Validation of DTI tractography-based measures of primary motor area connectivity in the squirrel monkey brain

A whole-brain analysis of functional connectivity and immediate early gene expression reveals functional network shifts after operant learning

Previous studies of operant learning have addressed neuronal activities and network changes in specific brain areas, such as the striatum, sensorimotor cortex, prefrontal/orbitofrontal cortices, and hippocampus. However, how changes in the whole-brain network are caused by cellular-level changes remains unclear. We, therefore, combined resting-state functional magnetic resonance imaging (rsfMRI) and whole-brain immunohistochemical analysis of early growth response 1 (EGR1), a marker of neural plasticity, to elucidate the temporal and spatial changes in functional networks and underlying cellular processes during operant learning. We used an 11.7-Tesla MRI scanner and whole-brain immunohistochemical analysis of EGR1 in mice during the early and late stages of operant learning. In the operant training, mice received a reward when they pressed left and right buttons alternately, and were punished with a bright light when they made a mistake. A group of mice (n = 22) underwent the first rsfMRI acquisition before behavioral sessions, the second acquisition after 3 training-session-days (early stage), and the third after 21 training-session-days (late stage). Another group of mice (n = 40) was subjected to histological analysis 15 min after the early or late stages of behavioral sessions. Functional connectivity increased between the limbic areas and thalamus or auditory cortex after the early stage of training, and between the motor cortex, sensory cortex, and striatum after the late stage of training. The density of EGR1-immunopositive cells in the motor and sensory cortices increased in both the early and late stages of training, whereas the density in the amygdala increased only in the early stage of training. The subcortical networks centered around the limbic areas that emerged in the early stage have been implicated in rewards, pleasures, and fears. The connectivities between the motor cortex, somatosensory cortex, and striatum that consolidated in the late stage have been implicated in motor learning. Our multimodal longitudinal study successfully revealed temporal shifts in brain regions involved in behavioral learning together with the underlying cellular-level plasticity between these regions. Our study represents a first step towards establishing a new experimental paradigm that combines rsfMRI and immunohistochemistry to link macroscopic and microscopic mechanisms involved in learning.

The spatial extent of anatomical connections within the thalamus varies across the cortical hierarchy in humans and macaques

Each cortical area has a distinct pattern of anatomical connections within the thalamus, a central subcortical structure composed of functionally and structurally distinct nuclei. Previous studies have suggested that certain cortical areas may have more extensive anatomical connections that target multiple thalamic nuclei, which potentially allows them to modulate distributed information flow. However, there is a lack of quantitative investigations into anatomical connectivity patterns within the thalamus. Consequently, it remains unknown if cortical areas exhibit systematic differences in the extent of their anatomical connections within the thalamus. To address this knowledge gap, we used diffusion magnetic resonance imaging (dMRI) to perform brain-wide probabilistic tractography for 828 healthy adults from the Human Connectome Project. We then developed a framework to quantify the spatial extent of each cortical area's anatomical connections within the thalamus. Additionally, we leveraged resting-state functional MRI, cortical myelin, and human neural gene expression data to test if the extent of anatomical connections within the thalamus varied along the cortical hierarchy. Our results revealed two distinct corticothalamic tractography motifs: 1) a sensorimotor cortical motif characterized by focal thalamic connections targeting posterolateral thalamus, associated with fast, feed-forward information flow; and 2) an associative cortical motif characterized by diffuse thalamic connections targeting anteromedial thalamus, associated with slow, feed-back information flow. These findings were consistent across human subjects and were also observed in macaques, indicating cross-species generalizability. Overall, our study demonstrates that sensorimotor and association cortical areas exhibit differences in the spatial extent of their anatomical connections within the thalamus, which may support functionally-distinct cortico-thalamic information flow.

Investigating the contribution of cytoarchitecture to diffusion MRI measures in gray matter using histology

Postmortem studies are currently considered a gold standard for investigating brain structure at the cellular level. To investigate cellular changes in the context of human development, aging, or disease treatment, non-invasive in-vivo imaging methods such as diffusion MRI (dMRI) are needed. However, dMRI measures are only indirect measures and require validation in gray matter (GM) in the context of their sensitivity to the underlying cytoarchitecture, which has been lacking. Therefore, in this study we conducted direct comparisons between in-vivo dMRI measures and histology acquired from the same four rhesus monkeys. Average and heterogeneity of fractional anisotropy and trace from diffusion tensor imaging and mean squared displacement (MSD) and return-to-origin-probability from biexponential model were calculated in nine cytoarchitectonically different GM regions using dMRI data. DMRI measures were compared with corresponding histology measures of regional average and heterogeneity in cell area density. Results show that both average and heterogeneity in trace and MSD measures are sensitive to the underlying cytoarchitecture (cell area density) and capture different aspects of cell composition and organization. Trace and MSD thus would prove valuable as non-invasive imaging biomarkers in future studies investigating GM cytoarchitectural changes related to development and aging as well as abnormal cellular pathologies in clinical studies.

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.

Deterministic Tractography Analysis of Rat Brain Using SIGMA Atlas in 9.4T MRI

Preclinical studies using rodents have been the choice for many neuroscience researchers due totheir close reflection of human biology. In particular, research involving rodents has utilized MRI to accurately identify brain regions and characteristics by acquiring high resolution cavity images with different contrasts non-invasively, and this has resulted in high reproducibility and throughput. In addition, tractographic analysis using diffusion tensor imaging to obtain information on the neural structure of white matter has emerged as a major methodology in the field of neuroscience due to its contribution in discovering significant correlations between altered neural connections and various neurological and psychiatric diseases. However, unlike image analysis studies with human subjects where a myriad of human image analysis programs and procedures have been thoroughly developed and validated, methods for analyzing rat image data using MRI in preclinical research settings have seen significantly less developed. Therefore, in this study, we present a deterministic tractographic analysis pipeline using the SIGMA atlas for a detailed structural segmentation and structural connectivity analysis of the rat brain’s structural connectivity. In addition, the structural connectivity analysis pipeline presented in this study was preliminarily tested on normal and stroke rat models for initial observation.

A critical review of connectome validation studies

Diffusion MRI tractography is the most widely used macroscale method for mapping connectomes in vivo. However, tractography is prone to various errors and biases, and thus tractography-derived connectomes require careful validation. Here, we critically review studies that have developed or utilized phantoms and tracer maps to validate tractography-derived connectomes, either quantitatively or qualitatively. We identify key factors impacting connectome reconstruction accuracy, including streamline seeding, propagation and filtering methods, and consider the strengths and limitations of state-of-the-art connectome phantoms and associated validation studies. These studies demonstrate the inherent limitations of current fiber orientation models and tractography algorithms and their impact on connectome reconstruction accuracy. Reconstructing connectomes with both high sensitivity and high specificity is challenging, given that some tractography methods can generate an abundance of spurious connections, while others can overlook genuine fiber bundles. We argue that streamline filtering can minimize spurious connections and potentially improve the biological plausibility of connectomes derived from tractography. We find that algorithmic choices such as the tractography seeding methodology, angular threshold, and streamline propagation method can substantially impact connectome reconstruction accuracy. Hence, careful application of tractography is necessary to reconstruct accurate connectomes. Improvements in diffusion MRI acquisition techniques will not necessarily overcome current tractography limitations without accompanying modeling and algorithmic advances.

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.

Enhanced Fiber Tractography Using Edema Correction: Application and Evaluation in High-Grade Gliomas

BACKGROUND

A limitation of diffusion tensor imaging (DTI)-based tractography is peritumoral edema that confounds traditional diffusion-based magnetic resonance metrics.

OBJECTIVE

To augment fiber-tracking through peritumoral regions by performing novel edema correction on clinically feasible DTI acquisitions and assess the accuracy of the fiber-tracks using intraoperative stimulation mapping (ISM), task-based functional magnetic resonance imaging (fMRI) activation maps, and postoperative follow-up as reference standards.

METHODS

Edema correction, using our bi-compartment free water modeling algorithm (FERNET), was performed on clinically acquired DTI data from a cohort of 10 patients presenting with suspected high-grade glioma and peritumoral edema in proximity to and/or infiltrating language or motor pathways. Deterministic fiber-tracking was then performed on the corrected and uncorrected DTI to identify tracts pertaining to the eloquent region involved (language or motor). Tracking results were compared visually and quantitatively using mean fiber count, voxel count, and mean fiber length. The tracts through the edematous region were verified based on overlay with the corresponding motor or language task-based fMRI activation maps and intraoperative ISM points, as well as at time points after surgery when peritumoral edema had subsided.

RESULTS

Volume and number of fibers increased with application of edema correction; concordantly, mean fractional anisotropy decreased. Overlay with functional activation maps and ISM-verified eloquence of the increased fibers. Comparison with postsurgical follow-up scans with lower edema further confirmed the accuracy of the tracts.

CONCLUSION

This method of edema correction can be applied to standard clinical DTI to improve visualization of motor and language tracts in patients with glioma-associated peritumoral edema.

Two cortical representations of voice control are differentially involved in speech fluency

Recent studies have identified two distinct cortical representations of voice control in humans, the ventral and the dorsal laryngeal motor cortex. Strikingly, while persistent developmental stuttering has been linked to a white-matter deficit in the ventral laryngeal motor cortex, intensive fluency-shaping intervention modulated the functional connectivity of the dorsal laryngeal motor cortical network. Currently, it is unknown whether the underlying structural network organization of these two laryngeal representations is distinct or differently shaped by stuttering intervention. Using probabilistic diffusion tractography in 22 individuals who stutter and participated in a fluency shaping intervention, in 18 individuals who stutter and did not participate in the intervention and in 28 control participants, we here compare structural networks of the dorsal laryngeal motor cortex and the ventral laryngeal motor cortex and test intervention-related white-matter changes. We show (i) that all participants have weaker ventral laryngeal motor cortex connections compared to the dorsal laryngeal motor cortex network, regardless of speech fluency, (ii) connections of the ventral laryngeal motor cortex were stronger in fluent speakers, (iii) the connectivity profile of the ventral laryngeal motor cortex predicted stuttering severity (iv) but the ventral laryngeal motor cortex network is resistant to a fluency shaping intervention. Our findings substantiate a weaker structural organization of the ventral laryngeal motor cortical network in developmental stuttering and imply that assisted recovery supports neural compensation rather than normalization. Moreover, the resulting dissociation provides evidence for functionally segregated roles of the ventral laryngeal motor cortical and dorsal laryngeal motor cortical networks.

Fine-Grained Topography and Modularity of the Macaque Frontal Pole Cortex Revealed by Anatomical Connectivity Profiles

The frontal pole cortex (FPC) plays key roles in various higher-order functions and is highly developed in non-human primates. An essential missing piece of information is the detailed anatomical connections for finer parcellation of the macaque FPC than provided by the previous tracer results. This is important for understanding the functional architecture of the cerebral cortex. Here, combining cross-validation and principal component analysis, we formed a tractography-based parcellation scheme that applied a machine learning algorithm to divide the macaque FPC (2 males and 6 females) into eight subareas using high-resolution diffusion magnetic resonance imaging with the 9.4T Bruker system, and then revealed their subregional connections. Furthermore, we applied improved hierarchical clustering to the obtained parcels to probe the modular structure of the subregions, and found that the dorsolateral FPC, which contains an extension to the medial FPC, was mainly connected to regions of the default-mode network. The ventral FPC was mainly involved in the social-interaction network and the dorsal FPC in the metacognitive network. These results enhance our understanding of the anatomy and circuitry of the macaque brain, and contribute to FPC-related clinical research.

Birth weight is associated with adolescent brain development: A multimodal imaging study in monozygotic twins

Previous research has shown that the prenatal environment, commonly indexed by birth weight (BW), is a predictor of morphological brain development. We previously showed in monozygotic (MZ) twins associations between BW and brain morphology that were independent of genetics. In the present study, we employed a longitudinal MZ twin design to investigate whether variations in prenatal environment (as indexed by discordance in BW) are associated with resting‐state functional connectivity (rs‐FC) and with structural connectivity. We focused on the limbic and default mode networks (DMNs), which are key regions for emotion regulation and internally generated thoughts, respectively. One hundred and six healthy adolescent MZ twins (53 pairs; 42% male pairs) followed longitudinally from birth underwent a magnetic resonance imaging session at age 15. Graph theoretical analysis was applied to rs‐FC measures. TrackVis was used to determine track count as an indicator of structural connectivity strength. Lower BW twins had less efficient limbic network connectivity as compared to their higher BW co‐twin, driven by differences in the efficiency of the right hippocampus and right amygdala. Lower BW male twins had fewer tracks connecting the right hippocampus and right amygdala as compared to their higher BW male co‐twin. There were no associations between BW and the DMN. These findings highlight the possible role of unique prenatal environmental influences in the later development of efficient spontaneous limbic network connections within healthy individuals, irrespective of DNA sequence or shared environment.

How Human Is Human Connectional Neuroanatomy?

The structure of the human brain has been studied extensively. Despite all the knowledge accrued, direct information about connections, from origin to termination, in the human brain is extremely limited. Yet there is a widespread misperception that human connectional neuroanatomy is well-established and validated. In this article, we consider what is known directly about human structural and connectional neuroanatomy. Information on neuroanatomical connections in the human brain is derived largely from studies in non-human experimental models in which the entire connectional pathway, including origins, course, and terminations, is directly visualized. Techniques to examine structural connectivity in the human brain are progressing rapidly; nevertheless, our present understanding of such connectivity is limited largely to data derived from homological comparisons, particularly with non-human primates. We take the position that an in-depth and more precise understanding of human connectional neuroanatomy will be obtained by a systematic application of this homological approach.

Multimodal mapping of the face connectome

Face processing supports our ability to recognize friend from foe, form tribes and understand the emotional implications of changes in facial musculature. This skill relies on a distributed network of brain regions, but how these regions interact is poorly understood. Here we integrate anatomical and functional connectivity measurements with behavioural assays to create a global model of the face connectome. We dissect key features, such as the network topology and fibre composition. We propose a neurocognitive model with three core streams; face processing along these streams occurs in a parallel and reciprocal manner. Although long-range fibre paths are important, the face network is dominated by short-range fibres. Finally, we provide evidence that the well-known right lateralization of face processing arises from imbalanced intra- and interhemispheric connections. In summary, the face network relies on dynamic communication across highly structured fibre tracts, enabling coherent face processing that underpins behaviour and cognition.

Structural brain networks and functional motor outcome after stroke-a prospective cohort study

The time course of topological reorganization that occurs in the structural connectome after an ischaemic stroke is currently not well understood. We aimed to determine the evolution of structural brain networks in stroke patients with motor deficits and relate changes in their global topology to residual symptom burden and functional impairment. In this prospective cohort study, ischaemic stroke patients with supratentorial infarcts and motor symptoms were assessed longitudinally by advanced diffusion MRI and detailed clinical testing of upper extremity motor function at four time points from the acute to the chronic stage. For each time point, structural connectomes were reconstructed, and whole-hemisphere global network topology was quantified in terms of integration and segregation parameters. Using non-linear joint mixed-effects regression modelling, network evolution was related to lesion volume and clinical outcome. Thirty patients were included for analysis. Graph-theoretical analysis demonstrated that, over time, brain networks became less integrated and more segregated with decreasing global efficiency and increasing modularity. Changes occurred in both stroke and intact hemispheres and, in the latter, were positively associated with lesion volume. Greater change in topology was associated with larger residual symptom burden and greater motor impairment 1, 3 and 12 months after stroke. After ischaemic stroke, brain networks underwent characteristic changes in both ipsi- and contralesional hemispheres. Topological network changes reflect the severity of damage to the structural network and are associated with functional outcome beyond the impact of lesion volume.

Comparison between diffusion MRI tractography and histological tract-tracing of cortico-cortical structural connectivity in the ferret brain

The anatomical wiring of the brain is a central focus in network neuroscience. Diffusion MRI tractography offers the unique opportunity to investigate the brain fiber architecture in vivo and noninvasively. However, its reliability is still highly debated. Here, we explored the ability of diffusion MRI tractography to match invasive anatomical tract-tracing connectivity data of the ferret brain. We also investigated the influence of several state-of-the-art tractography algorithms on this match to ground truth connectivity data. Tract-tracing connectivity data were obtained from retrograde tracer injections into the occipital, parietal, and temporal cortices of adult ferrets. We found that the relative densities of projections identified from the anatomical experiments were highly correlated with the estimates from all the studied diffusion tractography algorithms (Spearman's rho ranging from 0.67 to 0.91), while only small, nonsignificant variations appeared across the tractography algorithms. These results are comparable to findings reported in mouse and monkey, increasing the confidence in diffusion MRI tractography results. Moreover, our results provide insights into the variations of sensitivity and specificity of the tractography algorithms, and hence into the influence of choosing one algorithm over another.

A Web-Based Atlas Combining MRI and Histology of the Squirrel Monkey Brain

The squirrel monkey (Saimiri sciureus) is a commonly-used surrogate for humans in biomedical research. In the neuroimaging community, MRI and histological atlases serve as valuable resources for anatomical, physiological, and functional studies of the brain; however, no digital MRI/histology atlas is currently available for the squirrel monkey. This paper describes the construction of a web-based multi-modal atlas of the squirrel monkey brain. The MRI-derived information includes anatomical MRI contrast (i.e., T2-weighted and proton-density-weighted) and diffusion MRI metrics (i.e., fractional anisotropy and mean diffusivity) from data acquired both in vivo and ex vivo on a 9.4 Tesla scanner. The histological images include Nissl and myelin stains, co-registered to the corresponding MRI, allowing identification of cyto- and myelo-architecture. In addition, a bidirectional neuronal tracer, biotinylated dextran amine (BDA) was injected into the primary motor cortex, enabling highly specific identification of regions connected to the injection location. The atlas integrates the results of common image analysis methods including diffusion tensor imaging glyphs, labels of 57 white-matter tracts identified using DTI-tractography, and 18 cortical regions of interest identified from Nissl-revealed cyto-architecture. All data are presented in a common space, and all image types are accessible through a web-based atlas viewer, which allows visualization and interaction of user-selectable contrasts and varying resolutions. By providing an easy to use reference system of anatomical information, our web-accessible multi-contrast atlas forms a rich and convenient resource for comparisons of brain findings across subjects or modalities. The atlas is called the Combined Histology-MRI Integrated Atlas of the Squirrel Monkey (CHIASM). All images are accessible through our web-based viewer ( https://chiasm.vuse.vanderbilt.edu /), and data are available for download at ( https://www.nitrc.org/projects/smatlas/ ).

Histologically derived fiber response functions for diffusion MRI vary across white matter fibers-An ex vivo validation study in the squirrel monkey brain

Understanding the relationship between the diffusion-weighted MRI signal and the arrangement of white matter fibers is fundamental for accurate voxel-wise reconstruction of the fiber orientation distribution (FOD) and subsequent fiber tractography. Spherical deconvolution reconstruction techniques model the diffusion signal as the convolution of the FOD with a response function that represents the signal profile of a single fiber orientation. Thus, given the signal and a fiber response function, the FOD can be estimated in every imaging voxel by deconvolution. However, the selection of the appropriate response function remains relatively under-studied, and requires further validation. In this work, using 3D histologically defined FODs and the corresponding diffusion signal from three ex vivo squirrel monkey brains, we derive the ground truth response functions. We find that the histologically derived response functions differ from those conventionally used. Next, we find that response functions statistically vary across brain regions, which suggests that the practice of using the same kernel throughout the brain is not optimal. We show that different kernels lead to different FOD reconstructions, which in turn can lead to different tractography results depending on algorithmic parameters, with large variations in the accuracy of resulting reconstructions. Together, these results suggest there is room for improvement in estimating and understanding the relationship between the diffusion signal and the underlying FOD.

Building connectomes using diffusion MRI: why, how and but

Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments.

Limits to anatomical accuracy of diffusion tractography using modern approaches

Diffusion MRI fiber tractography is widely used to probe the structural connectivity of the brain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, tractography has well-known pitfalls that limits the anatomical accuracy of this technique. Numerous modern methods have been developed to address these shortcomings through advances in acquisition, modeling, and computation. To test whether these advances improve tractography accuracy, we organized the 3-D Validation of Tractography with Experimental MRI (3D-VoTEM) challenge at the ISBI 2018 conference. We made available three unique independent tractography validation datasets - a physical phantom and two ex vivo brain specimens - resulting in 176 distinct submissions from 9 research groups. By comparing results over a wide range of fiber complexities and algorithmic strategies, this challenge provides a more comprehensive assessment of tractography's inherent limitations than has been reported previously. The central results were consistent across all sub-challenges in that, despite advances in tractography methods, the anatomical accuracy of tractography has not dramatically improved in recent years. Taken together, our results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain.

Anatomical accuracy of standard-practice tractography algorithms in the motor system - A histological validation in the squirrel monkey brain

For two decades diffusion fiber tractography has been used to probe both the spatial extent of white matter pathways and the region to region connectivity of the brain. In both cases, anatomical accuracy of tractography is critical for sound scientific conclusions. Here we assess and validate the algorithms and tractography implementations that have been most widely used - often because of ease of use, algorithm simplicity, or availability offered in open source software. Comparing forty tractography results to a ground truth defined by histological tracers in the primary motor cortex on the same squirrel monkey brains, we assess tract fidelity on the scale of voxels as well as over larger spatial domains or regional connectivity. No algorithms are successful in all metrics, and, in fact, some implementations fail to reconstruct large portions of pathways or identify major points of connectivity. The accuracy is most dependent on reconstruction method and tracking algorithm, as well as the seed region and how this region is utilized. We also note a tremendous variability in the results, even though the same MR images act as inputs to all algorithms. In addition, anatomical accuracy is significantly decreased at increased distances from the seed. An analysis of the spatial errors in tractography reveals that many techniques have trouble properly leaving the gray matter, and many only reveal connectivity to adjacent regions of interest. These results show that the most commonly implemented algorithms have several shortcomings and limitations, and choices in implementations lead to very different results. This study should provide guidance for algorithm choices based on study requirements for sensitivity, specificity, or the need to identify particular connections, and should serve as a heuristic for future developments in tractography.

The VALiDATe29 MRI Based Multi-Channel Atlas of the Squirrel Monkey Brain

We describe the development of the first digital atlas of the normal squirrel monkey brain and present the resulting product, VALiDATe29. The VALiDATe29 atlas is based on multiple types of magnetic resonance imaging (MRI) contrast acquired on 29 squirrel monkeys, and is created using unbiased, nonlinear registration techniques, resulting in a population-averaged stereotaxic coordinate system. The atlas consists of multiple anatomical templates (proton density, T1, and T2* weighted), diffusion MRI templates (fractional anisotropy and mean diffusivity), and ex vivo templates (fractional anisotropy and a structural MRI). In addition, the templates are combined with histologically defined cortical labels, and diffusion tractography defined white matter labels. The combination of intensity templates and image segmentations make this atlas suitable for the fundamental atlas applications of spatial normalization and label propagation. Together, this atlas facilitates 3D anatomical localization and region of interest delineation, and enables comparisons of experimental data across different subjects or across different experimental conditions. This article describes the atlas creation and its contents, and demonstrates the use of the VALiDATe29 atlas in typical applications. The atlas is freely available to the scientific community.

Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics

Diffusion MRI (dMRI)-based tractography offers unique abilities to map whole-brain structural connections in human and animal brains. However, dMRI-based tractography indirectly measures white matter tracts, with suboptimal accuracy and reliability. Recently, sophisticated methods including constrained spherical deconvolution (CSD) and global tractography have been developed to improve tract reconstructions through modeling of more complex fiber orientations. Our study aimed to determine the accuracy of connectome reconstruction for three dMRI-based tractography approaches: diffusion tensor (DT)-based, CSD-based and global tractography. Therefore, we validated whole brain structural connectome reconstructions based on ten ultrahigh-resolution dMRI rat brain scans and 106 cortical regions, from which varying tractography parameters were compared against standardized neuronal tracer data. All tested tractography methods generated considerable numbers of false positive and false negative connections. There was a parameter range trade-off between sensitivity: 0.06-0.63 interhemispherically and 0.22-0.86 intrahemispherically; and specificity: 0.99-0.60 interhemispherically and 0.99-0.23 intrahemispherically. Furthermore, performance of all tractography methods decreased with increasing spatial distance between connected regions. Similar patterns and trade-offs were found, when we applied spherical deconvolution informed filtering of tractograms, streamline thresholding and group-based average network thresholding. Despite the potential of CSD-based and global tractography to handle complex fiber orientations at voxel level, reconstruction accuracy, especially for long-distance connections, remains a challenge. Hence, connectome reconstruction benefits from varying parameter settings and combination of tractography methods to account for anatomical variation of neuronal pathways.

Tests of cortical parcellation based on white matter connectivity using diffusion tensor imaging

The cerebral cortex is conventionally divided into a number of domains based on cytoarchitectural features. Diffusion tensor imaging (DTI) enables noninvasive parcellation of the cortex based on white matter connectivity patterns. However, the correspondence between DTI-connectivity-based and cytoarchitectural parcellation has not been systematically established. In this study, we compared histological parcellation of New World monkey neocortex to DTI- connectivity-based classification and clustering in the same brains. First, we used supervised classification to parcellate parieto-frontal cortex based on DTI tractograms and the cytoarchitectural prior (obtained using Nissl staining). We performed both within and across sample classification, showing reasonable classification performance in both conditions. Second, we used unsupervised clustering to parcellate the cortex and compared the clusters to the cytoarchitectonic standard. We then explored the similarities and differences with several post-hoc analyses, highlighting underlying principles that drive the DTI-connectivity-based parcellation. The differences in parcellation between DTI-connectivity and Nissl histology probably represent both DTI's bias toward easily-tracked bundles and true differences between cytoarchitectural and connectivity defined domains. DTI tractograms appear to cluster more according to functional networks, rather than mapping directly onto cytoarchitectonic domains. Our results show that caution should be used when DTI-tractography classification, based on data from another brain, is used as a surrogate for cytoarchitectural parcellation.

Tests of clustering thalamic nuclei based on various dMRI models in the squirrel monkey brain

Background

Clustering thalamic nuclei is important for both research and clinical purposes. For example, ventral intermediate nuclei in thalami serve as targets in both deep brain stimulation neurosurgery and radiosurgery for treating patients suffering from movement disorders (e.g., Parkinson's disease and essential tremor). Diffusion magnetic resonance imaging (dMRI) is able to reflect tissue microstructure in the central nervous system via fitting different models, such as, the diffusion tensor (DT), constrained spherical deconvolution (CSD), neurite orientation dispersion and density imaging (NODDI), diffusion kurtosis imaging (DKI) and the spherical mean technique (SMT).

Purpose

To test which of the above-mentioned dMRI models is better for thalamic parcellation, we proposed a framework of k-means clustering, implemented it on each model, and evaluated the agreement with histology.

Method

An ex vivo monkey brain was scanned in a 9.4T MRI scanner at 0.3mm resolution with b values of 3000, 6000, 9000 and 12000 s/mm². K-means clustering on each thalamus was implemented using maps of dMRI models fitted to the same data. Meanwhile, histological nuclei were identified by AChE and Nissl stains of the same brain. Overall agreement rate and agreement rate for each nucleus were calculated between clustering and histology. Sixteen thalamic nuclei on each hemisphere were included.

Results

Clustering with the DKI model has slightly higher overall agreement rate but clustering with other dMRI models result in higher agreement rate in some nuclei.

Conclusion

dMRl models should be carefully selected to better parcellate the thalamus, depending on the specific purpose of the parcellation.

Altered anatomical connections of associative and limbic cortico-basal-ganglia circuits in obsessive-compulsive disorder

BACKGROUND

Current neurocognitive models suppose dysfunctions of associative and limbic cortico-basal ganglia circuits to be at the core of obsessive-compulsive disorder (OCD). As little is known about the state of underlying anatomical connections, we investigated whether these connections were reduced and/or not properly organised in OCD patients compared to control.

METHODS

Diffusion magnetic resonance images were obtained in 37 OCD patients with predominant checking symptoms and 37 matched healthy controls. We developed indices to characterise the quantity (spatial extent and density) and the organisation (topography and segregation) of 24 anatomical connections between associative and limbic cortical (anterior cingulate, dorsolateral prefrontal, orbitofrontal cortices and the frontal pole), and subcortical (caudate nucleus, putamen and thalamus) areas in each hemisphere.

RESULTS

Associative and limbic cortico-basal-ganglia connections were reduced in OCD patients compared to controls: 19/24 connections had a reduced subcortical spatial extent, 9/24 had a reduced density. Moreover, while the general topography was conserved, the different cortical projection fields in the striatum and thalamus were hyper-segregated in OCD patients compared to controls.

CONCLUSION

These quantitative and qualitative differences of anatomical connections go beyond the current model of a reduced cortical control of automatic behaviour stored in the basal ganglia. The hyper-segregation in OCD could also impair the integration of cortical information in the thalamus and striatum and distort the subsequent behavioural selection process. This provides new working hypotheses for functional and behavioural studies on OCD.

Skeletal muscle diffusion tensor-MRI fiber tracking: rationale, data acquisition and analysis methods, applications and future directions

The mechanical functions of muscles involve the generation of force and the actuation of movement by shortening or lengthening under load. These functions are influenced, in part, by the internal arrangement of muscle fibers with respect to the muscle's mechanical line of action. This property is known as muscle architecture. In this review, we describe the use of diffusion tensor (DT)-MRI muscle fiber tracking for the study of muscle architecture. In the first section, the importance of skeletal muscle architecture to function is discussed. In addition, traditional and complementary methods for the assessment of muscle architecture (brightness-mode ultrasound imaging and cadaver analysis) are presented. Next, DT-MRI is introduced and the structural basis for the reduced and anisotropic diffusion of water in muscle is discussed. The third section discusses issues related to the acquisition of skeletal muscle DT-MRI data and presents recommendations for optimal strategies. The fourth section discusses methods for the pre-processing of DT-MRI data, the available approaches for the calculation of the diffusion tensor and the seeding and propagating of fiber tracts, and the analysis of the tracking results to measure structural properties pertinent to muscle biomechanics. Lastly, examples are presented of how DT-MRI fiber tracking has been used to provide new insights into how muscles function, and important future research directions are highlighted. Copyright © 2016 John Wiley & Sons, Ltd.

Altered structural and effective connectivity in anorexia and bulimia nervosa in circuits that regulate energy and reward homeostasis

Anorexia and bulimia nervosa are severe eating disorders that share many behaviors. Structural and functional brain circuits could provide biological links that those disorders have in common. We recruited 77 young adult women, 26 healthy controls, 26 women with anorexia and 25 women with bulimia nervosa. Probabilistic tractography was used to map white matter connectivity strength across taste and food intake regulating brain circuits. An independent multisample greedy equivalence search algorithm tested effective connectivity between those regions during sucrose tasting. Anorexia and bulimia nervosa had greater structural connectivity in pathways between insula, orbitofrontal cortex and ventral striatum, but lower connectivity from orbitofrontal cortex and amygdala to the hypothalamus (P<0.05, corrected for comorbidity, medication and multiple comparisons). Functionally, in controls the hypothalamus drove ventral striatal activity, but in anorexia and bulimia nervosa effective connectivity was directed from anterior cingulate via ventral striatum to the hypothalamus. Across all groups, sweetness perception was predicted by connectivity strength in pathways connecting to the middle orbitofrontal cortex. This study provides evidence that white matter structural as well as effective connectivity within the energy-homeostasis and food reward-regulating circuitry is fundamentally different in anorexia and bulimia nervosa compared with that in controls. In eating disorders, anterior cingulate cognitive-emotional top down control could affect food reward and eating drive, override hypothalamic inputs to the ventral striatum and enable prolonged food restriction.

Cerebello-cerebral connectivity in the developing brain

Disrupted cerebellar development and injury is associated with impairments in both motor and non-motor domains. Methods to non-invasively characterize cerebellar afferent and efferent connections during early development are lacking. The aim of this study was to assess the feasibility of delineating cortico-ponto-cerebellar (CPC) and cerebello-thalamo-cortical (CTC) white matter tracts during brain development using high angular resolution diffusion imaging (HARDI). HARDI data were obtained in 24 infants born between 24⁺⁶ and 39 weeks gestational age (median 33⁺⁴ weeks) and scanned between 29⁺¹ and 44 weeks postmenstrual age (PMA) (median 37⁺¹ weeks). Probabilistic tractography of CPC and CTC fibers was performed using constrained spherical deconvolution. Connections between cerebellum and contralateral cerebral hemisphere were identified in all infants studied. Fractional anisotropy (FA) values of CTC and CPC pathways increased with increasing PMA at scan (p < 0.001). The supratentorial regions connecting to contralateral cerebellum in most subjects, irrespective of PMA at scan, included the precentral cortex, superior frontal cortex, supplementary motor area, insula, postcentral cortex, precuneus, and paracentral lobule. This study demonstrates the feasibility of assessing CTC and CPC white matter connectivity in vivo during the early stages of development. The ability to assess cerebellar connectivity during this critical developmental period may help improve our understanding of the role of the cerebellum in a wide range of neuromotor and neurocognitive disorders.

Understanding Neuronal Architecture in Obesity through Analysis of White Matter Connection Strength

Despite the prevalence of obesity, our understanding of its neurobiological underpinnings is insufficient. Diffusion weighted imaging and calculation of white matter connection strength are methods to describe the architecture of anatomical white matter tracts. This study is aimed to characterize white matter architecture within taste-reward circuitry in a population of obese individuals. Obese (n = 18, age = 28.7 ± 8.3 years) and healthy control (n = 24, age = 27.4 ± 6.3 years) women underwent diffusion weighted imaging. Using probabilistic fiber tractography (FSL PROBTRACKX2 toolbox) we calculated connection strength within 138 anatomical white matter tracts. Obese women (OB) displayed lower and greater connectivity within taste-reward circuitry compared to controls (Wilks' λ < 0.001; p < 0.001). Connectivity was lower in white matter tracts connecting insula, amygdala, prefrontal cortex (PFC), orbitofrontal cortex (OFC) and striatum. Connectivity was greater between the amygdala and anterior cingulate cortex (ACC). This study indicates that lower white matter connectivity within white matter tracts of insula-fronto-striatal taste-reward circuitry are associated with obesity as well as greater connectivity within white matter tracts connecting the amygdala and ACC. The specificity of regions suggests sensory integration and reward processing are key associations that are altered in and might contribute to obesity.

A 3D high resolution ex vivo white matter atlas of the common squirrel monkey (Saimiri sciureus) based on diffusion tensor imaging

Modern magnetic resonance imaging (MRI) brain atlases are high quality 3-D volumes with specific structures labeled in the volume. Atlases are essential in providing a common space for interpretation of results across studies, for anatomical education, and providing quantitative image-based navigation. Extensive work has been devoted to atlas construction for humans, macaque, and several non-primate species (e.g., rat). One notable gap in the literature is the common squirrel monkey - for which the primary published atlases date from the 1960's. The common squirrel monkey has been used extensively as surrogate for humans in biomedical studies, given its anatomical neuro-system similarities and practical considerations. This work describes the continued development of a multi-modal MRI atlas for the common squirrel monkey, for which a structural imaging space and gray matter parcels have been previously constructed. This study adds white matter tracts to the atlas. The new atlas includes 49 white matter (WM) tracts, defined using diffusion tensor imaging (DTI) in three animals and combines these data to define the anatomical locations of these tracks in a standardized coordinate system compatible with previous development. An anatomist reviewed the resulting tracts and the inter-animal reproducibility (i.e., the Dice index of each WM parcel across animals in common space) was assessed. The Dice indices range from 0.05 to 0.80 due to differences of local registration quality and the variation of WM tract position across individuals. However, the combined WM labels from the 3 animals represent the general locations of WM parcels, adding basic connectivity information to the atlas.

Greater Insula White Matter Fiber Connectivity in Women Recovered from Anorexia Nervosa

Anorexia nervosa is a severe psychiatric disorder associated with reduced drive to eat. Altered taste-reward circuit white matter fiber organization in anorexia nervosa after recovery could indicate a biological marker that alters the normal motivation to eat. Women recovered from restricting-type anorexia (Recovered AN, n = 24, age = 30.3 ± 8.1 years) and healthy controls (n = 24, age = 27.4 ± 6.3 years) underwent diffusion weighted imaging of the brain. Probabilistic tractography analyses calculated brain white matter connectivity (streamlines) as an estimate of fiber connections in taste-reward-related white matter tracts, and microstructural integrity (fractional anisotropy, FA) was assessed using tract-based spatial statistics. Recovered AN showed significantly (range P<0.05-0.001, Bonferroni corrected) greater white matter connectivity between bilateral insula regions and ventral striatum, left insula and middle orbitofrontal cortex (OFC), and right insula projecting to gyrus rectus and medial OFC. Duration of illness predicted connectivity of tracts projecting from the insula to ventral striatum and OFC. Microstructural integrity was lower in Recovered AN in most insula white matter tracts, as was whole-brain FA in parts of the anterior corona radiata, external capsule, and cerebellum (P<0.05, family-wise error-corrected). This study indicates higher structural white matter connectivity, an estimate of fibers connections, in anorexia after recovery in tracts that connect taste-reward processing regions. Greater connectivity together with less-fiber integrity could indicate altered neural activity between those regions, which could interfere with normal food-reward circuit function. Correlations between connectivity and illness duration suggest that connectivity could be a marker for illness severity. Whether greater connectivity can predict prognosis of the disorder requires further study.

Human blindsight is mediated by an intact geniculo-extrastriate pathway

Although damage to the primary visual cortex (V1) causes hemianopia, many patients retain some residual vision; known as blindsight. We show that blindsight may be facilitated by an intact white-matter pathway between the lateral geniculate nucleus and motion area hMT+. Visual psychophysics, diffusion-weighted magnetic resonance imaging and fibre tractography were applied in 17 patients with V1 damage acquired during adulthood and 9 age-matched controls. Individuals with V1 damage were subdivided into blindsight positive (preserved residual vision) and negative (no residual vision) according to psychophysical performance. All blindsight positive individuals showed intact geniculo-hMT+ pathways, while this pathway was significantly impaired or not measurable in blindsight negative individuals. Two white matter pathways previously implicated in blindsight: (i) superior colliculus to hMT+ and (ii) between hMT+ in each hemisphere were not consistently present in blindsight positive cases. Understanding the visual pathways crucial for residual vision may direct future rehabilitation strategies for hemianopia patients.

DOI:http://dx.doi.org/10.7554/eLife.08935.001

Visual information from our eyes projects to a region at the back of the brain called the primary visual cortex, which is where the information is processed to allow us to see the world around us. If a person suffers a stroke that affects this primary visual cortex, he or she can become blind on one side. However, some people can still detect images within this ‘blind’ area, even if they are not consciously aware of it. This phenomenon is known as ‘blindsight’, but it remains unclear which pathways and structures in the brain might allow this information to be detected.

Ajina et al. have now examined the brains of a large group of patients with damage to the visual cortex. The results for the patients with blindsight were compared to those without, and to a group of sighted control participants. This analysis identified a pathway that seems to underlie blindsight. This pathway (which runs between an area of the brain called the lateral geniculate nucleus and another called the motion area hMT+) was present in all patients with blindsight, but was missing or disrupted in those patients without blindsight.

Ajina et al. then examined other pathways that had previously been suggested to support blindsight and revealed that they were unlikely to do so. This is because the suggested connections were not identifiable in all patients with blindsight, and were often intact in those patients without blindsight.

So far, this work has addressed the structure of the pathways rather than their activity. Future work will attempt to determine whether it is possible to strengthen such pathways to improve visual ability.

DOI:http://dx.doi.org/10.7554/eLife.08935.002

Optimization of large-scale mouse brain connectome via joint evaluation of DTI and neuron tracing data

Tractography based on diffusion tensor imaging (DTI) data has been used as a tool by a large number of recent studies to investigate structural connectome. Despite its great success in offering unique 3D neuroanatomy information, DTI is an indirect observation with limited resolution and accuracy and its reliability is still unclear. Thus, it is essential to answer this fundamental question: how reliable is DTI tractography in constructing large-scale connectome? To answer this question, we employed neuron tracing data of 1772 experiments on the mouse brain released by the Allen Mouse Brain Connectivity Atlas (AMCA) as the ground-truth to assess the performance of DTI tractography in inferring white matter fiber pathways and inter-regional connections. For the first time in the neuroimaging field, the performance of whole brain DTI tractography in constructing a large-scale connectome has been evaluated by comparison with tracing data. Our results suggested that only with the optimized tractography parameters and the appropriate scale of brain parcellation scheme, can DTI produce relatively reliable fiber pathways and a large-scale connectome. Meanwhile, a considerable amount of errors were also identified in optimized DTI tractography results, which we believe could be potentially alleviated by efforts in developing better DTI tractography approaches. In this scenario, our framework could serve as a reliable and quantitative test bed to identify errors in tractography results which will facilitate the development of such novel tractography algorithms and the selection of optimal parameters.

3D structure tensor analysis of light microscopy data for validating diffusion MRI

Diffusion magnetic resonance imaging (d-MRI) is a powerful non-invasive and non-destructive technique for characterizing brain tissue on the microscopic scale. However, the lack of validation of d-MRI by independent experimental means poses an obstacle to accurate interpretation of data acquired using this method. Recently, structure tensor analysis has been applied to light microscopy images, and this technique holds promise to be a powerful validation strategy for d-MRI. Advantages of this approach include its similarity to d-MRI in terms of averaging the effects of a large number of cellular structures, and its simplicity, which enables it to be implemented in a high-throughput manner. However, a drawback of previous implementations of this technique arises from it being restricted to 2D. As a result, structure tensor analyses have been limited to tissue sectioned in a direction orthogonal to the direction of interest. Here we describe the analytical framework for extending structure tensor analysis to 3D, and utilize the results to analyze serial image "stacks" acquired with confocal microscopy of rhesus macaque hippocampal tissue. Implementation of 3D structure tensor procedures requires removal of sources of anisotropy introduced in tissue preparation and confocal imaging. This is accomplished with image processing steps to mitigate the effects of anisotropic tissue shrinkage, and the effects of anisotropy in the point spread function (PSF). In order to address the latter confound, we describe procedures for measuring the dependence of PSF anisotropy on distance from the microscope objective within tissue. Prior to microscopy, ex vivo d-MRI measurements performed on the hippocampal tissue revealed three regions of tissue with mutually orthogonal directions of least restricted diffusion that correspond to CA1, alveus and inferior longitudinal fasciculus. We demonstrate the ability of 3D structure tensor analysis to identify structure tensor orientations that are parallel to d-MRI derived diffusion tensors in each of these three regions. It is concluded that the 3D generalization of structure tensor analysis will further improve the utility of this method for validation of d-MRI by making it a more flexible experimental technique that closer resembles the inherently 3D nature of d-MRI measurements.

Integrating histology and MRI in the first digital brain of common squirrel monkey, Saimiri sciureus

This effort is a continuation of development of a digital brain atlas of the common squirrel monkey, Saimiri sciureus, a New World monkey with functional and microstructural organization of central nervous system similar to that of humans. Here, we present the integration of histology with multi-modal magnetic resonance imaging (MRI) atlas constructed from the brain of an adult female squirrel monkey. The central concept of this work is to use block face photography to establish an intermediate common space in coordinate system which preserves the high resolution in-plane resolution of histology while enabling 3-D correspondence with MRI. In vivo MRI acquisitions include high resolution T2 structural imaging (300 µm isotropic) and low resolution diffusion tensor imaging (600 um isotropic). Ex vivo MRI acquisitions include high resolution T2 structural imaging and high resolution diffusion tensor imaging (both 300 µm isotropic). Cortical regions were manually annotated on the co-registered volumes based on published histological sections in-plane. We describe mapping of histology and MRI based data of the common squirrel monkey and construction of a viewing tool that enable online viewing of these datasets. The previously descried atlas MRI is used for its deformation to provide accurate conformation to the MRI, thus adding information at the histological level to the MRI volume. This paper presents the mapping of single 2D image slice in block face as a proof of concept and this can be extended to map the atlas space in 3D coordinate system as part of the future work and can be loaded to an XNAT system for further use.

Recent advancements in diffusion MRI for investigating cortical development after preterm birth-potential and pitfalls

Preterm infants are born during a critical period of brain maturation, in which even subtle events can result in substantial behavioral, motor and cognitive deficits, as well as psychiatric diseases. Recent evidence shows that the main source for these devastating disabilities is not necessarily white matter (WM) damage but could also be disruptions of cortical microstructure. Animal studies showed how moderate hypoxic-ischemic conditions did not result in significant neuronal loss in the developing brain, but did cause significantly impaired dendritic growth and synapse formation alongside a disturbed development of neuronal connectivity as measured using diffusion magnetic resonance imaging (dMRI). When using more advanced acquisition settings such as high-angular resolution diffusion imaging (HARDI), more advanced reconstruction methods can be applied to investigate the cortical microstructure with higher levels of detail. Recent advances in dMRI acquisition and analysis have great potential to contribute to a better understanding of neuronal connectivity impairment in preterm birth. We will review the current understanding of abnormal preterm cortical development, novel approaches in dMRI, and the pitfalls in scanning vulnerable preterm infants.

Aging alterations in whole-brain networks during adulthood mapped with the minimum spanning tree indices: the interplay of density, connectivity cost and life-time trajectory

The organizational network changes in the human brain across the lifespan have been mapped using functional and structural connectivity data. Brain network changes provide valuable insights into the processes underlying senescence. Nonetheless, the altered network density in the elderly severely compromises the usefulness of network analysis to study the aging brain. We successfully circumvented this problem by focusing on the critical structural network backbone, using a robust tree representation. Whole-brain networks' minimum spanning trees were determined in a dataset of diffusion-weighted images from 382 healthy subjects, ranging in age from 20.2 to 86.2 years. Tree-based metrics were compared with classical network metrics. In contrast to the tree-based metrics, classical metrics were highly influenced by age-related changes in network density. Tree-based metrics showed linear and non-linear correlation across adulthood and are in close accordance with results from previous histopathological characterizations of the changes in white matter integrity in the aging brain.

Adaptive distance metric learning for diffusion tensor image segmentation

High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework.

A brain MRI atlas of the common squirrel monkey, Saimiri sciureus

The common squirrel monkey, Saimiri sciureus, is a New World monkey with functional and microstructural organization of central nervous system similar to that of humans. It is one of the most commonly used South American primates in biomedical research. Unlike its Old World macaque cousins, no digital atlases have described the organization of the squirrel monkey brain. Here, we present a multi-modal magnetic resonance imaging (MRI) atlas constructed from the brain of an adult female squirrel monkey. In vivo MRI acquisitions include T2 structural imaging and diffusion tensor imaging. Ex vivo MRI acquisitions include T2 structural imaging and diffusion tensor imaging. Cortical regions were manually annotated on the co-registered volumes based on published histological sections.