Shared and unique heritability of hippocampal subregion volumes in children and adults.

Behavioral genetic analyses have not demonstrated robust, unique, genetic correlates of hippocampal subregion volume. Genetic differentiation of hippocampal longitudinal axis subregion volume has not yet been investigated in population-based samples, although this has been demonstrated in rodent and post-mortem human tissue work. The following study is the first population-based investigation of genetic factors that contribute to gray matter volume along the hippocampal longitudinal axis. Twin-based biometric analyses demonstrated that longitudinal axis subregions are associated with significant, unique, genetic variance, and that longitudinal axis subregions are also associated with significant shared, hippocampus-general, genetic factors. Our study's findings suggest that genetic differences in hippocampal longitudinal axis structure can be detected in individual differences in gray matter volume in population-level research designs.

The role of neural load effects in predicting individual differences in working memory function.

Studies of working memory (WM) function have tended to adopt either a within-subject approach, focusing on effects of load manipulations, or a between-subjects approach, focusing on individual differences. This dichotomy extends to WM neuroimaging studies, with different neural correlates being identified for within- and between-subjects variation in WM. Here, we examined this issue in a systematic fashion, leveraging the large-sample Human Connectome Project dataset, to conduct a well-powered, whole-brain analysis of the N-back WM task. We first demonstrate the advantages of parcellation schemes for dimension reduction, in terms of load-related effect sizes. This parcel-based approach is then utilized to directly compare the relationship between load-related (within-subject) and behavioral individual differences (between-subject) effects through both correlational and predictive analyses. The results suggest a strong linkage of within-subject and between-subject variation, with larger load-effects linked to stronger brain-behavior correlations. In frontoparietal cortex no hemispheric biases were found towards one type of variation, but the Dorsal Attention Network did exhibit greater sensitivity to between over within-subjects variation, whereas in the Somatomotor network, the reverse pattern was observed. Cross-validated predictive modeling capitalizing on this tight relationship between the two effects indicated greater predictive power for load-activated than load-deactivated parcels, while also demonstrating that load-related effect size can serve as an effective guide to feature (i.e., parcel) selection, in maximizing predictive power while maintaining interpretability. Together, the findings demonstrate an important consistency across within- and between-subjects approaches to identifying the neural substrates of WM, which can be effectively harnessed to develop more powerful predictive models.

Statistical Guidelines for Handling Missing Data in Traumatic Brain Injury Clinical Research.

Missing data is a persistent and unavoidable problem in even the most carefully designed traumatic brain injury (TBI) clinical research. Missing data patterns may result from participant dropout, non-compliance, technical issues, or even death. This review describes the types of missing data that are common in TBI research, and assesses the strengths and weaknesses of the statistical approaches used to draw conclusions and make clinical decisions from these data. We review recent innovations in missing values analysis (MVA), a relatively new branch of statistics, as applied to clinical TBI data. Our discussion focuses on studies from the International Traumatic Brain Injury Research (InTBIR) initiative project: Transforming Research and Clinical Knowledge in TBI (TRACK-TBI), Collaborative Research on Acute TBI in Intensive Care Medicine in Europe (CREACTIVE), and Approaches and Decisions in Acute Pediatric TBI Trial (ADAPT). In addition, using data from the TRACK-TBI pilot study (n = 586) and the completed clinical trial assessing valproate (VPA) for the treatment of post-traumatic epilepsy (n = 379) we present real-world examples of typical missing data patterns and the application of statistical techniques to mitigate the impact of missing data in order to draw sound conclusions from ongoing clinical studies.

Assessment of Neighborhood Poverty, Cognitive Function, and Prefrontal and Hippocampal Volumes in Children.

Importance: The association between poverty and unfavorable cognitive outcomes is robust, but most research has focused on individual household socioeconomic status (SES). There is increasing evidence that neighborhood context explains unique variance not accounted for by household SES. Objective: To evaluate whether neighborhood poverty (NP) is associated with cognitive function and prefrontal and hippocampal brain structure in ways that are dissociable from household SES. Design, Setting, and Participants: This cross-sectional study used a baseline sample of the ongoing longitudinal Adolescent Brain Cognitive Development (ABCD) Study. The ABCD Study will follow participants for assessments each year for 10 years. Data were collected at 21 US sites, mostly within urban and suburban areas, between September 2019 and October 2018. School-based recruitment was used to create a participant sample reflecting the US population. Data analysis was conducted from March to June 2019. Main Outcomes and Measures: NP and household SES were included as factors potentially associated with National Institutes of Health Toolbox Cognitive Battery subtests and hippocampal and prefrontal (dorsolateral prefrontal cortex [DLPFC], dorsomedial PFC [DMPFC], superior frontal gyrus [SFG]) volumes. Independent variables were first considered individually and then together in mixed-effects models with age, sex, and intracranial volume as covariates. Structural equation modeling (SEM) was used to assess shared variance in NP to brain structure and cognitive task associations. The tested hypotheses were formulated after data collection. Results: A total of 11 875 children aged 9 and 10 years (5678 [47.8%] girls) were analyzed. Greater NP was associated with lower scores across all cognitive domains (eg, total composite: ß = -0.18; 95% CI, -0.21 to -0.15; P < .001) and with decreased brain volume in the DLPFC (eg, right DLPFC: ß = -0.09; 95% CI, -0.12 to -0.07; P < .001), DMPFC (eg, right DMPC: ß = -0.07; 95% CI, -0.09 to -0.05; P < .001), SFG (eg, right SFG: ß = -0.05; 95% CI, -0.08 to -0.03; P < .001), and right hippocampus (ß = -0.04; 95% CI, -0.06 to -0.01; P = .01), even when accounting for household income. Greater household income was associated with higher scores across all cognitive domains (eg, total composite: ß = 0.30; 95% CI, 0.28 to 0.33; P < .001) and larger volume in all prefrontal and hippocampal brain regions (eg, right hippocampus: ß = 0.04; 95% CI, 0.02 to 0.07; P < .001) even when accounting for NP. The SEM model was a good fit across all cognitive domains, with prefrontal regions being associated with NP relations to language (picture vocabulary: estimate [SE], -0.03 [0.01]; P < .001; oral reading: estimate [SE], -0.02 [0.01]; P < .001), episodic memory (picture sequence: estimate [SE], -0.02 [0.01]; P = .008), and working memory (dimensional card sort: estimate [SE], -0.02 [0.01]; P = .001; flanker inhibitory control: estimate [SE], -0.01 [0.01]; P = .01; list sorting: estimate [SE], -0.03 [0.01]; P < .001) and hippocampal regions being associated with NP associations with language (picture vocabulary: estimate [SE], -0.01 [0.004]; P < .001) and episodic memory (picture sequence: estimate [SE], -0.01 [0.004]; P < 0.001). Conclusions and Relevance: In this study, NP accounted for unique variance in cognitive function and prefrontal and right hippocampal brain volume. These findings demonstrate the importance of including broader environmental influences when conceptualizing early life adversity.

Media Access is Associated with Knowledge of Optimal Water, Sanitation and Hygiene Practices in Tanzania.

The importance of water, sanitation and hygiene (WASH) behaviors in low- and middle-income countries in preventing childhood illness is well established. Tanzania is known to have high rates of chronic malnutrition and childhood stunting-both of which have been linked to poor WASH practices. Interviews were conducted with 5000 primary caregivers of children aged 0-23 months. Four composite WASH knowledge variables were created to assess the relationship between WASH knowledge and access to different forms of media, such as television, radio, and mobile phones. WASH knowledge variables measure knowledge of when to wash hands, the need for soap when washing hands, when to wash a baby's hands, and how eating soil or chicken feces can affect a baby's health. Logistic and linear regression analyses were conducted to measure the association between media access and WASH knowledge. Having watched television was positively associated with higher WASH knowledge indicators (all p < 0.05). Higher WASH knowledge was positively associated with more frequent handwashing after cleaning a baby's bottom (all p < 0.0001). The quantity of media access also had a positive linear effect on handwashing; more media items owned was associated with increases in handwashing. Study findings indicate media access is associated with WASH knowledge among caregivers in resource-poor settings.

Neuroimaging of individual differences: A latent variable modeling perspective.

Neuroimaging data is being increasingly utilized to address questions of individual difference. When examined with task-related fMRI (t-fMRI), individual differences are typically investigated via correlations between the BOLD activation signal at every voxel and a particular behavioral measure. This can be problematic because: 1) correlational designs require evaluation of t-fMRI psychometric properties, yet these are not well understood; and 2) bivariate correlations are severely limited in modeling the complexities of brain-behavior relationships. Analytic tools from psychometric theory such as latent variable modeling (e.g., structural equation modeling) can help simultaneously address both concerns. This review explores the advantages gained from integrating psychometric theory and methods with cognitive neuroscience for the assessment and interpretation of individual differences. The first section provides background on classic and modern psychometric theories and analytics. The second section details current approaches to t-fMRI individual difference analyses and their psychometric limitations. The last section uses data from the Human Connectome Project to provide illustrative examples of how t-fMRI individual differences research can benefit by utilizing latent variable models.

The Role of Psychometrics in Individual Differences Research in Cognition: A Case Study of the AX-CPT.

Investigating individual differences in cognition requires addressing questions not often thought about in standard experimental designs, especially regarding the psychometric properties of the task. Using the AX-CPT cognitive control task as a case study example, we address four concerns that one may encounter when researching the topic of individual differences in cognition. First, we demonstrate the importance of variability in task scores, which in turn directly impacts reliability, particularly when comparing correlations in different populations. Second, we demonstrate the importance of variability and reliability for evaluating potential failures to replicate predicted correlations, even within the same population. Third, we demonstrate how researchers can turn to evaluating psychometric properties as a way of evaluating the feasibility of utilizing the task in new settings (e.g., online administration). Lastly, we show how the examination of psychometric properties can help researchers make informed decisions when designing a study, such as determining the appropriate number of trials for a task.

Comparing Plasma Phospho Tau, Total Tau, and Phospho Tau-Total Tau Ratio as Acute and Chronic Traumatic Brain Injury Biomarkers.

Importance: Annually in the United States, at least 3.5 million people seek medical attention for traumatic brain injury (TBI). The development of therapies for TBI is limited by the absence of diagnostic and prognostic biomarkers. Microtubule-associated protein tau is an axonal phosphoprotein. To date, the presence of the hypophosphorylated tau protein (P-tau) in plasma from patients with acute TBI and chronic TBI has not been investigated. Objective: To examine the associations between plasma P-tau and total-tau (T-tau) levels and injury presence, severity, type of pathoanatomic lesion (neuroimaging), and patient outcomes in acute and chronic TBI. Design, Setting, and Participants: In the TRACK-TBI Pilot study, plasma was collected at a single time point from 196 patients with acute TBI admitted to 3 level I trauma centers (<24 hours after injury) and 21 patients with TBI admitted to inpatient rehabilitation units (mean [SD], 176.4 [44.5] days after injury). Control samples were purchased from a commercial vendor. The TRACK-TBI Pilot study was conducted from April 1, 2010, to June 30, 2012. Data analysis for the current investigation was performed from August 1, 2015, to March 13, 2017. Main Outcomes and Measures: Plasma samples were assayed for P-tau (using an antibody that specifically recognizes phosphothreonine-231) and T-tau using ultra-high sensitivity laser-based immunoassay multi-arrayed fiberoptics conjugated with rolling circle amplification. Results: In the 217 patients with TBI, 161 (74.2%) were men; mean (SD) age was 42.5 (18.1) years. The P-tau and T-tau levels and P-tau-T-tau ratio in patients with acute TBI were higher than those in healthy controls. Receiver operating characteristic analysis for the 3 tau indices demonstrated accuracy with area under the curve (AUC) of 1.000, 0.916, and 1.000, respectively, for discriminating mild TBI (Glasgow Coma Scale [GCS] score, 13-15, n = 162) from healthy controls. The P-tau level and P-tau-T-tau ratio were higher in individuals with more severe TBI (GCS, ≤12 vs 13-15). The P-tau level and P-tau-T-tau ratio outperformed the T-tau level in distinguishing cranial computed tomography-positive from -negative cases (AUC = 0.921, 0.923, and 0.646, respectively). Acute P-tau levels and P-tau-T-tau ratio weakly distinguished patients with TBI who had good outcomes (Glasgow Outcome Scale-Extended GOS-E, 7-8) (AUC = 0.663 and 0.658, respectively) and identified those with poor outcomes (GOS-E, ≤4 vs >4) (AUC = 0.771 and 0.777, respectively). Plasma samples from patients with chronic TBI also showed elevated P-tau levels and a P-tau-T-tau ratio significantly higher than that of healthy controls, with both P-tau indices strongly discriminating patients with chronic TBI from healthy controls (AUC = 1.000 and 0.963, respectively). Conclusions and Relevance: Plasma P-tau levels and P-tau-T-tau ratio outperformed T-tau level as diagnostic and prognostic biomarkers for acute TBI. Compared with T-tau levels alone, P-tau levels and P-tau-T-tau ratios show more robust and sustained elevations among patients with chronic TBI.

Uncovering precision phenotype-biomarker associations in traumatic brain injury using topological data analysis.

BACKGROUND: Traumatic brain injury (TBI) is a complex disorder that is traditionally stratified based on clinical signs and symptoms. Recent imaging and molecular biomarker innovations provide unprecedented opportunities for improved TBI precision medicine, incorporating patho-anatomical and molecular mechanisms. Complete integration of these diverse data for TBI diagnosis and patient stratification remains an unmet challenge. METHODS AND FINDINGS: The Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot multicenter study enrolled 586 acute TBI patients and collected diverse common data elements (TBI-CDEs) across the study population, including imaging, genetics, and clinical outcomes. We then applied topology-based data-driven discovery to identify natural subgroups of patients, based on the TBI-CDEs collected. Our hypothesis was two-fold: 1) A machine learning tool known as topological data analysis (TDA) would reveal data-driven patterns in patient outcomes to identify candidate biomarkers of recovery, and 2) TDA-identified biomarkers would significantly predict patient outcome recovery after TBI using more traditional methods of univariate statistical tests. TDA algorithms organized and mapped the data of TBI patients in multidimensional space, identifying a subset of mild TBI patients with a specific multivariate phenotype associated with unfavorable outcome at 3 and 6 months after injury. Further analyses revealed that this patient subset had high rates of post-traumatic stress disorder (PTSD), and enrichment in several distinct genetic polymorphisms associated with cellular responses to stress and DNA damage (PARP1), and in striatal dopamine processing (ANKK1, COMT, DRD2). CONCLUSIONS: TDA identified a unique diagnostic subgroup of patients with unfavorable outcome after mild TBI that were significantly predicted by the presence of specific genetic polymorphisms. Machine learning methods such as TDA may provide a robust method for patient stratification and treatment planning targeting identified biomarkers in future clinical trials in TBI patients. TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT01565551.

Screening for Post-Traumatic Stress Disorder in a Civilian Emergency Department Population with Traumatic Brain Injury.

Post-traumatic stress disorder (PTSD) is a condition associated with traumatic brain injury (TBI). While the importance of PTSD and TBI among military personnel is widely recognized, there is less awareness of PTSD associated with civilian TBI. We examined the incidence and factors associated with PTSD 6 months post-injury in a civilian emergency department population using measures from the National Institute of Neurological Disorders and Stroke TBI Common Data Elements Outcome Battery. Participants with mild TBI (mTBI) from the Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot study with complete 6-month outcome batteries (n = 280) were analyzed. Screening for PTSD symptoms was conducted using the PTSD Checklist-Civilian Version. Descriptive measures are summarized and predictors for PTSD were examined using logistic regression. Incidence of screening positive for PTSD was 26.8% at 6 months following mTBI. Screening positive for PTSD was significantly associated with concurrent functional disability, post-concussive and psychiatric symptomatology, decreased satisfaction with life, and decreased performance in visual processing and mental flexibility. Multi-variable regression showed injury mechanism of assault (odds ratio [OR] 3.59; 95% confidence interval [CI] 1.69-7.63; p = 0.001) and prior psychiatric history (OR 2.56; 95% CI 1.42-4.61; p = 0.002) remained significant predictors of screening positive for PTSD, while education (per year OR 0.88; 95% CI 0.79-0.98; p = 0.021) was associated with decreased odds of PTSD. Standardized data collection and review of pre-injury education, psychiatric history, and injury mechanism during initial hospital presentation can aid in identifying patients with mTBI at risk for developing PTSD symptoms who may benefit from closer follow-up after initial injury care.

Establishing a Traumatic Brain Injury Program of Care: Benchmarking Outcomes after Institutional Adoption of Evidence-Based Guidelines.

Traumatic brain injury (TBI) is a widespread global disease, often with widely varying outcomes. Standardization of care and adherence to established guidelines are central to the effort to improve outcomes. At our level I urban trauma center, we developed and implemented a Joint Commission-certified TBI Program of Care in 2011 and compared our post-implementation patient data set with historical controls, using the International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) prognostic model. Historical controls were drawn from the San Francisco General Hospital Traumatic Coma Data Bank (SFGH/TCDB) from 1987 to 1996. Recent era patients were drawn from the NeuroTracker database, a customized electronic medical record used in our clinical practice. Descriptive statistics were calculated. Adherence to four quality-of-care metrics on the clinical service was tracked for 2011-2013. The IMPACT prognostic model was used to calculate expected versus observed mortality for current and historical patient groups. In the historical control group, 832 patients were identified and 6-month mortality was available for 592. Observed 6-month mortality was 49%. In the recent era patient group, 211 patients were identified and 6-month mortality was 38%. The IMPACT prognostic model was applied to each patient group. Areas under the curve for each analysis were >0.85 and goodness of fit was satisfactory, indicating good performance of the IMPACT model. Comparison of observed versus expected deaths in the recent versus the control patient sets revealed a drop of 59% in early mortality. The greatest reductions in mortality were observed in the group of patients with IMPACT-predicted mortality ≤50%. Significant progress has been made in reducing the percentage of unexpected deaths in TBI patients. It is likely that major factors include more aggressive management and tracking of compliance with the implementation of guidelines for the management of TBI patients.

Plasma Anti-Glial Fibrillary Acidic Protein Autoantibody Levels during the Acute and Chronic Phases of Traumatic Brain Injury: A Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot Study.

We described recently a subacute serum autoantibody response toward glial fibrillary acidic protein (GFAP) and its breakdown products 5-10 days after severe traumatic brain injury (TBI). Here, we expanded our anti-GFAP autoantibody (AutoAb[GFAP]) investigation to the multicenter observational study Transforming Research and Clinical Knowledge in TBI Pilot (TRACK-TBI Pilot) to cover the full spectrum of TBI (Glasgow Coma Scale 3-15) by using acute (<24 h) plasma samples from 196 patients with acute TBI admitted to three Level I trauma centers, and a second cohort of 21 participants with chronic TBI admitted to inpatient TBI rehabilitation. We find that acute patients self-reporting previous TBI with loss of consciousness (LOC) (n = 43) had higher day 1 AutoAb[GFAP] (mean ± standard error: 9.11 ± 1.42; n = 43) than healthy controls (2.90 ± 0.92; n = 16; p = 0.032) and acute patients reporting no previous TBI (2.97 ± 0.37; n = 106; p < 0.001), but not acute patients reporting previous TBI without LOC (8.01 ± 1.80; n = 47; p = 0.906). These data suggest that while exposure to TBI may trigger the AutoAb[GFAP] response, circulating antibodies are elevated specifically in acute TBI patients with a history of TBI. AutoAb[GFAP] levels for participants with chronic TBI (average post-TBI time 176 days or 6.21 months) were also significantly higher (15.08 ± 2.82; n = 21) than healthy controls (p < 0.001). These data suggest a persistent upregulation of the autoimmune response to specific brain antigen(s) in the subacute to chronic phase after TBI, as well as after repeated TBI insults. Hence, AutoAb[GFAP] may be a sensitive assay to study the dynamic interactions between post-injury brain and patient-specific autoimmune responses across acute and chronic settings after TBI.

Association of a common genetic variant within ANKK1 with six-month cognitive performance after traumatic brain injury.

Genetic association analyses suggest that certain common single nucleotide polymorphisms (SNPs) may adversely impact recovery from traumatic brain injury (TBI). Delineating their causal relationship may aid in development of novel interventions and in identifying patients likely to respond to targeted therapies. We examined the influence of the (C/T) SNP rs1800497 of ANKK1 on post-TBI outcome using data from two prospective multicenter studies: the Citicoline Brain Injury Treatment (COBRIT) trial and Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot (TRACK-TBI Pilot). We included patients with ANKK1 genotyping results and cognitive outcomes at six months post-TBI (n = 492: COBRIT n = 272, TRACK-TBI Pilot n = 220). Using the California Verbal Learning Test Second Edition (CVLT-II) Trial 1-5 Standard Score, we found a dose-dependent effect for the T allele, with T/T homozygotes scoring lowest on the CVLT-II Trial 1-5 Standard Score (T/T 45.1, C/T 51.1, C/C 52.1, ANOVA, p = 0.008). Post hoc testing with multiple comparison-correction indicated that T/T patients performed significantly worse than C/T and C/C patients. Similar effects were observed in a test of non-verbal processing (Wechsler Adult Intelligence Scale, Processing Speed Index). Our findings extend those of previous studies reporting a negative relationship of the ANKK1 T allele with cognitive performance after TBI. In this study, we demonstrate the value of pooling shared clinical, biomarker, and outcome variables from two large datasets applying the NIH TBI Common Data Elements. The results have implications for future multicenter investigations to further elucidate the role of ANKK1 in post-TBI outcome.

Diffusion tensor imaging for outcome prediction in mild traumatic brain injury: a TRACK-TBI study.

We evaluated 3T diffusion tensor imaging (DTI) for white matter injury in 76 adult mild traumatic brain injury (mTBI) patients at the semiacute stage (11.2±3.3 days), employing both whole-brain voxel-wise and region-of-interest (ROI) approaches. The subgroup of 32 patients with any traumatic intracranial lesion on either day-of-injury computed tomography (CT) or semiacute magnetic resonance imaging (MRI) demonstrated reduced fractional anisotropy (FA) in numerous white matter tracts, compared to 50 control subjects. In contrast, 44 CT/MRI-negative mTBI patients demonstrated no significant difference in any DTI parameter, compared to controls. To determine the clinical relevance of DTI, we evaluated correlations between 3- and 6-month outcome and imaging, demographic/socioeconomic, and clinical predictors. Statistically significant univariable predictors of 3-month Glasgow Outcome Scale-Extended (GOS-E) included MRI evidence for contusion (odds ratio [OR] 4.9 per unit decrease in GOS-E; p=0.01), ≥1 ROI with severely reduced FA (OR, 3.9; p=0.005), neuropsychiatric history (OR, 3.3; p=0.02), age (OR, 1.07/year; p=0.002), and years of education (OR, 0.79/year; p=0.01). Significant predictors of 6-month GOS-E included ≥1 ROI with severely reduced FA (OR, 2.7; p=0.048), neuropsychiatric history (OR, 3.7; p=0.01), and years of education (OR, 0.82/year; p=0.03). For the subset of 37 patients lacking neuropsychiatric and substance abuse history, MRI surpassed all other predictors for both 3- and 6-month outcome prediction. This is the first study to compare DTI in individual mTBI patients to conventional imaging, clinical, and demographic/socioeconomic characteristics for outcome prediction. DTI demonstrated utility in an inclusive group of patients with heterogeneous backgrounds, as well as in a subset of patients without neuropsychiatric or substance abuse history.

Transforming research and clinical knowledge in traumatic brain injury pilot: multicenter implementation of the common data elements for traumatic brain injury.

Traumatic brain injury (TBI) is among the leading causes of death and disability worldwide, with enormous negative social and economic impacts. The heterogeneity of TBI combined with the lack of precise outcome measures have been central to the discouraging results from clinical trials. Current approaches to the characterization of disease severity and outcome have not changed in more than three decades. This prospective multicenter observational pilot study aimed to validate the feasibility of implementing the TBI Common Data Elements (TBI-CDEs). A total of 650 subjects who underwent computed tomography (CT) scans in the emergency department within 24 h of injury were enrolled at three level I trauma centers and one rehabilitation center. The TBI-CDE components collected included: 1) demographic, social and clinical data; 2) biospecimens from blood drawn for genetic and proteomic biomarker analyses; 3) neuroimaging studies at 2 weeks using 3T magnetic resonance imaging (MRI); and 4) outcome assessments at 3 and 6 months. We describe how the infrastructure was established for building data repositories for clinical data, plasma biomarkers, genetics, neuroimaging, and multidimensional outcome measures to create a high quality and accessible information commons for TBI research. Risk factors for poor follow-up, TBI-CDE limitations, and implementation strategies are described. Having demonstrated the feasibility of implementing the TBI-CDEs through successful recruitment and multidimensional data collection, we aim to expand to additional study sites. Furthermore, interested researchers will be provided early access to the Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) data set for collaborative opportunities to more precisely characterize TBI and improve the design of future clinical treatment trials. (ClinicalTrials.gov Identifier NCT01565551.).

Quantitative CT improves outcome prediction in acute traumatic brain injury.

The admission noncontrast head computed tomography (CT) scan has been demonstrated to be one of several key early clinical and imaging features in the challenging problem of prediction of long-term outcome after acute traumatic brain injury (TBI). In this study, we employ two novel approaches to the problem of imaging classification and outcome prediction in acute TBI. First, we employ the novel technique of quantitative CT (qCT) image analysis to provide more objective, reproducible measures of the abnormal features of the admission head CT in acute TBI. We show that the incorporation of quantitative, rather than qualitative, CT features results in a significant improvement in prediction of the 6-month Extended Glasgow Outcome Scale (GOS-E) score over a wide spectrum of injury severity. Second, we employ principal components analysis (PCA) to demonstrate the interdependence of certain predictive variables. Relatively few prior studies of outcome prediction in acute TBI have used a multivariate approach that explicitly takes into account the potential covariance among clinical and CT predictive variables. We demonstrate that several predictors, including midline shift, cistern effacement, subdural hematoma volume, and Glasgow Coma Scale (GCS) score are related to one another. Rather than being independent features, their importance may be related to their status as surrogate measures of a more fundamental underlying clinical feature, such as the severity of intracranial mass effect. We believe that objective computational tools and data-driven analytical methods hold great promise for neurotrauma research, and may ultimately have a role in image analysis for clinical care.

Independent component analysis of DTI reveals multivariate microstructural correlations of white matter in the human brain.

It has recently been demonstrated that specific patterns of correlation exist in diffusion tensor imaging (DTI) parameters across white matter tracts in the normal human brain. These microstructural correlations are thought to reflect phylogenetic and functional similarities between different axonal fiber pathways. However, this earlier work was limited in three major respects: (1) the analysis was restricted to only a dozen selected tracts; (2) the DTI measurements were averaged across whole tracts, whereas metrics such as fractional anisotropy (FA) are known to vary considerably within single tracts; and (3) a univariate measure of correlation was used. In this investigation, we perform an automated multivariate whole-brain voxel-based study of white matter FA correlations using independent component analysis (ICA) of tract-based spatial statistics computed from 3T DTI in 53 healthy adult volunteers. The resulting spatial maps of the independent components show voxels for which the FA values within each map co-vary across individuals. The strongest FA correlations were found in anatomically recognizable tracts and tract segments, either singly or in homologous pairs. Hence, ICA of DTI provides an automated unsupervised decomposition of the normal human brain into multiple separable microstructurally correlated white matter regions, many of which correspond to anatomically familiar classes of white matter pathways. Further research is needed to determine whether whole-brain ICA of DTI represents a novel alternative to tractography for feature extraction in studying the normal microstructure of human white matter as well as the abnormal white matter microstructure found in neurological and psychiatric disorders.

Microstructural correlations of white matter tracts in the human brain.

The purpose of this study is to investigate whether specific patterns of correlation exist in diffusion tensor imaging (DTI) parameters across different white matter tracts in the normal human brain, and whether the relative strengths of these putative microstructural correlations might reflect phylogenetic and functional similarities between tracts. We performed quantitative DTI fiber tracking on 44 healthy adult volunteers to obtain tract-based measures of mean diffusivity (MD), fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) from four homologous pairs of neocortical association pathways (arcuate fasciculi, inferior fronto-occipital fasciculi, inferior longitudinal fasciculi, and uncinate fasciculi bilaterally), a homologous pair of limbic association pathways (left and right dorsal cingulum bundles), and a homologous pair of cortical-subcortical projection pathways (left and right corticospinal tracts). From the resulting inter-tract correlation matrices, we show that there are statistically significant correlations of DTI parameters between tracts, and that there are statistically significant variations among these inter-tract correlations. Furthermore, we observe that many, but by no means all, of the strongest correlations are between homologous tracts in the left and right hemispheres. Even among homologous pairs of tracts, there are wide variations in the degree of coupling. Finally, we generate a data-driven hierarchical clustering of the fiber pathways based on pairwise FA correlations to demonstrate that the neocortical association pathways tend to group separately from the limbic pathways at trend-level statistical significance, and that the projection pathways of the left and right corticospinal tracts comprise the most distant outgroup with high confidence (p<0.01). Hence, specific patterns of microstructural correlation exist between tracts and may reflect phylogenetic and functional similarities between tracts. The study of these microstructural relationships between white matter pathways might aid research on the genetic basis and on the behavioral effects of axonal connectivity, as well as provide a revealing new perspective with which to investigate neurological and psychiatric disorders.