Streamline Count Research Articles

Radiomic white matter parameters of functional integrity of the corticospinal tract in high-grade glioma

Tractography has become a widely available tool for the planning of neurosurgical operations as well as for neuroscientific research. The absence of patient interaction makes it easily applicable. However, it leaves uncertainty about the functional relevance of the identified bundles. We retrospectively analyzed the correlation of white matter markers with their clinical function in 24 right-handed patients who underwent first surgery for high-grade glioma. Morphological affection of the corticospinal tract (CST) and grade of paresis were assessed before surgery. Tractography was performed manually with MRTrix3 and automatically with TractSeg. Median and mean fractional anisotropy (FA) from manual tractography showed a significant correlation with CST affection (p = 0.008) and paresis (p = 0.015, p = 0.026). CST affection correlated further most with energy, and surface-volume ratio (p = 0.014) from radiomic analysis. Paresis correlated most with maximum 2D column diameter (p = 0.005), minor axis length (p = 0.006), and kurtosis (p = 0.008) from radiomic analysis. Streamline count yielded no significant correlations. In conclusion, mean or median FA can be used for the assessment of CST integrity in high-grade glioma. Also, several radiomic parameters are suited to describe tract integrity and may be used to quantitatively analyze white matter in the future.

Decreased uncinate fasciculus integrity in functional seizures following traumatic brain injury.

The uncinate fasciculus (UF) has been implicated previously in contributing to the pathophysiology of functional (nonepileptic) seizures (FS). FS are frequently preceded by adverse life events (ALEs) and present with comorbid psychiatric symptoms, yet neurobiological correlates of these factors remain unclear. To address this gap, using advanced diffusion magnetic resonance imaging (dMRI), UF tracts in a large cohort of patients with FS and pre-existing traumatic brain injury (TBI + FS) were compared to those in patients with TBI without FS (TBI-only). We hypothesized that dMRI measures in UF structural connectivity would reveal UF differences when controlling for TBI status. Partial correlation tests assessed the potential relationships with psychiatric symptom severity measures. Participants with TBI-only (N = 46) and TBI + FS (N = 55) completed a series of symptom questionnaires and MRI scanning. Deterministic tractography via diffusion spectrum imaging (DSI) was implemented in DSI studio (https://dsi-studio.labsolver.org) with q-space diffeomorphic reconstruction (QSDR), streamline production, and manual segmentation to assess bilateral UF integrity. Fractional anisotropy (FA), radial diffusivity (RD), streamline counts, and their respective asymmetry indices (AIs) served as estimates of white matter integrity. Compared to TBI-only, TBI + FS participants demonstrated decreased left hemisphere FA and RD asymmetry index (AI) for UF tracts (both p < .05, false discovery rate [FDR] corrected). Additionally, TBI + FS reported higher symptom severity in depression, anxiety, and PTSD measures (all p < .01). Correlation tests comparing UF white matter integrity differences to psychiatric symptom severity failed to reach criteria for significance (all p > .05, FDR corrected). In a large, well-characterized sample, participants with FS had decreased white matter health after controlling for the history of TBI. Planned follow-up analysis found no evidence to suggest that UF connectivity measures are a feature of group differences in mood or anxiety comorbidities for FS. These findings suggest that frontolimbic structural connectivity may play a role in FS symptomology, after accounting for prior ALEs and comorbid psychopathology severity.

Feasibility study to unveil the potential: considerations of constrained spherical deconvolution tractography with unsedated neonatal diffusion brain MRI data.

The study aimed to (1) assess the feasibility constrained spherical deconvolution (CSD) tractography to reconstruct crossing fiber bundles with unsedated neonatal diffusion MRI (dMRI), and (2) demonstrate the impact of spatial and angular resolution and processing settings on tractography and derived quantitative measures. For the purpose of this study, the term-equivalent dMRIs (single-shell b800, and b2000, both 5 b0, and 45 gradient directions) of two moderate-late preterm infants (with and without motion artifacts) from a local cohort [Brain Imaging in Moderate-late Preterm infants (BIMP) study; Calgary, Canada] and one infant from the developing human connectome project with high-quality dMRI (using the b2600 shell, comprising 20 b0 and 128 gradient directions, from the multi-shell dataset) were selected. Diffusion tensor imaging (DTI) and CSD tractography were compared on b800 and b2000 dMRI. Varying image resolution modifications, (pre-)processing and tractography settings were tested to assess their impact on tractography. Each experiment involved visualizing local modeling and tractography for the corpus callosum and corticospinal tracts, and assessment of morphological and diffusion measures. Contrary to DTI, CSD enabled reconstruction of crossing fibers. Tractography was susceptible to image resolution, (pre-) processing and tractography settings. In addition to visual variations, settings were found to affect streamline count, length, and diffusion measures (fractional anisotropy and mean diffusivity). Diffusion measures exhibited variations of up to 23%. Reconstruction of crossing fiber bundles using CSD tractography with unsedated neonatal dMRI data is feasible. Tractography settings affected streamline reconstruction, warranting careful documentation of methods for reproducibility and comparison of cohorts.

Association between altered white matter networks and post operative ventricle volume in shunt-treated pediatric hydrocephalus

ObjectiveThe objective of this study was to use probabilistic tractography in combination with white matter microstructure metrics to characterize differences in white matter networks between shunt-treated pediatric hydrocephalus patients relative to healthy controls. We were also able to explore the relationship between these white matter networks and postoperative ventricle volume. MethodsNetwork-based statistics was used in combination with whole-brain probabilistic tractography to determine dysregulated white matter networks in a sample of patients with pediatric hydrocephalus (n = 8), relative to controls (n = 36). Metrics such as streamline count (SC), as well as the mean of the fractional anisotropy along a tract, axial diffusivity (AD), mean diffusivity (MD), and radial diffusivity (RD) were assessed. In networks that were found to be significantly different for patients with hydrocephalus, tracts were evaluated to assess their relationship with postoperative lateral ventricle volume. ResultsPatients with pediatric hydrocephalus had various networks that were either upregulated or downregulated relative to controls across all white matter measures. Predominately, network dysregulation occurred in tracts involving structures located outside of the frontal lobe. Furthermore tracts with values suggesting decreased white matter integrity were not only found between subcortical structures, but also cortical structures. While there were various tracts with white matter metrics that were initially predicted by lateral ventricle volume, only two tracts remained significant following multiple comparisons. ConclusionsThis cross-sectional study in pediatric patients with hydrocephalus and healthy controls demonstrated using whole-brain probabilistic tractography that there are various networks with dysregulated white matter integrity in hydrocephalus patients relative to controls. These dysregulated networks have tracts connecting structures throughout the brain, and the regions were predominately located centrally and posteriorly. Postoperative ventricle volume did not predict the white matter integrity of many tracts. Future studies with larger sample sizes are needed to further understand these results.

Structural abnormalities in the Fronto-Parietal Network: Linking white matter integrity to sustained attention deficits in Schizophrenia

Schizophrenia is associated with a range of cognitive deficits, among which impairments in sustained attention are particularly significant. Previous research has investigated functional changes in the fronto-parietal network (FPN) related to attentional control in schizophrenia. However, the role of structural connectivity within the FPN in sustained attention deficits remains under-explored. Utilizing diffusion tensor imaging (DTI), this study investigated white matter integrity in 75 participants, comprising 37 individuals with schizophrenia (SZ) and 38 healthy controls (HC). Psychomotor vigilance task (PVT) performance was assessed to gauge sustained attention. The SZ group showed a significant reduction in fractional anisotropy (FA) and streamline counts within white matter tracts connecting frontal and parietal regions, compared to the HC group. Further, significant negative correlations were found between PVT performance and white matter integrity measures within the SZ group, specifically in the left FPN. Our findings indicate that structural abnormalities in the FPN are associated with sustained attention deficits in schizophrenia. These results contribute to our understanding of the neurobiological mechanisms underlying cognitive impairments in schizophrenia and offer potential avenues for targeted therapeutic interventions. Further research is warranted to validate these outcomes and explore their clinical implications.

Symmetry differences of structural connectivity in multiple sclerosis and healthy state

Focal and diffuse cerebral damages occur in Multiple Sclerosis (MS) that promotes profound shifts in local and global structural connectivity parameters, mainly derived from diffusion tensor imaging. Most of the reconstruction analyses have applied conventional tracking algorithms largely based on the controversial streamline count. For a more credible explanation of the diffusion MRI signal, we used convex optimization modeling for the microstructure-informed tractography2 (COMMIT2) framework. All multi-shell diffusion data from 40 healthy controls (HCs) and 40 relapsing-remitting MS (RRMS) patients were transformed into COMMIT2-weighted matrices based on the Schefer-200 parcels atlas (7 networks) and 14 bilateral subcortical regions. The success of the classification process between MS and healthy state was efficiently predicted by the left DMN-related structures and visual network-associated pathways. Additionally, the lesion volume and age of onset were remarkably correlated with the components of the left DMN. Using complementary approaches such as global metrics revealed differences in WM microstructural integrity between MS and HCs (efficiency, strength). Our findings demonstrated that the cutting-edge diffusion MRI biomarkers could hold the potential for interpreting brain abnormalities in a more distinctive way.

Globally altered microstructural properties and network topology in Rasmussen's encephalitis.

Rasmussen's encephalitis is an immune-mediated brain disorder characterised by progressive unilateral cerebral atrophy, neuroinflammation, drug-resistant seizures and cognitive decline. However, volumetric changes and epileptiform EEG activity were also observed in the contralateral hemisphere, raising questions about the aetiology of contralateral involvement. In this study, we aim to investigate alterations of white matter integrity, structural network topology and network efficiency in Rasmussen's encephalitis using diffusion-tensor imaging. Fourteen individuals with Rasmussen's encephalitis (11 female, median onset 6 years, range 4-22, median disease duration at MRI 5 years, range 0-42) and 20 healthy control subjects were included. All subjects underwent T1-weighted structural and diffusion-tensor imaging. Diffusion-tensor images were analysed using the fixel-based analysis framework included in the MRtrix3 toolbox. Fibre density and cross-section served as a quantitative measure for microstructural white matter integrity. T1-weighted structural images were processed using FreeSurfer, subcortical segmentations and cortical parcellations using the Desikan-Killiany atlas served as nodes in a structural network model, edge weights were determined based on streamline count between pairs of nodes and compared using network-based statistics. Global efficiency was used to quantify network integration on an intrahemispheric level. All metrics were compared cross-sectionally between individuals with Rasmussen's encephalitis and healthy control subjects using sex and age as regressors and within the Rasmussen's encephalitis group using linear regression including age at onset and disease duration as independent variables. Relative to healthy control subjects, individuals with Rasmussen's encephalitis showed significantly (family-wise-error-corrected P < 0.05) lower fibre density and cross-section as well as edge weights in intrahemispheric connections within the ipsilesional hemisphere and in interhemispheric connections. Lower edge weights were noted in the contralesional hemisphere and in interhemispheric connections, with the latter being mainly affected within the first 2 years after disease onset. With longer disease duration, fibre density and cross-section significantly (uncorrected P < 0.01) decreased in both hemispheres. In the contralesional corticospinal tract, fibre density and cross-section significantly (uncorrected P < 0.01) increased with disease duration. Intrahemispheric edge weights (uncorrected P < 0.01) and global efficiency significantly increased with disease duration in both hemispheres (ipsilesional r = 0.74, P = 0.001; contralesional r = 0.67, P = 0.012). Early disease onset was significantly (uncorrected P < 0.01) negatively correlated with lower fibre density and cross-section bilaterally. Our results show that the disease process of Rasmussen's encephalitis is not limited to the cortex of the lesioned hemisphere but should be regarded as a network disease affecting white matter across the entire brain and causing degenerative as well as compensatory changes on a network level.

Individualized targeting is warranted in subcallosal cingulate gyrus deep brain stimulation for treatment-resistant depression: A tractography analysis.

Subcallosal cingulate gyrus (SCG) is a target of deep brain stimulation (DBS) for treatment-resistant depression. However, previous randomized controlled trials report that approximately 42% of patients are responders to this therapy of last resort, and suboptimal targeting of SCG is a potential underlying factor to this unsatisfactory efficacy. Tractography has been proposed as a supplementary method to enhance targeting strategy. We performed a connectivity-based segmentation in the SCG region via probabilistic tractography in 100 healthy volunteers from the Human Connectome Project. The SCG voxels with maximum connectivity to brain regions implicated in depression, including Brodmann Area 10 (BA10), cingulate cortex, thalamus, and nucleus accumbens were identified, and the conjunctions were deemed as tractography-based targets. We then performed deterministic tractography using these targets in additional 100 volunteers to calculate streamline counts compassing to relevant brain regions and fibers. We also evaluated the intra- and inter-subject variance using test-retest dataset. Two tractography-based targets were identified. Tractography-based target-1 had the highest streamline counts to right BA10 and bilateral cingulate cortex, while tractography-based target-2 had the highest streamline counts to bilateral nucleus accumbens and uncinate fasciculus. The mean linear distance from individual tractography-based target to anatomy-based target was 3.2 ± 1.8 mm and 2.5 ± 1.4 mm in left and right hemispheres. The mean ± SD of targets between intra- and inter-subjects were 2.2 ± 1.2 and 2.9 ± 1.4 in left hemisphere, and 2.3 ± 1.4 and 3.1 ± 1.7 in right hemisphere, respectively. Individual heterogeneity as well as inherent variability from diffusion imaging should be taken into account during SCG-DBS target planning procedure.

Structural connectivity mediates the relationship between blood oxygenation and cognitive function in sickle cell anemia.

In sickle cell disease, the relative importance of reduced hemoglobin (Hb) and peripheral oxygen saturation on brain structure remains uncertain. We applied graph-theoretical analysis to diffusion magnetic resonance imaging data to investigate the effect of structural brain connectivity on cognitive function, alongside the presence or absence, number, and volume of silent cerebral infarction. In patients, we investigated the relationships between network properties, blood oxygenation, and cognition (working memory and processing speed indices). Based on streamline counts and fractional anisotropy, we identified a subnetwork with weakened connectivity in 92 patients with sickle cell disease (91 homozygous for HbS [HbSS], 1 heterozygote with HbSβ0 thalassemia; 49 males; aged 8.0 to 38.8 y), compared with 54 control subjects (22 males; aged 6.7 to 30.6 y). Multiple regression analyses showed a significant effect of Hb on full-network edge density (P< .05) and of peripheral oxygen saturation on streamline-weighted subnetwork efficiency (P< .01). There were effects of fractional anisotropy-weighted full-network and subnetwork efficiency on working memory index (both P< .05), and of streamline-weighted subnetwork efficiency on processing speed index (P= .05). However, there were no effects of presence, number or volume of silent cerebral infarcts. Streamline-weighted efficiency was progressively lower with lower oxygen saturation, with a downstream effect on the processing speed index. In path analysis, indirect relationships between blood oxygenation and cognition, mediated by network properties, were better supported than direct alternatives, with an indirect relationship between low oxygen saturation and processing speed index in patients, mediated by structural connectivity efficiency in a subnetwork of the brain differing from control subjects. Our findings are consistent with the notion that cognitive impairment is primarily mediated by hypoxic-ischemic effects on normal-appearing white matter and highlight the utility of network-based methods in providing biomarkers of cognitive dysfunction in patients with sickle cell disease.

Young versus older subject diffusion magnetic resonance imaging data for virtual white matter lesion tractography.

White matter hyperintensity (WMH) lesions on T2 fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) and changes in adjacent normal-appearing white matter can disrupt computerized tract reconstruction and result in inaccurate measures of structural brain connectivity. The virtual lesion approach provides an alternative strategy for estimating structural connectivity changes due to WMH. To assess the impact of using young versus older subject diffusion MRI data for virtual lesion tractography, we leveraged recently available diffusion MRI data from the Human Connectome Project (HCP) Lifespan database. Neuroimaging data from 50 healthy young (39.2 ± 1.6 years) and 46 healthy older (74.2 ± 2.5 years) subjects were obtained from the publicly available HCP-Aging database. Three WMH masks with low, moderate, and high lesion burdens were extracted from the WMH lesion frequency map of locally acquired FLAIR MRI data. Deterministic tractography was conducted to extract streamlines in 21 WM bundles with and without the WMH masks as regions of avoidance in both young and older cohorts. For intact tractography without virtual lesion masks, 7 out of 21 WM pathways showed a significantly lower number of streamlines in older subjects compared to young subjects. A decrease in streamline count with higher native lesion burden was found in corpus callosum, corticostriatal tract, and fornix pathways. Comparable percentages of affected streamlines were obtained in young and older groups with virtual lesion tractography using the three WMH lesion masks of increasing severity. We conclude that using normative diffusion MRI data from young subjects for virtual lesion tractography of WMH is, in most cases, preferable to using age-matched normative data.

Characterizing Streamline Count Invariant Graph Measures of Structural Connectomes.

While graph measures are used increasingly to characterize human connectomes, uncertainty remains in how to use these metrics in a quantitative and reproducible manner. Specifically, there is a lack of community consensus regarding the number of streamlines needed to generate connectomes. The purpose was to define the relationship between streamline count and graph-measure value, reproducibility, and repeatability. Retrospective analysis of previously prospective study. Ten healthy subjects, 70% female, aged 25.3 ± 5.9 years. A 3-T, T1-weighted sequences and diffusion-weighted imaging (DWI) with two gradient strengths (b-values=1200 and 3000 sec/mm2 , echo time [TE]=68 msec, repetition time [TR]=5.4seconds, 120 slices, field of view=188 mm2 ). A total of 13 graph-theory measures were derived for each subject by generating probabilistic whole-brain tractography from DWI and mapping the structural connectivity to connectomes. The streamline count invariance from changes in mean, repeatability, and reproducibility were derived. Paired t-test with P value <0.05 was used to compare graph-measure means with a reference, intraclass correlation coefficient (ICC) to measure repeatability, and concordance correlation coefficient (CCC) to measure reproducibility. Modularity and global efficiency converged to their reference mean with ICC > 0.90 and CCC > 0.99. Edge count, small-worldness, randomness, and average betweenness centrality converged to the reference mean, with ICC > 0.90 and CCC > 0.95. Assortativity and average participation coefficient converged with ICC > 0.75 and CCC > 0.90. Density, average node strength, average node degree, characteristic path length, average local efficiency, and average clustering coefficient did not converge, though had ICC > 0.90 and CCC > 0.99. For these measures, alternate definitions that converge a reference mean are provided. Modularity and global efficiency are streamline count invariant for greater than 6 million and 100,000 streamlines, respectively. Density, average node strength, average node degree, characteristic path length, average local efficiency, and average clustering coefficient were strongly dependent on streamline count. 1. Stage 1.

Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes.

There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population-wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N=8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size.

Effects of White-Matter Tract Length in Sport-Related Concussion: A Tractography Study from the NCAA-DoD CARE Consortium.

Sport-related concussion (SRC) is an important public health issue. White-matter alterations after SRC are widely studied by neuroimaging approaches, such as diffusion magnetic resonance imaging (MRI). Although the exact anatomical location of the alterations may differ, significant white-matter alterations are commonly observed in long fiber tracts, but are never proven. In the present study, we performed streamline tractography to characterize the association between tract length and white-matter microstructural alterations after SRC. Sixty-eight collegiate athletes diagnosed with acute concussion (24-48 h post-injury) and 64 matched contact-sport controls were included in this study. The athletes underwent diffusion tensor imaging (DTI) in 3.0 T MRI scanners across three study sites. DTI metrics were used for tract-based spatial statistics to map white-matter regions-of-interest (ROIs) with significant group differences. Whole-brain white-mater streamline tractography was performed to extract "affected" white-matter streamlines (i.e., streamlines passing through the identified ROIs). In the concussed athletes, streamline counts and DTI metrics of the affected white-matter fiber tracts were summarized and compared with unaffected white-matter tracts across tract length in the same participant. The affected white-matter tracts had a high streamline count at length of 80-100 mm and high length-adjusted affected ratio for streamline length longer than 80 mm. DTI mean diffusivity was higher in the affected streamlines longer than 100 mm with significant associations with the Brief Symptom Inventory score. Our findings suggest that long fibers in the brains of collegiate athletes are more vulnerable to acute SRC with higher mean diffusivity and a higher affected ratio compared with the whole distribution.

Large-Scale Exploration of Whole-Brain Structural Connectivity in Anorexia Nervosa: Alterations in the Connectivity of Frontal and Subcortical Networks

BackgroundAnorexia nervosa (AN) is characterized by disturbances in cognition and behavior surrounding eating and weight. The severity of AN combined with the absence of localized brain abnormalities suggests distributed, systemic underpinnings that may be identified using diffusion-weighted magnetic resonance imaging and tractography to reconstruct white matter pathways. MethodsDiffusion-weighted magnetic resonance imaging data acquired from female patients with AN (n= 147) and female healthy control (HC) participants (n = 119), ages 12 to 40 years, were combined across 5 studies. Probabilistic tractography was completed, and full-cortex connectomes describing streamline counts between 84 brain regions were generated and harmonized. Graph theory methods were used to describe alterations in network organization in AN. The network-based statistic tested between-group differences in brain subnetwork connectivity. The metrics strength and efficiency indexed the connectivity of brain regions (network nodes) and were compared between groups using multiple linear regression. ResultsIndividuals with AN, relative to HC peers, had reduced connectivity in a network comprising subcortical regions and greater connectivity between frontal cortical regions (p < .05, familywise error corrected). Node-based analyses indicated reduced connectivity of the left hippocampus in patients relative to HC peers (p < .05, permutation corrected). Severity of illness, assessed by body mass index, was associated with subcortical connectivity (p < .05, uncorrected). ConclusionsAnalyses identified reduced structural connectivity of subcortical networks and regions, and stronger cortical network connectivity, among individuals with AN relative to HC peers. These findings are consistent with alterations in feeding, emotion, and executive control circuits in AN, and may direct hypothesis-driven research into mechanisms of persistent restrictive eating behavior.

The Case for Optimized Edge-Centric Tractography at Scale.

The anatomic validity of structural connectomes remains a significant uncertainty in neuroimaging. Edge-centric tractography reconstructs streamlines in bundles between each pair of cortical or subcortical regions. Although edge bundles provides a stronger anatomic embedding than traditional connectomes, calculating them for each region-pair requires exponentially greater computation. We observe that major speedup can be achieved by reducing the number of streamlines used by probabilistic tractography algorithms. To ensure this does not degrade connectome quality, we calculate the identifiability of edge-centric connectomes between test and re-test sessions as a proxy for information content. We find that running PROBTRACKX2 with as few as 1 streamline per voxel per region-pair has no significant impact on identifiability. Variation in identifiability caused by streamline count is overshadowed by variation due to subject demographics. This finding even holds true in an entirely different tractography algorithm using MRTrix. Incidentally, we observe that Jaccard similarity is more effective than Pearson correlation in calculating identifiability for our subject population.

Episodic Past, Future, and counterfactual thinking in Relapsing-Remitting Multiple sclerosis

Multiple sclerosis (MS) is a progressive disease characterized by widespread white matter lesions in the brain and spinal cord. In addition to well-characterized motor deficits, MS results in cognitive impairments in several domains, notably in episodic autobiographical memory. Recent studies have also revealed that patients with MS exhibit deficits in episodic future thinking, i.e., our capacity to imagine possible events that may occur in our personal future. Both episodic memory and episodic future thinking have been shown to share cognitive and neural mechanisms with a related kind of hypothetical simulation known as episodic counterfactual thinking: our capacity to imagine alternative ways in which past personal events could have occurred but did not. However, the extent to which episodic counterfactual thinking is affected in MS is still unknown. The current study sought to explore this issue by comparing performance in mental simulation tasks involving either past, future or counterfactual thoughts in relapsing-remitting MS. Diffusion weighted imaging (DWI) measures were also extracted to determine whether changes in structural pathways connecting the brain’s default mode network (DMN) would be associated with group differences in task performance. Relative to controls, patients showed marked reductions in the number of internal details across all mental simulations, but no differences in the number of external and semantic-based details. It was also found that, relative to controls, patients with relapsing-remitting MS reported reduced composition ratings for episodic simulations depicting counterfactual events, but not so for actual past or possible future episodes. Additionally, three DWI measures of white matter integrity—fractional anisotropy, radial diffusivity and streamline counts—showed reliable differences between patients with relapsing-remitting MS and matched healthy controls. Importantly, DWI measures associated with reduced white matter integrity in three association tracts on the DMN—the right superior longitudinal fasciculus, the left hippocampal portion of the cingulum and the left inferior longitudinal fasciculus—predicted reductions in the number of internal details during episodic counterfactual simulations. Taken together, these results help to illuminate impairments in episodic simulation in relapsing-remitting MS and show, for the first time, a differential association between white matter integrity and deficits in episodic counterfactual thinking in individuals with relapsing-remitting MS.

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.

Altered Structural Brain Networks in Refractory and Nonrefractory Idiopathic Generalized Epilepsy.

Introduction: Idiopathic generalized epilepsy (IGE) is a collection of generalized nonlesional epileptic network disorders. Around 20-40% of patients with IGE are refractory to antiseizure medication, and mechanisms underlying refractoriness are poorly understood. Here, we characterize structural brain network alterations and determine whether network alterations differ between patients with refractory and nonrefractory IGE. Methods: Thirty-three patients with IGE (10 nonrefractory and 23 refractory) and 39 age- and sex-matched healthy controls were studied. Network nodes were segmented from T1-weighted images, while connections between these nodes (edges) were reconstructed from diffusion magnetic resonance imaging (MRI). Diffusion networks of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and streamline count (Count) were studied. Differences between all patients, refractory, nonrefractory, and control groups were computed using network-based statistics. Nodal volume differences between groups were computed using Cohen's d effect size calculation. Results: Patients had significantly decreased bihemispheric FA and Count networks and increased MD and RD networks compared with controls. Alterations in network architecture, with respect to controls, differed depending on treatment outcome, including predominant FA network alterations in refractory IGE and increased nodal volume in nonrefractory IGE. Diffusion MRI networks were not influenced by nodal volume. Discussion: Although a nonlesional disorder, patients with IGE have bihemispheric structural network alterations that may differ between patients with refractory and nonrefractory IGE. Given that distinct nodal volume and FA network alterations were observed between treatment outcome groups, a multifaceted network analysis may be useful for identifying imaging biomarkers of refractory IGE.

The Social Cerebellum: A Large-Scale Investigation of Functional and Structural Specificity and Connectivity.

The cerebellum has been traditionally disregarded in relation to nonmotor functions, but recent findings indicate it may be involved in language, affective processing, and social functions. Mentalizing, or Theory of Mind (ToM), is the ability to infer mental states of others and this skill relies on a distributed network of brain regions. Here, we leveraged large-scale multimodal neuroimaging data to elucidate the structural and functional role of the cerebellum in mentalizing. We used functional activations to determine whether the cerebellum has a domain-general or domain-specific functional role, and effective connectivity and probabilistic tractography to map the cerebello-cerebral mentalizing network. We found that the cerebellum is organized in a domain-specific way and that there is a left cerebellar effective and structural lateralization, with more and stronger effective connections from the left cerebellar hemisphere to the right cerebral mentalizing areas, and greater cerebello-thalamo-cortical and cortico-ponto-cerebellar streamline counts from and to the left cerebellum. Our study provides novel insights to the network organization of the cerebellum, an overlooked brain structure, and mentalizing, one of humans' most essential abilities to navigate the social world.

Predicting MEG resting-state functional connectivity from microstructural information.

Understanding how human brain microstructure influences functional connectivity is an important endeavor. In this work, magnetic resonance imaging data from 90 healthy participants were used to calculate structural connectivity matrices using the streamline count, fractional anisotropy, radial diffusivity, and a myelin measure (derived from multicomponent relaxometry) to assign connection strength. Unweighted binarized structural connectivity matrices were also constructed. Magnetoencephalography resting-state data from those participants were used to calculate functional connectivity matrices, via correlations of the Hilbert envelopes of beamformer time series in the delta, theta, alpha, and beta frequency bands. Nonnegative matrix factorization was performed to identify the components of the functional connectivity. Shortest path length and search-information analyses of the structural connectomes were used to predict functional connectivity patterns for each participant. The microstructure-informed algorithms predicted the components of the functional connectivity more accurately than they predicted the total functional connectivity. This provides a methodology to understand functional mechanisms better. The shortest path length algorithm exhibited the highest prediction accuracy. Of the weights of the structural connectivity matrices, the streamline count and the myelin measure gave the most accurate predictions, while the fractional anisotropy performed poorly. Overall, different structural metrics paint very different pictures of the structural connectome and its relationship to functional connectivity.