MaPPeRTrac: A Massively Parallel, Portable, and Reproducible Tractography Pipeline.

Large-scale diffusion MRI tractography remains a significant challenge. Users must orchestrate a complex sequence of instructions that requires many software packages with complex dependencies and high computational costs. We developed MaPPeRTrac, an edge-centric tractography pipeline that simplifies and accelerates this process in a wide range of high-performance computing (HPC) environments. It fully automates either probabilistic or deterministic tractography, starting from a subject's magnetic resonance imaging (MRI) data, including structural and diffusion MRI images, to the edge density image (EDI) of their structural connectomes. Dependencies are containerized with Singularity (now called Apptainer) and decoupled from code to enable rapid prototyping and modification. Data derivatives are organized with the Brain Imaging Data Structure (BIDS) to ensure that they are findable, accessible, interoperable, and reusable following FAIR principles. The pipeline takes full advantage of HPC resources using the Parsl parallel programming framework, resulting in the creation of connectome datasets of unprecedented size. MaPPeRTrac is publicly available and tested on commercial and scientific hardware, so it can accelerate brain connectome research for a broader user community. MaPPeRTrac is available at: https://github.com/LLNL/mappertrac .

Modulation of quorum sensing activity by copper sulfate, potassium dichromate, and cadmium chloride in biosensor strains.

Beyond their biological roles, metals have a strong impact on the environment. It has been reported that metals are also inhibitory of Quorum Sensing (QS) mechanisms, ones of the best characterized signaling systems in bacteria and fungi. We analyzed the effect of CuSO4, CdCl2, and K2Cr2O7, on QS systems sharing or differing in the bacterial host or the QS signal. The results in this study show that CuSO4 can not only be inhibitory, but also stimulatory of QS activity: at 0.2 mM increased six fold the activity in Chromobacterium subtsugae CV026. This behavior is related to the concentration of the metal and the particular QS system: E. coli MT102 (pJBA132) was no affected, but CuSO4 decreased the QS activity of Pseudomonas putida F117 (pKR-C12) to half its control values. K2Cr2O7 increased four and three folds the QS activities of E. coli MT102 (pJBA132) and P. putida F117 (pAS-C8), respectively, but without effect when combined with CuSO4 or CdCl2. CdCl2 only showed a positive effect in CV026 when combined with CuSO4. Results suggest that factors related with the culture conditions impact on the influence of the metals, and reinforce the importance of the environment in the modulation of QS activity.

Emotional Resilience Predicts Preserved White Matter Microstructure Following Mild Traumatic Brain Injury.

BACKGROUND: Adult patients with mild traumatic brain injury (mTBI) exhibit distinct phenotypes of emotional and cognitive functioning identified by latent profile analysis of clinical neuropsychological assessments. When discerned early after injury, these latent clinical profiles have been found to improve prediction of long-term outcomes from mTBI. The present study hypothesized that white matter (WM) microstructure is better preserved in an emotionally resilient mTBI phenotype compared with a neuropsychiatrically distressed mTBI phenotype. METHODS: The present study used diffusion magnetic resonance imaging to investigate and compare WM microstructure in major association, projection, and commissural tracts between the two phenotypes and over time. Diffusion magnetic resonance images from 172 patients with mTBI were analyzed to compute individual diffusion tensor imaging maps at 2 weeks and 6 months after injury. RESULTS: By comparing the diffusion tensor imaging parameters between the two phenotypes at global, regional, and voxel levels, emotionally resilient patients were shown to have higher axial diffusivity compared with neuropsychiatrically distressed patients early after mTBI. Longitudinal analysis revealed greater compromise of WM microstructure in neuropsychiatrically distressed patients, with greater decrease of global axial diffusivity and more widespread decrease of regional axial diffusivity during the first 6 months after injury compared with emotionally resilient patients. CONCLUSIONS: These results provide neuroimaging evidence of WM microstructural differences underpinning mTBI phenotypes identified from neuropsychological assessments and show differing longitudinal trajectories of these biological effects. These findings suggest that diffusion magnetic resonance imaging can provide short- and long-term imaging biomarkers of resilience.

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.

Diffusion Tensor Imaging Reveals Elevated Diffusivity of White Matter Microstructure that Is Independently Associated with Long-Term Outcome after Mild Traumatic Brain Injury: A TRACK-TBI Study.

Diffusion tensor imaging (DTI) literature on single-center studies contains conflicting results regarding acute effects of mild traumatic brain injury (mTBI) on white matter (WM) microstructure and the prognostic significance. This larger-scale multi-center DTI study aimed to determine how acute mTBI affects WM microstructure over time and how early WM changes affect long-term outcome. From Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI), a cohort study at 11 United States level 1 trauma centers, a total of 391 patients with acute mTBI ages 17 to 60 years were included and studied at two weeks and six months post-injury. Demographically matched friends or family of the participants were the control group (n = 148). Axial diffusivity (AD), fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) were the measures of WM microstructure. The primary outcome was the Glasgow Outcome Scale Extended (GOSE) score of injury-related functional limitations across broad life domains at six months post-injury. The AD, MD, and RD were higher and FA was lower in mTBI versus friend control (FC) at both two weeks and six months post-injury throughout most major WM tracts of the cerebral hemispheres. In the mTBI group, AD and, to a lesser extent, MD decreased in WM from two weeks to six months post-injury. At two weeks post-injury, global WM AD and MD were both independently associated with six-month incomplete recovery (GOSE <8 vs = 8) even after accounting for demographic, clinical, and other imaging factors. DTI provides reliable imaging biomarkers of dynamic WM microstructural changes after mTBI that have utility for patient selection and treatment response in clinical trials. Continued technological advances in the sensitivity, specificity, and precision of diffusion magnetic resonance imaging hold promise for routine clinical application in mTBI.

Latent Profile Analysis of Neuropsychiatric Symptoms and Cognitive Function of Adults 2 Weeks After Traumatic Brain Injury: Findings From the TRACK-TBI Study.

Importance: Heterogeneity across patients with traumatic brain injury (TBI) presents challenges for clinical care and intervention design. Identifying distinct clinical phenotypes of TBI soon after injury may inform patient selection for precision medicine clinical trials. Objective: To investigate whether distinct neurobehavioral phenotypes can be identified 2 weeks after TBI and to characterize the degree to which early neurobehavioral phenotypes are associated with 6-month outcomes. Design, Setting, and Participants: This prospective cohort study included patients presenting to 18 US level 1 trauma centers within 24 hours of TBI from 2014 to 2019 as part of the Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) study. Data were analyzed from January 28, 2020, to January 11, 2021. Exposures: TBI. Main Outcomes and Measures: Latent profiles (LPs) were derived from common dimensions of neurobehavioral functioning at 2 weeks after injury, assessed through National Institutes of Health TBI Common Data Elements (ie, Brief Symptom Inventory-18, Patient Health Questionnaire-9 Depression checklist, Posttraumatic Stress Disorder Checklist for DSM-5, PROMIS Pain Intensity scale, Insomnia Severity Index, Rey Auditory Verbal Learning Test, Wechsler Adult Intelligence Scale-Fourth Edition Coding and Symbol Search subtests, Trail Making Test, and NIH Toolbox Cognitive Battery Pattern Comparison Processing Speed, Dimensional Change Card Sort, Flanker Inhibitory Control and Attention, and Picture Sequence Memory subtests). Six-month outcomes were the Satisfaction With Life Scale (SWLS), Quality of Life after Brain Injury-Overall Scale (QOLIBRI-OS), Glasgow Outcome Scale-Extended (GOSE), and Rivermead Post-Concussion Symptoms Questionnaire (RPQ). Results: Among 1757 patients with TBI included, 1184 (67.4%) were men, and the mean (SD) age was 39.9 (17.0) years. LP analysis revealed 4 distinct neurobehavioral phenotypes at 2 weeks after injury: emotionally resilient (419 individuals [23.8%]), cognitively impaired (368 individuals [20.9%]), cognitively resilient (620 individuals [35.3%]), and neuropsychiatrically distressed (with cognitive weaknesses; 350 individuals [19.9%]). Adding LP group to models including demographic characteristics, medical history, Glasgow Coma Scale score, and other injury characteristics was associated with significantly improved estimation of association with 6-month outcome (GOSE R2 increase = 0.09-0.19; SWLS R2 increase = 0.12-0.22; QOLIBRI-OS R2 increase = 0.14-0.32; RPQ R2 = 0.13-0.34). Conclusions and Relevance: In this cohort study of patients with TBI presenting to US level-1 trauma centers, qualitatively distinct profiles of symptoms and cognitive functioning were identified at 2 weeks after TBI. These distinct phenotypes may help optimize clinical decision-making regarding prognosis, as well as selection and stratification for randomized clinical trials.

Concussion Disrupts Normal Brain White Matter Microstructural Symmetry.

Injuries and illnesses can alter the normal bilateral symmetry of the brain, and determining the extent of this disruption may be useful in characterizing the pathology. One way of quantifying brain symmetry is in terms of bilateral correlation of diffusion tensor metrics between homologous white matter tracts. With this approach, we hypothesized that the brains of patients with a concussion are more asymmetrical than those of healthy individuals without a history of a concussion. We scanned the brains of 35 normal individuals and 15 emergency department patients with a recent concussion. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were determined for regions of interest (ROI) defined by a standard white-matter atlas that included 21 bilateral ROIs. For each ROI pair, bilateral correlation coefficients were calculated and compared between the two subject groups. A symmetry index, defined as the ratio between the difference and the sum of bilateral measures, was also calculated for each ROI pair and compared between the groups. We found that in normal subjects, the extent of symmetry varied among regions and individuals, and at least subtle forms of structural lateralization were common across regions. In patients, higher asymmetry was found overall as well as in the corticospinal tract specifically. Results indicate that a concussion can manifest in brain asymmetry that deviates from a normal state. The clinical utility of characterizing post-concussion pathology as abnormal brain asymmetry merits further exploration.

Connectome mapping with edge density imaging differentiates pediatric mild traumatic brain injury from typically developing controls: proof of concept.

BACKGROUND: Although acute neurologic impairment might be transient, other long-term effects can be observed with mild traumatic brain injury. However, when pediatric patients with mild traumatic brain injury present for medical care, conventional imaging with CT and MR imaging often does not reveal abnormalities. OBJECTIVE: To determine whether edge density imaging can separate pediatric mild traumatic brain injury from typically developing controls. MATERIALS AND METHODS: Subjects were recruited as part of the "Therapeutic Resources for Attention Improvement using Neuroimaging in Traumatic Brain Injury" (TRAIN-TBI) study. We included 24 adolescents (χ=14.1 years of age, σ=1.6 years, range 10-16 years), 14 with mild traumatic brain injury (TBI) and 10 typically developing controls. Neurocognitive assessments included the pediatric version of the California Verbal Learning Test (CVLT) and the Attention Network Task (ANT). Diffusion MR imaging was acquired on a 3-tesla (T) scanner. Edge density images were computed utilizing fiber tractography. Principal component analysis (PCA) and support vector machines (SVM) were used in an exploratory analysis to separate mild TBI and control groups. The diagnostic accuracy of edge density imaging, neurocognitive tests, and fractional anisotropy (FA) from diffusion tensor imaging (DTI) was computed with two-sample t-tests and receiver operating characteristic (ROC) metrics. RESULTS: Support vector machine-principal component analysis of edge density imaging maps identified three white matter regions distinguishing pediatric mild TBI from controls. The bilateral tapetum, sagittal stratum, and callosal splenium identified mild TBI subjects with sensitivity of 79% and specificity of 100%. Accuracy from the area under the ROC curve (AUC) was 94%. Neurocognitive testing provided an AUC of 61% (CVLT) and 71% (ANT). Fractional anisotropy yielded an AUC of 48%. CONCLUSION: In this proof-of-concept study, we show that edge density imaging is a new form of connectome mapping that provides better diagnostic delineation between pediatric mild TBI and healthy controls than DTI or neurocognitive assessments of memory or attention.

Spectral graph theory of brain oscillations.

The relationship between the brain's structural wiring and the functional patterns of neural activity is of fundamental interest in computational neuroscience. We examine a hierarchical, linear graph spectral model of brain activity at mesoscopic and macroscopic scales. The model formulation yields an elegant closed-form solution for the structure-function problem, specified by the graph spectrum of the structural connectome's Laplacian, with simple, universal rules of dynamics specified by a minimal set of global parameters. The resulting parsimonious and analytical solution stands in contrast to complex numerical simulations of high dimensional coupled nonlinear neural field models. This spectral graph model accurately predicts spatial and spectral features of neural oscillatory activity across the brain and was successful in simultaneously reproducing empirically observed spatial and spectral patterns of alpha-band (8-12 Hz) and beta-band (15-30 Hz) activity estimated from source localized magnetoencephalography (MEG). This spectral graph model demonstrates that certain brain oscillations are emergent properties of the graph structure of the structural connectome and provides important insights towards understanding the fundamental relationship between network topology and macroscopic whole-brain dynamics. .