Disentangling disorders of consciousness: Insights from diffusion tensor imaging and machine learning

Abstract

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.

Thalamic and extra-thalamic connections of the Globus Pallidus in the human brain: The ultradirect pathway

“…The mesocircuit hypothesis of DOC posits that following structural damages, functional disruptions at the BG-THAL level may also occur, leading to a reduced excitation of the anterior forebrain (Schiff 2010). Thalamo-prefrontal connections have been advocated to be necessary for sustaining organized behavior during wakefulness (Schiff 2008) and consistently implicated in DOC (Laureys et al 2000, Monti et al 2015, Zheng et al 2017. Yet, the current hypothesis fails to factor GPe into the model and attributes the reduced cortical excitation to the undertaking of the GPi through its excessive inhibition supposedly on the central thalamic nuclei (intralaminar complex and adjacent paralaminar portion of association nuclei-MD, VA, VL, and PUL) following insufficient inhibition from the striatum.…”

EEG power spectra and subcortical pathology in chronic disorders of consciousness

“…frequencies is related to atrophy in thalamic regions well known to be associated with severity of impairment after brain injury, as shown in post-mortem 36 and in vivo 21,37,38 studies, putatively secondary to delayed injury. Damage within thalamus, along with functional 4 and/or structural disconnection of thalamo-cortical projections 23 , is indeed central to current theories of recovery from severe brain injury 35 , and might be key to the network dysfunction and inferred loss of information processing measured with advanced neuroimaging approaches 6,7,26,28,39,40 . Furthermore, a recent cross-modal study in the acute and sub-acute patients has shown that EEG spectral profiles dominated by slow frequencies (i.e., delta) are predictive of poor outcome and increased thalamic atrophy at 6 months post injury in moderate-to-severe TBI patients recovering from coma 13 .…”

“…In particular, under this view, the evolving damage occurring after severe brain injury results in a reduction of thalamo-cortical and thalamo-striatal excitatory outflow, due to deafferentation and loss of neurons in central thalamus, which leads to a net decrease in excitatory input to the forebrain and striatum 20 . While indirect evidence exists in support of this model, with in vivo and post-mortem works demonstrating a relationship between damage to thalamus, loss of thalamo-cortical structural connectivity, and depth of impairment [21][22][23] , there is virtually no data directly uniting the patterns of EEG power spectra at the scalp in bedside recordings and patterns of subcortical damage in long-term DOC patients, a gap which is not only problematic for the clinician's interpretation of the observed EEG data, but also hampers our ability to monitor, through an unexpensive, repeatable technique, easily applicable at the patient's bedside, interventions and their effects. In what follows, we address, in a large cohort of patients with chronic DOC, the heretofore untested relationship between observed electrocortical rhythms, patterns of subcortical brain atrophy (including thalamus, brainstem, and basal ganglia), and behavioral measures of awareness and arousal, as indexed by the Coma Recovery Scale-revised (CRS-R; 24 ).…”

“…Then, the selected EEG activity was analyzed by dividing it into 90 non-overlapping 2 s segments. Absolute total power and relative power were evaluated in the delta (1-4 Hz), theta (4-8 Hz), alpha(8)(9)(10)(11)(12)(13), beta(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma (>30 Hz) bands, and averaged within each EEG channel.MRI data acquisition and processingNeuroimaging data were obtained with a 3T MR scanner (Achieva, Philips Healthcare BV, Best, NL) equipped with a 32-channel head coil. The MRI protocol comprised a high resolution 3D-TFE T1-weighted sequence (185 sagittal slices, TR = 9.781 ms, TE = 4.6 ms, FOV = 240 × 240 mm 2 , voxel size = 1 × 1 × 1 mm 3 , flip angle = 8 • ).…”

Degrees of functional connectome abnormality in disorders of consciousness

“…Problems of behavioral assessment of consciousness and the importance of correct diagnosis in rehabilitation and patient care decisions have led to an extensive search for objective methods of evaluating the level of consciousness based on different modalities of brain activity measurement. The latter include positron emission tomography (PET) (Stender et al, ), functional magnetic resonance imaging (fMRI) (Demertzi et al, ; Wu et al, ), diffusion tensor imaging (DTI) (Zheng et al, ), and recording of electroencephalographic responses to transcranial magnetic stimulation (TMS‐EEG) (Casali et al, ). These studies produced a number of important advances.…”