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Eichert N, DeKraker J, Howard AFD, Huszar IN, Zhu S, Sallet J, Miller KL, Mars RB, Jbabdi S, Bernhardt BC. Hippocampal connectivity patterns echo macroscale cortical evolution in the primate brain. Nat Commun 2024; 15:5963. [PMID: 39013855 PMCID: PMC11252401 DOI: 10.1038/s41467-024-49823-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 06/17/2024] [Indexed: 07/18/2024] Open
Abstract
While the hippocampus is key for human cognitive abilities, it is also a phylogenetically old cortex and paradoxically considered evolutionarily preserved. Here, we introduce a comparative framework to quantify preservation and reconfiguration of hippocampal organisation in primate evolution, by analysing the hippocampus as an unfolded cortical surface that is geometrically matched across species. Our findings revealed an overall conservation of hippocampal macro- and micro-structure, which shows anterior-posterior and, perpendicularly, subfield-related organisational axes in both humans and macaques. However, while functional organisation in both species followed an anterior-posterior axis, we observed a marked reconfiguration in the latter across species, which mirrors a rudimentary integration of the default-mode-network in non-human primates. Here we show that microstructurally preserved regions like the hippocampus may still undergo functional reconfiguration in primate evolution, due to their embedding within heteromodal association networks.
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Affiliation(s)
- Nicole Eichert
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK.
| | - Jordan DeKraker
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Amy F D Howard
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Istvan N Huszar
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Silei Zhu
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Jérôme Sallet
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
- INSERM U1208 Stem Cell and Brain Research Institute, Univ Lyon, Bron, France
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Boris C Bernhardt
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
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Zhong T, Wu X, Liang S, Ning Z, Wang L, Niu Y, Yang S, Kang Z, Feng Q, Li G, Zhang Y. nBEST: Deep-learning-based non-human primates Brain Extraction and Segmentation Toolbox across ages, sites and species. Neuroimage 2024; 295:120652. [PMID: 38797384 DOI: 10.1016/j.neuroimage.2024.120652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 05/21/2024] [Accepted: 05/22/2024] [Indexed: 05/29/2024] Open
Abstract
Accurate processing and analysis of non-human primate (NHP) brain magnetic resonance imaging (MRI) serves an indispensable role in understanding brain evolution, development, aging, and diseases. Despite the accumulation of diverse NHP brain MRI datasets at various developmental stages and from various imaging sites/scanners, existing computational tools designed for human MRI typically perform poor on NHP data, due to huge differences in brain sizes, morphologies, and imaging appearances across species, sites, and ages, highlighting the imperative for NHP-specialized MRI processing tools. To address this issue, in this paper, we present a robust, generic, and fully automated computational pipeline, called non-human primates Brain Extraction and Segmentation Toolbox (nBEST), whose main functionality includes brain extraction, non-cerebrum removal, and tissue segmentation. Building on cutting-edge deep learning techniques by employing lifelong learning to flexibly integrate data from diverse NHP populations and innovatively constructing 3D U-NeXt architecture, nBEST can well handle structural NHP brain MR images from multi-species, multi-site, and multi-developmental-stage (from neonates to the elderly). We extensively validated nBEST based on, to our knowledge, the largest assemblage dataset in NHP brain studies, encompassing 1,469 scans with 11 species (e.g., rhesus macaques, cynomolgus macaques, chimpanzees, marmosets, squirrel monkeys, etc.) from 23 independent datasets. Compared to alternative tools, nBEST outperforms in precision, applicability, robustness, comprehensiveness, and generalizability, greatly benefiting downstream longitudinal, cross-sectional, and cross-species quantitative analyses. We have made nBEST an open-source toolbox (https://github.com/TaoZhong11/nBEST) and we are committed to its continual refinement through lifelong learning with incoming data to greatly contribute to the research field.
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Affiliation(s)
- Tao Zhong
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Xueyang Wu
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Shujun Liang
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Zhenyuan Ning
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Yuyu Niu
- Yunnan Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China
| | - Shihua Yang
- College of Veterinary Medicine, South China Agricultural University, Guangzhou, China
| | - Zhuang Kang
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Qianjin Feng
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, USA.
| | - Yu Zhang
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China.
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3
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Liang L, Zimmermann Rollin I, Alikaya A, Ho JC, Santini T, Bostan AC, Schwerdt HN, Stauffer WR, Ibrahim TS, Pirondini E, Schaeffer DJ. An open-source MRI compatible frame for multimodal presurgical mapping in macaque and capuchin monkeys. J Neurosci Methods 2024; 407:110133. [PMID: 38588922 PMCID: PMC11127775 DOI: 10.1016/j.jneumeth.2024.110133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 03/06/2024] [Accepted: 04/03/2024] [Indexed: 04/10/2024]
Abstract
BACKGROUND High-precision neurosurgical targeting in nonhuman primates (NHPs) often requires presurgical anatomical mapping with noninvasive neuroimaging techniques (MRI, CT, PET), allowing for translation of individual anatomical coordinates to surgical stereotaxic apparatus. Given the varied tissue contrasts that these imaging techniques produce, precise alignment of imaging-based coordinates to surgical apparatus can be cumbersome. MRI-compatible stereotaxis with radiopaque fiducial markers offer a straight-forward and reliable solution, but existing commercial options do not fit in conformal head coils that maximize imaging quality. NEW METHOD We developed a compact MRI-compatible stereotaxis suitable for a variety of NHP species (Macaca mulatta, Macaca fascicularis, and Cebus apella) that allows multimodal alignment through technique-specific fiducial markers. COMPARISON WITH EXISTING METHODS With the express purpose of compatibility with clinically available MRI, CT, and PET systems, the frame is no larger than a human head, while allowing for imaging NHPs in the supinated position. This design requires no marker implantation, special software, or additional knowledge other than the operation of a common large animal stereotaxis. RESULTS We demonstrated the applicability of this 3D-printable apparatus across a diverse set of experiments requiring presurgical planning: 1) We demonstrate the accuracy of the fiducial system through a within-MRI cannula insertion and subcortical injection of a viral vector. 2) We also demonstrated accuracy of multimodal (MRI and CT) alignment and coordinate transfer to guide a surgical robot electrode implantation for deep-brain electrophysiology. CONCLUSIONS The computer-aided design files and engineering drawings are publicly available, with the modular design allowing for low cost and manageable manufacturing.
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Affiliation(s)
- Lucy Liang
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA; Center for Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Isabela Zimmermann Rollin
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA; Center for Neural Basis of Cognition, Pittsburgh, PA, USA; Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Aydin Alikaya
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Jonathan C Ho
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA; School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Tales Santini
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Andreea C Bostan
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Helen N Schwerdt
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - William R Stauffer
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Tamer S Ibrahim
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA; University of Pittsburgh, Psychiatry, Pittsburgh, PA, USA; University of Pittsburgh, Radiology, Pittsburgh, PA, USA
| | - Elvira Pirondini
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA; Center for Neural Basis of Cognition, Pittsburgh, PA, USA; Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA; Department of Physical Medicine & Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA; Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
| | - David J Schaeffer
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA; Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA.
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4
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Zhong T, Wang Y, Xu X, Wu X, Liang S, Ning Z, Wang L, Niu Y, Li G, Zhang Y. A brain subcortical segmentation tool based on anatomy attentional fusion network for developing macaques. Comput Med Imaging Graph 2024; 116:102404. [PMID: 38870599 DOI: 10.1016/j.compmedimag.2024.102404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 05/21/2024] [Accepted: 05/22/2024] [Indexed: 06/15/2024]
Abstract
Magnetic Resonance Imaging (MRI) plays a pivotal role in the accurate measurement of brain subcortical structures in macaques, which is crucial for unraveling the complexities of brain structure and function, thereby enhancing our understanding of neurodegenerative diseases and brain development. However, due to significant differences in brain size, structure, and imaging characteristics between humans and macaques, computational tools developed for human neuroimaging studies often encounter obstacles when applied to macaques. In this context, we propose an Anatomy Attentional Fusion Network (AAF-Net), which integrates multimodal MRI data with anatomical constraints in a multi-scale framework to address the challenges posed by the dynamic development, regional heterogeneity, and age-related size variations of the juvenile macaque brain, thus achieving precise subcortical segmentation. Specifically, we generate a Signed Distance Map (SDM) based on the initial rough segmentation of the subcortical region by a network as an anatomical constraint, providing comprehensive information on positions, structures, and morphology. Then we construct AAF-Net to fully fuse the SDM anatomical constraints and multimodal images for refined segmentation. To thoroughly evaluate the performance of our proposed tool, over 700 macaque MRIs from 19 datasets were used in this study. Specifically, we employed two manually labeled longitudinal macaque datasets to develop the tool and complete four-fold cross-validations. Furthermore, we incorporated various external datasets to demonstrate the proposed tool's generalization capabilities and promise in brain development research. We have made this tool available as an open-source resource at https://github.com/TaoZhong11/Macaque_subcortical_segmentation for direct application.
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Affiliation(s)
- Tao Zhong
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, China
| | - Ya Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, USA
| | - Xiaotong Xu
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, China
| | - Xueyang Wu
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, China
| | - Shujun Liang
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, China
| | - Zhenyuan Ning
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, China
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, USA
| | - Yuyu Niu
- Yunnan Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, China
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, USA.
| | - Yu Zhang
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, China.
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van der Grinten M, de Ruyter van Steveninck J, Lozano A, Pijnacker L, Rueckauer B, Roelfsema P, van Gerven M, van Wezel R, Güçlü U, Güçlütürk Y. Towards biologically plausible phosphene simulation for the differentiable optimization of visual cortical prostheses. eLife 2024; 13:e85812. [PMID: 38386406 PMCID: PMC10883675 DOI: 10.7554/elife.85812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 01/21/2024] [Indexed: 02/23/2024] Open
Abstract
Blindness affects millions of people around the world. A promising solution to restoring a form of vision for some individuals are cortical visual prostheses, which bypass part of the impaired visual pathway by converting camera input to electrical stimulation of the visual system. The artificially induced visual percept (a pattern of localized light flashes, or 'phosphenes') has limited resolution, and a great portion of the field's research is devoted to optimizing the efficacy, efficiency, and practical usefulness of the encoding of visual information. A commonly exploited method is non-invasive functional evaluation in sighted subjects or with computational models by using simulated prosthetic vision (SPV) pipelines. An important challenge in this approach is to balance enhanced perceptual realism, biologically plausibility, and real-time performance in the simulation of cortical prosthetic vision. We present a biologically plausible, PyTorch-based phosphene simulator that can run in real-time and uses differentiable operations to allow for gradient-based computational optimization of phosphene encoding models. The simulator integrates a wide range of clinical results with neurophysiological evidence in humans and non-human primates. The pipeline includes a model of the retinotopic organization and cortical magnification of the visual cortex. Moreover, the quantitative effects of stimulation parameters and temporal dynamics on phosphene characteristics are incorporated. Our results demonstrate the simulator's suitability for both computational applications such as end-to-end deep learning-based prosthetic vision optimization as well as behavioral experiments. The modular and open-source software provides a flexible simulation framework for computational, clinical, and behavioral neuroscientists working on visual neuroprosthetics.
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Affiliation(s)
| | | | - Antonio Lozano
- Netherlands Institute for Neuroscience, Vrije Universiteit, Amsterdam, Netherlands
| | - Laura Pijnacker
- Donders Institute for Brain Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Bodo Rueckauer
- Donders Institute for Brain Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Pieter Roelfsema
- Netherlands Institute for Neuroscience, Vrije Universiteit, Amsterdam, Netherlands
| | - Marcel van Gerven
- Donders Institute for Brain Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Richard van Wezel
- Donders Institute for Brain Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
- Biomedical Signals and Systems Group, University of Twente, Enschede, Netherlands
| | - Umut Güçlü
- Donders Institute for Brain Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Yağmur Güçlütürk
- Donders Institute for Brain Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
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Merchant H, Mendoza G, Pérez O, Betancourt A, García-Saldivar P, Prado L. Diverse Time Encoding Strategies Within the Medial Premotor Areas of the Primate. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1455:117-140. [PMID: 38918349 DOI: 10.1007/978-3-031-60183-5_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/27/2024]
Abstract
The measurement of time in the subsecond scale is critical for many sophisticated behaviors, yet its neural underpinnings are largely unknown. Recent neurophysiological experiments from our laboratory have shown that the neural activity in the medial premotor areas (MPC) of macaques can represent different aspects of temporal processing. During single interval categorization, we found that preSMA encodes a subjective category limit by reaching a peak of activity at a time that divides the set of test intervals into short and long. We also observed neural signals associated with the category selected by the subjects and the reward outcomes of the perceptual decision. On the other hand, we have studied the behavioral and neurophysiological basis of rhythmic timing. First, we have shown in different tapping tasks that macaques are able to produce predictively and accurately intervals that are cued by auditory or visual metronomes or when intervals are produced internally without sensory guidance. In addition, we found that the rhythmic timing mechanism in MPC is governed by different layers of neural clocks. Next, the instantaneous activity of single cells shows ramping activity that encodes the elapsed or remaining time for a tapping movement. In addition, we found MPC neurons that build neural sequences, forming dynamic patterns of activation that flexibly cover all the produced interval depending on the tapping tempo. This rhythmic neural clock resets on every interval providing an internal representation of pulse. Furthermore, the MPC cells show mixed selectivity, encoding not only elapsed time, but also the tempo of the tapping and the serial order element in the rhythmic sequence. Hence, MPC can map different task parameters, including the passage of time, using different cell populations. Finally, the projection of the time varying activity of MPC hundreds of cells into a low dimensional state space showed circular neural trajectories whose geometry represented the internal pulse and the tapping tempo. Overall, these findings support the notion that MPC is part of the core timing mechanism for both single interval and rhythmic timing, using neural clocks with different encoding principles, probably to flexibly encode and mix the timing representation with other task parameters.
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Affiliation(s)
- Hugo Merchant
- Instituto de Neurobiología, UNAM, Campus Juriquilla, Querétaro, Mexico.
| | - Germán Mendoza
- Instituto de Neurobiología, UNAM, Campus Juriquilla, Querétaro, Mexico
| | - Oswaldo Pérez
- Instituto de Neurobiología, UNAM, Campus Juriquilla, Querétaro, Mexico
| | | | | | - Luis Prado
- Instituto de Neurobiología, UNAM, Campus Juriquilla, Querétaro, Mexico
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吴 雪, 张 煜, 张 华, 钟 涛. [Whole-brain parcellation for macaque brain magnetic resonance images based on attention mechanism and multi-modality feature fusion]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2023; 43:2118-2125. [PMID: 38189399 PMCID: PMC10774098 DOI: 10.12122/j.issn.1673-4254.2023.12.17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Indexed: 01/09/2024]
Abstract
OBJECTIVE We propose a novel deep learning algorithm based on attention mechanism and multimodality feature fusion (DDAM) to achieve whole-brain parcellation of macaque brain magnetic resonance (MR) images. METHODS We collected multimodality brain MR images of 68 macaques (aged 13 to 36 months) with corresponding ground truth labels. To address the complexity and complementary nature of the multimodality data, we employed a multi- encoder structure to adapt to different modalities and performed feature extraction. In the decoder, an attention mechanism was introduced to construct the Attention-based Multimodality Feature Fusion module (AMFF). Leveraging the rich and complementary information between modalities, AMFF effectively integrated multimodality features of varying scales and complexities to enhance the performance of parcellation. We conducted ablation experiments and statistically analyzed the results. RESULTS The incorporation of the multi- encoder structure and attention mechanism significantly improved the performance of the network for integrating the multimodality features, and achieved an average DSC of 0.904 and an ASD as low as 0.131 for macaque whole-brain parcellation (P < 0.05). The ablation experiments validated the effectiveness of each component of the DDAM method. CONCLUSION The proposed DDAM method, with its enhanced ability to extract and integrate multimodality features, can significantly improve the accuracy of whole-brain MR image segmentation.
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Affiliation(s)
- 雪扬 吴
- />南方医科大学生物医学工程学院//广东省医学图像处理重点实验室,广东 广州 510515School of Biomedical Engineering//Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China
| | - 煜 张
- />南方医科大学生物医学工程学院//广东省医学图像处理重点实验室,广东 广州 510515School of Biomedical Engineering//Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China
| | - 华 张
- />南方医科大学生物医学工程学院//广东省医学图像处理重点实验室,广东 广州 510515School of Biomedical Engineering//Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China
| | - 涛 钟
- />南方医科大学生物医学工程学院//广东省医学图像处理重点实验室,广东 广州 510515School of Biomedical Engineering//Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China
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8
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Magielse N, Heuer K, Toro R, Schutter DJLG, Valk SL. A Comparative Perspective on the Cerebello-Cerebral System and Its Link to Cognition. CEREBELLUM (LONDON, ENGLAND) 2023; 22:1293-1307. [PMID: 36417091 PMCID: PMC10657313 DOI: 10.1007/s12311-022-01495-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/11/2022] [Indexed: 11/24/2022]
Abstract
The longstanding idea that the cerebral cortex is the main neural correlate of human cognition can be elaborated by comparative analyses along the vertebrate phylogenetic tree that support the view that the cerebello-cerebral system is suited to support non-motor functions more generally. In humans, diverse accounts have illustrated cerebellar involvement in cognitive functions. Although the neocortex, and its transmodal association cortices such as the prefrontal cortex, have become disproportionately large over primate evolution specifically, human neocortical volume does not appear to be exceptional relative to the variability within primates. Rather, several lines of evidence indicate that the exceptional volumetric increase of the lateral cerebellum in conjunction with its connectivity with the cerebral cortical system may be linked to non-motor functions and mental operation in primates. This idea is supported by diverging cerebello-cerebral adaptations that potentially coevolve with cognitive abilities across other vertebrates such as dolphins, parrots, and elephants. Modular adaptations upon the vertebrate cerebello-cerebral system may thus help better understand the neuroevolutionary trajectory of the primate brain and its relation to cognition in humans. Lateral cerebellar lobules crura I-II and their reciprocal connections to the cerebral cortical association areas appear to have substantially expanded in great apes, and humans. This, along with the notable increase in the ventral portions of the dentate nucleus and a shift to increased relative prefrontal-cerebellar connectivity, suggests that modular cerebellar adaptations support cognitive functions in humans. In sum, we show how comparative neuroscience provides new avenues to broaden our understanding of cerebellar and cerebello-cerebral functions in the context of cognition.
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Affiliation(s)
- Neville Magielse
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Center Jülich, Jülich, Germany
- Otto Hahn Cognitive Neurogenetics Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of Systems Neuroscience, Heinrich Heine University, Düsseldorf, Germany
| | - Katja Heuer
- Institute Pasteur, Unité de Neuroanatomie Appliquée et Théorique, Université Paris Cité, Paris, France
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Roberto Toro
- Institute Pasteur, Unité de Neuroanatomie Appliquée et Théorique, Université Paris Cité, Paris, France
| | - Dennis J L G Schutter
- Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands
| | - Sofie L Valk
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Center Jülich, Jülich, Germany.
- Otto Hahn Cognitive Neurogenetics Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
- Institute of Systems Neuroscience, Heinrich Heine University, Düsseldorf, Germany.
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9
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Demirci N, Hoffman ME, Holland MA. Systematic cortical thickness and curvature patterns in primates. Neuroimage 2023; 278:120283. [PMID: 37516374 PMCID: PMC10443624 DOI: 10.1016/j.neuroimage.2023.120283] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 07/11/2023] [Accepted: 07/17/2023] [Indexed: 07/31/2023] Open
Abstract
Humans are known to have significant and consistent differences in thickness throughout the cortex, with thick outer gyral folds and thin inner sulcal folds. Our previous work has suggested a mechanical basis for this thickness pattern, with the forces generated during cortical folding leading to thick gyri and thin sulci, and shown that cortical thickness varies along a gyral-sulcal spectrum in humans. While other primate species are expected to exhibit similar patterns of cortical thickness, it is currently unknown how these patterns scale across different sizes, forms, and foldedness. Among primates, brains vary enormously from roughly the size of a grape to the size of a grapefruit, and from nearly smooth to dramatically folded; of these, human brains are the largest and most folded. These variations in size and form make comparative neuroanatomy a rich resource for investigating common trends that transcend differences between species. In this study, we examine 12 primate species in order to cover a wide range of sizes and forms, and investigate the scaling of their cortical thickness relative to the surface geometry. The 12 species were selected due to the public availability of either reconstructed surfaces and/or population templates. After obtaining or reconstructing 3D surfaces from publicly available neuroimaging data, we used our surface-based computational pipeline (https://github.com/mholla/curveball) to analyze patterns of cortical thickness and folding with respect to size (total surface area), geometry (i.e. curvature, shape, and sulcal depth), and foldedness (gyrification). In all 12 species, we found consistent cortical thickness variations along a gyral-sulcal spectrum, with convex shapes thicker than concave shapes and saddle shapes in between. Furthermore, we saw an increasing thickness difference between gyri and sulci as brain size increases. Our results suggest a systematic folding mechanism relating local cortical thickness to geometry. Finally, all of our reconstructed surfaces and morphometry data are available for future research in comparative neuroanatomy.
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Affiliation(s)
- Nagehan Demirci
- Bioengineering Graduate Program, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Mia E Hoffman
- Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA; Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Maria A Holland
- Bioengineering Graduate Program, University of Notre Dame, Notre Dame, IN 46556, USA; Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA.
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de Sousa AA, Beaudet A, Calvey T, Bardo A, Benoit J, Charvet CJ, Dehay C, Gómez-Robles A, Gunz P, Heuer K, van den Heuvel MP, Hurst S, Lauters P, Reed D, Salagnon M, Sherwood CC, Ströckens F, Tawane M, Todorov OS, Toro R, Wei Y. From fossils to mind. Commun Biol 2023; 6:636. [PMID: 37311857 PMCID: PMC10262152 DOI: 10.1038/s42003-023-04803-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Accepted: 04/04/2023] [Indexed: 06/15/2023] Open
Abstract
Fossil endocasts record features of brains from the past: size, shape, vasculature, and gyrification. These data, alongside experimental and comparative evidence, are needed to resolve questions about brain energetics, cognitive specializations, and developmental plasticity. Through the application of interdisciplinary techniques to the fossil record, paleoneurology has been leading major innovations. Neuroimaging is shedding light on fossil brain organization and behaviors. Inferences about the development and physiology of the brains of extinct species can be experimentally investigated through brain organoids and transgenic models based on ancient DNA. Phylogenetic comparative methods integrate data across species and associate genotypes to phenotypes, and brains to behaviors. Meanwhile, fossil and archeological discoveries continuously contribute new knowledge. Through cooperation, the scientific community can accelerate knowledge acquisition. Sharing digitized museum collections improves the availability of rare fossils and artifacts. Comparative neuroanatomical data are available through online databases, along with tools for their measurement and analysis. In the context of these advances, the paleoneurological record provides ample opportunity for future research. Biomedical and ecological sciences can benefit from paleoneurology's approach to understanding the mind as well as its novel research pipelines that establish connections between neuroanatomy, genes and behavior.
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Affiliation(s)
| | - Amélie Beaudet
- Laboratoire de Paléontologie, Évolution, Paléoécosystèmes et Paléoprimatologie (PALEVOPRIM), UMR 7262 CNRS & Université de Poitiers, Poitiers, France.
- University of Cambridge, Cambridge, UK.
| | - Tanya Calvey
- Division of Clinical Anatomy and Biological Anthropology, University of Cape Town, Cape Town, South Africa.
| | - Ameline Bardo
- UMR 7194, CNRS-MNHN, Département Homme et Environnement, Musée de l'Homme, Paris, France
- Skeletal Biology Research Centre, School of Anthropology and Conservation, University of Kent, Canterbury, UK
| | - Julien Benoit
- Evolutionary Studies Institute, University of the Witwatersrand, Johannesburg, South Africa
| | - Christine J Charvet
- Department of Anatomy, Physiology and Pharmacology, College of Veterinary Medicine, Auburn University, Auburn, AL, USA
| | - Colette Dehay
- University of Lyon, Université Claude Bernard Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, F-69500, Bron, France
| | | | - Philipp Gunz
- Department of Human Origins, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, D-04103, Leipzig, Germany
| | - Katja Heuer
- Institut Pasteur, Université Paris Cité, Unité de Neuroanatomie Appliquée et Théorique, F-75015, Paris, France
| | | | - Shawn Hurst
- University of Indianapolis, Indianapolis, IN, USA
| | - Pascaline Lauters
- Institut royal des Sciences naturelles, Direction Opérationnelle Terre et Histoire de la Vie, Brussels, Belgium
| | - Denné Reed
- Department of Anthropology, University of Texas at Austin, Austin, TX, USA
| | - Mathilde Salagnon
- CNRS, CEA, IMN, GIN, UMR 5293, Université Bordeaux, Bordeaux, France
- PACEA UMR 5199, CNRS, Université Bordeaux, Pessac, France
| | - Chet C Sherwood
- Department of Anthropology, The George Washington University, Washington, DC, USA
| | - Felix Ströckens
- C. & O. Vogt Institute for Brain Research, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Mirriam Tawane
- Ditsong National Museum of Natural History, Pretoria, South Africa
| | - Orlin S Todorov
- School of Natural Sciences, Macquarie University, Sydney, NSW, Australia
| | - Roberto Toro
- Institut Pasteur, Université Paris Cité, Unité de Neuroanatomie Appliquée et Théorique, F-75015, Paris, France
| | - Yongbin Wei
- Beijing University of Posts and Telecommunications, Beijing, China
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12
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A framework and resource for global collaboration in non-human primate neuroscience. CURRENT RESEARCH IN NEUROBIOLOGY 2023. [DOI: 10.1016/j.crneur.2023.100079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2023] Open
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Janssen P, Isa T, Lanciego J, Leech K, Logothetis N, Poo MM, Mitchell AS. Visualizing advances in the future of primate neuroscience research. CURRENT RESEARCH IN NEUROBIOLOGY 2022; 4:100064. [PMID: 36582401 PMCID: PMC9792703 DOI: 10.1016/j.crneur.2022.100064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 09/30/2022] [Accepted: 11/24/2022] [Indexed: 12/15/2022] Open
Abstract
Future neuroscience and biomedical projects involving non-human primates (NHPs) remain essential in our endeavors to understand the complexities and functioning of the mammalian central nervous system. In so doing, the NHP neuroscience researcher must be allowed to incorporate state-of-the-art technologies, including the use of novel viral vectors, gene therapy and transgenic approaches to answer continuing and emerging research questions that can only be addressed in NHP research models. This perspective piece captures these emerging technologies and some specific research questions they can address. At the same time, we highlight some current caveats to global NHP research and collaborations including the lack of common ethical and regulatory frameworks for NHP research, the limitations involving animal transportation and exports, and the ongoing influence of activist groups opposed to NHP research.
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Affiliation(s)
- Peter Janssen
- Laboratory for Neuro- and Psychophysiology, KU Leuven, Belgium
| | - Tadashi Isa
- Graduate School of Medicine, Kyoto University, Japan
| | - Jose Lanciego
- Department Neurosciences, Center for Applied Medical Research (CIMA), University of Navarra, CiberNed., Pamplona, Spain
| | - Kirk Leech
- European Animal Research Association, United Kingdom
| | - Nikos Logothetis
- International Center for Primate Brain Research, Shanghai, China
| | - Mu-Ming Poo
- International Center for Primate Brain Research, Shanghai, China
| | - Anna S. Mitchell
- School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, New Zealand,Department of Experimental Psychology, University of Oxford, United Kingdom,Corresponding author. School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, New Zealand.
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14
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Tian X, Chen Y, Majka P, Szczupak D, Perl YS, Yen CCC, Tong C, Feng F, Jiang H, Glen D, Deco G, Rosa MGP, Silva AC, Liang Z, Liu C. An integrated resource for functional and structural connectivity of the marmoset brain. Nat Commun 2022; 13:7416. [PMID: 36456558 PMCID: PMC9715556 DOI: 10.1038/s41467-022-35197-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 11/21/2022] [Indexed: 12/02/2022] Open
Abstract
Comprehensive integration of structural and functional connectivity data is required to model brain functions accurately. While resources for studying the structural connectivity of non-human primate brains already exist, their integration with functional connectivity data has remained unavailable. Here we present a comprehensive resource that integrates the most extensive awake marmoset resting-state fMRI data available to date (39 marmoset monkeys, 710 runs, 12117 mins) with previously published cellular-level neuronal tracing data (52 marmoset monkeys, 143 injections) and multi-resolution diffusion MRI datasets. The combination of these data allowed us to (1) map the fine-detailed functional brain networks and cortical parcellations, (2) develop a deep-learning-based parcellation generator that preserves the topographical organization of functional connectivity and reflects individual variabilities, and (3) investigate the structural basis underlying functional connectivity by computational modeling. This resource will enable modeling structure-function relationships and facilitate future comparative and translational studies of primate brains.
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Affiliation(s)
- Xiaoguang Tian
- grid.21925.3d0000 0004 1936 9000Department of Neurobiology, University of Pittsburgh Brain Institute, University of Pittsburgh, Pittsburgh, PA 15261 USA
| | - Yuyan Chen
- grid.9227.e0000000119573309Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, China
| | - Piotr Majka
- grid.419305.a0000 0001 1943 2944Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of the Polish Academy of Sciences, 02-093 Warsaw, Poland ,grid.1002.30000 0004 1936 7857Department of Physiology and Neuroscience Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800 Australia
| | - Diego Szczupak
- grid.21925.3d0000 0004 1936 9000Department of Neurobiology, University of Pittsburgh Brain Institute, University of Pittsburgh, Pittsburgh, PA 15261 USA
| | - Yonatan Sanz Perl
- grid.5612.00000 0001 2172 2676Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona, 08018 Spain ,grid.441741.30000 0001 2325 2241Universidad de San Andrés, Vito Dumas 284 (B1644BID), Buenos Aires, Argentina
| | - Cecil Chern-Chyi Yen
- grid.94365.3d0000 0001 2297 5165Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health (NINDS/NIH), Bethesda, MD 20892 USA
| | - Chuanjun Tong
- grid.9227.e0000000119573309Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, China
| | - Furui Feng
- grid.9227.e0000000119573309Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, China
| | - Haiteng Jiang
- grid.13402.340000 0004 1759 700XDepartment of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Zhe Jiang Sheng, China ,grid.13402.340000 0004 1759 700XMOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou, China
| | - Daniel Glen
- grid.94365.3d0000 0001 2297 5165Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health (NIMH/NIH), Bethesda, MD 20892 USA
| | - Gustavo Deco
- grid.5612.00000 0001 2172 2676Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona, 08018 Spain ,grid.425902.80000 0000 9601 989XInstitució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona, 08010 Spain ,grid.419524.f0000 0001 0041 5028Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, 04103 Germany ,grid.1002.30000 0004 1936 7857School of Psychological Sciences, Monash University, Melbourne, Clayton, VIC 3800 Australia
| | - Marcello G. P. Rosa
- grid.1002.30000 0004 1936 7857Department of Physiology and Neuroscience Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800 Australia
| | - Afonso C. Silva
- grid.21925.3d0000 0004 1936 9000Department of Neurobiology, University of Pittsburgh Brain Institute, University of Pittsburgh, Pittsburgh, PA 15261 USA
| | - Zhifeng Liang
- grid.9227.e0000000119573309Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, China ,grid.511008.dShanghai Center for Brain Science and Brain-Inspired Intelligence Technology Shanghai, Shanghai, China
| | - Cirong Liu
- grid.9227.e0000000119573309Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, China ,grid.511008.dShanghai Center for Brain Science and Brain-Inspired Intelligence Technology Shanghai, Shanghai, China ,Lingang Laboratory, Shanghai, 200031 China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, Beijing, China
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15
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Wan B, Bayrak Ş, Ting Xu T, Schaare HL, Bethlehem RAI, Bernhardt BC, Valk SL. Heritability and cross-species comparisons of human cortical functional organization asymmetry. eLife 2022; 11:77215. [PMID: 35904242 PMCID: PMC9381036 DOI: 10.7554/elife.77215] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 07/28/2022] [Indexed: 11/17/2022] Open
Abstract
The human cerebral cortex is symmetrically organized along large-scale axes but also presents inter-hemispheric differences in structure and function. The quantified contralateral homologous difference, that is asymmetry, is a key feature of the human brain left-right axis supporting functional processes, such as language. Here, we assessed whether the asymmetry of cortical functional organization is heritable and phylogenetically conserved between humans and macaques. Our findings indicate asymmetric organization along an axis describing a functional trajectory from perceptual/action to abstract cognition. Whereas language network showed leftward asymmetric organization, frontoparietal network showed rightward asymmetric organization in humans. These asymmetries were heritable in humans and showed a similar spatial distribution with macaques, in the case of intra-hemispheric asymmetry of functional hierarchy. This suggests (phylo)genetic conservation. However, both language and frontoparietal networks showed a qualitatively larger asymmetry in humans relative to macaques. Overall, our findings suggest a genetic basis for asymmetry in intrinsic functional organization, linked to higher order cognitive functions uniquely developed in humans.
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Affiliation(s)
- Bin Wan
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Şeyma Bayrak
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Ting Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, United States
| | - H Lina Schaare
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | | | | | - Sofie Louise Valk
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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Ren W, Ji B, Guan Y, Cao L, Ni R. Recent Technical Advances in Accelerating the Clinical Translation of Small Animal Brain Imaging: Hybrid Imaging, Deep Learning, and Transcriptomics. Front Med (Lausanne) 2022; 9:771982. [PMID: 35402436 PMCID: PMC8987112 DOI: 10.3389/fmed.2022.771982] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 02/16/2022] [Indexed: 12/26/2022] Open
Abstract
Small animal models play a fundamental role in brain research by deepening the understanding of the physiological functions and mechanisms underlying brain disorders and are thus essential in the development of therapeutic and diagnostic imaging tracers targeting the central nervous system. Advances in structural, functional, and molecular imaging using MRI, PET, fluorescence imaging, and optoacoustic imaging have enabled the interrogation of the rodent brain across a large temporal and spatial resolution scale in a non-invasively manner. However, there are still several major gaps in translating from preclinical brain imaging to the clinical setting. The hindering factors include the following: (1) intrinsic differences between biological species regarding brain size, cell type, protein expression level, and metabolism level and (2) imaging technical barriers regarding the interpretation of image contrast and limited spatiotemporal resolution. To mitigate these factors, single-cell transcriptomics and measures to identify the cellular source of PET tracers have been developed. Meanwhile, hybrid imaging techniques that provide highly complementary anatomical and molecular information are emerging. Furthermore, deep learning-based image analysis has been developed to enhance the quantification and optimization of the imaging protocol. In this mini-review, we summarize the recent developments in small animal neuroimaging toward improved translational power, with a focus on technical improvement including hybrid imaging, data processing, transcriptomics, awake animal imaging, and on-chip pharmacokinetics. We also discuss outstanding challenges in standardization and considerations toward increasing translational power and propose future outlooks.
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Affiliation(s)
- Wuwei Ren
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
- Shanghai Engineering Research Center of Energy Efficient and Custom AI IC, Shanghai, China
| | - Bin Ji
- Department of Radiopharmacy and Molecular Imaging, School of Pharmacy, Fudan University, Shanghai, China
| | - Yihui Guan
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Lei Cao
- Shanghai Changes Tech, Ltd., Shanghai, China
| | - Ruiqing Ni
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
- Institute for Biomedical Engineering, ETH Zürich and University of Zurich, Zurich, Switzerland
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17
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Alenyá M, Wang X, Lefévre J, Auzias G, Fouquet B, Eixarch E, Rousseau F, Camara O. Computational pipeline for the generation and validation of patient-specific mechanical models of brain development. BRAIN MULTIPHYSICS 2022. [DOI: 10.1016/j.brain.2022.100045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
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18
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Applications of chemogenetics in non-human primates. Curr Opin Pharmacol 2022; 64:102204. [DOI: 10.1016/j.coph.2022.102204] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 01/10/2022] [Accepted: 02/11/2022] [Indexed: 11/23/2022]
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Bernhardt BC, Smallwood J, Keilholz S, Margulies DS. Gradients in Brain Organization. Neuroimage 2022; 251:118987. [PMID: 35151850 DOI: 10.1016/j.neuroimage.2022.118987] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 02/08/2022] [Indexed: 12/14/2022] Open
Affiliation(s)
- Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
| | | | - Shella Keilholz
- Biomedical Engineering, Emory University / Georgia Institute of Technology, Atlanta, Georgia
| | - Daniel S Margulies
- Integrative Neuroscience and Cognition Center, Centre National de la Recherche Scientifique (CNRS) and Université de Paris, Paris, France
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20
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Shahdloo M, Schüffelgen U, Papp D, Miller KL, Chiew M. Model-based dynamic off-resonance correction for improved accelerated fMRI in awake behaving nonhuman primates. Magn Reson Med 2022; 87:2922-2932. [PMID: 35081259 PMCID: PMC9306555 DOI: 10.1002/mrm.29167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 11/26/2021] [Accepted: 01/03/2022] [Indexed: 11/18/2022]
Abstract
Purpose To estimate dynamic off‐resonance due to vigorous body motion in accelerated fMRI of awake behaving nonhuman primates (NHPs) using the echo‐planar imaging reference navigator, in order to attenuate the effects of time‐varying off‐resonance on the reconstruction. Methods In NHP fMRI, the animal’s head is usually head‐posted, and the dynamic off‐resonance is mainly caused by motion in body parts that are distant from the brain and have low spatial frequency. Hence, off‐resonance at each frame can be approximated as a spatially linear perturbation of the off‐resonance at a reference frame, and is manifested as a relative linear shift in k‐space. Using GRAPPA operators, we estimated these shifts by comparing the navigator at each time frame with that at the reference frame. Estimated shifts were then used to correct the data at each frame. The proposed method was evaluated in phantom scans, simulations, and in vivo data. Results The proposed method is shown to successfully estimate spatially low‐order dynamic off‐resonance perturbations, including induced linear off‐resonance perturbations in phantoms, and is able to correct retrospectively corrupted data in simulations. Finally, it is shown to reduce ghosting artifacts and geometric distortions by up to 20% in simultaneous multislice in vivo acquisitions in awake‐behaving NHPs. Conclusion A method is proposed that does not need sequence modification or extra acquisitions and makes accelerated awake behaving NHP imaging more robust and reliable, reducing the gap between what is possible with NHP protocols and state‐of‐the‐art human imaging.
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Affiliation(s)
- Mo Shahdloo
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Urs Schüffelgen
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Daniel Papp
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.,NeuroPoly Lab, Electrical Engineering Department, Polytechnique Montréal, Montreal, Canada
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Mark Chiew
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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Milham M, Petkov C, Belin P, Ben Hamed S, Evrard H, Fair D, Fox A, Froudist-Walsh S, Hayashi T, Kastner S, Klink C, Majka P, Mars R, Messinger A, Poirier C, Schroeder C, Shmuel A, Silva AC, Vanduffel W, Van Essen DC, Wang Z, Roe AW, Wilke M, Xu T, Aarabi MH, Adolphs R, Ahuja A, Alvand A, Amiez C, Autio J, Azadi R, Baeg E, Bai R, Bao P, Basso M, Behel AK, Bennett Y, Bernhardt B, Biswal B, Boopathy S, Boretius S, Borra E, Boshra R, Buffalo E, Cao L, Cavanaugh J, Celine A, Chavez G, Chen LM, Chen X, Cheng L, Chouinard-Decorte F, Clavagnier S, Cléry J, Colcombe SJ, Conway B, Cordeau M, Coulon O, Cui Y, Dadarwal R, Dahnke R, Desrochers T, Deying L, Dougherty K, Doyle H, Drzewiecki CM, Duyck M, Arachchi WE, Elorette C, Essamlali A, Evans A, Fajardo A, Figueroa H, Franco A, Freches G, Frey S, Friedrich P, Fujimoto A, Fukunaga M, Gacoin M, Gallardo G, Gao L, Gao Y, Garside D, Garza-Villarreal EA, Gaudet-Trafit M, Gerbella M, Giavasis S, Glen D, Ribeiro Gomes AR, Torrecilla SG, Gozzi A, Gulli R, Haber S, Hadj-Bouziane F, Fujimoto SH, Hawrylycz M, He Q, He Y, Heuer K, Hiba B, Hoffstaedter F, Hong SJ, Hori Y, Hou Y, Howard A, de la Iglesia-Vaya M, Ikeda T, Jankovic-Rapan L, Jaramillo J, Jedema HP, Jin H, Jiang M, Jung B, Kagan I, Kahn I, Kiar G, Kikuchi Y, Kilavik B, Kimura N, Klatzmann U, Kwok SC, Lai HY, Lamberton F, Lehman J, Li P, Li X, Li X, Liang Z, Liston C, Little R, Liu C, Liu N, Liu X, Liu X, Lu H, Loh KK, Madan C, Magrou L, Margulies D, Mathilda F, Mejia S, Meng Y, Menon R, Meunier D, Mitchell A, Mitchell A, Murphy A, Mvula T, Ortiz-Rios M, Ortuzar Martinez DE, Pagani M, Palomero-Gallagher N, Pareek V, Perkins P, Ponce F, Postans M, Pouget P, Qian M, Ramirez J“B, Raven E, Restrepo I, Rima S, Rockland K, Rodriguez NY, Roger E, Hortelano ER, Rosa M, Rossi A, Rudebeck P, Russ B, Sakai T, Saleem KS, Sallet J, Sawiak S, Schaeffer D, Schwiedrzik CM, Seidlitz J, Sein J, Sharma J, Shen K, Sheng WA, Shi NS, Shim WM, Simone L, Sirmpilatze N, Sivan V, Song X, Tanenbaum A, Tasserie J, Taylor P, Tian X, Toro R, Trambaiolli L, Upright N, Vezoli J, Vickery S, Villalon J, Wang X, Wang Y, Weiss AR, Wilson C, Wong TY, Woo CW, Wu B, Xiao D, Xu AG, Xu D, Xufeng Z, Yacoub E, Ye N, Ying Z, Yokoyama C, Yu X, Yue S, Yuheng L, Yumeng X, Zaldivar D, Zhang S, Zhao Y, Zuo Z. Toward next-generation primate neuroscience: A collaboration-based strategic plan for integrative neuroimaging. Neuron 2022; 110:16-20. [PMID: 34731649 DOI: 10.1016/j.neuron.2021.10.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 09/30/2021] [Accepted: 10/11/2021] [Indexed: 12/22/2022]
Abstract
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.
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22
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Elam JS, Glasser MF, Harms MP, Sotiropoulos SN, Andersson JLR, Burgess GC, Curtiss SW, Oostenveld R, Larson-Prior LJ, Schoffelen JM, Hodge MR, Cler EA, Marcus DM, Barch DM, Yacoub E, Smith SM, Ugurbil K, Van Essen DC. The Human Connectome Project: A retrospective. Neuroimage 2021; 244:118543. [PMID: 34508893 PMCID: PMC9387634 DOI: 10.1016/j.neuroimage.2021.118543] [Citation(s) in RCA: 72] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 08/13/2021] [Accepted: 08/30/2021] [Indexed: 01/21/2023] Open
Abstract
The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances in human neuroimaging, particularly for measures of brain connectivity; apply these advances to study a large number of healthy young adults; and freely share the data and tools with the scientific community. NIH awarded grants to two consortia; this retrospective focuses on the "WU-Minn-Ox" HCP consortium centered at Washington University, the University of Minnesota, and University of Oxford. In just over 6 years, the WU-Minn-Ox consortium succeeded in its core objectives by: 1) improving MR scanner hardware, pulse sequence design, and image reconstruction methods, 2) acquiring and analyzing multimodal MRI and MEG data of unprecedented quality together with behavioral measures from more than 1100 HCP participants, and 3) freely sharing the data (via the ConnectomeDB database) and associated analysis and visualization tools. To date, more than 27 Petabytes of data have been shared, and 1538 papers acknowledging HCP data use have been published. The "HCP-style" neuroimaging paradigm has emerged as a set of best-practice strategies for optimizing data acquisition and analysis. This article reviews the history of the HCP, including comments on key events and decisions associated with major project components. We discuss several scientific advances using HCP data, including improved cortical parcellations, analyses of connectivity based on functional and diffusion MRI, and analyses of brain-behavior relationships. We also touch upon our efforts to develop and share a variety of associated data processing and analysis tools along with detailed documentation, tutorials, and an educational course to train the next generation of neuroimagers. We conclude with a look forward at opportunities and challenges facing the human neuroimaging field from the perspective of the HCP consortium.
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Affiliation(s)
| | | | - Michael P Harms
- Washington University School of Medicine, St. Louis, MO, USA
| | - Stamatios N Sotiropoulos
- Sir Peter Mansfield Imaging Centre & NIHR Nottingham Biomedical Research Centre, Queen's Medical Centre, School of Medicine, University of Nottingham, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK
| | | | | | | | - Robert Oostenveld
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, the Netherlands
| | | | - Jan-Mathijs Schoffelen
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, the Netherlands
| | - Michael R Hodge
- Washington University School of Medicine, St. Louis, MO, USA
| | - Eileen A Cler
- Washington University School of Medicine, St. Louis, MO, USA
| | - Daniel M Marcus
- Washington University School of Medicine, St. Louis, MO, USA
| | - Deanna M Barch
- Washington University School of Medicine, St. Louis, MO, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK
| | - Kamil Ugurbil
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
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23
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Klink PC, Chen X, Vanduffel V, Roelfsema P. Population receptive fields in non-human primates from whole-brain fMRI and large-scale neurophysiology in visual cortex. eLife 2021; 10:67304. [PMID: 34730515 PMCID: PMC8641953 DOI: 10.7554/elife.67304] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 10/24/2021] [Indexed: 01/07/2023] Open
Abstract
Population receptive field (pRF) modeling is a popular fMRI method to map the retinotopic organization of the human brain. While fMRI-based pRF maps are qualitatively similar to invasively recorded single-cell receptive fields in animals, it remains unclear what neuronal signal they represent. We addressed this question in awake nonhuman primates comparing whole-brain fMRI and large-scale neurophysiological recordings in areas V1 and V4 of the visual cortex. We examined the fits of several pRF models based on the fMRI blood-oxygen-level-dependent (BOLD) signal, multi-unit spiking activity (MUA), and local field potential (LFP) power in different frequency bands. We found that pRFs derived from BOLD-fMRI were most similar to MUA-pRFs in V1 and V4, while pRFs based on LFP gamma power also gave a good approximation. fMRI-based pRFs thus reliably reflect neuronal receptive field properties in the primate brain. In addition to our results in V1 and V4, the whole-brain fMRI measurements revealed retinotopic tuning in many other cortical and subcortical areas with a consistent increase in pRF size with increasing eccentricity, as well as a retinotopically specific deactivation of default mode network nodes similar to previous observations in humans.
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Affiliation(s)
| | - Xing Chen
- Vision and Cognition, Netherlands Institute for Neuroscience, Amsterdam, Netherlands
| | | | - Pieter Roelfsema
- Vision and Cognition, Netherlands Institute for Neuroscience, Amsterdam, Netherlands
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24
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Bryant KL, Ardesch DJ, Roumazeilles L, Scholtens LH, Khrapitchev AA, Tendler BC, Wu W, Miller KL, Sallet J, van den Heuvel MP, Mars RB. Diffusion MRI data, sulcal anatomy, and tractography for eight species from the Primate Brain Bank. Brain Struct Funct 2021; 226:2497-2509. [PMID: 34264391 PMCID: PMC8608778 DOI: 10.1007/s00429-021-02268-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 03/26/2021] [Indexed: 12/16/2022]
Abstract
Large-scale comparative neuroscience requires data from many species and, ideally, at multiple levels of description. Here, we contribute to this endeavor by presenting diffusion and structural MRI data from eight primate species that have not or rarely been described in the literature. The selected samples from the Primate Brain Bank cover a prosimian, New and Old World monkeys, and a great ape. We present preliminary labelling of the cortical sulci and tractography of the optic radiation, dorsal part of the cingulum bundle, and dorsal parietal-frontal and ventral temporal-frontal longitudinal white matter tracts. Both dorsal and ventral association fiber systems could be observed in all samples, with the dorsal tracts occupying much less relative volume in the prosimian than in other species. We discuss the results in the context of known primate specializations and present hypotheses for further research. All data and results presented here are available online as a resource for the scientific community.
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Affiliation(s)
- Katherine L Bryant
- Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Headington, Oxford, OX9 3DU, UK
| | - Dirk Jan Ardesch
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Lea Roumazeilles
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Lianne H Scholtens
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Alexandre A Khrapitchev
- Department of Oncology, University of Oxford, Cancer Research UK and Medical Research Council Oxford Institute for Radiation Oncology, Oxford, UK
| | - Benjamin C Tendler
- Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Headington, Oxford, OX9 3DU, UK
| | - Wenchuan Wu
- Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Headington, Oxford, OX9 3DU, UK
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Headington, Oxford, OX9 3DU, UK
| | - Jerome Sallet
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, 69500, Bron, France
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Headington, Oxford, OX9 3DU, UK.
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands.
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25
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Scott JT, Bourne JA. Modelling behaviors relevant to brain disorders in the nonhuman primate: Are we there yet? Prog Neurobiol 2021; 208:102183. [PMID: 34728308 DOI: 10.1016/j.pneurobio.2021.102183] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 10/27/2021] [Accepted: 10/27/2021] [Indexed: 12/30/2022]
Abstract
Recent years have seen a profound resurgence of activity with nonhuman primates (NHPs) to model human brain disorders. From marmosets to macaques, the study of NHP species offers a unique window into the function of primate-specific neural circuits that are impossible to examine in other models. Examining how these circuits manifest into the complex behaviors of primates, such as advanced cognitive and social functions, has provided enormous insights to date into the mechanisms underlying symptoms of numerous neurological and neuropsychiatric illnesses. With the recent optimization of modern techniques to manipulate and measure neural activity in vivo, such as optogenetics and calcium imaging, NHP research is more well-equipped than ever to probe the neural mechanisms underlying pathological behavior. However, methods for behavioral experimentation and analysis in NHPs have noticeably failed to keep pace with these advances. As behavior ultimately lies at the junction between preclinical findings and its translation to clinical outcomes for brain disorders, approaches to improve the integrity, reproducibility, and translatability of behavioral experiments in NHPs requires critical evaluation. In this review, we provide a unifying account of existing brain disorder models using NHPs, and provide insights into the present and emerging contributions of behavioral studies to the field.
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Affiliation(s)
- Jack T Scott
- Australian Regenerative Medicine Institute, Monash University, Clayton, VIC, Australia
| | - James A Bourne
- Australian Regenerative Medicine Institute, Monash University, Clayton, VIC, Australia.
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26
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Russ BE, Petkov CI, Kwok SC, Zhu Q, Belin P, Vanduffel W, Hamed SB. Common functional localizers to enhance NHP & cross-species neuroscience imaging research. Neuroimage 2021; 237:118203. [PMID: 34048898 PMCID: PMC8529529 DOI: 10.1016/j.neuroimage.2021.118203] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 05/15/2021] [Accepted: 05/24/2021] [Indexed: 11/25/2022] Open
Abstract
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.
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Affiliation(s)
- Brian E Russ
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, United States; Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York City, NY, United States; Department of Psychiatry, New York University at Langone, New York City, NY, United States.
| | - Christopher I Petkov
- Biosciences Institute, Newcastle University Medical School, Newcastle upon Tyne, United Kingdom
| | - Sze Chai Kwok
- Shanghai Key Laboratory of Brain Functional Genomics, Key Laboratory of Brain Functional Genomics Ministry of Education, Shanghai Key Laboratory of Magnetic Resonance, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China; Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan, Jiangsu, China; NYU-ECNU Institute of Brain and Cognitive Science at NYU Shanghai, Shanghai, China
| | - Qi Zhu
- Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin Center, 91191 Gif/Yvette, France; Laboratory for Neuro-and Psychophysiology, Department of Neurosciences, KU Leuven Medical School, Leuven, 3000, Belgium
| | - Pascal Belin
- Institut de Neurosciences de La Timone, Aix-Marseille Université et CNRS, Marseille, 13005, France
| | - Wim Vanduffel
- Laboratory for Neuro-and Psychophysiology, Department of Neurosciences, KU Leuven Medical School, Leuven, 3000, Belgium; Leuven Brain Institute, KU Leuven, Leuven, 3000, Belgium; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, United States; Department of Radiology, Harvard Medical School, Boston, MA 02144, United States.
| | - Suliann Ben Hamed
- Institut des Sciences Cognitives Marc Jeannerod, UMR 5229, Université de Lyon - CNRS, France.
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27
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Wang X, Li XH, Cho JW, Russ BE, Rajamani N, Omelchenko A, Ai L, Korchmaros A, Sawiak S, Benn RA, Garcia-Saldivar P, Wang Z, Kalin NH, Schroeder CE, Craddock RC, Fox AS, Evans AC, Messinger A, Milham MP, Xu T. U-net model for brain extraction: Trained on humans for transfer to non-human primates. Neuroimage 2021; 235:118001. [PMID: 33789137 PMCID: PMC8529630 DOI: 10.1016/j.neuroimage.2021.118001] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 03/22/2021] [Accepted: 03/25/2021] [Indexed: 01/21/2023] Open
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.
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Affiliation(s)
- Xindi Wang
- Montreal Neurological Institute, McGill University, Montreal, Québec, Canada.
| | - Xin-Hui Li
- The Child Mind Institute, 101 East 56th Street, New York, NY 10022, USA
| | - Jae Wook Cho
- The Child Mind Institute, 101 East 56th Street, New York, NY 10022, USA
| | - Brian E Russ
- Nathan Kline Institute, 140 Old Orangeburg Rd, Orangeburg, NY, USA; Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York City, NY, USA; Department of Psychiatry, New York University School of Medicine, New York City, NY, USA
| | - Nanditha Rajamani
- The Child Mind Institute, 101 East 56th Street, New York, NY 10022, USA
| | - Alisa Omelchenko
- The Child Mind Institute, 101 East 56th Street, New York, NY 10022, USA
| | - Lei Ai
- The Child Mind Institute, 101 East 56th Street, New York, NY 10022, USA
| | | | - Stephen Sawiak
- Translational Neuroimaging Laboratory, Department of Physiology, Development and Neuroscience University of Cambridge, Cambridge CB2 3EG, UK
| | - R Austin Benn
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
| | - Pamela Garcia-Saldivar
- Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, México
| | - Zheng Wang
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Science, Shanghai, China; University of Chinese Academy of Science, China
| | - Ned H Kalin
- Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, 6001 Research Park Blvd, Madison, WI 53719, USA
| | - Charles E Schroeder
- Nathan Kline Institute, 140 Old Orangeburg Rd, Orangeburg, NY, USA; Departments of Psychiatry and Neurology, Columbia University College of Physicians and Surgeons, New York, NY 10032, USA
| | - R Cameron Craddock
- Department of Diagnostic Medicine, The University of Texas at Austin Dell Medical School, USA
| | - Andrew S Fox
- Department of Psychology, and the California National Primate Research Center, University of California, Davis, One Shields Ave., Davis, CA 95616, USA
| | - Alan C Evans
- Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Adam Messinger
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, USA
| | - Michael P Milham
- The Child Mind Institute, 101 East 56th Street, New York, NY 10022, USA; Nathan Kline Institute, 140 Old Orangeburg Rd, Orangeburg, NY, USA
| | - Ting Xu
- The Child Mind Institute, 101 East 56th Street, New York, NY 10022, USA.
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28
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Klink PC, Aubry JF, Ferrera VP, Fox AS, Froudist-Walsh S, Jarraya B, Konofagou EE, Krauzlis RJ, Messinger A, Mitchell AS, Ortiz-Rios M, Oya H, Roberts AC, Roe AW, Rushworth MFS, Sallet J, Schmid MC, Schroeder CE, Tasserie J, Tsao DY, Uhrig L, Vanduffel W, Wilke M, Kagan I, Petkov CI. Combining brain perturbation and neuroimaging in non-human primates. Neuroimage 2021; 235:118017. [PMID: 33794355 PMCID: PMC11178240 DOI: 10.1016/j.neuroimage.2021.118017] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 03/07/2021] [Accepted: 03/22/2021] [Indexed: 12/11/2022] Open
Abstract
Brain perturbation studies allow detailed causal inferences of behavioral and neural processes. Because the combination of brain perturbation methods and neural measurement techniques is inherently challenging, research in humans has predominantly focused on non-invasive, indirect brain perturbations, or neurological lesion studies. Non-human primates have been indispensable as a neurobiological system that is highly similar to humans while simultaneously being more experimentally tractable, allowing visualization of the functional and structural impact of systematic brain perturbation. This review considers the state of the art in non-human primate brain perturbation with a focus on approaches that can be combined with neuroimaging. We consider both non-reversible (lesions) and reversible or temporary perturbations such as electrical, pharmacological, optical, optogenetic, chemogenetic, pathway-selective, and ultrasound based interference methods. Method-specific considerations from the research and development community are offered to facilitate research in this field and support further innovations. We conclude by identifying novel avenues for further research and innovation and by highlighting the clinical translational potential of the methods.
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Affiliation(s)
- P Christiaan Klink
- Department of Vision & Cognition, Netherlands Institute for Neuroscience, Meibergdreef 47, 1105 BA Amsterdam, the Netherlands.
| | - Jean-François Aubry
- Physics for Medicine Paris, Inserm U1273, CNRS UMR 8063, ESPCI Paris, PSL University, Paris, France
| | - Vincent P Ferrera
- Department of Neuroscience & Department of Psychiatry, Columbia University Medical Center, New York, NY, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Andrew S Fox
- Department of Psychology & California National Primate Research Center, University of California, Davis, CA, USA
| | | | - Béchir Jarraya
- NeuroSpin, Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA), Institut National de la Santé et de la Recherche Médicale (INSERM), Cognitive Neuroimaging Unit, Université Paris-Saclay, France; Foch Hospital, UVSQ, Suresnes, France
| | - Elisa E Konofagou
- Ultrasound and Elasticity Imaging Laboratory, Department of Biomedical Engineering, Columbia University, New York, NY, USA; Department of Radiology, Columbia University, New York, NY, USA
| | - Richard J Krauzlis
- Laboratory of Sensorimotor Research, National Eye Institute, Bethesda, MD, USA
| | - Adam Messinger
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA
| | - Anna S Mitchell
- Department of Experimental Psychology, Oxford University, Oxford, United Kingdom
| | - Michael Ortiz-Rios
- Newcastle University Medical School, Newcastle upon Tyne NE1 7RU, United Kingdom; German Primate Center, Leibniz Institute for Primate Research, Kellnerweg 4, 37077 Göttingen, Germany
| | - Hiroyuki Oya
- Iowa Neuroscience Institute, Carver College of Medicine, University of Iowa, Iowa City, IA, USA; Department of Neurosurgery, University of Iowa, Iowa city, IA, USA
| | - Angela C Roberts
- Department of Physiology, Development and Neuroscience, Cambridge University, Cambridge, United Kingdom
| | - Anna Wang Roe
- Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou 310029, China
| | | | - Jérôme Sallet
- Department of Experimental Psychology, Oxford University, Oxford, United Kingdom; Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute, U1208 Bron, France; Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Michael Christoph Schmid
- Newcastle University Medical School, Newcastle upon Tyne NE1 7RU, United Kingdom; Faculty of Science and Medicine, University of Fribourg, Chemin du Musée 5, CH-1700 Fribourg, Switzerland
| | - Charles E Schroeder
- Nathan Kline Institute, Orangeburg, NY, USA; Columbia University, New York, NY, USA
| | - Jordy Tasserie
- NeuroSpin, Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA), Institut National de la Santé et de la Recherche Médicale (INSERM), Cognitive Neuroimaging Unit, Université Paris-Saclay, France
| | - Doris Y Tsao
- Division of Biology and Biological Engineering, Tianqiao and Chrissy Chen Institute for Neuroscience; Howard Hughes Medical Institute; Computation and Neural Systems, Caltech, Pasadena, CA, USA
| | - Lynn Uhrig
- NeuroSpin, Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA), Institut National de la Santé et de la Recherche Médicale (INSERM), Cognitive Neuroimaging Unit, Université Paris-Saclay, France
| | - Wim Vanduffel
- Laboratory for Neuro- and Psychophysiology, Neurosciences Department, KU Leuven Medical School, Leuven, Belgium; Leuven Brain Institute, KU Leuven, Leuven Belgium; Harvard Medical School, Boston, MA, USA; Massachusetts General Hospital, Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Melanie Wilke
- German Primate Center, Leibniz Institute for Primate Research, Kellnerweg 4, 37077 Göttingen, Germany; Department of Cognitive Neurology, University Medicine Göttingen, Göttingen, Germany
| | - Igor Kagan
- German Primate Center, Leibniz Institute for Primate Research, Kellnerweg 4, 37077 Göttingen, Germany.
| | - Christopher I Petkov
- Newcastle University Medical School, Newcastle upon Tyne NE1 7RU, United Kingdom.
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29
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Dojat M, Bjaalie JG, Barbier EL. Editorial: APPNING: Animal Population Imaging. Front Neuroinform 2021; 15:676603. [PMID: 34054451 PMCID: PMC8158807 DOI: 10.3389/fninf.2021.676603] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 04/09/2021] [Indexed: 11/13/2022] Open
Affiliation(s)
- Michel Dojat
- University Grenoble Alpes, Inserm U1216, Grenoble Institut Neurosciences, Grenoble, France
| | - Jan G Bjaalie
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Emmanuel L Barbier
- University Grenoble Alpes, Inserm U1216, Grenoble Institut Neurosciences, Grenoble, France
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30
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Hartig R, Glen D, Jung B, Logothetis NK, Paxinos G, Garza-Villarreal EA, Messinger A, Evrard HC. The Subcortical Atlas of the Rhesus Macaque (SARM) for neuroimaging. Neuroimage 2021; 235:117996. [PMID: 33794360 DOI: 10.1016/j.neuroimage.2021.117996] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 03/15/2021] [Accepted: 03/19/2021] [Indexed: 12/20/2022] Open
Abstract
Digitized neuroanatomical atlases that can be overlaid onto functional data are crucial for localizing brain structures and analyzing functional networks identified by neuroimaging techniques. To aid in functional and structural data analysis, we have created a comprehensive parcellation of the rhesus macaque subcortex using a high-resolution ex vivo structural imaging scan. This anatomical 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) with 210 primary regions-of-interest (ROIs). The subcortical parcellation and nomenclature reflect those of the 4th edition of the Rhesus Monkey Brain in Stereotaxic Coordinates (Paxinos et al., in preparation), rather than proposing yet another novel atlas. The primary ROIs are organized across six spatial hierarchical scales from small, fine-grained ROIs to broader composites of multiple ROIs, making the SARM suitable for analysis at different resolutions and allowing broader labeling of functional signals when more accurate localization is not possible. As an example application of this atlas, we have included a functional localizer for the dorsal lateral geniculate (DLG) nucleus in three macaques using a visual flickering checkerboard stimulus, identifying and quantifying 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 MRI data processing software, such as AFNI, where the atlas has been embedded into the software alongside cortical macaque atlases.
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Affiliation(s)
- Renée Hartig
- Centre for Integrative Neurosciences, University of Tübingen, Tübingen, Germany; Max Planck Institute for Biological Cybernetics, Tübingen, Germany; Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Daniel Glen
- Scientific and Statistical Computing Core, National Institute of Mental Health, Bethesda, USA
| | - Benjamin Jung
- Department of Neuroscience, Brown University, Providence, RI, USA; Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, USA
| | - Nikos K Logothetis
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany; University of Manchester, Manchester, United Kingdom; International Center for Primate Brain Research, Songjiang, Shanghai, PR China
| | - George Paxinos
- Neuroscience Research Australia and The University of New South Wales, Sydney, NSW 2031, Australia
| | - Eduardo A Garza-Villarreal
- Instituto de Neurobiologia, Universidad Nacional Autónoma de México campus Juriquilla, Queretaro, Mexico.
| | - Adam Messinger
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, USA.
| | - Henry C Evrard
- Centre for Integrative Neurosciences, University of Tübingen, Tübingen, Germany; Max Planck Institute for Biological Cybernetics, Tübingen, Germany; Nathan S. Kline Institute for Psychiatric Research, Center for Biomedical Imaging and Neuromodulation, Orangeburg, NY, USA; International Center for Primate Brain Research, Songjiang, Shanghai, PR China.
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31
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Balezeau F, Nacef J, Kikuchi Y, Schneider F, Rocchi F, Muers RS, Fernandez-Palacios O'Connor R, Blau C, Wilson B, Saunders RC, Howard M, Thiele A, Griffiths TD, Petkov CI, Murphy K. MRI monitoring of macaque monkeys in neuroscience: Case studies, resource and normative data comparisons. Neuroimage 2021; 230:117778. [PMID: 33497775 PMCID: PMC8063182 DOI: 10.1016/j.neuroimage.2021.117778] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 12/17/2020] [Accepted: 01/13/2021] [Indexed: 12/14/2022] Open
Abstract
Information from Magnetic Resonance Imaging (MRI) is useful for diagnosis and treatment management of human neurological patients. MRI monitoring might also prove useful for non-human animals involved in neuroscience research provided that MRI is available and feasible and that there are no MRI contra-indications precluding scanning. However, MRI monitoring is not established in macaques and a resource is urgently needed that could grow with scientific community contributions. Here we show the utility and potential benefits of MRI-based monitoring in a few diverse cases with macaque monkeys. We also establish a PRIMatE MRI Monitoring (PRIME-MRM) resource within the PRIMatE Data Exchange (PRIME-DE) and quantitatively compare the cases to normative information drawn from MRI data from typical macaques in PRIME-DE. In the cases, the monkeys presented with no or mild/moderate clinical signs, were well otherwise and MRI scanning did not present a significant increase in welfare impact. Therefore, they were identified as suitable candidates for clinical investigation, MRI-based monitoring and treatment. For each case, we show MRI quantification of internal controls in relation to treatment steps and comparisons with normative data in typical monkeys drawn from PRIME-DE. We found that MRI assists in precise and early diagnosis of cerebral events and can be useful for visualising, treating and quantifying treatment response. The scientific community could now grow the PRIME-MRM resource with other cases and larger samples to further assess and increase the evidence base on the benefits of MRI monitoring of primates, complementing the animals’ clinical monitoring and treatment regime.
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Affiliation(s)
- Fabien Balezeau
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Jennifer Nacef
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Yukiko Kikuchi
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Felix Schneider
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Francesca Rocchi
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Ross S Muers
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | | | - Christoph Blau
- Comparative Biology Centre, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Benjamin Wilson
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Richard C Saunders
- Laboratory of Neuropsychology, National Institutes of Health (NIMH), Bethesda, MD, United States
| | - Matthew Howard
- Department of Neurosurgery, University of Iowa, Iowa City, IA, United States
| | - Alexander Thiele
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Timothy D Griffiths
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Christopher I Petkov
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom.
| | - Kathy Murphy
- Comparative Biology Centre, Newcastle University, Newcastle upon Tyne, United Kingdom.
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32
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Garcia-Saldivar P, Garimella A, Garza-Villarreal EA, Mendez FA, Concha L, Merchant H. PREEMACS: Pipeline for preprocessing and extraction of the macaque brain surface. Neuroimage 2020; 227:117671. [PMID: 33359348 DOI: 10.1016/j.neuroimage.2020.117671] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 12/04/2020] [Accepted: 12/16/2020] [Indexed: 01/18/2023] Open
Abstract
Accurate extraction of the cortical brain surface is critical for cortical thickness estimation and a key element to perform multimodal imaging analysis, where different metrics are integrated and compared in a common space. While brain surface extraction has become widespread practice in human studies, several challenges unique to neuroimaging of non-human primates (NHP) have hindered its adoption for the study of macaques. Although, some of these difficulties can be addressed at the acquisition stage, several common artifacts can be minimized through image preprocessing. Likewise, there are several image analysis pipelines for human MRIs, but very few automated methods for extraction of cortical surfaces have been reported for NHPs and none have been tested on data from diverse sources. We present PREEMACS, a pipeline that standardizes the preprocessing of structural MRI images (T1- and T2-weighted) and carries out an automatic surface extraction of the macaque brain. Building upon and extending pre-existing tools, the first module performs volume orientation, image cropping, intensity non-uniformity correction, and volume averaging, before skull-stripping through a convolutional neural network. The second module performs quality control using an adaptation of MRIqc method to extract objective quality metrics that are then used to determine the likelihood of accurate brain surface estimation. The third and final module estimates the white matter (wm) and pial surfaces from the T1-weighted volume (T1w) using an NHP customized version of FreeSurfer aided by the T2-weighted volumes (T2w). To evaluate the generalizability of PREEMACS, we tested the pipeline using 57 T1w/T2w NHP volumes acquired at 11 different sites from the PRIME-DE public dataset. Results showed an accurate and robust automatic brain surface extraction from images that passed the quality control segment of our pipeline. This work offers a robust, efficient and generalizable pipeline for the automatic standardization of MRI surface analysis on NHP.
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Affiliation(s)
- Pamela Garcia-Saldivar
- Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla. Blvd. Juriquilla, 3001 Querétaro, Querétaro, México
| | - Arun Garimella
- Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla. Blvd. Juriquilla, 3001 Querétaro, Querétaro, México; International Institute of Information Technology, Hyderabad, India
| | - Eduardo A Garza-Villarreal
- Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla. Blvd. Juriquilla, 3001 Querétaro, Querétaro, México
| | - Felipe A Mendez
- Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla. Blvd. Juriquilla, 3001 Querétaro, Querétaro, México
| | - Luis Concha
- Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla. Blvd. Juriquilla, 3001 Querétaro, Querétaro, México.
| | - Hugo Merchant
- Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla. Blvd. Juriquilla, 3001 Querétaro, Querétaro, México.
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