Objective: What mechanisms underlie the loss and recovery of consciousness after severe brain injury? We sought to establish, in the largest cohort of patients with disorders of consciousness (DOC) to date, the link between gold standard clinical measures of awareness and wakefulness, and specific patterns of local brain pathology-thereby possibly providing a mechanistic framework for patient diagnosis, prognosis, and treatment development. Methods: Structural T1-weighted magnetic resonance images were collected, in a continuous sample of 143 severely brain-injured patients with DOC (and 96 volunteers), across 2 tertiary expert centers. Brain atrophy in subcortical regions (bilateral thalamus, basal ganglia, hippocampus, basal forebrain, and brainstem) was assessed across (1) healthy volunteers and patients, (2) clinical entities (eg, vegetative state, minimally conscious state), (3) clinical measures of consciousness (Coma Recovery Scale-Revised), and (4) injury etiology. Results: Compared to volunteers, patients exhibited significant atrophy across all structures (p < 0.05, corrected). Strikingly, we found almost no significant differences across clinical entities. Nonetheless, the clinical measures of awareness and wakefulness upon which differential diagnosis rely were systematically associated with tissue atrophy within thalamic and basal ganglia nuclei, respectively; the basal forebrain was atrophied in proportion to patients' response to sensory stimulation. In addition, nontraumatic injuries exhibited more extensive thalamic atrophy. Interpretation: These findings provide, for the first time, a grounding in pathology for gold standard behavior-based clinical measures of consciousness, and reframe our current models of DOC by stressing the different links tying thalamic mechanisms to willful behavior and extrathalamic mechanisms to behavioral (and electrocortical) arousal.ANN NEUROL 2015;00:000-000 T he mechanisms supporting consciousness, as well as its loss and recovery after severe brain injury, remain largely unknown. In the context of disorders of consciousness (DOC) 1 such as the vegetative state (VS) and the minimally conscious state (MCS), the lack of a mechanistic understanding of the relationship between brain damage and neurological condition has direct consequences for our ability to make accurate diagnoses, prognoses, and to develop targeted interventions, thereby raising complicated medical and ethical questions. 2Although information concerning the nature and extent of a patient's brain damage is generally taken into consideration during clinical assessments, current differential diagnosis procedures rely exclusively-as per international guidelines-on behavioral presentation. 3-5 Consequently, although our understanding of DOC is continuously increasing, 6,7 little is known about the connection between behaviorally defined clinical entities and the underlying brain damage, [8][9][10] or the degree to which standard behavior-based clinical assessments (eg, JFK View this article online at wil...

The study of structural and functional magnetic resonance imaging data has greatly benefitted from the development of sophisticated and efficient algorithms aimed at automating and optimizing the analysis of brain data. We address, in the context of the segmentation of brain from non-brain tissue (i.e., brain extraction, also known as skull-stripping), the tension between the increased theoretical and clinical interest in patient data, and the difficulty of conventional algorithms to function optimally in the presence of gross brain pathology. Indeed, because of the reliance of many algorithms on priors derived from healthy volunteers, images with gross pathology can severely affect their ability to correctly trace the boundaries between brain and non-brain tissue, potentially biasing subsequent analysis. We describe and make available an optimized brain extraction script for the pathological brain (optiBET) robust to the presence of pathology. Rather than attempting to trace the boundary between tissues, optiBET performs brain extraction by (i) calculating an initial approximate brain extraction; (ii) employing linear and non-linear registration to project the approximate extraction into the MNI template space; (iii) back-projecting a standard brain-only mask from template space to the subject’s original space; and (iv) employing the back-projected brain-only mask to mask-out non-brain tissue. The script results in up to 94% improvement of the quality of extractions over those obtained with conventional software across a large set of severely pathological brains. Since optiBET makes use of freely available algorithms included in FSL, it should be readily employable by anyone having access to such tools.

Whether unique to humans or not, consciousness is a central aspect of our experience of the world. The neural fingerprint of this experience, however, remains one of the least understood aspects of the human brain. In this paper we employ graph-theoretic measures and support vector machine classification to assess, in 12 healthy volunteers, the dynamic reconfiguration of functional connectivity during wakefulness, propofol-induced sedation and loss of consciousness, and the recovery of wakefulness. Our main findings, based on resting-state fMRI, are three-fold. First, we find that propofol-induced anesthesia does not bear differently on long-range versus short-range connections. Second, our multi-stage design dissociated an initial phase of thalamo-cortical and cortico-cortical hyperconnectivity, present during sedation, from a phase of cortico-cortical hypoconnectivity, apparent during loss of consciousness. Finally, we show that while clustering is increased during loss of consciousness, as recently suggested, it also remains significantly elevated during wakefulness recovery. Conversely, the characteristic path length of brain networks (i.e., the average functional distance between any two regions of the brain) appears significantly increased only during loss of consciousness, marking a decrease of global information-processing efficiency uniquely associated with unconsciousness. These findings suggest that propofol-induced loss of consciousness is mainly tied to cortico-cortical and not thalamo-cortical mechanisms, and that decreased efficiency of information flow is the main feature differentiating the conscious from the unconscious brain.

Proton magnetic resonance spectroscopy ( 1 H MRS) neurometabolite abnormalities have been detected widely in subjects with and at risk for schizophrenia. We hypothesized that such abnormalities would be present both in patients with schizophrenia and in their unaffected twin siblings. We acquired magnetic resonance spectra (TR/TE = 3000/30 ms) at voxels in the mesial prefrontal gray matter, left prefrontal white matter and left hippocampus in 14 twin pairs discordant for schizophrenia (2 monozygotic, 12 dizygotic), 13 healthy twin pairs (4 monozygotic, 9 dizygotic) and 1 additional unaffected co-twin of a schizophrenia proband. In the mesial prefrontal gray matter voxel, N-acetylaspartate (NAA), creatine þ phosphocreatine (Cr), glycerophosphocholine þ phosphocholine (Cho) and myo-inositol (mI) did not differ significantly between patients with schizophrenia, their unaffected co-twins or healthy controls. However, glutamate (Glu) was significantly lower in patients with schizophrenia (31%, percent difference) and unaffected co-twins (21%) than in healthy controls (collapsed across twin pairs). In the left hippocampus voxel, levels of NAA (23%), Cr (22%) and Cho (36%) were higher in schizophrenia patients compared with controls. Hippocampal NAA (25%), Cr (22%) and Cho (37%) were also significantly higher in patients than in their unaffected co-twins. Region-to-region differences in metabolite levels were also notable within all three diagnosis groups. These findings suggest that 1 H MRS neurometabolite abnormalities are present not only in patients with schizophrenia, but also in their unaffected co-twins. Thus, reduced mesial prefrontal cortical Glu and elevated hippocampal NAA, Cr and Cho may represent trait markers of schizophrenia risk and, when exacerbated, state markers of schizophrenia itself.

Our results indicate that in a cohort of patients with a moderate-severe TBI, 1) lesion location specificity (e.g. the temporal lobe) is related to both a high incidence of early seizures and longitudinal development of PTE, 2) early seizures, whether convulsive or non-convulsive in nature, are associated with an increased risk for PTE development, and 3) patients who develop PTE have greater chronic temporal lobe atrophy and worse functional outcomes, compared to those who do not develop PTE, despite matched injury severity characteristics. This study provides the foundation for a future prospective study focused on elucidating the mechanisms and risk factors for epileptogenesis.

Schizophrenia has been thought of as a disorder of reduced functional and structural connectivity. Recent advances in neuroimaging techniques such as functional magnetic resonance imaging, structural magnetic resonance imaging, diffusion tensor imaging, and small animal imaging have advanced our ability to investigate this hypothesis. Moreover, the power of longitudinal designs possible with these noninvasive techniques enable the study of not just how connectivity is disrupted in schizophrenia, but when this disruption emerges during development. This article reviews genetic and neurodevelopmental influences on structural and functional connectivity in human populations with or at risk for schizophrenia and in animal models of the disorder. We conclude that the weight of evidence across these diverse lines of inquiry points to a developmental disruption of neural connectivity in schizophrenia and that this disrupted connectivity likely involves susceptibility genes that affect processes involved in establishing intra- and interregional connectivity.

Neurofibromatosis (NF1) represents the most common single gene cause of learning disabilities. NF1 patients have impairments in frontal lobe based cognitive functions such as attention, working memory, and inhibition. Due to its well–characterized genetic etiology, investigations of NF1 may shed light on neural mechanisms underlying such difficulties in the general population or other patient groups. Prior neuroimaging findings indicate global brain volume increases, consistent with neural over-proliferation. However, little is known about alterations in white matter microstructure in NF1. We performed diffusion tensor imaging (DTI) analyses using tract-based spatial statistics (TBSS) in 14 young adult NF1 patients and 12 healthy controls. We also examined brain volumetric measures in the same subjects. Consistent with prior studies, we found significantly increased overall gray and white matter volume in NF1 patients. Relative to healthy controls, NF1 patients showed widespread reductions in white matter integrity across the entire brain as reflected by decreased fractional anisotropy (FA) and significantly increased absolute diffusion (ADC). When radial and axial diffusion were examined we found pronounced differences in radial diffusion in NF1 patients, indicative of either decreased myelination or increased space between axons. Secondary analyses revealed that FA and radial diffusion effects were of greatest magnitude in the frontal lobe. Such alterations of white matter tracts connecting frontal regions could contribute to the observed cognitive deficits. Furthermore, although the cellular basis of these white matter microstructural alterations remains to be determined, our findings of disproportionately increased radial diffusion against a background of increased white matter volume suggest the novel hypothesis that one potential alteration contributing to increased cortical white matter in NF1 may be looser packing of axons, with or without myelination changes. Further, this indicates that axial and radial diffusivity can uniquely contribute as markers of NF1-associated brain pathology in conjunction with the typically investigated measures.

Previous studies have suggested that disorders of consciousness (DOC) after severe brain injury may result from disconnections of the thalamo-cortical system. However, thalamo-cortical connectivity differences between vegetative state (VS), minimally conscious state minus (MCS-, i.e., low-level behavior such as visual pursuit), and minimally conscious state plus (MCS+, i.e., high-level behavior such as language processing) remain unclear. Probabilistic tractography in a sample of 25 DOC patients was employed to assess whether structural connectivity in various thalamo-cortical circuits could differentiate between VS, MCS-, and MCS+ patients. First, the thalamus was individually segmented into seven clusters based on patterns of cortical connectivity and tested for univariate differences across groups. Second, reconstructed whole-brain thalamic tracks were used as features in a multivariate searchlight analysis to identify regions along the tracks that were most informative in distinguishing among groups. At the univariate level, it was found that VS patients displayed reduced connectivity in most thalamo-cortical circuits of interest, including frontal, temporal, and sensorimotor connections, as compared with MCS+, but showed more pulvinar-occipital connections when compared with MCS-. Moreover, MCS- exhibited significantly less thalamo-premotor and thalamo-temporal connectivity than MCS+. At the multivariate level, it was found that thalamic tracks reaching frontal, parietal, and sensorimotor regions, could discriminate, up to 100% accuracy, across each pairwise group comparison. Together, these findings highlight the role of thalamo-cortical connections in patients' behavioral profile and level of consciousness. Diffusion tensor imaging combined with machine learning algorithms could thus potentially facilitate diagnostic distinctions in DOC and shed light on the neural correlates of consciousness. Hum Brain Mapp 38:431-443, 2017. © 2016 Wiley Periodicals, Inc.