Accelerating the Evolution of Nonhuman Primate Neuroimaging

Milham, Michael P.; Petkov, Christopher I.; Margulies, Daniel S.; Schroeder, Charles E.; Basso, Michele A.; Belin, Pascal; Fair, Damien A.; Fox, Andrew S.; Kästner, Sabine; Mars, Rogier B.; Messinger, Adam; Poirier, Colline; Vanduffel, Wim; Essen, David C. Van; Alvand, Ashkan; Becker, Yannick; Hamed, Suliann Ben; Benn, Austin; Bodin, Clémentine; Boretius, Susann; Cagna, Bastien; Coulon, Olivier; El-Gohary, Sherif H.; Evrard, H; Forkel, Stephanie J.; Friedrich, Patrick; Froudist-Walsh, Seán; Garza-Villarreal, Eduardo A.; Gao, Yang; Gozzi, Alessandro; Grigis, Antoine; Hartig, Renée; Hayashi, Takuya; Heuer, Katja; Howells, Henrietta; Ardesch, Dirk Jan; Jarraya, Béchir; Jarrett, Wendy; Jedema, Hank P.; Kagan, Igor; Kelly, Clare; Kennedy, Henry; Klink, P. Christiaan; Kwok, Sze Chai; Leech, Robert; Liu, Xiaojin; Madan, Christopher R.; Madushanka, Wasana; Majka, Piotr; Mallon, Ann‐Marie; Marche, Kévin; Meguerditchian, Adrien; Menon, Ravi S.; Merchant, Hugo; Mitchell, Anna S.; Nenning, Karl-Heinz; Nikolaidis, Aki; Ortiz-Rios, Michael; Pagani, Maria Pia; Pareek, Vikas; Prescott, Mark J.; Procyk, Emmanuel; Rajimehr, Reza; Rautu, Sabina; Raz, Amir; Roe, Anna Wang; Rossi-Pool, Román; Roumazeilles, Léa; Sakai, Tomoko; Sallet, Jérôme; García-Saldivar, Pamela; Sato, Chika; Sawiak, Stephen J.; Schiffer, Anne-Marike; Schwiedrzik, Caspar M.; Seidlitz, Jakob; Sein, Julien; Shen, Zhiming; Shmuel, Amir; Silva, Afonso C.; Simone, Luciano; Sirmpilatze, Nikoloz; Sliwa, Julia; Smallwood, Jonathan; Tasserie, Jordy; Schotten, Michel Thiebaut de; Toro, Roberto; Trapeau, Régis; Uhrig, Lynn; Vezoli, Julien; Wang, Zheng; Wells, Sara; Williams, Bella; Xu, Ting; Xu, Augix Guohua; Yacoub, Essa; Zhan, Ming; Ai, Lei; 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; Fleysher, Lazar; Freiwald, Winrich A.; Griffiths, Timothy D.; Guedj, Carole; Hadj-Bouziane, Fadila; Harel, Noam; Hiba, Bassem; Jung, Benjamin; Koo, Bonhwang; Laland, Kevin N.; Leopold, David A.; Lindenfors, Patrik; Meunier, Martine; Mok, Kelvin; Morrison, John H.; Nacef, Jennifer; Nagy, Jamie; Pinsk, Mark A.; Reader, Simon M.; Roelfsema, Pieter; Rudko, David A.; Rushworth, Matthew; Russ, Brian E.; Schmid, Michael C.; Sullivan, Elinor L.; Thiele, Alexander; Todorov, Orlin S.; Tsao, Doris Y.; Ungerleider, Leslie G.; Wilson, Charles R.E.; Ye, Frank Q.; Zarco, Wilbert; Zhou, Yong-Di

doi:10.1016/j.neuron.2019.12.023

Cited by 97 publications

(59 citation statements)

References 7 publications

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“…Yet, much of this technique's physiological basis is not well understood. Since rhesus macaques offer an extremely promising translational model (Consortium et al, 2020;Hutchison et al, 2011;Milham et al, 2018), a detailed map of the rsfMRI 'macaque connectome' is needed to build the basis for future interventional work. While human rsfMRI studies have made significant advances in the ability to acquire highly sensitive and informative images of functional connectivity Glasser et al, 2016;Harms et al, 2018), including at relatively high spatial and temporal resolutions Vu et al, 2016), similar non-invasive neuroimaging advances in the macaque have not been as widespread or productive.…”

Section: Discussionmentioning

confidence: 99%

“…Taken together, we have achieved a highly detailed, individually robust delineation of RSNs in the macaque that closely resembles the quality and findings of connectomes from large human datasets, further demonstrating the benefits of using ultra-high magnetic fields in vivo. One countering recent argument and effort (Consortium et al, 2020;Milham et al, 2018) that has seen high translational success has been the cross laboratory pooling of macaque fMRI datasets. We argue that our rationale of advocating ultra-high field macaque neuroimaging does not negate or change the need for such databases, but rather aims at providing data and solutions for a different problem.…”

Section: Discussionmentioning

confidence: 99%

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Ultra-high field (10.5 T) resting state fMRI in the macaque

Yacoub

Grier

Auerbach

et al. 2020

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Resting state functional connectivity refers to the temporal correlations between spontaneous hemodynamic signals obtained using functional magnetic resonance imaging. This technique has demonstrated that the structure and dynamics of identifiable networks are altered in psychiatric and neurological disease states. Thus, resting state network organizations can be used as a diagnostic, or prognostic recovery indicator. However, much about the physiological basis of this technique is unknown. Thus, providing a translational bridge to an optimal animal model, the macaque, in which invasive circuit manipulations are possible, is of utmost importance. Current approaches to resting state measurements in macaques face unique challenges associated with signal-to-noise, the need for invasive contrast agents, and within-subject designs. These limitations can, in principle, be overcome through ultra-high magnetic fields. However, ultra-high field imaging has yet to be adapted for fMRI in macaques. Here, we demonstrate that the combination of high channel count transmitter and receiver arrays, optimized pulse sequences, and careful anesthesia regimens, allows for detailed withinsubject resting state analysis at ultra-high resolutions. In this study, we uncover thirty spatially detailed resting state components that are highly robust across individual macaques and closely resemble the quality and findings of connectomes from large human datasets. This detailed map of the rsfMRI 'macaque connectome' will be the basis for future neurobiological circuit manipulation work, providing valuable biological insights into human connectomics.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Ultra-high field (10.5 T) resting state fMRI in the macaque

Yacoub

Grier

Auerbach

et al. 2020

Preprint

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“…These Bayesian methods are presently available in AFNI (e.g., programs RBA and MBA) for ROI-based analyses. They offer additional advantages, including containing built-in model validation, allowing for full results reporting, and not requiring arbitrary thresholding (Chen et al, 2020(Chen et al, , 2019a. Having a hierarchical atlas such as the CHARM allows a great deal of flexibility for the researcher to perform such analyses on an appropriate scale for their study and experimental design.…”

Section: The Cortical Hierarchy Atlas Of the Rhesus Macaque (Charm)mentioning

confidence: 99%

“…These data are freely available to researchers, enabling larger multi-site studies. The PRIME-DE has been instrumental in leading this push towards openness and reproducibility in the macaque community (Milham et al, 2020), and data from the PRIME-DE has already been used in several studies (Froudist-Walsh et al, 2018;He et al, 2020;Heuer et al, 2019;Oligschläger et al, 2019;Valk et al, 2020;Xu et al, 2019bXu et al, , 2019aXu et al, , 2018.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive macaque fMRI pipeline and hierarchical atlas

Jung

Taylor²,

Seidlitz

et al. 2020

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Functional neuroimaging research in the non-human primate (NHP) has been advancing at a remarkable rate. The increase in available data establishes a need for robust analysis pipelines designed for NHP neuroimaging and accompanying template spaces to standardize the localization of neuroimaging results. Our group recently developed the NIMH Macaque Template (NMT), a high-resolution population average anatomical template and associated neuroimaging resources, providing researchers with a standard space for macaque neuroimaging (Seidlitz, Sponheim et al., 2018). Here, we release NMT v2, which includes both symmetric and asymmetric templates in stereotaxic orientation, with improvements in spatial contrast, processing efficiency, and segmentation. We also introduce the Cortical Hierarchy Atlas of the Rhesus Macaque (CHARM), a hierarchical parcellation of the macaque cerebral cortex with varying degrees of detail. These tools have been integrated into the neuroimaging analysis software AFNI (Cox, 1996) to provide a comprehensive and robust pipeline for fMRI processing, visualization and analysis of NHP data. AFNI’s new @animal_warper program can be used to efficiently align anatomical scans to the NMT v2 space, and afni_proc.py integrates these results with full fMRI processing using macaque-specific parameters: from motion correction through regression modeling. Taken together, the NMT v2 and AFNI represent an all-in-one package for macaque functional neuroimaging analysis, as demonstrated with available demos for both task and resting state fMRI.HighlightsThe NMT v2, a stereotaxically aligned symmetric macaque template, is introduced.A new atlas (CHARM), defined on NMT v2, parcellates the cortex at six spatial scales.AFNI’s @animal_warper aligns and maps data between monkey anatomicals and templates.AFNI’s afni_proc.py facilitates monkey fMRI analysis with automated scripting and QC.Demos of macaque task and resting state fMRI analysis with these tools are provided.

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“…Fox et al, 2018). The advent of multi-center data sharing (Milham et al, 2018) also allows for the possibility of larger sample sizes, and clearly calls for group-level analyses performed on data aligned to a population brain template with standardized atlases (Milham et al, 2020;Jung et al, this issue).…”

Section: Introductionmentioning

confidence: 99%

Subcortical Atlas of the Rhesus Macaque (SARM) for Magnetic Resonance Imaging

Hartig

Glen²,

Jung

et al. 2020

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Digitized neuroanatomical atlases are crucial for localizing brain structures and analyzing functional networks identified by magnetic resonance imaging (MRI). To aid in MRI data analysis, we have created a comprehensive parcellation of the rhesus macaque subcortex using a high-resolution ex vivo structural imaging scan. The structural scan and its parcellation were warped to the updated NIMH Macaque Template (NMT v2), an in vivo population template, where the parcellation was refined to produce the Subcortical Atlas of the Rhesus Macaque (SARM). The subcortical parcellation and nomenclature reflect those of the 4th edition of the Rhesus Monkey Brain in Stereotaxic Coordinates (RMBSC4; Paxinos et al., in preparation). The SARM features six parcellation levels, arranged hierarchically from fine regions-of-interest (ROIs) to broader composite regions, suited for fMRI studies. As a test, we ran a functional localizer for the dorsal lateral geniculate (DLG) nucleus in three macaques and found significant fMRI activation in this atlas region. The SARM has been made openly available to the neuroimaging community and can easily be used with common MR data processing software, such as AFNI, where the atlas can be embedded into the software alongside cortical macaque atlases.HighlightsWe present the Subcortical Atlas of the Rhesus Macaque (SARM).SARM provides a neuroanatomical reference frame for neuroimaging analysis.The entire subcortex is mapped, including the thalamus, basal ganglia, and brainstem.ROIs are grouped hierarchically, making SARM useful at multiple spatial resolutions.SARM is in the NMT v2 template space and complements the CHARM atlas for the cortex.

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Accelerating the Evolution of Nonhuman Primate Neuroimaging

Cited by 97 publications

References 7 publications

Ultra-high field (10.5 T) resting state fMRI in the macaque

Ultra-high field (10.5 T) resting state fMRI in the macaque

A comprehensive macaque fMRI pipeline and hierarchical atlas

Subcortical Atlas of the Rhesus Macaque (SARM) for Magnetic Resonance Imaging

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