Building connectomes using diffusion MRI: why, how and but

Multi-scale image analysis and prediction of visual field defects after selective amygdalohippocampectomy

Selective amygdalohippocampectomy is an effective treatment for patients with therapy-refractory temporal lobe epilepsy but may cause visual field defect (VFD). Here, we aimed to describe tissue-specific pre- and postoperative imaging correlates of the VFD severity using whole-brain analyses from voxel- to network-level. Twenty-eight patients with temporal lobe epilepsy underwent pre- and postoperative MRI (T1-MPRAGE and Diffusion Tensor Imaging) as well as kinetic perimetry according to Goldmann standard. We probed for whole-brain gray matter (GM) and white matter (WM) correlates of VFD using voxel-based morphometry and tract-based spatial statistics, respectively. We furthermore reconstructed individual structural connectomes and conducted local and global network analyses. Two clusters in the bihemispheric middle temporal gyri indicated a postsurgical GM volume decrease with increasing VFD severity (FWE-corrected p < 0.05). A single WM cluster showed a fractional anisotropy decrease with increasing severity of VFD in the ipsilesional optic radiation (FWE-corrected p < 0.05). Furthermore, patients with (vs. without) VFD showed a higher number of postoperative local connectivity changes. Neither in the GM, WM, nor in network metrics we found preoperative correlates of VFD severity. Still, in an explorative analysis, an artificial neural network meta-classifier could predict the occurrence of VFD based on presurgical connectomes above chance level.

The sensitivity of diffusion MRI to microstructural properties and experimental factors

Diffusion MRI is a non-invasive technique to study brain microstructure. Differences in the microstructural properties of tissue, including size and anisotropy, can be represented in the signal if the appropriate method of acquisition is used. However, to depict the underlying properties, special care must be taken when designing the acquisition protocol as any changes in the procedure might impact on quantitative measurements. This work reviews state-of-the-art methods for studying brain microstructure using diffusion MRI and their sensitivity to microstructural differences and various experimental factors. Microstructural properties of the tissue at a micrometer scale can be linked to the diffusion signal at a millimeter-scale using modeling. In this paper, we first give an introduction to diffusion MRI and different encoding schemes. Then, signal representation-based methods and multi-compartment models are explained briefly. The sensitivity of the diffusion MRI signal to the microstructural components and the effects of curvedness of axonal trajectories on the diffusion signal are reviewed. Factors that impact on the quality (accuracy and precision) of derived metrics are then reviewed, including the impact of random noise, and variations in the acquisition parameters (i.e., number of sampled signals, b-value and number of acquisition shells). Finally, yet importantly, typical approaches to deal with experimental factors are depicted, including unbiased measures and harmonization. We conclude the review with some future directions and recommendations on this topic.

Modelling white matter in gyral blades as a continuous vector field

Many brain imaging studies aim to measure structural connectivity with diffusion tractography. However, biases in tractography data, particularly near the boundary between white matter and cortical grey matter can limit the accuracy of such studies. When seeding from the white matter, streamlines tend to travel parallel to the convoluted cortical surface, largely avoiding sulcal fundi and terminating preferentially on gyral crowns. When seeding from the cortical grey matter, streamlines generally run near the cortical surface until reaching deep white matter. These so-called “gyral biases” limit the accuracy and effective resolution of cortical structural connectivity profiles estimated by tractography algorithms, and they do not reflect the expected distributions of axonal densities seen in invasive tracer studies or stains of myelinated fibres. We propose an algorithm that concurrently models fibre density and orientation using a divergence-free vector field within gyral blades to encourage an anatomically-justified streamline density distribution along the cortical white/grey-matter boundary while maintaining alignment with the diffusion MRI estimated fibre orientations. Using in vivo data from the Human Connectome Project, we show that this algorithm reduces tractography biases. We compare the structural connectomes to functional connectomes from resting-state fMRI, showing that our model improves cross-modal agreement. Finally, we find that after parcellation the changes in the structural connectome are very minor with slightly improved interhemispheric connections (i.e, more homotopic connectivity) and slightly worse intrahemi-spheric connections when compared to tracers.

Robust intra-individual estimation of structural connectivity by Principal Component Analysis

Fiber tractography based on diffusion-weighted MRI provides a non-invasive characterization of the structural connectivity of the human brain at the macroscopic level. Quantification of structural connectivity strength is challenging and mainly reduced to "streamline counting" methods. These are however highly dependent on the topology of the connectome and the particular specifications for seeding and filtering, which limits their intra-subject reproducibility across repeated measurements and, in consequence, also confines their validity. Here we propose a novel method for increasing the intra-subject reproducibility of quantitative estimates of structural connectivity strength. To this end, the connectome is described by a large matrix in positional-orientational space and reduced by Principal Component Analysis to obtain the main connectivity "modes". It was found that the proposed method is quite robust to structural variability of the data.

Structure-function coupling in the human connectome: A machine learning approach

While the function of most biological systems is tightly constrained by their structure, current evidence suggests that coupling between the structure and function of brain networks is relatively modest. We aimed to investigate whether the modest coupling between connectome structure and function is a fundamental property of nervous systems or a limitation of current brain network models. We developed a new deep learning framework to predict an individual's brain function from their structural connectome, achieving prediction accuracies that substantially exceeded state-of-the-art biophysical models (group: R=0.9±0.1, individual: R=0.55±0.1). Crucially, brain function predicted from an individual's structural connectome explained significant inter-individual variation in cognitive performance. Our results suggest that structure-function coupling in human brain networks is substantially tighter than previously suggested. We establish the margin by which current brain network models can be improved and demonstrate how deep learning can facilitate investigation of relations between brain function and behavior.

Tractography-Guided Deep Brain Stimulation of the Anteromedial Globus Pallidus Internus for Refractory Obsessive-Compulsive Disorder: Case Report

BACKGROUND AND IMPORTANCE

Obsessive-compulsive disorder (OCD) is a disabling psychiatric disorder, mainly treated with psychotherapy and pharmacotherapy. Surgical intervention may be appropriate for patients with treatment-refractory OCD. Deep brain stimulation (DBS) is an alternative for previously common ablative surgical procedures. Tractography has been proposed as a method for individualizing DBS treatment and may have the potential to improve efficacy.

CLINICAL PRESENTATION

We present a patient with treatment-refractory OCD previously treated with bilateral leucotomies, who underwent DBS surgery with targeting informed by tractography. Preoperative tractography to identify suitable DBS targets was undertaken. Structural images were also utilized for standard stereotactic surgical planning. The anteromedial globus pallidus internus (amGPi) was chosen as the target bilaterally after consideration of white matter projections to frontal cortical regions and neurosurgical approach. Bilateral amGPi DBS surgery was undertaken without adverse events. At 16-mo follow-up, there was a 48.5% reduction in OCD symptom severity as measured by the Yale-Brown Obsessive Compulsive Scale.

CONCLUSION

The amGPi can be a successful DBS target for OCD. This is the first known case to report on DBS surgery postleucotomies for OCD and highlights the utility of tractography for surgical planning in OCD.

Network communication models improve the behavioral and functional predictive utility of the human structural connectome

The connectome provides the structural substrate facilitating communication between brain regions. We aimed to establish whether accounting for polysynaptic communication in structural connectomes would improve prediction of interindividual variation in behavior as well as increase structure-function coupling strength. Connectomes were mapped for 889 healthy adults participating in the Human Connectome Project. To account for polysynaptic signaling, connectomes were transformed into communication matrices for each of 15 different network communication models. Communication matrices were (a) used to perform predictions of five data-driven behavioral dimensions and (b) correlated to resting-state functional connectivity (FC). While FC was the most accurate predictor of behavior, communication models, in particular communicability and navigation, improved the performance of structural connectomes. Communication also strengthened structure-function coupling, with the navigation and shortest paths models leading to 35-65% increases in association strength with FC. We combined behavioral and functional results into a single ranking that provides insight into which communication models may more faithfully recapitulate underlying neural signaling patterns. Comparing results across multiple connectome mapping pipelines suggested that modeling polysynaptic communication is particularly beneficial in sparse high-resolution connectomes. We conclude that network communication models can augment the functional and behavioral predictive utility of the human structural connectome.

From Coarse to Fine-Grained Parcellation of the Cortical Surface Using a Fiber-Bundle Atlas

In this article, we present a hybrid method to create fine-grained parcellations of the cortical surface, from a coarse-grained parcellation according to an anatomical atlas, based on cortico-cortical connectivity. The connectivity information is obtained from segmented superficial and deep white matter bundles, according to bundle atlases, instead of the whole tractography. Thus, a direct matching between the fiber bundles and the cortical regions is obtained, avoiding the problem of finding the correspondence of the cortical parcels among subjects. Generating parcels from segmented fiber bundles can provide a good representation of the human brain connectome since they are based on bundle atlases that contain the most reproducible short and long connections found on a population of subjects. The method first processes the tractography of each subject and extracts the bundles of the atlas, based on a segmentation algorithm. Next, the intersection between the fiber bundles and the cortical mesh is calculated, to define the initial and final intersection points of each fiber. A fiber filtering is then applied to eliminate misclassified fibers, based on the anatomical definition of each bundle and the labels of Desikan-Killiany anatomical parcellation. A parcellation algorithm is then performed to create a subdivision of the anatomical regions of the cortex, which is reproducible across subjects. This step resolves the overlapping of the fiber bundle extremities over the cortical mesh within each anatomical region. For the analysis, the density of the connections and the degree of overlapping, is considered and represented with a graph. One of our parcellations, an atlas composed of 160 parcels, achieves a reproducibility across subjects of ≈0.74, based on the average Dice's coefficient between subject's connectivity matrices, rather than ≈0.73 obtained for a macro anatomical parcellation of 150 parcels. Moreover, we compared two of our parcellations with state-of-the-art atlases, finding a degree of similarity with dMRI, functional, anatomical, and multi-modal atlases. The higher similarity was found for our parcellation composed of 185 sub-parcels with another parcellation based on dMRI data from the same database, but created with a different approach, leading to 130 parcels in common based on a Dice's coefficient ≥0.5.

Diffusion MRI tractography filtering techniques change the topology of structural connectomes

OBJECTIVE

The use of non-invasive techniques for the estimation of structural brain networks (i.e. connectomes) opened the door to large-scale investigations on the functioning and the architecture of the brain, unveiling the link between neurological disorders and topological changes of the brain network. This study aims at assessing if and how the topology of structural connectomes estimated non-invasively with diffusion MRI is affected by the employment of tractography filtering techniques in structural connectomic pipelines. Additionally, this work investigates the robustness of topological descriptors of filtered connectomes to the common practice of density-based thresholding.

APPROACH

We investigate the changes in global efficiency, characteristic path length, modularity and clustering coefficient on filtered connectomes obtained with the spherical deconvolution informed filtering of tractograms and using the convex optimization modelling for microstructure informed tractography. The analysis is performed on both healthy subjects and patients affected by traumatic brain injury and with an assessment of the robustness of the computed graph-theoretical measures with respect to density-based thresholding of the connectome.

MAIN RESULT

Our results demonstrate that tractography filtering techniques change the topology of brain networks, and thus alter network metrics both in the pathological and the healthy cases. Moreover, the measures are shown to be robust to density-based thresholding.

SIGNIFICANCE

The present work highlights how the inclusion of tractography filtering techniques in connectomic pipelines requires extra caution as they systematically change the network topology both in healthy subjects and patients affected by traumatic brain injury. Finally, the practice of low-to-moderate density-based thresholding of the connectomes is confirmed to have negligible effects on the topological analysis.

Analyzing the effects of free water modeling by deep learning on diffusion MRI structural connectivity estimates in glioma patients

Diffusion-weighted MRI makes it possible to quantify subvoxel brain microstructure and to reconstruct white matter fiber trajectories with which structural connectomes can be created. However, at the border between cerebrospinal fluid and white matter, or in the presence of edema, the obtained MRI signal originates from both the cerebrospinal fluid as well as from the white matter partial volume. Diffusion tractography can be strongly influenced by these free water partial volume effects. Thus, including a free water model can improve diffusion tractography in glioma patients. Here, we analyze how including a free water model influences structural connectivity estimates in healthy subjects as well as in brain tumor patients. During a clinical study, we acquired diffusion MRI data of 35 glioma patients and 28 age- and sex-matched controls, on which we applied an open-source deep learning based free water model. We performed deterministic as well as probabilistic tractography before and after free water modeling, and utilized the tractograms to create structural connectomes. Finally, we performed a quantitative analysis of the connectivity matrices. In our experiments, the number of tracked diffusion streamlines increased by 13% for high grade glioma patients, 9.25% for low grade glioma, and 7.65% for healthy controls. Intra-subject similarity of hemispheres increased significantly for the patient as well as for the control group, with larger effects observed in the patient group. Furthermore, inter-subject differences in connectivity between brain tumor patients and healthy subjects were reduced when including free water modeling. Our results indicate that free water modeling increases the similarity of connectivity matrices in brain tumor patients, while the observed effects are less pronounced in healthy subjects. As the similarity between brain tumor patients and healthy controls also increased, connectivity changes in brain tumor patients may have been overestimated in studies that did not perform free water modeling.

An MRI-Based, Data-Driven Model of Cortical Laminar Connectivity

Over the past two centuries, great scientific efforts have been spent on deciphering the structure and function of the cerebral cortex using a wide variety of methods. Since the advent of MRI neuroimaging, significant progress has been made in imaging of global white matter connectivity (connectomics), followed by promising new studies regarding imaging of grey matter laminar compartments. Despite progress in both fields, there still lacks mesoscale information regarding cortical laminar connectivity that could potentially bridge the gap between the current resolution of connectomics and the relatively higher resolution of cortical laminar imaging. Here, we systematically review a sample of prominent published articles regarding cortical laminar connectivity, in order to offer a simplified data-driven model that integrates white and grey matter MRI datasets into a novel way of exploring whole-brain tissue-level connectivity. Although it has been widely accepted that the cortex is exceptionally organized and interconnected, studies on the subject display a variety of approaches towards its structural building blocks. Our model addresses three principal cortical building blocks: cortical layer definitions (laminar grouping), vertical connections (intraregional, within the cortical microcircuit and subcortex) and horizontal connections (interregional, including connections within and between the hemispheres). While cortical partitioning into layers is more widely accepted as common knowledge, certain aspects of others such as cortical columns or microcircuits are still being debated. This study offers a broad and simplified view of histological and microscopical knowledge in laminar research that is applicable to the limitations of MRI methodologies, primarily regarding specificity and resolution.

Anatomical Characterization of the Human Structural Connectivity between the Pedunculopontine Nucleus and Globus Pallidus via Multi-Shell Multi-Tissue Tractography

Background and objectives: The internal (GPi) and external segments (GPe) of the globus pallidus represent key nodes in the basal ganglia system. Connections to and from pallidal segments are topographically organized, delineating limbic, associative and sensorimotor territories. The topography of pallidal afferent and efferent connections with brainstem structures has been poorly investigated. In this study we sought to characterize in-vivo connections between the globus pallidus and the pedunculopontine nucleus (PPN) via diffusion tractography. Materials and Methods: We employed structural and diffusion data of 100 subjects from the Human Connectome Project repository in order to reconstruct the connections between the PPN and the globus pallidus, employing higher order tractography techniques. We assessed streamline count of the reconstructed bundles and investigated spatial relations between pallidal voxels connected to the PPN and pallidal limbic, associative and sensorimotor functional territories. Results: We successfully reconstructed pallidotegmental tracts for the GPi and GPe in all subjects. The number of streamlines connecting the PPN with the GPi was greater than the number of those joining it with the GPe. PPN maps within pallidal segments exhibited a distinctive spatial organization, being localized in the ventromedial portion of the GPi and in the ventral-anterior portion in the GPe. Regarding their spatial relations with tractography-derived maps of pallidal functional territories, the highest value of percentage overlap was noticed between PPN maps and the associative territory. Conclusions: We successfully reconstructed the anatomical course of the pallidotegmental pathways and comprehensively characterized their topographical arrangement within both pallidal segments. PPM maps were localized in the ventromedial aspect of the GPi, while they occupied the anterior pole and the most ventral portion of the GPe. A better understanding of the spatial and topographical arrangement of the pallidotegmental pathways may have pathophysiological and therapeutic implications in movement disorders.

NODDI in clinical research

Diffusion MRI (dMRI) has proven to be a useful imaging approach for both clinical diagnosis and research investigating the microstructures of nervous tissues, and it has helped us to better understand the neurophysiological mechanisms of many diseases. Though diffusion tensor imaging (DTI) has long been the default tool to analyze dMRI data in clinical research, acquisition with stronger diffusion weightings beyond the DTI regimen is now possible with modern clinical scanners, potentially enabling even more detailed characterization of tissue microstructures. To take advantage of such data, neurite orientation dispersion and density imaging (NODDI) has been proposed as a way to relate the dMRI signal to tissue features via biophysically inspired modeling. The number of reports demonstrating the potential clinical utility of NODDI is rapidly increasing. At the same time, the pitfalls and limitations of NODDI, and general challenges in microstructure modeling, are becoming increasingly recognized by clinicians. dMRI microstructure modeling is a rapidly evolving field with great promise, where people from different scientific backgrounds, such as physics, medicine, biology, neuroscience, and statistics, are collaborating to build novel tools that contribute to improving human healthcare. Here, we review the applications of NODDI in clinical research and discuss future perspectives for investigations toward the implementation of dMRI microstructure imaging in clinical practice.

Interhemispheric Relationship of Genetic Influence on Human Brain Connectivity

To understand the origins of interhemispheric differences and commonalities/coupling in human brain wiring, it is crucial to determine how homologous interregional connectivities of the left and right hemispheres are genetically determined and related. To address this, in the present study, we analyzed human twin and pedigree samples with high-quality diffusion magnetic resonance imaging tractography and estimated the heritability and genetic correlation of homologous left and right white matter (WM) connections. The results showed that the heritability of WM connectivity was similar and coupled between the 2 hemispheres and that the degree of overlap in genetic factors underlying homologous WM connectivity (i.e., interhemispheric genetic correlation) varied substantially across the human brain: from complete overlap to complete nonoverlap. Particularly, the heritability was significantly stronger and the chance of interhemispheric complete overlap in genetic factors was higher in subcortical WM connections than in cortical WM connections. In addition, the heritability and interhemispheric genetic correlations were stronger for long-range connections than for short-range connections. These findings highlight the determinants of the genetics underlying WM connectivity and its interhemispheric relationships, and provide insight into genetic basis of WM connectivity asymmetries in both healthy and disease states.

Relationship between β-amyloid and structural network topology in decedents without dementia

OBJECTIVE

To investigate the association between β-amyloid (Aβ) load and postmortem structural network topology in decedents without dementia.

METHODS

Fourteen decedents (mean age at death 72.6 ± 7.2 years) without known clinical diagnosis of neurodegenerative disease and meeting pathology criteria only for no or low Alzheimer disease (AD) pathologic change were selected from the Normal Aging Brain Collection Amsterdam database. In situ brain MRI included 3D T1-weighted images for anatomical registration and diffusion tensor imaging for probabilistic tractography with subsequent structural network construction. Network topologic measures of centrality (degree), integration (global efficiency), and segregation (clustering and local efficiency) were calculated. Tissue sections from 12 cortical regions were sampled and immunostained for Aβ and hyperphosphorylated tau (p-tau), and histopathologic burden was determined. Linear mixed effect models were used to assess the relationship between Aβ and p-tau load and network topologic measures.

RESULTS

Aβ was present in 79% of cases and predominantly consisted of diffuse plaques; p-tau was sparsely present. Linear mixed effect models showed independent negative associations between Aβ load and global efficiency (β = -0.83 × 10^-3, p = 0.014), degree (β = -0.47, p = 0.034), and clustering (β = -0.55 × 10^-2, p = 0.043). A positive association was present between Aβ load and local efficiency (β = 3.16 × 10^-3, p = 0.035). Regionally, these results were significant in the posterior cingulate cortex (PCC) for degree (β = -2.22, p < 0.001) and local efficiency (β = 1.01 × 10^-2, p = 0.014) and precuneus for clustering (β = -0.91 × 10^-2, p = 0.017). There was no relationship between p-tau and network topology.

CONCLUSION

This study in deceased adults with AD-related pathologic change provides evidence for a relationship among early Aβ accumulation, predominantly of the diffuse type, and structural network topology, specifically of the PCC and precuneus.

Connectomic consistency: a systematic stability analysis of structural and functional connectivity

OBJECTIVE

Connectomics, the study of brain connectivity, has become an indispensable tool in neuroscientific research as it provides insights into brain organization. Connectomes are generated using different modalities such as diffusion MRI to capture structural organization of the brain or functional MRI to elaborate brain's functional organization. Understanding links between structural and functional organizations is crucial in explaining how observed behavior emerges from the underlying neurobiological mechanisms. Many studies have investigated how these two organizations relate to each other; however, we still lack a comparative understanding on how much variation should be expected in the two modalities, both between people and within a single person across scans.

APPROACH

In this study, we systematically analyzed the consistency of connectomes, that is the similarity between connectomes in terms of individual connections between brain regions and in terms of overall network topology. We present a comprehensive study of consistency in connectomes for a single subject examined longitudinally and across a large cohort of subjects cross-sectionally, in structure and function separately. Within structural connectomes, we compared connectomes generated by different tracking algorithms, parcellations, edge weighting schemes, and edge pruning techniques. In functional connectomes, we compared full, positive, and negative connectivity separately along with thresholding of weak edges. We evaluated consistency using correlation (incorporating information at the level of individual edges) and graph matching accuracy (evaluating connectivity at the level of network topology). We also examined the consistency of connectomes that are generated using different communication schemes.

MAIN RESULTS

Our results demonstrate varying degrees of consistency for the two modalities, with structural connectomes showing higher consistency than functional connectomes. Moreover, we observed a wide variation in consistency depending on how connectomes are generated.

SIGNIFICANCE

Our study sets a reference point for consistency of connectome types, which is especially important for structure-function coupling studies in evaluating mismatches between modalities.

GeoSP: A parallel method for a cortical surface parcellation based on geodesic distance

We present GeoSP, a parallel method that creates a parcellation of the cortical mesh based on a geodesic distance, in order to consider gyri and sulci topology. The method represents the mesh with a graph and performs a K-means clustering in parallel. It has two modes of use, by default, it performs the geodesic cortical parcellation based on the boundaries of the anatomical parcels provided by the Desikan-Killiany atlas. The other mode performs the complete parcellation of the cortex. Results for both modes and with different values for the total number of sub-parcels show homogeneous sub-parcels. Furthermore, the execution time is 82s for the whole cortex mode and 18s for the Desikan-Killiany atlas subdivision, for a parcellation into 350 sub-parcels. The proposed method will be available to the community to perform the evaluation of data-driven cortical parcellations. As an example, we compared GeoSP parcellation with Desikan-Killiany and Destrieux atlases in 50 subjects, obtaining more homogeneous parcels for GeoSP and minor differences in structural connectivity reproducibility across subjects.

Network Localisation of White Matter Damage in Cerebral Small Vessel Disease

Cerebral small vessel disease (CSVD) is a widespread condition associated to stroke, dementia and depression. To shed light on its opaque pathophysiology, we conducted a neuroimaging study aiming to assess the location of CSVD-induced damage in the human brain network. Structural connectomes of 930 subjects of the Hamburg City Health Study were reconstructed from diffusion weighted imaging. The connectome edges were partitioned into groups according to specific schemes: (1) connection to grey matter regions, (2) course and length of underlying streamlines. Peak-width of skeletonised mean diffusivity (PSMD) - a surrogate marker for CSVD - was related to each edge group's connectivity in a linear regression analysis allowing localisation of CSVD-induced effects. PSMD was associated with statistically significant decreases in connectivity of most investigated edge groups except those involved in connecting limbic, insular, temporal or cerebellar regions. Connectivity of interhemispheric and long intrahemispheric edges as well as edges connecting subcortical and frontal brain regions decreased most severely with increasing PSMD. In conclusion, MRI findings of CSVD are associated with widespread impairment of structural brain network connectivity, which supports the understanding of CSVD as a global brain disease. The pattern of regional preference might provide a link to clinical phenotypes of CSVD.

Conservation of brain connectivity and wiring across the mammalian class

Over 100 years ago, Ramon y Cajal hypothesized that two forces played a role in the evolution of mammalian brain connectivity: minimizing wiring costs and maximizing conductivity speed. Using diffusion MRI, we reconstructed the brain connectomes of 123 mammalian species. Network analysis revealed that both connectivity and the wiring cost are conserved across mammals. We describe a conservation principle that maintains the overall connectivity: species with fewer interhemispheric connections exhibit better intrahemispheric connectivity.

Subcallosal Cingulate Structural Connectivity Differs in Responders and Nonresponders to Electroconvulsive Therapy

BACKGROUND

Subcallosal cingulate (SCC) activity is associated with treatment response in major depressive disorder (MDD). Using electroconvulsive therapy (ECT) as a treatment model in this exploratory study, we addressed whether pretreatment SCC structural connectivity with corticolimbic-striatal circuitry relates to therapeutic outcome and whether these connectivity patterns change with treatment.

METHODS

Diffusion magnetic resonance imaging scans were acquired in 43 patients with MDD (mean [SD] age = 41 [13] years; men/women: 18/25) before and within 1 week of completing an ECT index series and in 31 healthy control subjects scanned twice (mean [SD] age = 38 [11] years; men/women: 17/18). Probabilistic tractography from subject-specific anatomically defined SCC seed regions to the ventral striatum (VS), anterior cingulate cortex (ACC), and bilateral medial prefrontal cortex (mPFC) was used to estimate structural connectivity in the target network.

RESULTS

SCC-mPFC connectivity was lower in responders (>50% symptom improvement) than nonresponders both before (p < .014) (difference 37%-96% left and right hemispheres) and after (p = .023) (difference 100% right hemisphere) treatment. SCC-mPFC connectivity in responders was also decreased compared with control subjects both at baseline (p = .012) and after ECT (p = .006), whereas nonresponders had SCC-right mPFC connectivity similar to that of control subjects. Subjects with MDD also showed decreased SCC-ACC connectivity compared with control subjects (baseline: p < .003, after ECT: p = .001), although SCC-ACC connectivity did not distinguish responders from nonresponders. Bilateral SCC-VS connectivity decreased (11%) with ECT (p = .021) regardless of treatment response.

CONCLUSIONS

While SCC-ACC connectivity may be a hallmark of MDD compared with control subjects, lower pretreatment SCC-mPFC connectivity in ECT responders (compared with nonresponders and control subjects) suggests that connectivity in this pathway may serve as a potential biomarker of therapeutic outcome and be relevant for treatment selection.

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.

Tractostorm: The what, why, and how of tractography dissection reproducibility

Investigative studies of white matter (WM) brain structures using diffusion MRI (dMRI) tractography frequently require manual WM bundle segmentation, often called "virtual dissection." Human errors and personal decisions make these manual segmentations hard to reproduce, which have not yet been quantified by the dMRI community. It is our opinion that if the field of dMRI tractography wants to be taken seriously as a widespread clinical tool, it is imperative to harmonize WM bundle segmentations and develop protocols aimed to be used in clinical settings. The EADC-ADNI Harmonized Hippocampal Protocol achieved such standardization through a series of steps that must be reproduced for every WM bundle. This article is an observation of the problematic. A specific bundle segmentation protocol was used in order to provide a real-life example, but the contribution of this article is to discuss the need for reproducibility and standardized protocol, as for any measurement tool. This study required the participation of 11 experts and 13 nonexperts in neuroanatomy and "virtual dissection" across various laboratories and hospitals. Intra-rater agreement (Dice score) was approximately 0.77, while inter-rater was approximately 0.65. The protocol provided to participants was not necessarily optimal, but its design mimics, in essence, what will be required in future protocols. Reporting tractometry results such as average fractional anisotropy, volume or streamline count of a particular bundle without a sufficient reproducibility score could make the analysis and interpretations more difficult. Coordinated efforts by the diffusion MRI tractography community are needed to quantify and account for reproducibility of WM bundle extraction protocols in this era of open and collaborative science.

Cytoarchitecture of the mouse brain by high resolution diffusion magnetic resonance imaging

MRI has been widely used to probe the neuroanatomy of the mouse brain, directly correlating MRI findings to histology is still challenging due to the limited spatial resolution and various image contrasts derived from water relaxation or diffusion properties. Magnetic resonance histology has the potential to become an indispensable research tool to mitigate such challenges. In the present study, we acquired high spatial resolution MRI datasets, including diffusion MRI (dMRI) at 25 ​μm isotropic resolution and quantitative susceptibility mapping (QSM) at 21.5 ​μm isotropic resolution to validate with conventional mouse brain histology. Diffusion weighted images (DWIs) show better delineation of cortical layers and glomeruli in the olfactory bulb than fractional anisotropy (FA) maps. However, among all the image contrasts, including quantitative susceptibility mapping (QSM), T1/T2∗ images and DTI metrics, FA maps highlight unique laminar architecture in sub-regions of the hippocampus, including the strata of the dentate gyrus and CA fields of the hippocampus. The mean diffusivity (MD) and axial diffusivity (AD) yield higher correlation with DAPI (0.62 and 0.71) and NeuN (0.78 and 0.74) than with NF-160 (-0.34 and -0.49). The correlations between FA and DAPI, NeuN, and NF-160 are 0.31, -0.01, and -0.49, respectively. Our findings demonstrate that MRI at microscopic resolution deliver a three-dimensional, non-invasive and non-destructive platform for characterization of fine structural detail in both gray matter and white matter of the mouse brain.

Mapping functional brain networks from the structural connectome: Relating the series expansion and eigenmode approaches

Functional brain networks are shaped and constrained by the underlying structural network. However, functional networks are not merely a one-to-one reflection of the structural network. Several theories have been put forward to understand the relationship between structural and functional networks. However, it remains unclear how these theories can be unified. Two existing recent theories state that 1) functional networks can be explained by all possible walks in the structural network, which we will refer to as the series expansion approach, and 2) functional networks can be explained by a weighted combination of the eigenmodes of the structural network, the so-called eigenmode approach. To elucidate the unique or common explanatory power of these approaches to estimate functional networks from the structural network, we analysed the relationship between these two existing views. Using linear algebra, we first show that the eigenmode approach can be written in terms of the series expansion approach, i.e., walks on the structural network associated with different hop counts correspond to different weightings of the eigenvectors of this network. Second, we provide explicit expressions for the coefficients for both the eigenmode and series expansion approach. These theoretical results were verified by empirical data from Diffusion Tensor Imaging (DTI) and functional Magnetic Resonance Imaging (fMRI), demonstrating a strong correlation between the mappings based on both approaches. Third, we analytically and empirically demonstrate that the fit of the eigenmode approach to measured functional data is always at least as good as the fit of the series expansion approach, and that errors in the structural data lead to large errors of the estimated coefficients for the series expansion approach. Therefore, we argue that the eigenmode approach should be preferred over the series expansion approach. Results hold for eigenmodes of the weighted adjacency matrices as well as eigenmodes of the graph Laplacian. ​Taken together, these results provide an important step towards unification of existing theories regarding the structure-function relationships in brain networks.

Mapping Short Association Fibers in the Early Cortical Visual Processing Stream Using In Vivo Diffusion Tractography

Short association fibers (U-fibers) connect proximal cortical areas and constitute the majority of white matter connections in the human brain. U-fibers play an important role in brain development, function, and pathology but are underrepresented in current descriptions of the human brain connectome, primarily due to methodological challenges in diffusion magnetic resonance imaging (dMRI) of these fibers. High spatial resolution and dedicated fiber and tractography models are required to reliably map the U-fibers. Moreover, limited quantitative knowledge of their geometry and distribution makes validation of U-fiber tractography challenging. Submillimeter resolution diffusion MRI—facilitated by a cutting-edge MRI scanner with 300 mT/m maximum gradient amplitude—was used to map U-fiber connectivity between primary and secondary visual cortical areas (V1 and V2, respectively) in vivo. V1 and V2 retinotopic maps were obtained using functional MRI at 7T. The mapped V1–V2 connectivity was retinotopically organized, demonstrating higher connectivity for retinotopically corresponding areas in V1 and V2 as expected. The results were highly reproducible, as demonstrated by repeated measurements in the same participants and by an independent replication group study. This study demonstrates a robust U-fiber connectivity mapping in vivo and is an important step toward construction of a more complete human brain connectome.

Connections, Tracts, Fractals, and the Rest: A Working Guide to Network and Connectivity Studies in Neurosurgery

Brain mapping and connectomics can probe networks that span the entire brain, producing a diverse range of outputs for probing specific clinically relevant questions. The potential for understanding the effect of focal lesions on brain function, cognition, and plasticity abounds, any one of which would likely yield more effective and safer neurosurgical strategies. However, the possibilities of advanced magnetic resonance imaging and connectomics have been somewhat underused in neurosurgery, arising from actual or perceived difficulties in either application or analysis. The present review builds on previous work describing the theoretical attractions of connectomics to deliberate on the practical details of performing high-quality connectomics studies in neurosurgery. First, the data and methods involved in deriving connectomics models will be considered, specifically for the purpose of determining the nature of inferences that can be made subsequently. Next, a selection of key analysis methods will be explored using practical examples that illustrate their effective implementation and the insights that can be gleaned. The principles of study design will be introduced, including analysis tips and methods for making efficient use of available resources. Finally, a review of the best research practices for neuroimaging studies will be discussed, including principles of open access data sharing, study preregistration, and methods for improving replicability. Ultimately, we hope readers will be better placed to appraise the current connectomics studies in neurosurgery and empowered to develop their own high-quality studies, both of which are key steps in realizing the true potential of connectomics and advanced neuroimaging analyses in general.

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.

Common misconceptions, hidden biases and modern challenges of dMRI tractography

The human brain is a complex and organized network, where the connection between regions is not achieved with single axons crisscrossing each other but rather millions of densely packed and well-ordered axons. Reconstruction from diffusion MRI tractography is only an attempt to capture the full complexity of this network, at the macroscale. This review provides an overview of the misconceptions, biases and pitfalls present in structural white matter bundle and connectome reconstruction using tractography. The goal is not to discourage readers, but rather to inform them of the limitations present in the methods used by researchers in the field in order to focus on what they can do and promote proper interpretations of their results. It also provides a list of open problems that could be solved in future research projects for the next generation of PhD students.

Toward an MRI-Based Mesoscale Connectome of the Squid Brain

Using high-resolution diffusion magnetic resonance imaging (dMRI) and a suite of old and new staining techniques, the beginnings of a multi-scale connectome map of the squid brain is erected. The first of its kind for a cephalopod, this includes the confirmation of 281 known connections with the addition of 145 previously undescribed pathways. These and other features suggest a suite of functional attributes, including (1) retinotopic organization through the optic lobes and into other brain areas well beyond that previously recognized, (2) a level of complexity and sub-division in the basal lobe supporting ideas of convergence with the vertebrate basal ganglia, and (3) differential lobe-dependent growth rates that mirror complexity and transitions in ontogeny.

Practical Application of Networks in Neurosurgery: Combined 3-Dimensional Printing, Neuronavigation, and Preoperative Surgical Planning

BACKGROUND

A plethora of cutting-edge neuroimaging analyses have been developed and published, yet they have not hitherto been realized as improvements in neurosurgical outcomes. In this paper we propose a novel interface between neuroimaging and neurosurgery for aiding translational research. Our objective is to create a method for applying advanced neuroimaging and network analysis findings to neurosurgery and illustrate its application through the presentation of 2 detailed case vignettes.

METHODS

This interface comprises a combination of network visualization, 3-dimensional printing, and ex-vivo neuronavigation to enable preoperative planning according to functional neuroanatomy. Clinical cases were selected from a prospective cohort study.

RESULTS

The first case vignette describes a low-grade glioma with potential language and executive function network involvement that underwent a successful complete resection of the lesion with preservation of network features. The second case describes a low-grade glioma in an apparently noneloquent location that underwent a subtotal resection but demonstrated unexpected and significant impairment in executive function postoperatively that subsequently abated during follow-up. In both examples the neuroimaging and network data highlight the complexity of the surrounding functional neuroanatomy at the individual level, beyond that which can be perceived on standard structural sequences.

CONCLUSIONS

The described interface has widespread applications for translational research including preoperative planning, neurosurgical training, and detailed patient counseling. A protocol for assessing its effectiveness and safety is proposed. Finally, recommendations for effective translation of findings from neuroimaging to neurosurgery are discussed, with the aim of making clinically meaningful improvements to neurosurgical practice.

The effect of network thresholding and weighting on structural brain networks in the UK Biobank

Whole-brain structural networks can be constructed using diffusion MRI and probabilistic tractography. However, measurement noise and the probabilistic nature of the tracking procedure result in an unknown proportion of spurious white matter connections. Faithful disentanglement of spurious and genuine connections is hindered by a lack of comprehensive anatomical information at the network-level. Therefore, network thresholding methods are widely used to remove ostensibly false connections, but it is not yet clear how different thresholding strategies affect basic network properties and their associations with meaningful demographic variables, such as age. In a sample of 3153 generally healthy volunteers from the UK Biobank Imaging Study (aged 44-77 years), we constructed whole-brain structural networks and applied two principled network thresholding approaches (consistency and proportional thresholding). These were applied over a broad range of threshold levels across six alternative network weightings (streamline count, fractional anisotropy, mean diffusivity and three novel weightings from neurite orientation dispersion and density imaging) and for four common network measures (mean edge weight, characteristic path length, network efficiency and network clustering coefficient). We compared network measures against age associations and found that: 1) measures derived from unthresholded matrices yielded the weakest age-associations (0.033 ​≤ ​|β| ​≤ ​0.409); and 2) the most commonly-used level of proportional-thresholding from the literature (retaining 68.7% of all possible connections) yielded significantly weaker age-associations (0.070 ​≤ ​|β| ​≤ ​0.406) than the consistency-based approach which retained only 30% of connections (0.140 ​≤ ​|β| ​≤ ​0.409). However, we determined that the stringency of the threshold was a stronger determinant of the network-age association than the choice of threshold method and the two thresholding approaches identified a highly overlapping set of connections (ICC ​= ​0.84), when matched at 70% network sparsity. Generally, more stringent thresholding resulted in more age-sensitive network measures in five of the six network weightings, except at the highest levels of sparsity (>90%), where crucial connections were then removed. At two commonly-used threshold levels, the age-associations of the connections that were discarded (mean β ​≤ ​|0.068|) were significantly smaller in magnitude than the corresponding age-associations of the connections that were retained (mean β ​≤ ​|0.219|, p ​< ​0.001, uncorrected). Given histological evidence of widespread degeneration of structural brain connectivity with increasing age, these results indicate that stringent thresholding methods may be most accurate in identifying true white matter connections.

The white matter connectome as an individualized biomarker of language impairment in temporal lobe epilepsy

OBJECTIVE

The distributed white matter network underlying language leads to difficulties in extracting clinically meaningful summaries of neural alterations leading to language impairment. Here we determine the predictive ability of the structural connectome (SC), compared with global measures of white matter tract microstructure and clinical data, to discriminate language impaired patients with temporal lobe epilepsy (TLE) from TLE patients without language impairment.

METHODS

T1- and diffusion-MRI, clinical variables (CVs), and neuropsychological measures of naming and verbal fluency were available for 82 TLE patients. Prediction of language impairment was performed using a robust tree-based classifier (XGBoost) for three models: (1) a CV-model which included demographic and epilepsy-related clinical features, (2) an atlas-based tract-model, including four frontotemporal white matter association tracts implicated in language (i.e., the bilateral arcuate fasciculus, inferior frontal occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus), and (3) a SC-model based on diffusion MRI. For the association tracts, mean fractional anisotropy was calculated as a measure of white matter microstructure for each tract using a diffusion tensor atlas (i.e., AtlasTrack). The SC-model used measurement of cortical-cortical connections arising from a temporal lobe subnetwork derived using probabilistic tractography. Dimensionality reduction of the SC was performed with principal components analysis (PCA). Each model was trained on 49 patients from one epilepsy center and tested on 33 patients from a different center (i.e., an independent dataset). Randomization was performed to test the stability of the results.

RESULTS

The SC-model yielded a greater area under the curve (AUC; .73) and accuracy (79%) compared to both the tract-model (AUC: .54, p < .001; accuracy: 70%, p < .001) and the CV-model (AUC: .59, p < .001; accuracy: 64%, p < .001). Within the SC-model, lateral temporal connections had the highest importance to model performance, including connections similar to language association tracts such as links between the superior temporal gyrus to pars opercularis. However, in addition to these connections many additional connections that were widely distributed, bilateral and interhemispheric in nature were identified as contributing to SC-model performance.

CONCLUSION

The SC revealed a white matter network contributing to language impairment that was widely distributed, bilateral, and lateral temporal in nature. The distributed network underlying language may be why the SC-model has an advantage in identifying sub-components of the complex fiber networks most relevant for aspects of language performance.

Default Mode Network structural alterations in Kocher-Monro trajectory white matter transection: A 3 and 7 tesla simulation modeling approach

The Kocher-Monro trajectory to the cerebral ventricular system represents one of the most common surgical procedures in the field of neurosurgery. Several studies have analyzed the specific white matter disruption produced during this intervention, which has no reported adverse neurological outcomes. In this study, a graph-theoretical approach was applied to quantify the structural alterations in whole-brain level connectivity. To this end, 132 subjects were randomly selected from the Human Connectome Project dataset and used to create 3 independent 44 subjects groups. Two of the groups underwent a simulated left/right Kocher-Monro trajectory and the third was kept as a control group. For the right Kocher-Monro approach, the nodal analysis revealed decreased strength in the anterior cingulate gyrus of the transected hemisphere. The network-based statistic analysis revealed a set of right lateralized subnetworks with decreased connectivity strength that is consistent with a subset of the Default Mode Network, Salience Network, and Cingulo-Opercular Network. These findings could allow for a better understanding of structural alterations caused by Kocher-Monro approaches that could reveal previously undetected clinical alterations and inform the process of designing safer and less invasive cerebral ventricular approaches.

Brain structural connectivity network alterations in insomnia disorder reveal a central role of the right angular gyrus

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People with insomnia show widespread brain structural hyperconnectivity.

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The right angular gyrus is central to the structural connectivity alterations.

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Connectivity of this angular gyrus subnetwork correlates with reactive hyperarousal.

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Brain structural hyperconnectivity may mark vulnerability to insomnia.

Insomnia Disorder (ID) is a prevalent and persistent condition, yet its neural substrate is not well understood. The cognitive, emotional, and behavioral characteristics of ID suggest that vulnerability involves distributed brain networks rather than a single brain area or connection. The present study utilized probabilistic diffusion tractography to compare the whole-brain structural connectivity networks of people with ID and those of matched controls without sleep complaints.

Diffusion-weighted images and T1-weighed images were acquired in 51 people diagnosed with ID (21–69 years of age, 37 female) and 48 matched controls without sleep complaints (22–70 years of age, 31 female). Probabilistic tractography was performed to construct the whole-brain structural connectivity network of each participant. Case–control differences in connectivity strength and network efficiency were evaluated with permutation tests.

People with ID showed structural hyperconnectivity within a subnetwork that spread over frontal, parietal, temporal, and subcortical regions and was anchored at the right angular gyrus. The result was robust across different edge-weighting strategies. Moreover, converging support was given by the finding of heightened right angular gyrus nodal efficiency (harmonic centrality) across varying graph density in people with ID. Follow-up correlation analyses revealed that subnetwork connectivity was associated with self-reported reactive hyperarousal.

The findings demonstrate that the right angular gyrus is a hub of enhanced structural connectivity in ID. Hyperconnectivity within the identified subnetwork may contribute to increased reactivity to stimuli and may signify vulnerability to ID.

MR g-ratio-weighted connectome analysis in patients with multiple sclerosis

Multiple sclerosis (MS) is a brain network disconnection syndrome. Although the brain network topology in MS has been evaluated using diffusion MRI tractography, the mechanism underlying disconnection in the disorder remains unclear. In this study, we evaluated the brain network topology in MS using connectomes with connectivity strengths based on the ratio of the inner to outer myelinated axon diameter (i.e., g-ratio), thereby providing enhanced sensitivity to demyelination compared with the conventional measures of connectivity. We mapped g-ratio-based connectomes in 14 patients with MS and compared them with those of 14 age- and sex-matched healthy controls. For comparison, probabilistic tractography was also used to map connectomes based on the number of streamlines (NOS). We found that g-ratio- and NOS-based connectomes comprised significant connectivity reductions in patients with MS, predominantly in the motor, somatosensory, visual, and limbic regions. However, only the g-ratio-based connectome enabled detection of significant increases in nodal strength in patients with MS. Finally, we found that the g-ratio-weighted nodal strength in motor, visual, and limbic regions significantly correlated with inter-individual variation in measures of disease severity. The g-ratio-based connectome can serve as a sensitive biomarker for diagnosing and monitoring disease progression.

The Seven Deadly Sins of Measuring Brain Structural Connectivity Using Diffusion MRI Streamlines Fibre-Tracking

There is great interest in the study of brain structural connectivity, as white matter abnormalities have been implicated in many disease states. Diffusion magnetic resonance imaging (MRI) provides a powerful means to characterise structural connectivity non-invasively, by using a fibre-tracking algorithm. The most widely used fibre-tracking strategy is based on the step-wise generation of streamlines. Despite their popularity and widespread use, there are a number of practical considerations that must be taken into account in order to increase the robustness of streamlines tracking results, particularly when these methods are used to study brain structural connectivity, and the connectome. This review article describes what we consider the ‘seven deadly sins’ of mapping structural connections using diffusion MRI streamlines fibre-tracking, with particular emphasis on ‘sins’ that can be practically avoided and they can have an important impact in the results. It is shown that there are important ‘deadly sins’ to be avoided at every step of the pipeline, such as during data acquisition, during data modelling to estimate local fibre architecture, during the fibre-tracking process itself, and during quantification of the tracking results. The recommendations here are intended to inform users on potential important shortcomings of their current tracking protocols, as well as to guide future users on some of the key issues and decisions that must be faced when designing their processing pipelines.

Comparative neuroanatomy: Integrating classic and modern methods to understand association fibers connecting dorsal and ventral visual cortex

Comparative neuroanatomy studies improve understanding of brain structure and function and provide insight regarding brain development, evolution, and also what features of the brain are uniquely human. With modern methods such as diffusion MRI (dMRI) and quantitative MRI (qMRI), we are able to measure structural features of the brain with the same methods across human and non-human primates. In this review article, we discuss how recent dMRI measurements of vertical occipital connections in humans and macaques can be compared with previous findings from invasive anatomical studies that examined connectivity, including relatively forgotten classic strychnine neuronography studies. We then review recent progress in understanding the neuroanatomy of vertical connections within the occipitotemporal cortex by combining modern quantitative MRI and classical histological measurements in human and macaque. Finally, we a) discuss current limitations of dMRI and tractography and b) consider potential paths for future investigations using dMRI and tractography for comparative neuroanatomical studies of white matter tracts between species. While we focus on vertical association connections in visual cortex in the present paper, this same approach can be applied to other white matter tracts. Similar efforts are likely to continue to advance our understanding of the neuroanatomical features of the brain that are shared across species, as well as to distinguish the features that are uniquely human.

Selective Brain Network and Cellular Responses Upon Dimethyl Fumarate Immunomodulation in Multiple Sclerosis

Background: Efficient personalized therapy paradigms are needed to modify the disease course and halt gray (GM) and white matter (WM) damage in patients with multiple sclerosis (MS). Presently, promising disease-modifying drugs show impressive efficiency, however, tailored markers of therapy responses are required. Here, we aimed to detect in a real-world setting patients with a more favorable brain network response and immune cell dynamics upon dimethyl fumarate (DMF) treatment. Methods: In a cohort of 78 MS patients we identified two thoroughly matched groups, based on age, disease duration, disability status and lesion volume, receiving DMF (n = 42) and NAT (n = 36) and followed them over 16 months. The rate of cortical atrophy and deep GM volumes were quantified. GM and WM network responses were characterized by brain modularization as a marker of regional and global structural alterations. In the DMF group, lymphocyte subsets were analyzed by flow cytometry and related to clinical and MRI parameters. Results: Sixty percent (25 patients) of the DMF and 36% (13 patients) of the NAT group had disease activity during the study period. The rate of cortical atrophy was higher in the DMF group (-2.4%) compared to NAT (-2.1%, p < 0.05) group. GM and WM network dynamics presented increased modularization in both groups. When dividing the DMF-treated cohort into patients free of disease activity (n = 17, DMF_R) and patients with disease activity (n = 25, DMF_NR) these groups differed significantly in CD8+ cell depletion counts (DMF_R: 197.7 ± 97.1/μl; DMF_NR: 298.4 ± 190.6/μl, p = 0.03) and also in cortical atrophy (DMF_R: -1.7%; DMF_NR: -3.2%, p = 0.01). DMF_R presented reduced longitudinal GM and WM modularization and less atrophy as markers of preserved structural global network integrity in comparison to DMF_NR and even NAT patients. Conclusions: NAT treatment contributes to a reduced rate of cortical atrophy compared to DMF therapy. However, patients under DMF treatment with a stronger CD8+ T cell depletion present a more favorable response in terms of cortical integrity and GM and WM network responses. Our findings may serve as basis for the development of personalized treatment paradigms.

Topographical Visualization of the Reciprocal Projection between the Medial Septum and the Hippocampus in the 5XFAD Mouse Model of Alzheimer's Disease

It is widely known that the degeneration of neural circuits is prominent in the brains of Alzheimer’s disease (AD) patients. The reciprocal connectivity of the medial septum (MS) and hippocampus, which constitutes the septo-hippocampo-septal (SHS) loop, is known to be associated with learning and memory. Despite the importance of the reciprocal projections between the MS and hippocampus in AD, the alteration of bidirectional connectivity between two structures has not yet been investigated at the mesoscale level. In this study, we adopted AD animal model, five familial AD mutations (5XFAD) mice, and anterograde and retrograde tracers, BDA and DiI, respectively, to visualize the pathology-related changes in topographical connectivity of the SHS loop in the 5XFAD brain. By comparing 4.5-month-old and 14-month-old 5XFAD mice, we successfully identified key circuit components of the SHS loop altered in 5XFAD brains. Remarkably, the SHS loop began to degenerate in 4.5-month-old 5XFAD mice before the onset of neuronal loss. The impairment of connectivity between the MS and hippocampus was accelerated in 14-month-old 5XFAD mice. These results demonstrate, for the first time, topographical evidence for the degradation of the interconnection between the MS and hippocampus at the mesoscale level in a mouse model of AD. Our results provide structural and functional insights into the interconnectivity of the MS and hippocampus, which will inform the use and development of various therapeutic approaches that target neural circuits for the treatment of AD.

Advances in computational and statistical diffusion MRI

Computational methods are crucial for the analysis of diffusion magnetic resonance imaging (MRI) of the brain. Computational diffusion MRI can provide rich information at many size scales, including local microstructure measures such as diffusion anisotropies or apparent axon diameters, whole-brain connectivity information that describes the brain's wiring diagram and population-based studies in health and disease. Many of the diffusion MRI analyses performed today were not possible five, ten or twenty years ago, due to the requirements for large amounts of computer memory or processor time. In addition, mathematical frameworks had to be developed or adapted from other fields to create new ways to analyze diffusion MRI data. The purpose of this review is to highlight recent computational and statistical advances in diffusion MRI and to put these advances into context by comparison with the more traditional computational methods that are in popular clinical and scientific use. We aim to provide a high-level overview of interest to diffusion MRI researchers, with a more in-depth treatment to illustrate selected computational advances.

The structural connectome in traumatic brain injury: A meta-analysis of graph metrics

Although recent structural connectivity studies of traumatic brain injury (TBI) have used graph theory to evaluate alterations in global integration and functional segregation, pooled analysis is needed to examine the robust patterns of change in graph metrics across studies. Following a systematic search, 15 studies met the inclusion criteria for review. Of these, ten studies were included in a random-effects meta-analysis of global graph metrics, and subgroup analyses examined the confounding effects of severity and time since injury. The meta-analysis revealed significantly higher values of normalised clustering coefficient (gö=ö1.445, CI=[0.512, 2.378], pö=ö0.002) and longer characteristic path length (gö=ö0.514, CI=[0.190, 0.838], pö=ö0.002) in TBI patients compared with healthy controls. Our findings suggest that the TBI structural network has shifted away from the balanced small-world network towards a regular lattice. Therefore, these graph metrics may be useful markers of neurocognitive dysfunction in TBI. We conclude that the pattern of change revealed by our analysis should be used to guide hypothesis-driven research into the role of graph metrics as diagnostic and prognostic biomarkers.

The Superoanterior Fasciculus (SAF): A Novel White Matter Pathway in the Human Brain?

Fiber tractography (FT) using diffusion magnetic resonance imaging (dMRI) is widely used for investigating microstructural properties of white matter (WM) fiber-bundles and for mapping structural connections of the human brain. While studying the architectural configuration of the brain's circuitry with FT is not without controversy, recent progress in acquisition, processing, modeling, analysis, and visualization of dMRI data pushes forward the reliability in reconstructing WM pathways. Despite being aware of the well-known pitfalls in analyzing dMRI data and several other limitations of FT discussed in recent literature, we present the superoanterior fasciculus (SAF), a novel bilateral fiber tract in the frontal region of the human brain that-to the best of our knowledge-has not been documented. The SAF has a similar shape to the anterior part of the cingulum bundle, but it is located more frontally. To minimize the possibility that these FT findings are based on acquisition or processing artifacts, different dMRI data sets and processing pipelines have been used to describe the SAF. Furthermore, we evaluated the configuration of the SAF with complementary methods, such as polarized light imaging (PLI) and human brain dissections. The FT results of the SAF demonstrate a long pathway, consistent across individuals, while the human dissections indicate fiber pathways connecting the postero-dorsal with the antero-dorsal cortices of the frontal lobe.

Structural network disruption markers explain disability in multiple sclerosis

OBJECTIVE

To evaluate whether structural brain network metrics correlate better with clinical impairment and information processing speed in multiple sclerosis (MS) beyond atrophy measures and white matter lesions.

METHODS

This cross-sectional study included 51 healthy controls and 122 patients comprising 58 relapsing-remitting, 28 primary progressive and 36 secondary progressive. Structural brain networks were reconstructed from diffusion-weighted MRIs and standard metrics reflecting network density, efficiency and clustering coefficient were derived and compared between subjects' groups. Stepwise linear regression analyses were used to investigate the contribution of network measures that explain clinical disability (Expanded Disability Status Scale (EDSS)) and information processing speed (Symbol Digit Modalities Test (SDMT)) compared with conventional MRI metrics alone and to determine the best statistical model that explains better EDSS and SDMT.

RESULTS

Compared with controls, network efficiency and clustering coefficient were reduced in MS while these measures were also reduced in secondary progressive relative to relapsing-remitting patients. Structural network metrics increase the variance explained by the statistical models for clinical and information processing dysfunction. The best model for EDSS showed that reduced network density and global efficiency and increased age were associated with increased clinical disability. The best model for SDMT showed that lower deep grey matter volume, reduced efficiency and male gender were associated with worse information processing speed.

CONCLUSIONS

Structural topological changes exist between subjects' groups. Network density and global efficiency explained disability above non-network measures, highlighting that network metrics can provide clinically relevant information about MS pathology.

Connectomics of bipolar disorder: a critical review, and evidence for dynamic instabilities within interoceptive networks

The notion that specific cognitive and emotional processes arise from functionally distinct brain regions has lately shifted toward a connectivity-based approach that emphasizes the role of network-mediated integration across regions. The clinical neurosciences have likewise shifted from a predominantly lesion-based approach to a connectomic paradigm-framing disorders as diverse as stroke, schizophrenia (SCZ), and dementia as "dysconnection syndromes". Here we position bipolar disorder (BD) within this paradigm. We first summarise the disruptions in structural, functional and effective connectivity that have been documented in BD. Not surprisingly, these disturbances show a preferential impact on circuits that support emotional processes, cognitive control and executive functions. Those at high risk (HR) for BD also show patterns of connectivity that differ from both matched control populations and those with BD, and which may thus speak to neurobiological markers of both risk and resilience. We highlight research fields that aim to link brain network disturbances to the phenotype of BD, including the study of large-scale brain dynamics, the principles of network stability and control, and the study of interoception (the perception of physiological states). Together, these findings suggest that the affective dysregulation of BD arises from dynamic instabilities in interoceptive circuits which subsequently impact on fear circuitry and cognitive control systems. We describe the resulting disturbance as a "psychosis of interoception".

Microstructural imaging of the human brain with a 'super-scanner': 10 key advantages of ultra-strong gradients for diffusion MRI

The key component of a microstructural diffusion MRI 'super-scanner' is a dedicated high-strength gradient system that enables stronger diffusion weightings per unit time compared to conventional gradient designs. This can, in turn, drastically shorten the time needed for diffusion encoding, increase the signal-to-noise ratio, and facilitate measurements at shorter diffusion times. This review, written from the perspective of the UK National Facility for In Vivo MR Imaging of Human Tissue Microstructure, an initiative to establish a shared 300 mT/m-gradient facility amongst the microstructural imaging community, describes ten advantages of ultra-strong gradients for microstructural imaging. Specifically, we will discuss how the increase of the accessible measurement space compared to a lower-gradient systems (in terms of Δ, b-value, and TE) can accelerate developments in the areas of 1) axon diameter distribution mapping; 2) microstructural parameter estimation; 3) mapping micro-vs macroscopic anisotropy features with gradient waveforms beyond a single pair of pulsed-gradients; 4) multi-contrast experiments, e.g. diffusion-relaxometry; 5) tractography and high-resolution imaging in vivo and 6) post mortem; 7) diffusion-weighted spectroscopy of metabolites other than water; 8) tumour characterisation; 9) functional diffusion MRI; and 10) quality enhancement of images acquired on lower-gradient systems. We finally discuss practical barriers in the use of ultra-strong gradients, and provide an outlook on the next generation of 'super-scanners'.

How do spatially distinct frequency specific MEG networks emerge from one underlying structural connectome? The role of the structural eigenmodes

Functional networks obtained from magnetoencephalography (MEG) from different frequency bands show distinct spatial patterns. It remains to be elucidated how distinct spatial patterns in MEG networks emerge given a single underlying structural network. Recent work has suggested that the eigenmodes of the structural network might serve as a basis set for functional network patterns in the case of functional MRI. Here, we take this notion further in the context of frequency band specific MEG networks. We show that a selected set of eigenmodes of the structural network can predict different frequency band specific networks in the resting state, ranging from delta (1-4 Hz) to the high gamma band (40-70 Hz). These predictions outperform predictions based from surrogate data, suggesting a genuine relationship between eigenmodes of the structural network and frequency specific MEG networks. We then show that the relevant set of eigenmodes can be excited in a network of neural mass models using linear stability analysis only by including delays. Excitation of an eigenmode in this context refers to a dynamic instability of a network steady state to a spatial pattern with a corresponding coherent temporal oscillation. Simulations verify the results from linear stability analysis and suggest that theta, alpha and beta band networks emerge very near to the bifurcation. The delta and gamma bands in the resting state emerges further away from the bifurcation. These results show for the first time how delayed interactions can excite the relevant set of eigenmodes that give rise to frequency specific functional connectivity patterns.