Search

Diffusion MRI-based tractography is the most commonly-used technique when inferring the structural brain connectome, i.e., the comprehensive map of the connections in the brain. The utility of graph theory-a powerful mathematical approach for modeling complex network systems-for analyzing tractography-based connectomes brings important opportunities to interrogate connectome data, providing novel insights into the connectivity patterns and topological characteristics of brain structural networks. When applying this framework, however, there are challenges, particularly regarding methodological and biological plausibility. This article describes the challenges surrounding quantitative tractography and potential solutions. In addition, challenges related to the calculation of global network metrics based on graph theory are discussed.Evidence Level: 5Technical Efficacy: Stage 1.

BackgroundNeuroimage 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).MethodsSixty-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.ResultsASD-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-II, relative to TDC, 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. Non-verbal 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.LimitationsWe 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.ConclusionsASD-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 sub-populations on the spectrum in research.

Brain network modularity analysis has attracted increasing interest due to its capability in measuring the level of integration and segregation across subnetworks. Most studies have focused on extracting modules at a single level, although brain network modules are known to be organized in a hierarchical manner. A few techniques have been developed to extract hierarchical modularity in human functional brain networks using resting-state functional magnetic resonance imaging (fMRI) data; however, the focus of those methods is binary networks produced by applying arbitrary thresholds of correlation coefficients to the connectivity matrices. In this study, we propose a new multisubject spectral clustering technique, called group-level network hierarchical clustering (GNetHiClus), to extract the hierarchical structure of the functional network based on full weighted connectivity information. The most reliable results of hierarchical clustering are then estimated using a bootstrap aggregation algorithm. Specifically, we employ a voting-based ensemble method, that is, majority voting; random subsamples with replacement are created for clustering brain regions, which are further aggregated to select the most reliable clustering results. The proposed method is evaluated over a range of group sample sizes, based on resting-state fMRI data from the Human Connectome Project. Our results show that GNetHiClus can extract relatively consistent hierarchical network structures across a range of sample sizes investigated. In addition, the results demonstrate that GNetHiClus can hierarchically cluster brain functional networks into specialized subnetworks from upper-to-lower level, including the high-level cognitive and the low-level perceptual networks. Conversely, from lower-to-upper level, information processed by specialized lower level subnetworks is integrated into upper level for achieving optimal efficiency for brain functional communications. Importantly, these findings are consistent with the concept of network segregation and integration, suggesting that the proposed technique can be helpful to promote the understanding of brain network from a hierarchical point of view.

Background and Purpose: Changes in connectivity of white matter fibers remote to a stroke lesion, suggestive of structural connectional diaschisis, may impact on clinical impairment and recovery after stroke. However, until recently, we have not had tract-specific techniques to map changes in white matter tracts in vivo in humans to enable investigation of potential mechanisms and clinical impact of such remote changes. Our aim was to identify and quantify white matter tracts that are affected remote from a stroke lesion and to investigate the associations between reductions in tract-specific connectivity and impaired touch discrimination function after stroke. Methods: We applied fixel-based analysis to diffusion magnetic resonance imaging data from 37 patients with stroke (right lesion =16; left lesion =21) and 26 age-matched healthy adults. Three quantitative metrics were compared between groups: fiber density; fiber-bundle cross-section; and a combined measure of both (fiber-bundle cross-section) that reflects axonal structural connectivity. Results: Compared with healthy adults, patients with stroke showed significant common fiber-bundle cross-section and fiber density reductions in 4 regions remote from focal lesions that play roles in somatosensory and spatial information processing. Structural connectivity along the somatosensory fibers of the lesioned hemisphere was correlated with contralesional hand touch function. Touch function of the ipsilesional hand was associated with connectivity of the superior longitudinal fasciculus, and, for the right-lesion group, the corpus callosum. Conclusions: Remote tract-specific reductions in axonal connectivity indicated by diffusion imaging measures are observed in the somatosensory network after stroke. These remote white matter connectivity reductions, indicative of structural connectional diaschisis, are associated with touch impairment in patients with stroke.

Highlights • A longitudinal fixel-based analysis in young brain-injured patients. • Lower fiber density and fiber cross-section in young brain-injured patients. • Restoration of left-hemispheric sensorimotor tracts following balance training. • Balance training improved patients’ postural control., Background and objectives Traumatic brain injury (TBI) is one of the leading causes of death and disability in children and adolescents. Young TBI patients suffer from gross motor deficits, such as postural control deficits, which can severely compromise their daily life activities. However, little attention has been devoted to uncovering the underlying white matter changes in response to training in TBI. In this study, we used longitudinal fixel-based analysis (FBA), an advanced diffusion imaging analysis technique, to investigate the effect of a balance training program on white matter fibre density and morphology in a group of young TBI patients. Methods Young patients with moderate-to-severe TBI (N = 17, 10 females, mean age = 13 ± 3 years) and age-matched controls (N = 17) underwent a home-based balance training program. Diffusion MRI scans together with gross motor assessments, including the gross motor items of the Bruininks-Oseretsky Test of Motor Proficiency, the Activities-Specific Balance Confidence (ABC) Scale, and the Sensory Organization Test (SOT) were administered before and at completion of 8-weeks of training. We used FBA to compare microstructural differences in fibre density (FD), macrostructural (morphological) changes in fibre cross-section (FC), and the combined FD and FC (FDC) metric across the whole brain. We then performed a longitudinal analysis to test whether training restores the white matter in the regions found to be damaged before treatment. Results Whole-brain fixel-based analysis revealed lower FD and FC in TBI patients compared to the control group across several commissural tracts, association fibres and projection fibres, with FD reductions of up to 50%. Following training, TBI patients showed a significant interaction effect between Group and Time for the SOT test, as well as significant increases in macrostructural white matter (i.e., FC & FDC) in left sensorimotor tracts. The amount of change in FC and FDC over time was, however, not associated with behavioural changes. Discussion Our fixel-based findings identified both microstructural and macrostructural abnormalities in young TBI patients. The longitudinal results provide a deeper understanding of the neurobiological mechanisms underlying balance training, which will allow clinicians to make more effective treatment decisions in everyday clinical practice with brain-injured patients.

Accumulating evidence shows that brain functional deficits may be impacted by damage to remote brain regions. Recent advances in neuroimaging suggest that stroke impairment can be better predicted based on disruption to brain networks rather than from lesion locations or volumes only. Our aim was to explore the feasibility of predicting post-stroke somatosensory function from brain functional connectivity through the application of machine learning techniques. Somatosensory impairment was measured using the Tactile Discrimination Test. Functional connectivity was employed to model the global brain function. Behavioral measures and MRI were collected at the same timepoint. Two machine learning models (linear regression and support vector regression) were chosen to predict somatosensory impairment from disrupted networks. Along with two feature pools (i.e., low-order and high-order functional connectivity, or low-order functional connectivity only) engineered, four predictive models were built and evaluated in the present study. Forty-three chronic stroke survivors participated this study. Results showed that the regression model employing both low-order and high-order functional connectivity can predict outcomes based on correlation coefficient of r = 0.54 (p = 0.0002). A machine learning predictive approach, involving high- and low-order modelling, is feasible for the prediction of residual somatosensory function in stroke patients using functional brain networks.

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. ispartof: NEUROIMAGE vol:202 ispartof: location:United States status: published

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.Declarations of interestnone.

Diffusion MRI streamlines tractography has become a major technique for inferring structural networks through reconstruction of brain connectome. However, quantification of structural connectivity based on the number of streamlines interconnecting brain grey matter regions is known to be problematic in a number of aspects, such as the ill-posed nature of streamlines terminations and the non-quantitative nature of streamline counts. This study investigates the effects of state-of-the-art connectome construction methods on the subsequent analyses of structural brain networks using graph theoretical approaches. Our results demonstrate that the characteristics of structural connectivity, including connectome variability, global network metrics, small-world attributes and network hubs, alter significantly following the improvement in biological accuracy of streamlines tractograms provided by anatomically-constrained tractography (ACT) and spherical-deconvolution informed filtering of tractograms (SIFT). Importantly, the commonly-used correction for connection density based on scaling the contribution of each streamline to the connectome by its inverse length is shown to provide incomplete correction, highlighting the necessity for the use of advanced tractogram reconstruction techniques in structural connectomics research.

The negative bias stress normally yields a negative threshold voltage shift of the thin film transistors due to the additional positive charges trapped in the gate dielectrics or at channel/gate insulator interface. However, a positive threshold voltage shift of the device with the post InGaZnO deposition annealing at 400oC is observed in our devices. The Na+ incorporation from Mo gate into the gate dielectric after 400oC annealing is responsible for this abnormal threshold voltage shift. The movement of Na+ ions toward the gate electrode by the negative gate bias decreases the distance between the gate electrode and the Na+ ions. Therefore, the voltage drop between the gate electrode and the Na+ ions reduces, and a corresponding positive threshold voltage shift is observed. Inserting a SiNx layer between the SiOx gate insulator and the Mo gate electrode can reduce the Na+ mobility, and thus a normal negative threshold voltage shift resumes. Experiment: A Mo layer with the thickness of 100 nm was sputtered on glass, and patterned as the gate electrode. Then 300 nm thick SiOx was deposited on the Mo gate electrode by plasma enhanced chemical vapor deposition (PECVD) at 350oC. A 50 nm a-IGZO active layer was sputtered on SiOx. PDA was performed with an O2/N2 gas with a mixing ratio of 1:4, and the annealing temperature is 350oC or 400oC for 1 hour. A 200 nm SiOx layer was deposited on the active layer by PECVD at 200oC as the etch-stop layer. After that, 100 nm Mo was sputtered as the source and drain electrodes. The channel width and length of all TFTs are 20μm and 10μm, respectively. Additional 100 nm thick SiOx was deposited by PECVD at 200oC as the passivation layer. Result and Discussion: The slightly negative VT shift of the device with 350oC PDA after negative bias stress (NBS) was mainly attributed to the hole-trapping in the gate insulator [1] or at the a-IGZO/gate insulator interface [2]-[3]. As the trapped holes increase, the VT shifts to the negative direction. However, the abnormally negative VT shift of the device with 400oC PDA after NBS was due to the motion of mobile Na+ in the gate insulator [4]. The Mo electrode has the Na+ concentration of ∼1×1015 cm−3, which was confirmed by second ion mass spectroscopy (SIMS). These results suggest that the origin of Na+ may be due to the contamination or the purity of Mo target. On the other hand, the peak concentration of Na+ in the device with 400oC PDA is approximately 5X of 350oC PDA. Fig. 1 shows the transfer characteristics of devices after NBS prebiased with a positive gate voltage (VGS=20V for 600 seconds). The larger positive VT shift of 400oC PDA TFT with prebias after NBS than the fresh devices is due to the large movement of Na+ toward gate electrode. Fig. 2(a) and (b) shows the band diagram to illustrate the Na+ effect. During the prebias period, Na+ near the SiOx/Mo gate electrode interface drifts toward the a-IGZO channel. When negative bias is applied at the Mo gate, Na+ moves toward the gate electrode. As the distance between Na+ and gate electrode decreases, the negative voltage drop at the gate electrode decreases, which “effectively” shifts the VT to the positive direction. In order to reduce the impact of Na+ movement, a 50nm SiNx layer was inserted between the SiOx insulator and the Mo gate electrode, and the VT shift after NBS with prebias recovered to negative direction. For SIMS measurement, the peak concentration of Na+ near SiOx and SiNx interface of the device with SiNx layer are smaller than near SiOx and Mo interface of the device without SiNx layer. On the other hand, the Na+ trapped in SiNx has low mobility and relatively low movement during NBS [5]. Therefore, the effectively positive voltage shift caused by Na+ movement is suppressed, and thus improves the reliability of 400oC PDA device. Acknowledgement: The support of INNOLUX and the Ministry of Science and Technology, Taiwan (MOST 108-2622-8-002-016, MOST 108-2218-E-002-027) is highly acknowledged. [1] Y. K. Moon et al., JJAP (2009) [2] E. N. Cho et al., Trans. Device Mater. Rel. (2011) [3] W. T. Chen et al., EDL (2011) [4] C. Lo et al., J-EDS (2016) [5] B.-D. Choi et al., JJAP (2005) Figure 1

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.

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.

The commercial technologies for aluminium production include production of alumina from bauxite and smelting of alumina to produce aluminium. The current technology is energy intensive, and a major source of greenhouse gas emissions and harmful fluoride emissions. Carbothermal reduction of bauxite is a promising alternative technology for aluminium and aluminoalloy production. Western Australia and Queensland bauxite ores were carbothermally reduced in steps in argon. Experiments were performed in a high temperature vertical tube furnace, and the off-gas composition was monitored using an infra-red gas analyser. The phase composition of reduced samples was characterised by XRD. Oxygen and carbon contents in reduced samples were determined by LECO analysers. The morphology of the surface and intersections was observed by SEM. The chemical compositions of the phases in the reduced samples were detected by EDS. The results of this study have proved the concept of stepwise reduction of bauxite ores in solid state by appropriate control of reduction temperature. Below 1100 °C, only iron oxides were reduced to metallic iron. A ferroalloy phase was formed at 1200 °C and above. The products in the bauxites reduced to 1600 °C include a ferroalloy of silicon and aluminium, carbides of titanium, silicon and aluminium, and unreacted alumina.

Diffusion orientation transform (DOT) is a powerful imaging technique that allows the reconstruction of the microgeometry of fibrous tissues based on diffusion MRI data. The three main error sources involving this methodology are the finite sampling of the q-space, the practical truncation of the series of spherical harmonics and the use of a mono-exponential model for the attenuation of the measured signal. In this work, a detailed mathematical description that provides an extension to the DOT methodology is presented. In particular, the limitations implied by the use of measurements with a finite support in q-space are investigated and clarified as well as the impact of the harmonic series truncation. Near- and far-field analytical patterns for the diffusion propagator are examined. The near-field pattern makes available the direct computation of the probability of return to the origin. The far-field pattern allows probing the limitations of the mono-exponential model, which suggests the existence of a limit of validity for DOT. In the regimen from moderate to large displacement lengths the isosurfaces of the diffusion propagator reveal aberrations in form of artifactual peaks. Finally, the major contribution of this work is the derivation of analytical equations that facilitate the accurate reconstruction of some orientational distribution functions (ODFs) and skewness ODFs that are relatively immune to these artifacts. The new formalism was tested using synthetic and real data from a phantom of intersecting capillaries. The results support the hypothesis that the revisited DOT methodology could enhance the estimation of the microgeometry of fiber tissues.

Purpose To reduce the scan time of high angular resolution diffusion imaging (HARDI) by using the hemispherical encoding scheme with the cross-term correction. Materials and Methods Unidirectional and 45° crossing phantoms were built to evaluate the accuracy of the fiber orientation estimation when using a hemispherical encoding scheme with and without the cross-term correction. The q-ball imaging using the spherical harmonic basis was adopted for estimation of fiber orientation. By recalculating the diffusion gradient strengths, we make the actual b value in all directions equal to the expected b value for the correction of the cross-term. Results The separation angles measured on the crossing phantom are 42.67° ± 1.50°, 42.75° ± 2.10°, and 42.93° ± 2.78° for cross-term-free results, for the results from the hemispherical encoding scheme with and without the cross-term correction. The angular errors in mapping the unidirectional phantom are 1.87°–2.29° and 4.01°–4.92° for the results using the hemispherical encoding scheme with and without correcting for the cross-term at different b values. Conclusion Using the hemispherical encoding scheme with the cross-term correction can potentially halve the scan time of HARDI and obtain accurate fiber orientation estimation simultaneously. This will be helpful in the clinical application of HARDI. J. Magn. Reson. Imaging 2009;29:1386–1394. © 2009 Wiley-Liss, Inc.

Diffusion-weighted magnetic resonance imaging has the ability to map neuronal architecture by estimating the 3D diffusion displacement within fibrous brain structures. This approach has non-invasively been demonstrated in the human brain with diffusion tensor tractography. Despite its valuable application in neuroscience and clinical studies however, it faces an inherent limit in mapping fiber tracts through areas with intervoxel incoherence. Recent advances in high angular resolution diffusion imaging have surpassed this limit and have the ability to resolve the complex fiber intercrossing within each MR voxel. To connect the fiber tracts from a multi-fiber system, this study proposed a modified fiber assignment using the continuous tracking (MFACT) algorithm and a tracking browser to propagate tracts along complex diffusion profiles. The Q-ball imaging method was adopted to acquire the diffusion displacements. Human motor pathways with seed points from the internal capsule, motor cortex, and pons were studied respectively. The results were consistent with known anatomy and demonstrated the promising potential of the MFACT method in mapping the complex neuronal architecture in the human brain.

Diffusion-weighted imaging can potentially be used to infer the connectivity of the human brain in vivo using fibre-tracking techniques, and is therefore of great interest to neuroscientists and clinicians. A key requirement for fibre tracking is the accurate estimation of white matter fibre orientations within each imaging voxel. The diffusion tensor model, which is widely used for this purpose, has been shown to be inadequate in crossing fibre regions. A number of approaches have recently been proposed to address this issue, based on high angular resolution diffusion-weighted imaging (HARDI) data. In this study, an experimental model of crossing fibres, consisting of water-filled plastic capillaries, is used to thoroughly assess three such techniques: constrained spherical deconvolution (CSD), super-resolved CSD (super-CSD) and Q-ball imaging (QBI). HARDI data were acquired over a range of crossing angles and b-values, from which fibre orientations were computed using each technique. All techniques were capable of resolving the two fibre populations down to a crossing angle of 45 degrees , and down to 30 degrees for super-CSD. A bias was observed in the fibre orientations estimated by QBI for crossing angles other than 90 degrees, consistent with previous simulation results. Finally, for a 45 degrees crossing, the minimum b-value required to resolve the fibre orientations was 4000 s/mm(2) for QBI, 2000 s/mm(2) for CSD, and 1000 s/mm(2) for super-CSD. The quality of estimation of fibre orientations may profoundly affect fibre tracking attempts, and the results presented provide important additional information regarding performance characteristics of well-known methods.

An essential step for fibre-tracking is the accurate estimation of neuronal fibre orientations within each imaging voxel, and a number of methods have been proposed to reconstruct the orientation distribution function based on sampling three-dimensional q-space. In the q-space formalism, very short (infinitesimal) gradient pulses are the basic requirement to obtain the true spin displacement probability density function. On current clinical MR systems however, the diffusion gradient pulse duration (delta) is inevitably finite due to the limit on the achievable gradient intensity. The failure to satisfy the short gradient pulse (SGP) requirement has been a recurrent criticism for fibre orientation estimation based on the q-space approach. In this study, the influence of a finite delta on the DW signal measured as a function of gradient direction is described theoretically and demonstrated through simulations and experimental models. Our results suggest that the current practice of using long delta for DW imaging on human clinical MR scanners, which is enforced by hardware limitations, might in fact be beneficial for estimating fibre orientations. For a given b-value, the prolongation of delta is advantageous for estimating fibre orientations for two reasons: first, it leads to a boost in DW signal in the transverse plane of the fibre. Second, it stretches out the shape of the measured diffusion profile, which improves the contrast between DW orientations. This is especially beneficial for resolving crossing fibres, as this contrast is essential to discriminate between different fibre directions.

The function of the corpus callosum (CC) is to distribute perceptual, motor, cognitive, learned, and voluntary information between the two hemispheres of the brain. Accurate parcellation of the CC according to fiber composition and fiber connection is of upmost important. In this work, population‐based probabilistic connection topographies of the CC, in the standard Montreal Neurological Institute (MNI) space, are estimated by incorporating anatomical cytoarchitectural parcellation with high angular resolution diffusion imaging (HARDI) tractography. First, callosal fibers are extracted using multiple fiber assignment by continuous tracking algorithm based on q‐ball imaging (QBI), on 12 healthy and young subjects. Then, the fiber tracts are aligned in the standard MNI coordinate system based on a tract‐based transformation scheme. Next, twenty‐eight Brodmann's areas on the surface of cortical cortex are registered to the MNI space to parcellate the aligned callosal fibers. Finally, the population‐based topological subdivisions of the midsagittal CC to each cortical target are then mapped. And the resulting subdivisions of the CC that connect to the frontal and somatosensory associated cortex are also showed. To our knowledge, it is the first topographic subdivisions of the CC done using HARDI tractography and cytoarchitectonic information. In conclusion, this sophisticated topography of the CC may serve as a landmark to further understand the correlations between the CC, brain intercommunication, and functional cytoarchitectures. Hum Brain Mapp 2009. © 2009 Wiley‐Liss, Inc.

Diffusion spectrum imaging (DSI) can map complex fiber microstructures in tissues by characterizing their 3-D water diffusion spectra. However, a long acquisition time is required for adequate q-space sampling to completely reconstruct the 3-D diffusion probability density function. Furthermore, to achieve a high q-value encoding for sufficient spatial resolution, the diffusion gradient duration and the diffusion time are usually lengthened on a clinical scanner, resulting in a long echo time and low signal-to-noise ratio of diffusion-weighted images. To bypass long acquisition times and strict gradient requirements, the reduced-encoding DSI (RE-DSI) with a bi-Gaussian diffusion model is presented in this study. The bi-Gaussian extrapolation kernel, based on the assumption of the bi-Gaussian diffusion signal curve across biological tissue, is applied to the reduced q-space sampling data in order to fulfill the high q-value requirement. The crossing phantom model and the manganese-enhanced rat model served as standards for accuracy assessment in RE-DSI. The errors of RE-DSI in estimating fiber orientations were close to the noise limit. Meanwhile, evidence from a human study demonstrated that RE-DSI significantly decreased the acquisition time required to resolve complex fiber orientations. The presented method facilitates the application of DSI analysis on a clinical magnetic resonance imaging system.

Q-ball imaging (QBI) has been proposed for the mapping of multiple intravoxel fiber structures using the Funk-Radon transform on high angular resolution diffusion images (HARDI). However, the accuracy and the angular resolution of QBI to define fiber orientations and its dependence on diffusion imaging parameters remain unclear. The phantom models, made up of sheets of parallel capillaries filled with water, were designed to evaluate the accuracy and the angular resolution of QBI at different |q| values. With an inner diameter of 20 mum and an outer diameter of 90 mum, the capillaries afforded a restrictive environment compared with the diffusion measurement scale. Further, the angular resolutions of QBI at various |q| value were also quantified on the corpus callosum in the human brain. The full width at half maximum (FWHM) of the main lobe of normalized orientation distribution function (nODF) was calculated and adopted to quantify the angular resolution of QBI. With the phantom model, a higher |q| value resulted in worse accuracy but better angular resolution for QBI. The same trend where a higher |q| value yielded a better angular resolution was also observed in the human study. Upon comparison of QBI with T2WI, QBI with |q|=277 cm(-1) (b=3000 s/mm(2)) was found to be insufficient to differentiate capillaries crossing at 45 degrees . However, when encoding with |q|=320, 358, and 392 cm(-1) (b=4000, 5000, and 6000 s/mm(2)), the deviation angles between the primary ODF and the 45 degrees phantoms were -4.91 degrees +/-2.72 degrees , -1.37 degrees +/-2.32 degrees , and -0.69 degrees +/-1.54 degrees with adequate signal-to-noise ratio (SNR). These results were consistent with the FWHM-nODF, which showed that a |q| value of 320 cm(-1) was the threshold to resolve capillaries intersecting at 45 degrees . Additionally, it was demonstrated in both the phantom model and the human brain that QBI encoding with lower |q| values may result in underestimation of the orientations of the crossing fibers. In conclusion, QBI was found to accurately resolve crossing fiber orientations and was highly dependent on the selected |q| value.

International audience; 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.

Available online 22 August 2021. White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process. This work was conducted in part using the resources of the Ad- vanced Computing Center for Research and Education at Vanderbilt University, Nashville, TN. KS, BL, CH were supported by the Na- tional Institutes of Health under award numbers R01EB017230, and T32EB001628, and in part by ViSE/VICTR VR3029 and the National Center for Research Resources, Grant UL1 RR024975-01. This work was also possible thanks to the support of the Institutional Research Chair in NeuroInformatics of Universitéde Sherbrooke, NSERC and Compute Canada (MD, FR). MP received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sk ł odowska-Curie grant agreement No 754462. The Wisconsin group acknowledges the support from a core grant to the Waisman Cen- ter from the National Institute of Child Health and Human Develop- ment (IDDRC U54 HD090256). NSF OAC-1916518, NSF IIS-1912270, NSF IIS-1636893, NSF BCS-1734853, NIH NIBIB 1R01EB029272-01, and a Microsoft Faculty Fellowship to F.P. LF acknowledges the sup- port of the Cluster of Excellence Matters of Activity. Image Space Mate- rial funded by the Deutsche Forschungsgemeinschaft (DFG, German Re- search Foundation) under Germany´s Excellence Strategy –EXC 2025. SW is supported by a Medical Research Council PhD Studentship UK [MR/N013913/1]. The Nottingham group’s processing was performed using the University of Nottingham’s Augusta HPC service and the Pre- cision Imaging Beacon Cluster. JPA, MA and SMS acknowledges the support of FCT - Fundação para a Ciência e a Tecnologia within CIN- TESIS, R&D Unit (reference UID/IC/4255/2013). MM was funded by the Wellcome Trust through a Sir Henry Wellcome Postdoctoral Fellow- ship [213722/Z/18/Z]. EJC-R is supported by the Swiss National Sci- ence Foundation (SNSF, Ambizione grant PZ00P2 185814/1). CMWT is supported by a Sir Henry Wellcome Fellowship (215944/Z/19/Z) and a Veni grant from the Dutch Research Council (NWO) (17331). FC acknowledges the support of the National Health and Medical Re- search Council of Australia (APP1091593 and APP1117724) and the Australian Research Council (DP170101815). NSF OAC-1916518, NSF IIS-1912270, NSF IIS-1636893, NSF BCS-1734853, Microsoft Faculty Fellowship to F.P. D.B. was partially supported by NIH NIMH T32- MH103213 to William Hetrick (Indiana University). CL is partly sup- ported by NIH grants P41 EB027061 and P30 NS076408 “Institutional Center Cores for Advanced Neuroimaging. JYMY received positional funding from the Royal Children’s Hospital Foundation (RCH 1000). JYMY, JC, and CEK acknowledge the support of the Royal Children’s Hospital Foundation, Murdoch Children’s Research Institute, The Uni- versity of Melbourne Department of Paediatrics, and the Victorian Gov- ernment’s Operational Infrastructure Support Program. C-HY is grateful to the Ministry of Science and Technology of Taiwan (MOST 109-2222- E-182-001-MY3) for the support. LC acknowledges support from CONA- CYT and UNAM. ARM acknowledges support from CONACYT. LJO, YR, and FZ were supported by NIH P41EB015902 and R01MH119222. AJG was supported by P41EB015898. NM was supported by R01MH119222, K24MH116366, and R01MH111917. This project has received funding from the European Union’s Horizon 2020 Research and Innovation Pro- gramme under Grant Agreement No. 785907 & 945539 (HBP SGA2 & SGA3), and from the ANR IFOPASUBA- 19-CE45-0022-01. PG, CR, NL and AV were partially supported by ANID-Basal FB0008 and ANID- FONDECYT 1190701 grants. We would like to acknowledge John C Gore, Hiromasa Takemura, Anastasia Yendiki, and Riccardo Galbusera for their helplful suggestions regarding the analysis, figures, and discussions.