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Yassin W, de Moura FB, Withey SL, Cao L, Kangas BD, Bergman J, Kohut SJ. Resting state networks of awake adolescent and adult squirrel monkeys using ultra-high field (9.4T) functional magnetic resonance imaging. eNeuro 2024; 11:ENEURO.0173-23.2024. [PMID: 38627065 DOI: 10.1523/eneuro.0173-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 02/07/2024] [Accepted: 02/07/2024] [Indexed: 04/30/2024] Open
Abstract
Resting state networks (RSNs) are increasingly forwarded as candidate biomarkers for neuropsychiatric disorders. Such biomarkers may provide objective measures for evaluating novel therapeutic interventions in nonhuman primates often used in translational neuroimaging research. This study aimed to characterize the RSNs of awake squirrel monkeys and compare the characteristics of those networks in adolescent and adult subjects. Twenty-seven squirrel monkeys (n=12 adolescents [6 male/6 female] ∼2.5 years and n=15 adults [7 male/8 female] ∼9.5 years) were gradually acclimated to awake scanning procedures; whole-brain fMRI images were acquired with a 9.4 Tesla scanner. Group level independent component (ICA) analysis (30 ICs) with dual regression was used to detect and compare RSNs. Twenty ICs corresponding to physiologically meaningful networks representing a range of neural functions, including motor, sensory, reward, and cognitive processes were identified in both adolescent and adult monkeys. The reproducibility of these RSNs was evaluated across several ICA model orders. Adults showed a trend for greater connectivity compared to adolescent subjects in two of the networks of interest: (1) in the right occipital region with the OFC network and (2) in the left temporal cortex, bilateral occipital cortex, and cerebellum with the posterior cingulate network. However, when age was entered into the above model, this trend for significance was lost. These results demonstrate that squirrel monkey RSNs are stable and consistent with RSNs previously identified in humans, rodents, and other nonhuman primate species. These data also identify several networks in adolescence that are conserved and others that may change into adulthood.Significance Statement Functional magnetic resonance imaging procedures have revealed important information about how the brain is modified by experimental manipulations, disease states, and aging throughout the lifespan. Preclinical neuroimaging, especially in nonhuman primates, has become a frequently used means to answer targeted questions related to brain resting-state functional connectivity. The present study characterized resting state networks (RSNs) in adult and adolescent squirrel monkeys; twenty RSNs corresponding to networks representing a range of neural functions were identified. The RSNs identified here can be utilized in future studies examining the effects of experimental manipulations on brain connectivity in squirrel monkeys. These data also may be useful for comparative analysis with other primate species to provide an evolutionary perspective for understanding brain function and organization.
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Affiliation(s)
- Walin Yassin
- Behavioral Neuroimaging Laboratory, McLean Hospital, Belmont, MA 02478
- Behavioral Biology Program, McLean Hospital, Belmont, MA 02478
- Department of Psychiatry, Harvard Medical School, Boston, MA 02478
| | - Fernando B de Moura
- Behavioral Neuroimaging Laboratory, McLean Hospital, Belmont, MA 02478
- Behavioral Biology Program, McLean Hospital, Belmont, MA 02478
- McLean Imaging Center, McLean Hospital, Belmont, MA 02478
- Department of Psychiatry, Harvard Medical School, Boston, MA 02478
| | - Sarah L Withey
- Behavioral Biology Program, McLean Hospital, Belmont, MA 02478
- Department of Psychiatry, Harvard Medical School, Boston, MA 02478
| | - Lei Cao
- Behavioral Neuroimaging Laboratory, McLean Hospital, Belmont, MA 02478
- Behavioral Biology Program, McLean Hospital, Belmont, MA 02478
- McLean Imaging Center, McLean Hospital, Belmont, MA 02478
| | - Brian D Kangas
- Behavioral Biology Program, McLean Hospital, Belmont, MA 02478
- Department of Psychiatry, Harvard Medical School, Boston, MA 02478
| | - Jack Bergman
- Behavioral Biology Program, McLean Hospital, Belmont, MA 02478
- Department of Psychiatry, Harvard Medical School, Boston, MA 02478
| | - Stephen J Kohut
- Behavioral Neuroimaging Laboratory, McLean Hospital, Belmont, MA 02478
- Behavioral Biology Program, McLean Hospital, Belmont, MA 02478
- McLean Imaging Center, McLean Hospital, Belmont, MA 02478
- Department of Psychiatry, Harvard Medical School, Boston, MA 02478
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2
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Sengupta A, Wang F, Mishra A, Reed JL, Chen LM, Gore JC. Detection and characterization of resting state functional networks in squirrel monkey brain. Cereb Cortex Commun 2023; 4:tgad018. [PMID: 37753115 PMCID: PMC10518810 DOI: 10.1093/texcom/tgad018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 08/22/2023] [Accepted: 08/23/2023] [Indexed: 09/28/2023] Open
Abstract
Resting-state fMRI based on analyzing BOLD signals is widely used to derive functional networks in the brain and how they alter during disease or injury conditions. Resting-state networks can also be used to study brain functional connectomes across species, which provides insights into brain evolution. The squirrel monkey (SM) is a non-human primate (NHP) that is widely used as a preclinical model for experimental manipulations to understand the organization and functioning of the brain. We derived resting-state networks from the whole brain of anesthetized SMs using Independent Component Analysis of BOLD acquisitions. We detected 15 anatomically constrained resting-state networks localized in the cortical and subcortical regions as well as in the white-matter. Networks encompassing visual, somatosensory, executive control, sensorimotor, salience and default mode regions, and subcortical networks including the Hippocampus-Amygdala, thalamus, basal-ganglia and brainstem region correspond well with previously detected networks in humans and NHPs. The connectivity pattern between the networks also agrees well with previously reported seed-based resting-state connectivity of SM brain. This study demonstrates that SMs share remarkable homologous network organization with humans and other NHPs, thereby providing strong support for their suitability as a translational animal model for research and additional insight into brain evolution across species.
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Affiliation(s)
- Anirban Sengupta
- Vanderbilt University Institute of Imaging Science, Nashville, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
| | - Feng Wang
- Vanderbilt University Institute of Imaging Science, Nashville, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
| | - Arabinda Mishra
- Vanderbilt University Institute of Imaging Science, Nashville, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
| | - Jamie L Reed
- Vanderbilt University Institute of Imaging Science, Nashville, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Department of Psychology, Vanderbilt University, Nashville, TN, United States of America
| | - Li Min Chen
- Vanderbilt University Institute of Imaging Science, Nashville, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Biomedical Engineering, Vanderbilt University, Nashville, TN, United States of America
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Nashville, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Biomedical Engineering, Vanderbilt University, Nashville, TN, United States of America
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, United States of America
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3
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Cao L, Kohut SJ, Frederick BD. Estimating and mitigating the effects of systemic low frequency oscillations (sLFO) on resting state networks in awake non-human primates using time lag dependent methodology. FRONTIERS IN NEUROIMAGING 2023; 1:1031991. [PMID: 37555145 PMCID: PMC10406257 DOI: 10.3389/fnimg.2022.1031991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 12/15/2022] [Indexed: 08/10/2023]
Abstract
AIM Resting-state fMRI (rs-fMRI) is often used to infer regional brain interactions from the degree of temporal correlation between spontaneous low-frequency fluctuations, thought to reflect local changes in the BOLD signal due to neuronal activity. One complication in the analysis and interpretation of rs-fMRI data is the existence of non-neuronal low frequency physiological noise (systemic low frequency oscillations; sLFOs) which occurs within the same low frequency band as the signal used to compute functional connectivity. Here, we demonstrate the use of a time lag mapping technique to estimate and mitigate the effects of the sLFO signal on resting state functional connectivity of awake squirrel monkeys. METHODS Twelve squirrel monkeys (6 male/6 female) were acclimated to awake scanning procedures; whole-brain fMRI images were acquired with a 9.4 Tesla scanner. Rs-fMRI data was preprocessed using an in-house pipeline and sLFOs were detected using a seed regressor generated by averaging BOLD signal across all voxels in the brain, which was then refined recursively within a time window of -16-12 s. The refined regressor was then used to estimate the voxel-wise sLFOs; these regressors were subsequently included in the general linear model to remove these moving hemodynamic components from the rs-fMRI data using general linear model filtering. Group level independent component analysis (ICA) with dual regression was used to detect resting-state networks and compare networks before and after sLFO denoising. RESULTS Results show sLFOs constitute ~64% of the low frequency fMRI signal in squirrel monkey gray matter; they arrive earlier in regions in proximity to the middle cerebral arteries (e.g., somatosensory cortex) and later in regions close to draining vessels (e.g., cerebellum). Dual regression results showed that the physiological noise was significantly reduced after removing sLFOs and the extent of reduction was determined by the brain region contained in the resting-state network. CONCLUSION These results highlight the need to estimate and remove sLFOs from fMRI data before further analysis.
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Affiliation(s)
- Lei Cao
- Behavioral Neuroimaging Laboratory, McLean Hospital, Belmont, MA, United States
- McLean Imaging Center, McLean Hospital, Belmont, MA, United States
| | - Stephen J. Kohut
- Behavioral Neuroimaging Laboratory, McLean Hospital, Belmont, MA, United States
- McLean Imaging Center, McLean Hospital, Belmont, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Blaise deB. Frederick
- McLean Imaging Center, McLean Hospital, Belmont, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
- Opto-Magnetic Group, McLean Hospital, Belmont, MA, United States
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4
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Yassin W, de Moura FB, Withey SL, Cao L, Kangas BD, Bergman J, Kohut SJ. Resting state networks of awake adolescent and adult squirrel monkeys using ultra-high field (9.4T) functional magnetic resonance imaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.08.523000. [PMID: 36711620 PMCID: PMC9881954 DOI: 10.1101/2023.01.08.523000] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Resting state networks (RSNs) are increasingly forwarded as candidate biomarkers for neuropsychiatric disorders. Such biomarkers may provide objective measures for evaluating novel therapeutic interventions in nonhuman primates often used in translational neuroimaging research. This study aimed to characterize the RSNs of awake squirrel monkeys and compare the characteristics of those networks in adolescent and adult subjects. Twenty-seven squirrel monkeys ( n =12 adolescents [6 male/6 female] ∼2.5 years and n =15 adults [7 male/8 female] ∼9.5 years) were gradually acclimated to awake scanning procedures; whole-brain fMRI images were acquired with a 9.4 Tesla scanner. Group level independent component (IC) analysis (30 ICs) with dual regression was used to detect and compare RSNs. Twenty ICs corresponding to physiologically meaningful networks representing a range of neural functions, including motor, sensory, reward (e.g., basal ganglia), and cognitive processes were identified in both adolescent and adult monkeys. Significant age-related differences between the adult and adolescent subjects (adult > adolescent) were found in two networks of interest: (1) the right upper occipital region with an OFC IC and (2) the left temporal cortex, bilateral visual areas, and cerebellum with the cingulate IC. These results demonstrate that squirrel monkey RSNs are stable and consistent with RSNs previously identified in humans, rodents, and other nonhuman primate species. These data also identify several networks in adolescence that are conserved and others that may change into adulthood. Significance Statement Functional magnetic resonance imaging procedures have revealed important information about how the brain is modified by experimental manipulations, disease states, and aging throughout the lifespan. Preclinical neuroimaging, especially in nonhuman primates, has become a frequently used means to answer targeted questions related to brain resting-state functional connectivity. The present study characterized resting state networks (RSNs) in adult and adolescent squirrel monkeys; twenty RSNs corresponding to networks representing a range of neural functions were identified. The RSNs identified here can be utilized in future studies examining the effects of experimental manipulations on brain connectivity in squirrel monkeys. These data also may be useful for comparative analysis with other primate species to provide an evolutionary perspective for understanding brain function and organization.
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5
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Yang Y, Zhang Q, Ren J, Zhu Q, Wang L, Geng Z. In vivo symmetric multi-contrast MRI brain templates and atlas for spontaneously hypertensive rats. Brain Struct Funct 2022; 227:1789-1801. [PMID: 35318503 DOI: 10.1007/s00429-022-02472-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 02/07/2022] [Indexed: 12/22/2022]
Abstract
Spontaneously hypertensive rats (SHRs) are a valuable animal model of essential hypertension. The increasing use of SHRs in neuroimaging has generated an urgent demand for a template set that provides a reference for advanced data analysis. Structural T2-weighted magnetic resonance imaging (MRI), diffusion tensor imaging (DTI) and functional MRI scans that were used to build the template set were obtained from 8 SHRs longitudinally scanned in vivo at 10, 24 and 52 weeks of age. These symmetric multi-contrast templates were constructed by iterative registration and averaging. The cortical atlas was derived from the Tohoku atlas, and the subcortical regions were manually delineated based on the templates. A set of SHR brain images named the Hebei Medical University rat brain template set (HRT) comprised 3D symmetric T2WI, raw T2-weighted signal with no added diffusion weighting (B0), fractional anisotropy (FA), mean diffusivity (MD) and blood oxygen level-dependent (BOLD) templates; tissue probability maps (TPMs) of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF); and a whole-brain atlas with 163 labels. We quantitatively validated the template and characterized the longitudinal changes in brain morphology in different brain tissues as SHRs aged. To our knowledge, the HRT is the first MRI template set for SHRs. We believe that the HRT can serve as a beneficial tool for precise analysis of the SHR brain using structural and functional MRI, which can promote neuroimaging studies on essential hypertension.
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Affiliation(s)
- Yingying Yang
- Graduate School, Hebei Medical University, Hebei, 050000, China.,Department of Imaging, The First Hospital of Qinhuangdao, Hebei, 066000, China
| | - Quan Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | | | - Qingfeng Zhu
- Department of Medical Imaging, The Second Hospital of Hebei Medical University, No. 215 Heping West Road, Xinhua District, Shijiazhuang City, 050000, Hebei Province, China
| | - Lixin Wang
- Department of Medical Imaging, The Second Hospital of Hebei Medical University, No. 215 Heping West Road, Xinhua District, Shijiazhuang City, 050000, Hebei Province, China
| | - Zuojun Geng
- Department of Medical Imaging, The Second Hospital of Hebei Medical University, No. 215 Heping West Road, Xinhua District, Shijiazhuang City, 050000, Hebei Province, China.
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6
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Roumazeilles L, Lange FJ, Benn RA, Andersson JLR, Bertelsen MF, Manger PR, Flach E, Khrapitchev AA, Bryant KL, Sallet J, Mars RB. Cortical Morphology and White Matter Tractography of Three Phylogenetically Distant Primates: Evidence for a Simian Elaboration. Cereb Cortex 2021; 32:1608-1624. [PMID: 34518890 PMCID: PMC9016287 DOI: 10.1093/cercor/bhab285] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 07/19/2021] [Accepted: 07/20/2021] [Indexed: 11/20/2022] Open
Abstract
Comparative neuroimaging has been used to identify changes in white matter architecture across primate species phylogenetically close to humans, but few have compared the phylogenetically distant species. Here, we acquired postmortem diffusion imaging data from ring-tailed lemurs (Lemur catta), black-capped squirrel monkeys (Saimiri boliviensis), and rhesus macaques (Macaca mulatta). We were able to establish templates and surfaces allowing us to investigate sulcal, cortical, and white matter anatomy. The results demonstrate an expansion of the frontal projections of the superior longitudinal fasciculus complex in squirrel monkeys and rhesus macaques compared to ring-tailed lemurs, which correlates with sulcal anatomy and the lemur’s smaller prefrontal granular cortex. The connectivity of the ventral pathway in the parietal region is also comparatively reduced in ring-tailed lemurs, with the posterior projections of the inferior longitudinal fasciculus not extending toward parietal cortical areas as in the other species. In the squirrel monkeys we note a very specific occipito-parietal anatomy that is apparent in their surface anatomy and the expansion of the posterior projections of the optical radiation. Our study supports the hypothesis that the connectivity of the prefrontal-parietal regions became relatively elaborated in the simian lineage after divergence from the prosimian lineage.
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Affiliation(s)
- Lea Roumazeilles
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford OX13TA, UK
| | - Frederik J Lange
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX39DU, UK
| | - R Austin Benn
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid 28029, Spain
| | - Jesper L R Andersson
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX39DU, UK
| | - Mads F Bertelsen
- Centre for Zoo and Wild Animal Health, Copenhagen Zoo, Frederiksberg 2000, Denmark
| | - Paul R Manger
- School of Anatomical Sciences, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg 2193, South Africa
| | - Edmund Flach
- Wildlife Health Services, Zoological Society of London, London NW14RY, UK (now retired)
| | - Alexandre A Khrapitchev
- MRC Oxford Institute for Radiation Oncology, Department of Oncology, University of Oxford, Oxford OX37DQ, UK
| | - Katherine L Bryant
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX39DU, UK
| | - Jérôme Sallet
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford OX13TA, UK.,Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron 69500, France
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX39DU, UK.,Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen 6525 HR, The Netherlands
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7
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Royo J, Forkel SJ, Pouget P, Thiebaut de Schotten M. The squirrel monkey model in clinical neuroscience. Neurosci Biobehav Rev 2021; 128:152-164. [PMID: 34118293 DOI: 10.1016/j.neubiorev.2021.06.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 04/27/2021] [Accepted: 06/01/2021] [Indexed: 12/11/2022]
Abstract
Clinical neuroscience research relying on animal models brought valuable translational insights into the function and pathologies of the human brain. The anatomical, physiological, and behavioural similarities between humans and mammals have prompted researchers to study cerebral mechanisms at different levels to develop and test new treatments. The vast majority of biomedical research uses rodent models, which are easily manipulable and have a broadly resembling organisation to the human nervous system but cannot satisfactorily mimic some disorders. For these disorders, macaque monkeys have been used as they have a more comparable central nervous system. Still, this research has been hampered by limitations, including high costs and reduced samples. This review argues that a squirrel monkey model might bridge the gap by complementing translational research from rodents, macaque, and humans. With the advent of promising new methods such as ultrasound imaging, tool miniaturisation, and a shift towards open science, the squirrel monkey model represents a window of opportunity that will potentially fuel new translational discoveries in the diagnosis and treatment of brain pathologies.
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Affiliation(s)
- Julie Royo
- Brain Connectivity and Behaviour Laboratory, Sorbonne University, Paris, France; Sorbonne University, Inserm U1127, CNRS UMR7225, UM75, ICM, Movement Investigation and Therapeutics Team, Paris, France.
| | - Stephanie J Forkel
- Brain Connectivity and Behaviour Laboratory, Sorbonne University, Paris, France; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA University of Bordeaux, Bordeaux, France; Department of Neuroimaging, Institute of Psychiatry, Psychology and Neurosciences, King's College London, UK
| | - Pierre Pouget
- Brain Connectivity and Behaviour Laboratory, Sorbonne University, Paris, France; Sorbonne University, Inserm U1127, CNRS UMR7225, UM75, ICM, Movement Investigation and Therapeutics Team, Paris, France
| | - Michel Thiebaut de Schotten
- Brain Connectivity and Behaviour Laboratory, Sorbonne University, Paris, France; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA University of Bordeaux, Bordeaux, France.
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8
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Yuan R, Nechvatal JM, Buckmaster CL, Ayash S, Parker KJ, Schatzberg AF, Lyons DM, Menon V. Long-term effects of intermittent early life stress on primate prefrontal-subcortical functional connectivity. Neuropsychopharmacology 2021; 46:1348-1356. [PMID: 33495547 PMCID: PMC8134590 DOI: 10.1038/s41386-021-00956-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 11/12/2020] [Accepted: 12/29/2020] [Indexed: 01/30/2023]
Abstract
Correlational studies of humans suggest that exposure to early life stress has long-term effects on neural circuits involved in vulnerability and resilience to mental health disorders. Stress-related mental health disorders are more prevalent in women than in men. Here, female squirrel monkeys are randomized to intermittently stressful (IS) social separations or a non-separated (NS) control condition conducted from 17 to 27 weeks of age. Nine years later in mid-life adulthood, resting-state functional magnetic resonance imaging was employed to parcellate prefrontal cortex (PFC). Resulting subdivisions were then used to characterize functional connectivity within PFC, and between PFC subdivisions and subcortical regions that are known to be altered by stress. Extensive hyper-connectivity of medial and orbitofrontal PFC with amygdala, hippocampus, and striatum was observed in IS compared to NS monkeys. Functional hyper-connectivity in IS monkeys was associated with previously reported indications of diminished anxiety-like behavior induced by prepubertal stress. Hyper-connectivity of PFC with amygdala and with hippocampus was also associated with increased ventral striatal dopamine D2 and/or D3 receptor (DRD2/3) availability assessed with positron emission tomography (PET) of [11C]raclopride binding in adulthood. Ventral striatal DRD2/3 availability has been linked to cognitive control, which plays a key role in stress coping as an aspect of emotion regulation. These findings provide causal support for enduring neurobiological effects of early life stress and suggest novel targets for new treatments of stress-related mental health disorders.
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Affiliation(s)
- Rui Yuan
- grid.168010.e0000000419368956Department of Psychiatry and Behavioral Sciences, Stanford, USA
| | - Jordan M. Nechvatal
- grid.168010.e0000000419368956Department of Psychiatry and Behavioral Sciences, Stanford, USA ,grid.240952.80000000087342732Department of Neurology and Neurological Sciences, Stanford, USA
| | - Christine L. Buckmaster
- grid.168010.e0000000419368956Department of Psychiatry and Behavioral Sciences, Stanford, USA
| | - Sarah Ayash
- grid.168010.e0000000419368956Department of Psychiatry and Behavioral Sciences, Stanford, USA ,grid.410607.4Translational Psychiatry, Department of Psychiatry and Psychotherapy, Johannes Gutenberg University Medical Center, Hanns-Dieter-Huesch Weg 19, 55128 Mainz, Germany ,grid.509458.50000 0004 8087 0005Leibniz Institute for Resilience Research, Hanns-Dieter-Huesch Weg 19, 55128 Mainz, Germany
| | - Karen J. Parker
- grid.168010.e0000000419368956Department of Psychiatry and Behavioral Sciences, Stanford, USA ,grid.168010.e0000000419368956Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA 94305 USA
| | - Alan F. Schatzberg
- grid.168010.e0000000419368956Department of Psychiatry and Behavioral Sciences, Stanford, USA ,grid.168010.e0000000419368956Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA 94305 USA
| | - David M. Lyons
- grid.168010.e0000000419368956Department of Psychiatry and Behavioral Sciences, Stanford, USA ,grid.168010.e0000000419368956Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA 94305 USA
| | - Vinod Menon
- Department of Psychiatry and Behavioral Sciences, Stanford, USA. .,Department of Neurology and Neurological Sciences, Stanford, USA. .,Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA, 94305, USA.
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9
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Charvet CJ, Palani A, Kabaria P, Takahashi E. Evolution of Brain Connections: Integrating Diffusion MR Tractography With Gene Expression Highlights Increased Corticocortical Projections in Primates. Cereb Cortex 2020; 29:5150-5165. [PMID: 30927350 DOI: 10.1093/cercor/bhz054] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 02/20/2019] [Indexed: 12/19/2022] Open
Abstract
Diffusion MR tractography permits investigating the 3D structure of cortical pathways as interwoven paths across the entire brain. We use high-resolution scans from diffusion spectrum imaging and high angular resolution diffusion imaging to investigate the evolution of cortical pathways within the euarchontoglire (i.e., primates, rodents) lineage. More specifically, we compare cortical fiber pathways between macaques (Macaca mulatta), marmosets (Callithrix jachus), and rodents (mice, Mus musculus). We integrate these observations with comparative analyses of Neurofilament heavy polypeptide (NEFH) expression across the cortex of mice and primates. We chose these species because their phylogenetic position serves to trace the early evolutionary history of the human brain. Our comparative analysis from diffusion MR tractography, cortical white matter scaling, and NEFH expression demonstrates that the examined primates deviate from mice in possessing increased long-range cross-cortical projections, many of which course across the anterior to posterior axis of the cortex. Our study shows that integrating gene expression data with diffusion MR data is an effective approach in identifying variation in connectivity patterns between species. The expansion of corticocortical pathways and increased anterior to posterior cortical integration can be traced back to an extension of neurogenetic schedules during development in primates.
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Affiliation(s)
| | - Arthi Palani
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02215, USA.,Medical Sciences in the College of Arts and Sciences, Boston University, Boston, MA 02215, USA
| | - Priya Kabaria
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02215, USA.,Department of Behavioral Neuroscience, Northeastern University, Boston, MA 02115, USA
| | - Emi Takahashi
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02215, USA
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10
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Charvet CJ, Das A, Song JW, Tindal-Burgess DJ, Kabaria P, Dai G, Kane T, Takahashi E. High Angular Resolution Diffusion MRI Reveals Conserved and Deviant Programs in the Paths that Guide Human Cortical Circuitry. Cereb Cortex 2020; 30:1447-1464. [PMID: 31667494 PMCID: PMC7132938 DOI: 10.1093/cercor/bhz178] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Revised: 06/13/2019] [Accepted: 07/10/2019] [Indexed: 02/07/2023] Open
Abstract
Diffusion magnetic resonance (MR) tractography represents a novel opportunity to investigate conserved and deviant developmental programs between humans and other species such as mice. To that end, we acquired high angular resolution diffusion MR scans of mice [embryonic day (E) 10.5 to postnatal week 4] and human brains [gestational week (GW) 17-30] at successive stages of fetal development to investigate potential evolutionary changes in radial organization and emerging pathways between humans and mice. We compare radial glial development as well as commissural development (e.g., corpus callosum), primarily because our findings can be integrated with previous work. We also compare corpus callosal growth trajectories across primates (i.e., humans and rhesus macaques) and rodents (i.e., mice). One major finding is that the developing cortex of humans is predominated by pathways likely associated with a radial glial organization at GW 17-20, which is not as evident in age-matched mice (E 16.5, 17.5). Another finding is that, early in development, the corpus callosum follows a similar developmental timetable in primates (i.e., macaques and humans) as in mice. However, the corpus callosum grows for an extended period of time in primates compared with rodents. Taken together, these findings highlight deviant developmental programs underlying the emergence of cortical pathways in the human brain.
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Affiliation(s)
| | - Avilash Das
- Medical Sciences in the College of Arts and Sciences, Boston University, Boston, MA 02215, USA
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02215, USA
- Fetal-Neonatal Brain Imaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Jae W Song
- Division of Neuroradiology, Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
| | | | - Priya Kabaria
- Department of Behavioral Neuroscience, Northeastern University, Boston, MA 02115, USA
| | - Guangping Dai
- Science Center, Wellesley College, Wellesley, MA 02481, USA
| | - Tara Kane
- Department of Behavioral Neuroscience, Northeastern University, Boston, MA 02115, USA
| | - Emi Takahashi
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02215, USA
- Fetal-Neonatal Brain Imaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02215, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
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11
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Thiebaut de Schotten M, Croxson PL, Mars RB. Large-scale comparative neuroimaging: Where are we and what do we need? Cortex 2019; 118:188-202. [PMID: 30661736 PMCID: PMC6699599 DOI: 10.1016/j.cortex.2018.11.028] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Revised: 11/26/2018] [Accepted: 11/29/2018] [Indexed: 01/26/2023]
Abstract
Neuroimaging has a lot to offer comparative neuroscience. Although invasive "gold standard" techniques have a better spatial resolution, neuroimaging allows fast, whole-brain, repeatable, and multi-modal measurements of structure and function in living animals and post-mortem tissue. In the past years, comparative neuroimaging has increased in popularity. However, we argue that its most significant potential lies in its ability to collect large-scale datasets of many species to investigate principles of variability in brain organisation across whole orders of species-an ambition that is presently unfulfilled but achievable. We briefly review the current state of the field and explore what the current obstacles to such an approach are. We propose some calls to action.
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Affiliation(s)
- Michel Thiebaut de Schotten
- Brain Connectivity and Behaviour Group, Sorbonne Universities, Paris France; Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR, Paris, France; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA University of Bordeaux, Bordeaux, France.
| | - Paula L Croxson
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
| | - 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, United Kingdom; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands.
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12
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Schilling KG, Gao Y, Christian M, Janve V, Stepniewska I, Landman BA, Anderson AW. A Web-Based Atlas Combining MRI and Histology of the Squirrel Monkey Brain. Neuroinformatics 2019; 17:131-145. [PMID: 30006920 DOI: 10.1007/s12021-018-9391-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
The squirrel monkey (Saimiri sciureus) is a commonly-used surrogate for humans in biomedical research. In the neuroimaging community, MRI and histological atlases serve as valuable resources for anatomical, physiological, and functional studies of the brain; however, no digital MRI/histology atlas is currently available for the squirrel monkey. This paper describes the construction of a web-based multi-modal atlas of the squirrel monkey brain. The MRI-derived information includes anatomical MRI contrast (i.e., T2-weighted and proton-density-weighted) and diffusion MRI metrics (i.e., fractional anisotropy and mean diffusivity) from data acquired both in vivo and ex vivo on a 9.4 Tesla scanner. The histological images include Nissl and myelin stains, co-registered to the corresponding MRI, allowing identification of cyto- and myelo-architecture. In addition, a bidirectional neuronal tracer, biotinylated dextran amine (BDA) was injected into the primary motor cortex, enabling highly specific identification of regions connected to the injection location. The atlas integrates the results of common image analysis methods including diffusion tensor imaging glyphs, labels of 57 white-matter tracts identified using DTI-tractography, and 18 cortical regions of interest identified from Nissl-revealed cyto-architecture. All data are presented in a common space, and all image types are accessible through a web-based atlas viewer, which allows visualization and interaction of user-selectable contrasts and varying resolutions. By providing an easy to use reference system of anatomical information, our web-accessible multi-contrast atlas forms a rich and convenient resource for comparisons of brain findings across subjects or modalities. The atlas is called the Combined Histology-MRI Integrated Atlas of the Squirrel Monkey (CHIASM). All images are accessible through our web-based viewer ( https://chiasm.vuse.vanderbilt.edu /), and data are available for download at ( https://www.nitrc.org/projects/smatlas/ ).
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Affiliation(s)
- Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA. .,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Matthew Christian
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Vaibhav Janve
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | | | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.,Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, USA.,Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.,Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, USA
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Schilling KG, Gao Y, Stepniewska I, Janve V, Landman BA, Anderson AW. Histologically derived fiber response functions for diffusion MRI vary across white matter fibers-An ex vivo validation study in the squirrel monkey brain. NMR IN BIOMEDICINE 2019; 32:e4090. [PMID: 30908803 PMCID: PMC6525086 DOI: 10.1002/nbm.4090] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 01/25/2019] [Accepted: 02/16/2019] [Indexed: 06/09/2023]
Abstract
Understanding the relationship between the diffusion-weighted MRI signal and the arrangement of white matter fibers is fundamental for accurate voxel-wise reconstruction of the fiber orientation distribution (FOD) and subsequent fiber tractography. Spherical deconvolution reconstruction techniques model the diffusion signal as the convolution of the FOD with a response function that represents the signal profile of a single fiber orientation. Thus, given the signal and a fiber response function, the FOD can be estimated in every imaging voxel by deconvolution. However, the selection of the appropriate response function remains relatively under-studied, and requires further validation. In this work, using 3D histologically defined FODs and the corresponding diffusion signal from three ex vivo squirrel monkey brains, we derive the ground truth response functions. We find that the histologically derived response functions differ from those conventionally used. Next, we find that response functions statistically vary across brain regions, which suggests that the practice of using the same kernel throughout the brain is not optimal. We show that different kernels lead to different FOD reconstructions, which in turn can lead to different tractography results depending on algorithmic parameters, with large variations in the accuracy of resulting reconstructions. Together, these results suggest there is room for improvement in estimating and understanding the relationship between the diffusion signal and the underlying FOD.
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Affiliation(s)
- Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | | | - Vaibhav Janve
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, United States
- Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, United States
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Discrimination of the hierarchical structure of cortical layers in 2-photon microscopy data by combined unsupervised and supervised machine learning. Sci Rep 2019; 9:7424. [PMID: 31092841 PMCID: PMC6520410 DOI: 10.1038/s41598-019-43432-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Accepted: 04/18/2019] [Indexed: 12/22/2022] Open
Abstract
The laminar organization of the cerebral cortex is a fundamental characteristic of the brain, with essential implications for cortical function. Due to the rapidly growing amount of high-resolution brain imaging data, a great demand arises for automated and flexible methods for discriminating the laminar texture of the cortex. Here, we propose a combined approach of unsupervised and supervised machine learning to discriminate the hierarchical cortical laminar organization in high-resolution 2-photon microscopic neural image data of mouse brain without observer bias, that is, without the prerequisite of manually labeled training data. For local cortical foci, we modify an unsupervised clustering approach to identify and represent the laminar cortical structure. Subsequently, supervised machine learning is applied to transfer the resulting layer labels across different locations and image data, to ensure the existence of a consistent layer label system. By using neurobiologically meaningful features, the discrimination results are shown to be consistent with the layer classification of the classical Brodmann scheme, and provide additional insight into the structure of the cerebral cortex and its hierarchical organization. Thus, our work paves a new way for studying the anatomical organization of the cerebral cortex, and potentially its functional organization.
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15
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Schilling KG, Daducci A, Maier-Hein K, Poupon C, Houde JC, Nath V, Anderson AW, Landman BA, Descoteaux M. Challenges in diffusion MRI tractography - Lessons learned from international benchmark competitions. Magn Reson Imaging 2019; 57:194-209. [PMID: 30503948 PMCID: PMC6331218 DOI: 10.1016/j.mri.2018.11.014] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 11/17/2018] [Indexed: 12/13/2022]
Abstract
Diffusion MRI (dMRI) fiber tractography has become a pillar of the neuroimaging community due to its ability to noninvasively map the structural connectivity of the brain. Despite widespread use in clinical and research domains, these methods suffer from several potential drawbacks or limitations. Thus, validating the accuracy and reproducibility of techniques is critical for sound scientific conclusions and effective clinical outcomes. Towards this end, a number of international benchmark competitions, or "challenges", has been organized by the diffusion MRI community in order to investigate the reliability of the tractography process by providing a platform to compare algorithms and results in a fair manner, and evaluate common and emerging algorithms in an effort to advance the state of the field. In this paper, we summarize the lessons from a decade of challenges in tractography, and give perspective on the past, present, and future "challenges" that the field of diffusion tractography faces.
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Affiliation(s)
- Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States of America.
| | | | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Cyril Poupon
- Neurospin, Frédéric Joliot Life Sciences Institute, CEA, Gif-sur-Yvette, France
| | - Jean-Christophe Houde
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Québec, Canada
| | - Vishwesh Nath
- Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, United States of America
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States of America; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States of America
| | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States of America; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States of America; Department of Electrical Engineering, Vanderbilt University, Nashville, TN, United States of America
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Québec, Canada
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Schilling KG, Yeh FC, Nath V, Hansen C, Williams O, Resnick S, Anderson AW, Landman BA. A fiber coherence index for quality control of B-table orientation in diffusion MRI scans. Magn Reson Imaging 2019; 58:82-89. [PMID: 30682379 DOI: 10.1016/j.mri.2019.01.018] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Revised: 01/17/2019] [Accepted: 01/19/2019] [Indexed: 12/19/2022]
Abstract
PURPOSE The diffusion MRI "b-vector" table describing the diffusion sensitization direction can be flipped and permuted in dimension due to different orientation conventions used in scanners and incorrect or improperly utilized file formats. This can lead to incorrect fiber orientation estimates and subsequent tractography failure. Here, we present an automated quality control procedure to detect when the b-table is flipped and/or permuted incorrectly. METHODS We define a "fiber coherence index" to describe how well fibers are connected to each other, and use it to automatically detect the correct configuration of b-vectors. We examined the performance on 3981 research subject scans (Baltimore Longitudinal Study of Aging), 1065 normal subject scans of high image quality (Human Connectome Project), and 202 patient scans (Vanderbilt University Medical Center), as well as 9 in-vivo and 9 ex-vivo animal data. RESULTS The coherence index resulted in a 99.9% (3979/3981) and 100% (1065/1065) success rate in normal subject scans, 98% (198/202) in patient scans, and 100% (18/18) in both in-vivo and ex-vivo animal data in detecting the correct gradient table in datasets without severe image artifacts. The four failing cases (4/202) in patient scans, and two failures in healthy subject scans (2/3981), all showed prominent motion or signal dropout artifacts. CONCLUSIONS The fiber coherence measure can be used as an automatic quality assurance check in any diffusion analysis pipeline. Additionally, the success of this fiber coherence measure suggests potential broader applications, including evaluating data quality, or even providing diagnostic value as a biomarker of white matter integrity.
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Affiliation(s)
- Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA.
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Vishwesh Nath
- Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Colin Hansen
- Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Owen Williams
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Susan Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
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Schilling KG, Nath V, Hansen C, Parvathaneni P, Blaber J, Gao Y, Neher P, Aydogan DB, Shi Y, Ocampo-Pineda M, Schiavi S, Daducci A, Girard G, Barakovic M, Rafael-Patino J, Romascano D, Rensonnet G, Pizzolato M, Bates A, Fischi E, Thiran JP, Canales-Rodríguez EJ, Huang C, Zhu H, Zhong L, Cabeen R, Toga AW, Rheault F, Theaud G, Houde JC, Sidhu J, Chamberland M, Westin CF, Dyrby TB, Verma R, Rathi Y, Irfanoglu MO, Thomas C, Pierpaoli C, Descoteaux M, Anderson AW, Landman BA. Limits to anatomical accuracy of diffusion tractography using modern approaches. Neuroimage 2018; 185:1-11. [PMID: 30317017 DOI: 10.1016/j.neuroimage.2018.10.029] [Citation(s) in RCA: 144] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 09/14/2018] [Accepted: 10/09/2018] [Indexed: 12/12/2022] Open
Abstract
Diffusion MRI fiber tractography is widely used to probe the structural connectivity of the brain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, tractography has well-known pitfalls that limits the anatomical accuracy of this technique. Numerous modern methods have been developed to address these shortcomings through advances in acquisition, modeling, and computation. To test whether these advances improve tractography accuracy, we organized the 3-D Validation of Tractography with Experimental MRI (3D-VoTEM) challenge at the ISBI 2018 conference. We made available three unique independent tractography validation datasets - a physical phantom and two ex vivo brain specimens - resulting in 176 distinct submissions from 9 research groups. By comparing results over a wide range of fiber complexities and algorithmic strategies, this challenge provides a more comprehensive assessment of tractography's inherent limitations than has been reported previously. The central results were consistent across all sub-challenges in that, despite advances in tractography methods, the anatomical accuracy of tractography has not dramatically improved in recent years. Taken together, our results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain.
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Affiliation(s)
- Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA.
| | - Vishwesh Nath
- Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Colin Hansen
- Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA
| | | | - Justin Blaber
- Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Peter Neher
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Dogu Baran Aydogan
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yonggang Shi
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - Simona Schiavi
- Computer Science Department, University of Verona, Verona, Italy
| | | | - Gabriel Girard
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Muhamed Barakovic
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Jonathan Rafael-Patino
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - David Romascano
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Gaëtan Rensonnet
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Marco Pizzolato
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Alice Bates
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Elda Fischi
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Jean-Philippe Thiran
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - Erick J Canales-Rodríguez
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - Chao Huang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Liming Zhong
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Ryan Cabeen
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Francois Rheault
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Canada
| | - Guillaume Theaud
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Canada
| | - Jean-Christophe Houde
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Canada
| | - Jasmeen Sidhu
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Canada
| | - Maxime Chamberland
- Cardiff University, Brain Research Imaging Centre, School of Psychology, Cardiff, UK
| | | | - Tim B Dyrby
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Ragini Verma
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, USA
| | - M Okan Irfanoglu
- National Institute of Biomedical Imaging and Bioengineering, NIH, Bethesda, MD, USA
| | - Cibu Thomas
- Section on Learning and Plasticity, Laboratory of Brain and Cognition, NIMH, Bethesda, MD, USA
| | - Carlo Pierpaoli
- National Institute of Biomedical Imaging and Bioengineering, NIH, Bethesda, MD, USA
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Canada
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA
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Schilling KG, Gao Y, Stepniewska I, Janve V, Landman BA, Anderson AW. Anatomical accuracy of standard-practice tractography algorithms in the motor system - A histological validation in the squirrel monkey brain. Magn Reson Imaging 2018; 55:7-25. [PMID: 30213755 DOI: 10.1016/j.mri.2018.09.004] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 08/06/2018] [Accepted: 09/06/2018] [Indexed: 01/15/2023]
Abstract
For two decades diffusion fiber tractography has been used to probe both the spatial extent of white matter pathways and the region to region connectivity of the brain. In both cases, anatomical accuracy of tractography is critical for sound scientific conclusions. Here we assess and validate the algorithms and tractography implementations that have been most widely used - often because of ease of use, algorithm simplicity, or availability offered in open source software. Comparing forty tractography results to a ground truth defined by histological tracers in the primary motor cortex on the same squirrel monkey brains, we assess tract fidelity on the scale of voxels as well as over larger spatial domains or regional connectivity. No algorithms are successful in all metrics, and, in fact, some implementations fail to reconstruct large portions of pathways or identify major points of connectivity. The accuracy is most dependent on reconstruction method and tracking algorithm, as well as the seed region and how this region is utilized. We also note a tremendous variability in the results, even though the same MR images act as inputs to all algorithms. In addition, anatomical accuracy is significantly decreased at increased distances from the seed. An analysis of the spatial errors in tractography reveals that many techniques have trouble properly leaving the gray matter, and many only reveal connectivity to adjacent regions of interest. These results show that the most commonly implemented algorithms have several shortcomings and limitations, and choices in implementations lead to very different results. This study should provide guidance for algorithm choices based on study requirements for sensitivity, specificity, or the need to identify particular connections, and should serve as a heuristic for future developments in tractography.
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Affiliation(s)
- Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | | | - Vaibhav Janve
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, USA; Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, USA
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Resting-state white matter-cortical connectivity in non-human primate brain. Neuroimage 2018; 184:45-55. [PMID: 30205207 DOI: 10.1016/j.neuroimage.2018.09.021] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 09/05/2018] [Accepted: 09/07/2018] [Indexed: 02/03/2023] Open
Abstract
Numerous studies have used functional magnetic resonance imaging (fMRI) to characterize functional connectivity between cortical regions by analyzing correlations in blood oxygenation level dependent (BOLD) signals in a resting state. However, to date, there have been only a handful of studies reporting resting state BOLD signals in white matter. Nonetheless, a growing number of reports has emerged in recent years suggesting white matter BOLD signals can be reliably detected, though their biophysical origins remain unclear. Moreover, recent studies have identified robust correlations in a resting state between signals from cortex and specific white matter tracts. In order to further validate and interpret these findings, we studied a non-human primate model to investigate resting-state connectivity patterns between parcellated cortical volumes and specific white matter bundles. Our results show that resting-state connectivity patterns between white and gray matter structures are not randomly distributed but share notable similarities with diffusion- and histology-derived anatomic connectivities. This suggests that resting-state BOLD correlations between white matter fiber tracts and the gray matter regions to which they connect are directly related to the anatomic arrangement and density of WM fibers. We also measured how different levels of baseline neural activity, induced by varying levels of anesthesia, modulate these patterns. As anesthesia levels were raised, we observed weakened correlation coefficients between specific white matter tracts and gray matter regions while key features of the connectivity pattern remained similar. Overall, results from this study provide further evidence that neural activity is detectable by BOLD fMRI in both gray and white matter throughout the resting brain. The combined use of gray and white matter functional connectivity could also offer refined full-scale functional parcellation of the entire brain to characterize its functional architecture.
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