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MRtrix3 is an open-source, cross-platform software package for medical image processing, analysis and visualisation, with a particular emphasis on the investigation of the brain using diffusion MRI. It is implemented using a fast, modular and flexible general-purpose code framework for image data access and manipulation, enabling efficient development of new applications, whilst retaining high computational performance and a consistent command-line interface between applications. In this article, we provide a high-level overview of the features of the MRtrix3 framework and general-purpose image processing applications provided with the software. [Display omitted] •Software package for medical image processing, analysis and visualisation.•Consistent and logical user interface.•Provides a fast, modular and flexible general-purpose code framework.•Focus on diffusion MRI, but software API is general-purpose.

•A high-level overview of how tractography is used to enable quantitative analysis of the brains structural connectivity.•Review methodology involved for tractography correction, segmentation and quantification.•Review studies using quantitative tractography approaches to study the brains white matter in health and disease. Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain’s white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain’s structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain’s structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain’s white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the “best” methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.

Recent advances in diffusion MRI tractography permit the generation of dense weighted structural connectomes that offer greater insight into brain organization. However, these efforts are hampered by the lack of consensus on how to extract topological measures from the resulting graphs. Here we evaluate the common practice of removing the graphs’ weak connections, which is primarily intended to eliminate spurious connections and emphasize strong connections. Because this processing step requires arbitrary or heuristic-based choices (e.g., setting a threshold level below which connections are removed), and such choices might complicate statistical analysis and inter-study comparisons, in this work we test whether removing weak connections is indeed necessary. To this end, we systematically evaluated the effect of removing weak connections on a range of popular graph-theoretical metrics. Specifically, we investigated if (and at what extent) removal of weak connections introduces a statistically significant difference between two otherwise equal groups of healthy subjects when only applied to one of the groups. Using data from the Human Connectome Project, we found that removal of weak connections had no statistical effect even when removing the weakest ∼70–90% connections. Removing yet a larger extent of weak connections, thus reducing connectivity density even further, did produce a predictably significant effect. However, metric values became sensitive to the exact connectivity density, which has ramifications regarding the stability of the statistical analysis. This pattern persisted whether connections were removed by connection strength threshold or connectivity density, and for connectomes generated using parcellations at different resolutions. Finally, we showed that the same pattern also applies for data from a clinical-grade MRI scanner. In conclusion, our analysis revealed that removing weak connections is not necessary for graph-theoretical analysis of dense weighted connectomes. Because removal of weak connections provides no practical utility to offset the undesirable requirement for arbitrary or heuristic-based choices, we recommend that this step is avoided in future studies. •We evaluate removal of weak connections from diffusion MRI dense weighted connectomes.•We calculate graph-theoretical metrics after enforcing various connectome densities.•Removal of the weakest connections is inconsequential for graph-theoretical analysis.•Removing larger extent of weak connections has ramifications to statistical analyses.•We advocate against removal of weak connections from dMRI dense weighted connectomes.

When using diffusion MRI streamlines tractograms to construct structural connectomes, ideally, each streamline should connect exactly 2 regions-of-interest (i.e. network nodes) as defined by a given brain parcellation scheme. However, the ill-posed nature of termination criteria in many tractography algorithms can cause streamlines apparently being associated with zero, one, or more than two grey matter (GM) nodes; streamlines that terminate in white matter or cerebrospinal fluid may even end up being assigned to nodes if the definitions of these nodes are not strictly constrained to genuine GM areas, resulting in a misleading connectome in non-trivial ways. Based on both in-house MRI data and state-of-the-art data provided by the Human Connectome Project, this study investigates the actual influence of streamline-to-node assignment methods, and their interactions with fibre-tracking terminations and brain parcellations, on the construction of pairwise regional connectivity and subsequent connectomic measures. Our results show that the frequency of generating successful pairwise connectivity is heavily affected by the convoluted interactions between the applied strategies for connectome construction, and that minor changes in the mechanism can cause significant variations in the within- and between-module connectivity strengths as well as in the commonly-used graph theory metrics. Our data suggest that these fundamental processes should not be overlooked in structural connectomics research, and that improved data quality is not in itself sufficient to solve the underlying problems associated with assigning streamlines to brain nodes. We demonstrate that the application of advanced fibre-tracking techniques that are designed to correct for inaccuracies of track terminations with respect to anatomical information at the fibre-tracking stage is advantageous to the subsequent connectome construction process, in which pairs of parcellation nodes can be more robustly identified from streamline terminations via a suitable assignment mechanism. •The process of assigning streamlines to brain nodes can be a significant problem.•Using over-dilated node images may result in biologically unrealistic connections.•Connectome topology is significantly affected by connection construction mechanisms.•Improved data quality cannot fix the underlying issues of connectome construction.•Advanced tractography methods can improve the construction of pairwise connections.

•Fractional anisotropy (FA) and radiality index (RI) sampled along cortical columns.•FA local max/min and RI max at intermediate cortical depths in most regions.•Results consistent between repeated scans and across different healthy subjects.•FA and RI peaks more notable at the banks than the crown of gyri or fundus of sulci.•FA and RI peaks more notable as the cortical thickness increases. High-resolution diffusion tensor imaging (DTI) can noninvasively probe the microstructure of cortical gray matter in vivo. In this study, 0.9-mm isotropic whole-brain DTI data were acquired in healthy subjects with an efficient multi-band multi-shot echo-planar imaging sequence. A column-based analysis that samples the fractional anisotropy (FA) and radiality index (RI) along radially oriented cortical columns was then performed to quantitatively analyze the FA and RI dependence on the cortical depth, cortical region, cortical curvature, and cortical thickness across the whole brain, which has not been simultaneously and systematically investigated in previous studies. The results showed characteristic FA and RI vs. cortical depth profiles, with an FA local maximum and minimum (or two inflection points) and a single RI maximum at intermediate cortical depths in most cortical regions, except for the postcentral gyrus where no FA peaks and a lower RI were observed. These results were consistent between repeated scans from the same subjects and across different subjects. They were also dependent on the cortical curvature and cortical thickness in that the characteristic FA and RI peaks were more pronounced i) at the banks than at the crown of gyri or at the fundus of sulci and ii) as the cortical thickness increases. This methodology can help characterize variations in microstructure along the cortical depth and across the whole brain in vivo, potentially providing quantitative biomarkers for neurological disorders. [Display omitted]

The human brain is a complex network, in which some brain regions, denoted as ‘hub’ regions, play critically important roles. Some of these hubs are highly interconnected forming a rich-club organization, which has been identified based on the degree metric from structural connectomes constructed using diffusion tensor imaging (DTI)-based fiber tractography. However, given the limitations of DTI, the yielded structural connectomes are largely compromised, possibly affecting the characterization of rich-club organizations. Recent progress in diffusion MRI and fiber tractography now enable more reliable but also very dense structural connectomes to be achieved. However, while the existing rich-club analysis method is based on weighted networks, it is essentially built upon degree metric and, therefore, not suitable for identifying rich-club organizations from such dense networks, as it yields nodes with indistinguishably high degrees. Therefore, we propose a novel method, i.e. Rich-club organization Identification using Combined H-degree and Effective strength to h-degree Ratio (RICHER), to identify rich-club organizations from dense weighted networks. Overall, it is shown that more robust rich-club organizations can be achieved using our proposed framework (i.e., state-of-the-art fiber tractography approaches and our proposed RICHER method) in comparison to the previous method focusing on weighted networks based on degree, i.e., RC-degree. Furthermore, by simulating network attacks in 3 ways, i.e., attack to non-rich-club/non-rich-club edges (NRC2NRC), rich-club/non-rich-club edges (RC2NRC), and rich-club/rich-club edges (RC2RC), brain network damage consequences have been evaluated in terms of global efficiency (GE) reductions. As expected, significant GE reductions have been detected using our proposed framework among conditions, i.e., NRC2NRC < RC2NRC, NRC2NRC < RC2RC and RC2NRC < RC2RC, which however have not been detected otherwise.

Older adults with major depressive disorder (MDD) or early cognitive decline during the subjective cognitive decline (SCD) stage may exhibit neuropsychiatric symptoms such as anxiety, depression, and subtle cognitive impairment. The clinicopathological features and biological mechanisms of MDD differ from those of SCD among older adults; these conditions thus require different treatment strategies. This study enrolled 82 participants above 50 years old with normal cognitive levels from the communities to examine biomarker–behavior correlations between MDD ( n  = 23) and SCD ( n  = 23) relative to a normal control (NC) group ( n  = 36). Multidomain assessments were performed for all participants, including immunomagnetic reduction tests to detect plasma beta-amyloid (Aβ), total tau (Tau), phosphorylated tau-181 (p-Tau181), neurofilament light chain, and glial fibrillary acidic protein (GFAP). This study observed that depressive symptoms in MDD were associated with amyloid pathology (plasma Aβ40 vs. HADS-D: R  = 0.45, p  = 0.031; Aβ42/Aβ40 vs. HADS-D: R  = −0.47, p  = 0.024), which was not observed in the NC (group difference p  < 0.05). Moreover, cognitive decline in MDD was distinguished by a mixed neurodegenerative process involving amyloid (plasma Aβ42 vs. facial memory test: R  = 0.48, p  = 0.025), tau (Tau/Aβ42 vs. digit symbol substitution test (DSST): R  = −0.53, p  = 0.01), and astrocytic injury (plasma GFAP vs. Montreal cognitive assessment score: R  = −0.44, p  = 0.038; plasma GFAP vs. DSST: R  = −0.52, p  = 0.014), findings that did not apply to the NC (group difference p  < 0.05). Moreover, this study revealed different biomarker–behavior correlations between individuals with SCD and the NC. Compared with the NC, cognitive decline in the SCD group might be unrelated to amyloid pathology and instead might be early manifestations of tau pathology. This study underscores the difference in clinicopathological features between MDD and SCD among older adults, which differ from those of the NC. These findings enhance our understanding of the mechanisms underlying MDD and SCD in older individuals.

This article describes the development and application of an integrated, generalized, and efficient Monte Carlo simulation system for diffusion magnetic resonance imaging (dMRI), named Diffusion Microscopist Simulator (DMS). DMS comprises a random walk Monte Carlo simulator and an MR image synthesizer. The former has the capacity to perform large-scale simulations of Brownian dynamics in the virtual environments of neural tissues at various levels of complexity, and the latter is flexible enough to synthesize dMRI datasets from a variety of simulated MRI pulse sequences. The aims of DMS are to give insights into the link between the fundamental diffusion process in biological tissues and the features observed in dMRI, as well as to provide appropriate ground-truth information for the development, optimization, and validation of dMRI acquisition schemes for different applications. The validity, efficiency, and potential applications of DMS are evaluated through four benchmark experiments, including the simulated dMRI of white matter fibers, the multiple scattering diffusion imaging, the biophysical modeling of polar cell membranes, and the high angular resolution diffusion imaging and fiber tractography of complex fiber configurations. We expect that this novel software tool would be substantially advantageous to clarify the interrelationship between dMRI and the microscopic characteristics of brain tissues, and to advance the biophysical modeling and the dMRI methodologies.

Background Subjective cognitive decline (SCD) is an early stage of dementia linked to Alzheimer's disease pathology. White matter changes were found in SCD using diffusion tensor imaging, but there are known limitations in voxel-wise tensor-based methods. Fixel-based analysis (FBA) can help understand changes in white matter fibers and how they relate to neurodegenerative proteins and multidomain behavior data in individuals with SCD. Methods Healthy adults with normal cognition were recruited in the Northeastern Taiwan Community Medicine Research Cohort in 2018–2022 and divided into SCD and normal control (NC). Participants underwent evaluations to assess cognitive abilities, mental states, physical activity levels, and susceptibility to fatigue. Neurodegenerative proteins were measured using an immunomagnetic reduction technique. Multi-shell diffusion MRI data were collected and analyzed using whole-brain FBA, comparing results between groups and correlating them with multidomain assessments. Results The final enrollment included 33 SCD and 46 NC participants, with no significant differences in age, sex, or education between the groups. SCD had a greater fiber-bundle cross-section than NC ( pFWE  < 0.05) at bilateral frontal superior longitudinal fasciculus II (SLFII). These white matter changes correlate negatively with plasma Aβ42 level ( r  = −0.38, p  = 0.01) and positively with the AD8 score for subjective cognitive complaints ( r  = 0.42, p  = 0.004) and the Hamilton Anxiety Rating Scale score for the degree of anxiety (Ham-A, r  = 0.35, p  = 0.019). The dimensional analysis of FBA metrics and blood biomarkers found positive correlations of plasma neurofilament light chain with fiber density at the splenium of corpus callosum ( pFWE  < 0.05) and with fiber-bundle cross-section at the right thalamus ( pFWE  < 0.05). Further examination of how SCD grouping interacts between the correlations of FBA metrics and multidomain assessments showed interactions between the fiber density at the corpus callosum with letter-number sequencing cognitive score ( pFWE  < 0.01) and with fatigue to leisure activities ( pFWE  < 0.05). Conclusion Based on FBA, our investigation suggests white matter structural alterations in SCD. The enlargement of SLFII's fiber cross-section is linked to plasma Aβ42 and neuropsychiatric symptoms, which suggests potential early axonal dystrophy associated with Alzheimer's pathology in SCD. The splenium of the corpus callosum is also a critical region of axonal degeneration and cognitive alteration for SCD.

Background Neuroimage literature of autism spectrum disorder (ASD) has a moderate-to-high risk of bias, partially because those combined with intellectual impairment (II) and/or minimally verbal (MV) status are generally ignored. We aimed to provide more comprehensive insights into white matter alterations of ASD, inclusive of individuals with II (ASD-II-Only) or MV expression (ASD-MV). Methods Sixty-five participants with ASD (ASD-Whole; 16.6 ± 5.9 years; comprising 34 intellectually able youth, ASD-IA, and 31 intellectually impaired youth, ASD-II, including 24 ASD-II-Only plus 7 ASD-MV) and 38 demographic-matched typically developing controls (TDC; 17.3 ± 5.6 years) were scanned in accelerated diffusion-weighted MRI. Fixel-based analysis was undertaken to investigate the categorical differences in fiber density (FD), fiber cross section (FC), and a combined index (FDC), and brain symptom/cognition associations. Results ASD-Whole had reduced FD in the anterior and posterior corpus callosum and left cerebellum Crus I, and smaller FDC in right cerebellum Crus II, compared to TDC. ASD-IA, relative to TDC, had no significant discrepancies, while ASD-II showed almost identical alterations to those from ASD-Whole vs. TDC. ASD-II-Only had greater FD/FDC in the isthmus splenium of callosum than ASD-MV. Autistic severity negatively correlated with FC in right Crus I. Nonverbal full-scale IQ positively correlated with FC/FDC in cerebellum VI. FD/FDC of the right dorsolateral prefrontal cortex showed a diagnosis-by-executive function interaction. Limitations We could not preclude the potential effects of age and sex from the ASD cohort, although statistical tests suggested that these factors were not influential. Our results could be confounded by variable psychiatric comorbidities and psychotropic medication uses in our ASD participants recruited from outpatient clinics, which is nevertheless closer to a real-world presentation of ASD. The outcomes related to ASD-MV were considered preliminaries due to the small sample size within this subgroup. Finally, our study design did not include intellectual impairment-only participants without ASD to disentangle the mixture of autistic and intellectual symptoms. Conclusions ASD-associated white matter alterations appear driven by individuals with II and potentially further by MV. Results suggest that changes in the corpus callosum and cerebellum are key for psychopathology and cognition associated with ASD. Our work highlights an essential to include understudied subpopulations on the spectrum in research.