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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. [ABSTRACT FROM AUTHOR]

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]

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. [ABSTRACT FROM AUTHOR]

To address controversy surrounding the most appropriate comparison group for mild traumatic brain injury (mTBI) research, mTBI patients 12–30 years of age were compared with an extracranial orthopedic injury (OI) patient group and an uninjured, typically developing (TD) participant group with comparable demographic backgrounds. Injured participants underwent subacute (within 96 h) and late (3 months) diffusion tensor imaging (DTI); TD controls underwent DTI once. Group differences in fractional anisotropy (FA) and mean diffusivity (MD) of commonly studied white matter tracts were assessed. For FA, subacute group differences occurred in the bilateral inferior frontal occipital fasciculus (IFOF) and right inferior longitudinal fasciculus (ILF), and for MD, differences were found in the total corpus callosum, right uncinate fasciculus, IFOF, ILF, and bilateral cingulum bundle (CB). In these analyses, differences (lower FA and higher MD) were generally observed between the mTBI and TD groups but not between the mTBI and OI groups. After a 3 month interval, groups significantly differed in left IFOF FA and in right IFOF and CB MD; the TD group had significantly higher FA and lower MD than both injury groups, which did not differ. There was one exception to this pattern, in which the OI group demonstrated significantly lower FA in the left ILF than the TD group, although neither group differed from the mTBI group. The mTBI and OI groups had generally similar longitudinal results. Findings suggest that different conclusions about group-level DTI analyses could be drawn, depending on the selected comparison group, highlighting the need for additional research in this area. Where possible, mTBI studies may benefit from the inclusion of both OI and TD controls. [ABSTRACT FROM AUTHOR]

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 mm2, 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. [ABSTRACT FROM AUTHOR]