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Quantitative Mapping of the Brain's Structural Connectivity Using Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: a review Fan Zhanga, Alessandro Daduccib, Yong Hec,d,e,f, Simona Schiavib, Caio Seguing,h, Robert Smithi,j, Chun-Hung Yehk, Tengda Zhaoc,d,e, Lauren J. O’Donnella aBrigham and Women’s Hospital, Harvard Medical School, Boston, USA bDepartment of Computer Science, University of Verona, Verona, Italy cState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China dBeijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China eIDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China fChinese Institute for Brain Research, Beijing, China gMelbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia hThe University of Sydney, School of Biomedical Engineering, Sydney, Australia iThe Florey Institute of Neuroscience and Mental Health, Melbourne, Australia jThe University of Melbourne, Melbourne, Australia kInstitute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital, Taoyuan, Taiwan Abstract Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo mapping of the brain’s white matter connections at macro scale. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain’s structural connectivity in health and disease. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain’s white matter, focusing on applications in neurodevelopment, aging, neurological dis- orders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the “best” methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications. 1. Introduction Many quantitative tractography approaches can be applied to study the brain’s structural connectivity in health and dis- ease. The fundamental goal of these analyses is to estimate Diffusion magnetic resonance imaging (dMRI) tractography quantitative measures of connectivity (or microstructure) of is an imaging method that uniquely enables in vivo mapping some pathway (or pathways) of interest. Quantitative analy- of the brain’s white matter connections at macro scale [28]. ses of tractography can be categorized into two main categories Since the first dMRI tractography methods were proposed in or styles: tract-specific analyses and connectome-based anal- 1998-2000 [27, 94, 296, 488, 28], tractography has enabled yses (this categorization is helpful but imperfect, as some ap- mapping of the brain’s structural connectivity in many neuro- proaches blend aspects of both analysis styles). Tract-specific logical applications such as aging, development and disease analysis refers to research that is typically hypothesis-driven [311, 83, 12, 507, 132, 402]. In this paper, we provide a high- arXiv:2104.11644v1 [q-bio.QM] 23 Apr 2021 and studies particular anatomical fiber tracts [5, 523, 394, 259]. level overview of how tractography is used to enable quantita- Tract-specific analysis has been increasingly adopted, particu- tive analysis of the brain’s structural connectivity. This review larly for the study of local white matter regions in health and is intended to be useful to researchers studying the white mat- disease [5, 523, 394]. Connectome-based analysis refers to re- ter, developers of quantitative analysis methods, and clinicians search that is more data-driven and generally studies the struc- interpreting results related to tractography. tural connectivity of the entire brain [429, 527, 211, 34]. This Due to the large number of proposed quantitative approaches type of analysis aims to understand patterns of whole-brain and the evolution of the field over two decades, there is a pro- anatomical connectivity, and therefore relies on tractography liferation of terminology related to the quantitative analysis of performed across the entire white matter. dMRI tractography. Therefore, we begin with a listing of com- mon terms used throughout this paper and their definitions, as In this paper, we first provide a brief introduction to tractog- provided in Table 1. We also provide a visualization of many raphy (Section 2), then a review of methodology for quantita- key concepts that will be used in the paper (Figure 1). tive tractography analysis (Sections 3–5), followed by a review Preprint submitted to Elsevier April 26, 2021 2 Table 1: Common terms used throughout this paper and their definitions. We note that there are traditional terms that are widely used, but are not technically or biologically precise; in this table, we emphasize such terms and encourage the avoidance of their usage in future studies. Term Definition Any computational process that estimates the anatomical trajectories of the white matter fiber Tractography / pathways from dMRI data. Fiber tracking Note: The term “Fiber tracing” is also widely used. However, we strongly suggest avoiding this term because “tracing” is frequently used in the context of ex vivo tracer injections. A set of ordered points in 3D space, encoding a trajectory estimated through performing tractography (see Figure 1(b)). Streamline Note: The term “fiber ” is also widely used. However, we strongly suggest avoiding this term because “fiber” is in reference to biology, while “streamline” implies the digital reconstruction of such. A set of streamlines, often generated in such a way as to cover the entire white matter in order to Tractogram capture any possible white matter connections. This can be referred to as a “whole-brain tractogram” (see Figure 1(c)). These terms have biological meanings as a set of white matter fibers (axons) forming a corticocortical or corticosubcortical connection in the brain [382]. In the neuroimaging literature, Fiber Bundle / these terms are commonly used instead to refer to white matter connections reconstructed using Fiber Tract / tractography. For clarity, in this paper, we will use the term “fiber pathway” to refer to a set of Fiber Fasciculus / streamlines resulting from the subdivision of a tractogram, while we will use the terms “fiber tract” Fiber Pathway or “fiber bundle” to refer to a fiber pathway that additionally corresponds to known anatomy with a traditional name (e.g., the corpus callosum or the corticospinal tract) (see Figure 1(d)). In neuroimaging, the somewhat elusive and ambiguous concept of brain connectivity refers to Brain Connectivity measures of the structural and/or functional relationship between different brain regions [369, 367, 202, 462]. A specific type of brain connectivity. Two brain regions are structurally connected if a fiber tract physically interconnects them. This is typically measured in vivo in humans using dMRI. However, there is no consensus on how this should be best quantified. Structural connectivity measures (also Structural called “weights” or “strengths”) can include a variety of quantitative connectivity measures Connectivity computed from a specific set of streamlines corresponding to a pathway of interest (e.g. those connecting two specific endpoints). The goal is often to approximate the true underlying fiber density or number of axons [227, 411]. A two-dimensional matrix wherein the rows and columns correspond to specific brain gray matter regions of interest (ROIs), and the value stored within each element of the matrix is the computed connectivity “ strength” between those regions corresponding to that row & column [429] (see Connectome / Figure 1(e)). Such data are described mathematically as a graph. This matrix representation is Brain Connectivity directly inspired by invasive axonal tract-tracing experiments in animals, where the results of Matrix multiple studies are expressed as a quantitative connectivity matrix [21]. Without further specification, we use “connectome” throughout to implicitly refer to the structural connectome constructed using dMRI tractography, as opposed to those derived through other imaging modalities (e.g., functional MRI). A theoretical model that connects the dMRI signal to salient features of tissue microstructure at the cellular level [310, 505, 327]. This includes tissue microstructure or biophysical models such as Diffusion Model Neurite Orientation Dispersion and Density Imaging (NODDI) [541] and Free Water (FW) [330, 329], as well as diffusion signal representations that include the diffusion tensor [29], diffusion kurtosis [222] and many others [6, 3]. Any parameter extracted from a diffusion model fit in each voxel that provides information Microstructural regarding the underlying tissue microstructure (e.g., the fractional anisotropy (FA) that describes Measure water diffusion anisotropy [30]). 3 Figure 1: Graphic illustration of tractography. (a) Example
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