Imaging of Concussion in Young Athletes

Abstract

Conventional neuroimaging examinations are typically normal in concussed young athletes. A current focus of research is the characterization of subtle abnormalities after concussion using advanced neuroimaging techniques. These techniques have the potential to identify biomarkers of concussion. In the future, such biomarkers will likely provide important clinical information regarding the appropriate time interval before return to play, as well as the risk for prolonged postconcussive symptoms and long-term cognitive impairment. This article discusses results from advanced imaging techniques and emphasizes imaging modalities that will likely become available in the near future for the clinical evaluation of concussed young athletes.

Validity of automated FreeSurfer segmentation compared to manual tracing in detecting prenatal alcohol exposure-related subcortical and corpus callosal alterations in 9- to 11-year-old children

“…While absolute volume differences may reflect differences in segmentation protocol, correlation analyses provide a measure of consistency between methods. Taken together, findings from previous studies suggest that performance of automated methods depends on the segmented structure as well as the protocol used and that discrepancy is greater in atrophic brains ( Sánchez-Benavides et al, 2010 ) and smaller brain regions ( Guenette et al, 2018 , Lehmann et al, 2010 , Schoemaker et al, 2016 ).…”

“…4 B) is the erroneous inclusion of the anterior commissure (indicated by a white circle) by FreeSurfer v.5.1 in the volume of the left NA, which manual tracing and FreeSurfer v.6.0 correctly excluded. The small size of the NA may additionally exacerbate the already high variability and poor reliability in this region ( Guenette et al, 2018 , Lehmann et al, 2010 , Schoemaker et al, 2016 ).

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“…Automated segmentation of a single brain performed with the same software on the same hardware and operating system has very high test–retest reliability ( Gronenschild et al, 2012 ). However, when comparing volumes from manual and automated segmentation in both healthy ( Cherbuin et al, 2009 , Morey et al, 2009 , Patenaude et al, 2011 ) and pathological ( Akhondi-Asl et al, 2011 , Dewey et al, 2010 , Doring et al, 2010 , Guenette et al, 2018 , Lehmann et al, 2010 , Nugent et al, 2013 , Pardoe et al, 2009 , Pipitone et al, 2014 , Sánchez-Benavides et al, 2010 , Shen et al, 2010 , Tae et al, 2008 ) adult populations, findings have varied. A recent study in which a typical neuropsychiatric magnetic resonance imaging (MRI) dataset was segmented using three automated segmentation methods (FreeSurfer; FSL-FIRST – FMRIB Integrated Registration and Segmentation Tool, Oxford University, Oxford, UK; volBrain – http://volbrain.upv.es ) demonstrated good partial correlation (0.77–0.87) to manually traced volumes on an “easy-to-segment” structure, such as the caudate nucleus, while a more difficult structure, such as the hippocampus, achieved moderate correlation (0.35–0.62) but poor absolute agreement (intraclass correlation (ICC) 0.07–0.10) ( Akudjedu et al, 2018 ).…”

“…For example, in patients with HIV, the association of caudate, putamen, amygdala, and hippocampal volumes with clinical measures of disease progression differed for outputs generated by FreeSurfer, IBASPM (Individual Brain Atlases using Statistical Parametric Mapping) and auto-assisted manual tracings ( Dewey et al, 2010 ). Depression-related hippocampal volume reductions were detected with FreeSurfer but not FSL-FIRST ( Morey et al, 2009 ), and in former National Football League (NFL) players with neurobehavioral symptoms, automated FreeSurfer segmentation identified group differences relative to age-matched controls in 4 of 11 regions, compared to 8 of 11 with manual correction, as well as different regions showing associations with neurobehavioral factors ( Guenette et al, 2018 ). Due to an absence of subgroups, Schoemaker et al (2016) could not examine this question in their study on segmentation accuracy in children.…”

Mild traumatic brain injury

“…Dlouho přetrvávající názor, že mTBI vedou jen k "funkčnímu postižení mozku a nikdy nezanechávají trvalé následky", je v současnosti překonaný především díky výsledkům moderních zobrazovacích, neuropatologických i experimentálních studií, které společně ukazují, že poranění hodnocená klinicky jako "lehká" (mTBI), zvláště pokud jsou opakovaná, mohou být spojena s různě dlouho přetrvávajícími nebo i trvalými změnami bílé i šedé hmoty (Bigler, 2017;Guenette et al, 2018;Mez et al, 2017;Tagge et al, 2018). Například studie hodnotící šedou hmotu mozku pomocí voxel based morfometrie a analýzy tloušťky kortexu dokládají, že anamnéza mTBI může být spojena s trvalou redukcí šedé hmoty ve frontálních, mediotemporálních a parietálních oblastech mozku (Bigler, 2017;Koerte et al, 2015;Monti et al, 2013).…”