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Structural and functional connectivity (FC) after sports-related concussion (SRC) may remain altered in adolescent athletes despite symptom resolution. Little is known, however, about how alterations in structural connectivity and FC co-present in female athletes whose symptom recovery tends to be prolonged. Despite resolution of symptoms, one month after her second SRC, an 18-year-old female athlete had decreased structural connectivity in the corpus callosum and cingulum, with altered FC near those regions, compared with other SRC and orthopedically injured athletes. Findings show persistent effects of SRC on advanced brain imaging and the possibility of greater vulnerability of white matter tracts in females.

Background and Purpose: Architecture of the cerebral network has been shown to associate with IQ in children with epilepsy. However, subject-level prediction on this basis, a crucial step toward harnessing network analyses for the benefit of children with epilepsy, has yet to be achieved. We compared two network normalization strategies in terms of their ability to optimize subject-level inferences on the relationship between brain network architecture and brain function., Materials and Methods: Patients with epilepsy and resting state fMRI were retrospectively identified. Brain network nodes were defined by anatomic parcellation, first in patient space (nodes defined for each patient) and again in template space (same nodes for all patients). Whole-brain weighted graphs were constructed according to pair-wise correlation of BOLD-signal time courses between nodes. The following metrics were then calculated: clustering coefficient, transitivity, modularity, path length, and global efficiency. Metrics computed on graphs in patient space were normalized to the same metric computed on a random network of identical size. A machine learning algorithm was used to predict patient IQ given access to only the network metrics., Results: Twenty-seven patients (8-18 years) comprised the final study group. All brain networks demonstrated expected small world properties. Accounting for intrinsic population heterogeneity had a significant effect on prediction accuracy. Specifically, transformation of all patients into a common standard space as well as normalization of metrics to those computed on a random network both substantially outperformed the use of non-normalized metrics., Conclusion: Normalization contributed significantly to accurate subject-level prediction of cognitive function in children with epilepsy. These findings support the potential for quantitative network approaches to contribute clinically meaningful information in children with neurological disorders., Competing Interests: The authors have declared that no competing interests exist.

Purpose: Metrics of the brain network architecture derived from resting-state fMRI have been shown to provide physiologically meaningful markers of IQ in children with epilepsy. However, traditional measures of functional connectivity (FC), specifically the Pearson correlation, assume a dominant linear relationship between BOLD time courses; this assumption may not be valid. Mutual information is an alternative measure of FC which has shown promise in the study of complex networks due to its ability to flexibly capture association of diverse forms. We aimed to compare network metrics derived from mutual information-defined FC to those derived from traditional correlation in terms of their capacity to predict patient-level IQ., Materials and Methods: Patients were retrospectively identified with the following: (1) focal epilepsy; (2) resting-state fMRI; and (3) full-scale IQ by a neuropsychologist. Brain network nodes were defined by anatomic parcellation. Parcellation was performed at the size threshold of 350 mm 2 , resulting in networks containing 780 nodes. Whole-brain, weighted graphs were then constructed according to the pairwise connectivity between nodes. In the traditional condition, edges (connections) between each pair of nodes were defined as the absolute value of the Pearson correlation coefficient between their BOLD time courses. In the mutual information condition, edges were defined as the mutual information between time courses. The following metrics were then calculated for each weighted graph: clustering coefficient, modularity, characteristic path length, and global efficiency. A machine learning algorithm was used to predict the IQ of each individual based on their network metrics. Prediction accuracy was assessed as the fractional variation explained for each condition., Results: Twenty-four patients met the inclusion criteria (age: 8-18 years). All brain networks demonstrated expected small-world properties. Network metrics derived from mutual information-defined FC significantly outperformed the use of the Pearson correlation. Specifically, fractional variation explained was 49% (95% CI: 46%, 51%) for the mutual information method; the Pearson correlation demonstrated a variation of 17% (95% CI: 13%, 19%)., Conclusion: Mutual information-defined functional connectivity captures physiologically relevant features of the brain network better than correlation., Clinical Relevance: Optimizing the capacity to predict cognitive phenotypes at the patient level is a necessary step toward the clinical utility of network-based biomarkers.

Background: An important component of the multicentre Chronic Effects of Neurotrauma Consortium (CENC) project is the development of improved quantitative magnetic resonance imaging (MRI) methods, including volumetric analysis. Although many studies routinely employ quality assurance (QA) procedures including MR and human phantoms to promote accuracy and monitor site differences, few studies perform rigorous direct comparisons of these data nor report findings that enable inference regarding site-to-site comparability. These gaps in evaluating cross-site differences are concerning, especially given the well-established differences that can occur between data acquired on scanners with different manufacturer, hardware or software., Methods: This study reports findings on (1) a series of studies utilizing two MR phantoms to interrogate machine-based variability using data collected on the same magnet, (2) a human phantom repeatedly imaged on the same scanner to investigate within-subject, within-site variability and (3) a human phantom imaged on three different scanners to examine within subject, between-site variability., Results: Although variability is relatively minimal for the phantom scanned on the same magnet, significantly more variability is introduced in a human subject, particularly when regions are relatively small or multiple sites used., Conclusion: Vigilance when combining data from different sites is suggested and that future efforts address these issues.

Objective: To study magnetic resonance imaging (MRI) correlates of novel (new-onset) psychiatric disorders (NPD) after traumatic brain injury (TBI) and orthopedic injury (OI)., Method: Participants were 7 to 17 years of age at the time of hospitalization for either TBI or OI. The study used a prospective, longitudinal, controlled design with standardized psychiatric assessments conducted at baseline (reflecting pre-injury function) and 3 months post-injury. MRI assessments including diffusion tensor imaging (DTI)-derived fractional anisotropy (FA), volumetric measures of gray and white matter regions, volumetric measures of lesions, and cortical thickness were conducted. Injury severity was assessed by standard clinical scales. The outcome measure was the presence of an NPD identified during the first 3 months after injury., Results: There were 88 participants (TBI, 44; OI, 44). NPD occurred more frequently in the TBI (21/44; 48%) versus the OI (6/44; 14%) group (Fisher's exact test, p = .001). NPD in TBI participants was not related to injury severity. Multivariate analysis of covariance of the relationship between FA in hypothesized regions of interest (bilateral frontal and temporal lobes, bilateral centrum semiovale, bilateral uncinate fasciculi) and NPD and group (TBI versus OI) was significant, and both variables (NPD, p < .05; group, p < .001) were jointly significantly related to FA. NPD was not significantly related to volumetric measures of white or gray matter structures, volumetric measures of lesions, or cortical thickness measures., Conclusions: Lowered white matter integrity may be more important in the pathophysiology of NPD than indices of gray matter or white matter atrophic changes, macroscopic lesions, and injury severity., (Copyright © 2012 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.)

Traumatic brain injury (TBI) is a leading cause of death and disability in children, yet little is known regarding the pattern of TBI-related microstructural change and its impact on subsequent development. Diffusion tensor imaging (DTI) was used to examine between-group differences at two time points (planned intervals of 3 months and 18 months post-injury) and within-group longitudinal change in a group of children and adolescents aged 7-17 years with moderate-to-severe TBI (n = 20) and a comparison group of children with orthopedic injury (OI) (n = 21). In the 3- and 18-month cross-sectional analyses, tract-based spatial statistics (TBSS) generally revealed decreased fractional anisotropy (FA) and increased apparent diffusion coefficient (ADC) in the TBI group in regions of frontal, temporal, parietal, and occipital white matter as well as several deep subcortical structures, though areas of FA decrease were more prominent at the 3-month assessment, and areas of ADC increase were more prominent at the 18 month assessment, particularly in the frontal regions. In terms of the within-group changes over time, the OI group demonstrated primarily diffuse increases in FA over time, consistent with previous findings of DTI-measured white matter developmental change. The TBI group demonstrated primarily regions of FA decrease and ADC increase over time, consistent with presumed continued degenerative change, though regions of ADC decrease were also appreciated. These results suggest that TBI-related microstructural changes are dynamic in children and continue until at least 18 months post-injury. Understanding the course of these changes in DTI metrics may be important in TBI for facilitating advances in management and intervention.

Primary objective: Brain structures and their white matter connections that may contribute to emotion processing and may be vulnerable to disruption by a traumatic brain injury (TBI) occurring in childhood have not been thoroughly explored. Research design and methods: The current investigation examines the relationship between diffusion tensor imaging (DTI) metrics, including fractional anisotropy (FA) and apparent diffusion coefficient (ADC), and 3-month post-injury performance on a task of emotion prosody recognition and a control task of phonological discrimination in a group of 91 children who sustained either a moderate-to-severe TBI ( n = 45) or orthopaedic injury (OI) ( n = 46). Main outcomes and results: Brain-behaviour findings within OI participants confirmed relationships between several significant white matter tracts in emotional prosody performance (i.e. the cingulum bundle, genu of the corpus callosum, inferior longitudinal fasciculus (ILF) and the inferior fronto-occipital fasciculus (IFOF). The cingulum and genu were also related to phonological discrimination performance. The TBI group demonstrated few strong brain behaviour relationships, with significant findings emerging only in the cingulum bundle for Emotional Prosody and the genu for Phonological Processing. Conclusion: The lack of clear relationships in the TBI group is discussed in terms of the likely disruption to cortical networks secondary to significant brain injuries. [ABSTRACT FROM AUTHOR]