Traumatic brain injury imaging research roadmap

Abstract

The past decade has seen impressive advances in the types of neuroimaging information that can be acquired in patients with traumatic brain injury. However, despite this increase in information, understanding of the contribution of this information to prognostic accuracy and treatment pathways for patients is limited. Available techniques often allow us to infer the presence of microscopic changes indicative of alterations in physiology and function in brain tissue. However, because histologic confirmation is typically lacking, conclusions reached by using these techniques remain solely inferential in almost all cases. Hence, a need exists for validation of these techniques by using data from large population samples that are obtained in a uniform manner, analyzed according to well-accepted procedures, and correlated with closely monitored clinical outcomes. At present, many of these approaches remain confined to population-based research rather than diagnosis at an individual level, particularly with regard to traumatic brain injury that is mild or moderate in degree. A need and a priority exist for patient-centered tools that will allow advanced neuroimaging tools to be brought into clinical settings. One barrier to developing these tools is a lack of an age-, sex-, and comorbidities-stratified, sequence-specific, reference imaging data base that could provide a clear understanding of normal variations across populations. Such a data base would provide researchers and clinicians with the information necessary to develop computational tools for the patient-based interpretation of advanced neuroimaging studies in the clinical setting. The recent "Joint ASNR-ACR HII-ASFNR TBI Workshop: Bringing Advanced Neuroimaging for Traumatic Brain Injury into the Clinic" on May 23, 2014, in Montreal, Quebec, Canada, brought together neuroradiologists, neurologists, psychiatrists, neuropsychologists, neuroimaging scientists, members of the National Institute of Neurologic Disorders and Stroke, industry representatives, and other traumatic brain injury stakeholders to attempt to reach consensus on issues related to and develop consensus recommendations in terms of creating both a well-characterized normative data base of comprehensive imaging and ancillary data to serve as a reference for tools that will allow interpretation of advanced neuroimaging tests at an individual level of a patient with traumatic brain injury. The workshop involved discussions concerning the following: 1) designation of the policies and infrastructure needed for a normative data base, 2) principles for characterizing normal control subjects, and 3) standardizing research neuroimaging protocols for traumatic brain injury. The present article summarizes these recommendations and examines practical steps to achieve them.

Figure.

Hypothetic distribution of an advanced neuroimaging-dependent measure for TBI (red) and non-TBI (blue) groups. A, Idealized separation of distributions between the TBI and non-TBI groups due to a very selective “normal” non-TBI control group (ie, supernormal, with no history of TBI, and no neurologic, psychiatric, or other detectable abnormalities), which would be amenable to conventional statistical analyses based on the general linear model. B, Marked overlap of distributions between the TBI and non-TBI groups due to a non-TBI control group comprising subjects with pre-existing abnormalities present in the general population, which would be unlikely to yield a statistically significant differentiation by using the general linear model. Despite relatively marked overlap between distributions, classification approaches may be able to identify features unique to each group and therefore discriminate whether an individual belongs in the TBI or non-TBI group. To pursue implementation of such a binary classification, it will be necessary to characterize the variability associated with neuroimaging methods expected from the general population in the absence of TBI, which can be facilitated by constructing a large comprehensive normative data base.