DTI is a diffusion-weighted imaging (DWI) technique that paved the way to new possibilities for investigating white matter in vivo as it provides information about white matter anatomy that is not available through any other methods.

DTI provides two essential types of information: a quantitative estimate of anisotropy and its spatial orientation. Tractography uses these microscopic data to track macroscopic axon fibers, and it is capable of noninvasive in vivo imaging of these structures [18, 19, 40]. In DAI, the white matter fibers are typically interrupted; measuring white matter anisotropy with DTI allows the tissue damage to be quantified [41] (Fig. 14.3).

Based on these considerations, Huisman et al. [35] proved that DTI can detect structural white matter changes and that these changes correlate with clinical parameters such as GCS score in the acute phase and at discharge. On the other hand, Zheng et al. [42] demonstrated the superior performance of DWI in identifying DAI compared with standard (T2-, T2*-weighted, FLAIR) sequences, but did not address its clinical relevance and thus the potential correlations with prognosis.

The technical characteristics of high-field MR have enhanced the quality of DTI data, particularly the spatial resolution, the spatial deformation induced by magnetic field inhomogeneities, and image SNR, thus improving the accuracy of nerve fiber tracking on mild, moderate, and severe TBI. Furthermore, the advantages of applying DTI to patients with head trauma are:

The potentiality of DTI to map and correlate the disruption in fiber tracts to clinical outcomes led researchers to adopt this technique for the quantification of the primary and secondary damage in DOC patients [43]. Recently, a large literature focused on this topic, i.e., to identify by DTI the different disorder of consciousness [e.g., vegetative state (VS) vs. minimally conscious state (MCS)] [44] or to correlate DTI findings with injury severity and clinical outcome [45–49].

Actually, the use of DTI to determine DOC severity, to aid diagnosis, and to ascertain prognostic outcomes is limited to the research setting. Despite this, DTI has proved a powerful tool as it grants insight into the pathogenesis and specific WM abnormalities underlying different DOC states, casting light on the neural basis of consciousness and the clinical features associated with DOC.

MR spectroscopy is a molecular-based neuroimaging technique that reflects the intracellular metabolic status that can be influenced by the presence of a microscopic injury.

The advantage of using 3.0 T MRS consists of an enhancement of the chemical shift with better separation of the metabolite peaks and high SNR [50]. MRS can document a reduction in the N-acetylaspartate (NAA) peak, a neuronal and axonal marker, and in ostensibly normal white matter free of macroscopic focal changes weeks or months after the trauma, and the extent of this reduction significantly correlates with scores on prognostic measures like GOS and Disability Rating Scale (DRS) [36, 37, 50]. Concomitant elevation of the levels of other metabolites, such as myo-inositol (Ins) and choline (Cho), is to be ascribed to glial proliferation or an inflammatory process [51–53]. Studies of white matter devoid of focal changes have demonstrated that the levels of these metabolites can eventually revert to normal [52] as well as a progressive reduction in NAA associated with increased Cho [51] or decreased Ins peaks [36]. Shutter et al. [37] studied a group of trauma patients in the subacute phase who underwent MRS within a week of the trauma to detect metabolic changes that could predict clinical outcome. Elevation of metabolite levels such as glutamate/glutamine (Glx) and Cho in apparently normal white and gray matter was found to be highly predictive of long-term adverse outcome with an accuracy of 89 % (Fig. 14.4).

fMRI studies typically measure signal changes induced in the blood volume, flow, and oxygenation when the normal/patient is performing a given task. The resulting acquired signal is hemodynamically coupled to the actual neural activity. In the past decades, fMRI has been used to identify regions specialized in cognitive and/or motor tasks, but more recently, the interest shifted toward a novel fMRI approach, i.e., functional connectivity MRI (fcMRI), which focuses on characterization of the brain’s intrinsic functional architecture especially at rest (absence of a specific task) (RS-fMRI) [54]. Therefore, high-field MR, one of whose advantages is a high sensitivity to the BOLD signal, could find applications in the study of the phases of motor and cognitive recovery after TBI, as suggested in the literature [38, 39, 55–57]. In particular, the degree of activation of specific brain areas at fMRI could provide an index of hemodynamic and functional activity in ostensibly healthy tissue in patients with DAI. Longitudinal variations of this index in conjunction with structural parameters and clinical examination could thus assume a prognostic value.

Typically, in task-based fMRI, data are collected with blocked design experiments in which the contrast between two conditions is evaluated in the presence of specific tasks [38, 39, 55–57]. At rest, on the other hand, participants do not perform any active task and are simply instructed to remain still either fixating or with eyes closed.

In a recent study [38], the observed cognitive and behavioral recovery significantly correlated with connectivity changes of resting-state networks (RSNs) in a group of severe TBI patients after a period of intensive neurorehabilitation. In particular, the cognitive recovery was more consistent in patients exhibiting the strongest changes in DMN connectivity (Fig.14.5).

Resting-state connectivity studies are appealing in DOC patients since they do not require the ability to participate in an active task in the scanner and it has been proposed as a prognostic tool in comatose patients [58–60]. The main advantage is that the identified functional systems (or networks) provide a wide coverage of several functional domains which can be assessed in a single acquisition session [54]. Therefore, there has been renewed interest, throughout the past decade, in the use of functional neuroimaging to distinguish between patients in vegetative state and individuals who are in a minimally conscious state or who exhibit locked-in syndrome [61, 62].

A recent study [59] describes the possibility to extract, from functional connectivity data at rest and during an auditory emotional stimulation, parameters able to characterize important global and local connectivity features, such as the degree of clustering, mean path length, and modularity, in DOC patients and characterize the clinical profile and diagnosis (Fig. 14.6). These results and the existing literature indicate the functional neuroimaging holds considerable promise for indicating valid biomarkers for the recovery and diagnosis in TBI patients. Further, it could generate important insight on the neural basis of consciousness and on the target for therapeutic and neurorehabilitative intervention.

In conclusion, high-field MR has the potential to enhance the accuracy of morphostructural, metabolic, and functional studies of the brain parenchyma in trauma patients, advancing the knowledge of the relationships between changes in nerve fiber ultrastructure and clinical symptoms and allowing the quantification of the brain plasticity phenomena that follow spontaneous clinical recovery or administration of pharmacological and rehabilitation therapies.