Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited

Diffusion Tractography in Deep Brain Stimulation Surgery: A Review

Deep brain stimulation (DBS) is believed to exert its therapeutic effects through modulation of brain circuitry, yet conventional preoperative planning does not allow direct targeting or visualization of white matter pathways. Diffusion MRI tractography (DT) is virtually the only non-invasive method of visualizing structural connectivity in the brain, leading many to suggest its use to guide DBS targeting. DT-guided DBS not only has the potential to allow direct white matter targeting for established applications [e.g., Parkinson’s disease (PD), essential tremor (ET), dystonia], but may also aid in the discovery of new therapeutic targets for a variety of other neurologic and psychiatric diseases. Despite these exciting opportunities, DT lacks standardization and rigorous anatomic validation, raising significant concern for the use of such data in stereotactic brain surgery. This review covers the technical details, proposed methods, and initial clinical data for the use of DT in DBS surgery. Rather than focusing on specific disease applications, this review focuses on methods that can be applied to virtually any DBS target.

Small Worldness in Dense and Weighted Connectomes

The human brain is a heterogeneous network of connected functional regions; however, most brain network studies assume that all brain connections can be described in a framework of binary connections. The brain is a complex structure of white matter tracts connected by a wide range of tract sizes, which suggests a broad range of connection strengths. Therefore, the assumption that the connections are binary yields an incomplete picture of the brain. Various thresholding methods have been used to remove spurious connections and reduce the graph density in binary networks. But these thresholds are arbitrary and make problematic the comparison of networks created at different thresholds. The heterogeneity of connection strengths can be represented in graph theory by applying weights to the network edges. Using our recently introduced edge weight parameter, we estimated the topological brain network organization using a complimentary weighted connectivity framework to the traditional framework of a binary network. To examine the reproducibility of brain networks in a controlled condition, we studied the topological network organization of a single healthy individual by acquiring 10 repeated diffusion-weighted magnetic resonance image datasets, over a 1-month period on the same scanner, and analyzing these networks with deterministic tractography. We applied a threshold to both the binary and weighted networks and determined that the extra degree of freedom that comes with the framework of weighting network connectivity provides a robust result as any threshold level. The proposed weighted connectivity framework provides a stable result and is able to demonstrate the small world property of brain networks in situations where the binary framework is inadequate and unable to demonstrate this network property.

Network-Level Structure-Function Relationships in Human Neocortex

The dynamics of spontaneous fluctuations in neural activity are shaped by underlying patterns of anatomical connectivity. While numerous studies have demonstrated edge-wise correspondence between structural and functional connections, much less is known about how large-scale coherent functional network patterns emerge from the topology of structural networks. In the present study, we deploy a multivariate statistical technique, partial least squares, to investigate the association between spatially extended structural networks and functional networks. We find multiple statistically robust patterns, reflecting reliable combinations of structural and functional subnetworks that are optimally associated with one another. Importantly, these patterns generally do not show a one-to-one correspondence between structural and functional edges, but are instead distributed and heterogeneous, with many functional relationships arising from nonoverlapping sets of anatomical connections. We also find that structural connections between high-degree hubs are disproportionately represented, suggesting that these connections are particularly important in establishing coherent functional networks. Altogether, these results demonstrate that the network organization of the cerebral cortex supports the emergence of diverse functional network configurations that often diverge from the underlying anatomical substrate.

Diffusion tensor imaging of dolphin brains reveals direct auditory pathway to temporal lobe

The brains of odontocetes (toothed whales) look grossly different from their terrestrial relatives. Because of their adaptation to the aquatic environment and their reliance on echolocation, the odontocetes' auditory system is both unique and crucial to their survival. Yet, scant data exist about the functional organization of the cetacean auditory system. A predominant hypothesis is that the primary auditory cortex lies in the suprasylvian gyrus along the vertex of the hemispheres, with this position induced by expansion of 'associative' regions in lateral and caudal directions. However, the precise location of the auditory cortex and its connections are still unknown. Here, we used a novel diffusion tensor imaging (DTI) sequence in archival post-mortem brains of a common dolphin (Delphinus delphis) and a pantropical dolphin (Stenella attenuata) to map their sensory and motor systems. Using thalamic parcellation based on traditionally defined regions for the primary visual (V1) and auditory cortex (A1), we found distinct regions of the thalamus connected to V1 and A1. But in addition to suprasylvian-A1, we report here, for the first time, the auditory cortex also exists in the temporal lobe, in a region near cetacean-A2 and possibly analogous to the primary auditory cortex in related terrestrial mammals (Artiodactyla). Using probabilistic tract tracing, we found a direct pathway from the inferior colliculus to the medial geniculate nucleus to the temporal lobe near the sylvian fissure. Our results demonstrate the feasibility of post-mortem DTI in archival specimens to answer basic questions in comparative neurobiology in a way that has not previously been possible and shows a link between the cetacean auditory system and those of terrestrial mammals. Given that fresh cetacean specimens are relatively rare, the ability to measure connectivity in archival specimens opens up a plethora of possibilities for investigating neuroanatomy in cetaceans and other species.

Advances in MRI-based computational neuroanatomy: from morphometry to in-vivo histology

PURPOSE OF REVIEW

Current computational neuroanatomy based on MRI focuses on morphological measures of the brain. We present recent methodological developments in quantitative MRI (qMRI) that provide standardized measures of the brain, which go beyond morphology. We show how biophysical modelling of qMRI data can provide quantitative histological measures of brain tissue, leading to the emerging field of in-vivo histology using MRI (hMRI).

RECENT FINDINGS

qMRI has greatly improved the sensitivity and specificity of computational neuroanatomy studies. qMRI metrics can also be used as direct indicators of the mechanisms driving observed morphological findings. For hMRI, biophysical models of the MRI signal are being developed to directly access histological information such as cortical myelination, axonal diameters or axonal g-ratio in white matter. Emerging results indicate promising prospects for the combined study of brain microstructure and function.

SUMMARY

Non-invasive brain tissue characterization using qMRI or hMRI has significant implications for both research and clinics. Both approaches improve comparability across sites and time points, facilitating multicentre/longitudinal studies and standardized diagnostics. hMRI is expected to shed new light on the relationship between brain microstructure, function and behaviour, both in health and disease, and become an indispensable addition to computational neuroanatomy.

Simple, Scalable Proteomic Imaging for High-Dimensional Profiling of Intact Systems

Combined measurement of diverse molecular and anatomical traits that span multiple levels remains a major challenge in biology. Here, we introduce a simple method that enables proteomic imaging for scalable, integrated, high-dimensional phenotyping of both animal tissues and human clinical samples. This method, termed SWITCH, uniformly secures tissue architecture, native biomolecules, and antigenicity across an entire system by synchronizing the tissue preservation reaction. The heat- and chemical-resistant nature of the resulting framework permits multiple rounds (>20) of relabeling. We have performed 22 rounds of labeling of a single tissue with precise co-registration of multiple datasets. Furthermore, SWITCH synchronizes labeling reactions to improve probe penetration depth and uniformity of staining. With SWITCH, we performed combinatorial protein expression profiling of the human cortex and also interrogated the geometric structure of the fiber pathways in mouse brains. Such integrated high-dimensional information may accelerate our understanding of biological systems at multiple levels.

Clarifying Human White Matter

Progress in magnetic resonance imaging (MRI) now makes it possible to identify the major white matter tracts in the living human brain. These tracts are important because they carry many of the signals communicated between different brain regions. MRI methods coupled with biophysical modeling can measure the tissue properties and structural features of the tracts that impact our ability to think, feel, and perceive. This review describes the fundamental ideas of the MRI methods used to identify the major white matter tracts in the living human brain.

The Structural Connectome of the Human Central Homeostatic Network

Homeostatic adaptations to stress are regulated by interactions between the brainstem and regions of the forebrain, including limbic sites related to respiratory, autonomic, affective, and cognitive processing. Neuroanatomic connections between these homeostatic regions, however, have not been thoroughly identified in the human brain. In this study, we perform diffusion spectrum imaging tractography using the MGH-USC Connectome MRI scanner to visualize structural connections in the human brain linking autonomic and cardiorespiratory nuclei in the midbrain, pons, and medulla oblongata with forebrain sites critical to homeostatic control. Probabilistic tractography analyses in six healthy adults revealed connections between six brainstem nuclei and seven forebrain regions, several over long distances between the caudal medulla and cerebral cortex. The strongest evidence for brainstem-homeostatic forebrain connectivity in this study was between the brainstem midline raphe and the medial temporal lobe. The subiculum and amygdala were the sampled forebrain nodes with the most extensive brainstem connections. Within the human brainstem-homeostatic forebrain connectome, we observed that a lateral forebrain bundle, whose connectivity is distinct from that of rodents and nonhuman primates, is the primary conduit for connections between the brainstem and medial temporal lobe. This study supports the concept that interconnected brainstem and forebrain nodes form an integrated central homeostatic network (CHN) in the human brain. Our findings provide an initial foundation for elucidating the neuroanatomic basis of homeostasis in the normal human brain, as well as for mapping CHN disconnections in patients with disorders of homeostasis, including sudden and unexpected death, and epilepsy.

A Mapping Between Structural and Functional Brain Networks

The relationship between structural and functional brain networks is still highly debated. Most previous studies have used a single functional imaging modality to analyze this relationship. In this work, we use multimodal data, from functional MRI, magnetoencephalography, and diffusion tensor imaging, and assume that there exists a mapping between the connectivity matrices of the resting-state functional and structural networks. We investigate this mapping employing group averaged as well as individual data. We indeed find a significantly high goodness of fit level for this structure-function mapping. Our analysis suggests that a functional connection is shaped by all walks up to the diameter in the structural network in both modality cases. When analyzing the inverse mapping, from function to structure, longer walks in the functional network also seem to possess minor influence on the structural connection strengths. Even though similar overall properties for the structure-function mapping are found for different functional modalities, our results indicate that the structure-function relationship is modality dependent.

Comparative Connectomics

We introduce comparative connectomics, the quantitative study of cross-species commonalities and variations in brain network topology that aims to discover general principles of network architecture of nervous systems and the identification of species-specific features of brain connectivity. By comparing connectomes derived from simple to more advanced species, we identify two conserved themes of wiring: the tendency to organize network topology into communities that serve specialized functionality and the general drive to enable high topological integration by means of investment of neural resources in short communication paths, hubs, and rich clubs. Within the space of wiring possibilities that conform to these common principles, we argue that differences in connectome organization between closely related species support adaptations in cognition and behavior.

D-BRAIN: Anatomically Accurate Simulated Diffusion MRI Brain Data

Diffusion Weighted (DW) MRI allows for the non-invasive study of water diffusion inside living tissues. As such, it is useful for the investigation of human brain white matter (WM) connectivity in vivo through fiber tractography (FT) algorithms. Many DW-MRI tailored restoration techniques and FT algorithms have been developed. However, it is not clear how accurately these methods reproduce the WM bundle characteristics in real-world conditions, such as in the presence of noise, partial volume effect, and a limited spatial and angular resolution. The difficulty lies in the lack of a realistic brain phantom on the one hand, and a sufficiently accurate way of modeling the acquisition-related degradation on the other. This paper proposes a software phantom that approximates a human brain to a high degree of realism and that can incorporate complex brain-like structural features. We refer to it as a Diffusion BRAIN (D-BRAIN) phantom. Also, we propose an accurate model of a (DW) MRI acquisition protocol to allow for validation of methods in realistic conditions with data imperfections. The phantom model simulates anatomical and diffusion properties for multiple brain tissue components, and can serve as a ground-truth to evaluate FT algorithms, among others. The simulation of the acquisition process allows one to include noise, partial volume effects, and limited spatial and angular resolution in the images. In this way, the effect of image artifacts on, for instance, fiber tractography can be investigated with great detail. The proposed framework enables reliable and quantitative evaluation of DW-MR image processing and FT algorithms at the level of large-scale WM structures. The effect of noise levels and other data characteristics on cortico-cortical connectivity and tractography-based grey matter parcellation can be investigated as well.

Hybrid PET/MR Imaging and Brain Connectivity

In recent years, brain connectivity is gaining ever-increasing interest from the interdisciplinary research community. The study of brain connectivity is characterized by a multifaceted approach providing both structural and functional evidence of the relationship between cerebral regions at different scales. Although magnetic resonance (MR) is the most established imaging modality for investigating connectivity in vivo, the recent advent of hybrid positron emission tomography (PET)/MR scanners paved the way for more comprehensive investigation of brain organization and physiology. Due to the high sensitivity and biochemical specificity of radiotracers, combining MR with PET imaging may enrich our ability to investigate connectivity by introducing the concept of metabolic connectivity and cometomics and promoting new insights on the physiological and molecular bases underlying high-level neural organization. This review aims to describe and summarize the main methods of analysis of brain connectivity employed in MR imaging and nuclear medicine. Moreover, it will discuss practical aspects and state-of-the-art techniques for exploiting hybrid PET/MR imaging to investigate the relationship of physiological processes and brain connectivity.

Brain structure and dynamics across scales: in search of rules

Louis Henry Sullivan, the father of skyscrapers, famously stated 'Form ever follows function'. In this short review, we will focus on the relationship between form (structure) and function (dynamics) in the brain. We summarize recent advances on the quantification of directed- and weighted-mesoscopic connectivity of mammalian cortex, the exponential distance rule for mesoscopic and microscopic circuit wiring, a spatially embedded random model of inter-areal cortical networks, and a large-scale dynamical circuit model of money's cortex that gives rise to a hierarchy of timescales. These findings demonstrate that inter-areal cortical networks are dense (hence such concepts as 'small-world' need to be refined when applied to the brain), spatially dependent (therefore purely topological approach of graph theory has limited applicability) and heterogeneous (consequently cortical areas cannot be treated as identical 'nodes').

Ensemble Tractography

Tractography uses diffusion MRI to estimate the trajectory and cortical projection zones of white matter fascicles in the living human brain. There are many different tractography algorithms and each requires the user to set several parameters, such as curvature threshold. Choosing a single algorithm with specific parameters poses two challenges. First, different algorithms and parameter values produce different results. Second, the optimal choice of algorithm and parameter value may differ between different white matter regions or different fascicles, subjects, and acquisition parameters. We propose using ensemble methods to reduce algorithm and parameter dependencies. To do so we separate the processes of fascicle generation and evaluation. Specifically, we analyze the value of creating optimized connectomes by systematically combining candidate streamlines from an ensemble of algorithms (deterministic and probabilistic) and systematically varying parameters (curvature and stopping criterion). The ensemble approach leads to optimized connectomes that provide better cross-validated prediction error of the diffusion MRI data than optimized connectomes generated using a single-algorithm or parameter set. Furthermore, the ensemble approach produces connectomes that contain both short- and long-range fascicles, whereas single-parameter connectomes are biased towards one or the other. In summary, a systematic ensemble tractography approach can produce connectomes that are superior to standard single parameter estimates both for predicting the diffusion measurements and estimating white matter fascicles.

Diffusion MRI and tractography opened a new avenue for studying white matter fascicles and their tissue properties in the living human brain. There are many different tractography methods, and each requires the user to set several parameters. A limitation of tractography is that the results depend on the selection of algorithms and parameters. Here, we analyze an ensemble method, Ensemble Tractography (ET), that reduces the effect of algorithm and parameter selection. ET creates a large set of candidate streamlines using an ensemble of algorithms and parameter values and then selects the streamlines with strong support from the data using a global fascicle evaluation method. Compared to single parameter connectomes, ET connectomes predict diffusion MRI signals better and cover a wider range of white matter volume. Importantly, ET connectomes include both short- and long-association fascicles, which are not typically found together in single-parameter connectomes.

Scale-Dependent Variability and Quantitative Regimes in Graph-Theoretic Representations of Human Cortical Networks

Studying brain connectivity is important due to potential differences in brain circuitry between health and disease. One drawback of graph-theoretic approaches to this is that their results are dependent on the spatial scale at which brain circuitry is examined and explicitly on how vertices and edges are defined in network models. To investigate this, magnetic resonance and diffusion tensor images were acquired from 136 healthy adults, and each subject's cortex was parceled into as many as 50,000 regions. Regions were represented as nodes in a reconstructed network representation, and interregional connectivity was inferred via deterministic tractography. Network model behavior was explored as a function of nodal number and connectivity weighing. Three distinct regimes of quantitative behavior assumed by network models as a function of spatial scale are identified, and their existence may be modulated by the spatial folding scale of the cortex. The maximum number of network nodes used to model human brain circuitry in this study (∼50,000) is larger than in previous macroscale neuroimaging studies. Results suggest that network model properties vary appreciably as a function of vertex assignment convention and edge weighing scheme and that graph-theoretic analysis results should not be compared across spatial scales without appropriate understanding of how spatial scale and model topology modulate network model properties. These findings have implications for comparing macro- to mesoscale studies of brain network models and understanding how choosing network-theoretic parameters affects the interpretation of brain connectivity studies.

Brain Networks and Cognitive Architectures

Most accounts of human cognitive architectures have focused on computational accounts of cognition while making little contact with the study of anatomical structures and physiological processes. A renewed convergence between neurobiology and cognition is well under way. A promising area arises from the overlap between systems/cognitive neuroscience on the one side and the discipline of network science on the other. Neuroscience increasingly adopts network tools and concepts to describe the operation of collections of brain regions. Beyond just providing illustrative metaphors, network science offers a theoretical framework for approaching brain structure and function as a multi-scale system composed of networks of neurons, circuits, nuclei, cortical areas, and systems of areas. This paper views large-scale networks at the level of areas and systems, mostly on the basis of data from human neuroimaging, and how this view of network structure and function has begun to illuminate our understanding of the biological basis of cognitive architectures.

The white matter query language: a novel approach for describing human white matter anatomy

We have developed a novel method to describe human white matter anatomy using an approach that is both intuitive and simple to use, and which automatically extracts white matter tracts from diffusion MRI volumes. Further, our method simplifies the quantification and statistical analysis of white matter tracts on large diffusion MRI databases. This work reflects the careful syntactical definition of major white matter fiber tracts in the human brain based on a neuroanatomist's expert knowledge. The framework is based on a novel query language with a near-to-English textual syntax. This query language makes it possible to construct a dictionary of anatomical definitions that describe white matter tracts. The definitions include adjacent gray and white matter regions, and rules for spatial relations. This novel method makes it possible to automatically label white matter anatomy across subjects. After describing this method, we provide an example of its implementation where we encode anatomical knowledge in human white matter for ten association and 15 projection tracts per hemisphere, along with seven commissural tracts. Importantly, this novel method is comparable in accuracy to manual labeling. Finally, we present results applying this method to create a white matter atlas from 77 healthy subjects, and we use this atlas in a small proof-of-concept study to detect changes in association tracts that characterize schizophrenia.

Merits and Limits of Tractography Techniques for the Uninitiated

The implementation of fiber tracking or tractography modules in commercial navigation systems resulted in a broad availability of visualization possibilities for major white matter tracts in the neurosurgical community. Unfortunately the implemented algorithms and tracking approaches do not represent the state of the art of tractography strategies and may lead to false tracking results. The application of advanced tractography techniques for neurosurgical procedures poses even additional challenges that relate to effects of the individual anatomy that might be altered by edema and tumor, to stereotactic inaccuracies due to image distortion, as well as to registration inaccuracies and brain shift.

Generative models of the human connectome

The human connectome represents a network map of the brain's wiring diagram and the pattern into which its connections are organized is thought to play an important role in cognitive function. The generative rules that shape the topology of the human connectome remain incompletely understood. Earlier work in model organisms has suggested that wiring rules based on geometric relationships (distance) can account for many but likely not all topological features. Here we systematically explore a family of generative models of the human connectome that yield synthetic networks designed according to different wiring rules combining geometric and a broad range of topological factors. We find that a combination of geometric constraints with a homophilic attachment mechanism can create synthetic networks that closely match many topological characteristics of individual human connectomes, including features that were not included in the optimization of the generative model itself. We use these models to investigate a lifespan dataset and show that, with age, the model parameters undergo progressive changes, suggesting a rebalancing of the generative factors underlying the connectome across the lifespan.

Multi-Shell Hybrid Diffusion Imaging (HYDI) at 7 Tesla in TgF344-AD Transgenic Alzheimer Rats

Diffusion weighted imaging (DWI) is widely used to study microstructural characteristics of the brain. Diffusion tensor imaging (DTI) and high-angular resolution imaging (HARDI) are frequently used in radiology and neuroscience research but can be limited in describing the signal behavior in composite nerve fiber structures. Here, we developed and assessed the benefit of a comprehensive diffusion encoding scheme, known as hybrid diffusion imaging (HYDI), composed of 300 DWI volumes acquired at 7-Tesla with diffusion weightings at b = 1000, 3000, 4000, 8000 and 12000 s/mm2 and applied it in transgenic Alzheimer rats (line TgF344-AD) that model the full clinico-pathological spectrum of the human disease. We studied and visualized the effects of the multiple concentric "shells" when computing three distinct anisotropy maps-fractional anisotropy (FA), generalized fractional anisotropy (GFA) and normalized quantitative anisotropy (NQA). We tested the added value of the multi-shell q-space sampling scheme, when reconstructing neural pathways using mathematical frameworks from DTI and q-ball imaging (QBI). We show a range of properties of HYDI, including lower apparent anisotropy when using high b-value shells in DTI-based reconstructions, and increases in apparent anisotropy in QBI-based reconstructions. Regardless of the reconstruction scheme, HYDI improves FA-, GFA- and NQA-aided tractography. HYDI may be valuable in human connectome projects and clinical research, as well as magnetic resonance research in experimental animals.

Recent advancement in the challenges to connectomics

Advancement of microscopic technologies established significant progress in our understanding of the brain. In the recent effort to elucidate the complete wiring map of the brain circuitry termed 'connectome', the different modalities of imaging technology, including those of light and electron microscopy, have started providing essential contribution in multiple organisms. The contribution would be impossible without the recent innovation in both acquisition and analyses of the big connectomic data. The current data demonstrated complicated networks with unidirectional and reciprocal connections of the cerebral circuits at the macroscopic and light microscopic ('mesoscopic') levels, and the unimaginable complexity of synaptic connections between axons and dendrites at the electron microscopic ('microscopic') level. At the same time, the data highlighted the necessity to make substantial advancement in methodology of the connectomic studies, including efficient handling and automated analyses of the acquired dataset. Further understanding about structural and functional connectome seems to be facilitated by combinations of the different imaging modalities. Such multidisciplinary approaches will give us the clues to address whether the complete connectome can elucidate fundamental mechanisms processing the basic and higher functions of human brains.

Spatiotemporal patterns of cortical fiber density in developing infants, and their relationship with cortical thickness

The intrinsic relationship between the convoluted cortical folding and the underlying complex whiter matter fiber connections has received increasing attention in current neuroscience studies. Recently, the axonal pushing hypothesis of cortical folding has been proposed to explain the finding that the axonal fibers (derived from diffusion tensor images) connecting to gyri are significantly denser than those connecting to sulci in both adult human and non-human primate brains. However, it is still unclear about the spatiotemporal patterns of the fiber density on the cortical surface of the developing infant brains from birth to 2 years of age, which is the most dynamic phase of postnatal brain development. In this paper, for the first time, we systemically characterized the spatial distributions and longitudinal developmental trajectories of the cortical fiber density in the first 2 postnatal years, via joint analysis of longitudinal structural and diffusion tensor imaging from 33 healthy infants. We found that the cortical fiber density increases dramatically in the first year and then keeps relatively stable in the second year. Moreover, we revealed that the cortical fiber density on gyral regions was significantly higher at 0, 1, and 2 years of age than that on sulcal regions in the frontal, temporal, and parietal lobes. Meanwhile, the cortical fiber density was strongly positively correlated with cortical thickness at several three-hinge junction regions of gyri. These results significantly advanced our understanding of the intrinsic relationship between the cortical folding, cortical thickness and axonal wiring during early postnatal stages.

The controversial existence of the human superior fronto-occipital fasciculus: Connectome-based tractographic study with microdissection validation

The superior fronto-occipital fasciculus (SFOF), a long association bundle that connects frontal and occipital lobes, is well-documented in monkeys but is controversial in human brain. Its assumed role is in visual processing and spatial awareness. To date, anatomical and neuroimaging studies on human and animal brains are not in agreement about the existence, course, and terminations of SFOF. To clarify the existence of the SFOF in human brains, we applied deterministic fiber tractography to a template of 488 healthy subjects and to 80 individual subjects from the Human Connectome Project (HCP) and validated the results with white matter microdissection of post-mortem human brains. The imaging results showed that previous reconstructions of the SFOF were generated by two false continuations, namely between superior thalamic peduncle (STP) and stria terminalis (ST), and ST and posterior thalamic peduncle. The anatomical microdissection confirmed this finding. No other fiber tracts in the previously described location of the SFOF were identified. Hence, our data suggest that the SFOF does not exist in the human brain.

In Vivo Mapping of Cortical Columnar Networks in the Monkey with Focal Electrical and Optical Stimulation

There are currently largescale efforts to understand the brain as a connection machine. However, there has been little emphasis on understanding connection patterns between functionally specific cortical columns. Here, we review development and application of focal electrical and optical stimulation methods combined with optical imaging and fMRI mapping in the non-human primate. These new approaches, when applied systematically on a large scale, will elucidate functionally specific intra-areal and inter-areal network connection patterns. Such functionally specific network data can provide accurate views of brain network topology.

Open Environment for Multimodal Interactive Connectivity Visualization and Analysis

Brain connectivity investigations are becoming increasingly multimodal and they present challenges for quantitatively characterizing and interactively visualizing data. In this study, we present a new set of network-based software tools for combining functional and anatomical connectivity from magnetic resonance imaging (MRI) data. The computational tools are available as part of Functional and Tractographic Connectivity Analysis Toolbox (FATCAT), a toolbox that interfaces with Analysis of Functional NeuroImages (AFNI) and SUrface MApping (SUMA) for interactive queries and visualization. This includes a novel, tractographic mini-probabilistic approach to improve streamline tracking in networks. We show how one obtains more robust tracking results for determining white matter connections by utilizing the uncertainty of the estimated diffusion tensor imaging (DTI) parameters and a few Monte Carlo iterations. This allows for thresholding based on the number of connections between target pairs to reduce the presence of tracts likely due to noise. To assist users in combining data, we describe an interface for navigating and performing queries in two-dimensional and three-dimensional data defined over voxel, surface, tract, and graph domains. These varied types of information can be visualized simultaneously and the queries performed interactively using SUMA and AFNI. The methods have been designed to increase the user's ability to visualize and combine functional MRI and DTI modalities, particularly in the context of single-subject inferences (e.g., in deep brain stimulation studies). Finally, we present a multivariate framework for statistically modeling network-based features in group analysis, which can be implemented for both functional and structural studies.

DBSproc: An open source process for DBS electrode localization and tractographic analysis

Deep brain stimulation (DBS) is an effective surgical treatment for movement disorders. Although stimulation sites for movement disorders such as Parkinson's disease are established, the therapeutic mechanisms of DBS remain controversial. Recent research suggests that specific white-matter tract and circuit activation mediates symptom relief. To investigate these questions, we have developed a patient-specific open-source software pipeline called 'DBSproc' for (1) localizing DBS electrodes and contacts from postoperative CT images, (2) processing structural and diffusion MRI data, (3) registering all images to a common space, (4) estimating DBS activation volume from patient-specific voltage and impedance, and (5) understanding the DBS contact-brain connectivity through probabilistic tractography. In this paper, we explain our methodology and provide validation with anatomical and tractographic data. This method can be used to help investigate mechanisms of action of DBS, inform surgical and clinical assessments, and define new therapeutic targets.

In vivo mapping of macroscopic neuronal projections in the mouse hippocampus using high-resolution diffusion MRI

Recent developments in diffusion magnetic resonance imaging (MRI) make it a promising tool for non-invasive mapping of the spatial organization of axonal and dendritic networks in gray matter regions of the brain. Given the complex cellular environments, in which these networks reside, evidence on the capability of diffusion MRI-based tractography to study these networks is still lacking. In this study, we used a localized diffusion MRI approach to acquire high spatial and angular resolution images of the live mouse hippocampus. The diffusion MRI and tractography results were compared with histology and the Allen mouse brain connectivity atlas using a multi-step image registration pipeline. The results demonstrated that in vivo diffusion MRI data at 0.1mm isotropic resolution revealed the organization of axonal and dendritic networks in the hippocampus and the tractography results shared remarkable similarity with the viral tracer data in term of their spatial projection patterns. Quantitative analysis showed significant correlations between tractography- and tracer-based projection density measurements in the mouse hippocampus. These findings suggest that high-resolution diffusion MRI and tractography can reveal macroscopic neuronal projections in the mouse hippocampus and are important for future development of advanced tractography methods.

Local but not long-range microstructural differences of the ventral temporal cortex in developmental prosopagnosia

Individuals with developmental prosopagnosia (DP) experience face recognition impairments despite normal intellect and low-level vision and no history of brain damage. Prior studies using diffusion tensor imaging in small samples of subjects with DP (n=6 or n=8) offer conflicting views on the neurobiological bases for DP, with one suggesting white matter differences in two major long-range tracts running through the temporal cortex, and another suggesting white matter differences confined to fibers local to ventral temporal face-specific functional regions of interest (fROIs) in the fusiform gyrus. Here, we address these inconsistent findings using a comprehensive set of analyzes in a sample of DP subjects larger than both prior studies combined (n=16). While we found no microstructural differences in long-range tracts between DP and age-matched control participants, we found differences local to face-specific fROIs, and relationships between these microstructural measures with face recognition ability. We conclude that subtle differences in local rather than long-range tracts in the ventral temporal lobe are more likely associated with developmental prosopagnosia.

Validation of tractography: Comparison with manganese tracing

In this study, we used invasive tracing to evaluate white matter tractography methods based on ex vivo diffusion-weighted magnetic resonance imaging (dwMRI) data. A representative selection of tractography methods were compared to manganese tracing on a voxel-wise basis, and a more qualitative assessment examined whether, and to what extent, certain fiber tracts and gray matter targets were reached. While the voxel-wise agreement was very limited, qualitative assessment revealed that tractography is capable of finding the major fiber tracts, although there were some differences between the methods. However, false positive connections were very common and, in particular, we discovered that it is not possible to achieve high sensitivity (i.e., few false negatives) and high specificity (i.e., few false positives) at the same time. Closer inspection of the results led to the conclusion that these problems mainly originate from regions with complex fiber arrangements or high curvature and are not easily resolved by sophisticated local models alone. Instead, the crucial challenge in making tractography a truly useful and reliable tool in brain research and neurology lies in the acquisition of better data. In particular, the increase of spatial resolution, under preservation of the signal-to-noise-ratio, is key.

Track Filtering via Iterative Correction of TDI Topology

We propose a new technique to clean outlier tracks from fiber bundles reconstructed by tractography. Previous techniques were mainly based on computing pair-wise distances and clustering methods to identify unwanted tracks, which relied heavy upon user inputs for parameter tuning. In this work, we propose the use of topological information in track density images (TDI) to achieve a more robust filtering of tracks. There are two main steps of our iterative algorithm. Given a fiber bundle, we first convert it to a TDI, then extract and score its critical points. After that, tracks that contribute to high scoring loops are identified and removed using the Reeb graph of the level set surface of the TDI. Our approach is geometrically intuitive and relies only on a single parameter that enables the user to decide on the length of insignificant loops. In our experiments, we use our method to reconstruct the optic radiation in human brain using the multi-shell HARDI data from the human connectome project (HCP). We compare our results against spectral filtering and show that our approach can achieve cleaner reconstructions. We also apply our method to 215 HCP subjects to test for asymmetry of the optic radiation and obtain statistically significant results that are consistent with post-mortem studies.

Wiring cost and topological participation of the mouse brain connectome

Brain connectomes are topologically complex systems, anatomically embedded in 3D space. Anatomical conservation of "wiring cost" explains many but not all aspects of these networks. Here, we examined the relationship between topology and wiring cost in the mouse connectome by using data from 461 systematically acquired anterograde-tracer injections into the right cortical and subcortical regions of the mouse brain. We estimated brain-wide weights, distances, and wiring costs of axonal projections and performed a multiscale topological and spatial analysis of the resulting weighted and directed mouse brain connectome. Our analysis showed that the mouse connectome has small-world properties, a hierarchical modular structure, and greater-than-minimal wiring costs. High-participation hubs of this connectome mediated communication between functionally specialized and anatomically localized modules, had especially high wiring costs, and closely corresponded to regions of the default mode network. Analyses of independently acquired histological and gene-expression data showed that nodal participation colocalized with low neuronal density and high expression of genes enriched for cognition, learning and memory, and behavior. The mouse connectome contains high-participation hubs, which are not explained by wiring-cost minimization but instead reflect competitive selection pressures for integrated network topology as a basis for higher cognitive and behavioral functions.

Comparison of diffusion tractography and tract-tracing measures of connectivity strength in rhesus macaque connectome

With the mapping of macroscale connectomes by means of in vivo diffusion-weighted MR Imaging (DWI) rapidly gaining in popularity, one of the necessary steps is the examination of metrics of connectivity strength derived from these reconstructions. In the field of human macroconnectomics the number of reconstructed fiber streamlines (NOS) is more and more used as a metric of cortico-cortical interareal connectivity strength, but the link between DWI NOS and in vivo animal tract-tracing measurements of anatomical connectivity strength remains poorly understood. In this technical report, we communicate on a comparison between DWI derived metrics and tract-tracing metrics of projection strength. Tract-tracing information on projection strength of interareal pathways was extracted from two commonly used macaque connectome datasets, including (1) the CoCoMac database of collated tract-tracing experiments of the macaque brain and (2) the high-resolution tract-tracing dataset of Markov and Kennedy and coworkers. NOS and density of reconstructed fiber pathways derived from DWI data acquired across 10 rhesus macaques was found to positively correlate to tract-tracing based measurements of connectivity strength across both the CoCoMac and Markov dataset (both P < 0.001), suggesting DWI NOS to form a valid method of assessment of the projection strength of white matter pathways. Our findings provide confidence of in vivo DWI connectome reconstructions to represent fairly realistic estimates of the wiring strength of white matter projections. Our cross-modal comparison supports the notion of in vivo DWI to be a valid methodology for robust description and interpretation of brain wiring.

In vivo characterization of the connectivity and subcomponents of the human globus pallidus

Projections from the substantia nigra and striatum traverse through the pallidum on the way to their targets. To date, in vivo characterization of these pathways remains elusive. Here we used high angular resolution diffusion imaging (N=138) to study the characteristics and structural subcompartments of the human pallidum. Our central result shows that the diffusion orientation distribution functions within the pallidum are asymmetrically oriented in a dorsal to dorsolateral direction, consistent with the orientation of underlying fiber systems. We also observed systematic differences in the diffusion signal between the two pallidal segments. Compared to the outer pallidal segment, the internal segment has more peaks in the diffusion orientation distribution and stronger anisotropy in the primary fiber direction, consistent with known cellular differences between the underlying nuclei. These differences in orientation, complexity, and degree of anisotropy are sufficiently robust to automatically segment the pallidal nuclei using diffusion properties. We characterize these patterns in one data set using diffusion spectrum imaging and replicate in a separate sample of subjects imaged using multi-shell imaging, highlighting the reliability of these diffusion patterns within pallidal nuclei. Thus the gray matter diffusion signal can be useful as an in vivo measure of the collective efferent pathways running through the human pallidum.

Dimensionless, Scale Invariant, Edge Weight Metric for the Study of Complex Structural Networks

High spatial and angular resolution diffusion weighted imaging (DWI) with network analysis provides a unique framework for the study of brain structure in vivo. DWI-derived brain connectivity patterns are best characterized with graph theory using an edge weight to quantify the strength of white matter connections between gray matter nodes. Here a dimensionless, scale-invariant edge weight is introduced to measure node connectivity. This edge weight metric provides reasonable and consistent values over any size scale (e.g. rodents to humans) used to quantify the strength of connection. Firstly, simulations were used to assess the effects of tractography seed point density and random errors in the estimated fiber orientations; with sufficient signal-to-noise ratio (SNR), edge weight estimates improve as the seed density increases. Secondly to evaluate the application of the edge weight in the human brain, ten repeated measures of DWI in the same healthy human subject were analyzed. Mean edge weight values within the cingulum and corpus callosum were consistent and showed low variability. Thirdly, using excised rat brains to study the effects of spatial resolution, the weight of edges connecting major structures in the temporal lobe were used to characterize connectivity in this local network. The results indicate that with adequate resolution and SNR, connections between network nodes are characterized well by this edge weight metric. Therefore this new dimensionless, scale-invariant edge weight metric provides a robust measure of network connectivity that can be applied in any size regime.

Automatic target validation based on neuroscientific literature mining for tractography

Target identification for tractography studies requires solid anatomical knowledge validated by an extensive literature review across species for each seed structure to be studied. Manual literature review to identify targets for a given seed region is tedious and potentially subjective. Therefore, complementary approaches would be useful. We propose to use text-mining models to automatically suggest potential targets from the neuroscientific literature, full-text articles and abstracts, so that they can be used for anatomical connection studies and more specifically for tractography. We applied text-mining models to three structures: two well-studied structures, since validated deep brain stimulation targets, the internal globus pallidus and the subthalamic nucleus and, the nucleus accumbens, an exploratory target for treating psychiatric disorders. We performed a systematic review of the literature to document the projections of the three selected structures and compared it with the targets proposed by text-mining models, both in rat and primate (including human). We ran probabilistic tractography on the nucleus accumbens and compared the output with the results of the text-mining models and literature review. Overall, text-mining the literature could find three times as many targets as two man-weeks of curation could. The overall efficiency of the text-mining against literature review in our study was 98% recall (at 36% precision), meaning that over all the targets for the three selected seeds, only one target has been missed by text-mining. We demonstrate that connectivity for a structure of interest can be extracted from a very large amount of publications and abstracts. We believe this tool will be useful in helping the neuroscience community to facilitate connectivity studies of particular brain regions. The text mining tools used for the study are part of the HBP Neuroinformatics Platform, publicly available at http://connectivity-brainer.rhcloud.com/.

Unraveling the multiscale structural organization and connectivity of the human brain: the role of diffusion MRI

The structural architecture and the anatomical connectivity of the human brain show different organizational principles at distinct spatial scales. Histological staining and light microscopy techniques have been widely used in classical neuroanatomical studies to unravel brain organization. Using such techniques is a laborious task performed on 2-dimensional histological sections by skilled anatomists possibly aided by semi-automated algorithms. With the recent advent of modern magnetic resonance imaging (MRI) contrast mechanisms, cortical layers and columns can now be reliably identified and their structural properties quantified post-mortem. These developments are allowing the investigation of neuroanatomical features of the brain at a spatial resolution that could be interfaced with that of histology. Diffusion MRI and tractography techniques, in particular, have been used to probe the architecture of both white and gray matter in three dimensions. Combined with mathematical network analysis, these techniques are increasingly influential in the investigation of the macro-, meso-, and microscopic organization of brain connectivity and anatomy, both in vivo and ex vivo. Diffusion MRI-based techniques in combination with histology approaches can therefore support the endeavor of creating multimodal atlases that take into account the different spatial scales or levels on which the brain is organized. The aim of this review is to illustrate and discuss the structural architecture and the anatomical connectivity of the human brain at different spatial scales and how recently developed diffusion MRI techniques can help investigate these.

A Diffusion MRI Tractography Connectome of the Mouse Brain and Comparison with Neuronal Tracer Data

Interest in structural brain connectivity has grown with the understanding that abnormal neural connections may play a role in neurologic and psychiatric diseases. Small animal connectivity mapping techniques are particularly important for identifying aberrant connectivity in disease models. Diffusion magnetic resonance imaging tractography can provide nondestructive, 3D, brain-wide connectivity maps, but has historically been limited by low spatial resolution, low signal-to-noise ratio, and the difficulty in estimating multiple fiber orientations within a single image voxel. Small animal diffusion tractography can be substantially improved through the combination of ex vivo MRI with exogenous contrast agents, advanced diffusion acquisition and reconstruction techniques, and probabilistic fiber tracking. Here, we present a comprehensive, probabilistic tractography connectome of the mouse brain at microscopic resolution, and a comparison of these data with a neuronal tracer-based connectivity data from the Allen Brain Atlas. This work serves as a reference database for future tractography studies in the mouse brain, and demonstrates the fundamental differences between tractography and neuronal tracer data.

Postmortem diffusion MRI of the human brainstem and thalamus for deep brain stimulator electrode localization

Deep brain stimulation (DBS) is an established surgical therapy for medically refractory tremor disorders including essential tremor (ET) and is currently under investigation for use in a variety of other neurologic and psychiatric disorders. There is growing evidence that the anti-tremor effects of DBS for ET are directly related to modulation of the dentatorubrothalamic tract (DRT), a white matter pathway that connects the cerebellum, red nucleus, and ventral intermediate nucleus of the thalamus. Emerging white matter targets for DBS, like the DRT, will require improved three-dimensional (3D) reference maps of deep brain anatomy and structural connectivity for accurate electrode targeting. High-resolution diffusion MRI of postmortem brain specimens can provide detailed volumetric images of important deep brain nuclei and 3D reconstructions of white matter pathways with probabilistic tractography techniques. We present a high spatial and angular resolution diffusion MRI template of the postmortem human brainstem and thalamus with 3D reconstructions of the nuclei and white matter tracts involved in ET circuitry. We demonstrate registration of these data to in vivo, clinical images from patients receiving DBS therapy, and correlate electrode proximity to tractography of the DRT with improvement of ET symptoms.

A diffusion tensor MRI atlas of the postmortem rhesus macaque brain

The rhesus macaque (Macaca mulatta) is the most widely used nonhuman primate for modeling the structure and function of the brain. Brain atlases, and particularly those based on magnetic resonance imaging (MRI), have become important tools for understanding normal brain structure, and for identifying structural abnormalities resulting from disease states, exposures, and/or aging. Diffusion tensor imaging (DTI)-based MRI brain atlases are widely used in both human and macaque brain imaging studies because of the unique contrasts, quantitative diffusion metrics, and diffusion tractography that they can provide. Previous MRI and DTI atlases of the rhesus brain have been limited by low contrast and/or low spatial resolution imaging. Here we present a microscopic resolution MRI/DTI atlas of the rhesus brain based on 10 postmortem brain specimens. The atlas includes both structural MRI and DTI image data, a detailed three-dimensional segmentation of 241 anatomic structures, diffusion tractography, cortical thickness estimates, and maps of anatomic variability among atlas specimens. This atlas incorporates many useful features from previous work, including anatomic label nomenclature and ontology, data orientation, and stereotaxic reference frame, and further extends prior analyses with the inclusion of high-resolution multi-contrast image data.

Transport for language south of the Sylvian fissure: The routes and history of the main tracts and stations in the ventral language network

It is now ten years since a 'ventral language pathway' was demonstrated in vivo in the human brain. In the intervening decade, this result has been replicated and expanded to include multiple possible pathways and functions. Despite this considerable level of research interest, age-old debates regarding the origin, course, termination and, indeed, the very existence of the tracts identified still remain. The current review examines four major tracts associated with the ventral 'semantic' language network, with the aim of elucidating and clarifying their structural and functional roles. Historical and modern conceptualisations of the tracts' neuroanatomical origins and terminations will be discussed, and key discrepancies and debates examined. It is argued that much of the controversy regarding the language pathways has resulted from inconsistencies in terminology, and the lack of a white matter 'lingua franca'.

Superficial white matter fiber systems impede detection of long-range cortical connections in diffusion MR tractography

In vivo tractography based on diffusion magnetic resonance imaging (dMRI) has opened new doors to study structure-function relationships in the human brain. Initially developed to map the trajectory of major white matter tracts, dMRI is used increasingly to infer long-range anatomical connections of the cortex. Because axonal projections originate and terminate in the gray matter but travel mainly through the deep white matter, the success of tractography hinges on the capacity to follow fibers across this transition. Here we demonstrate that the complex arrangement of white matter fibers residing just under the cortical sheet poses severe challenges for long-range tractography over roughly half of the brain. We investigate this issue by comparing dMRI from very-high-resolution ex vivo macaque brain specimens with histological analysis of the same tissue. Using probabilistic tracking from pure gray and white matter seeds, we found that ∼50% of the cortical surface was effectively inaccessible for long-range diffusion tracking because of dense white matter zones just beneath the infragranular layers of the cortex. Analysis of the corresponding myelin-stained sections revealed that these zones colocalized with dense and uniform sheets of axons running mostly parallel to the cortical surface, most often in sulcal regions but also in many gyral crowns. Tracer injection into the sulcal cortex demonstrated that at least some axonal fibers pass directly through these fiber systems. Current and future high-resolution dMRI studies of the human brain will need to develop methods to overcome the challenges posed by superficial white matter systems to determine long-range anatomical connections accurately.

Abnormal hippocampal-thalamic white matter tract development and positive symptom course in individuals at ultra-high risk for psychosis

Background/Objectives:

Abnormal development of the hippocampus has been reported in adolescents at ultra-high risk (UHR) for psychosis and thalamic abnormalities have been found. However, the white matter connections between the hippocampus and the thalamus have not been studied. The connections between these regions are of key importance to our understanding of the pathophysiology of psychosis.

Methods:

Twenty-six UHR and 21 healthy age-matched controls were tested at a baseline assessment and 12 months later. Symptoms were assessed at both the time points and all the participants underwent diffusion tensor imaging scans. We used tractography to trace the white matter connections in each individual between the thalamus and hippocampus and then extracted fractional anisotropy (FA) to assess white matter structural integrity.

Results:

There was a significant group by time interaction indicating that FA decreased in UHR, and increased in controls over 12 months. Across both groups, baseline FA of the thalamic–hippocampal tract was predictive of positive symptoms at 12-month follow-up. Critically, this pattern remained significant in UHR individual group alone. At baseline, those with higher FA, indicative of abnormal white matter development, show higher positive symptoms 1 year later.

Conclusions:

Here, we provide evidence to indicate that there are differences in white matter development in hippocampal–thalamic connections, both of which are important nodes in networks associated with schizophrenia. Furthermore, abnormal developmental patterns in UHR individuals are associated with positive symptom course.

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.

Converging structural and functional connectivity of orbitofrontal, dorsolateral prefrontal, and posterior parietal cortex in the human striatum

Modification of spatial attention via reinforcement learning (Lee and Shomstein, 2013) requires the integration of reward, attention, and executive processes. Corticostriatal pathways are an ideal neural substrate for this integration because these projections exhibit a globally parallel (Alexander et al., 1986), but locally overlapping (Haber, 2003), topographical organization. Here we explore whether there are unique striatal regions that exhibit convergent anatomical connections from orbitofrontal cortex, dorsolateral prefrontal cortex, and posterior parietal cortex. Deterministic fiber tractography on diffusion spectrum imaging data from neurologically healthy adults (N = 60) was used to map frontostriatal and parietostriatal projections. In general, projections from cortex were organized according to both a medial-lateral and a rostral-caudal gradient along the striatal nuclei. Within rostral aspects of the striatum, we identified two bilateral convergence zones (one in the caudate nucleus and another in the putamen) that consisted of voxels with unique projections from orbitofrontal cortex, dorsolateral prefrontal cortex, and parietal regions. The distributed cortical connectivity of these striatal convergence zones was confirmed with follow-up functional connectivity analysis from resting state fMRI data, in which a high percentage of structurally connected voxels also showed significant functional connectivity. The specificity of this convergent architecture to these regions of the rostral striatum was validated against control analysis of connectivity within the motor putamen. These results delineate a neurologically plausible network of converging corticostriatal projections that may support the integration of reward, executive control, and spatial attention that occurs during spatial reinforcement learning.