Abstract
There is a paucity of accurate and reliable biomarkers to detect traumatic brain injury, grade its severity, and model post-traumatic brain injury (TBI) recovery. This gap could be addressed via advances in brain mapping which define injury signatures and enable tracking of post-injury trajectories at the individual level. Mapping of molecular and anatomical changes and of modifications in functional activation supports the conceptual paradigm of TBI as a disorder of large-scale neural connectivity. Imaging approaches with particular relevance are magnetic resonance techniques (diffusion weighted imaging, diffusion tensor imaging, susceptibility weighted imaging, magnetic resonance spectroscopy, functional magnetic resonance imaging, and positron emission tomographic methods including molecular neuroimaging). Inferences from mapping represent unique endophenotypes which have the potential to transform classification and treatment of patients with TBI. Limitations of these methods, as well as future research directions, are highlighted.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Introduction
Traumatic brain injury (TBI) is recognized as a major public health concern because it is a leading cause of death in children and young adults and a significant driver of long-term disability in survivors [1–3], with direct and indirect healthcare expenditures between $50 and 100 billion in the USA alone [3, 4]. Despite this tremendous burden, there is a lack of accurate and reliable biological markers to detect TBI, classify its severity, stratify patients for treatment, and predict outcome [5, 6].
In the acute clinical setting, TBI severity is classified on the basis of level of consciousness [7] and on structural abnormalities identified on cranial CT scan [8, 9]. While the clinical classification may be confounded by the presence of concomitant sedation or physiological and metabolic perturbations, structural imaging is less susceptible to this problem. However, cranial CT scan, the most widely used structural neuroimaging tool, lacks sensitivity for the detection of smaller, microscopic lesions that constitute traumatic axonal injury (TAI), a pattern of multifocal white matter disruption seen in TBI of all severities (see below). TAI is a key determinant of clinical outcome following injury [10] and is identifiable with more sensitive magnetic resonance imaging techniques [11–16]. Lesions associated with TAI tend to be distributed in multiple sites across the central nervous system and evolve dynamically over time—disrupting systems responsible for integrated and temporally sustained cerebral function [17–19]. This disruption is more likely to affect domains of function which do not localize to a specific location site in the brain but are dependent on the connectivity and integrative capacity of distributed neural systems, e.g., conscious awareness, attention, memory, and planning—domains of cognition and behavior whose impairment are phenotypic hallmarks in patients with TBI [20–28]. This has led to the view that TBI is analogous to disorders of neural integration [18, 29], the corollary of which is that recovery of neurologic function following TBI will be closely linked with restoration or reconfiguration of integrative neural connectivity [30, 31•, 32].
In recent years, the detection and characterization of TBI have been transformed by the introduction of new MRI pulse sequences and enhanced sophistication in statistical and computational approaches to mapping of regional brain data [33]. The use of diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), and susceptibility-weighted imaging (SWI) has enlarged our understanding of TAI [14, 21, 25, 34–39]. Results obtained with MR spectroscopy reveal dynamic biochemical changes which track clinical severity and outcome following TBI [40–42]. Mapping of functional activation using positron emission tomography (PET), functional MRI (fMRI), electroencephalography (EEG), and magnetoencephalography (MEG) indicates changes in large-scale connectional architectures which may help differentiate cognitive phenotypes and classify outcome probabilities following TBI [30, 43–46, 47•]. Molecular probes used in conjunction with positron emission tomography reveal neurodegenerative and neuroinflammatory events associated with TBI in the acute or chronic setting [48–50]. Taken together, results from brain mapping are consistent with an emerging model of TBI “endophenotypes,” clinically latent patterns which represent biological links between underlying molecular and cellular phenomena and externally observable clinical syndromes. Here, recent inferences from brain mapping in patients with TBI are selectively reviewed, together with current knowledge gaps and priorities in the scientific agenda.
Neurobiological Considerations
Three pathological processes are believed to contribute substantively to the disorder of systems observed in TBI: axonal injury, synaptic dysfunction, and modifications in glia (Table 1). TAI (also referred to as “diffuse axonal injury”) is a unique pattern of multifocal neural damage induced by sudden shearing/stretching forces applied to projections lying within the white matter tracts of the central nervous system [51, 52•, 53–56]. TAI is linked to a sequence of events that may include cellular mechanotransduction, axoskeletal disruption, transport interruption, membrane failure, calcium entry, focal axonal swelling, mitochondrial dysfunction, lipid peroxidation, proteolysis, and, if the injury is severe or sustained enough, cell death [57–59]. Axonal damage is associated with increased inflammatory signaling mediated in part by activated microglial cells [60], and evidence of neuroinflammation may be detected years after the initial injury [48, 61, 62]. TAI is a common pathological substrate encountered in TBI of all severities. It is associated with many of the acute clinical manifestations of TBI including loss of consciousness, confusion, and impairments in cognition. Recent research suggests that TAI may be linked to neurodegenerative conditions whose clinical onset is observed years after exposure, such as Alzhemier’s disease [63], non-Alzheimer’s dementias [64], and chronic traumatic encephalopathy [19, 65••, 66].
The relationship between TBI and changes in synaptic structure and function is supported by an emerging body of literature [67]. In animal models of TBI, dendritic beading and fragmentation, decreased number of dendritic branches, and changes in dendritic spine density are observed in injured neurons and also in cells remote from, but monosynaptically connected to, sites of injury [68–70]. Experimental TBI is associated with downregulation in a number of key pre- and post-synaptic proteins including synaptotagmin, synaptophysin, synapsin, synaptojanin, post-synaptic density protein-93 and protein-95, and DISC1 [67, 71]. Finally, hippocampal long-term potentiation, a measure of synaptic efficiency and plasticity, is depressed acutely following experimental TBI [72–77]. However, this may be a time-dependent and/or location-specific effect, and increased synaptic efficacy and associated cortical hyperexcitability have been noted weeks after white focal matter transection [78], with implications for post-injury epileptogenesis and plasticity.
The central role of astrocytes and oligodendrocytes in central nervous system trauma is the object of intense investigation. Following injury, astrocytes may have both beneficial roles, such as restoration of neurotransmitter and ionic homeostasis in the extracellular milieu and modulation of inflammatory signaling, and deleterious effects, such as glial scar formation [79–81]. Impaired ability of astrocytes to exchange potassium across the cell membrane may contribute to acute neuronal or synaptic dysfunction [82]. There is also mounting evidence that TBI is associated with acute oligodendrocyte death, with consequent axonal demyelination, impaired action potential propagation, and spontaneous remyelination [83–85]—processes whose role in mediating chronic post-traumatic neurodegeneration is not well understood. Collectively, the post-injury cellular morphological and electrophysiological changes noted in axons, synapses, and glia represent a plausible biological framework to understand the alterations in anatomical and functional connectivity which have been identified with the help of brain mapping approaches [18].
Statistical Modeling
Meaningful advances in neuroimaging have been tightly coupled with the development of advanced novel analytical approaches. Early analysis of neuroimaging data was based on nonquantitative or semiquantitative examinations. Such methods were prone to bias since they overlook large areas of the brain, and they do not have standard application methods which challenge the reliability of the results. The establishment of standard image templates and advances in image registration and normalization have enabled innovative solutions for combining images across multiple subjects in one space for analysis [86]. Methods have evolved to quantitatively evaluate anatomical and functional differences in the whole brain across many subjects, generating large and rich datasets. New analytical tools have been developed to perform voxel-based analysis, such as voxel-based morphometry obtained with T1-weighted image sequences, or to generate tractographic representations of white matter using DTI data [87, 88].
The determination of neural connectivity, a measure of signal coherence or correlation across different regions of the brain, requires specific statistical modeling [86]. Three types of connectivity can be distinguished (Table 2): (1) structural connectivity which is based on the anatomical connections linking spatially discrete neural populations; (2) functional connectivity which describes the statistical covariance of time-dependent signals between neural populations; and (3) effective connectivity, which evaluates directionally specified cause-and-effect relationships between neural populations [89, 90]. The evaluation of connectivity in functional neuroimaging data is accomplished with either seed-based correlation or independent component analysis (ICA). In the seed-based approach, a region of interest is identified a priori on the basis of a model or hypothesis, and the correlation in signal time courses between the seed and other brain regions is computed [91]. Independent component analysis (ICA) is a model-free, data-driven technique which decomposes brain activation maps into discrete components each of which has its unique time course, then estimates covariance from predefined temporal windows [92].
Modeling and representation of brain connectivity data are increasingly reliant on graph theoretical methods. Nodes (vertices) are identified based on prior anatomical knowledge or brain atlases; then, internodal connections (edges) are mapped to produce a network graph [93, 94]. Different topological metrics may be used to describe the emergent graph. Clustering coefficient expresses the probability that two randomly selected nodes in a graph are connected to each other. Path length is another measure that reflects integration of a network and is measured by any unique sequence of edges that connects two nodes with one another. Finally, node degree, defined as the number of edges which are connected to the node or the centrality of the latter, captures the importance of a given node in a network; the most highly connected nodes are referred to as “hubs” [89]. In the following sections, we will selectively review recent findings obtained when brain mapping approaches have been applied in patients with TBI.
Magnetic Resonance Imaging
Magnetic resonance imaging (MRI) is multiparametric allowing versatile image reconstructions including morphological images, susceptibility-weighted images, diffusion-weighted images, and metabolic and perfusion images [95]. Overall, MRI is more sensitive in detecting acute TBI compared to CT scan which is often normal on initial presentation [11–16]. Even commonly used MRI pulse sequences such as T1 and T2 may lack sensitivity for the detection of TAI [96]. Recent progress in MRI acquisition suggests it is possible to detect and characterize subtle structural and functional changes associated with TBI [97].
Gradient Recalled Echo and Susceptibility-Weighted Imaging
T2*-weighted gradient-recalled echo (GRE; also sometimes referred to as just T2*) and susceptibility-weighted imaging (SWI) are MRI pulse sequences that are highly sensitive to the paramagnetic susceptibility of iron found in hemosiderin, a breakdown product of hemoglobin which is deposited in the brain tissue following parenchymal bleeds [98–102]. GRE and SWI have greater sensitivity than CT and conventional MRI sequences (T1- and T2-weighted imaging) in detecting cerebral microbleeds in patients with hypertension, stroke, and small vessel disease [100]. Studies suggest that SWI is even more sensitive than GRE in the detection of microbleeds [101, 103]. MRI-identified microbleeds are used to support diagnosis, characterize disease burden, and predict clinical outcomes in patients with cerebral amyloid angiopathy (CAA) [104, 105]. A recent study—whose relevance to TBI is apparent in the subsequent discussion—combined results from SWI, PET identification of amyloid using Pittsburgh compound B, and DTI to evaluate network characteristics in patients with CAA and aged-matched controls [106]. In this study, graph analysis suggested that CAA patients had reduced global network efficiency which was linked to diminished cognitive and gait performance, effects which remained significant in multivariable analysis.
Recent work indicates that focal parenchymal microhemorrhages frequently co-locate with evidence of axonal damage and could represent a valuable biomarker in a subset of patients with TBI [38, 107–114]. The current model is that biomechanical forces responsible for TAI can induce extravasation of blood from multifocal damage to small vessels [109], leading to a pattern sometimes referred to as “diffuse vascular injury” [112] or “hemorrhagic DAI” [55]. On GRE or SWI, traumatic microhemorrhages appear as focal areas of reduced signal intensity caused by the paramagnetic effects of deoxyhemoblogin which may be undetectable using T2-weighted or FLAIR MRI sequences [115, 116]. The sensitivity of these techniques in detecting microbleeds associated with DAI increases with the strength of the magnetic field [117]. In addition, SWI is reported to have higher sensitivity in detecting traumatic microbleeds compared to T2*-GRE [118]. The number and/or volume and distribution of SWI lesions have been found to correlate with clinical severity of TBI and with neurologic or cognitive outcome in both pediatric [38] and adult TBI populations [16, 108]. Some investigators have proposed traumatic microbleeds, when present, are unequivocal surrogate markers of TAI; building on this postulate and by training pattern classifiers via machine learning techniques, it is possible to accurately classify patients with TAI even if they do not have microbleeds and, moreover, to better explain variances in post-injury long-term cognitive performance [113, 119••].
Diffusion-Weighted and Diffusion Tensor Imaging
Contrast signal in magnetic resonance diffusion-weighted imaging (DWI) is produced when water diffusion probabilities within tissue become less random—as, for example, when water is sequestered inside cells due to cytotoxic edema [120]. This pulse sequence has been extensively validated in the diagnosis of acute ischemic stroke [121, 122]. Diffusion tensor imaging (DTI) takes advantage of the anisotropic diffusion properties of water molecules in tissue to enable inferences regarding underlying white matter microstructure [123]; fiber tractography based on DTI provides a three-dimensional representation of white matter tracts in the central nervous system [124].
Studies conducted over the past two decades indicate that tissue diffusion characteristics are sensitive diagnostic and prognostic variables in patients with TBI. Water diffusion becomes restricted in acute TBI, presumably secondary to cytotoxic edema. The biological significance of post-TBI restricted tissue water diffusion has been supported by animal studies demonstrating histological changes which co-localize with areas of abnormal signal on DWI [125–127]. Areas of restricted diffusion detected with DWI represent a biomarker of nonhemorrhagic TAI that is more sensitive than CT or conventional T1 and T2 pulse sequences [14, 27, 34, 96, 110, 128–146]. Data suggest that DWI lesion number, volume, and location can increase the accuracy of outcome prediction in patients with TBI across the severity spectrum [34, 110, 128, 130, 131, 137, 146–148]. In an analysis of 77 patients with moderate–severe TBI, higher whole brain apparent diffusion coefficients were associated with discharge to home or rehabilitation [146]. In an earlier study involving 26 patients with TAI, the volume of lesions identified with DWI strongly correlated with modified Rankin scale at discharge [34]. Results from a larger cohort demonstrated a strong association between DWI lesions in the corpus callosum and the extended Glasgow outcome scale at 12 months [110].
In recent investigations, it has been shown that DTI is a sensitive method to detect and characterize tissue changes associated with TBI of all severities [149, 150]. In rodent models, changes in white matter anisotropy have been linked to histological evidence of TAI [36, 151–159]. In humans, DTI demonstrates a range of white matter alterations which are observed in mild, moderate, and severe TBI, and which may help classify post-injury functional and cognitive recovery phenotypes [14, 20, 21, 24, 25, 27, 35, 37, 42, 52•, 111, 119••, 160, 161, 162, 163, 164••, 165, 166, 167]. White matter tracts in which early post-traumatic DTI changes are most tightly linked to long-term outcome include the corpus callosum [20, 21, 37, 149, 160, 161, 166, 168, 169], internal capsule [37, 42, 170, 171], and brainstem [28, 37, 168, 172–174]. In a recent prospective multicenter study [37], 105 patients with severe TBI underwent brain MRI a mean of 21 days after injury, and DTI variables were evaluated in 20 predefined white matter regions based on the atlas established by Mori et al. [175]. Here [37], early discrimination between functional outcome categories was significantly improved with a prediction model that included DTI data, as compared to a model based exclusively on the International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) score [176].
Repeated DTI assessments during post-TBI recovery reveal dynamic changes in white matter, suggesting an active process whose biological underpinning might relate to different contributions of axonal damage, neuronal repair, demyelination, remyelination, and gliosis [21, 28, 177–180]. In one study, 30 patients with severe TBI were scanned with DTI 8 weeks post-injury and again at 12 months [21]. At follow-up DTI, fractional anisotropy had increased and reached normal or supranormal levels in the internal capsule and in centrum semiovale and the presence and magnitude of this increase was correlated with favorable functional outcome, while the presence of persistently low fractional anisotropy in the corpus callosum and cerebral peduncle was correlated with poor outcome [21]. In another study on moderate TBI patients evaluated 5–14 days after injury, fractional anisotropy was significantly reduced in the corpus callosum and/or internal capsule compared to controls and remained depressed 6 month later [177]. Newcombe et al. recently reported on 12 patients with moderate or severe TBI who underwent serial MRI in the acute phase and again 2–4 times during a follow-up period of up to 2.7 years after injury [178]. While there was considerable heterogeneity in individual time-courses, analysis suggested a dynamic process with time-dependent decreases in fractional anisotropy which were contingent on increases in axial and radial diffusivities and which were linked to performance on visual memory and learning tasks [178].
A growing body of research indicates that DTI might support predictions of cognitive recovery, or the outcome of neurorehabilitation, following TBI [20, 25, 27, 111, 119••, 166, 181–184], a detailed review of which is beyond the scope of this paper. A general finding is that pathological changes in regional white matter integrity are associated with impairments in a range of cognitive domains; however, the type, specificity, and magnitude of these associations are inconsistent across studies. In a very recent report, graph analysis was used to interpret DTI tractography data in 52 subjects who were scanned a mean of 37.56 months after moderate or severe TBI and compared to age-matched controls [119••]. Using machine learning techniques, the presence of TBI was predicted with 93.4 % accuracy, while information processing speed, associative memory, and executive function were significantly associated with centrality measures in the cingulate cortex and caudate. In another recent study, Strangman et al. found that post-rehabilitation performance on tests of memory and learning were associated with reduced fractional anisotropy, respectively, in the parahippocampal white matter and anterior corpus callosum, left anterior internal capsule, and right anterior corona radiata [181]; however, fractional anisotropy predictions were no more accurate that pre-rehabilitation test scores.
Magnetic Resonance Spectroscopy and Magnetic Resonance Spectroscopic Imaging
Proton magnetic resonance spectroscopy (MRS) evaluates the signal of covalently bound protons to map regional concentrations of a range of chemical compounds. Commonly measured compounds include choline (Cho), creatinine/phosphocreatine (Cr), N-acetyl aspartate (NAA), lactate, and glutamate/glutamine (Glx) [185]. NAA is associated with neuronal and axonal integrity and decreased levels of NAA or NAA indexed to Cr (NAA/Cr) have been found after TBI in humans [40–42, 186–196]. Early reports indicated that brain NAA/Cr decreases acutely after TBI in the frontal lobe and that NAA/Cr correlated with injury severity (GCS) [188] and outcome (GOS) [40, 195]. Low NAA/Cr in the brainstem is strongly associated with unfavorable TBI outcome, often in tissue without discernible damage on conventional morphological MRI [186]. More recent work with serial MRS suggests that tissue NAA/Cr and NAA/Cho values decline rapidly then progressively recover over 1–4 weeks to near-normal levels in patients with favorable outcomes while levels remain lower in those with unfavorable outcome [41, 190, 196]. Linear discriminant analysis of combined MRS and DTI acquired 3–4 weeks after injury in patients with severe TBI suggested that these two approaches are complementary and when used in combination accurately discriminate between favorable and unfavorable 1-year functional outcomes [42]. Another study in mild TBI subjects which combined MRS with functional MRI suggested a positive correlation between NAA/Cr levels in the corpus callosum and inter-hemispheric functional connectivity [197]. Although results are promising, a number of questions remain regarding optimal methods for MRS data acquisition and analysis in patients with TBI [198, 199].
Functional Brain Mapping
Functional neuroimaging, the mapping of time-dependent signal changes to directly or indirectly track underlying neuronal activation, can be achieved with several modalities including positron emission tomography (PET), functional MRI (fMRI), electroencephalography (EEG), and magnetoencephalography (MEG).
Positron Emission Tomography
PET methods identify the distribution of molecular probes labeled with a positron-emitting tracer or radionuclide [200]. Radionuclides best studied in the evaluation of TBI patients include 18F Flourine-18-2-fluoro-deoxy-d-glucose (FDG) and 15O-H2O which represent indirect markers of brain glucose and oxygen uptake. Both of these methods have been used to infer regional functional activation in the central nervous system because of the physiologic coupling between cellular energy metabolism and blood flow in normal situations [201]. Recently, it has been proposed that absolute indices of global and regional cerebral glucose metabolism could help differentiate unresponsive subjects with different levels of conscious processing [202–204]. In severe TBI patients evaluated less than 5 days after injury, comatose subjects had significantly reduced FDG-PET uptake in the thalamus, brainstem, and cerebellum when compared to noncomatose subjects [205]. Reduced glucose uptake has been observed in bilateral medial and basal frontal lobes, the cingulate gyrus, and the thalamus in severe TBI patients with chronic disorders of consciousness [202, 203, 206, 207]. Functional disconnection between brainstem arousal centers and the precuneus was described in a 15O-H2O-PET study of patients who were in a persistent vegetative state. A remarkable case report combining DTI and FDG-PET documented recovery of verbal communication and motor function in a patient who remained unconscious for 19 years after severe TBI, a change which correlated with increased fractional anisotropy and glucose uptake in posteromedial parietal cortices that encompassed cuneus and precuneus [208].
In FDG-PET evaluations of patients in the subacute phase following severe TBI who did not have focal anatomical lesions on MRI, a correlation was established between reduced glucose uptake in the prefrontal cortex and the cingulate gyrus and impaired memory and executive function [209–211]. Investigation of memory task activation using 15O-H2O-PET indicated that recovering moderate and severe TBI patients engage a frontal, anterior cingulate, and occipital network which is larger and less asymmetric than healthy controls, suggesting an impairment in cortical processing efficiency [212]. Resting FDG-PET studies in patients with mild TBI/concussion have yielded a range of findings [213–217]. No difference in resting FDG-PET uptake was noted on one comparison of patients with post-concussion symptoms and healthy controls [217]. Others have reported that concussion patients have reduced resting glucose metabolism in temporal and frontal regions [214, 215] or in cerebellum, vermis, pons, and medial temporal lobe [216]. An intriguing recent study completed in military mild TBI subjects during wakefulness and REM sleep identified a reduced metabolic rate of glucose in the amygdala, hippocampus, parahippocampal gyrus, thalamus, insula, uncus, culmen, visual association cortices, and midline medial frontal cortices [213].
Functional Magnetic Resonance Imaging
fMRI has emerged as the leading approach to map brain functional activation in humans. Correlative analysis conducted in TBI patients suggests a good match between regional activations obtained with PET and fMRI, although the spatial resolution is higher with fMRI [218]. The blood-oxygen-level-dependent (BOLD) fMRI signal correlates with neuronal activity as suggested by concurrent local field potential recordings in the visual cortex of nonhuman primates [219]. The value of fMRI has become increasingly apparent in the assessment of patients with disorders of consciousness, many of whom have had a TBI. These studies employ three distinct experimental paradigms [220]: (i) active fMRI studies which map BOLD signal changes associated with a specific motor or cognitive task; (ii) passive fMRI studies which map BOLD signal changes associated with an auditory, visual or sensory stimulus, and (iii) resting-state fMRI studies which map BOLD signal in the absence of any given task or stimulus. This literature has been reviewed elsewhere [220–222]. Here, we selectively review some of the recent fMRI studies.
Convergent lines of research indicate that spontaneous brain activity is organized topographically in dissociable large-scale functional networks which have quantifiable intrinsic and extrinsic connectivities (Table 2) [29, 223–226]. The resting-state paradigm has particular relevance in brain-injured patients in whom task responsiveness may not be reliably present. Resting-state fMRI activity associated with loss and recovery of consciousness following severe TBI has been characterized in several recent studies [43, 45, 47•, 163, 164••, 184, 227, 228, 229, 230, 231, 232, 233, 234•].
A significant number of studies have centered on activity or connectivity within the structures of the default mode network (DMN), a neuronal system which includes nodes in the medial and lateral parietal, medial prefrontal, and medial and lateral temporal cortices [223, 235, 236]. Deactivation of the DMN is associated with engagement in goal-oriented activity [235], and changes in DMN activity or coherence have been identified in a range of neurological and psychiatric disorders. A recent coordinate-based meta-analysis of resting functional neuroimaging data (PET and fMRI) found that patients with disorders of consciousness have consistent reductions in activity within cortical structures associated with the DMN [43]. Resting functional connectivity of nodes within the DMN is significantly decreased in TBI patients across the severity spectrum [47•, 184, 229, 232, 233, 234•, 237–240], a pattern which has been linked to DTI evidence of white matter damage within the DMN [163]. Disruption of selected edges within the DMN may have particular significance in predicting emergence from coma: thus, in a cohort of patients who underwent fMRI within the first week after injury, connectivity strength between the posterior cingulate cortex (PCC) and medial prefrontal cortex was significantly greater in patients who recovered consciousness when compared to those who did not [233].
Recently, other non-DMN networks have been explored to describe neural changes following brain injury and to enhance the accuracy of phenotype and outcome classification. Deficiencies in task-induced deactivation of the DMN, or loss of the anticorrelation normally observed between the DMN and other networks, have been reported in patients with impaired consciousness [241–243], post-TBI cognitive impairment [164••, 239, 244, 245], and post-concussion syndrome [246]. In 133 brain-injured subjects who underwent fMRI 3–10 months after injury, functional connectivity within the salience network (in particular between the ACC and left anterior insula) accurately differentiated between vegetative state and minimally conscious state patients, while DMN connectivity (especially between PCC and left lateral parietal cortex) was linked to emergence from the vegetative state [234]. Using machine learning techniques, another group found that intrinsic network functional connectivity strength discriminated between vegetative state and minimally conscious state patients with an accuracy of >80 % in several large-scale networks (frontoparietal, salience, auditory, sensorimotor, and visual networks), with auditory network intrinsic connectivity providing the best discrimination, in particular, edges of the auditory network that connect auditory and visual cortical centers [47•].
In addition to investigating loss and return of consciousness in brain-injured patients, fMRI can map cognitive states and trajectories following TBI [227]. A graph analysis of resting fMRI data demonstrated that recovering TBI patients have longer average path lengths and reduced network efficiency most prominently affecting the posterior cingulate cortex hub [45]. While reduced network or edge functional connectivity is widely reported in many studies of TBI and could represent a valuable biomarker of TAI, selective increases in within-network fronto-parietal connectivity measures have been reported in patients in the chronic stage of post-TBI recovery and could represent compensatory mechanisms [163, 184, 230, 247•, 248, 249]. Task-fMRI studies conducted in TBI patients have indicated wider recruitment of cortical resources suggesting a loss processing efficiency in patients recovering from TBI [250–253]. Impaired working memory in chronic TBI patients has been correlated with decreased activation of frontal sites which are nodes in well-characterized memory networks [254]. Tracking of memory task-induced superior frontal activation in patients with TAI revealed a progressive normalization over time that correlated with improved task performance [255].
Arterial Spin Labeling
Arterial Spin Labeling (ASL) is an MRI acquisition sequence which allows a direct measure of regional cerebral blood flow [256, 257] and can serve as a marker of functional activation albeit with temporal and spatial characteristics that are distinct from BOLD [258, 259]. In ASL, arterial blood is labeled magnetically and serves as an endogenous tracer, which allows the noninvasive quantification of regional brain tissue perfusion. In the chronic phase following moderate to severe TBI, resting ASL demonstrates reductions in the cerebral blood flow with more prominent regional hypoperfusion in the posterior cingulate cortices, the thalami, and multiple locations in the frontal cortices correlating with structural changes on DTI [260, 261]. During sustained attention and working memory tasks, chronic phase TBI patients had paradoxical increased activation of superior occipital cortices and the left superior temporal cortex whereas these areas were deactivated in healthy controls [262]. ASL has recently been used to evaluate patients with mild TBI [263, 264]. A longitudinal evaluation of concussed collegiate football players who underwent ASL 1 day, 1 week, and 1 month after injury found a reduction in right insular and superior temporal sulcus perfusion acutely that normalized at 1 month, although dorsal insular perfusion remained lower in subjects with persisting post-concussion symptoms [264].
The relationship between ASL measures of cerebral blood flow and BOLD functional connectivity was recently evaluated in healthy controls both in the resting state and during a working memory task [265]. Findings suggest that nodes with high resting or task-evoked BOLD functional connectivity (i.e., hubs) are also regions with comparatively elevated perfusion, a correlation which was most prominent in the DMN and executive control network [265]. ASL time courses may be may be used to evaluate large-scale functional activation and connectivity patterns with significant homologies to those obtained with BOLD [266–269]. The relevance of ASL connectivity analysis in patients with or recovering from TBI is currently unknown.
Electroencephalography
Electroencephalography (EEG) expresses, in the frequency domain, the composite electrical signal generated by dendritic synapses in the superficial layers of the cerebral cortex. When compared to fMRI, EEG has higher temporal resolution but lower spatial resolution; however, the pragmatic possibilities of EEG which can be deployed at the bedside in hospitalized or critically ill patients are of considerable interest. Quantitative EEG employs computational approaches to analyze amplitude, frequency, power, phase, and coherence, either independently or in different combinations. The analysis of EEG coherence or connectivity represents a unique opportunity to evaluate the impact of TBI on functional integration. Although early work in patients with severe TBI suggested an association between EEG coherence and MRI markers of neural integrity [270], a relation was not found between interhemispheric EEG coherence and post-injury outcome [271]. Subsequent quantitative EEG studies provide evidence of a loss of functional connectivity in patients with TBI [272–280]. Spatially distributed neuronal synchronization in the gamma frequency—which has been associated with perceptual awareness [281]—is impaired in patients with severe TBI [274]. In mild TBI patients studied with a high-density EEG array, interhemispheric and intrahemispheric (fronto-parietal, fronto-temporal, and temporo-parietal) coherence was comparable to healthy controls in the resting condition but significantly decreased during a working memory task [272]. Graph theoretical analysis applied to high-density EEG data acquired in the resting state of mild TBI subjects indicates that while global network efficiency is unchanged, the connectivity of dorsolateral prefrontal cortex and inferior frontal gyrus was increased, supporting the hypothesis that TBI results in a reorganization of network topology that may represent injury-associated or post-injury compensatory processes [280]. Similarly, in subjects with mild TBI following blast exposure, reduced EEG phase synchrony (indicating diminished interhemispheric connectivity) was noted in the frontal region, a finding which was associated with DTI evidence of damage to frontal white matter tracts [279]. Application of ICA and graph theory to EEG data from mild TBI patients suggests a significant decrease in the long-distance connectivity associated with a loss of small-world network topology [273].
Magnetoencephalography
Magnetoencephalography (MEG) records magnetic fields produced by neuronal electrical activity. Magnetic course imaging (MSI) combines MEG and MRI to map functional activation in the brain. MEG and MSI may be particularly valuable in the evaluation of patients with mild TBI and post-concussion syndromes [282]. Among 84 patients with mild TBI, MEG identified focal abnormalities in prefontal, posterior parietal, inferior temporal, hippocampus, and cerebellum [283]. MEG recordings made during cognitive flexibility tasks demonstrated disorganized and inefficient cortical activation of executive networks in mild TBI patients when compared to healthy volunteers [284]. In a direct head-to-head comparison, MEG was found to be significantly more sensitive to focal abnormalities in patients with post-concussion syndrome than structural MRI or EEG [285], single-photon emission tomography [286], or even DTI [287]. Resting-state MEG recordings in the alpha band in TBI patients compared to healthy controls indicate reduced functional connectivity [288] or, analogously, reduced Lempel–Ziv complexity [289], changes which suggest underlying distributed network damage. MEG-identified deficiencies in functional connectivity were recently described in multiple frequency bands (delta, theta, and alpha) and linked to post-TBI symptoms of inattention, anxiety, and depression [290].
Data acquired with MEG may also represent sensitive biomarkers of post-injury adaptation and plasticity. In a remarkable longitudinal evaluation of patients recovering from moderate to severe TBI who were studied before and after rehabilitation, resting-state MEG data revealed a loss of local and long-distance slow-frequency (delta and theta bands) connectivity and an increase in higher-frequency (alpha and beta) connectivity, changes which correlated with improved performance on neuropsychological testing [30]; moreover, network topologies in the post-rehabilitation patients more closely matched those observed in healthy controls than topologies seen in the pre-rehabilitation state [30]. Graph analysis applied to the same dataset showed that topological parameters following TBI are differentially affected in the delta band and decreased in the alpha band and that, following rehabilitation, these parameters evolve towards those of the control group [291]. These results support the hypothesis of a balance or trade-off between neural synchrony in slow and fast spectral bands as a fundamental mechanism driving post-injury recovery [291].
Molecular Neuroimaging
Recent work conducted with PET radioligands in vivo has generated invaluable insights on molecular mechanisms which may drive axonal injury and disrupt brain connectivity in TBI, including the deposition of amyloid protein, tau protein, and the detection of cerebral inflammatory activity [48–50, 292–301].
There is extensive evidence of an association between brain amyloid pathology and TBI [19, 296, 302–305]. Imaging of amyloid protein using carbon 11-labeled Pittsburgh Compound B ([11C]PiB) in a heterogeneous sample of 15 patients who were assessed 1 day to 1 year after TBI demonstrated cortical and striatal amyloid deposition which was not detected in healthy controls, findings which were corroborated in the postmortem brains of subjects who died after TBI [50]. Among patients with mild cognitive impairment, PiB-defined amyloid deposition is significantly higher in subjects with a history of TBI than those without, a difference which was not observed in cognitively normal individuals [306].
The role of tau protein as a molecular determinant of TAI and in post-TBI neurodegenerative disorders has been illustrated in a number of recent reports [19, 52•, 65••, 66, 307, 308]. [F-18]FDDNP, a selective tau protein radioligand, has been used to evaluate retired American football players who had a documented history of TBI and presented with cognitive or psychiatric manifestations [49, 309]. Tau protein deposition was prominent in several subcortical structures including midbrain, basal ganglia, thalamus, amygdala, frontal, parietal, posterior cingulate, medial and lateral temporal—a distribution which closely matches postmortem neuropathological analyses in subjects who died of chronic traumatic encephalopathy, a neurodegenerative syndrome linked to TBI exposure [49, 66, 309].
Innate and adaptive immunologic responses occur both acutely and in the chronic setting after TBI and may represent targets for therapeutic modulation [310–315]. Two studies using selective PET markers of activated microglia documented persisting inflammation in the brain months to years after TBI [48, 62], corroborating neuropathological findings in deceased patients with a history of TBI [316]. Labeling of activated microglia was increased in several brain regions of retired American football players with a history of TBI, findings which appeared to correlate with performance on tests of learning of memory [297].
Conclusions and Future Directions
Recent work in brain mapping supports the paradigm of a “human connectome”: a multi-level representation of the neural matrix which constitutes the central nervous system [317]. The connectome model appears particularly valuable in the detection, diagnosis, classification, and recovery prediction in patients with TBI, which is a distributed, multifocal brain disorder. The connectome approach also suggests potentially transformative therapeutic possibilities such as the modulation of brain network activity to restore appropriate functional connectivity following TBI. Achieving these goals will require a comprehensive, organized approach to characterize and classify TBI and post-TBI recovery in cellular and molecular terms and to build models that link biological events to endophenotypes (including imaging) and clinical phenotypes. In the realm of functional neuroimaging, we need to understand how neural–hemodynamic coupling, which constitutes the basis of fMRI inference, might be confounded in the acute and chronic phases of TBI. The effects on the fMRI (or EEG) signal of prior neurological disease and cognitive impairment, concurrent physiologic variation, and sedative medication need to be identified and separated to reliably map neural activity. Registration methods should account for deformation associated with brain injury such as cerebral edema and mass effect in the acute phase and atrophy and hydrocephalus in the chronic phase. To enhance TBI data sharing, collaboration, and multisite investigation, standardized approaches will be needed for neuroimage acquisition, quality control, storage, analysis, and interpretation. Collectively, it is hoped that these advances will advance the goal of alleviating the burden associated with TBI.
References
Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance
Faul MXL, Wald MM, Coronado VG. Traumatic brain injury in the United States: emergency department visits, hospitalizations and deaths 2002–2006. Atlanta: Center for Disease Control and Prevention, National Center for Injury Prevention and Control; 2010.
Coronado VG, Xu L, Basavaraju SV, et al. Surveillance for traumatic brain injury-related deaths—United States, 1997–2007. Morbidity and mortality weekly report. Surveill Summ. 2011;60:1–32.
Selassie AW, Zaloshnja E, Langlois JA, et al. Incidence of long-term disability following traumatic brain injury hospitalization, United States, 2003. J Head Trauma Rehabil. 2008;23:123–31.
Rutland-Brown W, Langlois JA, Thomas KE, Xi YL. Incidence of traumatic brain injury in the United States, 2003. J Head Trauma Rehabil. 2006;21:544–8.
Carpenter KL, Czosnyka M, Jalloh I, et al. Systemic, local, and imaging biomarkers of brain injury: more needed, and better use of those already established? Front Neurol. 2015;6:26.
Stevens RD, Sutter R. Prognosis in severe brain injury. Crit Care Med. 2013;41:1104–23.
Teasdale G, Jennett B. Assessment of coma and impaired consciousness. A practical scale. Lancet (London, England). 1974;2:81–4.
Maas AI, Stocchetti N, Bullock R. Moderate and severe traumatic brain injury in adults. The Lancet. Neurology. 2008;7:728–41.
Marshall LF, Marshall SB, Klauber MR, et al. The diagnosis of head injury requires a classification based on computed axial tomography. J Neurotrauma. 1992;9 Suppl 1:S287–92.
Medana IM, Esiri MM. Axonal damage: a key predictor of outcome in human CNS diseases. Brain. 2003;126:515–30.
Gentry LR, Godersky JC, Thompson B, Dunn VD. Prospective comparative study of intermediate-field MR and CT in the evaluation of closed head trauma. AJR Am J Roentgenol. 1988;150:673–82.
Mittl RL, Grossman RI, Hiehle JF, et al. Prevalence of MR evidence of diffuse axonal injury in patients with mild head injury and normal head CT findings. AJNR Am J Neuroradiol. 1994;15:1583–9.
Arfanakis K, Haughton VM, Carew JD, et al. Diffusion tensor MR imaging in diffuse axonal injury. AJNR Am J Neuroradiol. 2002;23:794–802.
Huisman TA, Schwamm LH, Schaefer PW, et al. Diffusion tensor imaging as potential biomarker of white matter injury in diffuse axonal injury. AJNR Am J Neuroradiol. 2004;25:370–6.
Lee H, Wintermark M, Gean AD, et al. Focal lesions in acute mild traumatic brain injury and neurocognitive outcome: CT versus 3T MRI. J Neurotrauma. 2008;25:1049–56.
Yuh EL, Mukherjee P, Lingsma HF, et al. Magnetic resonance imaging improves 3-month outcome prediction in mild traumatic brain injury. Ann Neurol. 2013;73:224–35.
Castellanos NP, Bajo R, Cuesta P, et al. Alteration and reorganization of functional networks: a new perspective in brain injury study. Front Hum Neurosci. 2011;5:90.
Sharp DJ, Scott G, Leech R. Network dysfunction after traumatic brain injury. Nature reviews. Neurol. 2014;10:156–66.
Blennow K, Hardy J, Zetterberg H. The neuropathology and neurobiology of traumatic brain injury. Neuron. 2012;76:886–99.
Kraus MF, Susmaras T, Caughlin BP, et al. White matter integrity and cognition in chronic traumatic brain injury: a diffusion tensor imaging study. Brain. 2007;130:2508–19.
Sidaros A, Engberg AW, Sidaros K, et al. Diffusion tensor imaging during recovery from severe traumatic brain injury and relation to clinical outcome: a longitudinal study. Brain. 2008;131:559–72.
Salmond CH, Chatfield DA, Menon DK, et al. Cognitive sequelae of head injury: involvement of basal forebrain and associated structures. Brain. 2005;128:189–200.
Dikmen SS, Corrigan JD, Levin HS, et al. Cognitive outcome following traumatic brain injury. J Head Trauma Rehabil. 2009;24:430–8.
Palacios EM, Sala-Llonch R, Junque C, et al. White matter integrity related to functional working memory networks in traumatic brain injury. Neurol. 2012;78:852–60.
Wang JY, Bakhadirov K, Abdi H, et al. Longitudinal changes of structural connectivity in traumatic axonal injury. Neurol. 2011;77:818–26.
Newcombe VF, Menon DK. Cognitive deficits and mild traumatic brain injury. BMJ (Clinical research ed). 2013;346:f1522.
Newcombe VF, Outtrim JG, Chatfield DA, et al. Parcellating the neuroanatomical basis of impaired decision-making in traumatic brain injury. Brain. 2011;134:759–68.
Newcombe VF, Williams GB, Scoffings D, et al. Aetiological differences in neuroanatomy of the vegetative state: insights from diffusion tensor imaging and functional implications. J Neurol Neurosurg Psychiatry. 2010;81:552–61.
Stam CJ. Modern network science of neurological disorders. Nature reviews. Neurosci. 2014;15:683–95.
Castellanos NP, Paul N, Ordonez VE, et al. Reorganization of functional connectivity as a correlate of cognitive recovery in acquired brain injury. Brain. 2010;133:2365–81.
Guggenmos DJ, Azin M, Barbay S, et al. Restoration of function after brain damage using a neural prosthesis. Proc Natl Acad Sci U S A. 2013;110:21177–82. Implantation of a miconeuroprosthesis in a rat model of motor cortex injury enhanced functional connectivity between prefrontal and somatosensory cortices and was associated with rapid improvement in motor function.
Schiff ND, Giacino JT, Kalmar K, et al. Behavioural improvements with thalamic stimulation after severe traumatic brain injury. Nature. 2007;448:600–3.
Duckworth JL, Stevens RD. Imaging brain trauma. Curr Opin Crit Care. 2010;16:92–7.
Schaefer PW, Huisman TA, Sorensen AG, et al. Diffusion-weighted MR imaging in closed head injury: high correlation with initial Glasgow coma scale score and score on modified Rankin scale at discharge. Radiology. 2004;233:58–66.
Mac Donald CL, Johnson AM, Cooper D, et al. Detection of blast-related traumatic brain injury in U.S. military personnel. N Engl J Med. 2011;364:2091–100.
Mac Donald CL, Dikranian K, Bayly P, et al. Diffusion tensor imaging reliably detects experimental traumatic axonal injury and indicates approximate time of injury. J Neurosci : Off J Soc Neurosci. 2007;27:11869–76.
Galanaud D, Perlbarg V, Gupta R, et al. Assessment of white matter injury and outcome in severe brain trauma: a prospective multicenter cohort. Anesthesiology. 2012;117:1300–10.
Tong KA, Ashwal S, Holshouser BA, et al. Diffuse axonal injury in children: clinical correlation with hemorrhagic lesions. Ann Neurol. 2004;56:36–50.
Wong PK, Huang YL, Chen CJ, et al. Susceptibility-weighted MRI in mild traumatic brain injury. Neurology. 2015;85:921.
Garnett MR, Blamire AM, Corkill RG, et al. Early proton magnetic resonance spectroscopy in normal-appearing brain correlates with outcome in patients following traumatic brain injury. Brain : J neurol. 2000;123(Pt 10):2046–54.
Vagnozzi R, Signoretti S, Cristofori L, et al. Assessment of metabolic brain damage and recovery following mild traumatic brain injury: a multicentre, proton magnetic resonance spectroscopic study in concussed patients. Brain. 2010;133:3232–42.
Tollard E, Galanaud D, Perlbarg V, et al. Experience of diffusion tensor imaging and 1H spectroscopy for outcome prediction in severe traumatic brain injury: preliminary results. Crit Care Med. 2009;37:1448–55.
Hannawi Y, Lindquist MA, Caffo BS, et al. Resting brain activity in disorders of consciousness: a systematic review and meta-analysis. Neurology. 2015;84:1272–80.
Hartings JA, Wilson JA, Hinzman JM, et al. Spreading depression in continuous electroencephalography of brain trauma. Ann Neurol. 2014;76:681–94.
Pandit AS, Expert P, Lambiotte R, et al. Traumatic brain injury impairs small-world topology. Neurology. 2013;80:1826–33.
Cruse D, Chennu S, Chatelle C, et al. Bedside detection of awareness in the vegetative state: a cohort study. Lancet (London, England). 2011;378:2088–94.
Demertzi A, Antonopoulos G, Heine L, et al. Intrinsic functional connectivity differentiates minimally conscious from unresponsive patients. Brain. 2015;138:2619–31. Functional connectivity within default mode, frontoparietal, salience, auditory, sensorimotor and visual networks helped discriminate patients in a vegetative state from those in a minimally conscious state—supporting the hypothesis that intrinsic network connectivity is a fundamental biomarker of conscious processing.
Ramlackhansingh AF, Brooks DJ, Greenwood RJ, et al. Inflammation after trauma: microglial activation and traumatic brain injury. Ann Neurol. 2011;70:374–83.
Barrio JR, Small GW, Wong KP, et al. In vivo characterization of chronic traumatic encephalopathy using [F-18]FDDNP PET brain imaging. Proc Natl Acad Sci U S A. 2015;112:E2039–47.
Hong YT, Veenith T, Dewar D, et al. Amyloid imaging with carbon 11-labeled Pittsburgh compound B for traumatic brain injury. JAMA neurol. 2014;71:23–31.
Gennarelli TA, Thibault LE, Adams JH, et al. Diffuse axonal injury and traumatic coma in the primate. Ann Neurol. 1982;12:564–74.
Magnoni S, Mac Donald CL, Esparza TJ, et al. Quantitative assessments of traumatic axonal injury in human brain: concordance of microdialysis and advanced MRI. Brain. 2015;138:2263–77. In patients with severe TBI, brain interstitial tau protein concentrations obtained via cerebral microdialysis correlated with MRI-DTI assessments of anisotropy in the white matter adjacent to the microdialysis probe. The study increases confidence in DTI as a noninvasive approach to identify axonal injury.
Buki A, Povlishock JT. All roads lead to disconnection?—traumatic axonal injury revisited. Acta Neurochir. 2006;148:181–93. discussion 193–184.
Adams H, Mitchell DE, Graham DI, Doyle D. Diffuse brain damage of immediate impact type. Its relationship to ‘primary brain-stem damage’ in head injury. Brain. 1977;100:489–502.
Adams JH, Graham DI, Scott G, et al. Brain damage in fatal non-missile head injury. J Clin Pathol. 1980;33:1132–45.
Adams JH, Doyle D, Graham DI, et al. Diffuse axonal injury in head injuries caused by a fall. Lancet (London, England). 1984;2:1420–2.
Hemphill MA, Dauth S, Yu CJ, et al. Traumatic brain injury and the neuronal microenvironment: a potential role for neuropathological mechanotransduction. Neuron. 2015;85:1177–92.
Maxwell WL, MacKinnon MA, Stewart JE, Graham DI. Stereology of cerebral cortex after traumatic brain injury matched to the Glasgow outcome score. Brain. 2010;133:139–60.
Johnson VE, Stewart W, Smith DH. Axonal pathology in traumatic brain injury. Exp Neurol. 2013;246:35–43.
Kelley BJ, Lifshitz J, Povlishock JT. Neuroinflammatory responses after experimental diffuse traumatic brain injury. J Neuropathol Exp Neurol. 2007;66:989–1001.
Venkatesan C, Chrzaszcz M, Choi N, Wainwright MS. Chronic upregulation of activated microglia immunoreactive for galectin-3/Mac-2 and nerve growth factor following diffuse axonal injury. J Neuroinflammation. 2010;7:32.
Johnson VE, Stewart JE, Begbie FD, et al. Inflammation and white matter degeneration persist for years after a single traumatic brain injury. Brain. 2013;136:28–42.
Cole JH, Leech R, Sharp DJ. Prediction of brain age suggests accelerated atrophy after traumatic brain injury. Ann Neurol. 2015;77:571–81.
Nordstrom P, Michaelsson K, Gustafson Y, Nordstrom A. Traumatic brain injury and young onset dementia: a nationwide cohort study. Ann Neurol. 2014;75:374–81.
Kondo A, Shahpasand K, Mannix R, et al. Antibody against early driver of neurodegeneration cis P-tau blocks brain injury and tauopathy. Nature. 2015;523:431–6. Study demonstrating increased cis-phosphorylated tau protein (cisP-tau) expression acutely after murine TBI, leading to axonal microtubule disruption, impaired mitochondrial function, inter-neuronal spread, and apoptosis. In addition results suggest antibodies blocking cisP-tau could have diagnostic and therapeutic properties.
McKee AC, Stern RA, Nowinski CJ, et al. The spectrum of disease in chronic traumatic encephalopathy. Brain. 2013;136:43–64.
Merlo L, Cimino F, Angileri FF, et al. Alteration in synaptic junction proteins following traumatic brain injury. J Neurotrauma. 2014;31:1375–85.
Gao X, Deng P, Xu ZC, Chen J. Moderate traumatic brain injury causes acute dendritic and synaptic degeneration in the hippocampal dentate gyrus. PLoS One. 2011;6, e24566.
Sword J, Masuda T, Croom D, Kirov SA. Evolution of neuronal and astroglial disruption in the peri-contusional cortex of mice revealed by in vivo two-photon imaging. Brain. 2013;136:1446–61.
Winston CN, Chellappa D, Wilkins T, et al. Controlled cortical impact results in an extensive loss of dendritic spines that is not mediated by injury-induced amyloid-beta accumulation. J Neurotrauma. 2013;30:1966–72.
Yu C, Boutte A, Yu X, et al. A systems biology strategy to identify molecular mechanisms of action and protein indicators of traumatic brain injury. J Neurosci Res. 2015;93:199–214.
Aungst SL, Kabadi SV, Thompson SM, et al. Repeated mild traumatic brain injury causes chronic neuroinflammation, changes in hippocampal synaptic plasticity, and associated cognitive deficits. J Cereb Blood Flow Metab. 2014;34:1223–32.
Reeves TM, Lyeth BG, Povlishock JT. Long-term potentiation deficits and excitability changes following traumatic brain injury. Exp Brain Res. 1995;106:248–56.
Sanders MJ, Sick TJ, Perez-Pinzon MA, et al. Chronic failure in the maintenance of long-term potentiation following fluid percussion injury in the rat. Brain Res. 2000;861:69–76.
Schwarzbach E, Bonislawski DP, Xiong G, Cohen AS. Mechanisms underlying the inability to induce area CA1 LTP in the mouse after traumatic brain injury. Hippocampus. 2006;16:541–50.
Goforth PB, Ren J, Schwartz BS, Satin LS. Excitatory synaptic transmission and network activity are depressed following mechanical injury in cortical neurons. J Neurophysiol. 2011;105:2350–63.
Albensi BC, Sullivan PG, Thompson MB, et al. Cyclosporin ameliorates traumatic brain-injury-induced alterations of hippocampal synaptic plasticity. Exp Neurol. 2000;162:385–9.
Avramescu S, Timofeev I. Synaptic strength modulation after cortical trauma: a role in epileptogenesis. J Neurosci : Off J Soc Neurosci. 2008;28:6760–72.
Pekny M, Nilsson M. Astrocyte activation and reactive gliosis. Glia. 2005;50:427–34.
Kou Z, VandeVord PJ. Traumatic white matter injury and glial activation: from basic science to clinics. Glia. 2014;62:1831–55.
Myer DJ, Gurkoff GG, Lee SM, et al. Essential protective roles of reactive astrocytes in traumatic brain injury. Brain. 2006;129:2761–72.
D’Ambrosio R, Maris DO, Grady MS, et al. Impaired K(+) homeostasis and altered electrophysiological properties of post-traumatic hippocampal glia. J Neurosci : Off J Soc Neurosci. 1999;19:8152–62.
Shi H, Hu X, Leak RK et al. Demyelination as a rational therapeutic target for ischemic or traumatic brain injury. Experimental neurology 2015.
Armstrong RC, Mierzwa AJ, Sullivan GM, Sanchez MA. Myelin and oligodendrocyte lineage cells in white matter pathology and plasticity after traumatic brain injury. Neuropharmacology 2015.
Mierzwa AJ, Marion CM, Sullivan GM, et al. Components of myelin damage and repair in the progression of white matter pathology after mild traumatic brain injury. J Neuropathol Exp Neurol. 2015;74:218–32.
Friston KJ. Statistical parametric mapping : the analysis of functional brain images. Amsterdam: Elsevier/Academic; 2007.
Ridgway GR, Henley SM, Rohrer JD, et al. Ten simple rules for reporting voxel-based morphometry studies. NeuroImage. 2008;40:1429–35.
Mori S, van Zijl PC. Fiber tracking: principles and strategies—a technical review. NMR Biomed. 2002;15:468–80.
Sporns O. Structure and function of complex brain networks. Dialogues Clin Neurosci. 2013;15:247–62.
Park HJ, Friston K. Structural and functional brain networks: from connections to cognition. Science (New York, N.Y.) 2013; 342:1238411.
Larson-Prior LJ, Zempel JM, Nolan TS, et al. Cortical network functional connectivity in the descent to sleep. Proc Natl Acad Sci U S A. 2009;106:4489–94.
Calhoun VD, Miller R, Pearlson G, Adali T. The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron. 2014;84:262–74.
Deco G, Kringelbach ML. Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders. Neuron. 2014;84:892–905.
Deco G, Tononi G, Boly M, Kringelbach ML. Rethinking segregation and integration: contributions of whole-brain modelling. Nat Rev Neurosci. 2015;16:430–9.
Plewes DB, Kucharczyk W. Physics of MRI: a primer. J Magn Reson Imaging: JMRI. 2012;35:1038–54.
Huisman TA, Sorensen AG, Hergan K, et al. Diffusion-weighted imaging for the evaluation of diffuse axonal injury in closed head injury. J Comput Assist Tomogr. 2003;27:5–11.
Stevens RD, Hannawi Y, Puybasset L. MRI for coma emergence and recovery. Curr Opin Crit Care. 2014;20:168–73.
Greenberg SM, Vernooij MW, Cordonnier C, et al. Cerebral microbleeds: a guide to detection and interpretation. The Lancet Neurology. 2009;8:165–74.
Greenberg SM, O’Donnell HC, Schaefer PW, Kraft E. MRI detection of new hemorrhages: potential marker of progression in cerebral amyloid angiopathy. Neurology. 1999;53:1135–8.
Greenberg SM, Finklestein SP, Schaefer PW. Petechial hemorrhages accompanying lobar hemorrhage: detection by gradient-echo MRI. Neurology. 1996;46:1751–4.
Goos JD, van der Flier WM, Knol DL, et al. Clinical relevance of improved microbleed detection by susceptibility-weighted magnetic resonance imaging. Stroke; J of cereb circ. 2011;42:1894–900.
Charidimou A, Jager HR, Werring DJ. Cerebral microbleed detection and mapping: principles, methodological aspects and rationale in vascular dementia. Exp Gerontol. 2012;47:843–52.
Cheng AL, Batool S, McCreary CR, et al. Susceptibility-weighted imaging is more reliable than T2*-weighted gradient-recalled echo MRI for detecting microbleeds. Stroke; J Cereb Circ. 2013;44:2782–6.
Knudsen KA, Rosand J, Karluk D, Greenberg SM. Clinical diagnosis of cerebral amyloid angiopathy: validation of the Boston criteria. Neurology. 2001;56:537–9.
Viswanathan A, Greenberg SM. Cerebral amyloid angiopathy in the elderly. Ann Neurol. 2011;70:871–80.
Reijmer YD, Fotiadis P, Martinez-Ramirez S, et al. Structural network alterations and neurological dysfunction in cerebral amyloid angiopathy. Brain. 2015;138:179–88.
Scheid R, Preul C, Gruber O, et al. Diffuse axonal injury associated with chronic traumatic brain injury: evidence from T2*-weighted gradient-echo imaging at 3 T. AJNR Am J neuroradiol. 2003;24:1049–56.
Huang YL, Kuo YS, Tseng YC, et al. Susceptibility-weighted MRI in mild traumatic brain injury. Neurology. 2015;84:580–5.
Kenney K, Amyot F, Haber M, et al. Cerebral vascular injury in traumatic brain injury. Exp Neurol. 2015.
Moen KG, Brezova V, Skandsen T, et al. Traumatic axonal injury: the prognostic value of lesion load in corpus callosum, brain stem, and thalamus in different magnetic resonance imaging sequences. J Neurotrauma. 2014;31:1486–96.
Kinnunen KM, Greenwood R, Powell JH, et al. White matter damage and cognitive impairment after traumatic brain injury. Brain. 2011;134:449–63.
Iwamura A, Taoka T, Fukusumi A, et al. Diffuse vascular injury: convergent-type hemorrhage in the supratentorial white matter on susceptibility-weighted image in cases of severe traumatic brain damage. Neuroradiology. 2012;54:335–43.
Hellyer PJ, Leech R, Ham TE, et al. Individual prediction of white matter injury following traumatic brain injury. Ann Neurol. 2013;73:489–99.
Scheid R, Walther K, Guthke T, et al. Cognitive sequelae of diffuse axonal injury. Arch Neurol. 2006;63:418–24.
Geurts BH, Andriessen TM, Goraj BM, Vos PE. The reliability of magnetic resonance imaging in traumatic brain injury lesion detection. Brain Inj. 2012;26:1439–50.
Gerber DJ, Weintraub AH, Cusick CP, et al. Magnetic resonance imaging of traumatic brain injury: relationship of T2*SE and T2GE to clinical severity and outcome. Brain Inj. 2004;18:1083–97.
Scheid R, Ott DV, Roth H, et al. Comparative magnetic resonance imaging at 1.5 and 3 Tesla for the evaluation of traumatic microbleeds. J Neurotrauma. 2007;24:1811–6.
Hasiloglu ZI, Albayram S, Selcuk H, et al. Cerebral microhemorrhages detected by susceptibility-weighted imaging in amateur boxers. AJNR Am J neuroradiol. 2011;32:99–102.
Fagerholm ED, Hellyer PJ, Scott G, et al. Disconnection of network hubs and cognitive impairment after traumatic brain injury. Brain. 2015;138:1696–709. Graph theory analysis and machine learning techniques applied to DTI-tractography data indicate that betweenness centrality and eigenvector centrality are significantly reduced within network hubs in subjects with traumatic axonal injury.
Le Bihan D. Looking into the functional architecture of the brain with diffusion MRI. Nat Rev Neurosci. 2003;4:469–80.
Warach S, Chien D, Li W, et al. Fast magnetic resonance diffusion-weighted imaging of acute human stroke. Neurology. 1992;42:1717–23.
Muir KW, Buchan A, von Kummer R, et al. Imaging of acute stroke. The Lancet Neurology. 2006;5:755–68.
Mori S, Zhang J. Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron. 2006;51:527–39.
Melhem ER, Mori S, Mukundan G, et al. Diffusion tensor MR imaging of the brain and white matter tractography. AJR Am J Roentgenol. 2002;178:3–16.
Assaf Y, Beit-Yannai E, Shohami E, et al. Diffusion- and T2-weighted MRI of closed-head injury in rats: a time course study and correlation with histology. Magn Reson Imaging. 1997;15:77–85.
Albensi BC, Knoblach SM, Chew BG, et al. Diffusion and high resolution MRI of traumatic brain injury in rats: time course and correlation with histology. Exp Neurol. 2000;162:61–72.
Van Putten HP, Bouwhuis MG, Muizelaar JP, et al. Diffusion-weighted imaging of edema following traumatic brain injury in rats: effects of secondary hypoxia. J Neurotrauma. 2005;22:857–72.
Galloway NR, Tong KA, Ashwal S, et al. Diffusion-weighted imaging improves outcome prediction in pediatric traumatic brain injury. J Neurotrauma. 2008;25:1153–62.
Fernandez-Espejo D, Bekinschtein T, Monti MM, et al. Diffusion weighted imaging distinguishes the vegetative state from the minimally conscious state. NeuroImage. 2011;54:103–12.
Zheng WB, Liu GR, Kong KM, Wu RH. Coma duration prediction in diffuse axonal injury: analyses of apparent diffusion coefficient and clinical prognostic factors. Conference proceedings : … Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Ann Conf. 2006;1:1052–5.
Hou DJ, Tong KA, Ashwal S, et al. Diffusion-weighted magnetic resonance imaging improves outcome prediction in adult traumatic brain injury. J Neurotrauma. 2007;24:1558–69.
Topal NB, Hakyemez B, Erdogan C, et al. MR imaging in the detection of diffuse axonal injury with mild traumatic brain injury. Neurol Res. 2008;30:974–8.
Liu AY, Maldjian JA, Bagley LJ, et al. Traumatic brain injury: diffusion-weighted MR imaging findings. AJNR Am J Neuroradiol. 1999;20:1636–41.
Hergan K, Schaefer PW, Sorensen AG, et al. Diffusion-weighted MRI in diffuse axonal injury of the brain. Eur Radiol. 2002;12:2536–41.
Chan JH, Tsui EY, Peh WC, et al. Diffuse axonal injury: detection of changes in anisotropy of water diffusion by diffusion-weighted imaging. Neuroradiology. 2003;45:34–8.
Le TH, Mukherjee P, Henry RG, et al. Diffusion tensor imaging with three-dimensional fiber tractography of traumatic axonal shearing injury: an imaging correlate for the posterior callosal “disconnection” syndrome: case report. Neurosurgery. 2005;56:189.
Zheng WB, Liu GR, Li LP, Wu RH. Prediction of recovery from a post-traumatic coma state by diffusion-weighted imaging (DWI) in patients with diffuse axonal injury. Neuroradiology. 2007;49:271–9.
Moen KG, Skandsen T, Folvik M, et al. A longitudinal MRI study of traumatic axonal injury in patients with moderate and severe traumatic brain injury. Neurosurg Psychiatry. 2012;83:1193–200.
Haberg AK, Olsen A, Moen KG, et al. White matter microstructure in chronic moderate-to-severe traumatic brain injury: impact of acute-phase injury-related variables and associations with outcome measures. J Neurosci Res. 2015;93:1109–26.
Goetz P, Blamire A, Rajagopalan B, et al. Increase in apparent diffusion coefficient in normal appearing white matter following human traumatic brain injury correlates with injury severity. J Neurotrauma. 2004;21:645–54.
Pasco A, Ter Minassian A, Chapon C, et al. Dynamics of cerebral edema and the apparent diffusion coefficient of water changes in patients with severe traumatic brain injury. A prospective MRI study. Eur Radiol. 2006;16:1501–8.
Kinoshita T, Moritani T, Hiwatashi A, et al. Conspicuity of diffuse axonal injury lesions on diffusion-weighted MR imaging. Eur J Radiol. 2005;56:5–11.
Shanmuganathan K, Gullapalli RP, Mirvis SE, et al. Whole-brain apparent diffusion coefficient in traumatic brain injury: correlation with Glasgow Coma Scale score. AJNR Am J Neuroradiol. 2004;25:539–44.
Marmarou A, Signoretti S, Aygok G, et al. Traumatic brain edema in diffuse and focal injury: cellular or vasogenic? Acta neurochirurgica Supplement. 2006;96:24–9.
Moen KG, Haberg AK, Skandsen T, et al. A longitudinal magnetic resonance imaging study of the apparent diffusion coefficient values in corpus callosum during the first year after traumatic brain injury. J Neurotrauma. 2014;31:56–63.
Shakir A, Aksoy D, Mlynash M, et al. Prognostic value of quantitative diffusion-weighted MRI in patients with traumatic brain injury. J neuroimaging : Off J Am Soc Neuroimaging. 2015.
Hudak AM, Peng L, Marquez de la Plata C, et al. Cytotoxic and vasogenic cerebral oedema in traumatic brain injury: assessment with FLAIR and DWI imaging. Brain Inj. 2014;28:1602–9.
Yuan L, Wei X, Xu C, et al. Use of multisequence 3.0-T MRI to detect severe traumatic brain injury and predict the outcome. Br J Radiol. 2015;88:20150129.
Aoki Y, Inokuchi R, Gunshin M, et al. Diffusion tensor imaging studies of mild traumatic brain injury: a meta-analysis. Neurosurg Psychiatry. 2012;83:870–6.
Hulkower MB, Poliak DB, Rosenbaum SB, et al. A decade of DTI in traumatic brain injury: 10 years and 100 articles later. AJNR Am J Neuroradiol. 2013;34:2064–74.
Mac Donald CL, Dikranian K, Song SK, et al. Detection of traumatic axonal injury with diffusion tensor imaging in a mouse model of traumatic brain injury. Exp Neurol. 2007;205:116–31.
Li J, Li XY, Feng DF, Gu L. Quantitative evaluation of microscopic injury with diffusion tensor imaging in a rat model of diffuse axonal injury. Eur J Neurosci. 2011;33:933–45.
van de Looij Y, Mauconduit F, Beaumont M, et al. Diffusion tensor imaging of diffuse axonal injury in a rat brain trauma model. NMR Biomed. 2012;25:93–103.
Budde MD, Janes L, Gold E, et al. The contribution of gliosis to diffusion tensor anisotropy and tractography following traumatic brain injury: validation in the rat using Fourier analysis of stained tissue sections. Brain. 2011;134:2248–60.
Rubovitch V, Ten-Bosch M, Zohar O, et al. A mouse model of blast-induced mild traumatic brain injury. Exp Neurol. 2011;232:280–9.
Bennett RE, Mac Donald CL, Brody DL. Diffusion tensor imaging detects axonal injury in a mouse model of repetitive closed-skull traumatic brain injury. Neurosci Lett. 2012;513:160–5.
Calabrese E, Du F, Garman RH, et al. Diffusion tensor imaging reveals white matter injury in a rat model of repetitive blast-induced traumatic brain injury. J Neurotrauma. 2014;31:938–50.
Donovan V, Kim C, Anugerah AK, et al. Repeated mild traumatic brain injury results in long-term white-matter disruption. J Cereb Blood Flow Metab. 2014;34:715–23.
Long JA, Watts LT, Chemello J, et al. Multiparametric and longitudinal MRI characterization of mild traumatic brain injury in rats. J Neurotrauma. 2015;32:598–607.
Wilde EA, McCauley SR, Hunter JV, et al. Diffusion tensor imaging of acute mild traumatic brain injury in adolescents. Neurology. 2008;70:948–55.
Mayer AR, Ling J, Mannell MV, et al. A prospective diffusion tensor imaging study in mild traumatic brain injury. Neurology. 2010;74:643–50.
Mac Donald CL, Johnson AM, Wierzechowski L, et al. Prospectively assessed clinical outcomes in concussive blast vs nonblast traumatic brain injury among evacuated US military personnel. JAMA neurol. 2014;71:994–1002.
Sharp DJ, Beckmann CF, Greenwood R, et al. Default mode network functional and structural connectivity after traumatic brain injury. Brain. 2011;134:2233–47.
Bonnelle V, Ham TE, Leech R, et al. Salience network integrity predicts default mode network function after traumatic brain injury. Proc Natl Acad Sci U S A. 2012;109:4690–5. Combined fMRI and DTI demonstrates that task-induced default mode network deactivation is impaired in the chronic phase of TBI, and this impairment is proportional to the structural damage of the salience network. This work suggests that the salience network modulates default mode activity and explains why frontal injury might lead to deficits in inhibitory control phenotypically.
Ling JM, Pena A, Yeo RA, et al. Biomarkers of increased diffusion anisotropy in semi-acute mild traumatic brain injury: a longitudinal perspective. Brain. 2012;135:1281–92.
Croall ID, Cowie CJ, He J, et al. White matter correlates of cognitive dysfunction after mild traumatic brain injury. Neurology. 2014;83:494–501.
Adam O, Mac Donald CL, Rivet D, et al. Clinical and imaging assessment of acute combat mild traumatic brain injury in Afghanistan. Neurology. 2015;85:219–27.
Wang JY, Bakhadirov K, Devous Sr MD, et al. Diffusion tensor tractography of traumatic diffuse axonal injury. Arch Neurol. 2008;65:619–26.
Betz J, Zhuo J, Roy A, et al. Prognostic value of diffusion tensor imaging parameters in severe traumatic brain injury. J Neurotrauma. 2012;29:1292–305.
Choi GS, Kim OL, Kim SH, et al. Classification of cause of motor weakness in traumatic brain injury using diffusion tensor imaging. Arch Neurol. 2012;69:363–7.
Matsushita M, Hosoda K, Naitoh Y, et al. Utility of diffusion tensor imaging in the acute stage of mild to moderate traumatic brain injury for detecting white matter lesions and predicting long-term cognitive function in adults. J Neurosurg. 2011;115:130–9.
Perlbarg V, Puybasset L, Tollard E, et al. Relation between brain lesion location and clinical outcome in patients with severe traumatic brain injury: a diffusion tensor imaging study using voxel-based approaches. Hum Brain Mapp. 2009;30:3924–33.
Edlow BL, Haynes RL, Takahashi E, et al. Disconnection of the ascending arousal system in traumatic coma. J Neuropathol Exp Neurol. 2013;72:505–23.
Delano-Wood L, Bangen KJ, Sorg SF, et al. Brainstem white matter integrity is related to loss of consciousness and postconcussive symptomatology in veterans with chronic mild to moderate traumatic brain injury. Brain imaging behav. 2015;9:500–12.
Mori S, Crain BJ. MRI atlas of human white matter. Amsterdam. Boston: Elsevier; 2005.
Lingsma HF, Roozenbeek B, Steyerberg EW, et al. Early prognosis in traumatic brain injury: from prophecies to predictions. The Lancet Neurology. 2010;9:543–54.
Kumar R, Husain M, Gupta RK, et al. Serial changes in the white matter diffusion tensor imaging metrics in moderate traumatic brain injury and correlation with neuro-cognitive function. J Neurotrauma. 2009;26:481–95.
Newcombe VF, Correia MM, Ledig C, et al. Dynamic changes in white matter abnormalities correlate with late improvement and deterioration following TBI: a diffusion tensor imaging study. Neurorehabil Neural Repair. 2015.
Newcombe V, Chatfield D, Outtrim J, et al. Mapping traumatic axonal injury using diffusion tensor imaging: correlations with functional outcome. PLoS One. 2011;6, e19214.
van der Eerden AW, Khalilzadeh O, Perlbarg V, et al. White matter changes in comatose survivors of anoxic ischemic encephalopathy and traumatic brain injury: comparative diffusion-tensor imaging study. Radiology. 2014;270:506–16.
Strangman GE, O’Neil-Pirozzi TM, Supelana C, et al. Fractional anisotropy helps predicts memory rehabilitation outcome after traumatic brain injury. NeuroRehabilitation. 2012;31:295–310.
Spitz G, Maller JJ, O’Sullivan R, Ponsford JL. White matter integrity following traumatic brain injury: the association with severity of injury and cognitive functioning. Brain Topogr. 2013;26:648–60.
Niogi SN, Mukherjee P, Ghajar J, et al. Structural dissociation of attentional control and memory in adults with and without mild traumatic brain injury. Brain. 2008;131:3209–21.
Bonnelle V, Leech R, Kinnunen KM, et al. Default mode network connectivity predicts sustained attention deficits after traumatic brain injury. J Neurosci: Off J Soc Neurosci. 2011;31:13442–51.
Posse S, Otazo R, Dager SR, Alger J. MR spectroscopic imaging: principles and recent advances. J Magn Reson Imaging JMRI. 2013;37:1301–25.
Carpentier A, Galanaud D, Puybasset L, et al. Early morphologic and spectroscopic magnetic resonance in severe traumatic brain injuries can detect “invisible brain stem damage” and predict “vegetative states”. J Neurotrauma. 2006;23:674–85.
Gasparovic C, Yeo R, Mannell M, et al. Neurometabolite concentrations in gray and white matter in mild traumatic brain injury: an 1H-magnetic resonance spectroscopy study. J Neurotrauma. 2009;26:1635–43.
Garnett MR, Blamire AM, Rajagopalan B, et al. Evidence for cellular damage in normal-appearing white matter correlates with injury severity in patients following traumatic brain injury: a magnetic resonance spectroscopy study. Brain. 2000;123(Pt 7):1403–9.
Mamere AE, Saraiva LA, Matos AL, et al. Evaluation of delayed neuronal and axonal damage secondary to moderate and severe traumatic brain injury using quantitative MR imaging techniques. AJNR Am J neuroradiol. 2009;30:947–52.
Signoretti S, Marmarou A, Aygok GA, et al. Assessment of mitochondrial impairment in traumatic brain injury using high-resolution proton magnetic resonance spectroscopy. J Neurosurg. 2008;108:42–52.
Maudsley AA, Govind V, Levin B, et al. Distributions of magnetic resonance diffusion and spectroscopy measures with traumatic brain injury. J Neurotrauma. 2015;32:1056–63.
Marino S, Ciurleo R, Bramanti P, et al. 1H-MR spectroscopy in traumatic brain injury. Neurocrit Care. 2011;14:127–33.
Faden AI, Demediuk P, Panter SS, Vink R. The role of excitatory amino acids and NMDA receptors in traumatic brain injury. Science (New York, N.Y.) 1989; 244:798–800.
Carpenter KL, Jalloh I, Hutchinson PJ. Glycolysis and the significance of lactate in traumatic brain injury. Front Neurosci. 2015;9:112.
Friedman SD, Brooks WM, Jung RE, et al. Quantitative proton MRS predicts outcome after traumatic brain injury. Neurology. 1999;52:1384–91.
Holshouser BA, Tong KA, Ashwal S, et al. Prospective longitudinal proton magnetic resonance spectroscopic imaging in adult traumatic brain injury. J Magn Reson Imaging. 2006;24:33–40.
Johnson B, Zhang K, Gay M, et al. Metabolic alterations in corpus callosum may compromise brain functional connectivity in MTBI patients: an 1H-MRS study. Neurosci Lett. 2012;509:5–8.
Haacke EM, Duhaime AC, Gean AD, et al. Common data elements in radiologic imaging of traumatic brain injury. J magn reson imaging : JMRI. 2010;32:516–43.
Wintermark M, Coombs L, Druzgal TJ, et al. Traumatic brain injury imaging research roadmap. AJNR Am J Neuroradiol. 2015;36:E12–23.
Townsend DW. Physical principles and technology of clinical PET imaging. Ann Acad Med Singap. 2004;33:133–45.
Sokoloff L. Relationships among local functional activity, energy metabolism, and blood flow in the central nervous system. Fed Proc. 1981;40:2311–6.
Stender J, Kupers R, Rodell A, et al. Quantitative rates of brain glucose metabolism distinguish minimally conscious from vegetative state patients. J Cereb Blood Flow Metab. 2015;35:58–65.
Stender J, Gosseries O, Bruno MA et al. Diagnostic precision of PET imaging and functional MRI in disorders of consciousness: a clinical validation study. Lancet (London, England) 2014; 384:514–522.
Shulman RG, Hyder F, Rothman DL. Baseline brain energy supports the state of consciousness. Proc Natl Acad Sci U S A. 2009;106:11096–101.
Hattori N, Huang SC, Wu HM, et al. Correlation of regional metabolic rates of glucose with glasgow coma scale after traumatic brain injury. J Nucl Med : Off publication Soc Nucl Med. 2003;44:1709–16.
Nakayama N, Okumura A, Shinoda J, et al. Relationship between regional cerebral metabolism and consciousness disturbance in traumatic diffuse brain injury without large focal lesions: an FDG-PET study with statistical parametric mapping analysis. J Neurol Neurosurg Psychiatry. 2006;77:856–62.
Garcia-Panach J, Lull N, Lull JJ, et al. A voxel-based analysis of FDG-PET in traumatic brain injury: regional metabolism and relationship between the thalamus and cortical areas. J Neurotrauma. 2011;28:1707–17.
Voss HU, Ulug AM, Dyke JP, et al. Possible axonal regrowth in late recovery from the minimally conscious state. J Clin Invest. 2006;116:2005–11.
Fontaine A, Azouvi P, Remy P, et al. Functional anatomy of neuropsychological deficits after severe traumatic brain injury. Neurology. 1999;53:1963–8.
Lombardi WJ, Andreason PJ, Sirocco KY, et al. Wisconsin Card Sorting Test performance following head injury: dorsolateral fronto-striatal circuit activity predicts perseveration. J Clin Exp Neuropsychol. 1999;21:2–16.
Kato T, Nakayama N, Yasokawa Y, et al. Statistical image analysis of cerebral glucose metabolism in patients with cognitive impairment following diffuse traumatic brain injury. J Neurotrauma. 2007;24:919–26.
Levine B, Cabeza R, McIntosh AR, et al. Functional reorganisation of memory after traumatic brain injury: a study with H(2)(15)0 positron emission tomography. Neurosurg Psychiatry. 2002;73:173–81.
Stocker RP, Cieply MA, Paul B, et al. Combat-related blast exposure and traumatic brain injury influence brain glucose metabolism during REM sleep in military veterans. NeuroImage. 2014;99:207–14.
Gross H, Kling A, Henry G, et al. Local cerebral glucose metabolism in patients with long-term behavioral and cognitive deficits following mild traumatic brain injury. J neuropsychiatry clin neurosci. 1996;8:324–34.
Umile EM, Sandel ME, Alavi A, et al. Dynamic imaging in mild traumatic brain injury: support for the theory of medial temporal vulnerability. Arch Phys Med Rehabil. 2002;83:1506–13.
Peskind ER, Petrie EC, Cross DJ, et al. Cerebrocerebellar hypometabolism associated with repetitive blast exposure mild traumatic brain injury in 12 Iraq war veterans with persistent post-concussive symptoms. NeuroImage. 2011;54 Suppl 1:S76–82.
Chen SH, Kareken DA, Fastenau PS, et al. A study of persistent post-concussion symptoms in mild head trauma using positron emission tomography. Neurosurg Psychiatry. 2003;74:326–32.
Kremer S, Nicolas-Ong C, Schunck T, et al. Usefulness of functional MRI associated with PET scan and evoked potentials in the evaluation of brain functions after severe brain injury: preliminary results. Journal of neuroradiology. J Neuroradiol. 2010;37:159–66.
Logothetis NK, Pauls J, Augath M, et al. Neurophysiological investigation of the basis of the fMRI signal. Nature. 2001;412:150–7.
Laureys S, Schiff ND. Coma and consciousness: paradigms (re)framed by neuroimaging. NeuroImage. 2012;61:478–91.
Edlow BL, Giacino JT, Wu O. Functional MRI and outcome in traumatic coma. Current neurology and neuroscience reports. 2013;13:375.
Owen AM. Detecting consciousness: a unique role for neuroimaging. Annu Rev Psychol. 2013;64:109–33.
Greicius MD, Krasnow B, Reiss AL, Menon V. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci U S A. 2003;100:253–8.
Damoiseaux JS, Rombouts SA, Barkhof F, et al. Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci U S A. 2006;103:13848–53.
Fox MD, Snyder AZ, Vincent JL, et al. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A. 2005;102:9673–8.
Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med. 1995;34:537–41.
Irimia A, Van Horn JD. Functional neuroimaging of traumatic brain injury: advances and clinical utility. Neuropsychiatr Dis Treat. 2015;11:2355–65.
Eickhoff SB, Dafotakis M, Grefkes C, et al. fMRI reveals cognitive and emotional processing in a long-term comatose patient. Exp Neurol. 2008;214:240–6.
Vanhaudenhuyse A, Noirhomme Q, Tshibanda LJ, et al. Default network connectivity reflects the level of consciousness in non-communicative brain-damaged patients. Brain. 2010;133:161–71.
Palacios EM, Sala-Llonch R, Junque C, et al. Resting-state functional magnetic resonance imaging activity and connectivity and cognitive outcome in traumatic brain injury. JAMA Neurol. 2013;70:845–51.
Ovadia-Caro S, Nir Y, Soddu A, et al. Reduction in inter-hemispheric connectivity in disorders of consciousness. PLoS One. 2012;7, e37238.
Cauda F, Micon BM, Sacco K, et al. Disrupted intrinsic functional connectivity in the vegetative state. Neurosurg Psychiatry. 2009;80:429–31.
Silva S, de Pasquale F, Vuillaume C, et al. Disruption of posteromedial large-scale neural communication predicts recovery from coma. Neurology. 2015;85:1–9.
Qin P, Wu X, Huang Z, et al. How are different neural networks related to consciousness? Ann Neurol. 2015;78:594–605. Analysis of fMRI data revealed that differentiation of vegetative from minimally conscious state was predicted most accurately by functional connectivity within the salience network.
Raichle ME, MacLeod AM, Snyder AZ, et al. A default mode of brain function. Proc Natl Acad Sci U S A. 2001;98:676–82.
Raichle ME. The brain’s default mode network. Annu Rev Neurosci. 2015;38:433–47.
Soddu A, Vanhaudenhuyse A, Bahri MA, et al. Identifying the default-mode component in spatial IC analyses of patients with disorders of consciousness. Hum Brain Mapp. 2012;33:778–96.
Demertzi A, Gomez F, Crone JS, et al. Multiple fMRI system-level baseline connectivity is disrupted in patients with consciousness alterations. Cortex; a journal devoted to the study of the nervous system and behavior. 2014;52:35–46.
Mayer AR, Mannell MV, Ling J, et al. Functional connectivity in mild traumatic brain injury. Hum Brain Mapp. 2011;32:1825–35.
Zhu DC, Covassin T, Nogle S, et al. A potential biomarker in sports-related concussion: brain functional connectivity alteration of the default-mode network measured with longitudinal resting-state fMRI over thirty days. J Neurotrauma. 2015;32:327–41.
Fernandez-Espejo D, Junque C, Cruse D, et al. Combination of diffusion tensor and functional magnetic resonance imaging during recovery from the vegetative state. BMC Neurol. 2010;10:77.
Mikell CB, Banks GP, Frey HP, et al. Frontal networks associated with command following after hemorrhagic stroke. Stroke; J Cereb Circ. 2015;46:49–57.
Crone JS, Ladurner G, Holler Y, et al. Deactivation of the default mode network as a marker of impaired consciousness: an fMRI study. PLoS One. 2011;6, e26373.
Mayer AR, Yang Z, Yeo RA, et al. A functional MRI study of multimodal selective attention following mild traumatic brain injury. Brain Imaging Behav. 2012;6:343–54.
Jilka SR, Scott G, Ham T, et al. Damage to the salience network and interactions with the default mode network. J Neurosci: Official J Soc Neurosci. 2014;34:10798–807.
Stevens MC, Lovejoy D, Kim J, et al. Multiple resting state network functional connectivity abnormalities in mild traumatic brain injury. Brain Imaging Behav. 2012;6:293–318.
Caeyenberghs K, Leemans A, Heitger MH, et al. Graph analysis of functional brain networks for cognitive control of action in traumatic brain injury. Brain. 2012;135:1293–307. Graph theoretical analysis of task-fMRI timecourses revealed increased functional connectivity in the left premotor cortex in TBI patients when compared to controls, likely representing an aberrant compensatory process which reduces cortical processing efficiency.
Hillary FG, Slocomb J, Hills EC, et al. Changes in resting connectivity during recovery from severe traumatic brain injury. Int J psychophysiol : Off J Int Organ Psychophysiol. 2011;82:115–23.
Sours C, Zhuo J, Roys S, et al. Disruptions in resting state functional connectivity and cerebral blood flow in mild traumatic brain injury patients. PLoS One. 2015;10, e0134019.
Turner GR, Levine B. Augmented neural activity during executive control processing following diffuse axonal injury. Neurology. 2008;71:812–8.
Sinopoli KJ, Chen JK, Wells G, et al. Imaging “brain strain” in youth athletes with mild traumatic brain injury during dual-task performance. J Neurotrauma. 2014;31:1843–59.
Wylie GR, Freeman K, Thomas A, et al. Cognitive improvement after mild traumatic brain injury measured with functional neuroimaging during the acute period. PLoS One. 2015;10, e0126110.
Ham TE, Bonnelle V, Hellyer P, et al. The neural basis of impaired self-awareness after traumatic brain injury. Brain. 2014;137:586–97.
Kasahara M, Menon DK, Salmond CH, et al. Traumatic brain injury alters the functional brain network mediating working memory. Brain Inj. 2011;25:1170–87.
Sanchez-Carrion R, Fernandez-Espejo D, Junque C, et al. A longitudinal fMRI study of working memory in severe TBI patients with diffuse axonal injury. NeuroImage. 2008;43:421–9.
Detre JA, Zhang W, Roberts DA, et al. Tissue specific perfusion imaging using arterial spin labeling. NMR Biomed. 1994;7:75–82.
Wong EC, Buxton RB, Frank LR. Implementation of quantitative perfusion imaging techniques for functional brain mapping using pulsed arterial spin labeling. NMR Biomed. 1997;10:237–49.
Aguirre GK, Detre JA, Zarahn E, Alsop DC. Experimental design and the relative sensitivity of BOLD and perfusion fMRI. NeuroImage. 2002;15:488–500.
Weber MJ, Detre JA, Thompson-Schill SL, Avants BB. Reproducibility of functional network metrics and network structure: a comparison of task-related BOLD, resting ASL with BOLD contrast, and resting cerebral blood flow. Cogn Affect Behav Neurosci. 2013;13:627–40.
Kim J, Whyte J, Patel S, et al. Resting cerebral blood flow alterations in chronic traumatic brain injury: an arterial spin labeling perfusion FMRI study. J Neurotrauma. 2010;27:1399–411.
Liu AA, Voss HU, Dyke JP, et al. Arterial spin labeling and altered cerebral blood flow patterns in the minimally conscious state. Neurology. 2011;77:1518–23.
Kim J, Whyte J, Patel S, et al. A perfusion fMRI study of the neural correlates of sustained-attention and working-memory deficits in chronic traumatic brain injury. Neurorehabil Neural Repair. 2012;26:870–80.
Wang Y, Nelson LD, LaRoche AA, et al. Cerebral blood flow alterations in acute sport-related concussion. J Neurotrauma. 2015.
Meier TB, Bellgowan PS, Singh R, et al. Recovery of cerebral blood flow following sports-related concussion. JAMA Neurol. 2015;72:530–8.
Liang X, Zou Q, He Y, Yang Y. Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain. Proc Natl Acad Sci U S A. 2013;110:1929–34.
Viviani R, Messina I, Walter M. Resting state functional connectivity in perfusion imaging: correlation maps with BOLD connectivity and resting state perfusion. PLoS One. 2011;6, e27050.
Jann K, Orosz A, Dierks T, et al. Quantification of network perfusion in ASL cerebral blood flow data with seed based and ICA approaches. Brain Topogr. 2013;26:569–80.
Liang X, Connelly A, Calamante F. Voxel-wise functional connectomics using arterial spin labeling functional magnetic resonance imaging: the role of denoising. Brain connect. 2015;5:543–53.
Chen JJ, Jann K, Wang DJ. Characterizing resting-state brain function using arterial spin labeling. Brain connectivity. 2015;5:527–42.
Thatcher RW, Biver C, McAlaster R, Salazar A. Biophysical linkage between MRI and EEG coherence in closed head injury. NeuroImage. 1998;8:307–26.
Kane NM, Moss TH, Curry SH, Butler SR. Quantitative electroencephalographic evaluation of non-fatal and fatal traumatic coma. Electroencephalogr Clin Neurophysiol. 1998;106:244–50.
Kumar S, Rao SL, Chandramouli BA, Pillai SV. Reduction of functional brain connectivity in mild traumatic brain injury during working memory. J Neurotrauma. 2009;26:665–75.
Cao C, Slobounov S. Alteration of cortical functional connectivity as a result of traumatic brain injury revealed by graph theory, ICA, and sLORETA analyses of EEG signals. IEEE Trans neural Syst Rehabil Eng : Publication IEEE Eng Med Biol Soc. 2010;18:11–9.
Slewa-Younan S, Green AM, Baguley IJ, et al. Is ‘gamma’ (40 Hz) synchronous activity disturbed in patients with traumatic brain injury? Clin Neurophysiol : Off J Int Fed Clin Neurophysiol. 2002;113:1640–6.
Wiese H, Stude P, Nebel K, et al. Recovery of movement-related potentials in the temporal course after prefrontal traumatic brain injury: a follow-up study. Clin Neurophysiol : Off J Int Fed Clin Neurophysiol. 2004;115:2677–92.
Wiese H, Stude P, Nebel K, et al. Impaired movement-related potentials in acute frontal traumatic brain injury. Clin Neurophysiol : Off J Int Fed Clin Neurophysiol. 2004;115:289–98.
Leon-Carrion J, Leon-Dominguez U, Pollonini L, et al. Synchronization between the anterior and posterior cortex determines consciousness level in patients with traumatic brain injury (TBI). Brain Res. 2012;1476:22–30.
Pollonini L, Pophale S, Situ N, et al. Information communication networks in severe traumatic brain injury. Brain Topogr. 2010;23:221–6.
Sponheim SR, McGuire KA, Kang SS, et al. Evidence of disrupted functional connectivity in the brain after combat-related blast injury. NeuroImage. 2011;54 Suppl 1:S21–9.
Virji-Babul N, Hilderman CG, Makan N, et al. Changes in functional brain networks following sports-related concussion in adolescents. J Neurotrauma. 2014;31:1914–9.
Uhlhaas PJ, Singer W. Neural synchrony in brain disorders: relevance for cognitive dysfunctions and pathophysiology. Neuron. 2006;52:155–68.
Robb Swan A, Nichols S, Drake A, et al. Magnetoencephalography slow-wave detection in patients with mild traumatic brain injury and ongoing symptoms correlated with long-term neuropsychological outcome. J Neurotrauma. 2015;32:1510–21.
Huang MX, Nichols S, Baker DG, et al. Single-subject-based whole-brain MEG slow-wave imaging approach for detecting abnormality in patients with mild traumatic brain injury. NeuroImage Clin. 2014;5:109–19.
da Costa L, Robertson A, Bethune A, et al. Delayed and disorganised brain activation detected with magnetoencephalography after mild traumatic brain injury. Neurosurg Psychiatry. 2015;86:1008–15.
Lewine JD, Davis JT, Sloan JH, et al. Neuromagnetic assessment of pathophysiologic brain activity induced by minor head trauma. AJNR Am J neuroradiol. 1999;20:857–66.
Lewine JD, Davis JT, Bigler ED, et al. Objective documentation of traumatic brain injury subsequent to mild head trauma: multimodal brain imaging with MEG, SPECT, and MRI. J Head Trauma Rehabil. 2007;22:141–55.
Huang MX, Theilmann RJ, Robb A, et al. Integrated imaging approach with MEG and DTI to detect mild traumatic brain injury in military and civilian patients. J Neurotrauma. 2009;26:1213–26.
Tarapore PE, Findlay AM, Lahue SC, et al. Resting state magnetoencephalography functional connectivity in traumatic brain injury. J Neurosurg. 2013;118:1306–16.
Luo Q, Xu D, Roskos T, et al. Complexity analysis of resting state magnetoencephalography activity in traumatic brain injury patients. J Neurotrauma. 2013;30:1702–9.
Dunkley BT, Da Costa L, Bethune A, et al. Low-frequency connectivity is associated with mild traumatic brain injury. NeuroImage Clin. 2015;7:611–21.
Castellanos NP, Leyva I, Buldu JM, et al. Principles of recovery from traumatic brain injury: reorganization of functional networks. NeuroImage. 2011;55:1189–99.
Mannix R, Meehan WP, Mandeville J, et al. Clinical correlates in an experimental model of repetitive mild brain injury. Ann Neurol. 2013;74:65–75.
Cheng JS, Craft R, Yu GQ, et al. Tau reduction diminishes spatial learning and memory deficits after mild repetitive traumatic brain injury in mice. PLoS One. 2014;9, e115765.
Mouzon BC, Bachmeier C, Ferro A, et al. Chronic neuropathological and neurobehavioral changes in a repetitive mild traumatic brain injury model. Ann Neurol. 2014;75:241–54.
Ojo JO, Mouzon BC, Crawford F. Repetitive head trauma, chronic traumatic encephalopathy and tau: challenges in translating from mice to men. Exp Neurol. 2015.
Uryu K, Laurer H, McIntosh T, et al. Repetitive mild brain trauma accelerates Abeta deposition, lipid peroxidation, and cognitive impairment in a transgenic mouse model of Alzheimer amyloidosis. J Neurosci : Off J Soc Neurosci. 2002;22:446–54.
Coughlin JM, Wang Y, Munro CA, et al. Neuroinflammation and brain atrophy in former NFL players: an in vivo multimodal imaging pilot study. Neurobiol Dis. 2015;74:58–65.
Harish G, Mahadevan A, Pruthi N, et al. Characterization of traumatic brain injury in human brains reveals distinct cellular and molecular changes in contusion and pericontusion. J Neurochem. 2015;134:156–72.
Shitaka Y, Tran HT, Bennett RE, et al. Repetitive closed-skull traumatic brain injury in mice causes persistent multifocal axonal injury and microglial reactivity. J Neuropathol Exp Neurol. 2011;70:551–67.
Venneti S, Wagner AK, Wang G, et al. The high affinity peripheral benzodiazepine receptor ligand DAA1106 binds specifically to microglia in a rat model of traumatic brain injury: implications for PET imaging. Exp Neurol. 2007;207:118–27.
Weiner MW, Veitch DP, Hayes J, et al. Effects of traumatic brain injury and posttraumatic stress disorder on Alzheimer’s disease in veterans, using the Alzheimer’s Disease Neuroimaging Initiative. Alzheimers Dement: J Alzheimers Assoc. 2014;10:S226–35.
Roberts GW, Gentleman SM, Lynch A, Graham DI. beta A4 amyloid protein deposition in brain after head trauma. Lancet (London, England). 1991;338:1422–3.
Nicoll JA, Roberts GW, Graham DI. Apolipoprotein E epsilon 4 allele is associated with deposition of amyloid beta-protein following head injury. Nat Med. 1995;1:135–7.
Loane DJ, Pocivavsek A, Moussa CE, et al. Amyloid precursor protein secretases as therapeutic targets for traumatic brain injury. Nat Med. 2009;15:377–9.
Johnson VE, Stewart W, Smith DH. Traumatic brain injury and amyloid-beta pathology: a link to Alzheimer’s disease? Nature reviews. Neuroscience. 2010;11:361–70.
Mielke MM, Savica R, Wiste HJ, et al. Head trauma and in vivo measures of amyloid and neurodegeneration in a population-based study. Neurology. 2014;82:70–6.
Magnoni S, Esparza TJ, Conte V, et al. Tau elevations in the brain extracellular space correlate with reduced amyloid-beta levels and predict adverse clinical outcomes after severe traumatic brain injury. Brain. 2012;135:1268–80.
Goldstein LE, Fisher AM, Tagge CA et al. Chronic traumatic encephalopathy in blast-exposed military veterans and a blast neurotrauma mouse model. Science translational medicine 2012; 4:134ra160.
Small GW, Kepe V, Siddarth P, et al. PET scanning of brain tau in retired national football league players: preliminary findings. Am J Geriatr Psychiatry: Off J Am Assoc Geriatr Psychiatry. 2013;21:138–44.
Witcher KG, Eiferman DS, Godbout JP. Priming the inflammatory pump of the CNS after traumatic brain injury. Trends in neurosci. 2015;38:609–20.
Corps KN, Roth TL, McGavern DB. Inflammation and neuroprotection in traumatic brain injury. JAMA neurol. 2015;72:355–62.
Roth TL, Nayak D, Atanasijevic T, et al. Transcranial amelioration of inflammation and cell death after brain injury. Nature. 2014;505:223–8.
Kyritsis N, Kizil C, Zocher S, et al. Acute inflammation initiates the regenerative response in the adult zebrafish brain. Science (New York, NY). 2012;338:1353–6.
Wang G, Shi Y, Jiang X, et al. HDAC inhibition prevents white matter injury by modulating microglia/macrophage polarization through the GSK3beta/PTEN/Akt axis. Proc Natl Acad Sci U S A. 2015;112:2853–8.
Ruseva MM, Ramaglia V, Morgan BP, Harris CL. An anticomplement agent that homes to the damaged brain and promotes recovery after traumatic brain injury in mice. Proc Natl Acad Sci U S A. 2015;112:14319–24.
Velazquez A, Ortega M, Rojas S, et al. Widespread microglial activation in patients deceased from traumatic brain injury. Brain Inj. 2015;29:1126–33.
Sporns O. The human connectome: origins and challenges. NeuroImage. 2013;80:53–61.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
Yousef Hannawi and Robert D. Stevens declare that they have no conflict of interest.
Human and Animal Rights and Informed Consent
This article does not contain any studies with human or animal subjects performed by any of the authors.
Additional information
This article is part of the Topical Collection on Neurotrauma
Rights and permissions
About this article
Cite this article
Hannawi, Y., Stevens, R.D. Mapping the Connectome Following Traumatic Brain Injury. Curr Neurol Neurosci Rep 16, 44 (2016). https://doi.org/10.1007/s11910-016-0642-9
Published:
DOI: https://doi.org/10.1007/s11910-016-0642-9
Keywords
- Amyloid beta
- Arterial spin labeling
- Cognition
- Coma
- Diffusion tensor imaging
- Diffusion-weighted imaging
- Electroencephalography
- Functional magnetic resonance imaging
- Neurologic recovery
- Magnetic resonance imaging
- Magnetic resonance spectroscopy
- Magnetoencephalography
- Neural plasticity
- Positron imaging tomography
- Susceptibility-weighted imaging
- Tau protein
- Translocator protein
- Traumatic axonal injury
- Traumatic brain injury