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Takemura H, Kaneko T, Sherwood CC, Johnson GA, Axer M, Hecht EE, Ye FQ, Leopold DA. A prominent vertical occipital white matter fasciculus unique to primate brains. Curr Biol 2024; 34:3632-3643.e4. [PMID: 38991613 PMCID: PMC11338705 DOI: 10.1016/j.cub.2024.06.034] [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: 02/18/2024] [Revised: 06/09/2024] [Accepted: 06/11/2024] [Indexed: 07/13/2024]
Abstract
Vision in humans and other primates enlists parallel processing streams in the dorsal and ventral visual cortex, known to support spatial and object processing, respectively. These streams are bridged, however, by a prominent white matter tract, the vertical occipital fasciculus (VOF), identified in both classical neuroanatomy and recent diffusion-weighted magnetic resonance imaging (dMRI) studies. Understanding the evolution of the VOF may shed light on its origin, function, and role in visually guided behaviors. To this end, we acquired high-resolution dMRI data from the brains of select mammalian species, including anthropoid and strepsirrhine primates, a tree shrew, rodents, and carnivores. In each species, we attempted to delineate the VOF after first locating the optic radiations in the occipital white matter. In all primate species examined, the optic radiation was flanked laterally by a prominent and coherent white matter fasciculus recognizable as the VOF. By contrast, the equivalent analysis applied to four non-primate species from the same superorder as primates (tree shrew, ground squirrel, paca, and rat) failed to reveal white matter tracts in the equivalent location. Clear evidence for a VOF was also absent in two larger carnivore species (ferret and fox). Although we cannot rule out the existence of minor or differently organized homologous fiber pathways in the non-primate species, the results suggest that the VOF has greatly expanded, or possibly emerged, in the primate lineage. This adaptation likely facilitated the evolution of unique visually guided behaviors in primates, with direct impacts on manual object manipulation, social interactions, and arboreal locomotion.
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Affiliation(s)
- Hiromasa Takemura
- Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, 38 Nishigonaka Myodaiji, Okazaki-shi, Aichi 444-8585, Japan; The Graduate Institute for Advanced Studies, SOKENDAI, Shonan Village, Hayama-cho, Kanagawa 240-0193, Japan; Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, 1-4 Yamadaoka, Suita-shi, Osaka 565-0871, Japan.
| | - Takaaki Kaneko
- Center for the Evolutionary Origins of Human Behavior, Kyoto University, 41-2 Kanrin, Inuyama-shi, Aichi 484-8506, Japan; Division of Behavioral Development, Department of System Neuroscience, National Institute for Physiological Sciences, 38 Nishigonaka Myodaiji, Okazaki-shi, Aichi, Japan
| | - Chet C Sherwood
- Department of Anthropology, The George Washington University, 800 22nd St. NW, Washington, DC 20052, USA
| | - G Allan Johnson
- Department of Radiology, Duke Center for In Vivo Microscopy, Duke Medical Center, 311 Research Drive, Durham, NC 27710, USA; Department of Biomedical Engineering, Duke University, 101 Science Dive., Durham, NC 27705, USA
| | - Markus Axer
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich 52425, Germany; Department of Physics, School of Mathematics and Natural Sciences, University of Wuppertal, Gaußstraße 20 42119, Wuppertal, Germany
| | - Erin E Hecht
- Department of Human Evolutionary Biology, Harvard University, 11 Divinity Avenue, Cambridge, MA 02138, USA
| | - Frank Q Ye
- Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, National Institutes of Health, Bethesda, MD 20814, USA
| | - David A Leopold
- Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, National Institutes of Health, Bethesda, MD 20814, USA; Systems Neurodevelopment Laboratory, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20814, USA.
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Tanner J, Faskowitz J, Teixeira AS, Seguin C, Coletta L, Gozzi A, Mišić B, Betzel RF. A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity. Nat Commun 2024; 15:5865. [PMID: 38997282 PMCID: PMC11245624 DOI: 10.1038/s41467-024-50248-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 07/01/2024] [Indexed: 07/14/2024] Open
Abstract
The macroscale connectome is the network of physical, white-matter tracts between brain areas. The connections are generally weighted and their values interpreted as measures of communication efficacy. In most applications, weights are either assigned based on imaging features-e.g. diffusion parameters-or inferred using statistical models. In reality, the ground-truth weights are unknown, motivating the exploration of alternative edge weighting schemes. Here, we explore a multi-modal, regression-based model that endows reconstructed fiber tracts with directed and signed weights. We find that the model fits observed data well, outperforming a suite of null models. The estimated weights are subject-specific and highly reliable, even when fit using relatively few training samples, and the networks maintain a number of desirable features. In summary, we offer a simple framework for weighting connectome data, demonstrating both its ease of implementation while benchmarking its utility for typical connectome analyses, including graph theoretic modeling and brain-behavior associations.
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Affiliation(s)
- Jacob Tanner
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Andreia Sofia Teixeira
- LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
| | - Caio Seguin
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | | | - Alessandro Gozzi
- Functional Neuroimaging Lab, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems, Rovereto, Italy
| | - Bratislav Mišić
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Richard F Betzel
- Cognitive Science Program, Indiana University, Bloomington, IN, USA.
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA.
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
- Program in Neuroscience, Indiana University, Bloomington, IN, USA.
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Takemura H, Kruper JA, Miyata T, Rokem A. Tractometry of Human Visual White Matter Pathways in Health and Disease. Magn Reson Med Sci 2024; 23:316-340. [PMID: 38866532 PMCID: PMC11234945 DOI: 10.2463/mrms.rev.2024-0007] [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] [Indexed: 06/14/2024] Open
Abstract
Diffusion-weighted MRI (dMRI) provides a unique non-invasive view of human brain tissue properties. The present review article focuses on tractometry analysis methods that use dMRI to assess the properties of brain tissue within the long-range connections comprising brain networks. We focus specifically on the major white matter tracts that convey visual information. These connections are particularly important because vision provides rich information from the environment that supports a large range of daily life activities. Many of the diseases of the visual system are associated with advanced aging, and tractometry of the visual system is particularly important in the modern aging society. We provide an overview of the tractometry analysis pipeline, which includes a primer on dMRI data acquisition, voxelwise model fitting, tractography, recognition of white matter tracts, and calculation of tract tissue property profiles. We then review dMRI-based methods for analyzing visual white matter tracts: the optic nerve, optic tract, optic radiation, forceps major, and vertical occipital fasciculus. For each tract, we review background anatomical knowledge together with recent findings in tractometry studies on these tracts and their properties in relation to visual function and disease. Overall, we find that measurements of the brain's visual white matter are sensitive to a range of disorders and correlate with perceptual abilities. We highlight new and promising analysis methods, as well as some of the current barriers to progress toward integration of these methods into clinical practice. These barriers, such as variability in measurements between protocols and instruments, are targets for future development.
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Affiliation(s)
- Hiromasa Takemura
- Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Aichi, Japan
- Graduate Institute for Advanced Studies, SOKENDAI, Hayama, Kanagawa, Japan
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, Suita, Osaka, Japan
| | - John A Kruper
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
| | - Toshikazu Miyata
- Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Aichi, Japan
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, Suita, Osaka, Japan
| | - Ariel Rokem
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
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Vinci-Booher S, McDonald DJ, Berquist E, Pestilli F. Associative white matter tracts selectively predict sensorimotor learning. Commun Biol 2024; 7:762. [PMID: 38909103 PMCID: PMC11193801 DOI: 10.1038/s42003-024-06420-1] [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: 08/11/2023] [Accepted: 06/06/2024] [Indexed: 06/24/2024] Open
Abstract
Human learning varies greatly among individuals and is related to the microstructure of major white matter tracts in several learning domains, yet the impact of the existing microstructure of white matter tracts on future learning outcomes remains unclear. We employed a machine-learning model selection framework to evaluate whether existing microstructure might predict individual differences in learning a sensorimotor task, and further, if the mapping between tract microstructure and learning was selective for learning outcomes. We used diffusion tractography to measure the mean fractional anisotropy (FA) of white matter tracts in 60 adult participants who then practiced drawing a set of 40 unfamiliar symbols repeatedly using a digital writing tablet. We measured drawing learning as the slope of draw duration over the practice session and measured visual recognition learning for the symbols using an old/new 2-AFC task. Results demonstrated that tract microstructure selectively predicted learning outcomes, with left hemisphere pArc and SLF3 tracts predicting drawing learning and the left hemisphere MDLFspl predicting visual recognition learning. These results were replicated using repeat, held-out data and supported with complementary analyses. Results suggest that individual differences in the microstructure of human white matter tracts may be selectively related to future learning outcomes.
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Affiliation(s)
- S Vinci-Booher
- Department of Psychological and Brain Sciences, Program for Neuroscience, Indiana University, Bloomington, IN, USA
- Department of Psychology and Human Development, Vanderbilt University, Nashville, TN, USA
| | - D J McDonald
- Department of Statistics, University of British Columbia, Vancouver, BC, Canada
| | - E Berquist
- Department of Psychological and Brain Sciences, Program for Neuroscience, Indiana University, Bloomington, IN, USA
| | - F Pestilli
- Department of Psychological and Brain Sciences, Program for Neuroscience, Indiana University, Bloomington, IN, USA.
- Department of Psychology, Center for Perceptual Systems, Center for Theoretical and Computational Neuroscience, Center for Aging Populations Sciences, Center for Learning and Memory, University of Texas at Austin, Austin, TX, USA.
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Uesaki M, Furlan M, Smith AT, Takemura H. White matter tracts adjacent to the human cingulate sulcus visual area (CSv). PLoS One 2024; 19:e0300575. [PMID: 38578743 PMCID: PMC10997140 DOI: 10.1371/journal.pone.0300575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 02/29/2024] [Indexed: 04/07/2024] Open
Abstract
Human cingulate sulcus visual area (CSv) was first identified as an area that responds selectively to visual stimulation indicative of self-motion. It was later shown that the area is also sensitive to vestibular stimulation as well as to bodily motion compatible with locomotion. Understanding the anatomical connections of CSv will shed light on how CSv interacts with other parts of the brain to perform information processing related to self-motion and navigation. A previous neuroimaging study (Smith et al. 2018, Cerebral Cortex, 28, 3685-3596) used diffusion-weighted magnetic resonance imaging (dMRI) to examine the structural connectivity of CSv, and demonstrated connections between CSv and the motor and sensorimotor areas in the anterior and posterior cingulate sulcus. The present study aimed to complement this work by investigating the relationship between CSv and adjacent major white matter tracts, and to map CSv's structural connectivity onto known white matter tracts. By re-analysing the dataset from Smith et al. (2018), we identified bundles of fibres (i.e. streamlines) from the whole-brain tractography that terminate near CSv. We then assessed to which white matter tracts those streamlines may belong based on previously established anatomical prescriptions. We found that a significant number of CSv streamlines can be categorised as part of the dorsalmost branch of the superior longitudinal fasciculus (SLF I) and the cingulum. Given current thinking about the functions of these white matter tracts, our results support the proposition that CSv provides an interface between sensory and motor systems in the context of self-motion.
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Affiliation(s)
- Maiko Uesaki
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology (NICT), Suita, Osaka, Japan
- Graduate School of Frontier Biosciences, Osaka University, Suita, Osaka, Japan
- Open Innovation & Collaboration Research Organization, Ritsumeikan University, Ibaraki, Osaka, Japan
| | - Michele Furlan
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), Trieste, Trieste, Italy
| | - Andrew T. Smith
- Department of Psychology, Royal Holloway, University of London, Egham, Surrey, United Kingdom
| | - Hiromasa Takemura
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology (NICT), Suita, Osaka, Japan
- Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Aichi, Japan
- Graduate Institute for Advanced Studies, SOKENDAI, Hayama, Kanagawa, Japan
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Oshiro H, Hata J, Nakashima D, Hayashi N, Haga Y, Hagiya K, Yoshimaru D, Okano H. Influence of Diffusion Time and Temperature on Restricted Diffusion Signal: A Phantom Study. Magn Reson Med Sci 2024; 23:136-145. [PMID: 36754420 PMCID: PMC11024708 DOI: 10.2463/mrms.mp.2022-0103] [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: 08/16/2022] [Accepted: 12/17/2022] [Indexed: 02/10/2023] Open
Abstract
PURPOSE Diffusion MRI is a physical measurement method that quantitatively indicates the displacement of water molecules diffusing in voxels. However, there are insufficient data to characterize the diffusion process physically in a uniform structure such as a phantom. This study investigated the transitional relationship between structure scale, temperature, and diffusion time for simple restricted diffusion using a capillary phantom. METHODS We performed diffusion-weighted pulsed-gradient stimulated-echo acquisition mode (STEAM) MRI with a 9.4 Tesla MRI system (Bruker BioSpin, Ettlingen, Germany) and a quadrature coil with an inner diameter of 86 mm (Bruker BioSpin). We measured the diffusion coefficients (radial diffusivity [RD]) of capillary plates (pore sizes 6, 12, 25, 50, and 100 μm) with uniformly restricted structures at various temperatures (10ºC, 20ºC, 30ºC, and 40ºC) and multiple diffusion times (12-800 ms). We evaluated the characteristics of scale, temperature, and diffusion time for restricted diffusion. RESULTS The RD decayed and became constant depending on the structural scale. Diffusion coefficient fluctuations with temperature occurred mostly under conditions of a large structural scale and short diffusion time. We obtained data suggesting that temperature-dependent changes in the diffusion coefficients follow physical laws. CONCLUSION No water molecules were observed outside the glass tubes in the capillary plates, and the capillary plates only reflected a restricted diffusion process within the structure.We experimentally evaluated the characteristics of simple restricted diffusion to reveal the transitional relationship of the diffusion coefficient with diffusion time, structure scale, and temperature through composite measurement.
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Affiliation(s)
- Hinako Oshiro
- Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan
- Center for Brain Science, RIKEN, Wako, Saitama, Japan
| | - Junichi Hata
- Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan
- Center for Brain Science, RIKEN, Wako, Saitama, Japan
- School of Medicine, Keio University, Tokyo, Japan
- Division of Regenerative Medicine, The Jikei University School of Medicine, Tokyo, Japan
| | | | - Naoya Hayashi
- Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan
- Center for Brain Science, RIKEN, Wako, Saitama, Japan
| | - Yawara Haga
- Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan
- Center for Brain Science, RIKEN, Wako, Saitama, Japan
| | - Kei Hagiya
- Center for Brain Science, RIKEN, Wako, Saitama, Japan
| | - Daisuke Yoshimaru
- Center for Brain Science, RIKEN, Wako, Saitama, Japan
- School of Medicine, Keio University, Tokyo, Japan
- Division of Regenerative Medicine, The Jikei University School of Medicine, Tokyo, Japan
| | - Hideyuki Okano
- Center for Brain Science, RIKEN, Wako, Saitama, Japan
- School of Medicine, Keio University, Tokyo, Japan
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Dubner SE, Rickerich L, Bruckert L, Poblaciones RV, Sproul D, Scala M, Feldman HM, Travis KE. Early, low-dose hydrocortisone and near-term brain connectivity in extremely preterm infants. Pediatr Res 2024; 95:1028-1034. [PMID: 38030826 DOI: 10.1038/s41390-023-02903-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 10/17/2023] [Accepted: 10/25/2023] [Indexed: 12/01/2023]
Abstract
BACKGROUND Postnatal steroids are used to prevent bronchopulmonary dysplasia in extremely preterm infants but may have adverse effects on brain development. We assessed connectivity metrics of major cerebral and cerebellar white matter pathways at near-term gestational age among infants who did or did not receive a standardized regimen of hydrocortisone during the first 10 days of life. METHODS Retrospective cohort study. PARTICIPANTS Infants born <28 weeks: Protocol group (n = 33) received at least 50% and not more than 150% of an intended standard dose of 0.5 mg/kg hydrocortisone twice daily for 7 days, then 0.5 mg/kg per day for 3 days; Non-Protocol group (n = 22), did not receive protocol hydrocortisone or completed <50% of the protocol dose. We assessed group differences in near-term diffusion MRI mean fractional anisotropy (FA) and mean diffusivity (MD) across the corticospinal tract, inferior longitudinal fasciculus, corpus callosum and superior cerebellar peduncle. RESULTS Groups were comparable in gestational age, post-menstrual age at scan, medical complications, bronchopulmonary dysplasia, and necrotizing enterocolitis. No significant large effect group differences were identified in mean FA or MD in any cerebral or cerebellar tract. CONCLUSION(S) Low dose, early, postnatal hydrocortisone was not associated with significant differences in white matter tract microstructure at near-term gestational age. IMPACT This study compared brain microstructural connectivity as a primary outcome among extremely preterm infants who did or did not receive early postnatal hydrocortisone. Low dose hydrocortisone in the first 10 days of life was not associated with significant differences in white matter microstructure in major cerebral and cerebellar pathways. Hydrocortisone did not have a significant effect on early brain white matter circuits.
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Affiliation(s)
- Sarah E Dubner
- Division of Developmental-Behavioral Pediatrics, Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Lucy Rickerich
- Program in Human Biology, Stanford University, Stanford, CA, USA
| | - Lisa Bruckert
- Division of Developmental-Behavioral Pediatrics, Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Rocío Velasco Poblaciones
- Division of Developmental-Behavioral Pediatrics, Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Dawson Sproul
- Program in Human Biology, Stanford University, Stanford, CA, USA
| | - Melissa Scala
- Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Heidi M Feldman
- Division of Developmental-Behavioral Pediatrics, Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Katherine E Travis
- Division of Developmental-Behavioral Pediatrics, Department of Pediatrics, Stanford University, Stanford, CA, USA.
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Betzel R, Puxeddu MG, Seguin C, Bazinet V, Luppi A, Podschun A, Singleton SP, Faskowitz J, Parakkattu V, Misic B, Markett S, Kuceyeski A, Parkes L. Controlling the human connectome with spatially diffuse input signals. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.27.581006. [PMID: 38463980 PMCID: PMC10925126 DOI: 10.1101/2024.02.27.581006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
The human brain is never at "rest"; its activity is constantly fluctuating over time, transitioning from one brain state-a whole-brain pattern of activity-to another. Network control theory offers a framework for understanding the effort - energy - associated with these transitions. One branch of control theory that is especially useful in this context is "optimal control", in which input signals are used to selectively drive the brain into a target state. Typically, these inputs are introduced independently to the nodes of the network (each input signal is associated with exactly one node). Though convenient, this input strategy ignores the continuity of cerebral cortex - geometrically, each region is connected to its spatial neighbors, allowing control signals, both exogenous and endogenous, to spread from their foci to nearby regions. Additionally, the spatial specificity of brain stimulation techniques is limited, such that the effects of a perturbation are measurable in tissue surrounding the stimulation site. Here, we adapt the network control model so that input signals have a spatial extent that decays exponentially from the input site. We show that this more realistic strategy takes advantage of spatial dependencies in structural connectivity and activity to reduce the energy (effort) associated with brain state transitions. We further leverage these dependencies to explore near-optimal control strategies such that, on a per-transition basis, the number of input signals required for a given control task is reduced, in some cases by two orders of magnitude. This approximation yields network-wide maps of input site density, which we compare to an existing database of functional, metabolic, genetic, and neurochemical maps, finding a close correspondence. Ultimately, not only do we propose a more efficient framework that is also more adherent to well-established brain organizational principles, but we also posit neurobiologically grounded bases for optimal control.
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Affiliation(s)
- Richard Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN 47401
- Cognitive Science Program, Indiana University, Bloomington IN 47401
- Program in Neuroscience, Indiana University, Bloomington IN 47401
| | - Maria Grazia Puxeddu
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN 47401
| | - Caio Seguin
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN 47401
| | - Vincent Bazinet
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Andrea Luppi
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | | | | | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN 47401
| | - Vibin Parakkattu
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN 47401
- Cognitive Science Program, Indiana University, Bloomington IN 47401
| | - Bratislav Misic
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | | | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, NY
- Department of Computational Biology, Cornell University, Ithaca, NY
| | - Linden Parkes
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, USA
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9
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Lock C, Tan NSM, Long IJ, Keong NC. Neuroimaging data repositories and AI-driven healthcare-Global aspirations vs. ethical considerations in machine learning models of neurological disease. Front Artif Intell 2024; 6:1286266. [PMID: 38440234 PMCID: PMC10910099 DOI: 10.3389/frai.2023.1286266] [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: 09/03/2023] [Accepted: 12/27/2023] [Indexed: 03/06/2024] Open
Abstract
Neuroimaging data repositories are data-rich resources comprising brain imaging with clinical and biomarker data. The potential for such repositories to transform healthcare is tremendous, especially in their capacity to support machine learning (ML) and artificial intelligence (AI) tools. Current discussions about the generalizability of such tools in healthcare provoke concerns of risk of bias-ML models underperform in women and ethnic and racial minorities. The use of ML may exacerbate existing healthcare disparities or cause post-deployment harms. Do neuroimaging data repositories and their capacity to support ML/AI-driven clinical discoveries, have both the potential to accelerate innovative medicine and harden the gaps of social inequities in neuroscience-related healthcare? In this paper, we examined the ethical concerns of ML-driven modeling of global community neuroscience needs arising from the use of data amassed within neuroimaging data repositories. We explored this in two parts; firstly, in a theoretical experiment, we argued for a South East Asian-based repository to redress global imbalances. Within this context, we then considered the ethical framework toward the inclusion vs. exclusion of the migrant worker population, a group subject to healthcare inequities. Secondly, we created a model simulating the impact of global variations in the presentation of anosmia risks in COVID-19 toward altering brain structural findings; we then performed a mini AI ethics experiment. In this experiment, we interrogated an actual pilot dataset (n = 17; 8 non-anosmic (47%) vs. 9 anosmic (53%) using an ML clustering model. To create the COVID-19 simulation model, we bootstrapped to resample and amplify the dataset. This resulted in three hypothetical datasets: (i) matched (n = 68; 47% anosmic), (ii) predominant non-anosmic (n = 66; 73% disproportionate), and (iii) predominant anosmic (n = 66; 76% disproportionate). We found that the differing proportions of the same cohorts represented in each hypothetical dataset altered not only the relative importance of key features distinguishing between them but even the presence or absence of such features. The main objective of our mini experiment was to understand if ML/AI methodologies could be utilized toward modelling disproportionate datasets, in a manner we term "AI ethics." Further work is required to expand the approach proposed here into a reproducible strategy.
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Affiliation(s)
- Christine Lock
- Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore
| | - Nicole Si Min Tan
- Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore
| | - Ian James Long
- Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore
| | - Nicole C. Keong
- Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
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10
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Ragone E, Tanner J, Jo Y, Zamani Esfahlani F, Faskowitz J, Pope M, Coletta L, Gozzi A, Betzel R. Modular subgraphs in large-scale connectomes underpin spontaneous co-fluctuation events in mouse and human brains. Commun Biol 2024; 7:126. [PMID: 38267534 PMCID: PMC10810083 DOI: 10.1038/s42003-024-05766-w] [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: 06/08/2023] [Accepted: 01/02/2024] [Indexed: 01/26/2024] Open
Abstract
Previous studies have adopted an edge-centric framework to study fine-scale network dynamics in human fMRI. To date, however, no studies have applied this framework to data collected from model organisms. Here, we analyze structural and functional imaging data from lightly anesthetized mice through an edge-centric lens. We find evidence of "bursty" dynamics and events - brief periods of high-amplitude network connectivity. Further, we show that on a per-frame basis events best explain static FC and can be divided into a series of hierarchically-related clusters. The co-fluctuation patterns associated with each cluster centroid link distinct anatomical areas and largely adhere to the boundaries of algorithmically detected functional brain systems. We then investigate the anatomical connectivity undergirding high-amplitude co-fluctuation patterns. We find that events induce modular bipartitions of the anatomical network of inter-areal axonal projections. Finally, we replicate these same findings in a human imaging dataset. In summary, this report recapitulates in a model organism many of the same phenomena observed in previously edge-centric analyses of human imaging data. However, unlike human subjects, the murine nervous system is amenable to invasive experimental perturbations. Thus, this study sets the stage for future investigation into the causal origins of fine-scale brain dynamics and high-amplitude co-fluctuations. Moreover, the cross-species consistency of the reported findings enhances the likelihood of future translation.
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Affiliation(s)
| | - Jacob Tanner
- Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47401, USA
| | - Youngheun Jo
- Department of Psychological and Brain Sciences and Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA
| | - Farnaz Zamani Esfahlani
- Stephenson School of Biomedical Engineering, The University of Oklahoma, Norman, OK, 73019, USA
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences and Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA
| | - Maria Pope
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47401, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, 47401, USA
| | | | - Alessandro Gozzi
- Functional Neuroimaging Lab, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems, Rovereto, Italy
| | - Richard Betzel
- Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA.
- Department of Psychological and Brain Sciences and Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA.
- Program in Neuroscience, Indiana University, Bloomington, IN, 47401, USA.
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11
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Li S, Zhang W, Yao S, He J, Zhu C, Gao J, Xue T, Xie G, Chen Y, Torio EF, Feng Y, Bastos DC, Rathi Y, Makris N, Kikinis R, Bi WL, Golby AJ, O'Donnell LJ, Zhang F. Tractography-based automated identification of the retinogeniculate visual pathway with novel microstructure-informed supervised contrastive learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.03.574115. [PMID: 38260369 PMCID: PMC10802389 DOI: 10.1101/2024.01.03.574115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
The retinogeniculate visual pathway (RGVP) is responsible for carrying visual information from the retina to the lateral geniculate nucleus. Identification and visualization of the RGVP are important in studying the anatomy of the visual system and can inform the treatment of related brain diseases. Diffusion MRI (dMRI) tractography is an advanced imaging method that uniquely enables in vivo mapping of the 3D trajectory of the RGVP. Currently, identification of the RGVP from tractography data relies on expert (manual) selection of tractography streamlines, which is time-consuming, has high clinical and expert labor costs, and is affected by inter-observer variability. In this paper, we present a novel deep learning framework, DeepRGVP , to enable fast and accurate identification of the RGVP from dMRI tractography data. We design a novel microstructure-informed supervised contrastive learning method that leverages both streamline label and tissue microstructure information to determine positive and negative pairs. We propose a simple and successful streamline-level data augmentation method to address highly imbalanced training data, where the number of RGVP streamlines is much lower than that of non-RGVP streamlines. We perform comparisons with several state-of-the-art deep learning methods that were designed for tractography parcellation, and we show superior RGVP identification results using DeepRGVP. In addition, we demonstrate a good generalizability of DeepRGVP to dMRI tractography data from neurosurgical patients with pituitary tumors and we show DeepRGVP can successfully identify RGVPs despite the effect of lesions affecting the RGVPs. Overall, our study shows the high potential of using deep learning to automatically identify the RGVP.
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12
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Hosoki M, Eidsness MA, Bruckert L, Travis KE, Feldman HM. Associations of behavioral problems with white matter circuits connecting to the frontal lobes in school-aged children born at term and preterm. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.08.23298268. [PMID: 37986772 PMCID: PMC10659456 DOI: 10.1101/2023.11.08.23298268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Introduction This study investigated whether behavioral problems in children were associated with fractional anisotropy (FA) of white matter tracts connecting from other brain regions to right and left frontal lobes. We considered internalizing and externalizing behavioral problems separately and contrasted patterns of associations in children born at term and very preterm. Methods Parents completed the Child Behavior Checklist/6-18 questionnaire to quantify behavioral problems when their children were age 8 years (N=36 FT and 37 PT). Diffusion magnetic resonance scans were collected at the same age and analyzed using probabilistic tractography. We used multiple linear regression to investigate the strength of association between age-adjusted T-scores of internalizing and externalizing problems and mean fractional anisotropy (mean-FA) of right and left uncinate, arcuate, and anterior thalamic radiations, controlling for birth group and sex. Results Regression models predicting internalizing T-scores from mean-FA found significant group-by-tract interactions for the left and right arcuate and right uncinate. Internalizing scores were negatively associated with mean-FA of left and right arcuate only in children born at term (pleft AF =0.01, pright AF =0.01). Regression models predicting externalizing T-scores from mean-FA found significant group-by-tract interactions for the left arcuate and right uncinate. Externalizing scores were negatively associated with mean-FA of right uncinate in children born at term (pright UF =0.01) and positively associated in children born preterm (pright UF preterm =0.01). Other models were not significant. Conclusions In this sample of children with scores for behavioral problems across the full range, internalizing and externalizing behavioral problems were negatively associated with mean-FA of white matter tracts connecting to frontal lobes in children born at term; externalizing behavioral problems were positively associated with mean-FA of the right uncinate in children born preterm. The different associations by birth group suggest that the neurobiology of behavioral problems differs in the two birth groups.
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Affiliation(s)
- Machiko Hosoki
- Corresponding Author: Machiko Hosoki, Developmental-Behavioral Pediatrics, Stanford University School of Medicine, 3145 Porter Drive, MC 5395, Palo Alto, CA 94304,
| | - Margarita Alethea Eidsness
- Division of Developmental-Behavioral Pediatrics, Department of Pediatrics, Stanford University School of Medicine
| | | | - Katherine E. Travis
- Division of Developmental-Behavioral Pediatrics, Department of Pediatrics, Stanford University School of Medicine
| | - Heidi M Feldman
- Division of Developmental-Behavioral Pediatrics, Department of Pediatrics, Stanford University School of Medicine
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13
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Martinie O, Karan P, Traverse E, Mercier C, Descoteaux M, Robert MT. The Challenge of Diffusion Magnetic Resonance Imaging in Cerebral Palsy: A Proposed Method to Identify White Matter Pathways. Brain Sci 2023; 13:1386. [PMID: 37891755 PMCID: PMC10605121 DOI: 10.3390/brainsci13101386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 09/19/2023] [Accepted: 09/26/2023] [Indexed: 10/29/2023] Open
Abstract
Cerebral palsy (CP), a neuromotor disorder characterized by prenatal brain lesions, leads to white matter alterations and sensorimotor deficits. However, the CP-related diffusion neuroimaging literature lacks rigorous and consensual methodology for preprocessing and analyzing data due to methodological challenges caused by the lesion extent. Advanced methods are available to reconstruct diffusion signals and can update current advances in CP. Our study demonstrates the feasibility of analyzing diffusion CP data using a standardized and open-source pipeline. Eight children with CP (8-12 years old) underwent a single diffusion magnetic resonance imaging (MRI) session on a 3T scanner (Achieva 3.0T (TX), Philips Healthcare Medical Systems, Best, The Netherlands). Exclusion criteria were contraindication to MRI and claustrophobia. Anatomical and diffusion images were acquired. Data were corrected and analyzed using Tractoflow 2.3.0 version, an open-source and robust tool. The tracts were extracted with customized procedures based on existing atlases and freely accessed standardized libraries (ANTs, Scilpy). DTI, CSD, and NODDI metrics were computed for each tract. Despite lesion heterogeneity and size, we successfully reconstructed major pathways, except for a participant with a larger lesion. Our results highlight the feasibility of identifying and quantifying subtle white matter pathways. Ultimately, this will increase our understanding of the clinical symptoms to provide precision medicine and optimize rehabilitation.
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Affiliation(s)
- Ophélie Martinie
- Centre for Interdisciplinary Research in Rehabilitation and Social Integration, Québec, QC G1M 2S8, Canada; (O.M.); (E.T.); (C.M.)
- Department of Rehabilitation, Université Laval, Québec, QC G1V 0A6, Canada
| | - Philippe Karan
- Department of Computer Sciences, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada; (P.K.); (M.D.)
| | - Elodie Traverse
- Centre for Interdisciplinary Research in Rehabilitation and Social Integration, Québec, QC G1M 2S8, Canada; (O.M.); (E.T.); (C.M.)
- Department of Rehabilitation, Université Laval, Québec, QC G1V 0A6, Canada
| | - Catherine Mercier
- Centre for Interdisciplinary Research in Rehabilitation and Social Integration, Québec, QC G1M 2S8, Canada; (O.M.); (E.T.); (C.M.)
- Department of Rehabilitation, Université Laval, Québec, QC G1V 0A6, Canada
| | - Maxime Descoteaux
- Department of Computer Sciences, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada; (P.K.); (M.D.)
| | - Maxime T. Robert
- Centre for Interdisciplinary Research in Rehabilitation and Social Integration, Québec, QC G1M 2S8, Canada; (O.M.); (E.T.); (C.M.)
- Department of Rehabilitation, Université Laval, Québec, QC G1V 0A6, Canada
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14
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Pope M, Seguin C, Varley TF, Faskowitz J, Sporns O. Co-evolving dynamics and topology in a coupled oscillator model of resting brain function. Neuroimage 2023; 277:120266. [PMID: 37414231 DOI: 10.1016/j.neuroimage.2023.120266] [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: 01/25/2023] [Revised: 05/24/2023] [Accepted: 07/04/2023] [Indexed: 07/08/2023] Open
Abstract
Dynamic models of ongoing BOLD fMRI brain dynamics and models of communication strategies have been two important approaches to understanding how brain network structure constrains function. However, dynamic models have yet to widely incorporate one of the most important insights from communication models: the brain may not use all of its connections in the same way or at the same time. Here we present a variation of a phase delayed Kuramoto coupled oscillator model that dynamically limits communication between nodes on each time step. An active subgraph of the empirically derived anatomical brain network is chosen in accordance with the local dynamic state on every time step, thus coupling dynamics and network structure in a novel way. We analyze this model with respect to its fit to empirical time-averaged functional connectivity, finding that, with the addition of only one parameter, it significantly outperforms standard Kuramoto models with phase delays. We also perform analyses on the novel time series of active edges it produces, demonstrating a slowly evolving topology moving through intermittent episodes of integration and segregation. We hope to demonstrate that the exploration of novel modeling mechanisms and the investigation of dynamics of networks in addition to dynamics on networks may advance our understanding of the relationship between brain structure and function.
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Affiliation(s)
- Maria Pope
- Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States; School of Informatics, Computing & Engineering, Indiana University, Bloomington, IN 47405, United States.
| | - Caio Seguin
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States
| | - Thomas F Varley
- School of Informatics, Computing & Engineering, Indiana University, Bloomington, IN 47405, United States; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States
| | - Olaf Sporns
- Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States; Network Science Institute, Indiana University, Bloomington, IN 47405, United States; Cognitive Science Program, Indiana University, Bloomington, IN 47405, United States
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15
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Morita T, Takemura H, Naito E. Functional and Structural Properties of Interhemispheric Interaction between Bilateral Precentral Hand Motor Regions in a Top Wheelchair Racing Paralympian. Brain Sci 2023; 13:brainsci13050715. [PMID: 37239187 DOI: 10.3390/brainsci13050715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/14/2023] [Accepted: 04/23/2023] [Indexed: 05/28/2023] Open
Abstract
Long-term motor training can cause functional and structural changes in the human brain. Assessing how the training of specific movements affects specific parts of the neural circuitry is essential to understand better the underlying mechanisms of motor training-induced plasticity in the human brain. We report a single-case neuroimaging study that investigated functional and structural properties in a professional athlete of wheelchair racing. As wheelchair racing requires bilateral synchronization of upper limb movements, we hypothesized that functional and structural properties of interhemispheric interactions in the central motor system might differ between the professional athlete and controls. Functional and diffusion magnetic resonance imaging (fMRI and dMRI) data were obtained from a top Paralympian (P1) in wheelchair racing. With 23 years of wheelchair racing training starting at age eight, she holds an exceptional competitive record. Furthermore, fMRI and dMRI data were collected from three other paraplegic participants (P2-P4) with long-term wheelchair sports training other than wheelchair racing and 37 able-bodied control volunteers. Based on the fMRI data analyses, P1 showed activation in the bilateral precentral hand sections and greater functional connectivity between these sections during a right-hand unimanual task. In contrast, other paraplegic participants and controls showed activation in the contralateral hemisphere and deactivation in the ipsilateral hemisphere. Moreover, dMRI data analysis revealed that P1 exhibited significantly lower mean diffusivity along the transcallosal pathway connecting the bilateral precentral motor regions than control participants, which was not observed in the other paraplegic participants. These results suggest that long-term training with bilaterally synchronized upper-limb movements may promote bilateral recruitment of the precentral hand sections. Such recruitment may affect the structural circuitry involved in the interhemispheric interaction between the bilateral precentral regions. This study provides valuable evidence of the extreme adaptability of the human brain.
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Affiliation(s)
- Tomoyo Morita
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology (NICT), 2A6 1-4 Yamadaoka, Suita 565-0871, Osaka, Japan
- Graduate School of Frontier Biosciences, Osaka University, 1-3 Yamadaoka, Suita 565-0871, Osaka, Japan
| | - Hiromasa Takemura
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology (NICT), 2A6 1-4 Yamadaoka, Suita 565-0871, Osaka, Japan
- Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, 38 Nishigonaka Myodaiji, Okazaki 444-8585, Aichi, Japan
- The Graduate Institute for Advanced Studies, SOKENDAI, Shonan Village, Hayama 240-0193, Kanagawa, Japan
| | - Eiichi Naito
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology (NICT), 2A6 1-4 Yamadaoka, Suita 565-0871, Osaka, Japan
- Graduate School of Frontier Biosciences, Osaka University, 1-3 Yamadaoka, Suita 565-0871, Osaka, Japan
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16
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Vinci-Booher S, McDonald DJ, Berquist E, Pestilli F. Associative white matter tracts selectively predict sensorimotor learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.10.523345. [PMID: 37131816 PMCID: PMC10153388 DOI: 10.1101/2023.01.10.523345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Human learning is a complex phenomenon that varies greatly among individuals and is related to the microstructure of major white matter tracts in several learning domains, yet the impact of the existing myelination of white matter tracts on future learning outcomes remains unclear. We employed a machine-learning model selection framework to evaluate whether existing microstructure might predict individual differences in the potential for learning a sensorimotor task, and further, if the mapping between the microstructure of major white matter tracts and learning was selective for learning outcomes. We used diffusion tractography to measure the mean fractional anisotropy (FA) of white matter tracts in 60 adult participants who then underwent training and subsequent testing to evaluate learning. During training, participants practiced drawing a set of 40 novel symbols repeatedly using a digital writing tablet. We measured drawing learning as the slope of draw duration over the practice session and visual recognition learning as the performance accuracy in an old/new 2-AFC recognition task. Results demonstrated that the microstructure of major white matter tracts selectively predicted learning outcomes, with left hemisphere pArc and SLF 3 tracts predicting drawing learning and the left hemisphere MDLFspl predicting visual recognition learning. These results were replicated in a repeat, held-out data set and supported with complementary analyses. Overall, results suggest that individual differences in the microstructure of human white matter tracts may be selectively related to future learning outcomes and open avenues of inquiry concerning the impact of existing tract myelination in the potential for learning. Significance statement A selective mapping between tract microstructure and future learning has been demonstrated in the murine model and, to our knowledge, has not yet been demonstrated in humans. We employed a data-driven approach that identified only two tracts, the two most posterior segments of the arcuate fasciculus in the left hemisphere, to predict learning a sensorimotor task (drawing symbols) and this prediction model did not transfer to other learning outcomes (visual symbol recognition). Results suggest that individual differences in learning may be selectively related to the tissue properties of major white matter tracts in the human brain.
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17
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Lerma-Usabiaga G, Liu M, Paz-Alonso PM, Wandell BA. Reproducible Tract Profiles 2 (RTP2) suite, from diffusion MRI acquisition to clinical practice and research. Sci Rep 2023; 13:6010. [PMID: 37045891 PMCID: PMC10097625 DOI: 10.1038/s41598-023-32924-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 04/04/2023] [Indexed: 04/14/2023] Open
Abstract
Diffusion MRI is a complex technique, where new discoveries and implementations occur at a fast pace. The expertise needed for data analyses and accurate and reproducible results is increasingly demanding and requires multidisciplinary collaborations. In the present work we introduce Reproducible Tract Profiles 2 (RTP2), a set of flexible and automated methods to analyze anatomical MRI and diffusion weighted imaging (DWI) data for reproducible tractography. RTP2 reads structural MRI data and processes them through a succession of serialized containerized analyses. We describe the DWI algorithms used to identify white-matter tracts and their summary metrics, the flexible architecture of the platform, and the tools to programmatically access and control the computations. The combination of these three components provides an easy-to-use automatized tool developed and tested over 20 years, to obtain usable and reliable state-of-the-art diffusion metrics at the individual and group levels for basic research and clinical practice.
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Affiliation(s)
- Garikoitz Lerma-Usabiaga
- Department of Psychology, Stanford University, 450 Serra Mall, Jordan Hall Building, Stanford, CA, 94305, USA.
- BCBL, Basque Center on Cognition, Brain and Language, Mikeletegi Pasealekua 69, 20009, Donostia-San Sebastián, Gipuzkoa, Spain.
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, 94305, USA.
- IKERBASQUE, Basque Foundation for Science, 48013, Bilbao, Spain.
| | - Mengxing Liu
- BCBL, Basque Center on Cognition, Brain and Language, Mikeletegi Pasealekua 69, 20009, Donostia-San Sebastián, Gipuzkoa, Spain
| | - Pedro M Paz-Alonso
- BCBL, Basque Center on Cognition, Brain and Language, Mikeletegi Pasealekua 69, 20009, Donostia-San Sebastián, Gipuzkoa, Spain
- IKERBASQUE, Basque Foundation for Science, 48013, Bilbao, Spain
| | - Brian A Wandell
- Department of Psychology, Stanford University, 450 Serra Mall, Jordan Hall Building, Stanford, CA, 94305, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, 94305, USA
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18
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Cheng H, Vinci-Booher S, Wang J, Caron B, Wen Q, Newman S, Pestilli F. Denoising diffusion weighted imaging data using convolutional neural networks. PLoS One 2022; 17:e0274396. [PMID: 36108272 PMCID: PMC9477507 DOI: 10.1371/journal.pone.0274396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 08/26/2022] [Indexed: 11/17/2022] Open
Abstract
Diffusion weighted imaging (DWI) with multiple, high b-values is critical for extracting tissue microstructure measurements; however, high b-value DWI images contain high noise levels that can overwhelm the signal of interest and bias microstructural measurements. Here, we propose a simple denoising method that can be applied to any dataset, provided a low-noise, single-subject dataset is acquired using the same DWI sequence. The denoising method uses a one-dimensional convolutional neural network (1D-CNN) and deep learning to learn from a low-noise dataset, voxel-by-voxel. The trained model can then be applied to high-noise datasets from other subjects. We validated the 1D-CNN denoising method by first demonstrating that 1D-CNN denoising resulted in DWI images that were more similar to the noise-free ground truth than comparable denoising methods, e.g., MP-PCA, using simulated DWI data. Using the same DWI acquisition but reconstructed with two common reconstruction methods, i.e. SENSE1 and sum-of-square, to generate a pair of low-noise and high-noise datasets, we then demonstrated that 1D-CNN denoising of high-noise DWI data collected from human subjects showed promising results in three domains: DWI images, diffusion metrics, and tractography. In particular, the denoised images were very similar to a low-noise reference image of that subject, more than the similarity between repeated low-noise images (i.e. computational reproducibility). Finally, we demonstrated the use of the 1D-CNN method in two practical examples to reduce noise from parallel imaging and simultaneous multi-slice acquisition. We conclude that the 1D-CNN denoising method is a simple, effective denoising method for DWI images that overcomes some of the limitations of current state-of-the-art denoising methods, such as the need for a large number of training subjects and the need to account for the rectified noise floor.
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Affiliation(s)
- Hu Cheng
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States of America
- Program of Neuroscience, Indiana University, Bloomington, IN, United States of America
| | - Sophia Vinci-Booher
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States of America
- Department of Psychology and Human Development, Vanderbilt University, Nashville, TN, United States of America
| | - Jian Wang
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Bradley Caron
- Department of Psychology, Center for Perceptual Systems and Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Austin, TX, United States of America
| | - Qiuting Wen
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States of America
| | - Sharlene Newman
- Alabama Life Research Institute, The University of Alabama, Tuscaloosa, AL, United States of America
| | - Franco Pestilli
- Department of Psychology, Center for Perceptual Systems and Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Austin, TX, United States of America
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19
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Vinci-Booher S, Caron B, Bullock D, James K, Pestilli F. Development of white matter tracts between and within the dorsal and ventral streams. Brain Struct Funct 2022; 227:1457-1477. [PMID: 35267078 DOI: 10.1007/s00429-021-02414-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Accepted: 10/12/2021] [Indexed: 01/11/2023]
Abstract
The degree of interaction between the ventral and dorsal visual streams has been discussed in multiple scientific domains for decades. Recently, several white matter tracts that directly connect cortical regions associated with the dorsal and ventral streams have become possible to study due to advancements in automated and reproducible methods. The developmental trajectory of this set of tracts, here referred to as the posterior vertical pathway (PVP), has yet to be described. We propose an input-driven model of white matter development and provide evidence for the model by focusing on the development of the PVP. We used reproducible, cloud-computing methods and diffusion imaging from adults and children (ages 5-8 years) to compare PVP development to that of tracts within the ventral and dorsal pathways. PVP microstructure was more adult-like than dorsal stream microstructure, but less adult-like than ventral stream microstructure. Additionally, PVP microstructure was more similar to the microstructure of the ventral than the dorsal stream and was predicted by performance on a perceptual task in children. Overall, results suggest a potential role for the PVP in the development of the dorsal visual stream that may be related to its ability to facilitate interactions between ventral and dorsal streams during learning. Our results are consistent with the proposed model, suggesting that the microstructural development of major white matter pathways is related, at least in part, to the propagation of sensory information within the visual system.
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Affiliation(s)
- S Vinci-Booher
- Indiana University, 1101 E. 10th Street, Bloomington, IN, 47405, USA.
| | - B Caron
- Indiana University, 1101 E. 10th Street, Bloomington, IN, 47405, USA
| | - D Bullock
- Indiana University, 1101 E. 10th Street, Bloomington, IN, 47405, USA
| | - K James
- Indiana University, 1101 E. 10th Street, Bloomington, IN, 47405, USA
| | - F Pestilli
- Indiana University, 1101 E. 10th Street, Bloomington, IN, 47405, USA.
- The University of Texas, 108 E Dean Keeton St, Austin, TX, 78712, USA.
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20
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Grotheer M, Kubota E, Grill-Spector K. Establishing the functional relevancy of white matter connections in the visual system and beyond. Brain Struct Funct 2022; 227:1347-1356. [PMID: 34846595 PMCID: PMC9046284 DOI: 10.1007/s00429-021-02423-4] [Citation(s) in RCA: 2] [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: 05/24/2021] [Accepted: 11/02/2021] [Indexed: 01/04/2023]
Abstract
For over a century, researchers have examined the functional relevancy of white matter bundles. Consequently, many large-scale bundles spanning several centimeters have been associated in their entirety with specific brain functions, such as language or attention. However, these coarse structural-functional relationships are at odds with modern understanding of the fine-grained functional organization of human cortex, such as the mosaic of category-selective regions in ventral temporal cortex. Here, we review a multimodal approach that combines fMRI to define functional regions of interest within individual's brains with dMRI tractography to identify the white matter bundles of the same individual. Combining these data allows to determine which subsets of streamlines within a white matter bundle connect to specific functional regions in each individual. That is, this approach identifies the functionally defined white matter sub-bundles of the brain. We argue that this approach not only enhances the accuracy of interpreting the functional relevancy of white matter bundles, but also enables segmentation of these large-scale bundles into meaningful functional units, which can then be linked to behavior with enhanced precision. Importantly, this approach has the potential for making new discoveries of the fine-grained functional relevancy of white matter connections in the visual system and the brain more broadly, akin to the flurry of research that has identified functional regions in cortex.
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Affiliation(s)
- Mareike Grotheer
- Department of Psychology, Philipps-Universität Marburg, 35032, Marburg, Germany.
- Center for Mind, Brain and Behavior-CMBB, Philipps-Universität Marburg and Justus-Liebig-Universität Giessen, 35037, Marburg, Germany.
| | - Emily Kubota
- Psychology Department, Stanford University, Stanford, CA, 94305, USA
| | - Kalanit Grill-Spector
- Psychology Department, Stanford University, Stanford, CA, 94305, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, 94305, USA
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21
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Zamani Esfahlani F, Faskowitz J, Slack J, Mišić B, Betzel RF. Local structure-function relationships in human brain networks across the lifespan. Nat Commun 2022; 13:2053. [PMID: 35440659 PMCID: PMC9018911 DOI: 10.1038/s41467-022-29770-y] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 03/29/2022] [Indexed: 12/13/2022] Open
Abstract
A growing number of studies have used stylized network models of communication to predict brain function from structure. Most have focused on a small set of models applied globally. Here, we compare a large number of models at both global and regional levels. We find that globally most predictors perform poorly. At the regional level, performance improves but heterogeneously, both in terms of variance explained and the optimal model. Next, we expose synergies among predictors by using pairs to jointly predict FC. Finally, we assess age-related differences in global and regional coupling across the human lifespan. We find global decreases in the magnitude of structure-function coupling with age. We find that these decreases are driven by reduced coupling in sensorimotor regions, while higher-order cognitive systems preserve local coupling with age. Our results describe patterns of structure-function coupling across the cortex and how this may change with age.
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Affiliation(s)
- Farnaz Zamani Esfahlani
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, 47405, USA
| | - Jonah Slack
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA
| | - Bratislav Mišić
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Richard F Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA.
- Program in Neuroscience, Indiana University, Bloomington, IN, 47405, USA.
- Cognitive Science Program, Indiana University, Bloomington, IN, 47405, USA.
- Network Science Institute, Indiana University, Bloomington, IN, 47405, USA.
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22
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Zhang F, Daducci A, He Y, Schiavi S, Seguin C, Smith RE, Yeh CH, Zhao T, O'Donnell LJ. Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review. Neuroimage 2022; 249:118870. [PMID: 34979249 PMCID: PMC9257891 DOI: 10.1016/j.neuroimage.2021.118870] [Citation(s) in RCA: 88] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 12/03/2021] [Accepted: 12/31/2021] [Indexed: 12/13/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.
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Affiliation(s)
- Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | | | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia; The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia
| | - Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University, Taoyuan, Taiwan; Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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23
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Bullock DN, Hayday EA, Grier MD, Tang W, Pestilli F, Heilbronner SR. A taxonomy of the brain's white matter: twenty-one major tracts for the 21st century. Cereb Cortex 2022; 32:4524-4548. [PMID: 35169827 PMCID: PMC9574243 DOI: 10.1093/cercor/bhab500] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 12/06/2021] [Accepted: 12/07/2021] [Indexed: 01/26/2023] Open
Abstract
The functional and computational properties of brain areas are determined, in large part, by their connectivity profiles. Advances in neuroimaging and network neuroscience allow us to characterize the human brain noninvasively, but a comprehensive understanding of the human brain demands an account of the anatomy of brain connections. Long-range anatomical connections are instantiated by white matter, which itself is organized into tracts. These tracts are often disrupted by central nervous system disorders, and they can be targeted by neuromodulatory interventions, such as deep brain stimulation. Here, we characterized the connections, morphology, traversal, and functions of the major white matter tracts in the brain. There are major discrepancies across different accounts of white matter tract anatomy, hindering our attempts to accurately map the connectivity of the human brain. However, we are often able to clarify the source(s) of these discrepancies through careful consideration of both histological tract-tracing and diffusion-weighted tractography studies. In combination, the advantages and disadvantages of each method permit novel insights into brain connectivity. Ultimately, our synthesis provides an essential reference for neuroscientists and clinicians interested in brain connectivity and anatomy, allowing for the study of the association of white matter's properties with behavior, development, and disorders.
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Affiliation(s)
- Daniel N Bullock
- Department of Psychological and Brain Sciences, Program in Neuroscience, Indiana University Bloomington, Bloomington, IN 47405, USA,Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - Elena A Hayday
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - Mark D Grier
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | | | | | - Sarah R Heilbronner
- Address correspondence to Sarah R. Heilbronner, Department of Neuroscience, University of Minnesota, 2-164 Jackson Hall, 321 Church St SE, Minneapolis, MN 55455, USA.
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24
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Normal Retinotopy in Primary Visual Cortex in a Congenital Complete Unilateral Lesion of Lateral Geniculate Nucleus in Human: A Case Study. Int J Mol Sci 2022; 23:ijms23031055. [PMID: 35162977 PMCID: PMC8835673 DOI: 10.3390/ijms23031055] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 01/14/2022] [Accepted: 01/15/2022] [Indexed: 11/17/2022] Open
Abstract
Impairment of the geniculostriate pathway results in scotomas in the corresponding part of the visual field. Here, we present a case of patient IB with left eye microphthalmia and with lesions in most of the left geniculostriate pathway, including the Lateral Geniculate Nucleus (LGN). Despite the severe lesions, the patient has a very narrow scotoma in the peripheral part of the lower-right-hemifield only (beyond 15° of eccentricity) and complete visual field representation in the primary visual cortex. Population receptive field mapping (pRF) of the patient’s visual field reveals orderly eccentricity maps together with contralateral activation in both hemispheres. With diffusion tractography, we revealed connections between superior colliculus (SC) and cortical structures in the hemisphere affected by the lesions, which could mediate the retinotopic reorganization at the cortical level. Our results indicate an astonishing case for the flexibility of the developing retinotopic maps where the contralateral thalamus receives fibers from both the nasal and temporal retinae.
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25
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Caffarra S, Karipidis II, Yablonski M, Yeatman JD. Anatomy and physiology of word-selective visual cortex: from visual features to lexical processing. Brain Struct Funct 2021; 226:3051-3065. [PMID: 34636985 PMCID: PMC8639194 DOI: 10.1007/s00429-021-02384-8] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 09/07/2021] [Indexed: 12/20/2022]
Abstract
Over the past 2 decades, researchers have tried to uncover how the human brain can extract linguistic information from a sequence of visual symbols. The description of how the brain's visual system processes words and enables reading has improved with the progressive refinement of experimental methodologies and neuroimaging techniques. This review provides a brief overview of this research journey. We start by describing classical models of object recognition in non-human primates, which represent the foundation for many of the early models of visual word recognition in humans. We then review functional neuroimaging studies investigating the word-selective regions in visual cortex. This research led to the differentiation of highly specialized areas, which are involved in the analysis of different aspects of written language. We then consider the corresponding anatomical measurements and provide a description of the main white matter pathways carrying neural signals crucial to word recognition. Finally, in an attempt to integrate structural, functional, and electrophysiological findings, we propose a view of visual word recognition, accounting for spatial and temporal facets of word-selective neural processes. This multi-modal perspective on the neural circuitry of literacy highlights the relevance of a posterior-anterior differentiation in ventral occipitotemporal cortex for visual processing of written language and lexical features. It also highlights unanswered questions that can guide us towards future research directions. Bridging measures of brain structure and function will help us reach a more precise understanding of the transformation from vision to language.
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Affiliation(s)
- Sendy Caffarra
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA, 94305-5101, USA
- Stanford University Graduate School of Education, 485 Lasuen Mall, Stanford, CA, 94305, USA
- Basque Center on Cognition, Brain and Language, Mikeletegi 69, 20009, San Sebastian, Spain
- University of Modena and Reggio Emilia, Via Campi 287, 41125, Modena, Italy
| | - Iliana I Karipidis
- Department of Psychiatry and Behavioral Sciences, Center for Interdisciplinary Brain Sciences Research, School of Medicine, Stanford University, 401 Quarry Road, Stanford, CA, 94305-5717, USA.
| | - Maya Yablonski
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA, 94305-5101, USA
- Stanford University Graduate School of Education, 485 Lasuen Mall, Stanford, CA, 94305, USA
| | - Jason D Yeatman
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA, 94305-5101, USA
- Stanford University Graduate School of Education, 485 Lasuen Mall, Stanford, CA, 94305, USA
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26
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Abstract
We describe a collection of T1-, diffusion- and functional T2*-weighted magnetic resonance imaging data from human individuals with albinism and achiasma. This repository can be used as a test-bed to develop and validate tractography methods like diffusion-signal modeling and fiber tracking as well as to investigate the properties of the human visual system in individuals with congenital abnormalities. The MRI data is provided together with tools and files allowing for its preprocessing and analysis, along with the data derivatives such as manually curated masks and regions of interest for performing tractography.
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27
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Pope M, Fukushima M, Betzel RF, Sporns O. Modular origins of high-amplitude cofluctuations in fine-scale functional connectivity dynamics. Proc Natl Acad Sci U S A 2021; 118:e2109380118. [PMID: 34750261 PMCID: PMC8609635 DOI: 10.1073/pnas.2109380118] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/17/2021] [Indexed: 11/22/2022] Open
Abstract
The topology of structural brain networks shapes brain dynamics, including the correlation structure of brain activity (functional connectivity) as estimated from functional neuroimaging data. Empirical studies have shown that functional connectivity fluctuates over time, exhibiting patterns that vary in the spatial arrangement of correlations among segregated functional systems. Recently, an exact decomposition of functional connectivity into frame-wise contributions has revealed fine-scale dynamics that are punctuated by brief and intermittent episodes (events) of high-amplitude cofluctuations involving large sets of brain regions. Their origin is currently unclear. Here, we demonstrate that similar episodes readily appear in silico using computational simulations of whole-brain dynamics. As in empirical data, simulated events contribute disproportionately to long-time functional connectivity, involve recurrence of patterned cofluctuations, and can be clustered into distinct families. Importantly, comparison of event-related patterns of cofluctuations to underlying patterns of structural connectivity reveals that modular organization present in the coupling matrix shapes patterns of event-related cofluctuations. Our work suggests that brief, intermittent events in functional dynamics are partly shaped by modular organization of structural connectivity.
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Affiliation(s)
- Maria Pope
- Program in Neuroscience, Indiana University, Bloomington, IN 47405
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN 47405
| | - Makoto Fukushima
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Nara 630-0192, Japan
- Data Science Center, Nara Institute of Science and Technology, Nara 630-0192, Japan
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Osaka 565-0871, Japan
| | - Richard F Betzel
- Program in Neuroscience, Indiana University, Bloomington, IN 47405
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405
- Cognitive Science Program, Indiana University, Bloomington, IN 47405
- Network Science Institute, Indiana University, Bloomington, IN 47405
| | - Olaf Sporns
- Program in Neuroscience, Indiana University, Bloomington, IN 47405;
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405
- Cognitive Science Program, Indiana University, Bloomington, IN 47405
- Network Science Institute, Indiana University, Bloomington, IN 47405
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28
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He J, Zhang F, Xie G, Yao S, Feng Y, Bastos DCA, Rathi Y, Makris N, Kikinis R, Golby AJ, O'Donnell LJ. Comparison of multiple tractography methods for reconstruction of the retinogeniculate visual pathway using diffusion MRI. Hum Brain Mapp 2021; 42:3887-3904. [PMID: 33978265 PMCID: PMC8288095 DOI: 10.1002/hbm.25472] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/24/2021] [Accepted: 04/25/2021] [Indexed: 12/31/2022] Open
Abstract
The retinogeniculate visual pathway (RGVP) conveys visual information from the retina to the lateral geniculate nucleus. The RGVP has four subdivisions, including two decussating and two nondecussating pathways that cannot be identified on conventional structural magnetic resonance imaging (MRI). Diffusion MRI tractography has the potential to trace these subdivisions and is increasingly used to study the RGVP. However, it is not yet known which fiber tracking strategy is most suitable for RGVP reconstruction. In this study, four tractography methods are compared, including constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD-Stream) methods, and multi-fiber (UKF-2T) and single-fiber (UKF-1T) unscented Kalman filter (UKF) methods. Experiments use diffusion MRI data from 57 subjects in the Human Connectome Project. The RGVP is identified using regions of interest created by two clinical experts. Quantitative anatomical measurements and expert anatomical judgment are used to assess the advantages and limitations of the four tractography methods. Overall, we conclude that UKF-2T and iFOD1 produce the best RGVP reconstruction results. The iFOD1 method can better quantitatively estimate the percentage of decussating fibers, while the UKF-2T method produces reconstructed RGVPs that are judged to better correspond to the known anatomy and have the highest spatial overlap across subjects. Overall, we find that it is challenging for current tractography methods to both accurately track RGVP fibers that correspond to known anatomy and produce an approximately correct percentage of decussating fibers. We suggest that future algorithm development for RGVP tractography should take consideration of both of these two points.
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Affiliation(s)
- Jianzhong He
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of TechnologyHangzhouChina
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Fan Zhang
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Guoqiang Xie
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of NeurosurgeryNuclear Industry 215 Hospital of Shaanxi ProvinceXianyangChina
| | - Shun Yao
- Department of Neurosurgery, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Center for Pituitary Tumor Surgery, Department of NeurosurgeryThe First Affiliated Hospital, Sun Yat‐sen UniversityGuangzhouChina
| | - Yuanjing Feng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of TechnologyHangzhouChina
| | - Dhiego C. A. Bastos
- Department of Neurosurgery, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Yogesh Rathi
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of Psychiatry, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Nikos Makris
- Department of Psychiatry, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Departments of Psychiatry, Neurology and Radiology, Massachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Ron Kikinis
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Alexandra J. Golby
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of Neurosurgery, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Lauren J. O'Donnell
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
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29
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McPherson BC, Pestilli F. A single mode of population covariation associates brain networks structure and behavior and predicts individual subjects' age. Commun Biol 2021; 4:943. [PMID: 34354185 PMCID: PMC8342440 DOI: 10.1038/s42003-021-02451-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 06/15/2021] [Indexed: 02/07/2023] Open
Abstract
Multiple human behaviors improve early in life, peaking in young adulthood, and declining thereafter. Several properties of brain structure and function progress similarly across the lifespan. Cognitive and neuroscience research has approached aging primarily using associations between a few behaviors, brain functions, and structures. Because of this, the multivariate, global factors relating brain and behavior across the lifespan are not well understood. We investigated the global patterns of associations between 334 behavioral and clinical measures and 376 brain structural connections in 594 individuals across the lifespan. A single-axis associated changes in multiple behavioral domains and brain structural connections (r = 0.5808). Individual variability within the single association axis well predicted the age of the subject (r = 0.6275). Representational similarity analysis evidenced global patterns of interactions across multiple brain network systems and behavioral domains. Results show that global processes of human aging can be well captured by a multivariate data fusion approach.
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Affiliation(s)
- Brent C McPherson
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, USA
| | - Franco Pestilli
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, USA.
- Department of Psychology, The University of Texas at Austin, Austin, TX, USA.
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30
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Babo-Rebelo M, Puce A, Bullock D, Hugueville L, Pestilli F, Adam C, Lehongre K, Lambrecq V, Dinkelacker V, George N. Visual Information Routes in the Posterior Dorsal and Ventral Face Network Studied with Intracranial Neurophysiology and White Matter Tract Endpoints. Cereb Cortex 2021; 32:342-366. [PMID: 34339495 PMCID: PMC8754371 DOI: 10.1093/cercor/bhab212] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 05/03/2021] [Accepted: 06/02/2021] [Indexed: 11/13/2022] Open
Abstract
Occipitotemporal regions within the face network process perceptual and socioemotional information, but the dynamics and information flow between different nodes of this network are still debated. Here, we analyzed intracerebral EEG from 11 epileptic patients viewing a stimulus sequence beginning with a neutral face with direct gaze. The gaze could avert or remain direct, while the emotion changed to fearful or happy. N200 field potential peak latencies indicated that face processing begins in inferior occipital cortex and proceeds anteroventrally to fusiform and inferior temporal cortices, in parallel. The superior temporal sulcus responded preferentially to gaze changes with augmented field potential amplitudes for averted versus direct gaze, and large effect sizes relative to other network regions. An overlap analysis of posterior white matter tractography endpoints (from 1066 healthy brains) relative to active intracerebral electrodes in the 11 patients showed likely involvement of both dorsal and ventral posterior white matter pathways. Overall, our data provide new insight into the timing of face and social cue processing in the occipitotemporal brain and anchor the superior temporal cortex in dynamic gaze processing.
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Affiliation(s)
- M Babo-Rebelo
- Institut du Cerveau-Paris Brain Institute, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Centre de Neuroimagerie de Recherche, CENIR, Centre MEG-EEG and STIM Platform, Paris F-75013, France.,Sorbonne Université, Institut du Cerveau-Paris Brain Institute, ICM, Inserm U 1127, CNRS UMR 7225, Experimental Neurosurgery Team, Paris F-75013, France.,Institute of Cognitive Neuroscience, University College London, WC1N 3AZ, London, UK
| | - A Puce
- Department of Psychological and Brain Sciences, Programs in Neuroscience, Cognitive Science, Indiana University, Bloomington, IN 47401, USA
| | - D Bullock
- Department of Psychological and Brain Sciences, Programs in Neuroscience, Cognitive Science, Indiana University, Bloomington, IN 47401, USA
| | - L Hugueville
- Institut du Cerveau-Paris Brain Institute, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Centre de Neuroimagerie de Recherche, CENIR, Centre MEG-EEG and STIM Platform, Paris F-75013, France
| | - F Pestilli
- Department of Psychological and Brain Sciences, Programs in Neuroscience, Cognitive Science, Indiana University, Bloomington, IN 47401, USA
| | - C Adam
- Neurophysiology Department, AP-HP, GH Pitié-Salpêtrière-Charles Foix, Paris F-75013, France
| | - K Lehongre
- Institut du Cerveau-Paris Brain Institute, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Centre de Neuroimagerie de Recherche, CENIR, Centre MEG-EEG and STIM Platform, Paris F-75013, France
| | - V Lambrecq
- Institut du Cerveau-Paris Brain Institute, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Centre de Neuroimagerie de Recherche, CENIR, Centre MEG-EEG and STIM Platform, Paris F-75013, France.,Neurophysiology Department, AP-HP, GH Pitié-Salpêtrière-Charles Foix, Paris F-75013, France
| | - V Dinkelacker
- Department of Neurology, Rothschild Foundation, Paris F-75019, France
| | - N George
- Institut du Cerveau-Paris Brain Institute, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Centre de Neuroimagerie de Recherche, CENIR, Centre MEG-EEG and STIM Platform, Paris F-75013, France.,Sorbonne Université, Institut du Cerveau-Paris Brain Institute, ICM, Inserm U 1127, CNRS UMR 7225, Experimental Neurosurgery Team, Paris F-75013, France
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31
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Perani D, Scifo P, Cicchini GM, Rosa PD, Banfi C, Mascheretti S, Falini A, Marino C, Morrone MC. White matter deficits correlate with visual motion perception impairments in dyslexic carriers of the DCDC2 genetic risk variant. Exp Brain Res 2021; 239:2725-2740. [PMID: 34228165 PMCID: PMC8448712 DOI: 10.1007/s00221-021-06137-1] [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: 11/24/2020] [Accepted: 05/12/2021] [Indexed: 02/07/2023]
Abstract
Motion perception deficits in dyslexia show a large intersubjective variability, partly reflecting genetic factors influencing brain architecture development. In previous work, we have demonstrated that dyslexic carriers of a mutation of the DCDC2 gene have a very strong impairment in motion perception. In the present study, we investigated structural white matter alterations associated with the poor motion perception in a cohort of twenty dyslexics with a subgroup carrying the DCDC2 gene deletion (DCDC2d+) and a subgroup without the risk variant (DCDC2d–). We observed significant deficits in motion contrast sensitivity and in motion direction discrimination accuracy at high contrast, stronger in the DCDC2d+ group. Both motion perception impairments correlated significantly with the fractional anisotropy in posterior ventral and dorsal tracts, including early visual pathways both along the optic radiation and in proximity of occipital cortex, MT and VWFA. However, the DCDC2d+ group showed stronger correlations between FA and motion perception impairments than the DCDC2d– group in early visual white matter bundles, including the optic radiations, and in ventral pathways located in the left inferior temporal cortex. Our results suggest that the DCDC2d+ group experiences higher vulnerability in visual motion processing even at early stages of visual analysis, which might represent a specific feature associated with the genotype and provide further neurobiological support to the visual-motion deficit account of dyslexia in a specific subpopulation.
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Affiliation(s)
- Daniela Perani
- Vita-Salute San Raffaele University, Milan, Italy.,C.E.R.M.A.C. (Centro di Risonanza Magnetica ad Alto Campo), Milan, Italy.,Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Scifo
- C.E.R.M.A.C. (Centro di Risonanza Magnetica ad Alto Campo), Milan, Italy.,Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Guido M Cicchini
- Institute of Neuroscience, National Research Council (CNR), Pisa, Italy.
| | - Pasquale Della Rosa
- C.E.R.M.A.C. (Centro di Risonanza Magnetica ad Alto Campo), Milan, Italy.,Unit of Neuroradiology, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Chiara Banfi
- Institute of Psychology, University of Graz, Graz, Austria
| | - Sara Mascheretti
- Child Psychopathology Unit, Scientific Institute Eugenio Medea, Bosisio Parini, Italy
| | - Andrea Falini
- Vita-Salute San Raffaele University, Milan, Italy.,C.E.R.M.A.C. (Centro di Risonanza Magnetica ad Alto Campo), Milan, Italy.,Unit of Neuroradiology, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Cecilia Marino
- Department of Psychiatry, Unviersity of Toronto, Toronto, Canada.,Division of Child and Youth Psychiatry, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Maria Concetta Morrone
- Department of Translational Research on New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy.,Scientific Institute Stella Maris (IRCSS), Pisa, Italy
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32
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Legarreta JH, Petit L, Rheault F, Theaud G, Lemaire C, Descoteaux M, Jodoin PM. Filtering in tractography using autoencoders (FINTA). Med Image Anal 2021; 72:102126. [PMID: 34161915 DOI: 10.1016/j.media.2021.102126] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 04/20/2021] [Accepted: 05/26/2021] [Indexed: 10/21/2022]
Abstract
Current brain white matter fiber tracking techniques show a number of problems, including: generating large proportions of streamlines that do not accurately describe the underlying anatomy; extracting streamlines that are not supported by the underlying diffusion signal; and under-representing some fiber populations, among others. In this paper, we describe a novel autoencoder-based learning method to filter streamlines from diffusion MRI tractography, and hence, to obtain more reliable tractograms. Our method, dubbed FINTA (Filtering in Tractography using Autoencoders) uses raw, unlabeled tractograms to train the autoencoder, and to learn a robust representation of brain streamlines. Such an embedding is then used to filter undesired streamline samples using a nearest neighbor algorithm. Our experiments on both synthetic and in vivo human brain diffusion MRI tractography data obtain accuracy scores exceeding the 90% threshold on the test set. Results reveal that FINTA has a superior filtering performance compared to conventional, anatomy-based methods, and the RecoBundles state-of-the-art method. Additionally, we demonstrate that FINTA can be applied to partial tractograms without requiring changes to the framework. We also show that the proposed method generalizes well across different tracking methods and datasets, and shortens significantly the computation time for large (>1 M streamlines) tractograms. Together, this work brings forward a new deep learning framework in tractography based on autoencoders, which offers a flexible and powerful method for white matter filtering and bundling that could enhance tractometry and connectivity analyses.
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Affiliation(s)
- Jon Haitz Legarreta
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, Université de Sherbrooke, 2500, boul. de l'Université, Sherbrooke, Québec J1K 2R1, Canada; Videos & Images Theory and Analytics Laboratory (VITAL), Department of Computer Science, Université de Sherbrooke, 2500, boul. de l'Université, Sherbrooke, Québec J1K 2R1, Canada.
| | - Laurent Petit
- Groupe d'Imagerie Neurofonctionnelle (GIN), Univ. Bordeaux, CNRS, CEA, IMN, UMR 5293, Bordeaux F-33000, France
| | - François Rheault
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, Université de Sherbrooke, 2500, boul. de l'Université, Sherbrooke, Québec J1K 2R1, Canada
| | - Guillaume Theaud
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, Université de Sherbrooke, 2500, boul. de l'Université, Sherbrooke, Québec J1K 2R1, Canada
| | - Carl Lemaire
- Centre de Calcul Scientifique, Université de Sherbrooke, 2500, boul. de l'Université, Sherbrooke, Québec J1K 2R1, Canada
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, Université de Sherbrooke, 2500, boul. de l'Université, Sherbrooke, Québec J1K 2R1, Canada
| | - Pierre-Marc Jodoin
- Videos & Images Theory and Analytics Laboratory (VITAL), Department of Computer Science, Université de Sherbrooke, 2500, boul. de l'Université, Sherbrooke, Québec J1K 2R1, Canada
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33
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Finzi D, Gomez J, Nordt M, Rezai AA, Poltoratski S, Grill-Spector K. Differential spatial computations in ventral and lateral face-selective regions are scaffolded by structural connections. Nat Commun 2021; 12:2278. [PMID: 33859195 PMCID: PMC8050273 DOI: 10.1038/s41467-021-22524-2] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 03/12/2021] [Indexed: 01/01/2023] Open
Abstract
Face-processing occurs across ventral and lateral visual streams, which are involved in static and dynamic face perception, respectively. However, the nature of spatial computations across streams is unknown. Using functional MRI and population receptive field (pRF) mapping, we measured pRFs in face-selective regions. Results reveal that spatial computations by pRFs in ventral face-selective regions are concentrated around the center of gaze (fovea), but spatial computations in lateral face-selective regions extend peripherally. Diffusion MRI reveals that these differences are mirrored by a preponderance of white matter connections between ventral face-selective regions and foveal early visual cortex (EVC), while connections with lateral regions are distributed more uniformly across EVC eccentricities. These findings suggest a rethinking of spatial computations in face-selective regions, showing that they vary across ventral and lateral streams, and further propose that spatial computations in high-level regions are scaffolded by the fine-grain pattern of white matter connections from EVC.
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Affiliation(s)
- Dawn Finzi
- Department of Psychology, Stanford University, Stanford, CA, USA.
| | - Jesse Gomez
- Neurosciences Program, Stanford University, Stanford, CA, USA
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Marisa Nordt
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Alex A Rezai
- Department of Psychology, Stanford University, Stanford, CA, USA
| | | | - Kalanit Grill-Spector
- Department of Psychology, Stanford University, Stanford, CA, USA
- Neurosciences Program, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
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Richard N, Desmurget M, Teillac A, Beuriat PA, Bardi L, Coudé G, Szathmari A, Mottolese C, Sirigu A, Hiba B. Anatomical bases of fast parietal grasp control in humans: A diffusion-MRI tractography study. Neuroimage 2021; 235:118002. [PMID: 33789136 DOI: 10.1016/j.neuroimage.2021.118002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 01/26/2021] [Accepted: 03/24/2021] [Indexed: 11/26/2022] Open
Abstract
The dorso-posterior parietal cortex (DPPC) is a major node of the grasp/manipulation control network. It is assumed to act as an optimal forward estimator that continuously integrates efferent outflows and afferent inflows to modulate the ongoing motor command. In agreement with this view, a recent per-operative study, in humans, identified functional sites within DPPC that: (i) instantly disrupt hand movements when electrically stimulated; (ii) receive short-latency somatosensory afferences from intrinsic hand muscles. Based on these results, it was speculated that DPPC is part of a rapid grasp control loop that receives direct inputs from the hand-territory of the primary somatosensory cortex (S1) and sends direct projections to the hand-territory of the primary motor cortex (M1). However, evidence supporting this hypothesis is weak and partial. To date, projections from DPPC to M1 grasp zone have been identified in monkeys and have been postulated to exist in humans based on clinical and transcranial magnetic studies. This work uses diffusion-MRI tractography in two samples of right- (n = 50) and left-handed (n = 25) subjects randomly selected from the Human Connectome Project. It aims to determine whether direct connections exist between DPPC and the hand control sectors of the primary sensorimotor regions. The parietal region of interest, related to hand control (hereafter designated DPPChand), was defined permissively as the 95% confidence area of the parietal sites that were found to disrupt hand movements in the previously evoked per-operative study. In both hemispheres, irrespective of handedness, we found dense ipsilateral connections between a restricted part of DPPChand and focal sectors within the pre and postcentral gyrus. These sectors, corresponding to the hand territories of M1 and S1, targeted the same parietal zone (spatial overlap > 92%). As a sensitivity control, we searched for potential connections between the angular gyrus (AG) and the pre and postcentral regions. No robust pathways were found. Streamline densities identified using AG as the starting seed represented less than 5 % of the streamline densities identified from DPPChand. Together, these results support the existence of a direct sensory-parietal-motor loop suited for fast manual control and more generally, for any task requiring rapid integration of distal sensorimotor signals.
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Affiliation(s)
- Nathalie Richard
- Institute of Cognitive Neuroscience Marc Jeannerod, CNRS / UMR 5229, 69500 Bron, France; Université Claude Bernard, Lyon 1, 69100 Villeurbanne, France
| | - Michel Desmurget
- Institute of Cognitive Neuroscience Marc Jeannerod, CNRS / UMR 5229, 69500 Bron, France; Université Claude Bernard, Lyon 1, 69100 Villeurbanne, France
| | - Achille Teillac
- Institute of Cognitive Neuroscience Marc Jeannerod, CNRS / UMR 5229, 69500 Bron, France; Université Claude Bernard, Lyon 1, 69100 Villeurbanne, France; Institut de neurosciences cognitives et intégratives d'Aquitaine, CNRS / UMR 5287, 33076 Bordeaux, France
| | - Pierre-Aurélien Beuriat
- Institute of Cognitive Neuroscience Marc Jeannerod, CNRS / UMR 5229, 69500 Bron, France; Université Claude Bernard, Lyon 1, 69100 Villeurbanne, France; Department of Pediatric Neurosurgery, Hôpital Femme Mère Enfant, 69500, Bron, France
| | - Lara Bardi
- Institute of Cognitive Neuroscience Marc Jeannerod, CNRS / UMR 5229, 69500 Bron, France; Université Claude Bernard, Lyon 1, 69100 Villeurbanne, France
| | - Gino Coudé
- Institute of Cognitive Neuroscience Marc Jeannerod, CNRS / UMR 5229, 69500 Bron, France; Université Claude Bernard, Lyon 1, 69100 Villeurbanne, France
| | - Alexandru Szathmari
- Institute of Cognitive Neuroscience Marc Jeannerod, CNRS / UMR 5229, 69500 Bron, France; Université Claude Bernard, Lyon 1, 69100 Villeurbanne, France; Department of Pediatric Neurosurgery, Hôpital Femme Mère Enfant, 69500, Bron, France
| | - Carmine Mottolese
- Institute of Cognitive Neuroscience Marc Jeannerod, CNRS / UMR 5229, 69500 Bron, France; Université Claude Bernard, Lyon 1, 69100 Villeurbanne, France; Department of Pediatric Neurosurgery, Hôpital Femme Mère Enfant, 69500, Bron, France
| | - Angela Sirigu
- Institute of Cognitive Neuroscience Marc Jeannerod, CNRS / UMR 5229, 69500 Bron, France; Université Claude Bernard, Lyon 1, 69100 Villeurbanne, France
| | - Bassem Hiba
- Institute of Cognitive Neuroscience Marc Jeannerod, CNRS / UMR 5229, 69500 Bron, France; Université Claude Bernard, Lyon 1, 69100 Villeurbanne, France.
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35
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Caron B, Stuck R, McPherson B, Bullock D, Kitchell L, Faskowitz J, Kellar D, Cheng H, Newman S, Port N, Pestilli F. Collegiate athlete brain data for white matter mapping and network neuroscience. Sci Data 2021; 8:56. [PMID: 33574337 PMCID: PMC7878753 DOI: 10.1038/s41597-021-00823-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 12/14/2020] [Indexed: 12/13/2022] Open
Abstract
We describe a dataset of processed data with associated reproducible preprocessing pipeline collected from two collegiate athlete groups and one non-athlete group. The dataset shares minimally processed diffusion-weighted magnetic resonance imaging (dMRI) data, three models of the diffusion signal in the voxel, full-brain tractograms, segmentation of the major white matter tracts as well as structural connectivity matrices. There is currently a paucity of similar datasets openly shared. Furthermore, major challenges are associated with collecting this type of data. The data and derivatives shared here can be used as a reference to study the effects of long-term exposure to collegiate athletics, such as the effects of repetitive head impacts. We use advanced anatomical and dMRI data processing methods publicly available as reproducible web services at brainlife.io.
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Affiliation(s)
- Bradley Caron
- Program in Neuroscience, Indiana University, 702 North Walnut Grove St, Bloomington, IN, 47405, USA
- School of Optometry, Indiana University, 800 E. Atwater Avenue, Bloomington, IN, 47405, USA
| | - Ricardo Stuck
- Program in Neuroscience, Indiana University, 702 North Walnut Grove St, Bloomington, IN, 47405, USA
| | - Brent McPherson
- Department of Psychological and Brain Sciences, Indiana University, 1101 East 10th Street, Bloomington, IN, 47405, USA
| | - Daniel Bullock
- Program in Neuroscience, Indiana University, 702 North Walnut Grove St, Bloomington, IN, 47405, USA
- Department of Psychological and Brain Sciences, Indiana University, 1101 East 10th Street, Bloomington, IN, 47405, USA
| | - Lindsey Kitchell
- Department of Psychological and Brain Sciences, Indiana University, 1101 East 10th Street, Bloomington, IN, 47405, USA
- Program in Cognitive Science, Indiana University, 1101 East 10th Street, Bloomington, IN, 47405, USA
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD, 20723, USA
| | - Joshua Faskowitz
- Program in Neuroscience, Indiana University, 702 North Walnut Grove St, Bloomington, IN, 47405, USA
- Department of Psychological and Brain Sciences, Indiana University, 1101 East 10th Street, Bloomington, IN, 47405, USA
| | - Derek Kellar
- Department of Psychological and Brain Sciences, Indiana University, 1101 East 10th Street, Bloomington, IN, 47405, USA
| | - Hu Cheng
- Program in Neuroscience, Indiana University, 702 North Walnut Grove St, Bloomington, IN, 47405, USA
- Department of Psychological and Brain Sciences, Indiana University, 1101 East 10th Street, Bloomington, IN, 47405, USA
| | - Sharlene Newman
- Program in Neuroscience, Indiana University, 702 North Walnut Grove St, Bloomington, IN, 47405, USA
- Department of Psychological and Brain Sciences, Indiana University, 1101 East 10th Street, Bloomington, IN, 47405, USA
- Alabama Life Research Institute, The University of Alabama, 1402E Northeast Medical Building, Box 870328, Tuscaloosa, AL, USA
| | - Nicholas Port
- Program in Neuroscience, Indiana University, 702 North Walnut Grove St, Bloomington, IN, 47405, USA
- School of Optometry, Indiana University, 800 E. Atwater Avenue, Bloomington, IN, 47405, USA
- Department of Psychological and Brain Sciences, Indiana University, 1101 East 10th Street, Bloomington, IN, 47405, USA
- Program in Cognitive Science, Indiana University, 1101 East 10th Street, Bloomington, IN, 47405, USA
| | - Franco Pestilli
- Program in Neuroscience, Indiana University, 702 North Walnut Grove St, Bloomington, IN, 47405, USA.
- School of Optometry, Indiana University, 800 E. Atwater Avenue, Bloomington, IN, 47405, USA.
- Department of Psychological and Brain Sciences, Indiana University, 1101 East 10th Street, Bloomington, IN, 47405, USA.
- Program in Cognitive Science, Indiana University, 1101 East 10th Street, Bloomington, IN, 47405, USA.
- Department of Computer Science, School of Informatics, Indiana University, 700 North Woodlawn Avenue, Bloomington, IN, 47408, USA.
- Department of Intelligent Systems Engineering, School of Informatics, Indiana University, 700 North Woodlawn Avenue, Bloomington, IN, 47408, USA.
- Department of Psychology, The University of Texas at Austin, 108 E Dean Keeton St, Austin, TX, 78712, USA.
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36
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Reymbaut A, Caron AV, Gilbert G, Szczepankiewicz F, Nilsson M, Warfield SK, Descoteaux M, Scherrer B. Magic DIAMOND: Multi-fascicle diffusion compartment imaging with tensor distribution modeling and tensor-valued diffusion encoding. Med Image Anal 2021; 70:101988. [PMID: 33611054 DOI: 10.1016/j.media.2021.101988] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 01/25/2021] [Accepted: 01/29/2021] [Indexed: 01/05/2023]
Abstract
Diffusion tensor imaging provides increased sensitivity to microstructural tissue changes compared to conventional anatomical imaging but also presents limited specificity. To tackle this problem, the DIAMOND model subdivides the voxel content into diffusion compartments and draws from diffusion-weighted data to estimate compartmental non-central matrix-variate Gamma distributions of diffusion tensors. It models each sub-voxel fascicle separately, resolving crossing white-matter pathways and allowing for a fascicle-element (fixel) based analysis of microstructural features. Alternatively, specific features of the intra-voxel diffusion tensor distribution can be selectively measured using tensor-valued diffusion-weighted acquisition schemes. However, the impact of such schemes on estimating brain microstructural features has only been studied in a handful of parametric single-fascicle models. In this work, we derive a general Laplace transform for the non-central matrix-variate Gamma distribution, which enables the extension of DIAMOND to tensor-valued encoded data. We then evaluate this "Magic DIAMOND" model in silico and in vivo on various combinations of tensor-valued encoded data. Assessing uncertainty on parameter estimation via stratified bootstrap, we investigate both voxel-based and fixel-based metrics by carrying out multi-peak tractography. We demonstrate using in silico evaluations that tensor-valued diffusion encoding significantly improves Magic DIAMOND's accuracy. Most importantly, we show in vivo that our estimated metrics can be robustly mapped along tracks across regions of fiber crossing, which opens new perspectives for tractometry and microstructure mapping along specific white-matter tracts.
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Affiliation(s)
| | | | - Guillaume Gilbert
- MR Clinical Science, Philips Healthcare Canada, Markham, ON L6C 2S3, Canada
| | - Filip Szczepankiewicz
- Department of Clinical Sciences, Lund University, 22184, Lund, Sweden; Random Walk Imaging AB, 22224, Lund, Sweden
| | - Markus Nilsson
- Department of Clinical Sciences, Lund University, 22184, Lund, Sweden
| | - Simon K Warfield
- Department of Radiology, Boston Children's Hospital, Boston, MA 02115, United States
| | | | - Benoit Scherrer
- Department of Radiology, Boston Children's Hospital, Boston, MA 02115, United States
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37
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Sani I, Stemmann H, Caron B, Bullock D, Stemmler T, Fahle M, Pestilli F, Freiwald WA. The human endogenous attentional control network includes a ventro-temporal cortical node. Nat Commun 2021; 12:360. [PMID: 33452252 PMCID: PMC7810878 DOI: 10.1038/s41467-020-20583-5] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Accepted: 12/07/2020] [Indexed: 01/29/2023] Open
Abstract
Endogenous attention is the cognitive function that selects the relevant pieces of sensory information to achieve goals and it is known to be controlled by dorsal fronto-parietal brain areas. Here we expand this notion by identifying a control attention area located in the temporal lobe. By combining a demanding behavioral paradigm with functional neuroimaging and diffusion tractography, we show that like fronto-parietal attentional areas, the human posterior inferotemporal cortex exhibits significant attentional modulatory activity. This area is functionally distinct from surrounding cortical areas, and is directly connected to parietal and frontal attentional regions. These results show that attentional control spans three cortical lobes and overarches large distances through fiber pathways that run orthogonally to the dominant anterior-posterior axes of sensory processing, thus suggesting a different organizing principle for cognitive control.
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Affiliation(s)
- Ilaria Sani
- grid.134907.80000 0001 2166 1519Laboratory of Neural Systems, The Rockefeller University, 1230 York Avenue, New York, NY 10065 USA ,grid.8591.50000 0001 2322 4988Laboratory of Neurology & Imaging of Cognition, University of Geneva, Chemin de mines 9, 1202 Geneva, CH Switzerland
| | - Heiko Stemmann
- grid.7704.40000 0001 2297 4381Institute for Brain Research and Center for Advanced Imaging, University of Bremen, 28334 Bremen, Germany
| | - Bradley Caron
- grid.411377.70000 0001 0790 959XDepartment of Psychological and Brain Sciences, Indiana University, Bloomington, IN USA
| | - Daniel Bullock
- grid.411377.70000 0001 0790 959XDepartment of Psychological and Brain Sciences, Indiana University, Bloomington, IN USA
| | - Torsten Stemmler
- grid.7704.40000 0001 2297 4381Institute for Brain Research and Center for Advanced Imaging, University of Bremen, 28334 Bremen, Germany
| | - Manfred Fahle
- grid.7704.40000 0001 2297 4381Institute for Brain Research and Center for Advanced Imaging, University of Bremen, 28334 Bremen, Germany
| | - Franco Pestilli
- grid.411377.70000 0001 0790 959XDepartment of Psychological and Brain Sciences, Indiana University, Bloomington, IN USA ,grid.89336.370000 0004 1936 9924Department of Psychology, The University of Texas at Austin, Austin, TX 78712 USA
| | - Winrich A. Freiwald
- grid.134907.80000 0001 2166 1519Laboratory of Neural Systems, The Rockefeller University, 1230 York Avenue, New York, NY 10065 USA ,Center for Brains, Minds & Machines, Cambridge, MA USA
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38
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Lerma-Usabiaga G, Mukherjee P, Perry ML, Wandell BA. Data-science ready, multisite, human diffusion MRI white-matter-tract statistics. Sci Data 2020; 7:422. [PMID: 33257659 PMCID: PMC7705748 DOI: 10.1038/s41597-020-00760-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 11/12/2020] [Indexed: 02/08/2023] Open
Abstract
The white matter tracts in the living human brain are critical for healthy function, and the diffusion MRI measured in these tracts is correlated with diverse behavioral measures. The technical skills required to analyze diffusion MRI data are complex: data acquisition requires MRI sequence development and acquisition expertise, analyzing raw-data into meaningful summary statistics requires computational neuroimaging and neuroanatomy expertise. The human white matter study field will advance faster if the tract summaries are available in plain data-science-ready format for non-diffusion MRI experts, such as statisticians, computer graphic researchers or data scientists in general. Here, we share a curated and processed dataset from three different MRI centers in a format that is data-science ready. The multisite data we share include measures of within and between MRI center variation in white-matter-tract diffusion measurements. Along with the dataset description and summary statistics, we describe the state-of-the-art computational system that guarantees reproducibility and provenance from the original scanner output.
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Affiliation(s)
- Garikoitz Lerma-Usabiaga
- Department of Psychology, Stanford University, 450 Jane Stanford Way, Jordan Hall Building, 94305, Stanford, California, USA.
- BCBL. Basque Center on Cognition, Brain and Language, Mikeletegi Pasealekua 69, Donostia - San Sebastián, 20009, Gipuzkoa, Spain.
- Wu Tsai Neurosciences Institute, Stanford University, 94305, Stanford, California, USA.
| | - Pratik Mukherjee
- Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
- Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | - Michael L Perry
- Department of Psychology, Stanford University, 450 Jane Stanford Way, Jordan Hall Building, 94305, Stanford, California, USA
| | - Brian A Wandell
- Department of Psychology, Stanford University, 450 Jane Stanford Way, Jordan Hall Building, 94305, Stanford, California, USA
- Wu Tsai Neurosciences Institute, Stanford University, 94305, Stanford, California, USA
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39
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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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40
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He B, Cao L, Xia X, Zhang B, Zhang D, You B, Fan L, Jiang T. Fine-Grained Topography and Modularity of the Macaque Frontal Pole Cortex Revealed by Anatomical Connectivity Profiles. Neurosci Bull 2020; 36:1454-1473. [PMID: 33108588 PMCID: PMC7719154 DOI: 10.1007/s12264-020-00589-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 07/30/2020] [Indexed: 11/25/2022] Open
Abstract
The frontal pole cortex (FPC) plays key roles in various higher-order functions and is highly developed in non-human primates. An essential missing piece of information is the detailed anatomical connections for finer parcellation of the macaque FPC than provided by the previous tracer results. This is important for understanding the functional architecture of the cerebral cortex. Here, combining cross-validation and principal component analysis, we formed a tractography-based parcellation scheme that applied a machine learning algorithm to divide the macaque FPC (2 males and 6 females) into eight subareas using high-resolution diffusion magnetic resonance imaging with the 9.4T Bruker system, and then revealed their subregional connections. Furthermore, we applied improved hierarchical clustering to the obtained parcels to probe the modular structure of the subregions, and found that the dorsolateral FPC, which contains an extension to the medial FPC, was mainly connected to regions of the default-mode network. The ventral FPC was mainly involved in the social-interaction network and the dorsal FPC in the metacognitive network. These results enhance our understanding of the anatomy and circuitry of the macaque brain, and contribute to FPC-related clinical research.
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Affiliation(s)
- Bin He
- School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin, 150080, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, 100190, China
| | - Long Cao
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xiaoluan Xia
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030600, China
| | - Baogui Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, 100190, China
| | - Dan Zhang
- Core Facility, Center of Biomedical Analysis, Tsinghua University, Beijing, 100084, China
| | - Bo You
- School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin, 150080, China.
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. .,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, 100190, China. .,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, CAS, Beijing, 100190, China. .,University of CAS, Beijing, 100049, China.
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. .,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, 100190, China. .,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, CAS, Beijing, 100190, China. .,Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China. .,The Queensland Brain Institute, University of Queensland, Brisbane, QLD, 4072, Australia. .,University of CAS, Beijing, 100049, China. .,Chinese Institute for Brain Research, Beijing, 102206, China.
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41
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Bertò G, Bullock D, Astolfi P, Hayashi S, Zigiotto L, Annicchiarico L, Corsini F, De Benedictis A, Sarubbo S, Pestilli F, Avesani P, Olivetti E. Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation. Neuroimage 2020; 224:117402. [PMID: 32979520 DOI: 10.1016/j.neuroimage.2020.117402] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 09/12/2020] [Accepted: 09/18/2020] [Indexed: 12/18/2022] Open
Abstract
Virtual delineation of white matter bundles in the human brain is of paramount importance for multiple applications, such as pre-surgical planning and connectomics. A substantial body of literature is related to methods that automatically segment bundles from diffusion Magnetic Resonance Imaging (dMRI) data indirectly, by exploiting either the idea of connectivity between regions or the geometry of fiber paths obtained with tractography techniques, or, directly, through the information in volumetric data. Despite the remarkable improvement in automatic segmentation methods over the years, their segmentation quality is not yet satisfactory, especially when dealing with datasets with very diverse characteristics, such as different tracking methods, bundle sizes or data quality. In this work, we propose a novel, supervised streamline-based segmentation method, called Classifyber, which combines information from atlases, connectivity patterns, and the geometry of fiber paths into a simple linear model. With a wide range of experiments on multiple datasets that span from research to clinical domains, we show that Classifyber substantially improves the quality of segmentation as compared to other state-of-the-art methods and, more importantly, that it is robust across very diverse settings. We provide an implementation of the proposed method as open source code, as well as web service.
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Affiliation(s)
- Giulia Bertò
- NeuroInformatics Laboratory (NILab), Bruno Kessler Foundation (FBK), Trento, Italy; Center for Mind and Brain Sciences (CIMeC), University of Trento, Italy
| | - Daniel Bullock
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, USA
| | - Pietro Astolfi
- NeuroInformatics Laboratory (NILab), Bruno Kessler Foundation (FBK), Trento, Italy; Center for Mind and Brain Sciences (CIMeC), University of Trento, Italy; PAVIS, Italian Institute of Technology (IIT), Genova, Italy
| | - Soichi Hayashi
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, USA
| | - Luca Zigiotto
- Division of Neurosurgery, Structural and Functional Connectivity Lab, S. Chiara Hospital, Trento, Italy
| | - Luciano Annicchiarico
- Division of Neurosurgery, Structural and Functional Connectivity Lab, S. Chiara Hospital, Trento, Italy
| | - Francesco Corsini
- Division of Neurosurgery, Structural and Functional Connectivity Lab, S. Chiara Hospital, Trento, Italy
| | - Alessandro De Benedictis
- Neurosurgery Unit, Department of Neuroscience, Bambino Gesù Children's Hospital IRCCS, Rome, Italy
| | - Silvio Sarubbo
- Division of Neurosurgery, Structural and Functional Connectivity Lab, S. Chiara Hospital, Trento, Italy
| | - Franco Pestilli
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, USA
| | - Paolo Avesani
- NeuroInformatics Laboratory (NILab), Bruno Kessler Foundation (FBK), Trento, Italy; Center for Mind and Brain Sciences (CIMeC), University of Trento, Italy
| | - Emanuele Olivetti
- NeuroInformatics Laboratory (NILab), Bruno Kessler Foundation (FBK), Trento, Italy; Center for Mind and Brain Sciences (CIMeC), University of Trento, Italy.
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42
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Reconstructing the Brain's Wiring Diagram Is No Monkey Business. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:840-841. [PMID: 32896296 DOI: 10.1016/j.bpsc.2020.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 07/10/2020] [Indexed: 11/23/2022]
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43
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Girard G, Caminiti R, Battaglia-Mayer A, St-Onge E, Ambrosen KS, Eskildsen SF, Krug K, Dyrby TB, Descoteaux M, Thiran JP, Innocenti GM. On the cortical connectivity in the macaque brain: A comparison of diffusion tractography and histological tracing data. Neuroimage 2020; 221:117201. [PMID: 32739552 DOI: 10.1016/j.neuroimage.2020.117201] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 07/17/2020] [Accepted: 07/22/2020] [Indexed: 12/22/2022] Open
Abstract
Diffusion-weighted magnetic resonance imaging (DW-MRI) tractography is a non-invasive tool to probe neural connections and the structure of the white matter. It has been applied successfully in studies of neurological disorders and normal connectivity. Recent work has revealed that tractography produces a high incidence of false-positive connections, often from "bottleneck" white matter configurations. The rich literature in histological connectivity analysis studies in the macaque monkey enables quantitative evaluation of the performance of tractography algorithms. In this study, we use the intricate connections of frontal, cingulate, and parietal areas, well established by the anatomical literature, to derive a symmetrical histological connectivity matrix composed of 59 cortical areas. We evaluate the performance of fifteen diffusion tractography algorithms, including global, deterministic, and probabilistic state-of-the-art methods for the connectivity predictions of 1711 distinct pairs of areas, among which 680 are reported connected by the literature. The diffusion connectivity analysis was performed on a different ex-vivo macaque brain, acquired using multi-shell DW-MRI protocol, at high spatial and angular resolutions. Across all tested algorithms, the true-positive and true-negative connections were dominant over false-positive and false-negative connections, respectively. Moreover, three-quarters of streamlines had endpoints location in agreement with histological data, on average. Furthermore, probabilistic streamline tractography algorithms show the best performances in predicting which areas are connected. Altogether, we propose a method for quantitative evaluation of tractography algorithms, which aims at improving the sensitivity and the specificity of diffusion-based connectivity analysis. Overall, those results confirm the usefulness of tractography in predicting connectivity, although errors are produced. Many of the errors result from bottleneck white matter configurations near the cortical grey matter and should be the target of future implementation of methods.
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Affiliation(s)
- Gabriel Girard
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Center for BioMedical Imaging, Lausanne, Switzerland; Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
| | - Roberto Caminiti
- Neuroscience and Behavior Laboratory, Istituto Italiano di Tecnologia, Rome, Italy
| | | | - Etienne St-Onge
- Sherbrooke Connectivity Imaging Lab, Computer Science Department, Faculty of Science, Université de Sherbrooke, Sherbrooke, Canada
| | - Karen S Ambrosen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark; Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Simon F Eskildsen
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Kristine Krug
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom; Institute of Biology, Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany; Leibniz-Insitute for Neurobiology, Magdeburg, Germany
| | - Tim B Dyrby
- Danish Research Centre for Magnetic Resonance, Center 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
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab, Computer Science Department, Faculty of Science, Université de Sherbrooke, Sherbrooke, Canada
| | - Jean-Philippe Thiran
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Center for BioMedical Imaging, Lausanne, Switzerland; Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Giorgio M Innocenti
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden; Brain and Mind Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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44
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Tozzi L, Staveland B, Holt-Gosselin B, Chesnut M, Chang SE, Choi D, Shiner M, Wu H, Lerma-Usabiaga G, Sporns O, Barch DM, Gotlib IH, Hastie TJ, Kerr AB, Poldrack RA, Wandell BA, Wintermark M, Williams LM. The human connectome project for disordered emotional states: Protocol and rationale for a research domain criteria study of brain connectivity in young adult anxiety and depression. Neuroimage 2020; 214:116715. [PMID: 32147367 PMCID: PMC8597395 DOI: 10.1016/j.neuroimage.2020.116715] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 03/03/2020] [Indexed: 12/31/2022] Open
Abstract
Through the Human Connectome Project (HCP) our understanding of the functional connectome of the healthy brain has been dramatically accelerated. Given the pressing public health need, we must increase our understanding of how connectome dysfunctions give rise to disordered mental states. Mental disorders arising from high levels of negative emotion or from the loss of positive emotional experience affect over 400 million people globally. Such states of disordered emotion cut across multiple diagnostic categories of mood and anxiety disorders and are compounded by accompanying disruptions in cognitive function. Not surprisingly, these forms of psychopathology are the leading cause of disability worldwide. The Research Domain Criteria (RDoC) initiative spearheaded by NIMH offers a framework for characterizing the relations among connectome dysfunctions, anchored in neural circuits and phenotypic profiles of behavior and self-reported symptoms. Here, we report on our Connectomes Related to Human Disease protocol for integrating an RDoC framework with HCP protocols to characterize connectome dysfunctions in disordered emotional states, and present quality control data from a representative sample of participants. We focus on three RDoC domains and constructs most relevant to depression and anxiety: 1) loss and acute threat within the Negative Valence System (NVS) domain; 2) reward valuation and responsiveness within the Positive Valence System (PVS) domain; and 3) working memory and cognitive control within the Cognitive System (CS) domain. For 29 healthy controls, we present preliminary imaging data: functional magnetic resonance imaging collected in the resting state and in tasks matching our constructs of interest ("Emotion", "Gambling" and "Continuous Performance" tasks), as well as diffusion-weighted imaging. All functional scans demonstrated good signal-to-noise ratio. Established neural networks were robustly identified in the resting state condition by independent component analysis. Processing of negative emotional faces significantly activated the bilateral dorsolateral prefrontal and occipital cortices, fusiform gyrus and amygdalae. Reward elicited a response in the bilateral dorsolateral prefrontal, parietal and occipital cortices, and in the striatum. Working memory was associated with activation in the dorsolateral prefrontal, parietal, motor, temporal and insular cortices, in the striatum and cerebellum. Diffusion tractography showed consistent profiles of fractional anisotropy along known white matter tracts. We also show that results are comparable to those in a matched sample from the HCP Healthy Young Adult data release. These preliminary data provide the foundation for acquisition of 250 subjects who are experiencing disordered emotional states. When complete, these data will be used to develop a neurobiological model that maps connectome dysfunctions to specific behaviors and symptoms.
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Affiliation(s)
- Leonardo Tozzi
- Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Brooke Staveland
- Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | | | - Megan Chesnut
- Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Sarah E Chang
- Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - David Choi
- Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Melissa Shiner
- Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Hua Wu
- Center for Cognitive and Neurobiological Imaging, Stanford University, CA, USA
| | - Garikoitz Lerma-Usabiaga
- Psychology, Stanford University, CA, USA; BCBL. Basque Center on Cognition, Brain and Language, Donostia - San Sebastián, Gipuzkoa, Spain
| | - Olaf Sporns
- Psychological and Brain Sciences, Indiana University, IN, USA
| | - Deanna M Barch
- Psychological and Brain Sciences, Psychiatry & Radiology Washington University in St. Louis, MO, USA
| | | | | | - Adam B Kerr
- Center for Cognitive and Neurobiological Imaging, Stanford University, CA, USA; Department of Electrical Engineering, Stanford University, CA, USA
| | | | - Brian A Wandell
- Center for Cognitive and Neurobiological Imaging, Stanford University, CA, USA
| | | | - Leanne M Williams
- Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA; Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC) Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.
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45
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Kurzawski JW, Mikellidou K, Morrone MC, Pestilli F. The visual white matter connecting human area prostriata and the thalamus is retinotopically organized. Brain Struct Funct 2020; 225:1839-1853. [PMID: 32535840 PMCID: PMC7321903 DOI: 10.1007/s00429-020-02096-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2019] [Accepted: 06/05/2020] [Indexed: 11/30/2022]
Abstract
The human visual system is capable of processing visual information from fovea to the far peripheral visual field. Recent fMRI studies have shown a full and detailed retinotopic map in area prostriata, located ventro-dorsally and anterior to the calcarine sulcus along the parieto-occipital sulcus with strong preference for peripheral and wide-field stimulation. Here, we report the anatomical pattern of white matter connections between area prostriata and the thalamus encompassing the lateral geniculate nucleus (LGN). To this end, we developed and utilized an automated pipeline comprising a series of Apps that run openly on the cloud computing platform brainlife.io to analyse 139 subjects of the Human Connectome Project (HCP). We observe a continuous and extended bundle of white matter fibers from which two subcomponents can be extracted: one passing ventrally parallel to the optic radiations (OR) and another passing dorsally circumventing the lateral ventricle. Interestingly, the loop travelling dorsally connects the thalamus with the central visual field representation of prostriata located anteriorly, while the other loop travelling more ventrally connects the LGN with the more peripheral visual field representation located posteriorly. We then analyse an additional cohort of 10 HCP subjects using a manual plane extraction method outside brainlife.io to study the relationship between the two extracted white matter subcomponents and eccentricity, myelin and cortical thickness gradients within prostriata. Our results are consistent with a retinotopic segregation recently demonstrated in the OR, connecting the LGN and V1 in humans and reveal for the first time a retinotopic segregation regarding the trajectory of a fiber bundle between the thalamus and an associative visual area.
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Affiliation(s)
| | - Kyriaki Mikellidou
- Department of Psychology and Center for Applied Neuroscience, University of Cyprus, Nicosia, Cyprus
| | - Maria Concetta Morrone
- IRCCS Stella Maris, Viale del Tirreno, 331, Pisa, Italy.,Department of Translational Research On New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Franco Pestilli
- Department of Psychological and Brain Sciences, Program in Neuroscience and Program in Cognitive Science, Indiana University, 1101 E 10th Street, Bloomington, IN, 47401, USA.
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46
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Kaneko T, Takemura H, Pestilli F, Silva AC, Ye FQ, Leopold DA. Spatial organization of occipital white matter tracts in the common marmoset. Brain Struct Funct 2020; 225:1313-1326. [PMID: 32253509 PMCID: PMC7577349 DOI: 10.1007/s00429-020-02060-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Accepted: 03/18/2020] [Indexed: 11/30/2022]
Abstract
The primate brain contains a large number of interconnected visual areas, whose spatial organization and intracortical projections show a high level of conservation across species. One fiber pathway of recent interest is the vertical occipital fasciculus (VOF), which is thought to support communication between dorsal and ventral visual areas in the occipital lobe. A recent comparative diffusion MRI (dMRI) study reported that the VOF in the macaque brain bears a similar topology to that of the human, running superficial and roughly perpendicular to the optic radiation. The present study reports a comparative investigation of the VOF in the common marmoset, a small New World monkey whose lissencephalic brain is approximately tenfold smaller than the macaque and 150-fold smaller than the human. High-resolution ex vivo dMRI of two marmoset brains revealed an occipital white matter structure that closely resembles that of the larger primate species, with one notable difference. Namely, unlike in the macaque and the human, the VOF in the marmoset is spatially fused with other, more anterior vertical tracts, extending anteriorly between the parietal and temporal cortices. We compare several aspects of this continuous structure, which we term the VOF complex (VOF +), and neighboring fasciculi to those of macaques and humans. We hypothesize that the essential topology of the VOF+ is a conserved feature of the posterior cortex in anthropoid primates, with a clearer fragmentation into multiple named fasciculi in larger, more gyrified brains.
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Affiliation(s)
- Takaaki Kaneko
- RIKEN Center for Brain Science (CBS), 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan.
- Systems Neuroscience Section, Primate Research Institute, Kyoto University, 41 Kanrin, Inuyamas-shi, Aichi, 484-8506, Japan.
| | - Hiromasa Takemura
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, 1-4 Yamadaoka, Suita-shi, Osaka, 565-0871, Japan.
- Graduate School of Frontier Biosciences, Osaka University, 1-4 Yamadaoka, Suita-shi, Osaka, 565-0871, Japan.
| | - Franco Pestilli
- Department of Psychological and Brain Sciences, Indiana University, 1101 E 10th Street, Bloomington, IN, 47405, USA
| | - Afonso C Silva
- Department of Neurobiology, University of Pittsburgh Brain Institute, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Frank Q Ye
- Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | - David A Leopold
- Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
- Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
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47
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Ahmadi K, Fracasso A, Puzniak RJ, Gouws AD, Yakupov R, Speck O, Kaufmann J, Pestilli F, Dumoulin SO, Morland AB, Hoffmann MB. Triple visual hemifield maps in a case of optic chiasm hypoplasia. Neuroimage 2020; 215:116822. [PMID: 32276070 DOI: 10.1016/j.neuroimage.2020.116822] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 02/27/2020] [Accepted: 04/02/2020] [Indexed: 12/18/2022] Open
Abstract
In humans, each hemisphere comprises an overlay of two visuotopic maps of the contralateral visual field, one from each eye. Is the capacity of the visual cortex limited to these two maps or are plastic mechanisms available to host more maps? We determined the cortical organization of the visual field maps in a rare individual with chiasma hypoplasia, where visual cortex plasticity is challenged to accommodate three hemifield maps. Using high-resolution fMRI at 7T and diffusion-weighted MRI at 3T, we found three hemiretinal inputs, instead of the normal two, to converge onto the left hemisphere. fMRI-based population receptive field mapping of the left V1-V3 at 3T revealed three superimposed hemifield representations in the left visual cortex, i.e. two representations of opposing visual hemifields from the left eye and one right hemifield representation from the right eye. We conclude that developmental plasticity including the re-wiring of local intra- and cortico-cortical connections is pivotal to support the coexistence and functioning of three hemifield maps within one hemisphere.
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Affiliation(s)
- Khazar Ahmadi
- Department of Ophthalmology, Otto-von-Guericke University, Magdeburg, 39120, Germany; Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, 22362, Sweden
| | - Alessio Fracasso
- Department of Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, 3584 CS, the Netherlands; Department of Radiology, University Medical Center Utrecht, Utrecht, 3584 CX, the Netherlands; Spinoza Centre for Neuroimaging, Amsterdam, 1105 BK, the Netherlands; Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, G12 8QB, UK
| | - Robert J Puzniak
- Department of Ophthalmology, Otto-von-Guericke University, Magdeburg, 39120, Germany
| | - Andre D Gouws
- Department of Psychology, York Neuroimaging Centre, University of York, York, YO10 5NY, UK
| | - Renat Yakupov
- Department of Biomedical Magnetic Resonance, Institute for Physics, Otto-von-Guericke University, Magdeburg, 39120, Germany; German Center for Neurodegenerative Diseases, Magdeburg, 39120, Germany
| | - Oliver Speck
- Department of Biomedical Magnetic Resonance, Institute for Physics, Otto-von-Guericke University, Magdeburg, 39120, Germany; German Center for Neurodegenerative Diseases, Magdeburg, 39120, Germany; Leibniz Institute for Neurobiology, Magdeburg, 39118, Germany; Center for Behavioral Brain Sciences, Magdeburg, 39106, Germany
| | - Joern Kaufmann
- Department of Neurology, Otto-von-Guericke-University, Magdeburg, 39120, Germany
| | - Franco Pestilli
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 1101 E, USA
| | - Serge O Dumoulin
- Department of Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, 3584 CS, the Netherlands; Spinoza Centre for Neuroimaging, Amsterdam, 1105 BK, the Netherlands; Department of Experimental and Applied Psychology, VU University Amsterdam, Amsterdam, 1081 BT, the Netherlands
| | - Antony B Morland
- Department of Psychology, York Neuroimaging Centre, University of York, York, YO10 5NY, UK; Centre for Neuroscience, Hull-York Medical School, University of York, York, YO10 5DD, UK
| | - Michael B Hoffmann
- Department of Ophthalmology, Otto-von-Guericke University, Magdeburg, 39120, Germany; Center for Behavioral Brain Sciences, Magdeburg, 39106, Germany.
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48
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Innocenti GM, Caminiti R, Rouiller EM, Knott G, Dyrby TB, Descoteaux M, Thiran JP. Diversity of Cortico-descending Projections: Histological and Diffusion MRI Characterization in the Monkey. Cereb Cortex 2020; 29:788-801. [PMID: 29490005 DOI: 10.1093/cercor/bhx363] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2017] [Accepted: 12/30/2017] [Indexed: 01/16/2023] Open
Abstract
The axonal composition of cortical projections originating in premotor, supplementary motor (SMA), primary motor (a4), somatosensory and parietal areas and descending towards the brain stem and spinal cord was characterized in the monkey with histological tract tracing, electron microscopy (EM) and diffusion MRI (dMRI). These 3 approaches provided complementary information. Histology provided accurate assessment of axonal diameters and size of synaptic boutons. dMRI revealed the topography of the projections (tractography), notably in the internal capsule. From measurements of axon diameters axonal conduction velocities were computed. Each area communicates with different diameter axons and this generates a hierarchy of conduction delays in this order: a4 (the shortest), SMA, premotor (F7), parietal, somatosensory, premotor F4 (the longest). We provide new interpretations for i) the well-known different anatomical and electrophysiological estimates of conduction velocity; ii) why conduction delays are probably an essential component of the cortical motor command; and iii) how histological and dMRI tractography can be integrated.
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Affiliation(s)
- Giorgio M Innocenti
- Department of Neuroscience, Karolinska Institutet, Retzius vág 8, Stockholm, Sweden.,Brain and Mind Institute, EPFL, Lausanne, Switzerland.,EPFL-STI-IEL-LTS5, Station 11, Lausanne, Switzerland
| | - Roberto Caminiti
- Dipartimento di Fisiologia e Farmacologia, SAPIENZA Universitá di Roma, Piazzale Aldo Moro 5, Roma, Italy
| | - Eric M Rouiller
- Department of Medicine, Swiss Primate Competence Center for Research, Fribourg Cognition Center, University of Fribourg, Ch du Musee 5, Fribourg, Switzerland
| | - Graham Knott
- BioEM Facility, Faculty of Life Sciences, EPFL SV PTECH PTBIOEM AI 0143 Station 19, Lausanne, Switzerland
| | - Tim B Dyrby
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital, Hvidovre, Copenhagen, Denmark.,Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Capital Region, Denmark
| | - Maxime Descoteaux
- Department of Computer Science, Sherbrooke Connectivity Imaging Laboratory (SCIL), Centre de Recherche CHUS, Sherbrooke University, 2500 Boul, Québec, Canada
| | - Jean-Philippe Thiran
- EPFL-STI-IEL-LTS5, Station 11, Lausanne, Switzerland.,Department of Radiology, University Hospital Center (CHUV), University of Lausanne (UNIL), Ecole Polytechnique Fédérale de Lausanne (EPFL) Signal Processing Lab (LTS5) EPFL-STI-IEL-LTS5 Station 11, Lausanne, Switzerland
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49
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Witulla B, Goerig N, Putz F, Frey B, Engelhorn T, Dörfler A, Uder M, Fietkau R, Bert C, Laun FB. On PTV definition for glioblastoma based on fiber tracking of diffusion tensor imaging data. PLoS One 2020; 15:e0227146. [PMID: 31905221 PMCID: PMC6944332 DOI: 10.1371/journal.pone.0227146] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 12/11/2019] [Indexed: 01/20/2023] Open
Abstract
Radiotherapy (RT) is commonly applied for the treatment of glioblastoma multiforme (GBM). Following the planning target volume (PTV) definition procedure standardized in guidelines, a 20% risk of missing non-local recurrences is present. Purpose of this study was to evaluate whether diffusion tensor imaging (DTI)-based fiber tracking may be beneficial for PTV definition taking into account the prediction of distant recurrences. 56 GBM patients were examined with magnetic resonance imaging (MRI) including DTI performed before RT after resection of the primary tumor. Follow-up MRIs were acquired in three month intervals. For the seven patients with a distant recurrence, fiber tracking was performed with three algorithms and it was evaluated whether connections existed from the primary tumor region to the distant recurrence. It depended strongly on the used tracking algorithm and the used tracking parameters whether a connection was observed. Most of the connections were weak and thus not usable for PTV definition. Only in one of the seven patients with a recurring tumor, a clear connection was present. It seems unlikely that DTI-based fiber tracking can be beneficial for predicting distant recurrences in the planning of PTVs for glioblastoma multiforme.
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Affiliation(s)
- Barbara Witulla
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Nicole Goerig
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Florian Putz
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Benjamin Frey
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Tobias Engelhorn
- Department of Neuroradiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Arnd Dörfler
- Department of Neuroradiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Michael Uder
- Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Rainer Fietkau
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Christoph Bert
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- * E-mail:
| | - Frederik Bernd Laun
- Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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50
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Lerma-Usabiaga G, Mukherjee P, Ren Z, Perry ML, Wandell BA. Replication and generalization in applied neuroimaging. Neuroimage 2019; 202:116048. [PMID: 31356879 PMCID: PMC6819246 DOI: 10.1016/j.neuroimage.2019.116048] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 04/29/2019] [Accepted: 07/22/2019] [Indexed: 10/26/2022] Open
Abstract
There is much interest in translating neuroimaging findings into meaningful clinical diagnostics. The goal of scientific discoveries differs from clinical diagnostics. Scientific discoveries must replicate under a specific set of conditions; to translate to the clinic we must show that findings using purpose-built scientific instruments will be observable in clinical populations and instruments. Here we describe and evaluate data and computational methods designed to translate a scientific observation to a clinical setting. Using diffusion weighted imaging (DWI), Wahl et al. (2010) observed that across subjects the mean fractional anisotropy (FA) of homologous pairs of tracts is highly correlated. We hypothesize that this is a fundamental biological trait that should be present in most healthy participants, and deviations from this assessment may be a useful diagnostic metric. Using this metric as an illustration of our methods, we analyzed six pairs of homologous white matter tracts in nine different DWI datasets with 44 subjects each. Considering the original FA measurement as a baseline, we show that the new metric is between 2 and 4 times more precise when used in a clinical context. Our framework to translate research findings into clinical practice can be applied, in principle, to other neuroimaging results.
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Affiliation(s)
- Garikoitz Lerma-Usabiaga
- Department of Psychology, Stanford University, 450 Serra Mall, Jordan Hall Building, 94305, Stanford, CA, USA; BCBL. Basque Center on Cognition, Brain and Language, Mikeletegi Pasealekua 69, Donostia - San Sebastián, 20009, Gipuzkoa, Spain.
| | - Pratik Mukherjee
- Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA; Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Zhimei Ren
- Department of Statistics, Stanford University, 390 Serra Mall, Sequoia Hall Building, 94305, Stanford, CA, USA
| | - Michael L Perry
- Department of Psychology, Stanford University, 450 Serra Mall, Jordan Hall Building, 94305, Stanford, CA, USA
| | - Brian A Wandell
- Department of Psychology, Stanford University, 450 Serra Mall, Jordan Hall Building, 94305, Stanford, CA, USA
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