On the cortical connectivity in the macaque brain: A comparison of diffusion tractography and histological tracing data

Preoperative diffusion tensor imaging: Fiber-trajectory-distribution-based tractography to identify facial nerve in vestibular schwannoma

PURPOSE

Tractography of the facial nerve based on diffusion MRI is instrumental before surgery for the resection of vestibular schwannoma, but no excellent methods usable for the suppression of motion and image noise have been proposed. The aim of this study was to effectively suppress noise and provide accurate facial nerve reconstruction by extend a fiber trajectory distribution function based on the fourth-order streamline differential equations.

METHODS

Preoperative MRI from 33 patients with vestibular schwannoma who underwent surgical resection were utilized in this study. First, T1WI and T2WI were used to obtain mask images and regions of interest. Second, probabilistic tractography was employed to obtain the fibers representing the approximate facial nerve pathway, and these fibers were subsequently translated into orientation information for each voxel. Last, the voxel orientation information and the peaks of the fiber orientation distribution were combined to generate a fiber trajectory distribution function, which was used to parameterize the anatomical information. The parameters were determined by minimizing the cost between the trajectory of fibers and the estimated directions.

RESULTS

Qualitative and visual analyses were used to compare facial nerve reconstruction with intraoperative recordings. Compared with other methods (SD_Stream, iFOD1, iFOD2, unscented Kalman filter, parallel transport tractography), the fiber-trajectory-distribution-based tractography provided the most accurate facial nerve reconstructions.

CONCLUSION

The fiber-trajectory-distribution-based tractography can effectively suppress the effect of noise. It is a more valuable aid for surgeons before vestibular schwannoma resection, which may ultimately improve the postsurgical patient's outcome.

Age Trajectories of the Structural Connectome in Child and Adolescent Offspring of Individuals With Bipolar Disorder or Schizophrenia

Background

Offspring of parents with severe mental illness (e.g., bipolar disorder or schizophrenia) are at elevated risk of developing psychiatric illness owing to both genetic predisposition and increased burden of environmental stress. Emerging evidence indicates a disruption of brain network connectivity in young offspring of patients with bipolar disorder and schizophrenia, but the age trajectories of these brain networks in this high-familial-risk population remain to be elucidated.

Methods

A total of 271 T1-weighted and diffusion-weighted scans were obtained from 174 offspring of at least 1 parent diagnosed with bipolar disorder (n = 74) or schizophrenia (n = 51) and offspring of parents without severe mental illness (n = 49). The age range was 8 to 23 years; 97 offspring underwent 2 scans. Anatomical brain networks were reconstructed into structural connectivity matrices. Network analysis was performed to investigate anatomical brain connectivity.

Results

Offspring of parents with schizophrenia had differential trajectories of connectivity strength and clustering compared with offspring of parents with bipolar disorder and parents without severe mental illness, of global efficiency compared with offspring of parents without severe mental illness, and of local connectivity compared with offspring of parents with bipolar disorder.

Conclusions

The findings of this study suggest that familial high risk of schizophrenia is related to deviations in age trajectories of global structural connectome properties and local connectivity strength.

White matter tract crossing and bottleneck regions in the fetal brain

There is a growing interest in using diffusion MRI to study the white matter tracts and structural connectivity of the fetal brain. Recent progress in data acquisition and processing suggests that this imaging modality has a unique role in elucidating the normal and abnormal patterns of neurodevelopment in utero. However, there have been no efforts to quantify the prevalence of crossing tracts and bottleneck regions, important issues that have been extensively researched for adult brains. In this work, we determined the brain regions with crossing tracts and bottlenecks between 23 and 36 gestational weeks. We performed probabilistic tractography on 59 fetal brain scans and extracted a set of 51 distinct white tracts, which we grouped into 10 major tract bundle groups. We analyzed the results to determine the patterns of tract crossings and bottlenecks. Our results showed that 20-25% of the white matter voxels included two or three crossing tracts. Bottlenecks were more prevalent. Between 75-80% of the voxels were characterized as bottlenecks, with more than 40% of the voxels involving four or more tracts. The results of this study highlight the challenge of fetal brain tractography and structural connectivity assessment and call for innovative image acquisition and analysis methods to mitigate these problems.

Structural connectivity matures along a sensorimotor-association connectional axis in youth

Childhood and adolescence are associated with protracted developmental remodeling of cortico-cortical structural connectivity. However, how heterochronous development in white matter structural connectivity spatially and temporally unfolds across the macroscale human connectome remains unknown. Leveraging non-invasive diffusion MRI data from both cross-sectional (N = 590) and longitudinal (baseline: N = 3,949; two-year follow-up: N = 3,155) developmental datasets, we found that structural connectivity development diverges along a pre-defined sensorimotor-association (S-A) connectional axis from ages 8.1 to 21.9 years. Specifically, we observed a continuum of developmental profiles that spans from an early childhood increase in connectivity strength in sensorimotor-sensorimotor connections to a late adolescent increase in association-association connectional strength. The S-A connectional axis also captured spatial variations in associations between structural connectivity and both higher-order cognition and general psychopathology. Together, our findings reveal a hierarchical axis in the development of structural connectivity across the human connectome.

Can hubs of the human connectome be identified consistently with diffusion MRI?

Recent years have seen a surge in the use of diffusion MRI to map connectomes in humans, paralleled by a similar increase in processing and analysis choices. Yet these different steps and their effects are rarely compared systematically. Here, in a healthy young adult population (n = 294), we characterized the impact of a range of analysis pipelines on one widely studied property of the human connectome: its degree distribution. We evaluated the effects of 40 pipelines (comparing common choices of parcellation, streamline seeding, tractography algorithm, and streamline propagation constraint) and 44 group-representative connectome reconstruction schemes on highly connected hub regions. We found that hub location is highly variable between pipelines. The choice of parcellation has a major influence on hub architecture, and hub connectivity is highly correlated with regional surface area in most of the assessed pipelines (ρ > 0.70 in 69% of the pipelines), particularly when using weighted networks. Overall, our results demonstrate the need for prudent decision-making when processing diffusion MRI data, and for carefully considering how different processing choices can influence connectome organization.

One-shot segmentation of novel white matter tracts via extensive data augmentation and adaptive knowledge transfer

The use of convolutional neural networks (CNNs) has allowed accurate white matter (WM) tract segmentation on diffusion magnetic resonance imaging (dMRI). To train the CNN-based segmentation models, a large number of scans on which WM tracts are annotated need to be collected, and these annotated scans can be accumulated over a long period of time. However, when novel WM tracts that are different from existing annotated WM tracts are of interest, additional annotations are required for their segmentation. Due to the cost of manual annotations, methods have been developed for few-shot segmentation of novel WM tracts, where the segmentation knowledge is transferred from existing WM tracts to novel WM tracts and the amount of annotated data for novel WM tracts is reduced. Despite these developments, it is desirable to further reduce the amount of annotated data to the one-shot setting with a single annotated image. To address this problem, we develop an approach to one-shot segmentation of novel WM tracts. Our method follows the existing pretraining/fine-tuning framework that transfers segmentation knowledge from existing to novel WM tracts. First, as there is extremely scarce annotated data in the one-shot setting, we design several different data augmentation strategies so that extensive data augmentation can be performed to obtain extra synthetic training data. The data augmentation strategies are based on image masking and thus applicable to the one-shot setting. Second, to address overfitting and knowledge forgetting in the fine-tuning stage that can be more severe given limited training data, we propose an adaptive knowledge transfer strategy that selects the network weights to be updated. The data augmentation and adaptive knowledge transfer strategies are combined to train the segmentation model. Considering that the different data augmentation strategies can generate synthetic data that contain potentially conflicting information, we apply the data augmentation strategies separately, each leading to a different segmentation model. The results predicted by the different models are fused to produce the final segmentation. We validated our method on two brain dMRI datasets, including a public dataset and an in-house dataset. Different settings were considered for the validation, and the results show that the proposed method improves the one-shot segmentation of novel WM tracts.

Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods

The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body. The CST exhibits a somatotopic organization, which means that the motor neurons that control specific body parts are arranged in order within the CST. Diffusion magnetic resonance imaging (MRI) tractography is increasingly used to study the anatomy of the CST. However, despite many advances in tractography algorithms over the past decade, modern, state-of-the-art methods still face challenges. In this study, we compare the performance of six widely used tractography methods for reconstructing the CST and its somatotopic organization. These methods include constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD-Stream) methods, unscented Kalman filter (UKF) tractography methods including multi-fiber (UKF2T) and single-fiber (UKF1T) models, the generalized q-sampling imaging (GQI) based deterministic tractography method, and the TractSeg method. We investigate CST somatotopy by dividing the CST into four subdivisions per hemisphere that originate in the leg, trunk, hand, and face areas of the primary motor cortex. A quantitative and visual comparison is performed using diffusion MRI data (N = 100 subjects) from the Human Connectome Project. Quantitative evaluations include the reconstruction rate of the eight anatomical subdivisions, the percentage of streamlines in each subdivision, and the coverage of the white matter-gray matter (WM-GM) interface. CST somatotopy is further evaluated by comparing the percentage of streamlines in each subdivision to the cortical volumes for the leg, trunk, hand, and face areas. Overall, UKF2T has the highest reconstruction rate and cortical coverage. It is the only method with a significant positive correlation between the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex. However, our experimental results show that all compared tractography methods are biased toward generating many trunk streamlines (ranging from 35.10% to 71.66% of total streamlines across methods). Furthermore, the coverage of the WM-GM interface in the largest motor area (face) is generally low (under 40%) for all compared tractography methods. Different tractography methods give conflicting results regarding the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex, indicating that there is generally no clear relationship, and that reconstruction of CST somatotopy is still a large challenge. Overall, we conclude that while current tractography methods have made progress toward the well-known challenge of improving the reconstruction of the lateral projections of the CST, the overall problem of performing a comprehensive CST reconstruction, including clinically important projections in the lateral (hand and face areas) and medial portions (leg area), remains an important challenge for diffusion MRI tractography.

Whole-brain, gray, and white matter time-locked functional signal changes with simple tasks and model-free analysis

Recent studies have revealed the production of time-locked blood oxygenation level-dependent (BOLD) functional MRI (fMRI) signals throughout the entire brain in response to tasks, challenging the existence of sparse and localized brain functions and highlighting the pervasiveness of potential false negative fMRI findings. "Whole-brain" actually refers to gray matter, the only tissue traditionally studied with fMRI. However, several reports have demonstrated reliable detection of BOLD signals in white matter, which have previously been largely ignored. Using simple tasks and analyses, we demonstrate BOLD signal changes across the whole brain, in both white and gray matters, in similar manner to previous reports of whole brain studies. We investigated whether white matter displays time-locked BOLD signals across multiple structural pathways in response to a stimulus in a similar manner to the cortex. We find that both white and gray matter show time-locked activations across the whole brain, with a majority of both tissue types showing statistically significant signal changes for all task stimuli investigated. We observed a wide range of signal responses to tasks, with different regions showing different BOLD signal changes to the same task. Moreover, we find that each region may display different BOLD responses to different stimuli. Overall, we present compelling evidence that, just like all gray matter, essentially all white matter in the brain shows time-locked BOLD signal changes in response to multiple stimuli, challenging the idea of sparse functional localization and the prevailing wisdom of treating white matter BOLD signals as artifacts to be removed.

Controversies and progress on standardization of large-scale brain network nomenclature

Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt.

Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge

Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and false negative connections were consistently estimated across all methods. Although the challenge dataset doesn't capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods.

The Brain/MINDS Marmoset Connectivity Resource: An open-access platform for cellular-level tracing and tractography in the primate brain

The primate brain has unique anatomical characteristics, which translate into advanced cognitive, sensory, and motor abilities. Thus, it is important that we gain insight on its structure to provide a solid basis for models that will clarify function. Here, we report on the implementation and features of the Brain/MINDS Marmoset Connectivity Resource (BMCR), a new open-access platform that provides access to high-resolution anterograde neuronal tracer data in the marmoset brain, integrated to retrograde tracer and tractography data. Unlike other existing image explorers, the BMCR allows visualization of data from different individuals and modalities in a common reference space. This feature, allied to an unprecedented high resolution, enables analyses of features such as reciprocity, directionality, and spatial segregation of connections. The present release of the BMCR focuses on the prefrontal cortex (PFC), a uniquely developed region of the primate brain that is linked to advanced cognition, including the results of 52 anterograde and 164 retrograde tracer injections in the cortex of the marmoset. Moreover, the inclusion of tractography data from diffusion MRI allows systematic analyses of this noninvasive modality against gold-standard cellular connectivity data, enabling detection of false positives and negatives, which provide a basis for future development of tractography. This paper introduces the BMCR image preprocessing pipeline and resources, which include new tools for exploring and reviewing the data.

Towards reliable reconstruction of the mouse brain corticothalamic connectivity using diffusion MRI

Diffusion magnetic resonance imaging (dMRI) tractography has yielded intriguing insights into brain circuits and their relationship to behavior in response to gene mutations or neurological diseases across a number of species. Still, existing tractography approaches suffer from limited sensitivity and specificity, leading to uncertain interpretation of the reconstructed connections. Hence, in this study, we aimed to optimize the imaging and computational pipeline to achieve the best possible spatial overlaps between the tractography and tracer-based axonal projection maps within the mouse brain corticothalamic network. We developed a dMRI-based atlas of the mouse forebrain with structural labels imported from the Allen Mouse Brain Atlas (AMBA). Using the atlas and dMRI tractography, we first reconstructed detailed node-to-node mouse brain corticothalamic structural connectivity matrices using different imaging and tractography parameters. We then investigated the effects of each condition for accurate reconstruction of the corticothalamic projections by quantifying the similarities between the tractography and the tracer data from the Allen Mouse Brain Connectivity Atlas (AMBCA). Our results suggest that these parameters significantly affect tractography outcomes and our atlas can be used to investigate macroscopic structural connectivity in the mouse brain. Furthermore, tractography in mouse brain gray matter still face challenges and need improved imaging and tractography methods.

Superficial white matter across development, young adulthood, and aging: volume, thickness, and relationship with cortical features

Superficial white matter (SWM) represents a significantly understudied part of the human brain, despite comprising a large portion of brain volume and making up a majority of cortico-cortical white matter connections. Using multiple, high-quality datasets with large sample sizes (N = 2421, age range 5-100) in combination with methodological advances in tractography, we quantified features of SWM volume and thickness across the brain and across development, young adulthood, and aging. We had four primary aims: (1) characterize SWM thickness across brain regions (2) describe associations between SWM volume and age (3) describe associations between SWM thickness and age, and (4) quantify relationships between SWM thickness and cortical features. Our main findings are that (1) SWM thickness varies across the brain, with patterns robust across individuals and across the population at the region-level and vertex-level; (2) SWM volume shows unique volumetric trajectories with age that are distinct from gray matter and other white matter trajectories; (3) SWM thickness shows nonlinear cross-sectional changes across the lifespan that vary across regions; and (4) SWM thickness is associated with features of cortical thickness and curvature. For the first time, we show that SWM volume follows a similar trend as overall white matter volume, peaking at a similar time in adolescence, leveling off throughout adulthood, and decreasing with age thereafter. Notably, the relative fraction of total brain volume of SWM continuously increases with age, and consequently takes up a larger proportion of total white matter volume, unlike the other tissue types that decrease with respect to total brain volume. This study represents the first characterization of SWM features across the large portion of the lifespan and provides the background for characterizing normal aging and insight into the mechanisms associated with SWM development and decline.

Mapping Human Brain Pathways: Challenges and Opportunities in the Integration of Scales

The human brain is composed of a complex web of pathways. Diffusion magnetic resonance (MR) tractography is a neuroimaging technique that relies on the principle of diffusion to reconstruct brain pathways. Its tractography is broadly applicable to a range of problems as it is amenable for study in individuals of any age and from any species. However, it is well known that this technique can generate biologically implausible pathways, especially in regions of the brain where multiple fibers cross. This review highlights potential misconnections in two cortico-cortical association pathways with a focus on the aslant tract and inferior frontal occipital fasciculus. The lack of alternative methods to validate observations from diffusion MR tractography means there is a need to develop new integrative approaches to trace human brain pathways. This review discusses integrative approaches in neuroimaging, anatomical, and transcriptional variation as having much potential to trace the evolution of human brain pathways.

Whole-brain, gray and white matter time-locked functional signal changes with simple tasks and model-free analysis

Recent studies have revealed the production of time-locked blood oxygenation-level dependent (BOLD) functional MRI (fMRI) signals throughout the entire brain in response to a task, challenging the idea of sparse and localized brain functions, and highlighting the pervasiveness of potential false negative fMRI findings. In these studies, 'whole-brain' refers to gray matter regions only, which is the only tissue traditionally studied with fMRI. However, recent reports have also demonstrated reliable detection and analyses of BOLD signals in white matter which have been largely ignored in previous reports. Here, using model-free analysis and simple tasks, we investigate BOLD signal changes in both white and gray matters. We aimed to evaluate whether white matter also displays time-locked BOLD signals across all structural pathways in response to a stimulus. We find that both white and gray matter show time-locked activations across the whole-brain, with a majority of both tissue types showing statistically significant signal changes for all task stimuli investigated. We observed a wide range of signal responses to tasks, with different regions showing very different BOLD signal changes to the same task. Moreover, we find that each region may display different BOLD responses to different stimuli. Overall, we present compelling evidence that the whole brain, including both white and gray matter, show time-locked activation to multiple stimuli, not only challenging the idea of sparse functional localization, but also the prevailing wisdom of treating white matter BOLD signals as artefacts to be removed.

The parietal lobe evolution and the emergence of material culture in the human genus

Traditional and new disciplines converge in suggesting that the parietal lobe underwent a considerable expansion during human evolution. Through the study of endocasts and shape analysis, paleoneurology has shown an increased globularity of the braincase and bulging of the parietal region in modern humans, as compared to other human species, including Neandertals. Cortical complexity increased in both the superior and inferior parietal lobules. Emerging fields bridging archaeology and neuroscience supply further evidence of the involvement of the parietal cortex in human-specific behaviors related to visuospatial capacity, technological integration, self-awareness, numerosity, mathematical reasoning and language. Here, we complement these inferences on the parietal lobe evolution, with results from more classical neuroscience disciplines, such as behavioral neurophysiology, functional neuroimaging, and brain lesions; and apply these to define the neural substrates and the role of the parietal lobes in the emergence of functions at the core of material culture, such as tool-making, tool use and constructional abilities.

Exploring personalized structural connectomics for moderate to severe traumatic brain injury

Graph theoretical analysis of the structural connectome has been employed successfully to characterize brain network alterations in patients with traumatic brain injury (TBI). However, heterogeneity in neuropathology is a well-known issue in the TBI population, such that group comparisons of patients against controls are confounded by within-group variability. Recently, novel single-subject profiling approaches have been developed to capture inter-patient heterogeneity. We present a personalized connectomics approach that examines structural brain alterations in five chronic patients with moderate to severe TBI who underwent anatomical and diffusion magnetic resonance imaging. We generated individualized profiles of lesion characteristics and network measures (including personalized graph metric GraphMe plots, and nodal and edge-based brain network alterations) and compared them against healthy reference cases (N = 12) to assess brain damage qualitatively and quantitatively at the individual level. Our findings revealed alterations of brain networks with high variability between patients. With validation and comparison to stratified, normative healthy control comparison cohorts, this approach could be used by clinicians to formulate a neuroscience-guided integrative rehabilitation program for TBI patients, and for designing personalized rehabilitation protocols based on their unique lesion load and connectome.

Structural and functional connectivity of the inferior temporal numeral area

A growing body of evidence suggests that in adults, there is a spatially consistent "inferior temporal numeral area" (ITNA) in the occipitotemporal cortex that appears to preferentially process Arabic digits relative to non-numerical symbols and objects. However, very little is known about why the ITNA is spatially segregated from regions that process other orthographic stimuli such as letters, and why it is spatially consistent across individuals. In the present study, we used diffusion-weighted imaging and functional magnetic resonance imaging to contrast structural and functional connectivity between left and right hemisphere ITNAs and a left hemisphere letter-preferring region. We found that the left ITNA had stronger structural and functional connectivity than the letter region to inferior parietal regions involved in numerical magnitude representation and arithmetic. Between hemispheres, the left ITNA showed stronger structural connectivity with the left inferior frontal gyrus (Broca's area), while the right ITNA showed stronger structural connectivity to the ipsilateral inferior parietal cortex and stronger functional coupling with the bilateral IPS. Based on their relative connectivity, our results suggest that the left ITNA may be more readily involved in mapping digits to verbal number representations, while the right ITNA may support the mapping of digits to quantity representations.

Gyral and sulcal connectivity in the human cerebral cortex

The rapid evolution of image acquisition and data analytic methods has established in vivo whole-brain tractography as a routine technology over the last 20 years. Imaging-based methods provide an additional approach to classic neuroanatomical studies focusing on biomechanical principles of anatomical organization and can in turn overcome the complexity of inter-individual variability associated with histological and tractography studies. In this work we propose a novel, reliable framework for determining brain tracts resolving the anatomical variance of brain regions. We distinguished 4 region types based on anatomical considerations: (i) gyral regions at borders between cortical communities; (ii) gyral regions within communities; (iii) sulcal regions at invariant locations across subjects; and (iv) other sulcal regions. Region types showed strikingly different anatomical and connection properties. Results allowed complementing the current understanding of the brain’s communication structure with a model of its anatomical underpinnings.

Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI

Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.

Post mortem mapping of connectional anatomy for the validation of diffusion MRI

Diffusion MRI (dMRI) is a unique tool for the study of brain circuitry, as it allows us to image both the macroscopic trajectories and the microstructural properties of axon bundles in vivo. The Human Connectome Project ushered in an era of impressive advances in dMRI acquisition and analysis. As a result of these efforts, the quality of dMRI data that could be acquired in vivo improved substantially, and large collections of such data became widely available. Despite this progress, the main limitation of dMRI remains: it does not image axons directly, but only provides indirect measurements based on the diffusion of water molecules. Thus, it must be validated by methods that allow direct visualization of axons but that can only be performed in post mortem brain tissue. In this review, we discuss methods for validating the various features of connectional anatomy that are extracted from dMRI, both at the macro-scale (trajectories of axon bundles), and at micro-scale (axonal orientations and other microstructural properties). We present a range of validation tools, including anatomic tracer studies, Klingler's dissection, myelin stains, label-free optical imaging techniques, and others. We provide an overview of the basic principles of each technique, its limitations, and what it has taught us so far about the accuracy of different dMRI acquisition and analysis approaches.

Ultra-high field (10.5T) diffusion-weighted MRI of the macaque brain

Diffu0sion-weighted magnetic resonance imaging (dMRI) is a non-invasive imaging technique that provides information about the barriers to the diffusion of water molecules in tissue. In the brain, this information can be used in several important ways, including to examine tissue abnormalities associated with brain disorders and to infer anatomical connectivity and the organization of white matter bundles through the use of tractography algorithms. However, dMRI also presents certain challenges. For example, historically, the biological validation of tractography models has shown only moderate correlations with anatomical connectivity as determined through invasive tract-tracing studies. Some of the factors contributing to such issues are low spatial resolution, low signal-to-noise ratios, and long scan times required for high-quality data, along with modeling challenges like complex fiber crossing patterns. Leveraging the capabilities provided by an ultra-high field scanner combined with denoising, we have acquired whole-brain, 0.58 mm isotropic resolution dMRI with a 2D-single shot echo planar imaging sequence on a 10.5 Tesla scanner in anesthetized macaques. These data produced high-quality tractograms and maps of scalar diffusion metrics in white matter. This work demonstrates the feasibility and motivation for in-vivo dMRI studies seeking to benefit from ultra-high fields.

Intra-Areal Visual Topography in Primate Brains Mapped with Probabilistic Tractography of Diffusion-Weighted Imaging

Noninvasive diffusion-weighted magnetic resonance imaging (dMRI) can be used to map the neural connectivity between distinct areas in the intact brain, but the standard resolution achieved fundamentally limits the sensitivity of such maps. We investigated the sensitivity and specificity of high-resolution postmortem dMRI and probabilistic tractography in rhesus macaque brains to produce retinotopic maps of the lateral geniculate nucleus (LGN) and extrastriate cortical visual area V5/MT based on their topographic connections with the previously established functional retinotopic map of primary visual cortex (V1). We also replicated the differential connectivity of magnocellular and parvocellular LGN compartments with V1 across visual field positions. Predicted topographic maps based on dMRI data largely matched the established retinotopy of both LGN and V5/MT. Furthermore, tractography based on in vivo dMRI data from the same macaque brains acquired at standard field strength (3T) yielded comparable topographic maps in many cases. We conclude that tractography based on dMRI is sensitive enough to reveal the intrinsic organization of ordered connections between topographically organized neural structures and their resultant functional organization.

Evaluating reproducibility and subject-specificity of microstructure-informed connectivity

Tractography enables identifying and evaluating the healthy and diseased brain's white matter pathways from diffusion-weighted magnetic resonance imaging data. As previous evaluation studies have reported significant false-positive estimation biases, recent microstructure-informed tractography algorithms have been introduced to improve the trade-off between specificity and sensitivity. However, a major limitation for characterizing the performance of these techniques is the lack of ground truth brain data. In this study, we compared the performance of two relevant microstructure-informed tractography methods, SIFT2 and COMMIT, by assessing the subject specificity and reproducibility of their derived white matter pathways. Specifically, twenty healthy young subjects were scanned at eight different time points at two different sites. Subject specificity and reproducibility were evaluated using the whole-brain connectomes and a subset of 29 white matter bundles. Our results indicate that although the raw tractograms are more vulnerable to the presence of false-positive connections, they are highly reproducible, suggesting that the estimation bias is subject-specific. This high reproducibility was preserved when microstructure-informed tractography algorithms were used to filter the raw tractograms. Moreover, the resulting track-density images depicted a more uniform coverage of streamlines throughout the white matter, suggesting that these techniques could increase the biological meaning of the estimated fascicles. Notably, we observed an increased subject specificity by employing connectivity pre-processing techniques to reduce the underlaying noise and the data dimensionality (using principal component analysis), highlighting the importance of these tools for future studies. Finally, no strong bias from the scanner site or time between measurements was found. The largest intraindividual variance originated from the sole repetition of data measurements (inter-run).

Mapping brain-wide excitatory projectome of primate prefrontal cortex at submicron resolution and comparison with diffusion tractography

Resolving trajectories of axonal pathways in the primate prefrontal cortex remains crucial to gain insights into higher-order processes of cognition and emotion, which requires a comprehensive map of axonal projections linking demarcated subdivisions of prefrontal cortex and the rest of brain. Here, we report a mesoscale excitatory projectome issued from the ventrolateral prefrontal cortex (vlPFC) to the entire macaque brain by using viral-based genetic axonal tracing in tandem with high-throughput serial two-photon tomography, which demonstrated prominent monosynaptic projections to other prefrontal areas, temporal, limbic, and subcortical areas, relatively weak projections to parietal and insular regions but no projections directly to the occipital lobe. In a common 3D space, we quantitatively validated an atlas of diffusion tractography-derived vlPFC connections with correlative green fluorescent protein-labeled axonal tracing, and observed generally good agreement except a major difference in the posterior projections of inferior fronto-occipital fasciculus. These findings raise an intriguing question as to how neural information passes along long-range association fiber bundles in macaque brains, and call for the caution of using diffusion tractography to map the wiring diagram of brain circuits.

In the brain is a web of interconnected nerve cells that send messages to one another via spindly projections called axons. These axons join together at junctions called synapses to create circuits of nerve cells which connect neighboring or distant brain regions. Notably, long-range neural connections underpin higher-order cognitive skills (such as planning and emotion regulation) which make humans distinct from our primate relatives. Only by untangling these far-reaching networks can researchers begin to delineate what sets the human brain apart from other species.

Researchers deploy a range of imaging techniques to map neural networks: scanning entire brains using MRI machines, or imaging thin slices of fluorescently labelled brain tissue using powerful microscopes. However, tracing long-range axons at a high resolution is challenging, and has stirred up debate about whether some neural tracts, such as the inferior fronto-occipital fasciculus, are present in all primates or only humans.

To address these discrepancies, Yan, Yu et al. employed a two-pronged approach to map neural circuits in the brains of macaques. First, two techniques – called viral tracing and two-photon microscopy – were used to create a three-dimensional, fine-grain map showing how the ventrolateral prefrontal cortex (vlPFC), which regulates complex behaviors, connects to the rest of the brain. This revealed prominent axons from the vlPFC projecting via a single synapse to distant brain regions involved in higher-order functions, such as encoding memories and processing emotion. However, there were no direct, monosynaptic connections between the vlPFC and the occipital lobe, the brain’s visual processing center at the back of the head.

Next, Yan, Yu et al. used a specialized MRI scanner to create an atlas of neural circuits connected to the vlPFC, and compared these results to a technique tracing axons stained with a fluorescent dye. In general, there was good agreement between the two methods, except for major differences in the rear-end projections that typically form the inferior fronto-occipital fasciculus. This suggests that this long-range neural pathway exists in monkeys, but it connects via multiple synapses instead of a single junction as was previously thought.

The findings of Yan, Yu et al. provide new insights on the far-reaching neural pathways connecting distant parts of the macaque brain. It also suggests that atlases of neural circuits from whole brain scans should be taken with caution and validated using neural tracing experiments.

Multi-tract multi-symptom relationships in pediatric concussion

Background

The heterogeneity of white matter damage and symptoms in concussion has been identified as a major obstacle to therapeutic innovation. In contrast, most diffusion MRI (dMRI) studies on concussion have traditionally relied on group-comparison approaches that average out heterogeneity. To leverage, rather than average out, concussion heterogeneity, we combined dMRI and multivariate statistics to characterize multi-tract multi-symptom relationships.

Methods

Using cross-sectional data from 306 previously concussed children aged 9-10 from the Adolescent Brain Cognitive Development Study, we built connectomes weighted by classical and emerging diffusion measures. These measures were combined into two informative indices, the first representing microstructural complexity, the second representing axonal density. We deployed pattern-learning algorithms to jointly decompose these connectivity features and 19 symptom measures.

Results

Early multi-tract multi-symptom pairs explained the most covariance and represented broad symptom categories, such as a general problems pair, or a pair representing all cognitive symptoms, and implicated more distributed networks of white matter tracts. Further pairs represented more specific symptom combinations, such as a pair representing attention problems exclusively, and were associated with more localized white matter abnormalities. Symptom representation was not systematically related to tract representation across pairs. Sleep problems were implicated across most pairs, but were related to different connections across these pairs. Expression of multi-tract features was not driven by sociodemographic and injury-related variables, as well as by clinical subgroups defined by the presence of ADHD. Analyses performed on a replication dataset showed consistent results.

Conclusions

Using a double-multivariate approach, we identified clinically-informative, cross-demographic multi-tract multi-symptom relationships. These results suggest that rather than clear one-to-one symptom-connectivity disturbances, concussions may be characterized by subtypes of symptom/connectivity relationships. The symptom/connectivity relationships identified in multi-tract multi-symptom pairs were not apparent in single-tract/single-symptom analyses. Future studies aiming to better understand connectivity/symptom relationships should take into account multi-tract multi-symptom heterogeneity.

Funding

Financial support for this work came from a Vanier Canada Graduate Scholarship from the Canadian Institutes of Health Research (G.I.G.), an Ontario Graduate Scholarship (S.S.), a Restracomp Research Fellowship provided by the Hospital for Sick Children (S.S.), an Institutional Research Chair in Neuroinformatics (M.D.), as well as a Natural Sciences and Engineering Research Council CREATE grant (M.D.).

Imaging versus electrographic connectivity in human mood-related fronto-temporal networks

BACKGROUND

The efficacy of psychiatric DBS is thought to be driven by the connectivity of stimulation targets with mood-relevant fronto-temporal networks, which is typically evaluated using diffusion-weighted tractography.

OBJECTIVE

Leverage intracranial electrophysiology recordings to better predict the circuit-wide effects of neuromodulation to white matter targets. We hypothesize strong convergence between tractography-predicted structural connectivity and stimulation-induced electrophysiological responses.

METHODS

Evoked potentials were elicited by single-pulse stimulation to two common DBS targets for treatment-resistant depression - the subcallosal cingulate (SCC) and ventral capsule/ventral striatum (VCVS) - in two patients undergoing DBS with stereo-electroencephalographic (sEEG) monitoring. Evoked potentials were compared with predicted structural connectivity between DBS leads and sEEG contacts using probabilistic, patient-specific diffusion-weighted tractography.

RESULTS

Evoked potentials and tractography showed strong convergence in both patients in orbitofrontal, ventromedial prefrontal, and lateral prefrontal cortices for both SCC and VCVS stimulation targets. Low convergence was found in anterior cingulate (ACC), where tractography predicted structural connectivity from SCC targets but produced no evoked potentials during SCC stimulation. Further, tractography predicted no connectivity to ACC from VCVS targets, but VCVS stimulation produced robust evoked potentials.

CONCLUSION

The two connectivity methods showed significant convergence, but important differences emerged with respect to the ability of tractography to predict electrophysiological connectivity between SCC and VCVS to regions of the mood-related network. This multimodal approach raises intriguing implications for the use of tractography in surgical targeting and provides new data to enhance our understanding of the network-wide effects of neuromodulation.

Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review

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.

CONTINUITY: CONnectivity Tool with INtegration of sUbcortical regions, regIstration and visualization of TractographY

In this work, we present CONTINUITY, a novel, open-source interactive computation and visualization tool for brain connectome data. The connectome processing pipeline performs surface based processing as the main mode of operation. The automated processing includes structural-to-diffusion image co-registration, surface reconstruction for subcortical structures, as well as fiber tractography. The tool supports 3 different probabilistic methods of tractography offered by the tractography frameworks in FSL, MRtrix and DIPY. All methods employ brain and subcortical surfaces as seeds to initialize the tractography algorithms. CONTINUITY implements a friendly Graphical User Interface (GUI) to make the workflow accessible for nontechnical users. Additionally, it offers the possibility to visualize the results of the brain connectome in several interactive plot types such as a hierarchical edge bundling circle plot and over 2D/3D brain templates. This visualization tool can also be applied to connectome matrices computed with other tools and pipelines.

Volumetric Segmentation of White Matter Tracts with Label Embedding

Convolutional neural networks have achieved state-of-the-art performance for white matter (WM) tract segmentation based on diffusion magnetic resonance imaging (dMRI). However, the segmentation can still be difficult for challenging WM tracts with thin bodies or complicated shapes; the segmentation is even more problematic in challenging scenarios with reduced data quality or domain shift between training and test data, which can be easily encountered in clinical settings. In this work, we seek to improve the segmentation of WM tracts, especially for challenging WM tracts in challenging scenarios. In particular, our method is based on volumetric WM tract segmentation, where voxels are directly labeled without performing tractography. To improve the segmentation, we exploit the characteristics of WM tracts that different tracts can cross or overlap and revise the network design accordingly. Specifically, because multiple tracts can co-exist in a voxel, we hypothesize that the different tract labels can be correlated. The tract labels at a single voxel are concatenated as a label vector, the length of which is the number of tract labels. Due to the tract correlation, this label vector can be projected into a lower-dimensional space-referred to as the embedded space-for each voxel, which allows the segmentation network to solve a simpler problem. By predicting the coordinate in the embedded space for the tracts at each voxel and subsequently mapping the coordinate to the label vector with a reconstruction module, the segmentation result can be achieved. To facilitate the learning of the embedded space, an auxiliary label reconstruction loss is integrated with the segmentation accuracy loss during network training, and network training and inference are end-to-end. Our method was validated on two dMRI datasets under various settings. The results show that the proposed method improves the accuracy of WM tract segmentation, and the improvement is more prominent for challenging tracts in challenging scenarios.

The Arcuate Fasciculus and language origins: Disentangling existing conceptions that influence evolutionary accounts

The Arcuate Fasciculus (AF) is of considerable interdisciplinary interest, because of its major implication in language processing. Theories about language brain evolution are based on anatomical differences in the AF across primates. However, changing methodologies and nomenclatures have resulted in conflicting findings regarding interspecies AF differences: Historical knowledge about the AF originated from human blunt dissections and later from monkey tract-tracing studies. Contemporary tractography studies reinvestigate the fasciculus' morphology, but remain heavily bound to unclear anatomical priors and methodological limitations. First, we aim to disentangle the influences of these three epistemological steps on existing AF conceptions, and to propose a contemporary model to guide future work. Second, considering the influence of various AF conceptions, we discuss four key evolutionary changes that propagated current views about language evolution: 1) frontal terminations, 2) temporal terminations, 3) greater Dorsal- versus Ventral Pathway expansion, 4) lateralisation. We conclude that new data point towards a more shared AF anatomy across primates than previously described. Language evolution theories should incorporate this continuous AF evolution across primates.

The role of spatial embedding in mouse brain networks constructed from diffusion tractography and tracer injections

Diffusion MRI tractography is the only noninvasive method to measure the structural connectome in humans. However, recent validation studies have revealed limitations of modern tractography approaches, which lead to significant mistracking caused in part by local uncertainties in fiber orientations that accumulate to produce larger errors for longer streamlines. Characterizing the role of this length bias in tractography is complicated by the true underlying contribution of spatial embedding to brain topology. In this work, we compare graphs constructed with ex vivo tractography data in mice and neural tracer data from the Allen Mouse Brain Connectivity Atlas to random geometric surrogate graphs which preserve the low-order distance effects from each modality in order to quantify the role of geometry in various network properties. We find that geometry plays a substantially larger role in determining the topology of graphs produced by tractography than graphs produced by tracers. Tractography underestimates weights at long distances compared to neural tracers, which leads tractography to place network hubs close to the geometric center of the brain, as do corresponding tractography-derived random geometric surrogates, while tracer graphs place hubs further into peripheral areas of the cortex. We also explore the role of spatial embedding in modular structure, network efficiency and other topological measures in both modalities. Throughout, we compare the use of two different tractography streamline node assignment strategies and find that the overall differences between tractography approaches are small relative to the differences between tractography- and tracer-derived graphs. These analyses help quantify geometric biases inherent to tractography and promote the use of geometric benchmarking in future tractography validation efforts.

Species and individual differences and connectional asymmetry of Broca's area in humans and macaques

To reveal the connectional specialization of the Broca's area (or its homologue), voxel-wise inter-species and individual differences, and inter-hemispheric asymmetry were respectively inspected in humans and macaques at both whole-brain connectivity and single tract levels. It was discovered that the developed connectivity blueprint approach is able to localize connectionally comparable voxels between the two species in Broca's area, whereas the quantitative differences between blueprints of locationally or connectionally corresponding voxels enable us to generate inter-hemispheric, inter-subject, and inter-species connectional variabilities, respectively. More importantly, the inter-species and inter-subject variabilities exhibited positive correlation in both two primates, and relatively higher variabilities were detected in the anatomically defined pars triangularis. By contrast, negative relationship was identified between the inter-species variability and hemispheric asymmetry in human brain. In particular, relatively higher asymmetry was revealed in the anatomically defined pars opercularis. Therefore, our novel findings demonstrated that pars triangularis, as compared to pars opercularis, might be a more active area during primate evolution, in which the brain connectivity and possible functions of pars triangularis show relatively higher degree in species specialization, yet lower in hemispheric specialization. Meanwhile, brain connectivity and possible functions of pars opercularis manifested an opposite pattern. At the tract level, functional roles related to the ventral stream in speech comprehension were relatively conservative and bilaterally organized, while those related to the dorsal stream in speech production show relatively higher species and hemispheric specializations.

The functional characterization of callosal connections

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The functional characterization of callosal connections is informed by anatomical data.

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Callosal connections play a conditional driving role depending on the brain state and behavioral demands.

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Callosal connections play a modulatory function, in addition to a driving role.

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The corpus callosum participates in learning and interhemispheric transfer of sensorimotor habits.

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The corpus callosum contributes to language processing and cognitive functions.

The brain operates through the synaptic interaction of distant neurons within flexible, often heterogeneous, distributed systems. Histological studies have detailed the connections between distant neurons, but their functional characterization deserves further exploration. Studies performed on the corpus callosum in animals and humans are unique in that they capitalize on results obtained from several neuroscience disciplines. Such data inspire a new interpretation of the function of callosal connections and delineate a novel road map, thus paving the way toward a general theory of cortico-cortical connectivity. Here we suggest that callosal axons can drive their post-synaptic targets preferentially when coupled to other inputs endowing the cortical network with a high degree of conditionality. This might depend on several factors, such as their pattern of convergence-divergence, the excitatory and inhibitory operation mode, the range of conduction velocities, the variety of homotopic and heterotopic projections and, finally, the state-dependency of their firing. We propose that, in addition to direct stimulation of post-synaptic targets, callosal axons often play a conditional driving or modulatory role, which depends on task contingencies, as documented by several recent studies.

Physical and digital phantoms for validating tractography and assessing artifacts

Fiber tractography is widely used to non-invasively map white-matter bundles in vivo using diffusion-weighted magnetic resonance imaging (dMRI). As it is the case for all scientific methods, proper validation is a key prerequisite for the successful application of fiber tractography, be it in the area of basic neuroscience or in a clinical setting. It is well-known that the indirect estimation of the fiber tracts from the local diffusion signal is highly ambiguous and extremely challenging. Furthermore, the validation of fiber tractography methods is hampered by the lack of a real ground truth, which is caused by the extremely complex brain microstructure that is not directly observable non-invasively and that is the basis of the huge network of long-range fiber connections in the brain that are the actual target of fiber tractography methods. As a substitute for in vivo data with a real ground truth that could be used for validation, a widely and successfully employed approach is the use of synthetic phantoms. In this work, we are providing an overview of the state-of-the-art in the area of physical and digital phantoms, answering the following guiding questions: "What are dMRI phantoms and what are they good for?", "What would the ideal phantom for validation fiber tractography look like?" and "What phantoms, phantom datasets and tools used for their creation are available to the research community?". We will further discuss the limitations and opportunities that come with the use of dMRI phantoms, and what future direction this field of research might take.

Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

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.

Diffusion MRI and anatomic tracing in the same brain reveal common failure modes of tractography

Anatomic tracing is recognized as a critical source of knowledge on brain circuitry that can be used to assess the accuracy of diffusion MRI (dMRI) tractography. However, most prior studies that have performed such assessments have used dMRI and tracer data from different brains and/or have been limited in the scope of dMRI analysis methods allowed by the data. In this work, we perform a quantitative, voxel-wise comparison of dMRI tractography and anatomic tracing data in the same macaque brain. An ex vivo dMRI acquisition with high angular resolution and high maximum b-value allows us to compare a range of q-space sampling, orientation reconstruction, and tractography strategies. The availability of tracing in the same brain allows us to localize the sources of tractography errors and to identify axonal configurations that lead to such errors consistently, across dMRI acquisition and analysis strategies. We find that these common failure modes involve geometries such as branching or turning, which cannot be modeled well by crossing fibers. We also find that the default thresholds that are commonly used in tractography correspond to rather conservative, low-sensitivity operating points. While deterministic tractography tends to have higher sensitivity than probabilistic tractography in that very conservative threshold regime, the latter outperforms the former as the threshold is relaxed to avoid missing true anatomical connections. On the other hand, the q-space sampling scheme and maximum b-value have less of an impact on accuracy. Finally, using scans from a set of additional macaque brains, we show that there is enough inter-individual variability to warrant caution when dMRI and tracer data come from different animals, as is often the case in the tractography validation literature. Taken together, our results provide insights on the limitations of current tractography methods and on the critical role that anatomic tracing can play in identifying potential avenues for improvement.

Ventral intermediate nucleus structural connectivity-derived segmentation: anatomical reliability and variability

The Ventral intermediate nucleus (Vim) of thalamus is the most targeted structure for the treatment of drug-refractory tremors. Since methodological differences across existing studies are remarkable and no gold-standard pipeline is available, in this study, we tested different parcellation pipelines for tractography-derived putative Vim identification. Thalamic parcellation was performed on a high quality, multi-shell dataset and a downsampled, clinical-like dataset using two different diffusion signal modeling techniques and two different voxel classification criteria, thus implementing a total of four parcellation pipelines. The most reliable pipeline in terms of inter-subject variability has been picked and parcels putatively corresponding to motor thalamic nuclei have been selected by calculating similarity with a histology-based mask of Vim. Then, spatial relations with optimal stimulation points for the treatment of essential tremor have been quantified. Finally, effect of data quality and parcellation pipelines on a volumetric index of connectivity clusters has been assessed. We found that the pipeline characterized by higher-order signal modeling and threshold-based voxel classification criteria was the most reliable in terms of inter-subject variability regardless data quality. The maps putatively corresponding to Vim were those derived by precentral and dentate nucleus-thalamic connectivity. However, tractography-derived functional targets showed remarkable differences in shape and sizes when compared to a ground truth model based on histochemical staining on seriate sections of human brain. Thalamic voxels connected to contralateral dentate nucleus resulted to be the closest to literature-derived stimulation points for essential tremor but at the same time showing the most remarkable inter-subject variability. Finally, the volume of connectivity parcels resulted to be significantly influenced by data quality and parcellation pipelines. Hence, caution is warranted when performing thalamic connectivity-based segmentation for stereotactic targeting.

The Complex Hodological Architecture of the Macaque Dorsal Intraparietal Areas as Emerging from Neural Tracers and DW-MRI Tractography

In macaque monkeys, dorsal intraparietal areas are involved in several daily visuomotor actions. However, their border and sources of cortical afferents remain loosely defined. Combining retrograde histologic tracing and MRI diffusion-based tractography, we found a complex hodology of the dorsal bank of the intraparietal sulcus (db-IPS), which can be subdivided into a rostral intraparietal area PEip, projecting to the spinal cord, and a caudal medial intraparietal area MIP lacking such projections. Both include an anterior and a posterior sector, emerging from their ipsilateral, gradient-like connectivity profiles. As tractography estimations, we used the cross-sectional area of the white matter bundles connecting each area with other parietal and frontal regions, after selecting regions of interest (ROIs) corresponding to the injection sites of neural tracers. For most connections, we found a significant correlation between the proportions of cells projecting to all sectors of PEip and MIP along the continuum of the db-IPS and tractography. The latter also revealed “false positive” but plausible connections awaiting histologic validation.

Cortical Surfaces Integration with Tractography for Structural Connectivity Analysis

Background: Mapping diffusion MRI tractography streamlines to the cortical surface facilitates the integration of white matter features onto gray matter, especially for connectivity analysis. Method: In this work, we present methods that combine cortical surface meshes with tractography reconstruction to improve endpoint precision and coverage. This cortical mapping also enables the study of structural measures from tractography along the cortex and subcortical structures. In addition to structural connectivity analysis, novel adaptive and dynamic surface seeding methods are proposed. These improvements are made by incorporating cortical maps such as endpoint density. Results: The proposed dynamic surface seeding increases the cortical coverage and reduces endpoint location biases. Our results suggest that the use of cortical and subcortical meshes together with a proper seeding strategy can reduce the variability in structural connectivity analysis. Conclusion: The proposed adaptive and dynamic seeding utilize cortical maps to better distribute tractography interconnections, thus increasing cortical coverage and reducing endpoint bias. This also facilitates the analysis of white matter & diffusion MRI features along the cortex, combined with cortical measures or functional activation. Impact statement This research presents an overview of surface mapping methods for tractography to reduce structural connectivity variability. The proposed adaptive and dynamic seeding utilize cortical maps to better distribute tractography interconnections, thus increasing cortical coverage and reducing end-point bias. This also facilitates the analysis of white matter and diffusion magnetic resonance imaging features along the cortex, combined with cortical measures or functional activation.

Longitudinal predictive modeling of tau progression along the structural connectome

Tau neurofibrillary tangles, a pathophysiological hallmark of Alzheimer's disease (AD), exhibit a stereotypical spatiotemporal trajectory that is strongly correlated with disease progression and cognitive decline. Personalized prediction of tau progression is, therefore, vital for the early diagnosis and prognosis of AD. Evidence from both animal and human studies is suggestive of tau transmission along the brains preexisting neural connectivity conduits. We present here an analytic graph diffusion framework for individualized predictive modeling of tau progression along the structural connectome. To account for physiological processes that lead to active generation and clearance of tau alongside passive diffusion, our model uses an inhomogenous graph diffusion equation with a source term and provides closed-form solutions to this equation for linear and exponential source functionals. Longitudinal imaging data from two cohorts, the Harvard Aging Brain Study (HABS) and the Alzheimer's Disease Neuroimaging Initiative (ADNI), were used to validate the model. The clinical data used for developing and validating the model include regional tau measures extracted from longitudinal positron emission tomography (PET) scans based on the ¹⁸F-Flortaucipir radiotracer and individual structural connectivity maps computed from diffusion tensor imaging (DTI) by means of tractography and streamline counting. Two-timepoint tau PET scans were used to assess the goodness of model fit. Three-timepoint tau PET scans were used to assess predictive accuracy via comparison of predicted and observed tau measures at the third timepoint. Our results show high consistency between predicted and observed tau and differential tau from region-based analysis. While the prognostic value of this approach needs to be validated in a larger cohort, our preliminary results suggest that our longitudinal predictive model, which offers an in vivo macroscopic perspective on tau progression in the brain, is potentially promising as a personalizable predictive framework for AD.

Volumetric white matter tract segmentation with nested self-supervised learning using sequential pretext tasks

White matter (WM) tract segmentation based on diffusion magnetic resonance imaging (dMRI) provides an important tool for the analysis of brain development, function, and disease. Deep learning based methods of WM tract segmentation have been proposed, which greatly improve the accuracy of the segmentation. However, the training of the deep networks usually requires a large number of manual delineations of WM tracts, which can be especially difficult to obtain and unavailable in many scenarios. Therefore, in this work, we explore how to perform deep learning based WM tract segmentation when annotated training data is scarce. To this end, we seek to exploit the abundant unannotated dMRI data in the self-supervised learning framework. From the unannotated data, knowledge about image context can be learned with pretext tasks that do not require manual annotations. Specifically, a deep network can be pretrained for the pretext task, and the knowledge learned from the pretext task is then transferred to the subsequent WM tract segmentation task with only a small number of annotated scans via fine-tuning. We explore two designs of pretext tasks that are related to WM tracts. The first pretext task predicts the density map of fiber streamlines, which are representations of generic WM pathways, and the training data can be obtained automatically with tractography. The second pretext task learns to mimic the results of registration-based WM tract segmentation, which, although inaccurate, is more relevant to WM tract segmentation and provides a good target for learning context knowledge. Then, we combine the two pretext tasks and develop a nested self-supervised learning strategy. In the nested self-supervised learning strategy, the first pretext task provides initial knowledge for the second pretext task, and the knowledge learned from the second pretext task with the initial knowledge is transferred to the target WM tract segmentation task via fine-tuning. To evaluate the proposed method, experiments were performed on brain dMRI scans from the Human Connectome Project dataset with various experimental settings. The results show that the proposed method improves the performance of WM tract segmentation when tract annotations are scarce.

Hierarchical Microstructure Informed Tractography

Background: Tractography uses diffusion magnetic resonance imaging to noninvasively infer the macroscopic pathways of white matter fibers and it is the only available technique to probe in vivo the structural connectivity of the brain. However, despite this unique and compelling ability and its wide range of possible neurological applications, tractography is still limited, lacks anatomical precision, and suffers from a serious sensitivity/specificity trade-off. For this reason, in the past few years, tractography postprocessing techniques have emerged and proved effective for improving the quality of the reconstructions. Among them, the Convex Optimization Modeling for Microstructure Informed Tractography formulation allows incorporating the anatomical prior that fibers are naturally organized in fascicles, and has obtained exceptional results in increasing the accuracy of the estimated tractograms. Methods: We propose an extension to this idea and introduce a multilevel grouping of the streamlines to capture the white matter arrangement in fascicles and subfascicles. We tested our proposed formulation in synthetic and in vivo data. Results: Our experiments show that using multiple levels allows considering information about the white matter organization more adequately and helps to improve further the accuracy of the resulting tractograms. Conclusion: This new formulation represents a further important step toward a more accurate structural connectivity estimation.