bickel, Stephan, additional, Van Wouwe, Nelleke C., additional, González-Martínez, Jorge A., additional, Neimat, Joseph S., additional, Abel, Taylor J., additional, Bagić, Anto, additional, Parker, Lisa S., additional, Richardson, R. Mark, additional, Schroeder, Charles E., additional, Morency, Louis–Philippe, additional, and Ghuman, Avniel Singh, additional

Published
2021
Full Text
View/download PDF

46. Alpha and broadband high‐frequency activity track task dynamics and predict performance in controlled decision‐making

Author

Haegens, Saskia, primary, Pathak, Yagna J., additional, Smith, Elliot H., additional, Mikell, Charles B., additional, Banks, Garrett P., additional, Yates, Mark, additional, Bijanki, Kelly R., additional, Schevon, Catherine A., additional, McKhann, Guy M., additional, Schroeder, Charles E., additional, and Sheth, Sameer A., additional

Published
2021
Full Text
View/download PDF

47. U-net model for brain extraction: Trained on humans for transfer to non-human primates

Author

Wang, Xindi, primary, Li, Xin-Hui, additional, Cho, Jae Wook, additional, Russ, Brian E., additional, Rajamani, Nanditha, additional, Omelchenko, Alisa, additional, Ai, Lei, additional, Korchmaros, Annachiara, additional, Sawiak, Stephen, additional, Benn, R. Austin, additional, Garcia-Saldivar, Pamela, additional, Wang, Zheng, additional, Kalin, Ned H., additional, Schroeder, Charles E., additional, Craddock, R. Cameron, additional, Fox, Andrew S., additional, Evans, Alan C., additional, Messinger, Adam, additional, Milham, Michael P., additional, and Xu, Ting, additional

Published
2021
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results