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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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2
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Puzniak RJ, Ahmadi K, Kaufmann J, Gouws A, Morland AB, Pestilli F, Hoffmann MB. Quantifying nerve decussation abnormalities in the optic chiasm. NEUROIMAGE-CLINICAL 2019; 24:102055. [PMID: 31722288 PMCID: PMC6849426 DOI: 10.1016/j.nicl.2019.102055] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Revised: 10/14/2019] [Accepted: 10/22/2019] [Indexed: 12/26/2022]
Abstract
Diffusion MRI is capable of detecting structural abnormalities of the optic chiasm. Quantification of crossing strength in optic chiasm is of promise for albinism diagnostics. Optic chiasm is a powerful test model for neuroimaging methods resolving crossing fibers.
Objective The human optic chiasm comprises partially crossing optic nerve fibers. Here we used diffusion MRI (dMRI) for the in-vivo identification of the abnormally high proportion of crossing fibers found in the optic chiasm of people with albinism. Methods In 9 individuals with albinism and 8 controls high-resolution 3T dMRI data was acquired and analyzed with a set of methods for signal modeling [Diffusion Tensor (DT) and Constrained Spherical Deconvolution (CSD)], tractography, and streamline filtering (LiFE, COMMIT, and SIFT2). The number of crossing and non-crossing streamlines and their weights after filtering entered ROC-analyses to compare the discriminative power of the methods based on the area under the curve (AUC). The dMRI results were cross-validated with fMRI estimates of misrouting in a subset of 6 albinotic individuals. Results We detected significant group differences in chiasmal crossing for both unfiltered DT (p = 0.014) and CSD tractograms (p = 0.0009) also reflected by AUC measures (for DT and CSD: 0.61 and 0.75, respectively), underlining the discriminative power of the approach. Estimates of crossing strengths obtained with dMRI and fMRI were significantly correlated for CSD (R2 = 0.83, p = 0.012). The results show that streamline filtering methods in combination with probabilistic tracking, both optimized for the data at hand, can improve the detection of crossing in the human optic chiasm. Conclusions Especially CSD-based tractography provides an efficient approach to detect structural abnormalities in the optic chiasm. The most realistic results were obtained with filtering methods with parameters optimized for the data at hand. Significance Our findings demonstrate a novel anatomy-driven approach for the individualized diagnostics of optic chiasm abnormalities.
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Affiliation(s)
- Robert J Puzniak
- Department of Ophthalmology, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
| | - Khazar Ahmadi
- Department of Ophthalmology, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
| | - Jörn Kaufmann
- Department of Neurology, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
| | - Andre Gouws
- York Neuroimaging Centre, Department of Psychology, University of York, York, United Kingdom
| | - Antony B Morland
- York Neuroimaging Centre, Department of Psychology, University of York, York, United Kingdom; York Biomedical Research Institute, University of York, York, United Kingdom
| | - Franco Pestilli
- Department of Psychological and Brain Sciences, Program in Neuroscience and Program in Cognitive Science, Indiana University, Bloomington, USA
| | - Michael B Hoffmann
- Department of Ophthalmology, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany; Center for Behavioral Brain Sciences, Magdeburg, Germany.
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3
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Rossini P, Di Iorio R, Bentivoglio M, Bertini G, Ferreri F, Gerloff C, Ilmoniemi R, Miraglia F, Nitsche M, Pestilli F, Rosanova M, Shirota Y, Tesoriero C, Ugawa Y, Vecchio F, Ziemann U, Hallett M. Methods for analysis of brain connectivity: An IFCN-sponsored review. Clin Neurophysiol 2019; 130:1833-1858. [DOI: 10.1016/j.clinph.2019.06.006] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 05/08/2019] [Accepted: 06/18/2019] [Indexed: 01/05/2023]
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4
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Avesani P, McPherson B, Hayashi S, Caiafa CF, Henschel R, Garyfallidis E, Kitchell L, Bullock D, Patterson A, Olivetti E, Sporns O, Saykin AJ, Wang L, Dinov I, Hancock D, Caron B, Qian Y, Pestilli F. The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services. Sci Data 2019; 6:69. [PMID: 31123325 PMCID: PMC6533280 DOI: 10.1038/s41597-019-0073-y] [Citation(s) in RCA: 45] [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: 01/24/2018] [Accepted: 04/11/2019] [Indexed: 12/31/2022] Open
Abstract
We describe the Open Diffusion Data Derivatives (O3D) repository: an integrated collection of preserved brain data derivatives and processing pipelines, published together using a single digital-object-identifier. The data derivatives were generated using modern diffusion-weighted magnetic resonance imaging data (dMRI) with diverse properties of resolution and signal-to-noise ratio. In addition to the data, we publish all processing pipelines (also referred to as open cloud services). The pipelines utilize modern methods for neuroimaging data processing (diffusion-signal modelling, fiber tracking, tractography evaluation, white matter segmentation, and structural connectome construction). The O3D open services can allow cognitive and clinical neuroscientists to run the connectome mapping algorithms on new, user-uploaded, data. Open source code implementing all O3D services is also provided to allow computational and computer scientists to reuse and extend the processing methods. Publishing both data-derivatives and integrated processing pipeline promotes practices for scientific reproducibility and data upcycling by providing open access to the research assets for utilization by multiple scientific communities.
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Affiliation(s)
- Paolo Avesani
- Neuroinformatics Laboratory, Center for Information Technology, Fondazione Bruno Kessler, via Sommarive 18, 38123, Trento, Italy
- Center for Mind/Brain Sciences (CIMeC), University of Trento, via Delle Regole 101, 38123, Trento, Italy
| | - Brent McPherson
- Pestilli Lab. Department of Psychological and Brain Sciences, Program in Cognitive Science, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Soichi Hayashi
- Department of Psychological and Brain Sciences and Pervasive Technology Institute, University Information Technology Services, Indiana University, 1101 E 10th Street, Bloomington, IN, 47405, USA
| | - Cesar F Caiafa
- Pestilli Lab. Department of Psychological and Brain Sciences, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
- Instituto Argentino de Radioastronomía (CCT-La Plata, CONICET; CICPBA), CC5 V, Elisa, 1894, Argentina
- Facultad de Ingeniería, Universidad de Buenos Aires, Buenos Aires, C1063ACV, Argentina
| | - Robert Henschel
- Pervasive Technology Institute, Indiana University Bloomington, 2709 E 10th Street, Bloomington, IN, 47408, USA
| | - Eleftherios Garyfallidis
- Department of Intelligent Systems Engineering, Programs in Neuroscience and Cognitive Science, Indiana University Bloomington, 700N Woodlawn Ave, Bloomington, Indiana, 47408, USA
| | - Lindsey Kitchell
- Pestilli Lab. Department of Psychological and Brain Sciences, Program in Cognitive Science, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Daniel Bullock
- Pestilli Lab. Department of Psychological and Brain Sciences, Program in Neuroscience, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Andrew Patterson
- Pestilli Lab. Department of Psychological and Brain Sciences, Program in Neuroscience, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Emanuele Olivetti
- Neuroinformatics Laboratory, Center for Information Technology, Fondazione Bruno Kessler, via Sommarive 18, 38123, Trento, Italy
- Center for Mind/Brain Sciences (CIMeC), University of Trento, via Delle Regole 101, 38123, Trento, Italy
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Programs in Neuroscience and Cognitive Science, and Indiana Network Science Institute, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Andrew J Saykin
- Indiana University School of Medicine, Departments of Radiology and Imaging Sciences and Medical and Molecular Genetics, and the Indiana Alzheimer Disease Center, Indiana University, 355 W 16th St., Indianapolis, Indiana, 46202, USA
| | - Lei Wang
- Departments of Psychiatry and Behavioral Sciences and Radiology, Northwestern University Feinberg School of Medicine, 710N. Lake Shore Drive, Abbott Hall 1322, Chicago, IL, 60611, USA
| | - Ivo Dinov
- Statistics Online Computational Resource (SOCR), Center for Complexity of Self-Management in Chronic Disease (CSCD), Health Behavior and Biological Sciences, Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI, 49109, USA
| | - David Hancock
- Pervasive Technology Institute, Indiana University Bloomington, 2709 E 10th Street, Bloomington, IN, 47408, USA
| | - Bradley Caron
- Pestilli Lab. Indiana University School of Optometry and Program in Neuroscience, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, USA
| | - Yiming Qian
- Pestilli Lab. Department of Psychological and Brain Sciences, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Franco Pestilli
- Pestilli Lab. Department of Psychological and Brain Sciences, Engineering, Computer Science, Programs in Neuroscience and Cognitive Science, School of Optometry, and Indiana Network Science Institute, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA.
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5
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Yoshimine S, Ogawa S, Horiguchi H, Terao M, Miyazaki A, Matsumoto K, Tsuneoka H, Nakano T, Masuda Y, Pestilli F. Age-related macular degeneration affects the optic radiation white matter projecting to locations of retinal damage. Brain Struct Funct 2018; 223:3889-3900. [PMID: 29951918 DOI: 10.1007/s00429-018-1702-5] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Accepted: 06/17/2018] [Indexed: 12/16/2022]
Abstract
We investigated the impact of age-related macular degeneration (AMD) on visual acuity and the visual white matter. We combined an adaptive cortical atlas and diffusion-weighted magnetic resonance imaging (dMRI) and tractography to separate optic radiation (OR) projections to different retinal eccentricities in human primary visual cortex. We exploited the known anatomical organization of the OR and clinically relevant data to segment the OR into three primary components projecting to fovea, mid- and far-periphery. We measured white matter tissue properties-fractional anisotropy, linearity, planarity, sphericity-along the aforementioned three components of the optic radiation to compare AMD patients and controls. We found differences in white matter properties specific to OR white matter fascicles projecting to primary visual cortex locations corresponding to the location of retinal damage (fovea). Additionally, we show that the magnitude of white matter properties in AMD patients' correlates with visual acuity. In sum, we demonstrate a specific relation between visual loss, anatomical location of retinal damage and white matter damage in AMD patients. Importantly, we demonstrate that these changes are so profound that can be detected using magnetic resonance imaging data with clinical resolution. The conserved mapping between retinal and white matter damage suggests that retinal neurodegeneration might be a primary cause of white matter degeneration in AMD patients. The results highlight the impact of eye disease on brain tissue, a process that may become an important target to monitor during the course of treatment.
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Affiliation(s)
- Shoyo Yoshimine
- Department of Ophthalmology, The Jikei University School of Medicine, 3-25-8 Nishi-Shinbashi, Minato-ku, Tokyo, 105-8461, Japan.
| | - Shumpei Ogawa
- Department of Ophthalmology, The Jikei University School of Medicine, 3-25-8 Nishi-Shinbashi, Minato-ku, Tokyo, 105-8461, Japan.,Department of Ophthalmology, Atsugi City Hospital, Kanagawa, Japan
| | - Hiroshi Horiguchi
- Department of Ophthalmology, The Jikei University School of Medicine, 3-25-8 Nishi-Shinbashi, Minato-ku, Tokyo, 105-8461, Japan
| | - Masahiko Terao
- Research Institute for Time Studies, Yamaguchi University, Yamaguchi, Japan
| | | | - Kenji Matsumoto
- Tamagawa University Brain Science Institute, Machida, Tokyo, Japan
| | - Hiroshi Tsuneoka
- Department of Ophthalmology, The Jikei University School of Medicine, 3-25-8 Nishi-Shinbashi, Minato-ku, Tokyo, 105-8461, Japan
| | - Tadashi Nakano
- Department of Ophthalmology, The Jikei University School of Medicine, 3-25-8 Nishi-Shinbashi, Minato-ku, Tokyo, 105-8461, Japan
| | - Yoichiro Masuda
- Department of Ophthalmology, The Jikei University School of Medicine, 3-25-8 Nishi-Shinbashi, Minato-ku, Tokyo, 105-8461, Japan
| | - Franco Pestilli
- Department of Psychological and Brain Sciences, Indiana Network Science Institute, Indiana University, Bloomington, IN, 47405, USA. .,Department of Computer Science, Indiana University, Bloomington, USA. .,Department of Intelligent Systems Engineering, Indiana University, Bloomington, USA. .,Program in Neuroscience, Indiana University, Bloomington, USA. .,Program in Cognitive Science, Indiana University, Bloomington, USA. .,School of Optometry, Indiana University, Bloomington, USA.
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6
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Wang HE, Friston KJ, Bénar CG, Woodman MM, Chauvel P, Jirsa V, Bernard C. MULAN: Evaluation and ensemble statistical inference for functional connectivity. Neuroimage 2017; 166:167-184. [PMID: 29111409 DOI: 10.1016/j.neuroimage.2017.10.036] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2017] [Revised: 09/27/2017] [Accepted: 10/17/2017] [Indexed: 01/12/2023] Open
Abstract
Many analysis methods exist to extract graphs of functional connectivity from neuronal networks. Confidence in the results is limited because, (i) different methods give different results, (ii) parameter setting directly influences the final result, and (iii) systematic evaluation of the results is not always performed. Here, we introduce MULAN (MULtiple method ANalysis), which assumes an ensemble based approach combining multiple analysis methods and fuzzy logic to extract graphs with the most probable structure. In order to reduce the dependency on parameter settings, we determine the best set of parameters using a genetic algorithm on simulated datasets, whose temporal structure is similar to the experimental one. After a validation step, the selected set of parameters is used to analyze experimental data. The final step cross-validates experimental subsets of data and provides a direct estimate of the most likely graph and our confidence in the proposed connectivity. A systematic evaluation validates our strategy against empirical stereotactic electroencephalography (SEEG) and functional magnetic resonance imaging (fMRI) data.
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Affiliation(s)
- Huifang E Wang
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.
| | - Karl J Friston
- The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK
| | - Christian G Bénar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | | | - Patrick Chauvel
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France; Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Viktor Jirsa
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
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7
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Abstract
The ability to map brain networks in living individuals is fundamental in efforts to chart the relation between human behavior, health and disease. Advances in network neuroscience may benefit from developing new frameworks for mapping brain connectomes. We present a framework to encode structural brain connectomes and diffusion-weighted magnetic resonance (dMRI) data using multidimensional arrays. The framework integrates the relation between connectome nodes, edges, white matter fascicles and diffusion data. We demonstrate the utility of the framework for in vivo white matter mapping and anatomical computing by evaluating 1,490 connectomes, thirteen tractography methods, and three data sets. The framework dramatically reduces storage requirements for connectome evaluation methods, with up to 40x compression factors. Evaluation of multiple, diverse datasets demonstrates the importance of spatial resolution in dMRI. We measured large increases in connectome resolution as function of data spatial resolution (up to 52%). Moreover, we demonstrate that the framework allows performing anatomical manipulations on white matter tracts for statistical inference and to study the white matter geometrical organization. Finally, we provide open-source software implementing the method and data to reproduce the results.
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Affiliation(s)
- Cesar F Caiafa
- Department of Psychological and, Brain Sciences Indiana University Bloomington, IN, 47405, USA
- Instituto Argentino de Radioastronomía (IAR), CONICET CCT, La Plata Villa Elisa, 1894, Argentina
- Facultad de Ingeniería - Departamento de Computación, UBA Buenos Aires, C1063ACV, Argentina
| | - Franco Pestilli
- Department of Psychological and, Brain Sciences Indiana University Bloomington, IN, 47405, USA.
- Department of Intelligent Systems, Engineering Indiana University Bloomington, IN, 47405, USA.
- Department of Computer Science, Indiana University Bloomington, IN, 47405, USA.
- Program in Neuroscience Indiana University Bloomington, IN, 47405, USA.
- Program in Cognitive Science Indiana University Bloomington, IN, 47405, USA.
- School of Optometry Indiana University Bloomington, IN, 47405, USA.
- Indiana Network Science Institute Indiana University Bloomington, IN, 47405, USA.
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8
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Madan CR, Kensinger EA. Test-retest reliability of brain morphology estimates. Brain Inform 2017; 4:107-121. [PMID: 28054317 PMCID: PMC5413592 DOI: 10.1007/s40708-016-0060-4] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Accepted: 12/26/2016] [Indexed: 12/17/2022] Open
Abstract
Metrics of brain morphology are increasingly being used to examine inter-individual differences, making it important to evaluate the reliability of these structural measures. Here we used two open-access datasets to assess the intersession reliability of three cortical measures (thickness, gyrification, and fractal dimensionality) and two subcortical measures (volume and fractal dimensionality). Reliability was generally good, particularly with the gyrification and fractal dimensionality measures. One dataset used a sequence previously optimized for brain morphology analyses and had particularly high reliability. Examining the reliability of morphological measures is critical before the measures can be validly used to investigate inter-individual differences.
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Affiliation(s)
- Christopher R Madan
- Department of Psychology, Boston College, McGuinn 300, 140 Commonwealth Ave., Chestnut Hill, MA, 02467, USA.
| | - Elizabeth A Kensinger
- Department of Psychology, Boston College, McGuinn 300, 140 Commonwealth Ave., Chestnut Hill, MA, 02467, USA
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9
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Rokem A, Takemura H, Bock AS, Scherf KS, Behrmann M, Wandell BA, Fine I, Bridge H, Pestilli F. The visual white matter: The application of diffusion MRI and fiber tractography to vision science. J Vis 2017; 17:4. [PMID: 28196374 PMCID: PMC5317208 DOI: 10.1167/17.2.4] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2016] [Accepted: 12/12/2016] [Indexed: 12/19/2022] Open
Abstract
Visual neuroscience has traditionally focused much of its attention on understanding the response properties of single neurons or neuronal ensembles. The visual white matter and the long-range neuronal connections it supports are fundamental in establishing such neuronal response properties and visual function. This review article provides an introduction to measurements and methods to study the human visual white matter using diffusion MRI. These methods allow us to measure the microstructural and macrostructural properties of the white matter in living human individuals; they allow us to trace long-range connections between neurons in different parts of the visual system and to measure the biophysical properties of these connections. We also review a range of findings from recent studies on connections between different visual field maps, the effects of visual impairment on the white matter, and the properties underlying networks that process visual information supporting visual face recognition. Finally, we discuss a few promising directions for future studies. These include new methods for analysis of MRI data, open datasets that are becoming available to study brain connectivity and white matter properties, and open source software for the analysis of these data.
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Affiliation(s)
- Ariel Rokem
- The University of Washington eScience Institute, Seattle, WA, ://arokem.org
| | - Hiromasa Takemura
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, Suita-shi, JapanGraduate School of Frontier Biosciences, Osaka University, Suita-shi,
| | | | | | | | | | - Ione Fine
- University of Washington, Seattle, WA,
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10
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Goldstone RL, Pestilli F, Börner K. Self-portraits of the brain: cognitive science, data visualization, and communicating brain structure and function. Trends Cogn Sci 2015; 19:462-74. [PMID: 26187032 DOI: 10.1016/j.tics.2015.05.012] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2015] [Revised: 05/20/2015] [Accepted: 05/27/2015] [Indexed: 11/15/2022]
Affiliation(s)
- Robert L Goldstone
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA; Cognitive Science Program, Indiana University, Bloomington, IN, USA.
| | - Franco Pestilli
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA; Cognitive Science Program, Indiana University, Bloomington, IN, USA; Program in Neuroscience, Indiana University, Bloomington, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA
| | - Katy Börner
- Department of Information and Library Science, School of Informatics and Computing, Indiana University, Bloomington, IN, USA; Cognitive Science Program, Indiana University, Bloomington, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA
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