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

Blurred streamlines: A novel representation to reduce redundancy in tractography

Tractography is a powerful tool to study brain connectivity in vivo, but it is well known to suffer from an intrinsic trade-off between sensitivity and specificity. A critical - but usually underrated - parameter to choose that can heavily impact the quality of the estimates is the number of streamlines to be reconstructed for a given data set. In fact, sensitivity can be improved by generating more and more streamlines, as all real anatomical connections are likely reconstructed, but lots of false positives are inevitably introduced, too. Consequently, so-called tractography filtering techniques have become increasingly popular to get rid of these false positives and improve specificity. However, increasing number of streamlines introduces redundancy in tractography reconstructions, which may negatively impact the performance of filtering algorithms, especially those based on linear formulations. To address this problem, we introduce a novel streamlines representation, called "blurred streamlines", which drastically reduces the redundancy among streamlines by (i) clustering similar trajectories and (ii) spatially blurring the corresponding signal contributions. We tested the effectiveness of the blurred streamlines both on synthetic and in vivo data. Our results clearly show that this new representation is as accurate as state-of-the-art methods despite using only 5% of the input streamlines, thus significantly decreasing the computational complexity of filtering algorithms as well as storage requirements of the resulting reconstructions.

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

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

A systematic review of the relationship between magnetic resonance imaging based resting-state and structural networks in the rodent brain

Recent developments in rodent brain imaging have enabled translational characterization of functional and structural connectivity at the whole brain level in vivo. Nevertheless, fundamental questions about the link between structural and functional networks remain unsolved. In this review, we systematically searched for experimental studies in rodents investigating both structural and functional network measures, including studies correlating functional connectivity using resting-state functional MRI with diffusion tensor imaging or viral tracing data. We aimed to answer whether functional networks reflect the architecture of the structural connectome, how this reciprocal relationship changes throughout a disease, how structural and functional changes relate to each other, and whether changes follow the same timeline. We present the knowledge derived exclusively from studies that included in vivo imaging of functional and structural networks. The limited number of available reports makes it difficult to draw general conclusions besides finding a spatial and temporal decoupling between structural and functional networks during brain disease. Data suggest that when overcoming the currently limited evidence through future studies with combined imaging in various disease models, it will be possible to explore the interaction between both network systems as a disease or recovery biomarker.

Connectome-based prediction of functional impairment in experimental stroke models

Experimental rat models of stroke and hemorrhage are important tools to investigate cerebrovascular disease pathophysiology mechanisms, yet how significant patterns of functional impairment induced in various models of stroke are related to changes in connectivity at the level of neuronal populations and mesoscopic parcellations of rat brains remain unresolved. To address this gap in knowledge, we employed two middle cerebral artery occlusion models and one intracerebral hemorrhage model with variant extent and location of neuronal dysfunction. Motor and spatial memory function was assessed and the level of hippocampal activation via Fos immunohistochemistry. Contribution of connectivity change to functional impairment was analyzed for connection similarities, graph distances and spatial distances as well as the importance of regions in terms of network architecture based on the neuroVIISAS rat connectome. We found that functional impairment correlated with not only the extent but also the locations of the injury among the models. In addition, via coactivation analysis in dynamic rat brain models, we found that lesioned regions led to stronger coactivations with motor function and spatial learning regions than with other unaffected regions of the connectome. Dynamic modeling with the weighted bilateral connectome detected changes in signal propagation in the remote hippocampus in all 3 stroke types, predicting the extent of hippocampal hypoactivation and impairment in spatial learning and memory function. Our study provides a comprehensive analytical framework in predictive identification of remote regions not directly altered by stroke events and their functional implication.

Simulated Attack Reveals How Lesions Affect Network Properties in Poststroke Aphasia

Aphasia is a prevalent cognitive syndrome caused by stroke. The rarity of premorbid imaging and heterogeneity of lesion obscures the links between the local effects of the lesion, global anatomic network organization, and aphasia symptoms. We applied a simulated attack approach in humans to examine the effects of 39 stroke lesions (16 females) on anatomic network topology by simulating their effects in a control sample of 36 healthy (15 females) brain networks. We focused on measures of global network organization thought to support overall brain function and resilience in the whole brain and within the left hemisphere. After removing lesion volume from the network topology measures and behavioral scores [the Western Aphasia Battery Aphasia Quotient (WAB-AQ), four behavioral factor scores obtained from a neuropsychological battery, and a factor sum], we compared the behavioral variance accounted for by simulated poststroke connectomes to that observed in the randomly permuted data. Global measures of anatomic network topology in the whole brain and left hemisphere accounted for 10% variance or more of the WAB-AQ and the lexical factor score beyond lesion volume and null permutations. Streamline networks provided more reliable point estimates than FA networks. Edge weights and network efficiency were weighted most highly in predicting the WAB-AQ for FA networks. Overall, our results suggest that global network measures provide modest statistical value beyond lesion volume when predicting overall aphasia severity, but less value in predicting specific behaviors. Variability in estimates could be induced by premorbid ability, deafferentation and diaschisis, and neuroplasticity following stroke.SIGNIFICANCE STATEMENT Poststroke, the remaining neuroanatomy maintains cognition and supports recovery. However, studies often use small, cross-sectional samples that cannot fully model the interactions between lesions and other variables that affect networks in stroke. Alternate methods are required to account for these effects. "Simulated attack" models are computational approaches that apply virtual damage to the brain and measure their putative consequences. Using a simulated attack model, we estimated how simulated damage to anatomic networks could account for language performance. Overall, our results reveal that global network measures can provide modest statistical value predicting overall aphasia severity, but less value in predicting specific behaviors. These findings suggest that more theoretically precise network models could be necessary to robustly predict individual outcomes in aphasia.

The brainstem connectome database

Connectivity data of the nervous system and subdivisions, such as the brainstem, cerebral cortex and subcortical nuclei, are necessary to understand connectional structures, predict effects of connectional disorders and simulate network dynamics. For that purpose, a database was built and analyzed which comprises all known directed and weighted connections within the rat brainstem. A longterm metastudy of original research publications describing tract tracing results form the foundation of the brainstem connectome (BC) database which can be analyzed directly in the framework neuroVIISAS. The BC database can be accessed directly by connectivity tables, a web-based tool and the framework. Analysis of global and local network properties, a motif analysis, and a community analysis of the brainstem connectome provides insight into its network organization. For example, we found that BC is a scale-free network with a small-world connectivity. The Louvain modularity and weighted stochastic block matching resulted in partially matching of functions and connectivity. BC modeling was performed to demonstrate signal propagation through the somatosensory pathway which is affected in Multiple sclerosis.

Measurement(s)	brainstem
Technology Type(s)	tract tracing metastudy
Factor Type(s)	brain region
Sample Characteristic - Organism	Rattus rattus
Sample Characteristic - Environment	Experimental setup
Sample Characteristic - Location	Germany

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

Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.

What's New and What's Next in Diffusion MRI Preprocessing

Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B₁ bias fields, and spatial normalization. The focus will be on “what’s new” since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on “Mapping the Connectome” in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on “what’s next” in dMRI preprocessing.

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.

Mapping covariance in brain FDG uptake to structural connectivity

PURPOSE

Inter-subject covariance of regional 18F-fluorodeoxyglucose (FDG) PET measures (FDG_cov) as proxy of brain connectivity has been gaining an increasing acceptance in the community. Yet, it is still unclear to what extent FDG_cov is underlied by actual structural connectivity via white matter fiber tracts. In this study, we quantified the degree of spatial overlap between FDG_cov and structural connectivity networks.

METHODS

We retrospectively analyzed neuroimaging data from 303 subjects, both patients with suspected neurodegenerative disorders and healthy individuals. For each subject, structural magnetic resonance, diffusion tensor imaging, and FDG-PET data were available. The images were spatially normalized to a standard space and segmented into 62 anatomical regions using a probabilistic atlas. Sparse inverse covariance estimation was employed to estimate FDG_cov. Structural connectivity was measured by streamline tractography through fiber assignment by continuous tracking.

RESULTS

For the whole brain, 55% of detected connections were found to be convergent, i.e., present in both FDG_cov and structural networks. This metric for random networks was significantly lower, i.e., 12%. Convergent were 80% of intralobe connections and only 30% of interhemispheric interlobe connections.

CONCLUSION

Structural connectivity via white matter fiber tracts is a relevant substrate of FDG_cov, underlying around a half of connections at the whole brain level. Short-range white matter tracts appear to be a major substrate of intralobe FDG_cov connections.

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

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

Extensive Cortical Connectivity of the Human Hippocampal Memory System: Beyond the "What" and "Where" Dual Stream Model

The human hippocampus is involved in forming new memories: damage impairs memory. The dual stream model suggests that object "what" representations from ventral stream temporal cortex project to the hippocampus via the perirhinal and then lateral entorhinal cortex, and spatial "where" representations from the dorsal parietal stream via the parahippocampal gyrus and then medial entorhinal cortex. The hippocampus can then associate these inputs to form episodic memories of what happened where. Diffusion tractography was used to reveal the direct connections of hippocampal system areas in humans. This provides evidence that the human hippocampus has extensive direct cortical connections, with connections that bypass the entorhinal cortex to connect with the perirhinal and parahippocampal cortex, with the temporal pole, with the posterior and retrosplenial cingulate cortex, and even with early sensory cortical areas. The connections are less hierarchical and segregated than in the dual stream model. This provides a foundation for a conceptualization for how the hippocampal memory system connects with the cerebral cortex and operates in humans. One implication is that prehippocampal cortical areas such as the parahippocampal TF and TH subregions and perirhinal cortices may implement specialized computations that can benefit from inputs from the dorsal and ventral streams.

Graph theoretical structural connectome analysis of the brain in patients with chronic spinal cord injury: preliminary investigation

STUDY DESIGN

Retrospective study.

OBJECTIVES

We aimed to characterize the convergent disruptions of the structural connectivity based on network modeling technique (i.e., graph theory) to identify significant changes in network organization/reorganization between uninjured and chronic spinal cord injury (SCI) participants.

SETTING

USA.

METHODS

Ten adult participants including 4 with chronic SCI and 6 uninjured were scanned using a multi-shell diffusion imaging on a 3.0 T MR scanner. Whole brain structural connectivity matrix was estimated by performing the quantification of the number of white matter fibers (called edges) connecting each possible pair of brain region (called nodes). Brain regions were defined according to Desikan-Killiany cortical atlas. Using connectivity matrix, connectivity strength as well as six different graph theoretical measurements were computed for each participant. They include: (1) global efficiency; (2) local efficiency; (3) degree; (4) betweenness centrality; (5) average shortest length and (6) clustering coefficient. Finally network based statistics was applied to extract nodes/connections with significant differences between groups (uninjured vs SCI).

RESULTS

The SCI group showed significant decreases in betweenness centrality in the left precentral gyrus (T-score=2.98, p value=0.02), and the right caudal middle frontal gyrus (score = 2.35, p value=0.047). It also showed significant decrease in left transverse temporal gyrus (T-score=2.36, p value=0.046) in clustering coefficient. In addition, altered regions in the occipital and parietal lobe were also identified.

CONCLUSION

These results suggest that not only local but also global alterations of the white matter occur after SCI. The proposed modeling technique has the potential to serve as a screening tool to identify any areas of the brain affected after SCI.

Structural connectivity changes in the cerebral pain matrix in burning mouth syndrome: a multi-shell, multi-tissue-constrained spherical deconvolution model analysis

PURPOSE

Burning mouth syndrome (BMS) is a chronic intraoral pain syndrome. Previous studies have attempted to determine the brain connectivity features in BMS using functional and structural magnetic resonance imaging. However, no study has investigated the structural connectivity using multi-shell, multi-tissue-constrained spherical deconvolution (MSMT-CSD), anatomically constrained tractography (ACT), and spherical deconvolution informed filtering of tractograms (SIFT). Therefore, this study aimed to assess the differences in brain structural connectivity of patients with BMS and healthy controls using probabilistic tractography with these methods, and graph analysis.

METHODS

Fourteen patients with BMS and 11 age- and sex-matched healthy volunteers underwent 3-T magnetic resonance imaging. MSMT-CSD-based probabilistic structural connectivity was computed using the second-order integration over fiber orientation distributions algorithm based on nodes set in 84 anatomical cortical regions with ACT and SIFT. A t-test was performed for comparisons between the BMS and healthy control brain networks.

RESULTS

The betweenness centrality was significantly higher in the left insula, right amygdala, and right lateral orbitofrontal cortex and significantly lower in the right inferotemporal cortex in the BMS group than that in healthy controls. However, no significant difference was found in the clustering coefficient, node degree, and small-worldness between the two groups.

CONCLUSION

Graph analysis of brain probabilistic structural connectivity, based on diffusion imaging using an MSMT-CSD model with ACT and SIFT, revealed alterations in the regions comprising the pain matrix and medial pain ascending pathway. These results highlight the emotional-affective profile of BMS, which is a type of chronic pain syndrome.

The Topography of the Frontal Terminations of the Uncinate Fasciculus Revisited Through Focused Fiber Dissections: Shedding Light on a Current Controversy and Introducing the Insular Apex as a Key Anatomoclinical Area

BACKGROUND

Recent studies advocate a connectivity pattern wider than previously believed of the uncinate fasciculus that extends to the ventrolateral and dorsolateral prefrontal cortices. These new percepts on the connectivity of the tract suggest a more expansive role for the uncinate fasciculus. Our aim was to shed light on this controversy through fiber dissections.

METHODS

Twenty normal adult human formalin-fixed cerebral hemispheres were used. Focused dissections on the insular, orbitofrontal, ventromedial, ventrolateral, and dorsolateral prefrontal areas were performed to record the topography of the frontal terminations of the uncinate fasciculus.

RESULTS

Three discrete fiber layers were consistently disclosed: the first layer was recorded to terminate at the posterior orbital gyrus and pars orbitalis, the second layer at the posterior two thirds of the gyrus rectus, and the last layer at the posterior one third of the paraolfactory gyrus. The insular apex was documented as a crucial landmark regarding the topographic differentiation of the uncinate and occipitofrontal fasciculi (i.e., fibers that travel ventrally belong to the uncinate fasciculus whereas those traveling dorsally are occipitofrontal fibers).

CONCLUSIONS

The frontal terminations of the uncinate fasciculus were consistently documented to project to the posterior orbitofrontal area. The area of the insular apex is introduced for the first time as a crucial surface landmark to effectively distinguish the stems of the uncinate and occipitofrontal fasciculi. This finding could refine the spatial resolution of awake subcortical mapping, especially for insular lesions, and improve the accuracy of in vivo diffusion tensor imaging protocols.

Interactions Between Aging and Alzheimer's Disease on Structural Brain Networks

Normative aging and Alzheimer's disease (AD) propagation alter anatomical connections among brain parcels. However, the interaction between the trajectories of age- and AD-linked alterations in the topology of the structural brain network is not well understood. In this study, diffusion-weighted magnetic resonance imaging (MRI) datasets of 139 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were used to document their structural brain networks. The 139 participants consist of 45 normal controls (NCs), 37 with early mild cognitive impairment (EMCI), 27 with late mild cognitive impairment (LMCI), and 30 AD patients. All subjects were further divided into three subgroups based on their age (56-65, 66-75, and 71-85 years). After the structural connectivity networks were built using anatomically-constrained deterministic tractography, their global and nodal topological properties were estimated, including network efficiency, characteristic path length, transitivity, modularity coefficient, clustering coefficient, and betweenness. Statistical analyses were then performed on these metrics using linear regression, and one- and two-way ANOVA testing to examine group differences and interactions between aging and AD propagation. No significant interactions were found between aging and AD propagation in the global topological metrics (network efficiency, characteristic path length, transitivity, and modularity coefficient). However, nodal metrics (clustering coefficient and betweenness centrality) of some cortical parcels exhibited significant interactions between aging and AD propagation, with affected parcels including left superior temporal, right pars triangularis, and right precentral. The results collectively confirm the age-related deterioration of structural networks in MCI and AD patients, providing novel insight into the cross effects of aging and AD disorder on brain structural networks. Some early symptoms of AD may also be due to age-associated anatomic vulnerability interacting with early anatomic changes associated with AD.

The R1-weighted connectome: complementing brain networks with a myelin-sensitive measure

Myelin plays a crucial role in how well information travels between brain regions. Complementing the structural connectome, obtained with diffusion MRI tractography, with a myelin-sensitive measure could result in a more complete model of structural brain connectivity and give better insight into white-matter myeloarchitecture. In this work we weight the connectome by the longitudinal relaxation rate (R1), a measure sensitive to myelin, and then we assess its added value by comparing it with connectomes weighted by the number of streamlines (NOS). Our analysis reveals differences between the two connectomes both in the distribution of their weights and the modular organization. Additionally, the rank-based analysis shows that R1 can be used to separate transmodal regions (responsible for higher-order functions) from unimodal regions (responsible for low-order functions). Overall, the R1-weighted connectome provides a different perspective on structural connectivity taking into account white matter myeloarchitecture.

In the present work, we show that by using a myelin-sensitive measure we can complement the diffusion MRI-based connectivity and provide a different picture of the brain organization. We show that the R1-weighted average distribution does not follow the same trend as the number of streamlines strength distribution, and the two connectomes exhibit different modular organization. We also show that unimodal cortical regions tend to be connected by more streamlines, but the connections exhibit a lower R1-weighted average, while the transmodal regions have higher R1-weighted average but fewer streamlines. This could imply that the unimodal regions require more connections with lower myelination, whereas the transmodal regions rely on connections with higher myelination.

Preserved cholinergic forebrain integrity reduces structural connectome vulnerability in mild cognitive impairment

Neurodegeneration leads to redistribution of processing, which is reflected in a reorganisation of the structural connectome. This might affect its vulnerability to structural damage. Cortical acetylcholine allows favourable adaptation to pathology within the memory circuit. However, it remains unclear if it acts on a broader scale, affecting reconfiguration of whole-brain networks. To investigate the role of the cholinergic basal forebrain (CBFB) in strategic lesions, twenty patients with mild cognitive impairment (MCI) and twenty elderly controls underwent magnetic resonance imaging. Whole-brain tractograms were represented as network graphs. Lesions of individual nodes were simulated by removing a node and its connections from the graph. The impact of simulated lesions was quantified as the proportional change in global efficiency. Relationships between subregional CBFB volumes, global efficiency of intact connectomes and impacts of individual simulated lesions of network nodes were assessed. In MCI but not controls, larger CBFB volumes were associated with efficient network topology and reduced impact of hippocampal, thalamic and entorhinal lesions, indicating a protective effect against the global impact of simulated strategic lesions. This suggests that the cholinergic system shapes the configuration of the connectome, thereby reducing the impact of localised damage in MCI.

Microsurgical anatomy of the amygdaloid body and its connections

The amygdaloid body is a limbic nuclear complex characterized by connections with the thalamus, the brainstem and the neocortex. The recent advances in functional neurosurgery regarding the treatment of refractory epilepsy and several neuropsychiatric disorders renewed the interest in the study of its functional Neuroanatomy. In this scenario, we felt that a morphological study focused on the amygdaloid body and its connections could improve the understanding of the possible  implications in functional neurosurgery. With this purpose we performed a morfological study using nine formalin-fixed human hemispheres dissected under microscopic magnification by using the fiber dissection technique originally described by Klingler. In our results the  amygdaloid body presents two divergent projection systems named dorsal and ventral amygdalofugal pathways connecting the nuclear complex with the septum and the hypothalamus. Furthermore, the amygdaloid body is connected with the hippocampus through the amygdalo-hippocampal bundle, with the anterolateral temporal cortex through the amygdalo-temporalis fascicle, the anterior commissure and the temporo-pulvinar bundle of Arnold, with the insular cortex through the lateral olfactory stria, with the ambiens gyrus, the para-hippocampal gyrus and the basal forebrain through the cingulum, and with the frontal cortex through the uncinate fascicle. Finally, the amygdaloid body is connected with the brainstem through the medial forebrain bundle. Our description of the topographic anatomy of the amygdaloid body and its connections, hopefully represents a useful tool for clinicians and scientists, both in the scope of application and speculation.

Secondary Degeneration of White Matter After Focal Sensorimotor Cortical Ischemic Stroke in Rats

Ischemic lesions could lead to secondary degeneration in remote regions of the brain. However, the spatial distribution of secondary degeneration along with its role in functional deficits is not well understood. In this study, we explored the spatial and connectivity properties of white matter (WM) secondary degeneration in a focal unilateral sensorimotor cortical ischemia rat model, using advanced microstructure imaging on a 14 T MRI system. Significant axonal degeneration was observed in the ipsilateral external capsule and even remote regions including the contralesional external capsule and corpus callosum. Further fiber tractography analysis revealed that only fibers having direct axonal connections with the primary lesion exhibited a significant degeneration. These results suggest that focal ischemic lesions may induce remote WM degeneration, but limited to fibers tied to the primary lesion. These “direct” fibers mainly represent perilesional, interhemispheric, and subcortical axonal connections. At last, we found that primary lesion volume might be the determining factor of motor function deficits.

Use of computational fluid dynamics for 3D fiber tract visualization on human high-thickness histological slices: histological mesh tractography

Understanding the intricate three-dimensional relationship between fiber bundles and subcortical nuclei is not a simple task. It is of paramount importance in neurosciences, especially in the field of functional neurosurgery. The current methods for in vivo and post mortem fiber tract visualization have shortcomings and contributions to the field are welcome. Several tracts were chosen to implement a new technique to help visualization of white matter tracts, using high-thickness histology and dark field images. Our study describes the use of computational fluid dynamic simulations for visualization of 3D fiber tracts segmented from dark field microscopy in high-thickness histological slices (histological mesh tractography). A post mortem human brain was MRI scanned prior to skull extraction, histologically processed and serially cut at 430 µm thickness as previously described by our group. High-resolution dark field images were used to segment the outlines of the structures. These outlines served as basis for the construction of a 3D structured mesh, were a Finite Volume Method (FVM) simulation of water flow was performed to generate streamlines representing the geometry. The simulations were accomplished by an open source computer fluid dynamics software. The resulting simulation rendered a realistic 3D impression of the segmented anterior commissure, the left anterior limb of the internal capsule, the left uncinate fascicle, and the dentato-rubral tracts. The results are in line with clinical findings, diffusion MR imaging and anatomical dissection methods.

Optimization and validation of diffusion MRI-based fiber tracking with neural tracer data as a reference

Diffusion-weighted magnetic resonance imaging (dMRI) allows non-invasive investigation of whole-brain connectivity, which can reveal the brain’s global network architecture and also abnormalities involved in neurological and mental disorders. However, the reliability of connection inferences from dMRI-based fiber tracking is still debated, due to low sensitivity, dominance of false positives, and inaccurate and incomplete reconstruction of long-range connections. Furthermore, parameters of tracking algorithms are typically tuned in a heuristic way, which leaves room for manipulation of an intended result. Here we propose a general data-driven framework to optimize and validate parameters of dMRI-based fiber tracking algorithms using neural tracer data as a reference. Japan’s Brain/MINDS Project provides invaluable datasets containing both dMRI and neural tracer data from the same primates. A fundamental difference when comparing dMRI-based tractography and neural tracer data is that the former cannot specify the direction of connectivity; therefore, evaluating the fitting of dMRI-based tractography becomes challenging. The framework implements multi-objective optimization based on the non-dominated sorting genetic algorithm II. Its performance is examined in two experiments using data from ten subjects for optimization and six for testing generalization. The first uses a seed-based tracking algorithm, iFOD2, and objectives for sensitivity and specificity of region-level connectivity. The second uses a global tracking algorithm and a more refined set of objectives: distance-weighted coverage, true/false positive ratio, projection coincidence, and commissural passage. In both experiments, with optimized parameters compared to default parameters, fiber tracking performance was significantly improved in coverage and fiber length. Improvements were more prominent using global tracking with refined objectives, achieving an average fiber length from 10 to 17 mm, voxel-wise coverage of axonal tracts from 0.9 to 15%, and the correlation of target areas from 40 to 68%, while minimizing false positives and impossible cross-hemisphere connections. Optimized parameters showed good generalization capability for test brain samples in both experiments, demonstrating the flexible applicability of our framework to different tracking algorithms and objectives. These results indicate the importance of data-driven adjustment of fiber tracking algorithms and support the validity of dMRI-based tractography, if appropriate adjustments are employed.

U-shape short-range extrinsic connectivity organisation around the human central sulcus

The central sulcus is probably one of the most studied folds in the human brain, owing to its clear relationship with primary sensory-motor functional areas. However, due to the difficulty of estimating the trajectories of the U-shape fibres from diffusion MRI, the short structural connectivity of this sulcus remains relatively unknown. In this context, we studied the spatial organization of these U-shape fibres along the central sulcus. Based on high quality diffusion MRI data of 100 right-handed subjects and state-of-the-art pre-processing pipeline, we first define a connectivity space that provides a comprehensive and continuous description of the short-range anatomical connectivity around the central sulcus at both the individual and group levels. We then infer the presence of five major U-shape fibre bundles at the group level in both hemispheres by applying unsupervised clustering in the connectivity space. We propose a quantitative investigation of their position and number of streamlines as a function of hemisphere, sex and functional scores such as handedness and manual dexterity. Main findings of this study are twofold: a description of U-shape short-range connectivity along the central sulcus at group level and the evidence of a significant relationship between the position of three hand related U-shape fibre bundles and the handedness score of subjects.

Diffusion Tensor Imaging-Based Studies at the Group-Level Applied to Animal Models of Neurodegenerative Diseases

The understanding of human and non-human microstructural brain alterations in the course of neurodegenerative diseases has substantially improved by the non-invasive magnetic resonance imaging (MRI) technique of diffusion tensor imaging (DTI). Animal models (including disease or knockout models) allow for a variety of experimental manipulations, which are not applicable to humans. Thus, the DTI approach provides a promising tool for cross-species cross-sectional and longitudinal investigations of the neurobiological targets and mechanisms of neurodegeneration. This overview with a systematic review focuses on the principles of DTI analysis as used in studies at the group level in living preclinical models of neurodegeneration. The translational aspect from in-vivo animal models toward (clinical) applications in humans is covered as well as the DTI-based research of the non-human brains' microstructure, the methodological aspects in data processing and analysis, and data interpretation at different abstraction levels. The aim of integrating DTI in multiparametric or multimodal imaging protocols will allow the interrogation of DTI data in terms of directional flow of information and may identify the microstructural underpinnings of neurodegeneration-related patterns.

The efficacy of different preprocessing steps in reducing motion-related confounds in diffusion MRI connectomics

Head motion is a major confounding factor in neuroimaging studies. While numerous studies have investigated how motion impacts estimates of functional connectivity, the effects of motion on structural connectivity measured using diffusion MRI have not received the same level of attention, despite the fact that, like functional MRI, diffusion MRI relies on elaborate preprocessing pipelines that require multiple choices at each step. Here, we report a comprehensive analysis of how these choices influence motion-related contamination of structural connectivity estimates. Using a healthy adult sample (N = 294), we evaluated 240 different preprocessing pipelines, devised using plausible combinations of different choices related to explicit head motion correction, tractography propagation algorithms, track seeding methods, track termination constraints, quantitative metrics derived for each connectome edge, and parcellations. We found that an approach to motion correction that includes outlier replacement and within-slice volume correction led to a dramatic reduction in cross-subject correlations between head motion and structural connectivity strength, and that motion contamination is more severe when quantifying connectivity strength using mean tract fractional anisotropy rather than streamline count. We also show that the choice of preprocessing strategy can significantly influence subsequent inferences about network organization, with the location of network hubs varying considerably depending on the specific preprocessing steps applied. Our findings indicate that the impact of motion on structural connectivity can be successfully mitigated using recent motion-correction algorithms that include outlier replacement and within-slice motion correction.

Indirect frontocingulate structural connectivity predicts clinical response to accelerated rTMS in major depressive disorder

BACKGROUND

Repetitive transcranial magnetic stimulation (rTMS) is an established treatment for major depressive disorder (MDD), but its clinical efficacy remains rather modest. One reason for this could be that the propagation of rTMS effects via structural connections from the stimulated area to deeper brain structures (such as the cingulate cortices) is suboptimal.

METHODS

We investigated whether structural connectivity — derived from diffusion MRI data — could serve as a biomarker to predict treatment response. We hypothesized that stronger structural connections between the patient-specific stimulation position in the left dorsolateral prefrontal cortex (dlPFC) and the cingulate cortices would predict better clinical outcomes. We applied accelerated intermittent theta burst stimulation (aiTBS) to the left dlPFC in 40 patients with MDD. We correlated baseline structural connectivity, quantified using various metrics (fractional anisotropy, mean diffusivity, tract density, tract volume and number of tracts), with changes in depression severity scores after aiTBS.

RESULTS

Exploratory results (p < 0.05) showed that structural connectivity between the patient-specific stimulation site and the caudal and posterior parts of the cingulate cortex had predictive potential for clinical response to aiTBS.

LIMITATIONS

We used the diffusion tensor to perform tractography. A main limitation was that multiple fibre directions within voxels could not be resolved, which might have led to missing connections in some patients.

CONCLUSION

Stronger structural frontocingular connections may be of essence to optimally benefit from left dlPFC rTMS treatment in MDD. Even though the results are promising, further investigation with larger numbers of patients, more advanced tractography algorithms and classic daily rTMS treatment paradigms is warranted.

CLINICAL TRIAL REGISTRATION

http://clinicaltrials.gov/show/NCT01832805

Sensory-motor network topology in multiple sclerosis: Structural connectivity analysis accounting for intrinsic density discrepancy

Graph theory and network modelling have been previously applied to characterize motor network structural topology in multiple sclerosis (MS). However, between‐group differences disclosed by graph analysis might be primarily driven by discrepancy in density, which is likely to be reduced in pathologic conditions as a consequence of macroscopic damage and fibre loss that may result in less streamlines properly traced. In this work, we employed the convex optimization modelling for microstructure informed tractography (COMMIT) framework, which, given a tractogram, estimates the actual contribution (or weight) of each streamline in order to optimally explain the diffusion magnetic resonance imaging signal, filtering out those that are implausible or not necessary. Then, we analysed the topology of this ‘COMMIT‐weighted sensory‐motor network’ in MS accounting for network density. By comparing with standard connectivity analysis, we also tested if abnormalities in network topology are still identifiable when focusing on more ‘quantitative’ network properties. We found that topology differences identified with standard tractography in MS seem to be mainly driven by density, which, in turn, is strongly influenced by the presence of lesions. We were able to identify a significant difference in density but also in network global and local properties when accounting for density discrepancy. Therefore, we believe that COMMIT may help characterize the structural organization in pathological conditions, allowing a fair comparison of connectomes which considers discrepancies in network density. Moreover, discrepancy‐corrected network properties are clinically meaningful and may help guide prognosis assessment and treatment choice.

MonkeyCBP: A Toolbox for Connectivity-Based Parcellation of Monkey Brain

Non-human primate models are widely used in studying the brain mechanism underlying brain development, cognitive functions, and psychiatric disorders. Neuroimaging techniques, such as magnetic resonance imaging, play an important role in the examinations of brain structure and functions. As an indispensable tool for brain imaging data analysis, brain atlases have been extensively investigated, and a variety of versions constructed. These atlases diverge in the criteria based on which they are plotted. The criteria range from cytoarchitectonic features, neurotransmitter receptor distributions, myelination fingerprints, and transcriptomic patterns to structural and functional connectomic profiles. Among them, brainnetome atlas is tightly related to brain connectome information and built by parcellating the brain on the basis of the anatomical connectivity profiles derived from structural neuroimaging data. The pipeline for building the brainnetome atlas has been published as a toolbox named ATPP (A Pipeline for Automatic Tractography-Based Brain Parcellation). In this paper, we present a variation of ATPP, which is dedicated to monkey brain parcellation, to address the significant differences in the process between the two species. The new toolbox, MonkeyCBP, has major alterations in three aspects: brain extraction, image registration, and validity indices. By parcellating two different brain regions (posterior cingulate cortex) and (frontal pole) of the rhesus monkey, we demonstrate the efficacy of these alterations. The toolbox has been made public (https://github.com/bheAI/MonkeyCBP_CLI, https://github.com/bheAI/MonkeyCBP_GUI). It is expected that the toolbox can benefit the non-human primate neuroimaging community with high-throughput computation and low labor involvement.

Dissecting the default mode network: direct structural evidence on the morphology and axonal connectivity of the fifth component of the cingulum bundle

OBJECTIVE

Although a growing body of data support the functional connectivity between the precuneus and the medial temporal lobe during states of resting consciousness as well as during a diverse array of higher-order functions, direct structural evidence on this subcortical circuitry is scarce. Here, the authors investigate the very existence, anatomical consistency, morphology, and spatial relationships of the cingulum bundle V (CB-V), a fiber tract that has been reported to reside close to the inferior arm of the cingulum (CingI).

METHODS

Fifteen normal, formalin-fixed cerebral hemispheres from adults were treated with Klingler's method and subsequently investigated through the fiber microdissection technique in a medial to lateral direction.

RESULTS

A distinct group of fibers is invariably identified in the subcortical territory of the posteromedial cortex, connecting the precuneus and the medial temporal lobe. This tract follows the trajectory of the parietooccipital sulcus in a close spatial relationship with the CingI and the sledge runner fasciculus. It extends inferiorly to the parahippocampal place area and retrosplenial complex area, followed by a lateral curve to terminate toward the fusiform face area (Brodmann area [BA] 37) and lateral piriform area (BA35). Taking into account the aforementioned subcortical architecture, the CB-V allegedly participates as a major subcortical stream within the default mode network, possibly subserving the transfer of multimodal cues relevant to visuospatial, facial, and mnemonic information to the precuneal hub. Although robust clinical evidence on the functional role of this stream is lacking, the modern neurosurgeon should be aware of this tract when manipulating cerebral areas en route to lesions residing in or around the ventricular trigone.

CONCLUSIONS

Through the fiber microdissection technique, the authors were able to provide original, direct structural evidence on the existence, morphology, axonal connectivity, and correlative anatomy of what proved to be a discrete white matter pathway, previously described as the CB-V, connecting the precuneus and medial temporal lobe.

Primate homologs of mouse cortico-striatal circuits

With the increasing necessity of animal models in biomedical research, there is a vital need to harmonise findings across species by establishing similarities and differences in rodent and primate neuroanatomy. Using connectivity fingerprint matching, we compared cortico-striatal circuits across humans, non-human primates, and mice using resting-state fMRI data in all species. Our results suggest that the connectivity patterns for the nucleus accumbens and cortico-striatal motor circuits (posterior/lateral putamen) were conserved across species, making them reliable targets for cross-species comparisons. However, a large number of human and macaque striatal voxels were not matched to any mouse cortico-striatal circuit (mouse->human: 85% unassigned; mouse->macaque 69% unassigned; macaque->human; 31% unassigned). These unassigned voxels were localised to the caudate nucleus and anterior putamen, overlapping with executive function and social/language regions of the striatum and connected to prefrontal-projecting cerebellar lobules and anterior prefrontal cortex, forming circuits that seem to be unique for non-human primates and humans.

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.

Altered brain white matter connectome in children and adolescents with prenatal alcohol exposure

Diffuson tensor imaging (DTI) has demonstrated widespread alterations of brain white matter structure in children with prenatal alcohol exposure (PAE), yet it remains unclear how these alterations affect the structural brain network as a whole. The present study aimed to examine changes in the DTI-based structural connectome in children and adolescents with PAE compared to unexposed controls. Participants were 121 children and adolescents with PAE (51 females) and 119 typically-developing controls (49 females) aged 5-18 years with DTI data collected at one of four research centers across Canada. Graph-theory based analysis was performed on the connectivity matrix constructed from whole-brain white matter fibers via deterministic tractography. The PAE group had significantly decreased whole-brain global efficiency, degree centrality, and participation coefficients, as well as increased shortest path length and betweenness centrality compared to unexposed controls. Individuals with PAE had decreased connectivity between the attention, somatomotor, and default mode networks compared to controls. This study demonstrates decreased structural white matter connectivity in children and adolescents with PAE at a whole-brain level, suggesting widespread alterations in how networks are connected with each other. This decreased connectivity may underlie cognitive and behavioural difficulties in children with PAE.

Modulation of Nigrofugal and Pallidofugal Pathways in Deep Brain Stimulation for Parkinson Disease

BACKGROUND

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a well-established surgical therapy for patients with Parkinson disease (PD).

OBJECTIVE

To define the role of adjacent white matter stimulation in the effectiveness of STN-DBS.

METHODS

We retrospectively evaluated 43 patients with PD who received bilateral STN-DBS. The volumes of activated tissue were analyzed to obtain significant stimulation clusters predictive of 4 clinical outcomes: improvements in bradykinesia, rigidity, tremor, and reduction of dopaminergic medication. Tractography of the nigrofugal and pallidofugal pathways was performed. The significant clusters were used to calculate the involvement of the nigrofugal and pallidofugal pathways and the STN.

RESULTS

The clusters predictive of rigidity and tremor improvement were dorsal to the STN with most of the clusters outside of the STN. These clusters preferentially involved the pallidofugal pathways. The cluster predictive of bradykinesia improvement was located in the central part of the STN with an extension outside of the STN. The cluster predictive of dopaminergic medication reduction was located ventrolateral and caudal to the STN. These clusters preferentially involved the nigrofugal pathways.

CONCLUSION

Improvements in rigidity and tremor mainly involved the pallidofugal pathways dorsal to the STN. Improvement in bradykinesia mainly involved the central part of the STN and the nigrofugal pathways ventrolateral to the STN. Maximal reduction in dopaminergic medication following STN-DBS was associated with an exclusive involvement of the nigrofugal pathways.

Distinct structure-function relationships across cortical regions and connectivity scales in the rat brain

An improved understanding of the structure-function relationship in the brain is necessary to know to what degree structural connectivity underpins abnormal functional connectivity seen in disorders. We integrated high-field resting-state fMRI-based functional connectivity with high-resolution macro-scale diffusion-based and meso-scale neuronal tracer-based structural connectivity, to obtain an accurate depiction of the structure-function relationship in the rat brain. Our main goal was to identify to what extent structural and functional connectivity strengths are correlated, macro- and meso-scopically, across the cortex. Correlation analyses revealed a positive correspondence between functional and macro-scale diffusion-based structural connectivity, but no significant correlation between functional connectivity and meso-scale neuronal tracer-based structural connectivity. Zooming in on individual connections, we found strong functional connectivity in two well-known resting-state networks: the sensorimotor and default mode network. Strong functional connectivity within these networks coincided with strong short-range intrahemispheric structural connectivity, but with weak heterotopic interhemispheric and long-range intrahemispheric structural connectivity. Our study indicates the importance of combining measures of connectivity at distinct hierarchical levels to accurately determine connectivity across networks in the healthy and diseased brain. Although characteristics of the applied techniques may affect where structural and functional networks (dis)agree, distinct structure-function relationships across the brain could also have a biological basis.

Structural connectivity alterations in chronic and episodic migraine: A diffusion magnetic resonance imaging connectomics study

OBJECTIVE

To identify possible structural connectivity alterations in patients with episodic and chronic migraine using magnetic resonance imaging data.

METHODS

Fifty-four episodic migraine, 56 chronic migraine patients and 50 controls underwent T1-weighted and diffusion-weighted magnetic resonance imaging acquisitions. Number of streamlines (trajectories of estimated fiber-tracts), mean fractional anisotropy, axial diffusivity and radial diffusivity were the connectome measures. Correlation analysis between connectome measures and duration and frequency of migraine was performed.

RESULTS

Higher and lower number of streamlines were found in connections involving regions like the superior frontal gyrus when comparing episodic and chronic migraineurs with controls (p < .05 false discovery rate). Between the left caudal anterior cingulate and right superior frontal gyri, more streamlines were found in chronic compared to episodic migraine. Higher and lower fractional anisotropy, axial diffusivity, and radial diffusivity were found between migraine groups and controls in connections involving regions like the hippocampus. Lower radial diffusivity and axial diffusivity were found in chronic compared to episodic migraine in connections involving regions like the putamen. In chronic migraine, duration of migraine was positively correlated with fractional anisotropy and axial diffusivity.

CONCLUSIONS

Structural strengthening of connections involving subcortical regions associated with pain processing and weakening in connections involving cortical regions associated with hyperexcitability may coexist in migraine.

The road ahead in clinical network neuroscience

Clinical network neuroscience, the study of brain network topology in neurological and psychiatric diseases, has become a mainstay field within clinical neuroscience. Being a multidisciplinary group of clinical network neuroscience experts based in The Netherlands, we often discuss the current state of the art and possible avenues for future investigations. These discussions revolve around questions like "How do dynamic processes alter the underlying structural network?" and "Can we use network neuroscience for disease classification?" This opinion paper is an incomplete overview of these discussions and expands on ten questions that may potentially advance the field. By no means intended as a review of the current state of the field, it is instead meant as a conversation starter and source of inspiration to others.

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.

Test-retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering

There are two popular approaches for automated white matter parcellation using diffusion MRI tractography, including fiber clustering strategies that group white matter fibers according to their geometric trajectories and cortical-parcellation-based strategies that focus on the structural connectivity among different brain regions of interest. While multiple studies have assessed test-retest reproducibility of automated white matter parcellations using cortical-parcellation-based strategies, there are no existing studies of test-retest reproducibility of fiber clustering parcellation. In this work, we perform what we believe is the first study of fiber clustering white matter parcellation test-retest reproducibility. The assessment is performed on three test-retest diffusion MRI datasets including a total of 255 subjects across genders, a broad age range (5-82 years), health conditions (autism, Parkinson's disease and healthy subjects), and imaging acquisition protocols (three different sites). A comprehensive evaluation is conducted for a fiber clustering method that leverages an anatomically curated fiber clustering white matter atlas, with comparison to a popular cortical-parcellation-based method. The two methods are compared for the two main white matter parcellation applications of dividing the entire white matter into parcels (i.e., whole brain white matter parcellation) and identifying particular anatomical fiber tracts (i.e., anatomical fiber tract parcellation). Test-retest reproducibility is measured using both geometric and diffusion features, including volumetric overlap (wDice) and relative difference of fractional anisotropy. Our experimental results in general indicate that the fiber clustering method produced more reproducible white matter parcellations than the cortical-parcellation-based method.

Finding maximally disconnected subnetworks with shortest path tractography

Connectome-based lesion symptom mapping (CLSM) can be used to relate disruptions of brain network connectivity with clinical measures. We present a novel method that extends current CLSM approaches by introducing a fast reliable and accurate way for computing disconnectomes, i.e. identifying damaged or lesioned connections. We introduce a new algorithm that finds the maximally disconnected subgraph containing regions and region pairs with the greatest shared connectivity loss. After normalizing a stroke patient's segmented MRI lesion into template space, probability weighted structural connectivity matrices are constructed from shortest paths found in white matter voxel graphs of 210 subjects from the Human Connectome Project. Percent connectivity loss matrices are constructed by measuring the proportion of shortest-path probability weighted connections that are lost because of an intersection with the patient's lesion. Maximally disconnected subgraphs of the overall connectivity loss matrix are then derived using a computationally fast greedy algorithm that closely approximates the exact solution. We illustrate the approach in eleven stroke patients with hemiparesis by identifying expected disconnections of the corticospinal tract (CST) with cortical sensorimotor regions. Major disconnections are found in the thalamus, basal ganglia, and inferior parietal cortex. Moreover, the size of the maximally disconnected subgraph quantifies the extent of cortical disconnection and strongly correlates with multiple clinical measures. The methods provide a fast, reliable approach for both visualizing and quantifying the disconnected portion of a patient's structural connectome based on their routine clinical MRI, without reliance on concomitant diffusion weighted imaging. The method can be extended to large databases of stroke patients, multiple sclerosis or other diseases causing focal white matter injuries helping to better characterize clinically relevant white matter lesions and to identify biomarkers for the recovery potential of individual patients.

•

Significantly accelerated shortest path tractography approach for constructing connectomes and disconnectomes.

•

New algorithm extracts the subnetwork containing cortical connections and regions with maximal shared connectivity loss.

•

The size of the maximally disconnected subnetwork quantifies the extent of disconnection and correlates with stroke measures.

•

Fast and accurate approach for visualizing and analyzing the disconnected portion of a patient's structural connectome.

Quantification of structural brain connectivity via a conductance model

Connectomics has proved promising in quantifying and understanding the effects of development, aging and an array of diseases on the brain. In this work, we propose a new structural connectivity measure from diffusion MRI that allows us to incorporate direct brain connections, as well as indirect ones that would not be otherwise accounted for by standard techniques and that may be key for the better understanding of function from structure. From our experiments on the Human Connectome Project dataset, we find that our measure of structural connectivity better correlates with functional connectivity than streamline tractography does, meaning that it provides new structural information related to function. Through additional experiments on the ADNI-2 dataset, we demonstrate the ability of this new measure to better discriminate different stages of Alzheimer's disease. Our findings suggest that this measure is useful in the study of the normal brain structure, and for quantifying the effects of disease on the brain structure.

A systematic review on the quantitative relationship between structural and functional network connectivity strength in mammalian brains

The mammalian brain is composed of densely connected and interacting regions, which form structural and functional networks. An improved understanding of the structure-function relation is crucial to understand the structural underpinnings of brain function and brain plasticity after injury. It is currently unclear how functional connectivity strength relates to structural connectivity strength. We obtained an overview of recent papers that report on correspondences between quantitative functional and structural connectivity measures in the mammalian brain. We included network studies in which functional connectivity was measured with resting-state fMRI, and structural connectivity with either diffusion-weighted MRI or neuronal tract tracers. Twenty-seven of the 28 included studies showed a positive structure-function relationship. Large inter-study variations were found comparing functional connectivity strength with either quantitative diffusion-based (correlation coefficient (r) ranges: 0.18-0.82) or neuronal tracer-based structural connectivity measures (r = 0.24-0.74). Two functional datasets demonstrated lower structure-function correlations with neuronal tracer-based (r = 0.22 and r = 0.30) than with diffusion-based measures (r = 0.49 and r = 0.65). The robust positive quantitative structure-function relationship supports the hypothesis that structural connectivity provides the hardware from which functional connectivity emerges. However, methodological differences between the included studies complicate the comparison across studies, which emphasize the need for validation and standardization in brain structure-function studies.

The Nomenclature of Human White Matter Association Pathways: Proposal for a Systematic Taxonomic Anatomical Classification

The heterogeneity and complexity of white matter (WM) pathways of the human brain were discretely described by pioneers such as Willis, Stenon, Malpighi, Vieussens and Vicq d'Azyr up to the beginning of the 19th century. Subsequently, novel approaches to the gross dissection of brain internal structures have led to a new understanding of WM organization, notably due to the works of Reil, Gall and Burdach highlighting the fascicular organization of WM. Meynert then proposed a definitive tripartite organization in association, commissural and projection WM pathways. The enduring anatomical work of Dejerine at the turn of the 20th century describing WM pathways in detail has been the paramount authority on this topic (including its terminology) for over a century, enriched sporadically by studies based on blunt Klingler dissection. Currently, diffusion-weighted magnetic resonance imaging (DWI) is used to reveal the WM fiber tracts of the human brain in vivo by measuring the diffusion of water molecules, especially along axons. It is then possible by tractography to reconstitute the WM pathways of the human brain step by step at an unprecedented level of precision in large cohorts. However, tractography algorithms, although powerful, still face the complexity of the organization of WM pathways, and there is a crucial need to benefit from the exact definitions of the trajectories and endings of all WM fascicles. Beyond such definitions, the emergence of DWI-based tractography has mostly revealed strong heterogeneity in naming the different bundles, especially the long-range association pathways. This review addresses the various terminologies known for the WM association bundles, aiming to describe the rules of arrangements followed by these bundles and to propose a new nomenclature based on the structural wiring diagram of the human brain.