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Diffusion MRI tractography has been used to map the axonal structure of the human brain, but its ability to detect neuronal injury is yet to be explored. Here we report differential tractography, a new type of tractography that utilizes repeat MRI scans and a novel tracking strategy to map the exact segment of fiber pathways with a neuronal injury. We examined differential tractography on multiple sclerosis, Huntington’s disease, amyotrophic lateral sclerosis, and epileptic patients. The results showed that the affected pathways shown by differential tractography matched well with the unique clinical symptoms of the patients, and the false discovery rate of the findings could be estimated using a sham setting to provide a reliability measurement. This novel approach enables a quantitative and objective method to monitor neuronal injury in individuals, allowing for diagnostic and prognostic evaluation of brain diseases. [Display omitted] •Conventional tractography maps all pathways and is insensitive to neuronal change.•Differential tractography compares two longitudinal scans to map neuronal injury.•Differential tractography tracks the exact segment with a decrease in anisotropy.•The injury pattern revealed can assist with diagnostic and prognostic evaluations.•The false discovery rate of the findings can be estimated using a sham analysis. Differential tractography utilizes repeat diffusion MRI scans to identify the exact segment of tracks with a neuronal injury.

We propose and validate a novel experimental technique to measure two-point statistics of turbulent flows. It consists of spreading rigid fibers in the flow and tracking their position and orientation in time and is therefore named “fiber tracking velocimetry.” By choosing different fiber lengths, i.e., within the inertial or dissipative range of scales, the statistics of turbulence fluctuations at the selected length scale can be probed accurately by simply measuring the fiber velocity at its two ends and projecting it along the transverse-to-fiber direction. By means of fully resolved direct numerical simulations and experiments, we show that these fiber-based transverse velocity increments are statistically equivalent to the (unperturbed) flow transverse velocity increments. Moreover, we show that the turbulent energy-dissipation rate can be accurately measured exploiting sufficiently short fibers. The technique is tested against standard particle tracking velocimetry (PTV) of flow tracers with excellent agreement. Our technique overcomes the well-known problem of PTV to probe two-point statistics reliably because of the fast relative diffusion in turbulence that prevents the mutual distance between particles to remain constant at the length scale of interest. This problem, making it difficult to obtain converged statistics for a fixed separation distance, is even more dramatic for natural flows in open domains. A prominent example is oceanic currents, where drifters (i.e., the tracer-particle counterpart used in field measurements) disperse quickly, but at the same time their number has to be limited to save costs. Inspired by our laboratory experiments, we propose pairs of connected drifters as a viable option to solve the issue.

Shape analysis has been widely used in digital image processing and computer vision, but they have not been utilized to compare the structural characteristics of the human association pathways. Here we used shape analysis to derive length, area, volume, and shape metrics from diffusion MRI tractography and utilized them to study the morphology of human association pathways. The reliability analysis showed that shape descriptors achieved moderate to good test-retest reliability. Further analysis on association pathways showed left dominance in the arcuate fasciculus, cingulum, uncinate fasciculus, frontal aslant tract, and right dominance in the inferior fronto-occipital fasciculus and inferior longitudinal fasciculus. The superior longitudinal fasciculus has a mixed lateralization profile with different metrics showing either left or right dominance. The analysis of between-subject variations shows that the overall layout of the association pathways does not variate a lot across subjects, as shown by low between-subject variation in length, span, diameter, and radius. In contrast, the area of the pathway innervation region has a considerable between-subject variation. A follow-up analysis is warranted to thoroughly investigate the nature of population variations and their structure-function correlation. [Display omitted]

Virtual dissection of diffusion MRI tractograms is cumbersome and needs extensive knowledge of white matter anatomy. This virtual dissection often requires several inclusion and exclusion regions-of-interest that make it a process that is very hard to reproduce across experts. Having automated tools that can extract white matter bundles for tract-based studies of large numbers of people is of great interest for neuroscience and neurosurgical planning. The purpose of our proposed method, named RecoBundles, is to segment white matter bundles and make virtual dissection easier to perform. This can help explore large tractograms from multiple persons directly in their native space. RecoBundles leverages latest state-of-the-art streamline-based registration and clustering to recognize and extract bundles using prior bundle models. RecoBundles uses bundle models as shape priors for detecting similar streamlines and bundles in tractograms. RecoBundles is 100% streamline-based, is efficient to work with millions of streamlines and, most importantly, is robust and adaptive to incomplete data and bundles with missing components. It is also robust to pathological brains with tumors and deformations. We evaluated our results using multiple bundles and showed that RecoBundles is in good agreement with the neuroanatomical experts and generally produced more dense bundles. Across all the different experiments reported in this paper, RecoBundles was able to identify the core parts of the bundles, independently from tractography type (deterministic or probabilistic) or size. Thus, RecoBundles can be a valuable method for exploring tractograms and facilitating tractometry studies. [Display omitted]

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.

Diffusion MRI fiber tracking provides a non-invasive method for mapping the trajectories of human brain connections, but its false connection problem has been a major challenge. This study introduces topology-informed pruning (TIP), a method that automatically identifies singular tracts and eliminates them to improve the tracking accuracy. The accuracy of the tractography with and without TIP was evaluated by a team of 6 neuroanatomists in a blinded setting to examine whether TIP could improve the accuracy. The results showed that TIP improved the tracking accuracy by 11.93% in the single-shell scheme and by 3.47% in the grid scheme. The improvement is significantly different from a random pruning (p value < 0.001). The diagnostic agreement between TIP and neuroanatomists was comparable to the agreement between neuroanatomists. The proposed TIP algorithm can be used to automatically clean-up noisy fibers in deterministic tractography, with a potential to confirm the existence of a fiber connection in basic neuroanatomical studies or clinical neurosurgical planning.

White matter fiber tracking using diffusion magnetic resonance imaging (dMRI) provides a noninvasive approach to map brain connections, but improving anatomical accuracy has been a significant challenge since the birth of tractography methods. Utilizing tractography in brain studies therefore requires understanding of its technical limitations to avoid shortcomings and pitfalls. This review explores tractography limitations and how different white matter pathways pose different challenges to fiber tracking methodologies. We summarize the pros and cons of commonly-used methods, aiming to inform how tractography and its related analysis may lead to questionable results. Extending these experiences, we review the clinical utilization of tractography in patients with brain tumors and traumatic brain injury, starting from tensor-based tractography to more advanced methods. We discuss current limitations and highlight novel approaches in the context of these two conditions to inform future tractography developments.

Neuroimaging advances have given rise to major progress in neurosciences and neurology, as ever more subtle and specific imaging methods reveal new aspects of the brain. One major limitation of current methods is the spatial scale of the information available. We present an approach to gain spatial resolution using post-processing methods based on diffusion MRI fiber-tracking, to reveal structures beyond the resolution of the acquired imaging voxel; we term such a method as super-resolution track-density imaging (TDI). A major unmet challenge in imaging is the identification of abnormalities in white matter as a cause of illness; super-resolution TDI is shown to produce high-quality white matter images, with high spatial resolution and outstanding anatomical contrast. A unique property of these maps is demonstrated: their spatial resolution and signal-to-noise ratio can be tailored depending on the chosen image resolution and total number of fiber-tracks generated. Super-resolution TDI should greatly enhance the study of white matter in disorders of the brain and mind. ►A new super-resolution method based on fiber-tracking MRI is presented. ►It produces high-quality white matter images. ►It produces high spatial resolution and outstanding anatomical contrast. ►The spatial resolution and signal-to-noise ratio can be tailored. ►Novel contrast mechanism for tissues of mixed grey matter and white matter.

BACKGROUND:Deep brain stimulation alleviates tremor of various origins. Several regions like the ventralis intermediate nucleus of thalamus, the caudal zona incerta, and the posterior subthalamic region are generally targeted. Previous work with fiber tractography has shown the involvement of the cerebello-thalamo-cortical network in tremor control. OBJECTIVE:To report the results of a prospective trial in a group of patients with tremor who underwent post hoc tractographic analysis after treatment with traditional thalamic deep brain stimulation. METHODS:A total of 11 patients (aged 64 ± 17 years, 6 male) were enrolled (essential tremor [6], Parkinson tremor [3], and myoclonic tremor in myoclonus dystonia [2]). Patients received 1 (3 patients), 2 (7 patients), or 3 (1 patient) quadripolar electrodes. A 32-direction diffusion tensor magnetic resonance imaging sequence was acquired preoperatively. Tractography was processed postoperatively for evaluation and the dentato-rubro-thalamic tract (DRT) was individually tracked. Electrode positions were determined with helical computed tomography. Electric fields (EFs) were simulated according to individual stimulation parameters in a standardized atlas brain space (ICBM-MNI 152). RESULTS:Tremor was reduced in all patients (69.4% mean) on the global (bilateral) tremor score. Effective contacts were located inside or in proximity to the DRT. In moderate tremor reduction (2 patients), the EFs were centered on its anterior border. In good and excellent tremor reduction (9 patients), EFs focused on its center. CONCLUSION:Deep brain stimulation of the cerebello-thalamo-cortical network reduces tremor. The DRT connects 3 traditional target regions for deep brain stimulation in tremor disease. Tractography techniques can be used to directly visualize the DRT and, therefore, optimize target definition in individual patients. ABBREVIATIONS:ACPC, anterior commissure-posterior commissureCeTCoCe, cerebello-thalamo-cortical-cerebellar loopCoTCo, cortico-thalamo-cortical loopcZI, caudal zona incertaDBS, deep brain stimulationDRT, dentato-rubro-thalamic tractDTI, diffusion tensor magnetic resonance imagingEF, electric fieldET, essential tremorETRS, Essential Tremor Rating ScaleFOV, field of viewFT, fiber tractographyICBM-MNI 152, Montreal Neurological Institute Brain AtlasVOP, ventralis oralis posterior nucleus of thalamuspSTR, posterior subthalamic regionSTN, subthalamic nucleusTE, echo timeTR, repetition timeVim, ventral intermediate nucleus of thalamus

There is ongoing debate whether using a higher spatial resolution (sampling k-space) or a higher angular resolution (sampling q-space angles) is the better way to improve diffusion MRI (dMRI) based tractography results in living humans. In both cases, the limiting factor is the signal-to-noise ratio (SNR), due to the restricted acquisition time. One possible way to increase the spatial resolution without sacrificing either SNR or angular resolution is to move to a higher magnetic field strength. Nevertheless, dMRI has not been the preferred application for ultra-high field strength (7T). This is because single-shot echo-planar imaging (EPI) has been the method of choice for human in vivo dMRI. EPI faces several challenges related to the use of a high resolution at high field strength, for example, distortions and image blurring. These problems can easily compromise the expected SNR gain with field strength. In the current study, we introduce an adapted EPI sequence in conjunction with a combination of ZOOmed imaging and Partially Parallel Acquisition (ZOOPPA). We demonstrate that the method can produce high quality diffusion-weighted images with high spatial and angular resolution at 7T. We provide examples of in vivo human dMRI with isotropic resolutions of 1mm and 800μm. These data sets are particularly suitable for resolving complex and subtle fiber architectures, including fiber crossings in the white matter, anisotropy in the cortex and fibers entering the cortex. [Display omitted] ► Diffusion MRI (dMRI) at ultra-high field strength. ► Using ZOOPPA to exploit the SNR benefit of the high field strength. ► Enables dMRI with sub-millimeter isotropic resolution. ► High spatial and angular resolution with sufficient SNR to resolve crossing fibers.