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.

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. .

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.

Regional vulnerability of hippocampal subfields to aging measured by structural and diffusion MRI.

In the past few years, there has been an increasing awareness of the regional vulnerability of the hippocampus to age-related processes. However, to date, no studies have assessed the effects of age on different structural magnetic resonance parameters in the specific hippocampal subfields. In this study, we measured volume, mean diffusivity (MD) and fractional anisotropy (FA) in the presubiculum, subiculum, fimbria, cornu ammonis (CA) 1,2-3,4-DG and the whole hippocampus in fifty cognitively intact elder adults between 50 and 75 years of age (20 men, 30 women). Segmentation of hippocampal subfields was performed using FreeSurfer. Individual MD and FA images were coregistered to T1-weighted volumes using FLIRT of FSL. Linear regression analyses were performed to assess the effects of age on the anatomical measures of each subfield. In addition, multiple regression analyses were also carried out to assess which of the anatomical measures that showed a correlation with age in the previous analyses, were the best age predictors in the hippocampus. In agreement with previous studies, our results showed a significant association between age and volume (P < 0.001) as well as MD (P < 0.001) in the whole hippocampus. Regarding the specific hippocampal subfields, we found that age had a significant negative effect on volume in CA2-3 (P < 0.001) and CA4-DG (P < 0.001). Importantly, we found a positive effect of age on MD in CA2-3 (P < 0.001) and fimbria (P < 0.001) as well as a negative age effect on FA in the subiculum (P < 0.001). Multiple regression analyses revealed that the best overall predictors of age in the hippocampus were MD in the fimbria and volume of CA2-3, which explained 73.8% of the age variance. These results indicate that age has an effect both on volume and diffusion tensor imaging measures in different subfields, suggesting they provide complementary information on age-related processes in the hippocampus.

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 .

A role for the default mode network in the bases of disorders of consciousness.

OBJECTIVE: Functional connectivity in the default mode network (DMN) is known to be reduced in patients with disorders of consciousness, to a different extent depending on their clinical severity. Nevertheless, the integrity of the structural architecture supporting this network and its relation with the exhibited functional disconnections are very poorly understood. We investigated the structural connectivity and white matter integrity of the DMN in patients with disorders of consciousness of varying clinical severity. METHODS: Fifty-two patients--19 in a vegetative state (VS), 27 in a minimally conscious state (MCS), and 6 emerging from a minimally conscious state (EMCS)--and 23 healthy volunteers participated in the study. Structural connectivity was assessed by means of probabilistic tractography, and the integrity of the resulting fibers was characterized by their mean fractional anisotropy values. RESULTS: Patients showed significant impairments in all of the pathways connecting cortical regions within this network, as well as the pathway connecting the posterior cingulate cortex/precuneus with the thalamus, relative to the healthy volunteers. Moreover, the structural integrity of this pathway, as well as that of those connecting the posterior areas of the network, was correlated with the patients' behavioral signs for awareness, being higher in EMCS patients than those in the upper and lower ranges of the MCS patients, and lowest in VS patients. INTERPRETATION: These results provide a possible neural substrate for the functional disconnection previously described in these patients, and reinforce the importance of the DMN in the genesis of awareness and the neural bases of its disorders.

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.

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.

Diffusion tensor imaging differences relate to memory deficits in diffuse traumatic brain injury.

BACKGROUND: Memory is one of the most impaired functions after traumatic brain injury (TBI). We used diffusion tensor imaging (DTI) to determine the structural basis of memory deficit. We correlated fractional anisotropy (FA) of the fasciculi connecting the main cerebral regions that are involved in declarative and working memory functions. METHODS: Fifteen patients with severe and diffuse TBI and sixteen healthy controls matched by age and years of education were scanned. The neuropsychological assessment included: Letter-number sequencing test (LNS), 2-back task, digit span (forwards and backwards) and the Rivermead profilet. DTI was analyzed by a tract-based spatial statics (TBSS) approach. RESULTS: Whole brain DTI analysis showed a global decrease in FA values that correlated with the 2-back d-prime index, but not with the Rivermead profile. ROI analysis revealed positive correlations between working memory performance assessed by 2-back d-prime and superior longitudinal fasciculi, corpus callosum, arcuate fasciculi and fornix. Declarative memory assessed by the Rivermead profile scores correlated with the fornix and the corpus callosum. CONCLUSIONS: Diffuse TBI is associated with a general decrease of white matter integrity. Nevertheless deficits in specific memory domains are related to different patterns of white matter damage.

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.

Altered Cerebellar White Matter in Sensory Processing Dysfunction Is Associated With Impaired Multisensory Integration and Attention.

Sensory processing dysfunction (SPD) is characterized by a behaviorally observed difference in the response to sensory information from the environment. While the cerebellum is involved in normal sensory processing, it has not yet been examined in SPD. Diffusion tensor imaging scans of children with SPD (n = 42) and typically developing controls (TDC; n = 39) were compared for fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) across the following cerebellar tracts: the middle cerebellar peduncles (MCP), superior cerebellar peduncles (SCP), and cerebral peduncles (CP). Compared to TDC, children with SPD show reduced microstructural integrity of the SCP and MCP, characterized by reduced FA and increased MD and RD, which correlates with abnormal auditory behavior, multisensory integration, and attention, but not tactile behavior or direct measures of auditory discrimination. In contradistinction, decreased CP microstructural integrity in SPD correlates with abnormal tactile and auditory behavior and direct measures of auditory discrimination, but not multisensory integration or attention. Hence, altered cerebellar white matter organization is associated with complex sensory behavior and attention in SPD, which prompts further consideration of diagnostic measures and treatments to better serve affected individuals.

Disrupted White Matter Microstructure of the Cerebellar Peduncles in Scholastic Athletes After Concussion.

Concussion, or mild traumatic brain injury (mTBI), is a major public health concern, linked with persistent post-concussive syndrome, and chronic traumatic encephalopathy. At present, standard clinical imaging fails to reliably detect traumatic axonal injury associated with concussion and post-concussive symptoms. Diffusion tensor imaging (DTI) is an MR imaging technique that is sensitive to changes in white matter microstructure. Prior studies using DTI did not jointly investigate white matter microstructure in athletes, a population at high risk for concussive and subconcussive head traumas, with those in typical emergency room (ER) patients. In this study, we determine DTI scalar metrics in both ER patients and scholastic athletes who suffered concussions and compared them to those in age-matched healthy controls. In the early subacute post-concussion period, athletes demonstrated an elevated rate of regional decreases in axial diffusivity (AD) compared to controls. These regional decreases of AD were especially pronounced in the cerebellar peduncles, and were more frequent in athletes compared to the ER patient sample. The group differences may indicate differences in the mechanisms of the concussive impacts as well as possible compound effects of cumulative subconcussive impacts in athletes. The prevalence of white matter abnormality in cerebellar tracts lends credence to the hypothesis that post-concussive symptoms are caused by shearing of axons within an attention network mediated by the cerebellum, and warrant further study of the correlation between cerebellar DTI findings and clinical, neurocognitive, oculomotor, and vestibular outcomes in mTBI patients.

White Matter Connectome Correlates of Auditory Over-Responsivity: Edge Density Imaging and Machine-Learning Classifiers.

Sensory over-responsivity (SOR) commonly involves auditory and/or tactile domains, and can affect children with or without additional neurodevelopmental challenges. In this study, we examined white matter microstructural and connectome correlates of auditory over-responsivity (AOR), analyzing prospectively collected data from 39 boys, aged 8-12 years. In addition to conventional diffusion tensor imaging (DTI) maps - including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD); we used DTI and high-resolution T1 scans to develop connectome Edge Density (ED) maps. The tract-based spatial statistics was used for voxel-wise comparison of diffusion and ED maps. Then, stepwise penalized logistic regression was applied to identify independent variable (s) predicting AOR, as potential imaging biomarker (s) for AOR. Finally, we compared different combinations of machine learning algorithms (i.e., naïve Bayes, random forest, and support vector machine (SVM) and tract-based DTI/connectome metrics for classification of children with AOR. In direct sensory phenotype assessment, 15 (out of 39) boys exhibited AOR (with or without neurodevelopmental concerns). Voxel-wise analysis demonstrates extensive impairment of white matter microstructural integrity in children with AOR on DTI maps - evidenced by lower FA and higher MD and RD; moreover, there was lower connectome ED in anterior-superior corona radiata, genu and body of corpus callosum. In stepwise logistic regression, the average FA of left superior longitudinal fasciculus (SLF) was the single independent variable distinguishing children with AOR (p = 0.007). Subsequently, the left SLF average FA yielded an area under the curve of 0.756 in receiver operating characteristic analysis for prediction of AOR (p = 0.008) as a region-of-interest (ROI)-based imaging biomarker. In comparative study of different combinations of machine-learning models and DTI/ED metrics, random forest algorithms using ED had higher accuracy for AOR classification. Our results demonstrate extensive white matter microstructural impairment in children with AOR, with specifically lower connectomic ED in anterior-superior tracts and associated commissural pathways. Also, average FA of left SLF can be applied as ROI-based imaging biomarker for prediction of SOR. Finally, machine-learning models can provide accurate and objective image-based classifiers for identification of children with AOR based on white matter tracts connectome ED.

Diffusion tensor tractography in children with sensory processing disorder: Potentials for devising machine learning classifiers.

The "sensory processing disorder" (SPD) refers to brain's inability to organize sensory input for appropriate use. In this study, we determined the diffusion tensor imaging (DTI) microstructural and connectivity correlates of SPD, and apply machine learning algorithms for identification of children with SPD based on DTI/tractography metrics. A total of 44 children with SPD and 41 typically developing children (TDC) were prospectively recruited and scanned. In addition to fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD), we applied probabilistic tractography to generate edge density (ED) and track density (TD) from DTI maps. For identification of children with SPD, accurate classification rates from a combination of DTI microstructural (FA, MD, AD, and RD), connectivity (TD) and connectomic (ED) metrics with different machine learning algorithms - including naïve Bayes, random forest, support vector machine, and neural networks - were determined. In voxel-wise analysis, children with SPD had lower FA, ED, and TD but higher MD and RD compared to TDC - predominantly in posterior white matter tracts including posterior corona radiata, posterior thalamic radiation, and posterior body and splenium of corpus callosum. In stepwise penalized logistic regression, the only independent variable distinguishing children with SPD from TDC was the average TD in the splenium (p < 0.001). Among different combinations of machine learning algorithms and DTI/connectivity metrics, random forest models using tract-based TD yielded the highest accuracy in classification of SPD - 77.5% accuracy, 73.8% sensitivity, and 81.6% specificity. Our findings demonstrate impaired microstructural and connectivity/connectomic integrity in children with SPD, predominantly in posterior white matter tracts, and with reduced TD of the splenium of corpus callosum as the most distinctive pattern. Applying machine learning algorithms, these connectivity metrics can be used to devise novel imaging biomarkers for neurodevelopmental disorders.

White Matter Connectome Edge Density in Children with Autism Spectrum Disorders: Potential Imaging Biomarkers Using Machine-Learning Models.

Prior neuroimaging studies have reported white matter network underconnectivity as a potential mechanism for autism spectrum disorder (ASD). In this study, we examined the structural connectome of children with ASD using edge density imaging (EDI), and then applied machine-learning algorithms to identify children with ASD based on tract-based connectivity metrics. Boys aged 8-12 years were included: 14 with ASD and 33 typically developing children. The edge density (ED) maps were computed from probabilistic streamline tractography applied to high angular resolution diffusion imaging. Tract-based spatial statistics was used for voxel-wise comparison and coregistration of ED maps in addition to conventional diffusion tensor imaging (DTI) metrics of fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD). Tract-based average DTI/connectome metrics were calculated and used as input for different machine-learning models: naïve Bayes, random forest, support vector machines (SVMs), and neural networks. For these models, cross-validation was performed with stratified random sampling ( × 1,000 permutations). The average accuracy among validation samples was calculated. In voxel-wise analysis, the body and splenium of corpus callosum, bilateral superior and posterior corona radiata, and left superior longitudinal fasciculus showed significantly lower ED in children with ASD; whereas, we could not find significant difference in FA, MD, and RD maps between the two study groups. Overall, machine-learning models using tract-based ED metrics had better performance in identification of children with ASD compared with those using FA, MD, and RD. The EDI-based random forest models had greater average accuracy (75.3%), specificity (97.0%), and positive predictive value (81.5%), whereas EDI-based polynomial SVM had greater sensitivity (51.4%) and negative predictive values (77.7%). In conclusion, we found reduced density of connectome edges in the posterior white matter tracts of children with ASD, and demonstrated the feasibility of connectome-based machine-learning algorithms in identification of children with ASD.

Recovery After Mild Traumatic Brain Injury in Patients Presenting to US Level I Trauma Centers: A Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Study.

IMPORTANCE: Most traumatic brain injuries (TBIs) are classified as mild (mTBI) based on admission Glasgow Coma Scale (GCS) scores of 13 to 15. The prevalence of persistent functional limitations for these patients is unclear. OBJECTIVES: To characterize the natural history of recovery of daily function following mTBI vs peripheral orthopedic traumatic injury in the first 12 months postinjury using data from the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study, and, using clinical computed tomographic (CT) scans, examine whether the presence (CT+) or absence (CT-) of acute intracranial findings in the mTBI group was associated with outcomes. DESIGN, SETTING, AND PARTICIPANTS: TRACK-TBI, a cohort study of patients with mTBI presenting to US level I trauma centers, enrolled patients from February 26, 2014, to August 8, 2018, and followed up for 12 months. A total of 1453 patients at 11 level I trauma center emergency departments or inpatient units met inclusion criteria (ie, mTBI [n = 1154] or peripheral orthopedic traumatic injury [n = 299]) and were enrolled within 24 hours of injury; mTBI participants had admission GCS scores of 13 to 15 and clinical head CT scans. Patients with peripheral orthopedic trauma injury served as the control (OTC) group. EXPOSURES: Participants with mTBI or OTC. MAIN OUTCOMES AND MEASURES: The Glasgow Outcome Scale Extended (GOSE) scale score, reflecting injury-related functional limitations across broad life domains at 2 weeks and 3, 6, and 12 months postinjury was the primary outcome. The possible score range of the GOSE score is 1 (dead) to 8 (upper good recovery), with a score less than 8 indicating some degree of functional impairment. RESULTS: Of the 1453 participants, 953 (65.6%) were men; mean (SD) age was 40.9 (17.1) years in the mTBI group and 40.9 (15.4) years in the OTC group. Most participants (mTBI, 87%; OTC, 93%) reported functional limitations (GOSE <8) at 2 weeks postinjury. At 12 months, the percentage of mTBI participants reporting functional limitations was 53% (95% CI, 49%-56%) vs 38% (95% CI, 30%-45%) for OTCs. A higher percentage of CT+ patients reported impairment (61%) compared with the mTBI CT- group (49%; relative risk [RR], 1.24; 95% CI, 1.08-1.43) and a higher percentage in the mTBI CT-group compared with the OTC group (RR, 1.28; 95% CI, 1.02-1.60). CONCLUSIONS AND RELEVANCE: Most patients with mTBI presenting to US level I trauma centers report persistent, injury-related life difficulties at 1 year postinjury, suggesting the need for more systematic follow-up of patients with mTBI to provide treatments and reduce the risk of chronic problems after mTBI.