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An open-access dataset of naturalistic viewing using simultaneous EEG-fMRI

Telesford, Qawi K; Gonzalez-Moreira, Eduardo; Xu, Ting; Tian, Yiwen; Colcombe, Stanley J; Cloud, Jessica; Russ, Brian E; Falchier, Arnaud; Nentwich, Maximilian; Madsen, Jens; Parra, Lucas C; Schroeder, Charles E; Milham, Michael P; Franco, Alexandre R
In this work, we present a dataset that combines functional magnetic imaging (fMRI) and electroencephalography (EEG) to use as a resource for understanding human brain function in these two imaging modalities. The dataset can also be used for optimizing preprocessing methods for simultaneously collected imaging data. The dataset includes simultaneously collected recordings from 22 individuals (ages: 23-51) across various visual and naturalistic stimuli. In addition, physiological, eye tracking, electrocardiography, and cognitive and behavioral data were collected along with this neuroimaging data. Visual tasks include a flickering checkerboard collected outside and inside the MRI scanner (EEG-only) and simultaneous EEG-fMRI recordings. Simultaneous recordings include rest, the visual paradigm Inscapes, and several short video movies representing naturalistic stimuli. Raw and preprocessed data are openly available to download. We present this dataset as part of an effort to provide open-access data to increase the opportunity for discoveries and understanding of the human brain and evaluate the correlation between electrical brain activity and blood oxygen level-dependent (BOLD) signals.
PMID: 37612297
ISSN: 2052-4463
CID: 5561192

Anatomical and functional connectivity support the existence of a salience network node within the caudal ventrolateral prefrontal cortex

Trambaiolli, Lucas R; Peng, Xiaolong; Lehman, Julia F; Linn, Gary; Russ, Brian E; Schroeder, Charles E; Liu, Hesheng; Haber, Suzanne N
Three large-scale networks are considered essential to cognitive flexibility: the ventral and dorsal attention (VANet and DANet) and salience (SNet) networks. The ventrolateral prefrontal cortex (vlPFC) is a known component of the VANet and DANet, but there is a gap in the current knowledge regarding its involvement in the SNet. Herein, we used a translational and multimodal approach to demonstrate the existence of a SNet node within the vlPFC. First, we used tract-tracing methods in non-human primates (NHP) to quantify the anatomical connectivity strength between different vlPFC areas and the frontal and insular cortices. The strongest connections were with the dorsal anterior cingulate cortex (dACC) and anterior insula (AI) - the main cortical SNet nodes. These inputs converged in the caudal area 47/12, an area that has strong projections to subcortical structures associated with the SNet. Second, we used resting-state functional MRI (rsfMRI) in NHP data to validate this SNet node. Third, we used rsfMRI in the human to identify a homologous caudal 47/12 region that also showed strong connections with the SNet cortical nodes. Taken together, these data confirm a SNet node in the vlPFC, demonstrating that the vlPFC contains nodes for all three cognitive networks: VANet, DANet, and SNet. Thus, the vlPFC is in a position to switch between these three networks, pointing to its key role as an attentional hub. Its additional connections to the orbitofrontal, dorsolateral, and premotor cortices, place the vlPFC at the center for switching behaviors based on environmental stimuli, computing value, and cognitive control.
PMID: 35510840
ISSN: 2050-084x
CID: 5249422

Toward next-generation primate neuroscience: A collaboration-based strategic plan for integrative neuroimaging

Milham, Michael; Petkov, Chris; Belin, Pascal; Ben Hamed, Suliann; Evrard, Henry; Fair, Damien; Fox, Andrew; Froudist-Walsh, Sean; Hayashi, Takuya; Kastner, Sabine; Klink, Chris; Majka, Piotr; Mars, Rogier; Messinger, Adam; Poirier, Colline; Schroeder, Charles; Shmuel, Amir; Silva, Afonso C; Vanduffel, Wim; Van Essen, David C; Wang, Zheng; Roe, Anna Wang; Wilke, Melanie; Xu, Ting; Aarabi, Mohammad Hadi; Adolphs, Ralph; Ahuja, Aarit; Alvand, Ashkan; Amiez, Celine; Autio, Joonas; Azadi, Reza; Baeg, Eunha; Bai, Ruiliang; Bao, Pinglei; Basso, Michele; Behel, Austin K; Bennett, Yvonne; Bernhardt, Boris; Biswal, Bharat; Boopathy, Sethu; Boretius, Susann; Borra, Elena; Boshra, Rober; Buffalo, Elizabeth; Cao, Long; Cavanaugh, James; Celine, Amiez; Chavez, Gianfranco; Chen, Li Min; Chen, Xiaodong; Cheng, Luqi; Chouinard-Decorte, Francois; Clavagnier, Simon; Cléry, Justine; Colcombe, Stan J; Conway, Bevil; Cordeau, Melina; Coulon, Olivier; Cui, Yue; Dadarwal, Rakshit; Dahnke, Robert; Desrochers, Theresa; Deying, Li; Dougherty, Kacie; Doyle, Hannah; Drzewiecki, Carly M; Duyck, Marianne; Arachchi, Wasana Ediri; Elorette, Catherine; Essamlali, Abdelhadi; Evans, Alan; Fajardo, Alfonso; Figueroa, Hector; Franco, Alexandre; Freches, Guilherme; Frey, Steve; Friedrich, Patrick; Fujimoto, Atsushi; Fukunaga, Masaki; Gacoin, Maeva; Gallardo, Guillermo; Gao, Lixia; Gao, Yang; Garside, Danny; Garza-Villarreal, Eduardo A; Gaudet-Trafit, Maxime; Gerbella, Marzio; Giavasis, Steven; Glen, Daniel; Ribeiro Gomes, Ana Rita; Torrecilla, Sandra Gonzalez; Gozzi, Alessandro; Gulli, Roberto; Haber, Suzanne; Hadj-Bouziane, Fadila; Fujimoto, Satoka Hashimoto; Hawrylycz, Michael; He, Quansheng; He, Ye; Heuer, Katja; Hiba, Bassem; Hoffstaedter, Felix; Hong, Seok-Jun; Hori, Yuki; Hou, Yujie; Howard, Amy; de la Iglesia-Vaya, Maria; Ikeda, Takuro; Jankovic-Rapan, Lucija; Jaramillo, Jorge; Jedema, Hank P; Jin, Hecheng; Jiang, Minqing; Jung, Benjamin; Kagan, Igor; Kahn, Itamar; Kiar, Gregory; Kikuchi, Yuki; Kilavik, Bjørg; Kimura, Nobuyuki; Klatzmann, Ulysse; Kwok, Sze Chai; Lai, Hsin-Yi; Lamberton, Franck; Lehman, Julia; Li, Pengcheng; Li, Xinhui; Li, Xinjian; Liang, Zhifeng; Liston, Conor; Little, Roger; Liu, Cirong; Liu, Ning; Liu, Xiaojin; Liu, Xinyu; Lu, Haidong; Loh, Kep Kee; Madan, Christopher; Magrou, Loïc; Margulies, Daniel; Mathilda, Froesel; Mejia, Sheyla; Meng, Yao; Menon, Ravi; Meunier, David; Mitchell, A J; Mitchell, Anna; Murphy, Aidan; Mvula, Towela; Ortiz-Rios, Michael; Ortuzar Martinez, Diego Emanuel; Pagani, Marco; Palomero-Gallagher, Nicola; Pareek, Vikas; Perkins, Pierce; Ponce, Fernanda; Postans, Mark; Pouget, Pierre; Qian, Meizhen; Ramirez, Julian Bene; Raven, Erika; Restrepo, Isabel; Rima, Samy; Rockland, Kathleen; Rodriguez, Nadira Yusif; Roger, Elise; Hortelano, Eduardo Rojas; Rosa, Marcello; Rossi, Andrew; Rudebeck, Peter; Russ, Brian; Sakai, Tomoko; Saleem, Kadharbatcha S; Sallet, Jerome; Sawiak, Stephen; Schaeffer, David; Schwiedrzik, Caspar M; Seidlitz, Jakob; Sein, Julien; Sharma, Jitendra; Shen, Kelly; Sheng, Wei-An; Shi, Neo Sunhang; Shim, Won Mok; Simone, Luciano; Sirmpilatze, Nikoloz; Sivan, Virginie; Song, Xiaowei; Tanenbaum, Aaron; Tasserie, Jordy; Taylor, Paul; Tian, Xiaoguang; Toro, Roberto; Trambaiolli, Lucas; Upright, Nick; Vezoli, Julien; Vickery, Sam; Villalon, Julio; Wang, Xiaojie; Wang, Yufan; Weiss, Alison R; Wilson, Charlie; Wong, Ting-Yat; Woo, Choong-Wan; Wu, Bichan; Xiao, Du; Xu, Augix Guohua; Xu, Dongrong; Xufeng, Zhou; Yacoub, Essa; Ye, Ningrong; Ying, Zhang; Yokoyama, Chihiro; Yu, Xiongjie; Yue, Shasha; Yuheng, Lu; Yumeng, Xin; Zaldivar, Daniel; Zhang, Shaomin; Zhao, Yuguang; Zuo, Zhanguang
Open science initiatives are creating opportunities to increase research coordination and impact in nonhuman primate (NHP) imaging. The PRIMatE Data and Resource Exchange community recently developed a collaboration-based strategic plan to advance NHP imaging as an integrative approach for multiscale neuroscience.
PMID: 34731649
ISSN: 1097-4199
CID: 5499342

Common functional localizers to enhance NHP & cross-species neuroscience imaging research

Russ, Brian E; Petkov, Christopher I; Kwok, Sze Chai; Zhu, Qi; Belin, Pascal; Vanduffel, Wim; Hamed, Suliann Ben
Functional localizers are invaluable as they can help define regions of interest, provide cross-study comparisons, and most importantly, allow for the aggregation and meta-analyses of data across studies and laboratories. To achieve these goals within the non-human primate (NHP) imaging community, there is a pressing need for the use of standardized and validated localizers that can be readily implemented across different groups. The goal of this paper is to provide an overview of the value of localizer protocols to imaging research and we describe a number of commonly used or novel localizers within NHPs, and keys to implement them across studies. As has been shown with the aggregation of resting-state imaging data in the original PRIME-DE submissions, we believe that the field is ready to apply the same initiative for task-based functional localizers in NHP imaging. By coming together to collect large datasets across research group, implementing the same functional localizers, and sharing the localizers and data via PRIME-DE, it is now possible to fully test their robustness, selectivity and specificity. To do this, we reviewed a number of common localizers and we created a repository of well-established localizer that are easily accessible and implemented through the PRIME-RE platform.
PMID: 34048898
ISSN: 1095-9572
CID: 4888452

Minimal specifications for non-human primate MRI: Challenges in standardizing and harmonizing data collection

Autio, Joonas A; Zhu, Qi; Li, Xiaolian; Glasser, Matthew F; Schwiedrzik, Caspar M; Fair, Damien A; Zimmermann, Jan; Yacoub, Essa; Menon, Ravi S; Van Essen, David C; Hayashi, Takuya; Russ, Brian; Vanduffel, Wim
Recent methodological advances in MRI have enabled substantial growth in neuroimaging studies of non-human primates (NHPs), while open data-sharing through the PRIME-DE initiative has increased the availability of NHP MRI data and the need for robust multi-subject multi-center analyses. Streamlined acquisition and analysis protocols would accelerate and improve these efforts. However, consensus on minimal standards for data acquisition protocols and analysis pipelines for NHP imaging remains to be established, particularly for multi-center studies. Here, we draw parallels between NHP and human neuroimaging and provide minimal guidelines for harmonizing and standardizing data acquisition. We advocate robust translation of widely used open-access toolkits that are well established for analyzing human data. We also encourage the use of validated, automated pre-processing tools for analyzing NHP data sets. These guidelines aim to refine methodological and analytical strategies for small and large-scale NHP neuroimaging data. This will improve reproducibility of results, and accelerate the convergence between NHP and human neuroimaging strategies which will ultimately benefit fundamental and translational brain science.
PMID: 33882349
ISSN: 1095-9572
CID: 4878072

Piecing together the orbitofrontal puzzle

Elorette, Catherine; Fujimoto, Atsushi; Fredericks, J Megan; Stoll, Frederic M; Russ, Brian E; Rudebeck, Peter H
For almost a century, researchers have puzzled over how the orbitofrontal cortex (OFC) contributes to behavior. Our understanding of the functions of this area has evolved as each new finding and piece of information is added to complete the larger picture. Despite this, the full picture of OFC function is incomplete. Here we begin by reviewing recent (and not so recent) theories of how OFC contributes to behavior. We then go onto highlight emerging work that has helped to broaden perspectives on the role that OFC plays in contingent learning, interoception, and social behavior. How OFC contributes to these aspects of behavior is not well understood. Here we argue that only by establishing where and how these and other functions fit within the puzzle of OFC, either alone or as part of larger brain-wide circuits, will we be able to fully realize the functions of this area. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
PMID: 34060882
ISSN: 1939-0084
CID: 4891182

U-Net Model for Brain Extraction: Trained on Humans for Transfer to Non-human Primates

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; Xu, Ting
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 (20s∼10min). 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
PMID: 33789137
ISSN: 1095-9572
CID: 4830892

Dynamic Suppression of Average Facial Structure Shapes Neural Tuning in Three Macaque Face Patches

Koyano, Kenji W; Jones, Adam P; McMahon, David B T; Waidmann, Elena N; Russ, Brian E; Leopold, David A
The visual perception of identity in humans and other primates is thought to draw upon cortical areas specialized for the analysis of facial structure. A prominent theory of face recognition holds that the brain computes and stores average facial structure, which it then uses to efficiently determine individual identity, though the neural mechanisms underlying this process are controversial. Here, we demonstrate that the dynamic suppression of average facial structure plays a prominent role in the responses of neurons in three fMRI-defined face patches of the macaque. Using photorealistic face stimuli that systematically varied in identity level according to a psychophysically based face space, we found that single units in the AF, AM, and ML face patches exhibited robust tuning around average facial structure. This tuning emerged after the initial excitatory response to the face and was expressed as the selective suppression of sustained responses to low-identity faces. The coincidence of this suppression with increased spike timing synchrony across the population suggests a mechanism of active inhibition underlying this effect. Control experiments confirmed that the diminished responses to low-identity faces were not due to short-term adaptation processes. We propose that the brain's neural suppression of average facial structure facilitates recognition by promoting the extraction of distinctive facial characteristics and suppressing redundant or irrelevant responses across the population.
PMID: 33065012
ISSN: 1879-0445
CID: 4641702

An Open Resource for Non-human Primate Imaging

Milham, Michael P; Ai, Lei; Koo, Bonhwang; Xu, Ting; Amiez, Céline; Balezeau, Fabien; Baxter, Mark G; Blezer, Erwin L A; 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; Ben Hamed, Suliann; 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 F S; 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 R E; Yacoub, Essa; Ye, Frank Q; Zarco, Wilbert; Zhou, Yong-di; Margulies, Daniel S; Schroeder, Charles E
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.
PMID: 30269990
ISSN: 1097-4199
CID: 3372772

The Basal Forebrain Regulates Global Resting-State fMRI Fluctuations

Turchi, Janita; Chang, Catie; Ye, Frank Q; Russ, Brian E; Yu, David K; Cortes, Carlos R; Monosov, Ilya E; Duyn, Jeff H; Leopold, David A
Patterns of spontaneous brain activity, typically measured in humans at rest with fMRI, are used routinely to assess the brain's functional organization. The mechanisms that generate and coordinate the underlying neural fluctuations are largely unknown. Here we investigate the hypothesis that the nucleus basalis of Meynert (NBM), the principal source of widespread cholinergic and GABAergic projections to the cortex, contributes critically to such activity. We reversibly inactivated two distinct sites of the NBM in macaques while measuring fMRI activity across the brain. We found that inactivation led to strong, regionalized suppression of shared or "global" signal components of cortical fluctuations ipsilateral to the injection. At the same time, the commonly studied resting-state networks retained their spatial structure under this suppression. The results indicate that the NBM contributes selectively to the global component of functional connectivity but plays little if any role in the specific correlations that define resting-state networks.
PMID: 29398365
ISSN: 1097-4199
CID: 4087162