Population-averaged atlas of the macroscale human structural connectome and its network topology

Sex differences in the connectome of the human brain according to an MR-tractography study

Background: The gender differences in the brain anatomy play an important role in planning and analysis in a lot of studies of the brain. Despite most animal studies being performed on the animals of only one sex, clinical studies generally enroll both males and females. Keeping this fact in mind, learning the gender differences in the white matter structure is important for those studies which deal with the white matter changes. These differences should be considered on the stages of planning and evaluation of the results. Aims: Evaluation of the gender differences in the white matter pathways in healthy subjects. Methods: 21 women and 20 men were enrolled in the study. All the subjects underwent MR-tractography, then the anatomic connectome was composed and the differences were evaluated using the tracts quantitative anisotropy (QA) evaluation. Results: The gender differences were found in the white matter pathways with the prevalence of quantitative anisotropy in women, observed in a larger number of tracts than in those of men. QA was prevalent in a lot of fascicli that form major pathways in both groups: corpus callosum, dominant arcuate fasciclus, inferior fronto-occipital, inferior and superior right longitudinal pathways. Conclusions: The white matter pathways in males and females are different not only within the major tracts but also for small fascicli that form tracts.

Post-stroke reorganization of transient brain activity characterizes deficits and recovery of cognitive functions

Functional magnetic resonance imaging (fMRI) has been widely employed to study stroke pathophysiology. In particular, analyses of fMRI signals at rest were directed at quantifying the impact of stroke on spatial features of brain networks. However, brain networks have intrinsic time features that were, so far, disregarded in these analyses. In consequence, standard fMRI analysis failed to capture temporal imbalance resulting from stroke lesions, hence restricting their ability to reveal the interdependent pathological changes in structural and temporal network features following stroke. Here, we longitudinally analyzed hemodynamic-informed transient activity in a large cohort of stroke patients (n = 103) to assess spatial and temporal changes of brain networks after stroke. Metrics extracted from the hemodynamic-informed transient activity were replicable within- and between-individuals in healthy participants, hence supporting their robustness and their clinical applicability. While large-scale spatial patterns of brain networks were preserved after stroke, their durations were altered, with stroke subjects exhibiting a varied pattern of longer and shorter network activations compared to healthy individuals. Specifically, patients showed a longer duration in the lateral precentral gyrus and anterior cingulum, and a shorter duration in the occipital lobe and in the cerebellum. These temporal alterations were associated with white matter damage in projection and association pathways. Furthermore, they were tied to deficits in specific behavioral domains as restoration of healthy brain dynamics paralleled recovery of cognitive functions (attention, language and spatial memory), but was not significantly correlated to motor recovery. These findings underscore the critical importance of network temporal properties in dissecting the pathophysiology of brain changes after stroke, thus shedding new light on the clinical potential of time-resolved methods for fMRI analysis.

Dissecting neuropathic from poststroke pain: the white matter within

ABSTRACT

Poststroke pain (PSP) is a heterogeneous term encompassing both central neuropathic (ie, central poststroke pain [CPSP]) and nonneuropathic poststroke pain (CNNP) syndromes. Central poststroke pain is classically related to damage in the lateral brainstem, posterior thalamus, and parietoinsular areas, whereas the role of white matter connecting these structures is frequently ignored. In addition, the relationship between stroke topography and CNNP is not completely understood. In this study, we address these issues comparing stroke location in a CPSP group of 35 patients with 2 control groups: 27 patients with CNNP and 27 patients with stroke without pain. Brain MRI images were analyzed by 2 complementary approaches: an exploratory analysis using voxel-wise lesion symptom mapping, to detect significant voxels damaged in CPSP across the whole brain, and a hypothesis-driven, region of interest-based analysis, to replicate previously reported sites involved in CPSP. Odds ratio maps were also calculated to demonstrate the risk for CPSP in each damaged voxel. Our exploratory analysis showed that, besides known thalamic and parietoinsular areas, significant voxels carrying a high risk for CPSP were located in the white matter encompassing thalamoinsular connections (one-tailed threshold Z > 3.96, corrected P value <0.05, odds ratio = 39.7). These results show that the interruption of thalamocortical white matter connections is an important component of CPSP, which is in contrast with findings from nonneuropathic PSP and from strokes without pain. These data can aid in the selection of patients at risk to develop CPSP who could be candidates to pre-emptive or therapeutic interventions.

Quantification of changes in white matter tract fibers in idiopathic normal pressure hydrocephalus based on diffusion spectrum imaging

PURPOSE

Patients with idiopathic normal pressure hydrocephalus (iNPH) present white-matter abnormalities. The analytical methods described to date only measure mean diffusion parameter alterations of iNPH-specific brain regions or in a certain fasciculus. This study quantitatively analyzed whether iNPH-tract abnormalities are confined to specific sections or involve entire fibers based on diffusion spectrum imaging (DSI).

METHOD

Twenty-two patients with iNPH and 20 normally aging subjects were included. The 18 main tracts in the brain of each subject were extracted, and the diffusion parameters of 100 equidistant nodes on each fiber were calculated to quantitatively evaluate integrity changes in different regions along these tracts. Two diffusion metrics were measured, i.e., general fractional anisotropy (GFA) and fractional anisotropy (FA).

RESULTS

Compared to normally aging (P < 0.05), in iNPH, the GFA and FA of the left uncinate fasciculus and FA of the bilateral superior longitudinal fasciculus 1 were reduced in areas where the entire fiber was involved (%nodes with significant differences > 90%). Most other fasciculi detected presented GFA or FA alterations limited to specific regions. Increased and decreased GFA or FA co-occurred in different sections of the same fibers, including the corticospinal tract and left thalamic radiation posterior in iNPH.

CONCLUSIONS

Few iNPH fibers presented diffusion abnormalities involving nearly all tracts. Most fiber abnormalities in iNPH were confined to specific areas, and different parts of the same fasciculus showed diverse diffusion alterations in few cases. This DSI-based tract analysis provided detailed information on iNPH white-matter changes.

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.

Learning white matter subject-specific segmentation from structural MRI

PURPOSE

Mapping brain white matter (WM) is essential for building an understanding of brain anatomy and function. Tractography-based methods derived from diffusion-weighted MRI (dMRI) are the principal tools for investigating WM. These procedures rely on time-consuming dMRI acquisitions that may not always be available, especially for legacy or time-constrained studies. To address this problem, we aim to generate WM tracts from structural magnetic resonance imaging (MRI) image by deep learning.

METHODS

Following recently proposed innovations in structural anatomical segmentation, we evaluate the feasibility of training multiply spatial localized convolution neural networks to learn context from fixed spatial patches from structural MRI on standard template. We focus on six widely used dMRI tractography algorithms (TractSeg, RecoBundles, XTRACT, Tracula, automated fiber quantification (AFQ), and AFQclipped) and train 125 U-Net models to learn these techniques from 3870 T1-weighted images from the Baltimore Longitudinal Study of Aging, the Human Connectome Project S1200 release, and scans acquired at Vanderbilt University.

RESULTS

The proposed framework identifies fiber bundles with high agreement against tractography-based pathways with a median Dice coefficient from 0.62 to 0.87 on a test cohort, achieving improved subject-specific accuracy when compared to population atlas-based methods. We demonstrate the generalizability of the proposed framework on three externally available datasets.

CONCLUSIONS

We show that patch-wise convolutional neural network can achieve robust bundle segmentation from T1w. We envision the use of this framework for visualizing the expected course of WM pathways when dMRI is not available.

Multivariate genomic and transcriptomic determinants of imaging-derived personalized therapeutic needs in Parkinson's disease

Due to the marked interpersonal neuropathologic and clinical heterogeneity of Parkinson's disease (PD), current interventions are not personalized and fail to benefit all patients. Furthermore, we continue to lack well-established methods and clinical tests to tailor interventions at the individual level in PD. Here, we identify the genetic determinants of individual-tailored treatment needs derived from longitudinal multimodal neuroimaging data in 294 PD patients (PPMI data). Advanced multivariate statistical analysis revealed that both genomic and blood transcriptomic data significantly explain (P < 0.01, FWE-corrected) the interindividual variability in therapeutic needs associated with dopaminergic, functional, and structural brain reorganization. We confirmed a high overlap between the identified highly predictive molecular pathways and determinants of levodopa clinical responsiveness, including well-known (Wnt signaling, angiogenesis, dopaminergic activity) and recently discovered (immune markers, gonadotropin-releasing hormone receptor) pathways/components. In addition, the observed strong correspondence between the identified genomic and baseline-transcriptomic determinants of treatment needs/response supports the genome's active role at the time of patient evaluation (i.e., beyond individual genetic predispositions at birth). This study paves the way for effectively combining genomic, transcriptomic and neuroimaging data for implementing successful individually tailored interventions in PD and extending our pathogenetic understanding of this multifactorial and heterogeneous disorder.

Brainstem Diffusion Tensor Tractography and Clinical Applications in Pain

The brainstem is one of the most vulnerable brain structures in many neurological conditions, such as pain, sleep problems, autonomic dysfunctions, and neurodegenerative disorders. Diffusion tensor imaging and tractography provide structural details and quantitative measures of brainstem fiber pathways. Until recently, diffusion tensor tractographic studies have mainly focused on whole-brain MRI acquisition. Due to the brainstem's spatial localization, size, and tissue characteristics, and limits of imaging techniques, brainstem diffusion MRI poses particular challenges in tractography. We provide a brief overview on recent advances in diffusion tensor tractography in revealing human pathways connecting the brainstem to the subcortical regions (e.g., basal ganglia, mesolimbic, basal forebrain), and cortical regions. Each of these pathways contains different distributions of fiber tracts from known neurotransmitter-specific nuclei in the brainstem. We compare the brainstem tractographic approaches in literature and our in-lab developed automated brainstem tractography in terms of atlas building, technical advantages, and neuroanatomical implications on neurotransmitter systems. Lastly, we summarize recent investigations of using brainstem tractography as a promising tool in association with pain.

Temporally and functionally distinct large-scale brain network dynamics supporting task switching

Objective:

Our daily activities require frequent switches among competing responses at the millisecond time scale. We determined the spatiotemporal characteristics and functional significance of rapid, large-scale brain network dynamics during task switching.

Methods:

This cross-sectional study investigated patients with drug-resistant focal epilepsy who played a Lumosity cognitive flexibility training game during intracranial electroencephalography (iEEG) recording. According to a given task rule, unpredictably switching across trials, participants had to swipe the screen in the direction the stimulus was pointing or moving. Using this data, we described the spatiotemporal characteristics of iEEG high-gamma augmentation occurring more intensely during switch than repeat trials, unattributable to the effect of task rule (pointing or moving), within-stimulus congruence (the direction of stimulus pointing and moving was same or different in a given trial), or accuracy of an immediately preceding response. Diffusion-weighted imaging (DWI) tractography determined whether distant cortical regions showing enhanced activation during task switch trials were directly connected by white matter tracts. Trial-by-trial iEEG analysis deduced whether the intensity of task switch-related high-gamma augmentation was altered through practice and whether high-gamma amplitude predicted the accuracy of an upcoming response among switch trials.

Results:

The average number of completed trials during five-minute gameplay was 221.4 per patient (range: 171–285). Task switch trials increased the response times, whereas later trials reduced them. Analysis of iEEG signals sampled from 860 brain sites effectively elucidated the distinct spatiotemporal characteristics of task switch, task rule, and post-error-specific high-gamma modulations. Post-cue, task switch-related high-gamma augmentation was initiated in the right calcarine cortex after 260 ms, right precuneus after 330 ms, right entorhinal after 420 ms, and bilateral anterior middle-frontal gyri after 450 ms. DWI tractography successfully showed the presence of direct white matter tracts connecting the right visual areas to the precuneus and anterior middle-frontal regions but not between the right precuneus and anterior middle-frontal regions. Task-related high-gamma amplitudes in later trials were reduced in the calcarine, entorhinal and anterior middle-frontal regions, but increased in the precuneus. Functionally, enhanced post-cue precuneus high-gamma augmentation improved the accuracy of subsequent responses among switch trials.

Conclusions:

Our multimodal analysis uncovered two temporally and functionally distinct network dynamics supporting task switching. High-gamma augmentation in the visual-precuneus pathway may reflect the neural process facilitating an attentional shift to a given updated task rule. High-gamma activity in the visual-dorsolateral prefrontal pathway, rapidly reduced through practice, may reflect the cost of executing appropriate stimulus-response translation.

Lower fractional anisotropy of the corticothalamic tract and increased response time variability in adult patients with ADHD

BACKGROUND

Intraindividual intertrial variability has been suggested as an endophenotype of attention-deficit/hyperactivity disorder (ADHD). It is usually evaluated as response time variability (RTV) in reaction time tasks, and RTV has emerged as a robust and stable feature of ADHD. Among attempts to elucidate the neurobiological underpinnings of RTV, it has been suggested that alterations in white matter microstructure may explain RTV.

METHODS

We used diffusion tensor imaging (DTI) in a group of 53 adults with ADHD and 50 healthy controls. We obtained RTV parameters from a simple reaction-time task, in which participants were asked to respond to the appearance of white crosses on a screen using button presses.

RESULTS

We observed significant between-group differences for the ex-Gaussian parameter τ, indicating that the mean of extremely slow responses was greater for adults with ADHD than controls. Fractional anisotropy (FA) derived from DTI was significantly different between groups in 2 clusters of the corticothalamic tract. In the ADHD group, relatively decreased FA values were significantly associated with the parameter τ, such that lower FA values in the corticothalamic tract predicted greater τ as an index of RTV. We did not observe this association in healthy controls.

LIMITATIONS

For comparison with previous studies, we used FA as a dependent variable of interest. However, although this metric is sensitive to white matter structural properties, there are ambiguities in its interpretation.

CONCLUSION

Even in a simple reaction-time task, RTV proved again to be a stable feature of ADHD. It was associated with altered white matter structural properties of the corticothalamic tract in adults with ADHD.

Connectomic analysis of unilateral dual lead thalamic deep brain stimulation for treatment of multiple sclerosis tremor

Tremor is a common symptom in multiple sclerosis and can present as a severe postural and action tremor, leading to significant disability. Owing to the diffuse and progressive nature of the disease, it has been challenging to characterize the pathophysiology underlying multiple sclerosis tremor. Deep brain stimulation of the ventralis intermedius and the ventralis oralis posterior thalamic nuclei has been used to treat medically refractory multiple sclerosis tremors with variable results. The aim of this study was to characterize multiple sclerosis tremor at the network level by applying modern connectomic techniques to data from a previously completed single-centre, randomized, single-blind prospective trial of 12 subjects who were treated with unilateral dual-lead (ventralis intermedius + ventralis oralis posterior) thalamic deep brain stimulation. Preoperative T₁-weighted MRI and postoperative head CTs were used, along with applied programming settings, to estimate the volume of tissue activated for each patient. The volumes of tissue activated were then used to make voxel-wise and structural connectivity correlations with clinically observed tremor suppression. The volume of the tissue-activated analyses identified the optimal region of stimulation at the ventralis oralis posterior ventralis intermedius border intersecting with the dentato-rubro-thalamic tract. A regression model showed strong connectivity to the supplemental motor area was positively associated with tremor suppression (r = 0.66) in this cohort, whereas connectivity to the primary motor cortex was negatively associated with tremor suppression (r = −0.69), a finding opposite to that seen in ventralis intermedius deep brain stimulation for essential tremor. Comparing the structural connectivity to that of an essential tremor cohort revealed a distinct network that lies anterior to the essential tremor network. Overall, the volumes of tissue activated and connectivity observations converge to suggest that optimal suppression of multiple sclerosis tremor will likely be achieved by directing stimulation more anteriorly toward the ventralis oralis posterior and that a wide field of stimulation synergistically modulating the ventralis oralis posterior and ventralis intermedius nuclei may be more effective than traditional ventralis intermedius deep brain stimulation at suppressing the severe tremors commonly seen in complex tremor syndromes such as multiple sclerosis tremor.

A 4D infant brain volumetric atlas based on the UNC/UMN baby connectome project (BCP) cohort

Spatiotemporal (four-dimensional) infant-dedicated brain atlases are essential for neuroimaging analysis of early dynamic brain development. However, due to the substantial technical challenges in the acquisition and processing of infant brain MR images, 4D atlases densely covering the dynamic brain development during infancy are still scarce. Few existing ones generally have fuzzy tissue contrast and low spatiotemporal resolution, leading to degraded accuracy of atlas-based normalization and subsequent analyses. To address this issue, in this paper, we construct a 4D structural MRI atlas for infant brains based on the UNC/UMN Baby Connectome Project (BCP) dataset, which features a high spatial resolution, extensive age-range coverage, and densely sampled time points. Specifically, 542 longitudinal T1w and T2w scans from 240 typically developing infants up to 26-month of age were utilized for our atlas construction. To improve the co-registration accuracy of the infant brain images, which typically exhibit dynamic appearance with low tissue contrast, we employed the state-of-the-art registration method and leveraged our generated reliable brain tissue probability maps in addition to the intensity images to improve the alignment of individual images. To achieve consistent region labeling on both infant and adult brain images for facilitating region-based analysis across ages, we mapped the widely used Desikan cortical parcellation onto our atlas by following an age-decreasing mapping manner. Meanwhile, the typical subcortical structures were manually delineated to facilitate the studies related to the subcortex. Compared with the existing infant brain atlases, our 4D atlas has much higher spatiotemporal resolution and preserves more structural details, and thus can boost accuracy in neurodevelopmental analysis during infancy.

Prevalence of white matter pathways coming into a single white matter voxel orientation: The bottleneck issue in tractography

Characterizing and understanding the limitations of diffusion MRI fiber tractography is a prerequisite for methodological advances and innovations which will allow these techniques to accurately map the connections of the human brain. The so-called "crossing fiber problem" has received tremendous attention and has continuously triggered the community to develop novel approaches for disentangling distinctly oriented fiber populations. Perhaps an even greater challenge occurs when multiple white matter bundles converge within a single voxel, or throughout a single brain region, and share the same parallel orientation, before diverging and continuing towards their final cortical or sub-cortical terminations. These so-called "bottleneck" regions contribute to the ill-posed nature of the tractography process, and lead to both false positive and false negative estimated connections. Yet, as opposed to the extent of crossing fibers, a thorough characterization of bottleneck regions has not been performed. The aim of this study is to quantify the prevalence of bottleneck regions. To do this, we use diffusion tractography to segment known white matter bundles of the brain, and assign each bundle to voxels they pass through and to specific orientations within those voxels (i.e. fixels). We demonstrate that bottlenecks occur in greater than 50-70% of fixels in the white matter of the human brain. We find that all projection, association, and commissural fibers contribute to, and are affected by, this phenomenon, and show that even regions traditionally considered "single fiber voxels" often contain multiple fiber populations. Together, this study shows that a majority of white matter presents bottlenecks for tractography which may lead to incorrect or erroneous estimates of brain connectivity or quantitative tractography (i.e., tractometry), and underscores the need for a paradigm shift in the process of tractography and bundle segmentation for studying the fiber pathways of the human brain.

An atlas of white matter anatomy, its variability, and reproducibility based on constrained spherical deconvolution of diffusion MRI

Virtual dissection of white matter (WM) using diffusion MRI tractography is confounded by its poor reproducibility. Despite the increased adoption of advanced reconstruction models, early region-of-interest driven protocols based on diffusion tensor imaging (DTI) remain the dominant reference for virtual dissection protocols. Here we bridge this gap by providing a comprehensive description of typical WM anatomy reconstructed using a reproducible automated subject-specific parcellation-based approach based on probabilistic constrained-spherical deconvolution (CSD) tractography. We complement this with a WM template in MNI space comprising 68 bundles, including all associated anatomical tract selection labels and associated automated workflows. Additionally, we demonstrate bundle inter- and intra-subject variability using 40 (20 test-retest) datasets from the human connectome project (HCP) and 5 sessions with varying b-values and number of b-shells from the single-subject Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation (MASSIVE) dataset. The most reliably reconstructed bundles were the whole pyramidal tracts, primary corticospinal tracts, whole superior longitudinal fasciculi, frontal, parietal and occipital segments of the corpus callosum and middle cerebellar peduncles. More variability was found in less dense bundles, e.g., the fornix, dentato-rubro-thalamic tract (DRTT), and premotor pyramidal tract. Using the DRTT as an example, we show that this variability can be reduced by using a higher number of seeding attempts. Overall inter-session similarity was high for HCP test-retest data (median weighted-dice = 0.963, stdev = 0.201 and IQR = 0.099). Compared to the HCP-template bundles there was a high level of agreement for the HCP test-retest data (median weighted-dice = 0.747, stdev = 0.220 and IQR = 0.277) and for the MASSIVE data (median weighted-dice = 0.767, stdev = 0.255 and IQR = 0.338). In summary, this WM atlas provides an overview of the capabilities and limitations of automated subject-specific probabilistic CSD tractography for mapping white matter fasciculi in healthy adults. It will be most useful in applications requiring a reproducible parcellation-based dissection protocol, and as an educational resource for applied neuroimaging and clinical professionals.

Brain Connectometry Changes in Space Travelers After Long-Duration Spaceflight

Humans undergo extreme physiological changes when subjected to long periods of weightlessness, and as we continue to become a space-faring species, it is imperative that we fully understand the physiological changes that occur in the human body, including the brain. In this study, we present findings of brain structural changes associated with long-duration spaceflight based on diffusion magnetic resonance imaging (dMRI) data. Twelve cosmonauts who spent an average of six months aboard the International Space Station (ISS) were scanned in an MRI scanner pre-flight, ten days after flight, and at a follow-up time point seven months after flight. We performed differential tractography, a technique that confines white matter fiber tracking to voxels showing microstructural changes. We found significant microstructural changes in several large white matter tracts, such as the corpus callosum, arcuate fasciculus, corticospinal, corticostriatal, and cerebellar tracts. This is the first paper to use fiber tractography to investigate which specific tracts exhibit structural changes after long-duration spaceflight and may direct future research to investigate brain functional and behavioral changes associated with these white matter pathways.

The forgotten tract of vision in multiple sclerosis: vertical occipital fasciculus, its fiber properties, and visuospatial memory

Visual disturbances are a common disease manifestation in multiple sclerosis (MS) due to lesions damaging white matter tracts involved in vision. Vertical occipital fasciculus (VOF), a tract located vertically in the occipital lobe, was neglected for more than a century. We hypothesize that VOF is involved in integrating information between dorsal and ventral visual streams. Thus, its damage in MS, as well as its probable role in visual processing (by using MS as a VOF damage model) needs to be clarified. To study fiber characteristics of VOF in MS, and their clinical and visual learning associations, 57 relapsing-remitting MS (RRMS) and 25 healthy controls (HC) were recruited. We acquired MS Functional Composite, Expanded Disability Status Scale (EDSS), and Brief Visuospatial Memory Test-Revised (BVMT-R), and diffusion MRI scans. Tractography of VOF and optic radiation (OR) was done. VOF's metrics were statistically tested for between-group differences and clinical and visual tests associations. Along-tract analysis and laterality were also tested. RRMS patients had higher mean, axial, and radial diffusivity (nearly in all fiber points), and lower fractional anisotropy in bilateral VOFs compared to HC. No laterality was noted. These were associated with poor clinical outcomes, poor visual scores in EDSS, and lower total immediate and delayed recall in BVMT-R in RRMS, after adjusting for age, gender, and fiber metrics of OR. VOF damage is present in RRMS and is associated with visual symptoms and visuospatial learning impairments. It seems VOF is involved in integrating information between visual streams.

An interdisciplinary computational model for predicting traumatic brain injury: Linking biomechanics and functional neural networks

The brain is a complex network consisting of neuron cell bodies in the gray matter and their axonal projections, forming the white matter tracts. These neurons are supported by an equally complex vascular network as well as glial cells. Traumatic brain injury (TBI) can lead to the disruption of the structural and functional brain networks due to disruption of both neuronal cell bodies in the gray matter as well as their projections and supporting cells. To explore how an impact can alter the function of brain networks, we integrated a finite element (FE) brain mechanics model with linked models of brain dynamics (Kuramoto oscillator) and vascular perfusion (Balloon-Windkessel) in this study. We used empirical resting-state functional magnetic resonance imaging (MRI) data to optimize the fit of our brain dynamics and perfusion models to clinical data. Results from the FE model were used to mimic injury in these optimized brain dynamics models: injury to the nodes (gray matter) led to a decrease in the nodal oscillation frequency, while damage to the edges (axonal connections/white matter) progressively decreased coupling among connected nodes. A total of 53 cases, including 33 non-injurious and 20 concussive head impacts experienced by professional American football players were simulated using this integrated model. We examined the correlation of injury outcomes with global measures of structural connectivity, neural dynamics, and functional connectivity of the brain networks when using different lesion methods. Results show that injurious head impacts cause significant alterations in global network topology regardless of lesion methods. Changes between the disrupted and healthy functional connectivity (measured by Pearson correlation) consistently correlated well with injury outcomes (AUC≥0.75), although the predictive performance is not significantly different (p>0.05) to that of traditional kinematic measures (angular acceleration). Intriguingly, our lesion model for gray matter damage predicted increases in global efficiency and clustering coefficient with increases in injury risk, while disrupting axonal connections led to lower network efficiency and clustering. When both injury mechanisms were combined into a single injury prediction model, the injury prediction performance depended on the thresholds used to determine neurodegeneration and mechanical tolerance for axonal injury. Together, these results point towards complex effects of mechanical trauma to the brain and provide a new framework for understanding brain injury at a causal mechanistic level and developing more effective diagnostic methods and therapeutic interventions.

Controversy over the temporal cortical terminations of the left arcuate fasciculus: a reappraisal

The arcuate fasciculus has been considered a major dorsal fronto-temporal white matter pathway linking frontal language production regions with auditory perception in the superior temporal gyrus, the so-called Wernicke's area. In line with this tradition, both historical and contemporary models of language function have assigned primacy to superior temporal projections of the arcuate fasciculus. However, classical anatomical descriptions and emerging behavioural data are at odds with this assumption. On one hand, fronto-temporal projections to Wernicke's area may not be unique to the arcuate fasciculus. On the other hand, dorsal stream language deficits have been reported also for damage to middle, inferior and basal temporal gyri which may be linked to arcuate disconnection. These findings point to a reappraisal of arcuate projections in the temporal lobe. Here, we review anatomical and functional evidence regarding the temporal cortical terminations of the left arcuate fasciculus by incorporating dissection and tractography findings with stimulation data using cortico-cortical evoked potentials and direct electrical stimulation mapping in awake patients. Firstly, we discuss the fibers of the arcuate fasciculus projecting to the superior temporal gyrus and the functional rostro-caudal gradient in this region where both phonological encoding and auditory-motor transformation may be performed. Caudal regions within the temporoparietal junction may be involved in articulation and associated with temporoparietal projections of the third branch of the superior longitudinal fasciculus, while more rostral regions may support encoding of acoustic phonetic features, supported by arcuate fibres. We then move to examine clinical data showing that multimodal phonological encoding is facilitated by projections of the arcuate fasciculus to superior, but also middle, inferior and basal temporal regions. Hence, we discuss how projections of the arcuate fasciculus may contribute to acoustic (middle-posterior superior and middle temporal gyri), visual (posterior inferior temporal/fusiform gyri comprising the visual word form area) and lexical (anterior-middle inferior temporal/fusiform gyri in the basal temporal language area) information in the temporal lobe to be processed, encoded and translated into a dorsal phonological route to the frontal lobe. Finally, we point out surgical implications for this model in terms of the prediction and avoidance of neurological deficit.

Tractography of the Stria Terminalis in the Human Brain

The aim of this study was to investigate the trajectory of the stria terminalis and develop a protocol for mapping the stria terminalis using multi-shell diffusion images based tractography. The stria terminalis was reconstructed by combining one region of interest at the amygdala with another region of interest at the bed nucleus of stria terminalis. In addition, one region of avoidance was placed on the fornix at the interventricular foramen and another was set at the anterior perforated substance. The fiber-tracking protocol was tested in a Human Connectome Project-842 template, 35 healthy subjects from Massachusetts General Hospital, and 20 healthy subjects from the Human Connectome Project using generalized q-sampling imaging based tractography. The stria terminalis was reconstructed in the Human Connectome Project-842 template, 35 Massachusetts General Hospital healthy subjects, and 20 Human Connectome Project healthy subjects with our protocol. The stria terminalis originated from the amygdala and travelled parallel to the fornix. Then, the stria terminalis followed a C-shaped trajectory around the inferior, posterior, and dorsal surfaces of the thalamus before projecting to the bed nucleus of stria terminalis between the thalamus and caudate nucleus. There were no significant differences in the quantitative anisotropy and fractional anisotropy values between the left and right stria terminalis. The stria terminalis was accurately visualized across subjects using multi-shell diffusion images through generalized q-sampling imaging based tractography. This method could be an important tool for the reconstruction and evaluation of the stria terminalis in various neurological disorders. One Sentence Summary The visualizetion of the stria terminalis through the multi-shell diffusion images using generalized q-sampling imaging based tractography.

Differential Tractography and Correlation Tractography Findings on Patients With Mild Traumatic Brain Injury: A Pilot Study

Differential tractography and correlation tractography are new tractography modalities to study neuronal changes in brain diseases, but their performances in detecting neuronal injuries are yet to be investigated in patients with mild traumatic brain injury (mTBI). Here we investigated the white matter injury in mTBI patients using differential and correlation tractography. The diffusion MRI was acquired at 33 mTBI patients and 31 health controls. 7 of the mTBI patients had one-year follow-up scans, and differential tractography was used to evaluate injured fiber bundles on these 7 patients. All subjects were evaluated using digital symbol substitution test (DSST) and trail making test A (TMT-A), and the correlation tractography was performed to explore the exact pathways related to the cognitive performance. Our results showed that differential tractography revealed neuronal changes in the corpus callosum in all 7 follow-up mTBI patients with FDR between 0.007 and 0.17. Further, the correlation tractography showed that the splenium of the corpus callosum, combined with the right superior longitudinal fasciculus and right cingulum, were correlated with DSST (FDR = 0.001669) in the acute mTBI patients. The cognitive impairment findings in the acute stage and the longitudinal findings in the corpus callosum in the chronic stage of mTBI patients suggest that differential tractography and correlation tractography are valuable tools in the diagnostic and prognostic evaluation of neuronal injuries in mTBI patients.

Validating atlas-based lesion disconnectomics in multiple sclerosis: A retrospective multi-centric study

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Structural disconnectomes can be modelled without diffusion using tractography atlases.

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Atlas-based and DTI-derived disconnectome topological metrics correlate strongly.

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MS patient disconnectomes relate to clinical scores.

The translational potential of MR-based connectivity modelling is limited by the need for advanced diffusion imaging, which is not part of clinical protocols for many diseases. In addition, where diffusion data is available, brain connectivity analyses rely on tractography algorithms which imply two major limitations. First, tracking algorithms are known to be sensitive to the presence of white matter lesions and therefore leading to interpretation pitfalls and poor inter-subject comparability in clinical applications such as multiple sclerosis. Second, tractography quality is highly dependent on the acquisition parameters of diffusion sequences, leading to a trade-off between acquisition time and tractography precision.

Here, we propose an atlas-based approach to study the interplay between structural disconnectivity and lesions without requiring individual diffusion imaging. In a multi-centric setting involving three distinct multiple sclerosis datasets (containing both 1.5 T and 3 T data), we compare our atlas-based structural disconnectome computation pipeline to disconnectomes extracted from individual tractography and explore its clinical utility for reducing the gap between radiological findings and clinical symptoms in multiple sclerosis. Results using topological graph properties showed that overall, our atlas-based disconnectomes were suitable approximations of individual disconnectomes from diffusion imaging. Small-worldness was found to decrease for larger total lesion volumes thereby suggesting a loss of efficiency in brain connectivity of MS patients. Finally, the global efficiency of the created brain graph, combined with total lesion volume, allowed to stratify patients into subgroups with different clinical scores in all three cohorts.

Contribution of the medial eye field network to the voluntary deployment of visuospatial attention

Historically, the study of patients with spatial neglect has provided fundamental insights into the neural basis of spatial attention. However, lesion mapping studies have been unsuccessful in establishing the potential role of associative networks spreading on the dorsal-medial axis, mainly because they are uncommonly targeted by vascular injuries. Here we combine machine learning-based lesion-symptom mapping, disconnection analyses and the longitudinal behavioral data of 128 patients with well-delineated surgical resections. The analyses show that surgical resections in a location compatible with both the supplementary and the cingulate eye fields, and disrupting the dorsal-medial fiber network, are specifically associated with severely diminished performance on a visual search task (i.e., visuo-motor exploratory neglect) with intact performance on a task probing the perceptual component of neglect. This general finding provides causal evidence for a role of the frontal-medial network in the voluntary deployment of visuo-spatial attention.

Association Between Anatomical Location of Surgically Induced Lesions and Postoperative Seizure Outcome in Temporal Lobe Epilepsy

BACKGROUND AND OBJECTIVES

To determine the association between surgical lesions of distinct gray and white structures and connections with favorable postoperative seizure outcomes.

METHODS

Patients with drug-resistant temporal lobe epilepsy (TLE) from 3 epilepsy centers were included. We employed a voxel-based and connectome-based mapping approach to determine the association between favorable outcomes and surgery-induced temporal lesions. Analyses were conducted controlling for multiple confounders, including total surgical resection/ablation volume, hippocampal volumes, side of surgery, and site where the patient was treated.

RESULTS

The cohort included 113 patients with TLE (54 women; 86 right-handed; mean age at seizure onset 16.5 years [SD 11.9]; 54.9% left) who were 61.1% free of disabling seizures (Engel Class 1) at follow-up. Postoperative seizure freedom in TLE was associated with (1) surgical lesions that targeted the hippocampus as well as the amygdala-piriform cortex complex and entorhinal cortices; (2) disconnection of temporal, frontal, and limbic regions through loss of white matter tracts within the uncinate fasciculus, anterior commissure, and fornix; and (3) functional disconnection of the frontal (superior and middle frontal gyri, orbitofrontal region) and temporal (superior and middle pole) lobes.

DISCUSSION

Better postoperative seizure freedom is associated with surgical lesions of specific structures and connections throughout the temporal lobes. These findings shed light on the key components of epileptogenic networks in TLE and constitute a promising source of new evidence for future improvements in surgical interventions.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that for patients with TLE, postoperative seizure freedom is associated with surgical lesions of specific temporal lobe structures and connections.

Group-Level Ranking-Based Hubness Analysis of Human Brain Connectome Reveals Significant Interhemispheric Asymmetry and Intraparcel Heterogeneities

Objective: Graph theory applications are commonly used in connectomics research to better understand connectivity architecture and characterize its role in cognition, behavior and disease conditions. One of the numerous open questions in the field is how to represent inter-individual differences with graph theoretical methods to make inferences for the population. Here, we proposed and tested a simple intuitive method that is based on finding the correlation between the rank-ordering of nodes within each connectome with respect to a given metric to quantify the differences/similarities between different connectomes.

Methods: We used the diffusion imaging data of the entire HCP-1065 dataset of the Human Connectome Project (HCP) (n = 1,065 subjects). A customized cortical subparcellation of HCP-MMP atlas (360 parcels) (yielding a total of 1,598 ROIs) was used to generate connectivity matrices. Six graph measures including degree, strength, coreness, betweenness, closeness, and an overall “hubness” measure combining all five were studied. Group-level ranking-based aggregation method (“measure-then-aggregate”) was used to investigate network properties on population level.

Results: Measure-then-aggregate technique was shown to represent population better than commonly used aggregate-then-measure technique (overall r_s: 0.7 vs 0.5). Hubness measure was shown to highly correlate with all five graph measures (r_s: 0.88–0.99). Minimum sample size required for optimal representation of population was found to be 50 to 100 subjects. Network analysis revealed a widely distributed set of cortical hubs on both hemispheres. Although highly-connected hub clusters had similar distribution between two hemispheres, average ranking values of homologous parcels of two hemispheres were significantly different in 71% of all cortical parcels on group-level.

Conclusion: In this study, we provided experimental evidence for the robustness, limits and applicability of a novel group-level ranking-based hubness analysis technique. Graph-based analysis of large HCP dataset using this new technique revealed striking hemispheric asymmetry and intraparcel heterogeneities in the structural connectivity of the human brain.

Fast Streamline Search: An Exact Technique for Diffusion MRI Tractography

In this work, a hierarchical search algorithm is proposed to efficiently compute the distance between similar tractography streamlines. This hierarchical framework offers an upper bound and a lower bound for the point-wise distance between two streamlines, which guarantees the validity of a proximity search. The proposed streamline representation enables the use of space-partitioning search trees to increase the tractography clustering speed without reducing its accuracy. The resulting approach enables a fast reconstruction a sparse distance matrix between two sets of streamlines, for all similar streamlines within a given radius. Alongside a white matter atlas, this fast streamline search can be used for accurate and reproducible tractogram clustering.

Sex-Specific Patterns of Body Mass Index Relationship with White Matter Connectivity

BACKGROUND

Obesity is an increasingly recognized modifiable risk factor for Alzheimer's disease (AD). Increased body mass index (BMI) is related to distinct changes in white matter (WM) fiber density and connectivity.

OBJECTIVE

We investigated whether sex differentially affects the relationship between BMI and WM structural connectivity.

METHODS

A cross-sectional sample of 231 cognitively normal participants were enrolled from the Knight Alzheimer Disease Research Center. Connectome analyses were done with diffusion data reconstructed using q-space diffeomorphic reconstruction to obtain the spin distribution function and tracts were selected using a deterministic fiber tracking algorithm.

RESULTS

We identified an inverse relationship between higher BMI and lower connectivity in the associational fibers of the temporal lobe in overweight and obese men. Normal to overweight women showed a significant positive association between BMI and connectivity in a wide array of WM fibers, an association that reversed in obese and morbidly obese women. Interaction analyses revealed that with increasing BMI, women showed higher WM connectivity in the bilateral frontoparietal and parahippocampal parts of the cingulum, while men showed lower connectivity in right sided corticostriatal and corticopontine tracts. Subgroup analyses demonstrated comparable results in participants with and without positron emission tomography or cerebrospinal fluid evidence of brain amyloidosis, indicating that the relationship between BMI and structural connectivity in men and women is independent of AD biomarker status.

CONCLUSION

BMI influences structural connectivity of WM differently in men and women across BMI categories and this relationship does not vary as a function of preclinical AD.

TractEM: Evaluation of protocols for deterministic tractography white matter atlas

Reproducible identification of white matter pathways across subjects is essential for the study of structural connectivity of the human brain. One of the key challenges is anatomical differences between subjects and human rater subjectivity in labeling. Labeling white matter regions of interest presents many challenges due to the need to integrate both local and global information. Clearly communicating the manual processes to capture this information is cumbersome, yet essential to lay a solid foundation for comprehensive atlases. Segmentation protocols must be designed so the interpretation of the requested tasks as well as locating structural landmarks is anatomically accurate, intuitive and reproducible. In this work, we quantified the reproducibility of a first iteration of an open/public multi-bundle segmentation protocol. This allowed us to establish a baseline for its reproducibility as well as to identify the limitations for future iterations. The protocol was tested/evaluated on both typical 3 T research acquisition Baltimore Longitudinal Study of Aging (BLSA) and high-acquisition quality Human Connectome Project (HCP) datasets. The results show that a rudimentary protocol can produce acceptable intra-rater and inter-rater reproducibility. However, this work highlights the difficulty in generalizing reproducible results and the importance of reaching consensus on anatomical description of white matter pathways. The protocol has been made available in open source to improve generalizability and reliability in collaboration. The goal is to improve upon the first iteration and initiate a discussion on the anatomical validity (or lack thereof) of some bundle definitions and the importance of reproducibility of tractography segmentation.

Individual differences in white matter of the uncinate fasciculus and inferior fronto-occipital fasciculus: possible early biomarkers for callous-unemotional behaviors in young children with disruptive behavior problems

BACKGROUND

Callous-unemotional (CU) behaviors are important for identifying severe patterns of conduct problems (CP). One major fiber tract implicated in the development of CP is the uncinate fasciculus (UF), which connects amygdala and orbitofrontal cortex (OFC). The goals of the current study were to (a) explore differences in the white matter microstructure in the UF and other major fiber tracks between young typically developing (TD) children and those with a disruptive behavior disorder (DBD) and (b) explore, within the DBD group, whether individual differences in these white matter tracts relate to co-occurring CP and CU behaviors.

METHODS

Participants included 198 young children (69% boys, Mage  = 5.66 years; 80% Latinx; 48.5% TD). CU behaviors and CP were measured via a combination of teacher/parent ratings. Non-invasive diffusion-weighted imaging (DWI) was used to measure fractional anisotropy (FA), an indirect indicator of white matter properties.

RESULTS

Relative to TD children, children in the DBD group had reduced FA on four out of the five fiber tracks we examined (except for cingulum and right ILF), even after accounting for whole brain FA, sex, movement, parental income, and IQ. Within the DBD group, no associations were found between CP and reduced white matter integrity across any of the fiber tracks examined. However, we found that even after accounting for CP, ADHD symptomology, and a host of covariates (whole brain FA, sex, movement, parental income, and IQ), CU behaviors were independently related to reduced FA in bilateral UF and left inferior fronto-occipital fasciculus (IFOF) in the DBD group, but this was not the case for TD children.

CONCLUSIONS

Alterations in the white matter microstructure within bilateral UF and left IFOF may be biomarkers of CU behaviors, even in very young children.

Gait Abnormalities in Parkinson's Disease Are Associated with Extracellular Free-Water Characteristics in the Substantia Nigra

BACKGROUND

Gait impairments are common in Parkinson's disease (PD). The pathological mechanisms are complex and not thoroughly elucidated, thus quantitative and objective parameters that closely relate to gait characteristics are critically needed to improve the diagnostic assessments and monitor disease progression. The substantia nigra is a relay structure within basal ganglia brainstem loops that is centrally involved in gait modulation.

OBJECTIVE

We tested the hypothesis that quantitative gait biomechanics are related to the microstructural integrity of the substantia nigra and PD-relevant gait abnormalities are independent from bradykinesia-linked speed reductions.

METHODS

Thirty-eight PD patients and 33 age-matched control participants walked on a treadmill at fixed speeds. Gait parameters were fed into a principal component analysis to delineate relevant features. We applied the neurite orientation dispersion and density imaging (NODDI) model on diffusion-weighted MR-images to calculate the free-water content as an advanced marker of microstructural integrity of the substantia nigra and tested its associations with gait parameters.

RESULTS

Patients showed increased duration of stance phase, load response, pre-swing, and double support time, as well as reduced duration of single support and swing time. Gait rhythmic alterations associated positively with the free-water content in the right substantia nigra in PD, indicating that patients with more severe neurodegeneration extend the duration of stance phase, load response, and pre-swing.

CONCLUSION

The results provide evidence that gait alterations are not merely a byproduct of bradykinesia-related reduced walking speed. The data-supported association between free-water and the rhythmic component highlights the potential of substantia nigra microstructure imaging as a measure of gait-dysfunction and disease-progression.

White Matter Connectivity between Structures of the Basal Ganglia using 3T and 7T

Analysis of the basal ganglia has been important in investigating the effects of Parkinson's disease as well as treatments for Parkinson's disease. One method of analysis has been using MRI for non-invasively segmenting the basal ganglia, then investigating significant parameters that involve the basal ganglia, such as fiber orientations and positional markers for deep brain stimulation (DBS). Following enhancements to optimizations and improvements to 3T and 7T MRI acquisitions, we utilized Lead-DBS on human connectome project data to automatically segment the basal ganglia of 49 human connectome project subjects, reducing the reliance on manual segmentation for more consistency. We generated probabilistic tractography streamlines between each segmentation pair using 3T and 7T human connectome diffusion data to observe any major differences in tractography streamline patterns that can arise due to tradeoffs from different field strengths and acquisitions. Tractography streamlines generated between basal ganglia structures using 3T images showed less standard deviation in streamline count than using 7T images. Mean tractography streamline counts generated using 3T diffusion images were all higher in count than streamlines generated using 7T diffusion images. We illustrate a potential method for analyzing the structural connectivity between basal ganglia structures, as well as visualize possible differences in probabilistic tractography that can arise from different acquisition protocols.

Age-Related Trajectories of Brain Structure–Function Coupling in Female Roller Derby Athletes

Contact and collision sports are believed to accelerate brain aging. Postmortem studies of the human brain have implicated tau deposition in and around the perivascular space as a biomarker of an as yet poorly understood neurodegenerative process. Relatively little is known about the effects that collision sport participation has on the age-related trajectories of macroscale brain structure and function, particularly in female athletes. Diffusion MRI and resting-state functional MRI were obtained from female collision sport athletes (n = 19 roller derby (RD) players; 23–45 years old) and female control participants (n = 14; 20–49 years old) to quantify structural coupling (S_C) and decoupling (S_D). The novel and interesting finding is that RD athletes, but not controls, exhibited increasing S_C with age in two association networks: the frontoparietal network, important for cognitive control, and default-mode network, a task-negative network (permuted p = 0.0006). Age-related increases in S_C were also observed in sensorimotor networks (RD, controls) and age-related increases in S_D were observed in association networks (controls) (permuted p ≤ 0.0001). These distinct patterns suggest that competing in RD results in compressed neuronal timescales in critical networks as a function of age and encourages the broader study of female athlete brains across the lifespan.

White matter disconnectivity fingerprints causally linked to dissociated forms of alexia

For over 150 years, the study of patients with acquired alexia has fueled research aimed at disentangling the neural system critical for reading. An unreached goal, however, relates to the determination of the fiber pathways that root the different visual and linguistic processes needed for accurate word reading. In a unique series of neurosurgical patients with a tumor close to the visual word form area, we combine direct electrostimulation and population-based streamline tractography to map the disconnectivity fingerprints characterizing dissociated forms of alexia. Comprehensive analyses of disconnectivity matrices establish similarities and dissimilarities in the disconnection patterns associated with pure, phonological and lexical-semantic alexia. While disconnections of the inferior longitudinal and posterior arcuate fasciculi are common to all alexia subtypes, disconnections of the long arcuate and vertical occipital fasciculi are specific to phonological and pure alexia, respectively. These findings provide a strong anatomical background for cognitive and neurocomputational models of reading.

Six-dimensional dynamic tractography atlas of language connectivity in the developing brain

During a verbal conversation, our brain moves through a series of complex linguistic processing stages: sound decoding, semantic comprehension, retrieval of semantically coherent words, and overt production of speech outputs. Each process is thought to be supported by a network consisting of local and long-range connections bridging between major cortical areas. Both temporal and extratemporal lobe regions have functional compartments responsible for distinct language domains, including the perception and production of phonological and semantic components. This study provides quantitative evidence of how directly connected inter-lobar neocortical networks support distinct stages of linguistic processing across brain development. Novel six-dimensional tractography was used to intuitively visualize the strength and temporal dynamics of direct inter-lobar effective connectivity between cortical areas activated during each linguistic processing stage. We analysed 3401 non-epileptic intracranial electrode sites from 37 children with focal epilepsy (aged 5-20 years) who underwent extra-operative electrocorticography recording. Principal component analysis of auditory naming-related high-gamma modulations determined the relative involvement of each cortical area during each linguistic processing stage. To quantify direct effective connectivity, we delivered single-pulse electrical stimulation to 488 temporal and 1581 extratemporal lobe sites and measured the early cortico-cortical spectral responses at distant electrodes. Mixed model analyses determined the effects of naming-related high-gamma co-augmentation between connecting regions, age, and cerebral hemisphere on the strength of effective connectivity independent of epilepsy-related factors. Direct effective connectivity was strongest between extratemporal and temporal lobe site pairs, which were simultaneously activated between sentence offset and verbal response onset (i.e. response preparation period); this connectivity was approximately twice more robust than that with temporal lobe sites activated during stimulus listening or overt response. Conversely, extratemporal lobe sites activated during overt response were equally connected with temporal lobe language sites. Older age was associated with increased strength of inter-lobar effective connectivity especially between those activated during response preparation. The arcuate fasciculus supported approximately two-thirds of the direct effective connectivity pathways from temporal to extratemporal auditory language-related areas but only up to half of those in the opposite direction. The uncinate fasciculus consisted of <2% of those in the temporal-to-extratemporal direction and up to 6% of those in the opposite direction. We, for the first time, provided an atlas which quantifies and animates the strength, dynamics, and direction specificity of inter-lobar neural communications between language areas via the white matter pathways. Language-related effective connectivity may be strengthened in an age-dependent manner even after the age of 5.

The trajectory of the medial longitudinal fasciculus in the human brain: A diffusion imaging-based tractography study

The aim of this study is to investigate the trajectory of medial longitudinal fasciculus (MLF) and explore its anatomical relationship with the oculomotor nerve using tractography technique. The MLF and oculomotor nerve were reconstructed at the same time with preset three region of interests (ROIs): one set at the area of rostral midbrain, one placed on the MLF area at the upper pons, and one placed at the cisternal part of the oculomotor nerve. This mapping protocol was tested in an HCP‐1065 template, 35 health subjects from Massachusetts General Hospital (MGH), 20 healthy adults and 6 brainstem cavernous malformation (BCM) patients with generalized q‐sampling imaging (GQI)‐based tractography. Finally, the 200 μm brainstem template from Center for In Vivo Microscopy, Duke University (Duke CIVM), was used to validate the trajectory of reconstructed MLF. The MLF and oculomotor nerve were reconstructed in the HCP‐1065 template, 35 MGH health subjects, 20 healthy adults and 6 BCM patients. The MLF was in conjunction with the ipsilateral mesencephalic part of the oculomotor nerve. The displacement of MLF was identified in all BCM patients. Decreased QA, RDI and FA were found in the MLF of lesion side, indicating axonal loss and/or edema of displaced MLF. The reconstructed MLF in Duke CIVM brainstem 200 μm template corresponded well with histological anatomy. The MLF and oculomotor nerve were visualized accurately with our protocol using GQI‐based fiber tracking. This GQI‐based tractography is an important tool in the reconstruction and evaluation of MLF.

Tractography methods and findings in brain tumors and traumatic brain injury

White matter fiber tracking using diffusion magnetic resonance imaging (dMRI) provides a noninvasive approach to map brain connections, but improving anatomical accuracy has been a significant challenge since the birth of tractography methods. Utilizing tractography in brain studies therefore requires understanding of its technical limitations to avoid shortcomings and pitfalls. This review explores tractography limitations and how different white matter pathways pose different challenges to fiber tracking methodologies. We summarize the pros and cons of commonly-used methods, aiming to inform how tractography and its related analysis may lead to questionable results. Extending these experiences, we review the clinical utilization of tractography in patients with brain tumors and traumatic brain injury, starting from tensor-based tractography to more advanced methods. We discuss current limitations and highlight novel approaches in the context of these two conditions to inform future tractography developments.

White matter hyperintensities are associated with grey matter atrophy and cognitive decline in Alzheimer's disease and frontotemporal dementia

White matter hyperintensities (WMHs) are commonly assumed to represent non-specific cerebrovascular disease comorbid to neurodegenerative processes, rather than playing a synergistic role. We compared the impact of WMHs on grey matter (GM) atrophy and cognition in normal aging (n = 571), mild cognitive impairment (MCI, n = 551), Alzheimer's dementia (AD, n = 212), fronto-temporal dementia (FTD, n = 125), and Parkinson's disease (PD, n = 271). Longitudinal data were obtained from ADNI, FTLDNI, and PPMI datasets. Mixed-effects models were used to compare WMHs and GM atrophy between patients and controls and assess the impact of WMHs on GM atrophy and cognition. MCI, AD, and FTD patients had significantly higher WMH loads than controls. WMHs were related to GM atrophy in insular and parieto-occipital regions in MCI/AD, and frontal regions and basal ganglia in FTD. In addition, WMHs contributed to more severe cognitive deficits in AD and FTD compared to controls, whereas their impact in MCI and PD was not significantly different from controls. These results suggest potential synergistic effects between WMHs and proteinopathies in the neurodegenerative process in MCI, AD and FTD.

Spatiotemporal changes in diffusivity and anisotropy in fetal brain tractography

Population averaged diffusion atlases can be utilized to characterize complex microstructural changes with less bias than data from individual subjects. In this study, a fetal diffusion tensor imaging (DTI) atlas was used to investigate tract-based changes in anisotropy and diffusivity in vivo from 23 to 38 weeks of gestational age (GA). Healthy pregnant volunteers with typically developing fetuses were imaged at 3 T. Acquisition included structural images processed with a super-resolution algorithm and DTI images processed with a motion-tracked slice-to-volume registration algorithm. The DTI from individual subjects were used to generate 16 templates, each specific to a week of GA; this was accomplished by means of a tensor-to-tensor diffeomorphic deformable registration method integrated with kernel regression in age. Deterministic tractography was performed to outline the forceps major, forceps minor, bilateral corticospinal tracts (CST), bilateral inferior fronto-occipital fasciculus (IFOF), bilateral inferior longitudinal fasciculus (ILF), and bilateral uncinate fasciculus (UF). The mean fractional anisotropy (FA) and mean diffusivity (MD) was recorded for all tracts. For a subset of tracts (forceps major, CST, and IFOF) we manually divided the tractograms into anatomy conforming segments to evaluate within-tract changes. We found tract-specific, nonlinear, age related changes in FA and MD. Early in gestation, these trends appear to be dominated by cytoarchitectonic changes in the transient white matter fetal zones while later in gestation, trends conforming to the progression of myelination were observed. We also observed significant (local) heterogeneity in within-tract developmental trajectories for the CST, IFOF, and forceps major.

Longitudinal changes in brain parenchyma due to mild traumatic brain injury during the first year after injury

Chronic gray matter (GM) atrophy is a known consequence of moderate and severe traumatic brain injuries but has not been consistently shown in mild traumatic brain injury (mTBI). The aim of this study was to investigate the longitudinal effect of uncomplicated mTBI on the brain's GM and white matter (WM) from 6 weeks to 12 months after injury. Voxel-based-morphometry (VBM) was computed with the T1-weighted images of 48 uncomplicated mTBI patients and 37 orthopedic controls. Over the period from 6 weeks to 12 months, only patients who experienced uncomplicated mTBI, but not control participants, showed significant GM decrease predominantly in the right hemisphere along the GM-CSF border in lateral and medial portions of the sensorimotor cortex extending into the rolandic operculum, middle frontal gyrus, insula, and temporal pole. Additionally, only mTBI patients, but not controls, experienced significant WM decrease predominantly in the right hemisphere in the superior fasciculus longitudinalis, arcuate fasciculus, and cortical-pontine tracts as well as a significant WM increase in left arcuate fasciculus and left capsula extrema. We did not observe any significant change in the controls for the same time interval or any significant group differences in GM and WM probability at each of the two timepoints. This suggests that the changes along the brain tissue borders observed in the mTBI group represent a reorganization associated with subtle microscopical changes in intracortical myelin and not a direct degenerative process as a result of mTBI.

Accumbofrontal tract integrity is related to early life adversity and feedback learning

Abuse, neglect, exposure to violence, and other forms of early life adversity (ELA) are incredibly common and significantly impact physical and mental development. While important progress has been made in understanding the impacts of ELA on behavior and the brain, the preponderance of past work has primarily centered on threat processing and vigilance while ignoring other potentially critical neurobehavioral processes, such as reward-responsiveness and learning. To advance our understanding of potential mechanisms linking ELA and poor mental health, we center in on structural connectivity of the corticostriatal circuit, specifically accumbofrontal white matter tracts. Here, in a sample of 77 youth (Mean age = 181 months), we leveraged rigorous measures of ELA, strong diffusion neuroimaging methodology, and computational modeling of reward learning. Linking these different forms of data, we hypothesized that higher ELA would be related to lower quantitative anisotropy in accumbofrontal white matter. Furthermore, we predicted that lower accumbofrontal quantitative anisotropy would be related to differences in reward learning. Our primary predictions were confirmed, but similar patterns were not seen in control white matter tracts outside of the corticostriatal circuit. Examined collectively, our work is one of the first projects to connect ELA to neural and behavioral alterations in reward-learning, a critical potential mechanism linking adversity to later developmental challenges. This could potentially provide windows of opportunity to address the effects of ELA through interventions and preventative programming.

Jargonaphasia as a disconnection syndrome: A study combining white matter electrical stimulation and disconnectome mapping

BACKGROUND

In jargonaphasia, speech is fluent but meaningless. While neuropsychological evaluation may distinguish a neologistic component characterised by non-word production and a semantic component where pronounced words are real but speech is senseless, how this relates to the underlying white matter anatomy is debated.

OBJECTIVE

To identify white matter pathways causally involved in jargonaphasia.

METHODS

We retrospectively screened the intraoperative brain mapping data of 571 awake oncological resections using direct cortico-subcortical electrostimulation. Jargonaphasia was induced in 17 patients (19 sites) during a naming task. Stimulation sites were normalized to the Montreal Neurological Institute template space and used to generate individual disconnectome maps. Non-parametric voxelwise one and two sample t-tests were performed to identify the underlying white matter anatomy.

RESULTS

Jargonaphasia was induced only during stimulation of the left hemisphere. No cortical stimulation generated jargonaphasia. Subcortical sites causally associated with jargonaphasia clustered in 3 regions: in the temporal lobe (middle to inferior temporal gyri; n = 12), in the parietal lobe (supramarginal gyrus; n = 3) and in the temporal stem (n = 4). Disconnectome analysis indicated the inferior-fronto-occipital fasciculus (IFOF) was damaged in both neologistic and semantic jargonaphasia, while the involvement of the arcuate fasciculus was specific to neologistic jargonaphasia.

CONCLUSION

For the first time, we show that jargonaphasia is induced by white matter stimulation, hinting at disconnection. As IFOF disconnection unites both variants, these may represent a continuum of disorders distinguished by semantic impairment. Conversely, damage to the arcuate fasciculus in addition to the IFOF is specific to neologistic jargonaphasia, thus suggesting a dual-disconnection syndrome.

Fiber tractography bundle segmentation depends on scanner effects, vendor effects, acquisition resolution, diffusion sampling scheme, diffusion sensitization, and bundle segmentation workflow

When investigating connectivity and microstructure of white matter pathways of the brain using diffusion tractography bundle segmentation, it is important to understand potential confounds and sources of variation in the process. While cross-scanner and cross-protocol effects on diffusion microstructure measures are well described (in particular fractional anisotropy and mean diffusivity), it is unknown how potential sources of variation effect bundle segmentation results, which features of the bundle are most affected, where variability occurs, nor how these sources of variation depend upon the method used to reconstruct and segment bundles. In this study, we investigate six potential sources of variation, or confounds, for bundle segmentation: variation (1) across scan repeats, (2) across scanners, (3) across vendors (4) across acquisition resolution, (5) across diffusion schemes, and (6) across diffusion sensitization. We employ four different bundle segmentation workflows on two benchmark multi-subject cross-scanner and cross-protocol databases, and investigate reproducibility and biases in volume overlap, shape geometry features of fiber pathways, and microstructure features within the pathways. We find that the effects of acquisition protocol, in particular acquisition resolution, result in the lowest reproducibility of tractography and largest variation of features, followed by vendor-effects, scanner-effects, and finally diffusion scheme and b-value effects which had similar reproducibility as scan-rescan variation. However, confounds varied both across pathways and across segmentation workflows, with some bundle segmentation workflows more (or less) robust to sources of variation. Despite variability, bundle dissection is consistently able to recover the same location of pathways in the deep white matter, with variation at the gray matter/ white matter interface. Next, we show that differences due to the choice of bundle segmentation workflows are larger than any other studied confound, with low-to-moderate overlap of the same intended pathway when segmented using different methods. Finally, quantifying microstructure features within a pathway, we show that tractography adds variability over-and-above that which exists due to noise, scanner effects, and acquisition effects. Overall, these confounds need to be considered when harmonizing diffusion datasets, interpreting or combining data across sites, and when attempting to understand the successes and limitations of different methodologies in the design and development of new tractography or bundle segmentation methods.

Effective connectivity extracts clinically relevant prognostic information from resting state activity in stroke

Recent resting-state functional MRI studies in stroke patients have identified two robust biomarkers of acute brain dysfunction: a reduction of inter-hemispheric functional connectivity between homotopic regions of the same network, and an abnormal increase of ipsi-lesional functional connectivity between task-negative and task-positive resting-state networks. Whole-brain computational modelling studies, at the individual subject level, using undirected effective connectivity derived from empirically measured functional connectivity, have shown a reduction of measures of integration and segregation in stroke as compared to healthy brains. Here we employ a novel method, first, to infer whole-brain directional effective connectivity from zero-lagged and lagged covariance matrices, then, to compare it to empirically measured functional connectivity for predicting stroke versus healthy status, and patient performance (zero, one, multiple deficits) across neuropsychological tests. We also investigated the accuracy of functional connectivity versus model effective connectivity in predicting the long-term outcome from acute measures. Both functional and effective connectivity predicted healthy from stroke individuals significantly better than the chance-level; however, accuracy for the effective connectivity was significantly higher than for functional connectivity at 1- to 2-week, 3-month and 1-year post-stroke. Predictive functional connections mainly included those reported in previous studies (within-network inter-hemispheric and between task-positive and -negative networks intra-hemispherically). Predictive effective connections included additional between-network links. Effective connectivity was a better predictor than functional connectivity of the number of behavioural domains in which patients suffered deficits, both at 2-week and 1-year post-onset of stroke. Interestingly, patient deficits at 1-year time-point were better predicted by effective connectivity values at 2 weeks rather than at 1-year time-point. Our results thus demonstrate that the second-order statistics of functional MRI resting-state activity at an early stage of stroke, derived from a whole-brain effective connectivity, estimated in a model fitted to reproduce the propagation of neuronal activity, has pertinent information for clinical prognosis.

Impulsivity is related to overhasty risk learning in attention-deficit/hyperactivity disorder: A computational psychiatric approach

Attention-deficit/hyperactivity disorder (ADHD) is often accompanied by excessive risky behavior, and an impulsive trait has been proposed to be associated with risk-taking. However, the aspect of the cognitive process that impulsivity influences is not well understood. We hypothesized that impulsivity could be related to an overhasty shifting of beliefs during risk learning, thereby resulting in enhanced risk-taking behavior. In this study, we tested our hypothesis using the Bayesian modeling approach and predicted overhasty learning by a data-driven approach. We used an openly available task-based functional magnetic resonance imaging (fMRI) dataset. Participants with adult ADHD (n = 42) completed the balloon analog risk task (BART). By fitting our computational model that encapsulates the degree of overhasty learning, we estimated the degree of learning bias and investigated its relationship with Barratt Impulsiveness Scale (BIS) outcomes. Moreover, we created a connectome-based predictive model (CPM) based on fMRI data to predict the degree of risk-learning bias. The degree of overhasty learning in ADHD patients was significantly correlated with the BIS score (r = 0.424, p = 0.009). The CPM predicted the 'learning bias' parameter using negatively correlated edges (r = 0.341, p = 0.041; q² = 0.092). The 'hub nodes' in the predictive network were in the frontal lobe, including the orbitofrontal area. Our findings suggest that impulsivity in ADHD patients is associated with overhasty updating of beliefs during risk learning. Weak functional connectivity to the both dorso-lateral prefrontal and orbitofrontal lobes is predictive of the degree of overhasty learning.

Assessing different approaches to estimate single-subject metabolic connectivity from dynamic [18F]fluorodeoxyglucose Positron Emission Tomography data

Metabolic connectivity is conventionally calculated in terms of correlation of static positron emission tomography (PET) measurements across subjects. There is increasing interest in deriving metabolic connectivity at the single-subject level from dynamic PET data, in a similar way to functional magnetic resonance imaging. However, the strong multicollinearity among region-wise PET time-activity curves (TACs), their non-Gaussian distribution, and the choice of the best strategy for TAC standardization before metabolic connectivity estimation, are non-trivial methodological issues to be tackled.In this work we test four different approaches to estimate sparse inverse covariance matrices, as well as three similarity-based methods to derive adjacency matrices. These approaches, combined with three different TAC standardization strategies, are employed to quantify metabolic connectivity from dynamic [¹⁸F]fluorodeoxyglucose ([¹⁸F]FDG) PET data in four healthy subjects.

Transitive Inverse Consistent Rigid Longitudinal Registration of Diffusion Weighted Magnetic Resonance Imaging: A Case Study in Athletes With Repetitive Non-Concussive Head Injuries

Significant longitudinal changes in metrics derived from diffusion weighted magnetic resonance (MR) images of the brain have been observed in athletes subject to repetitive non-concussive head injuries (RHIs). Accurate alignment of longitudinal scans of a subject is an important step in detecting and quantifying these changes. Currently, tools such as DSI Studio [1], FreeSurfer [2], and FSL [3] perform pairwise rigid registration of all scans in a longitudinal sequence to the first time-point scan (or to another reference scan or template). While the rigid transformations obtained using this strategy can be computed in a manner that enforces inverse consistency, for the case of three or more scans, the transformations are not transitive. This can lead to discrepancy in the rigid transformations that can be measured in physical units. Using a diffusion MRI dataset collected and analyzed as part of a larger study in [4], [5], [6], we illustrate this discrepancy, and we show how it can lead to uncertainty in local/regional estimates of diffusion metrics including fractional anistropy (FA), mean diffusivity (MD), and quantitatve anisotropy (QA). Additionally, we propose a method to perform transitive longitudinal rigid registration of a sequence of scans in a manner that guarantees that the discrepancy in the transformations will be eliminated.Clinical relevance- This paper establishes that standard processing pipelines for performing longitudinal analysis of diffusion MR images of the brain exhibit registration discrepancies that can be eliminated.

Mapping the corticoreticular pathway from cortex-wide anterograde axonal tracing in the mouse

The corticoreticular pathway (CRP) has been implicated as an important mediator of motor recovery and rehabilitation after central nervous system damage. However, its origins, trajectory and laterality are not well understood. This study mapped the mouse CRP in comparison with the corticospinal tract (CST). We systematically searched the Allen Mouse Brain Connectivity Atlas (© 2011 Allen Institute for Brain Science) for experiments that used anterograde tracer injections into the right isocortex in mice. For each eligible experiment (N = 607), CRP and CST projection strength were quantified by the tracer volume reaching the reticular formation motor nuclei (RF_motor ) and pyramids, respectively. Tracer density in each brain voxel was also correlated with RF_motor versus pyramids projection strength to explore the relative trajectories of the CRP and CST. We found significant CRP projections originating from the primary and secondary motor cortices, anterior cingulate, primary somatosensory cortex, and medial prefrontal cortex. Compared with the CST, the CRP had stronger projections from each region except the primary somatosensory cortex. Ipsilateral projections were stronger than contralateral for both tracts (above the pyramidal decussation), but the CRP projected more bilaterally than the CST. The estimated CRP trajectory was anteromedial to the CST in the internal capsule and dorsal to the CST in the brainstem. Our findings reveal a widespread distribution of CRP origins and confirm strong bilateral CRP projections, theoretically increasing the potential for partial sparing after brain lesions and contralesional compensation after unilateral injury.

Discrete spectral eigenmode-resonance network of brain dynamics and connectivity

The problem of finding a compact natural representation of brain dynamics and connectivity is addressed using an expansion in terms of physical spatial eigenmodes and their frequency resonances. It is demonstrated that this discrete expansion via the system transfer function enables linear and nonlinear dynamics to be analyzed in compact form in terms of natural dynamic "atoms," each of which is a frequency resonance of an eigenmode. Because these modal resonances are determined by the system dynamics, not the investigator, they are privileged over widely used phenomenological patterns, and obviate the need for artificial discretizations and thresholding in coordinate space. It is shown that modal resonances participate as nodes of a discrete spectral network, are noninteracting in the linear regime, but are linked nonlinearly by wave-wave coalescence and decay processes. The modal resonance formulation is shown to be capable of speeding numerical calculations of strongly nonlinear interactions. Recent work in brain dynamics, especially based on neural field theory (NFT) approaches, allows eigenmodes and their resonances to be estimated from data without assuming a specific brain model. This means that dynamic equations can be inferred using system identification methods from control theory, rather than being assumed, and resonances can be interpreted as control-systems data filters. The results link brain activity and connectivity with control-systems functions such as prediction and attention via gain control and can also be linked to specific NFT predictions if desired, thereby providing a convenient bridge between physiologically based theories and experiment. Amplitudes of modes and resonances can also be tracked to provide a more direct and temporally localized representation of the dynamics than correlations and covariances, which are widely used in the field. By synthesizing many different lines of research, this work provides a way to link quantitative electrophysiological and imaging measurements, connectivity, brain dynamics, and function. This underlines the need to move between coordinate and spectral representations as required. Moreover, standard theoretical-physics approaches and mathematical methods can be used in place of ad hoc statistical measures such as those based on graph theory of artificially discretized and decimated networks, which are highly prone to selection effects and artifacts.