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      Mapping the Microstructure and Striae of the Human Olfactory Tract with Diffusion MRI

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          Abstract

          The human sense of smell plays an important role in appetite and food intake, detecting environmental threats, social interactions, and memory processing. However, little is known about the neural circuity supporting its function. The olfactory tracts project from the olfactory bulb along the base of the frontal cortex, branching into several striae to meet diverse cortical regions. Historically, using diffusion magnetic resonance imaging (dMRI) to reconstruct the human olfactory tracts has been prevented by susceptibility and motion artifacts. Here, we used a dMRI method with readout segmentation of long variable echo-trains (RESOLVE) to minimize image distortions and characterize the human olfactory tracts in vivo. We collected high-resolution dMRI data from 25 healthy human participants (12 male and 13 female) and performed probabilistic tractography using constrained spherical deconvolution (CSD). At the individual subject level, we identified the lateral, medial, and intermediate striae with their respective cortical connections to the piriform cortex and amygdala (AMY), olfactory tubercle (OT), and anterior olfactory nucleus (AON). We combined individual results across subjects to create a normalized, probabilistic atlas of the olfactory tracts. We then investigated the relationship between olfactory perceptual scores and measures of white matter integrity, including mean diffusivity (MD). Importantly, we found that olfactory tract MD negatively correlated with odor discrimination performance. In summary, our results provide a detailed characterization of the connectivity of the human olfactory tracts and demonstrate an association between their structural integrity and olfactory perceptual function.

          SIGNIFICANCE STATEMENT This study provides the first detailed in vivo description of the cortical connectivity of the three olfactory tract striae in the human brain, using diffusion magnetic resonance imaging (dMRI). Additionally, we show that tract microstructure correlates with performance on an odor discrimination task, suggesting a link between the structural integrity of the olfactory tracts and odor perception. Lastly, we generated a normalized probabilistic atlas of the olfactory tracts that may be used in future research to study its integrity in health and disease.

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          Most cited references62

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          FSL.

          FSL (the FMRIB Software Library) is a comprehensive library of analysis tools for functional, structural and diffusion MRI brain imaging data, written mainly by members of the Analysis Group, FMRIB, Oxford. For this NeuroImage special issue on "20 years of fMRI" we have been asked to write about the history, developments and current status of FSL. We also include some descriptions of parts of FSL that are not well covered in the existing literature. We hope that some of this content might be of interest to users of FSL, and also maybe to new research groups considering creating, releasing and supporting new software packages for brain image analysis. Copyright © 2011 Elsevier Inc. All rights reserved.
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            Advances in functional and structural MR image analysis and implementation as FSL.

            The techniques available for the interrogation and analysis of neuroimaging data have a large influence in determining the flexibility, sensitivity, and scope of neuroimaging experiments. The development of such methodologies has allowed investigators to address scientific questions that could not previously be answered and, as such, has become an important research area in its own right. In this paper, we present a review of the research carried out by the Analysis Group at the Oxford Centre for Functional MRI of the Brain (FMRIB). This research has focussed on the development of new methodologies for the analysis of both structural and functional magnetic resonance imaging data. The majority of the research laid out in this paper has been implemented as freely available software tools within FMRIB's Software Library (FSL).
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              User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability.

              Active contour segmentation and its robust implementation using level set methods are well-established theoretical approaches that have been studied thoroughly in the image analysis literature. Despite the existence of these powerful segmentation methods, the needs of clinical research continue to be fulfilled, to a large extent, using slice-by-slice manual tracing. To bridge the gap between methodological advances and clinical routine, we developed an open source application called ITK-SNAP, which is intended to make level set segmentation easily accessible to a wide range of users, including those with little or no mathematical expertise. This paper describes the methods and software engineering philosophy behind this new tool and provides the results of validation experiments performed in the context of an ongoing child autism neuroimaging study. The validation establishes SNAP intrarater and interrater reliability and overlap error statistics for the caudate nucleus and finds that SNAP is a highly reliable and efficient alternative to manual tracing. Analogous results for lateral ventricle segmentation are provided.
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                Author and article information

                Journal
                J Neurosci
                J Neurosci
                jneuro
                J. Neurosci
                The Journal of Neuroscience
                Society for Neuroscience
                0270-6474
                1529-2401
                5 January 2022
                5 January 2022
                : 42
                : 1
                : 58-68
                Affiliations
                [1] 1Department of Neurology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois 60611
                [2] 2The Graduate School, Northwestern University Interdepartmental Neuroscience (NUIN), Evanston, Illinois 60208
                [3] 3Department of Psychology, Northwestern University, Weinberg College of Arts and Sciences, Evanston, Illinois 60208
                [4] 4Department of Psychology, The University of Texas at Austin, Austin, Texas 78712
                [5] 5Center for Perceptual Systems, The University of Texas at Austin, Austin, Texas 78712
                [6] 6Department of Radiology, Northwestern University, Chicago, Illinois 60611
                Author notes
                Correspondence should be addressed to Thorsten Kahnt at thorsten.kahnt@ 123456northwestern.edu

                Author contributions: S.L.E.-C., F.P., T.B.P., and T.K. designed research; S.L.E.-C. performed research; G.Z. and C.Z. contributed unpublished reagents/analytic tools; S.L.E.-C. analyzed data; S.L.E.-C. wrote the first draft of the paper; G.Z., C.Z., F.P., and T.B.P. edited the paper; T.K. wrote the paper.

                Author information
                https://orcid.org/0000-0002-2469-0494
                https://orcid.org/0000-0002-3575-2670
                Article
                JN-RM-1552-21
                10.1523/JNEUROSCI.1552-21.2021
                8741165
                34759031
                30356aaf-6494-415e-9b1a-f7338ce0dc89
                Copyright © 2022 Echevarria-Cooper et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.

                History
                : 29 July 2021
                : 30 September 2021
                : 31 October 2021
                Funding
                Funded by: HHS | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD), doi 10.13039/100000055;
                Award ID: R01DC015426
                Funded by: HHS | NIH | National Institute on Aging (NIA), doi 10.13039/100000049;
                Award ID: P30AG013854
                Award ID: T32 AG020506
                Categories
                Research Articles
                Systems/Circuits

                diffusion mri,human,odor perception,olfaction,olfactory tract,tractography

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