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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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52
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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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53
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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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54
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Takemura H, Pestilli F, Weiner KS. Comparative neuroanatomy: Integrating classic and modern methods to understand association fibers connecting dorsal and ventral visual cortex. Neurosci Res 2019; 146:1-12. [PMID: 30389574 PMCID: PMC6491271 DOI: 10.1016/j.neures.2018.10.011] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 10/19/2018] [Accepted: 10/25/2018] [Indexed: 12/13/2022]
Abstract
Comparative neuroanatomy studies improve understanding of brain structure and function and provide insight regarding brain development, evolution, and also what features of the brain are uniquely human. With modern methods such as diffusion MRI (dMRI) and quantitative MRI (qMRI), we are able to measure structural features of the brain with the same methods across human and non-human primates. In this review article, we discuss how recent dMRI measurements of vertical occipital connections in humans and macaques can be compared with previous findings from invasive anatomical studies that examined connectivity, including relatively forgotten classic strychnine neuronography studies. We then review recent progress in understanding the neuroanatomy of vertical connections within the occipitotemporal cortex by combining modern quantitative MRI and classical histological measurements in human and macaque. Finally, we a) discuss current limitations of dMRI and tractography and b) consider potential paths for future investigations using dMRI and tractography for comparative neuroanatomical studies of white matter tracts between species. While we focus on vertical association connections in visual cortex in the present paper, this same approach can be applied to other white matter tracts. Similar efforts are likely to continue to advance our understanding of the neuroanatomical features of the brain that are shared across species, as well as to distinguish the features that are uniquely human.
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Affiliation(s)
- Hiromasa Takemura
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, Suita, Japan; Graduate School of Frontier Biosciences, Osaka University, Suita, Japan.
| | - Franco Pestilli
- Departments of Psychological and Brain Sciences, Computer Science and Intelligent Systems Engineering, Programs in Neuroscience and Cognitive Science, School of Optometry, Indiana University, Bloomington, IN, USA
| | - Kevin S Weiner
- Department of Psychology, University of California, Berkeley, CA, USA; Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
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55
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Grotheer M, Zhen Z, Lerma-Usabiaga G, Grill-Spector K. Separate lanes for adding and reading in the white matter highways of the human brain. Nat Commun 2019; 10:3675. [PMID: 31417075 PMCID: PMC6695422 DOI: 10.1038/s41467-019-11424-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Accepted: 07/09/2019] [Indexed: 01/11/2023] Open
Abstract
Math and reading involve distributed brain networks and have both shared (e.g. encoding of visual stimuli) and dissociated (e.g. quantity processing) cognitive components. Yet, to date, the shared vs. dissociated gray and white matter substrates of the math and reading networks are unknown. Here, we define these networks and evaluate the structural properties of their fascicles using functional MRI, diffusion MRI, and quantitative MRI. Our results reveal that there are distinct gray matter regions which are preferentially engaged in either math (adding) or reading, and that the superior longitudinal and arcuate fascicles are shared across the math and reading networks. Strikingly, within these fascicles, reading- and math-related tracts are segregated into parallel sub-bundles and show structural differences related to myelination. These findings open a new avenue of research that examines the contribution of sub-bundles within fascicles to specific behaviors.
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Affiliation(s)
- Mareike Grotheer
- Psychology Department, Stanford University, Stanford, CA, 94305, USA.
| | - Zonglei Zhen
- Beijing Key Laboratory of Applied Experimental Psychology, Faculty of Psychology, Beijing Normal University, Beijing, 100875, China
| | - Garikoitz Lerma-Usabiaga
- Psychology Department, Stanford University, Stanford, CA, 94305, USA
- BCBL. Basque Center on Cognition, Brain and Language, Mikeletegi Pasealekua 69, Donostia - San Sebastián, 20009, Gipuzkoa, Spain
| | - Kalanit Grill-Spector
- Psychology Department, Stanford University, Stanford, CA, 94305, USA
- Stanford Neurosciences Institute, Stanford University, Stanford, CA, 94305, USA
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56
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Associative white matter connecting the dorsal and ventral posterior human cortex. Brain Struct Funct 2019; 224:2631-2660. [DOI: 10.1007/s00429-019-01907-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Accepted: 06/07/2019] [Indexed: 02/05/2023]
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Kumar S, Sreenivasan V, Talukdar P, Pestilli F, Sridharan D. ReAl-LiFE: Accelerating the Discovery of Individualized Brain Connectomes on GPUs. PROCEEDINGS OF THE ... AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE. AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE 2019; 33:630-638. [PMID: 31355051 DOI: 10.1609/aaai.v33i01.3301630] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Diffusion imaging and tractography enable mapping structural connections in the human brain, in-vivo. Linear Fascicle Evaluation (LiFE) is a state-of-the-art approach for pruning spurious connections in the estimated structural connectome, by optimizing its fit to the measured diffusion data. Yet, LiFE imposes heavy demands on computing time, precluding its use in analyses of large connectome databases. Here, we introduce a GPU-based implementation of LiFE that achieves 50-100x speedups over conventional CPU-based implementations for connectome sizes of up to several million fibers. Briefly, the algorithm accelerates generalized matrix multiplications on a compressed tensor through efficient GPU kernels, while ensuring favorable memory access patterns. Leveraging these speedups, we advance LiFE's algorithm by imposing a regularization constraint on estimated fiber weights during connectome pruning. Our regularized, accelerated, LiFE algorithm ("ReAl-LiFE") estimates sparser connectomes that also provide more accurate fits to the underlying diffusion signal. We demonstrate the utility of our approach by classifying pathological signatures of structural connectivity in patients with Alzheimer's Disease (AD). We estimated million fiber whole-brain connectomes, followed by pruning with ReAl-LiFE, for 90 individuals (45 AD patients and 45 healthy controls). Linear classifiers, based on support vector machines, achieved over 80% accuracy in classifying AD patients from healthy controls based on their ReAl-LiFE pruned structural connectomes alone. Moreover, classification based on the ReAl-LiFE pruned connectome outperformed both the unpruned connectome, as well as the LiFE pruned connectome, in terms of accuracy. We propose our GPU-accelerated approach as a widely relevant tool for non-negative least squares optimization, across many domains.
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Affiliation(s)
- Sawan Kumar
- Computational and Data Sciences, Indian Institute of Science Bangalore, India
| | | | - Partha Talukdar
- Computational and Data Sciences, and Computer Science and Automation, Indian Institute of Science Bangalore, India
| | - Franco Pestilli
- Psychological and Brain Sciences, Indiana University Bloomington, USA
| | - Devarajan Sridharan
- Centre for Neuroscience, and Computer Science and Automation, Indian Institute of Science Bangalore, India
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58
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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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Bain JS, Yeatman JD, Schurr R, Rokem A, Mezer AA. Evaluating arcuate fasciculus laterality measurements across dataset and tractography pipelines. Hum Brain Mapp 2019; 40:3695-3711. [PMID: 31106944 DOI: 10.1002/hbm.24626] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 04/18/2019] [Accepted: 05/01/2019] [Indexed: 11/11/2022] Open
Abstract
The arcuate fasciculi are white-matter pathways that connect frontal and temporal lobes in each hemisphere. The arcuate plays a key role in the language network and is believed to be left-lateralized, in line with left hemisphere dominance for language. Measuring the arcuate in vivo requires diffusion magnetic resonance imaging-based tractography, but asymmetry of the in vivo arcuate is not always reliably detected in previous studies. It is unknown how the choice of tractography algorithm, with each method's freedoms, constraints, and vulnerabilities to false-positive and -negative errors, impacts findings of arcuate asymmetry. Here, we identify the arcuate in two independent datasets using a number of tractography strategies and methodological constraints, and assess their impact on estimates of arcuate laterality. We test three tractography methods: a deterministic, a probabilistic, and a tractography-evaluation (LiFE) algorithm. We extract the arcuate from the whole-brain tractogram, and compare it to an arcuate bundle constrained even further by selecting only those streamlines that connect to anatomically relevant cortical regions. We test arcuate macrostructure laterality, and also evaluate microstructure profiles for properties such as fractional anisotropy and quantitative R1. We find that both tractography choice and implementing the cortical constraints substantially impact estimates of all indices of arcuate laterality. Together, these results emphasize the effect of the tractography pipeline on estimates of arcuate laterality in both macrostructure and microstructure.
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Affiliation(s)
- Jonathan S Bain
- The Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Jason D Yeatman
- Institute for Learning & Brain Sciences and Department of Speech and Hearing Science, The University of Washington, Seattle, Washington, USA
| | - Roey Schurr
- The Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ariel Rokem
- The University of Washington eScience Institute, The University of Washington, Seattle, Washington, USA
| | - Aviv A Mezer
- The Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
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60
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Maffei C, Sarubbo S, Jovicich J. Diffusion-based tractography atlas of the human acoustic radiation. Sci Rep 2019; 9:4046. [PMID: 30858451 PMCID: PMC6411970 DOI: 10.1038/s41598-019-40666-8] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 02/08/2019] [Indexed: 02/06/2023] Open
Abstract
Diffusion MRI tractography allows in-vivo characterization of white matter architecture, including the localization and description of brain fibre bundles. However, some primary bundles are still only partially reconstructed, or not reconstructed at all. The acoustic radiation (AR) represents a primary sensory pathway that has been largely omitted in many tractography studies because its location and anatomical features make it challenging to reconstruct. In this study, we investigated the effects of acquisition and tractography parameters on the AR reconstruction using publicly available Human Connectome Project data. The aims of this study are: (i) using a subgroup of subjects and a reference AR for each subject, define an optimum set of parameters for AR reconstruction, and (ii) use the optimum parameters set on the full group to build a tractography-based atlas of the AR. Starting from the same data, the use of different acquisition and tractography parameters lead to very different AR reconstructions. Optimal results in terms of topographical accuracy and correspondence to the reference were obtained for probabilistic tractography, high b-values and default tractography parameters: these parameters were used to build an AR probabilistic tractography atlas. A significant left-hemispheric lateralization was found in the AR reconstruction of the 34 subjects.
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Affiliation(s)
- Chiara Maffei
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, USA.
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto (TN), Italy.
| | - Silvio Sarubbo
- Division of Neurosurgery, Structural and Functional Connectivity Lab (SFC-LSB) Project, "S.Chiara" Hospital, Trento APSS, Italy
| | - Jorge Jovicich
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto (TN), Italy
- Department of Psychology and Cognitive Sciences, University of Trento, Trento, Italy
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61
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Sani I, McPherson BC, Stemmann H, Pestilli F, Freiwald WA. Functionally defined white matter of the macaque monkey brain reveals a dorso-ventral attention network. eLife 2019; 8:e40520. [PMID: 30601116 PMCID: PMC6345568 DOI: 10.7554/elife.40520] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2018] [Accepted: 12/20/2018] [Indexed: 12/18/2022] Open
Abstract
Classical studies of attention have identified areas of parietal and frontal cortex as sources of attentional control. Recently, a ventral region in the macaque temporal cortex, the posterior infero-temporal dorsal area PITd, has been suggested as a third attentional control area. This raises the question of whether and how spatially distant areas coordinate a joint focus of attention. Here we tested the hypothesis that parieto-frontal attention areas and PITd are directly interconnected. By combining functional MRI with ex-vivo high-resolution diffusion MRI, we found that PITd and dorsal attention areas are all directly connected through three specific fascicles. These results ascribe a new function, the communication of attention signals, to two known fiber-bundles, highlight the importance of vertical interactions across the two visual streams, and imply that the control of endogenous attention, hitherto thought to reside in macaque dorsal cortical areas, is exerted by a dorso-ventral network.
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Affiliation(s)
- Ilaria Sani
- Laboratory of Neural SystemsThe Rockefeller UniversityNew YorkUnited States
| | - Brent C McPherson
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonUnited States
| | - Heiko Stemmann
- Institute for Brain Research and Center for Advanced ImagingUniversity of BremenBremenGermany
| | - Franco Pestilli
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonUnited States
| | - Winrich A Freiwald
- Laboratory of Neural SystemsThe Rockefeller UniversityNew YorkUnited States
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Aydogan DB, Shi Y. Tracking and validation techniques for topographically organized tractography. Neuroimage 2018; 181:64-84. [PMID: 29986834 PMCID: PMC6139055 DOI: 10.1016/j.neuroimage.2018.06.071] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2018] [Revised: 05/18/2018] [Accepted: 06/26/2018] [Indexed: 12/22/2022] Open
Abstract
Topographic regularity of axonal connections is commonly understood as the preservation of spatial relationships between nearby neurons and is a fundamental structural property of the brain. In particular the retinotopic mapping of the visual pathway can even be quantitatively computed. Inspired from this previously untapped anatomical knowledge, we propose a novel tractography method that preserves both topographic and geometric regularity. We make use of parameterized curves with Frenet-Serret frame and introduce a highly flexible mechanism for controlling geometric regularity. At the same time, we incorporate a novel local data support term in order to account for topographic organization. Unifying geometry with topographic regularity, we develop a Bayesian framework for generating highly organized streamlines that accurately follow neuroanatomy. We additionally propose two novel validation techniques to quantify topographic regularity. In our experiments, we studied the results of our approach with respect to connectivity, reproducibility and topographic regularity aspects. We present both qualitative and quantitative comparisons of our technique against three algorithms from MRtrix3. We show that our method successfully generates highly organized fiber tracks while capturing bundle anatomy that are geometrically challenging for other approaches.
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Affiliation(s)
- Dogu Baran Aydogan
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yonggang Shi
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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Schurr R, Duan Y, Norcia AM, Ogawa S, Yeatman JD, Mezer AA. Tractography optimization using quantitative T1 mapping in the human optic radiation. Neuroimage 2018; 181:645-658. [DOI: 10.1016/j.neuroimage.2018.06.060] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 06/03/2018] [Accepted: 06/20/2018] [Indexed: 12/31/2022] Open
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Wang Y, Metoki A, Alm KH, Olson IR. White matter pathways and social cognition. Neurosci Biobehav Rev 2018; 90:350-370. [PMID: 29684403 PMCID: PMC5993647 DOI: 10.1016/j.neubiorev.2018.04.015] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2017] [Revised: 03/02/2018] [Accepted: 04/15/2018] [Indexed: 12/13/2022]
Abstract
There is a growing consensus that social cognition and behavior emerge from interactions across distributed regions of the "social brain". Researchers have traditionally focused their attention on functional response properties of these gray matter networks and neglected the vital role of white matter connections in establishing such networks and their functions. In this article, we conduct a comprehensive review of prior research on structural connectivity in social neuroscience and highlight the importance of this literature in clarifying brain mechanisms of social cognition. We pay particular attention to three key social processes: face processing, embodied cognition, and theory of mind, and their respective underlying neural networks. To fully identify and characterize the anatomical architecture of these networks, we further implement probabilistic tractography on a large sample of diffusion-weighted imaging data. The combination of an in-depth literature review and the empirical investigation gives us an unprecedented, well-defined landscape of white matter pathways underlying major social brain networks. Finally, we discuss current problems in the field, outline suggestions for best practice in diffusion-imaging data collection and analysis, and offer new directions for future research.
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Affiliation(s)
- Yin Wang
- Department of Psychology, Temple University, Philadelphia, 19107, USA.
| | - Athanasia Metoki
- Department of Psychology, Temple University, Philadelphia, 19107, USA
| | - Kylie H Alm
- Department of Psychology, Temple University, Philadelphia, 19107, USA
| | - Ingrid R Olson
- Department of Psychology, Temple University, Philadelphia, 19107, USA.
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65
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The Original Social Network: White Matter and Social Cognition. Trends Cogn Sci 2018; 22:504-516. [PMID: 29628441 DOI: 10.1016/j.tics.2018.03.005] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2017] [Revised: 03/06/2018] [Accepted: 03/12/2018] [Indexed: 01/24/2023]
Abstract
Social neuroscience has traditionally focused on the functionality of gray matter regions, ignoring the critical role played by axonal fiber pathways in supporting complex social processes. In this paper, we argue that research on white matter is essential for understanding a range of topics in social neuroscience, such as face processing, theory of mind, empathy, and imitation, as well as clinical disorders defined by aberrant social behavior, such as prosopagnosia, autism, and schizophrenia. We provide practical advice on how best to carry out these studies, which ultimately will substantially deepen our understanding of the neurobiological basis of social behavior.
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66
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Betzel RF, Medaglia JD, Bassett DS. Diversity of meso-scale architecture in human and non-human connectomes. Nat Commun 2018; 9:346. [PMID: 29367627 PMCID: PMC5783945 DOI: 10.1038/s41467-017-02681-z] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2017] [Accepted: 12/19/2017] [Indexed: 12/21/2022] Open
Abstract
Brain function is reflected in connectome community structure. The dominant view is that communities are assortative and segregated from one another, supporting specialized information processing. However, this view precludes the possibility of non-assortative communities whose complex inter-community interactions could engender a richer functional repertoire. We use weighted stochastic blockmodels to uncover the meso-scale architecture of Drosophila, mouse, rat, macaque, and human connectomes. We find that most communities are assortative, though others form core-periphery and disassortative structures, which better recapitulate observed patterns of functional connectivity and gene co-expression in human and mouse connectomes compared to standard community detection techniques. We define measures for quantifying the diversity of communities in which brain regions participate, showing that this measure is peaked in control and subcortical systems in humans, and that inter-individual differences are correlated with cognitive performance. Our report paints a more diverse portrait of connectome communities and demonstrates their cognitive relevance.
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Affiliation(s)
- Richard F Betzel
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - John D Medaglia
- Department of Psychology, Drexel University, Philadelphia, PA, 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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67
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Uesaki M, Takemura H, Ashida H. Computational neuroanatomy of human stratum proprium of interparietal sulcus. Brain Struct Funct 2018; 223:489-507. [PMID: 28871500 PMCID: PMC5772143 DOI: 10.1007/s00429-017-1492-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Accepted: 08/04/2017] [Indexed: 11/19/2022]
Abstract
Recent advances in diffusion-weighted MRI (dMRI) and tractography have enabled identification of major long-range white matter tracts in the human brain. Yet, our understanding of shorter tracts, such as those within the parietal lobe, remains limited. Over a century ago, a tract connecting the superior and inferior parts of the parietal cortex was identified in a post-mortem study: stratum proprium of interparietal sulcus (SIPS; Sachs, Das hemisphärenmark des menschlichen grosshirns. Verlag von georg thieme, Leipzig, 1892). The tract has since been replicated in another fibre dissection study (Vergani et al., Cortex 56:145-156, 2014), however, it has not been fully investigated in the living human brain and its precise anatomical properties are yet to be described. We used dMRI and tractography to identify and characterise SIPS in vivo, and explored its spatial proximity to the cortical areas associated with optic-flow processing using fMRI. SIPS was identified bilaterally in all subjects, and its anatomical position and trajectory are consistent with previous post-mortem studies. Subsequent evaluation of the tractography results using the linear fascicle evaluation and virtual lesion analysis yielded strong statistical evidence for SIPS. We also found that the SIPS endpoints are adjacent to the optic-flow selective areas. In sum, we show that SIPS is a short-range tract connecting the superior and inferior parts of the parietal cortex, wrapping around the intraparietal sulcus, and that it may be a crucial anatomy underlying optic-flow processing. In vivo identification and characterisation of SIPS will facilitate further research on SIPS in relation to cortical functions, their development, and diseases that affect them.
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Affiliation(s)
- Maiko Uesaki
- Department of Psychology, Graduate School of Letters, Kyoto University, Kyoto, Japan.
- Japan Society for the Promotion of Science, Tokyo, Japan.
- Open Innovation and Collaboration Research Organization, Ritsumeikan University, Osaka, Japan.
| | - Hiromasa Takemura
- Japan Society for the Promotion of Science, Tokyo, Japan.
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, Suita, Japan.
- Graduate School of Frontier Biosciences, Osaka University, Suita, Japan.
| | - Hiroshi Ashida
- Department of Psychology, Graduate School of Letters, Kyoto University, Kyoto, Japan
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69
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Lubeiro A, de Luis-García R, Rodríguez M, Álvarez A, de la Red H, Molina V. Biological and cognitive correlates of cortical curvature in schizophrenia. Psychiatry Res Neuroimaging 2017; 270:68-75. [PMID: 29107210 DOI: 10.1016/j.pscychresns.2017.10.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Revised: 09/07/2017] [Accepted: 10/23/2017] [Indexed: 01/03/2023]
Abstract
Mean cortical curvature may relate to cortico-cortical connections integrity. We explored the association between prefrontal (PFC) cortical curvature and fractional anisotropy (FA) values for tracts connecting PFC and relevant cortical regions. In schizophrenia Anatomical and diffusion magnetic resonance images were obtained from 34 patients (16 of them first-episodes) and 32 healthy controls. We calculated curvature at rostral lateral prefrontal (RLPF) and superior medial prefrontal (SMPF) areas and mean FA for the tracts respectively connecting RLPF and SMPF areas with anterior caudal cingulate (ACC), superior temporal gyrus (STG) and superior parietal SP regions. Cognitive and clinical data were collected, including baseline symptoms, Clinical Global Impression change scores from baseline to follow-up, illness duration and treatment dosage. Patients showed significantly lower FA values in the tracts linking right RLPF-ACC, right SMPF-SPG and bilaterally PFC-STG. FA values in short-range cortico-cortical connections (linking PFC and ACC) were inversely associated with PFC curvature. In patients, cognitive performance was negatively associated with PFC curvature. Larger curvature values were associated to lack of clinical improvement at follow-up. We conclude that cortical curvature is influenced by integrity in short-range cortico-cortical connections and relates to cognition and clinical outcome in schizophrenia patients.
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Affiliation(s)
- Alba Lubeiro
- Psychiatry Department, School of Medicine, University of Valladolid, Av. Ramón y Cajal, 7, 47005 Valladolid, Spain
| | - Rodrigo de Luis-García
- Imaging Processing Laboratory, University of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain
| | - Margarita Rodríguez
- Radiology Service, University Hospital of Valladolid, Ramón y Cajal, 3, 47003 Valladolid, Spain
| | - Aldara Álvarez
- Psychiatry Service, Clinical Hospital of Valladolid, Ramón y Cajal, 3, 47003 Valladolid, Spain
| | - Henar de la Red
- Psychiatry Service, Clinical Hospital of Valladolid, Ramón y Cajal, 3, 47003 Valladolid, Spain
| | - Vicente Molina
- Psychiatry Department, School of Medicine, University of Valladolid, Av. Ramón y Cajal, 7, 47005 Valladolid, Spain; Psychiatry Service, Clinical Hospital of Valladolid, Ramón y Cajal, 3, 47003 Valladolid, Spain; Neurosciences Institute of Castilla y León (INCYL), University of Salamanca, Pintor Fernando Gallego, 1, 37007, Spain; CIBERSAM (Biomedical Research Network in Mental Health; Instituto de Salud Carlos III), Spain.
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70
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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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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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Grinberg F, Maximov II, Farrher E, Shah NJ. Microstructure-informed slow diffusion tractography in humans enhances visualisation of fibre pathways. Magn Reson Imaging 2017; 45:7-17. [PMID: 28870514 DOI: 10.1016/j.mri.2017.08.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Revised: 08/15/2017] [Accepted: 08/30/2017] [Indexed: 11/26/2022]
Abstract
Conventional fibre tractography methods based on diffusion tensor imaging exploit diffusion anisotropy and directionality in the range of low diffusion weightings (b-values). High b-value Biexponential Diffusion Tensor Analysis reported previously has demonstrated that fractional anisotropy of the slow diffusion component is essentially higher than that of conventional diffusion tensor imaging whereas popular compartment models associate this slow diffusion component with axonal water fraction. One of the primary aims of this study is to elucidate the feasibility and potential benefits of "microstructure-informed" whole-brain slow-diffusion fibre tracking (SDIFT) in humans. In vivo diffusion-weighted images in humans were acquired in the extended range of diffusion weightings≤6000smm-2 at 3T. Fast and slow diffusion tensors were reconstructed using the bi-exponential tensor decomposition, and a detailed statistical analysis of the relevant whole-brain tensor metrics was performed. We visualised three-dimensional fibre tracts in in vivo human brains using deterministic streamlining via the major eigenvector of the slow diffusion tensor. In particular, we demonstrated that slow-diffusion fibre tracking provided considerably higher fibre counts of long association fibres and allowed one to reconstruct more short association fibres than conventional diffusion tensor imaging. SDIFT is suggested to be useful as a complimentary method capable to enhance reliability and visualisation of the evaluated fibre pathways. It is especially informative in precortical areas where the uncertainty of the mono-exponential tensor evaluation becomes too high due to decreased anisotropy of low b-value diffusion in these areas. Benefits can be expected in assessment of the residual axonal integrity in tissues affected by various pathological conditions, in surgical planning, and in evaluation of cortical connectivity, in particular, between Brodmann's areas.
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Affiliation(s)
- Farida Grinberg
- Institute of Neuroscience and Medicine - 4, Forschungszentrum Juelich GmbH, Juelich, Germany,; Department of Neurology, Faculty of Medicine, RWTH Aachen University, JARA, Aachen, Germany.
| | - Ivan I Maximov
- Institute of Neuroscience and Medicine - 4, Forschungszentrum Juelich GmbH, Juelich, Germany
| | - Ezequiel Farrher
- Institute of Neuroscience and Medicine - 4, Forschungszentrum Juelich GmbH, Juelich, Germany
| | - N Jon Shah
- Institute of Neuroscience and Medicine - 4, Forschungszentrum Juelich GmbH, Juelich, Germany,; Department of Neurology, Faculty of Medicine, RWTH Aachen University, JARA, Aachen, Germany
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Takemura H, Pestilli F, Weiner KS, Keliris GA, Landi SM, Sliwa J, Ye FQ, Barnett MA, Leopold DA, Freiwald WA, Logothetis NK, Wandell BA. Occipital White Matter Tracts in Human and Macaque. Cereb Cortex 2017; 27:3346-3359. [PMID: 28369290 PMCID: PMC5890896 DOI: 10.1093/cercor/bhx070] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2016] [Revised: 03/01/2017] [Accepted: 03/04/2017] [Indexed: 12/17/2022] Open
Abstract
We compare several major white-matter tracts in human and macaque occipital lobe using diffusion magnetic resonance imaging. The comparison suggests similarities but also significant differences in the tracts. There are several apparently homologous tracts in the 2 species, including the vertical occipital fasciculus (VOF), optic radiation, forceps major, and inferior longitudinal fasciculus (ILF). There is one large human tract, the inferior fronto-occipital fasciculus, with no corresponding fasciculus in macaque. We could identify the macaque VOF (mVOF), which has been little studied. Its position is consistent with classical invasive anatomical studies by Wernicke. VOF homology is supported by similarity of the endpoints in V3A and ventral V4 across species. The mVOF fibers intertwine with the dorsal segment of the ILF, but the human VOF appears to be lateral to the ILF. These similarities and differences between the occipital lobe tracts will be useful in establishing which circuitry in the macaque can serve as an accurate model for human visual cortex.
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Affiliation(s)
- Hiromasa Takemura
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, Suita-shi, Osaka 565-0871, Japan
- Graduate School of Frontier Biosciences, Osaka University, Suita-shi, Osaka 565-0871, Japan
| | - Franco Pestilli
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Kevin S. Weiner
- Department of Psychology, Stanford University, Stanford, CA 94305, USA
| | - Georgios A. Keliris
- Max Planck Institute for Biological Cybernetics, 72072 Tübingen, Germany
- Bio-Imaging Laboratory, Department of Biomedical Sciences, University of Antwerp, Wilrijk 2610, Belgium
| | - Sofia M. Landi
- Laboratory of Neural Systems, The Rockefeller University, New York, NY 10065, USA
| | - Julia Sliwa
- Laboratory of Neural Systems, The Rockefeller University, New York, NY 10065, 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
| | - Winrich A. Freiwald
- Laboratory of Neural Systems, The Rockefeller University, New York, NY 10065, USA
| | | | - Brian A. Wandell
- Department of Psychology, Stanford University, Stanford, CA 94305, USA
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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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Chamberland M, Scherrer B, Prabhu SP, Madsen J, Fortin D, Whittingstall K, Descoteaux M, Warfield SK. Active delineation of Meyer's loop using oriented priors through MAGNEtic tractography (MAGNET). Hum Brain Mapp 2017; 38:509-527. [PMID: 27647682 PMCID: PMC5333642 DOI: 10.1002/hbm.23399] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Revised: 08/04/2016] [Accepted: 08/31/2016] [Indexed: 12/19/2022] Open
Abstract
Streamline tractography algorithms infer connectivity from diffusion MRI (dMRI) by following diffusion directions which are similarly aligned between neighboring voxels. However, not all white matter (WM) fascicles are organized in this manner. For example, Meyer's loop is a highly curved portion of the optic radiation (OR) that exhibits a narrow turn, kissing and crossing pathways, and changes in fascicle dispersion. From a neurosurgical perspective, damage to Meyer's loop carries a potential risk of inducing vision deficits to the patient, especially during temporal lobe resection surgery. To prevent such impairment, achieving an accurate delineation of Meyer's loop with tractography is thus of utmost importance. However, current algorithms tend to under-estimate the full extent of Meyer's loop, mainly attributed to the aforementioned rule for connectivity which requires a direction to be chosen across a field of orientations. In this article, it was demonstrated that MAGNEtic Tractography (MAGNET) can benefit Meyer's loop delineation by incorporating anatomical knowledge of the expected fiber orientation to overcome local ambiguities. A new ROI-mechanism was proposed which supplies additional information to streamline reconstruction algorithms by the means of oriented priors. Their results showed that MAGNET can accurately generate Meyer's loop in all of our 15 child subjects (8 males; mean age 10.2 years ± 3.1). It effectively improved streamline coverage when compared with deterministic tractography, and significantly reduced the distance between the anterior-most portion of Meyer's loop and the temporal pole by 16.7 mm on average, a crucial landmark used for preoperative planning of temporal lobe surgery. Hum Brain Mapp 38:509-527, 2017. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Maxime Chamberland
- Centre de Recherche CHUSUniversity of SherbrookeSherbrookeCanada
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Faculty of ScienceUniversity of SherbrookeSherbrookeCanada
- Department of Nuclear Medicine and Radiobiology, Faculty of Medicine and Health ScienceUniversity of SherbrookeSherbrookeCanada
| | - Benoit Scherrer
- Department of RadiologyBoston Children's Hospital and Harvard Medical School300 Longwood AvenueBostonMassachusettsUSA
| | - Sanjay P. Prabhu
- Department of RadiologyBoston Children's Hospital and Harvard Medical School300 Longwood AvenueBostonMassachusettsUSA
| | - Joseph Madsen
- Department of RadiologyBoston Children's Hospital and Harvard Medical School300 Longwood AvenueBostonMassachusettsUSA
| | - David Fortin
- Centre de Recherche CHUSUniversity of SherbrookeSherbrookeCanada
- Division of Neurosurgery and Neuro‐Oncology, Faculty of Medicine and Health ScienceUniversity of SherbrookeSherbrookeCanada
| | - Kevin Whittingstall
- Centre de Recherche CHUSUniversity of SherbrookeSherbrookeCanada
- Department of Nuclear Medicine and Radiobiology, Faculty of Medicine and Health ScienceUniversity of SherbrookeSherbrookeCanada
- Department of Diagnostic Radiology, Faculty of Medicine and Health ScienceUniversity of SherbrookeSherbrookeCanada
| | - Maxime Descoteaux
- Centre de Recherche CHUSUniversity of SherbrookeSherbrookeCanada
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Faculty of ScienceUniversity of SherbrookeSherbrookeCanada
| | - Simon K. Warfield
- Department of RadiologyBoston Children's Hospital and Harvard Medical School300 Longwood AvenueBostonMassachusettsUSA
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Sommer S, Kozerke S, Seifritz E, Staempfli P. Fiber up-sampling and quality assessment of tractograms - towards quantitative brain connectivity. Brain Behav 2017; 7:e00588. [PMID: 28127510 PMCID: PMC5256175 DOI: 10.1002/brb3.588] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2016] [Revised: 08/17/2016] [Accepted: 08/23/2016] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND AND PURPOSE Diffusion MRI tractography enables to investigate white matter pathways noninvasively by reconstructing estimated fiber pathways. However, such tractograms remain biased and nonquantitative. Several techniques have been proposed to reestablish the link between tractography and tissue microstructure by modeling the diffusion signal or fiber orientation distribution (FOD) with the given tractogram and optimizing each fiber or compartment contribution according to the diffusion signal or FOD. Nevertheless, deriving a reliable quantification of connectivity strength between different brain areas is still a challenge. Moreover, evaluating the quality of a tractogram and measuring the possible error sources contained in a specific reconstructed fiber bundle also remains difficult. Lastly, all of these optimization techniques fail if specific fiber populations within a tractogram are underrepresented, for example, due to algorithmic constraints, anatomical properties, fiber geometry or seeding patterns. METHODS In this work, we propose an approach which enables the inspection of the quality of a tractogram optimization by evaluating the residual error signal and its FOD representation. The automated fiber quantification (AFQ) is applied, whereby the framework is extended to reflect not only scalar diffusion metrics along a fiber bundle, but also directionally dependent FOD amplitudes along and perpendicular to the fiber direction. Furthermore, we also present an up-sampling procedure to increase the number of streamlines of a given fiber population. The introduced error metrics and fiber up-sampling method are tested and evaluated on single-shell diffusion data sets of 16 healthy volunteers. RESULTS AND CONCLUSION Analyzing the introduced error measures on specific fiber bundles shows a considerable improvement in applying the up-sampling method. Additionally, the error metrics provide a useful tool to spot and identify potential error sources in tractograms.
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Affiliation(s)
- Stefan Sommer
- Institute for Biomedical Engineering, University and ETH Zurich Zurich Switzerland; MR-Center of the Psychiatric Hospital and the Department of Child and Adolescent Psychiatry University of Zurich Zurich Switzerland
| | - Sebastian Kozerke
- Institute for Biomedical Engineering, University and ETH Zurich Zurich Switzerland
| | - Erich Seifritz
- Department of Psychiatry, Psychotherapy and Psychosomatics Hospital of Psychiatry University of Zurich Zurich Switzerland
| | - Philipp Staempfli
- MR-Center of the Psychiatric Hospital and the Department of Child and Adolescent Psychiatry University of Zurich Zurich Switzerland; Department of Psychiatry, Psychotherapy and Psychosomatics Hospital of Psychiatry University of Zurich Zurich Switzerland
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Abstract
Progress in magnetic resonance imaging (MRI) now makes it possible to identify the major white matter tracts in the living human brain. These tracts are important because they carry many of the signals communicated between different brain regions. MRI methods coupled with biophysical modeling can measure the tissue properties and structural features of the tracts that impact our ability to think, feel, and perceive. This review describes the fundamental ideas of the MRI methods used to identify the major white matter tracts in the living human brain.
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Affiliation(s)
- Brian A Wandell
- Department of Psychology and Stanford Neurosciences Institute, Stanford University, Stanford, California 94305;
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