Increased cerebral responses to salient transitions between alternating stimuli in chronic migraine with medication overuse headache and during migraine attacks.

INTRODUCTION: In a previous study exploring central pain modulation with heterotopic stimuli in healthy volunteers, we found that transitions between sustained noxious and innocuous thermal stimulations on the foot activated the "salience matrix". Knowing that central sensory processing is abnormal in migraine, we searched in the present study for possible abnormalities of these salient transitional responses in different forms of migraine and at different time points of the migraine cycle. METHODS: Participants of both sexes, mostly females, took part in a conditioned pain modulation experiment: Migraineurs between (n = 14) and during attacks (n = 5), chronic migraine patients with medication overuse headache (n = 7) and healthy volunteers (n = 24). To evoke the salience response, continuous noxious cold or innocuous warm stimulations were alternatively applied on the right foot. Cerebral blood oxygenation level dependent responses were recorded with fMRI. RESULTS: Switching between the two stimulations caused a significant transition response in the "salience matrix" in all subject groups (effect of the condition). Moreover, some group effects appeared on subsequent post-hoc analyses. Augmented transitional blood oxygenation level dependent responses in the motor cortex and superior temporal sulcus were found in two patient groups compared to healthy controls: chronic migraine with medication overuse headache patients and migraineurs recorded during an attack. In chronic migraine with medication overuse headache patients, salience-related responses were moreover greater in the premotor cortex, supplementary motor area, lingual gyrus and dorso-medial prefrontal cortex and other "salience matrix" areas, such as the anterior cingulate and primary somatosensory cortices. CONCLUSION: This study shows salience-related hyperactivation of affective and motor control areas in chronic migraine with medication overuse headache patients and, to a lesser extent, in episodic migraine patients during an attack. The greater extension of exaggerated blood oxygenation level dependent responses to unspecific salient stimuli in chronic migraine with medication overuse headache than during a migraine attack could be relevant for headache chronification.

"Look at my classifier's result": Disentangling unresponsive from (minimally) conscious patients.

Given the fact that clinical bedside examinations can have a high rate of misdiagnosis, machine learning techniques based on neuroimaging and electrophysiological measurements are increasingly being considered for comatose patients and patients with unresponsive wakefulness syndrome, a minimally conscious state or locked-in syndrome. Machine learning techniques have the potential to move from group-level statistical results to personalized predictions in a clinical setting. They have been applied for the purpose of (1) detecting changes in brain activation during functional tasks, equivalent to a behavioral command-following test and (2) estimating signs of consciousness by analyzing measurement data obtained from multiple subjects in resting state. In this review, we provide a comprehensive overview of the literature on both approaches and discuss the translation of present findings to clinical practice. We found that most studies struggle with the difficulty of establishing a reliable behavioral assessment and fluctuations in the patient's levels of arousal. Both these factors affect the training and validation of machine learning methods to a considerable degree. In studies involving more than 50 patients, small to moderate evidence was found for the presence of signs of consciousness or good outcome, where one study even showed strong evidence for good outcome.

Dynamic change of global and local information processing in propofol-induced loss and recovery of consciousness.

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.

Volitional electromyographic responses in disorders of consciousness.

OBJECTIVE: The aim of the study was to validate the use of electromyography (EMG) for detecting responses to command in patients in vegetative state/unresponsive wakefulness syndrome (VS/UWS) or in minimally conscious state (MCS). METHODS: Thirty-eight patients were included in the study (23 traumatic, 25 patients >1 year post-onset), 10 diagnosed as being in VS/UWS, eight in MCS- (no response to command) and 20 in MCS+ (response to command). Eighteen age-matched controls participated in the experiment. The paradigm consisted of three commands (i.e. 'Move your hands', 'Move your legs' and 'Clench your teeth') and one control sentence (i.e. 'It is a sunny day') presented in random order. Each auditory stimulus was repeated 4 times within one block with a stimulus-onset asynchrony of 30 seconds. RESULTS: Post-hoc analyses with Bonferroni correction revealed that EMG activity was higher solely for the target command in one patient in permanent VS/UWS and in three patients in MCS+. CONCLUSION: The use of EMG could help clinicians to detect conscious patients who do not show any volitional response during standard behavioural assessments. However, further investigations should determine the sensitivity of EMG as compared to neuroimaging and electrophysiological assessments.

Connectivity changes underlying spectral EEG changes during propofol-induced loss of consciousness.

The mechanisms underlying anesthesia-induced loss of consciousness remain a matter of debate. Recent electrophysiological reports suggest that while initial propofol infusion provokes an increase in fast rhythms (from beta to gamma range), slow activity (from delta to alpha range) rises selectively during loss of consciousness. Dynamic causal modeling was used to investigate the neural mechanisms mediating these changes in spectral power in humans. We analyzed source-reconstructed data from frontal and parietal cortices during normal wakefulness, propofol-induced mild sedation, and loss of consciousness. Bayesian model selection revealed that the best model for explaining spectral changes across the three states involved changes in corticothalamic interactions. Compared with wakefulness, mild sedation was accounted for by an increase in thalamic excitability, which did not further increase during loss of consciousness. In contrast, loss of consciousness per se was accompanied by a decrease in backward corticocortical connectivity from frontal to parietal cortices, while thalamocortical connectivity remained unchanged. These results emphasize the importance of recurrent corticocortical communication in the maintenance of consciousness and suggest a direct effect of propofol on cortical dynamics.

Electroencephalographic profiles for differentiation of disorders of consciousness.

BACKGROUND: Electroencephalography (EEG) is best suited for long-term monitoring of brain functions in patients with disorders of consciousness (DOC). Mathematical tools are needed to facilitate efficient interpretation of long-duration sleep-wake EEG recordings. METHODS: Starting with matching pursuit (MP) decomposition, we automatically detect and parametrize sleep spindles, slow wave activity, K-complexes and alpha, beta and theta waves present in EEG recordings, and automatically construct profiles of their time evolution, relevant to the assessment of residual brain function in patients with DOC. RESULTS: Above proposed EEG profiles were computed for 32 patients diagnosed as minimally conscious state (MCS, 20 patients), vegetative state/unresponsive wakefulness syndrome (VS/UWS, 11 patients) and Locked-in Syndrome (LiS, 1 patient). Their interpretation revealed significant correlations between patients' behavioral diagnosis and: (a) occurrence of sleep EEG patterns including sleep spindles, slow wave activity and light/deep sleep cycles, (b) appearance and variability across time of alpha, beta, and theta rhythms. Discrimination between MCS and VS/UWS based upon prominent features of these profiles classified correctly 87% of cases. CONCLUSIONS: Proposed EEG profiles offer user-independent, repeatable, comprehensive and continuous representation of relevant EEG characteristics, intended as an aid in differentiation between VS/UWS and MCS states and diagnostic prognosis. To enable further development of this methodology into clinically usable tests, we share user-friendly software for MP decomposition of EEG (http://braintech.pl/svarog) and scripts used for creation of the presented profiles (attached to this article).

Identifying the default-mode component in spatial IC analyses of patients with disorders of consciousness.

OBJECTIVES: Recent fMRI studies have shown that it is possible to reliably identify the default-mode network (DMN) in the absence of any task, by resting-state connectivity analyses in healthy volunteers. We here aimed to identify the DMN in the challenging patient population of disorders of consciousness encountered following coma. EXPERIMENTAL DESIGN: A spatial independent component analysis-based methodology permitted DMN assessment, decomposing connectivity in all its different sources either neuronal or artifactual. Three different selection criteria were introduced assessing anticorrelation-corrected connectivity with or without an automatic masking procedure and calculating connectivity scores encompassing both spatial and temporal properties. These three methods were validated on 10 healthy controls and applied to an independent group of 8 healthy controls and 11 severely brain-damaged patients [locked-in syndrome (n = 2), minimally conscious (n = 1), and vegetative state (n = 8)]. PRINCIPAL OBSERVATIONS: All vegetative patients showed fewer connections in the default-mode areas, when compared with controls, contrary to locked-in patients who showed near-normal connectivity. In the minimally conscious-state patient, only the two selection criteria considering both spatial and temporal properties were able to identify an intact right lateralized BOLD connectivity pattern, and metabolic PET data suggested its neuronal origin. CONCLUSIONS: When assessing resting-state connectivity in patients with disorders of consciousness, it is important to use a methodology excluding non-neuronal contributions caused by head motion, respiration, and heart rate artifacts encountered in all studied patients.

Electrophysiological correlates of behavioural changes in vigilance in vegetative state and minimally conscious state.

The existence of normal sleep in patients in a vegetative state is still a matter of debate. Previous electrophysiological sleep studies in patients with disorders of consciousness did not differentiate patients in a vegetative state from patients in a minimally conscious state. Using high-density electroencephalographic sleep recordings, 11 patients with disorders of consciousness (six in a minimally conscious state, five in a vegetative state) were studied to correlate the electrophysiological changes associated with sleep to behavioural changes in vigilance (sustained eye closure and muscle inactivity). All minimally conscious patients showed clear electroencephalographic changes associated with decreases in behavioural vigilance. In the five minimally conscious patients showing sustained behavioural sleep periods, we identified several electrophysiological characteristics typical of normal sleep. In particular, all minimally conscious patients showed an alternating non-rapid eye movement/rapid eye movement sleep pattern and a homoeostatic decline of electroencephalographic slow wave activity through the night. In contrast, for most patients in a vegetative state, while preserved behavioural sleep was observed, the electroencephalographic patterns remained virtually unchanged during periods with the eyes closed compared to periods of behavioural wakefulness (eyes open and muscle activity). No slow wave sleep or rapid eye movement sleep stages could be identified and no homoeostatic regulation of sleep-related slow wave activity was observed over the night-time period. In conclusion, we observed behavioural, but no electrophysiological, sleep wake patterns in patients in a vegetative state, while there were near-to-normal patterns of sleep in patients in a minimally conscious state. These results shed light on the relationship between sleep electrophysiology and the level of consciousness in severely brain-damaged patients. We suggest that the study of sleep and homoeostatic regulation of slow wave activity may provide a complementary tool for the assessment of brain function in minimally conscious state and vegetative state patients.

Brain-computer interfacing in disorders of consciousness.

BACKGROUND: Recent neuroimaging research has strikingly demonstrated the existence of covert awareness in some patients with disorders of consciousness (DoC). These findings have highlighted the potential for the development of simple brain-computer interfaces (BCI) as a diagnosis in behaviourally unresponsive patients. OBJECTIVES: This study here reviews current EEG-based BCIs that hold potential for assessing and eventually assisting patients with DoC. It highlights key areas for further development that might eventually make their application feasible in this challenging patient group. METHODS: The major types of BCIs proposed in the literature are considered, namely those based on the P3 potential, sensorimotor rhythms, steady state oscillations and slow cortical potentials. In each case, a brief overview of the relevant literature is provided and then their relative merits for BCI applications in DoC are considered. RESULTS: A range of BCI designs have been proposed and tested for enabling communication in fully conscious, paralysed patients. Although many of these have potential applicability for patients with DoC, they share some key challenges that need to be overcome, including limitations of stimulation modality, feedback, user training and consistency. CONCLUSION: Future work will need to address the technical and practical challenges facing reliable implementation at the patient's bedside.

Resting-state EEG study of comatose patients: a connectivity and frequency analysis to find differences between vegetative and minimally conscious states.

The aim of this study was to look for differences in the power spectra and in EEG connectivity measures between patients in the vegetative state (VS/UWS) and patients in the minimally conscious state (MCS). The EEG of 31 patients was recorded and analyzed. Power spectra were obtained using modern multitaper methods. Three connectivity measures (coherence, the imaginary part of coherency and the phase lag index) were computed. Of the 31 patients, 21 were diagnosed as MCS and 10 as VS/UWS using the Coma Recovery Scale-Revised (CRS-R). EEG power spectra revealed differences between the two conditions. The VS/UWS patients showed increased delta power but decreased alpha power compared with the MCS patients. Connectivity measures were correlated with the CRS-R diagnosis; patients in the VS/UWS had significantly lower connectivity than MCS patients in the theta and alpha bands. Standard EEG recorded in clinical conditions could be used as a tool to help the clinician in the diagnosis of disorders of consciousness.

Two distinct neuronal networks mediate the awareness of environment and of self.

Evidence from functional neuroimaging studies on resting state suggests that there are two distinct anticorrelated cortical systems that mediate conscious awareness: an "extrinsic" system that encompasses lateral fronto-parietal areas and has been linked with processes of external input (external awareness), and an "intrinsic" system which encompasses mainly medial brain areas and has been associated with internal processes (internal awareness). The aim of our study was to explore the neural correlates of resting state by providing behavioral and neuroimaging data from healthy volunteers. With no a priori assumptions, we first determined behaviorally the relationship between external and internal awareness in 31 subjects. We found a significant anticorrelation between external and internal awareness with a mean switching frequency of 0.05 Hz (range: 0.01-0.1 Hz). Interestingly, this frequency is similar to BOLD fMRI slow oscillations. We then evaluated 22 healthy volunteers in an fMRI paradigm looking for brain areas where BOLD activity correlated with "internal" and "external" scores. Activation of precuneus/posterior cingulate, anterior cingulate/mesiofrontal cortices, and parahippocampal areas ("intrinsic system") was linearly linked to intensity of internal awareness, whereas activation of lateral fronto-parietal cortices ("extrinsic system") was linearly associated with intensity of external awareness.

"Relevance vector machine" consciousness classifier applied to cerebral metabolism of vegetative and locked-in patients.

The vegetative state is a devastating condition where patients awaken from their coma (i.e., open their eyes) but fail to show any behavioural sign of conscious awareness. Locked-in syndrome patients also awaken from their coma and are unable to show any motor response to command (except for small eye movements or blinks) but recover full conscious awareness of self and environment. Bedside evaluation of residual cognitive function in coma survivors often is difficult because motor responses may be very limited or inconsistent. We here aimed to disentangle vegetative from "locked-in" patients by an automatic procedure based on machine learning using fluorodeoxyglucose PET data obtained in 37 healthy controls and in 13 patients in a vegetative state. Next, the trained machine was tested on brain scans obtained in 8 patients with locked-in syndrome. We used a sparse probabilistic Bayesian learning framework called "relevance vector machine" (RVM) to classify the scans. The trained RVM classifier, applied on an input scan, returns a probability value (p-value) of being in one class or the other, here being "conscious" or not. Training on the control and vegetative state groups was assessed with a leave-one-out cross-validation procedure, leading to 100% classification accuracy. When applied on the locked-in patients, all scans were classified as "conscious" with a mean p-value of .95 (min .85). In conclusion, even with this relatively limited data set, we could train a classifier distinguishing between normal consciousness (i.e., wakeful conscious awareness) and the vegetative state (i.e., wakeful unawareness). Cross-validation also indicated that the clinical classification and the one predicted by the automatic RVM classifier were in accordance. Moreover, when applied on a third group of "locked-in" consciously aware patients, they all had a strong probability of being similar to the normal controls, as expected. Therefore, RVM classification of cerebral metabolic images obtained in coma survivors could become a useful tool for the automated PET-based diagnosis of altered states of consciousness.

Default network connectivity reflects the level of consciousness in non-communicative brain-damaged patients.

The 'default network' is defined as a set of areas, encompassing posterior-cingulate/precuneus, anterior cingulate/mesiofrontal cortex and temporo-parietal junctions, that show more activity at rest than during attention-demanding tasks. Recent studies have shown that it is possible to reliably identify this network in the absence of any task, by resting state functional magnetic resonance imaging connectivity analyses in healthy volunteers. However, the functional significance of these spontaneous brain activity fluctuations remains unclear. The aim of this study was to test if the integrity of this resting-state connectivity pattern in the default network would differ in different pathological alterations of consciousness. Fourteen non-communicative brain-damaged patients and 14 healthy controls participated in the study. Connectivity was investigated using probabilistic independent component analysis, and an automated template-matching component selection approach. Connectivity in all default network areas was found to be negatively correlated with the degree of clinical consciousness impairment, ranging from healthy controls and locked-in syndrome to minimally conscious, vegetative then coma patients. Furthermore, precuneus connectivity was found to be significantly stronger in minimally conscious patients as compared with unconscious patients. Locked-in syndrome patient's default network connectivity was not significantly different from controls. Our results show that default network connectivity is decreased in severely brain-damaged patients, in proportion to their degree of consciousness impairment. Future prospective studies in a larger patient population are needed in order to evaluate the prognostic value of the presented methodology.

Resting state activity in patients with disorders of consciousness.

Recent advances in the study of spontaneous brain activity have demonstrated activity patterns that emerge with no task performance or sensory stimulation; these discoveries hold promise for the study of higher-order associative network functionality. Additionally, such advances are argued to be relevant in pathological states, such as disorders of consciousness (DOC), i.e., coma, vegetative and minimally conscious states. Recent studies on resting state activity in DOC, measured with functional magnetic resonance imaging (fMRI) techniques, show that functional connectivity is disrupted in the task-negative or the default mode network. However, the two main approaches employed in the analysis of resting state functional connectivity data (i.e., hypothesis-driven seed-voxel and data-driven independent component analysis) present multiple methodological difficulties, especially in non-collaborative DOC patients. Improvements in motion artifact removal and spatial normalization are needed before fMRI resting state data can be used as proper biomarkers in severe brain injury. However, we anticipate that such developments will boost clinical resting state fMRI studies, allowing for easy and fast acquisitions and ultimately improve the diagnosis and prognosis in the absence of DOC patients' active collaboration in data acquisition.

Automated EEG entropy measurements in coma, vegetative state/unresponsive wakefulness syndrome and minimally conscious state.

Monitoring the level of consciousness in brain-injured patients with disorders of consciousness is crucial as it provides diagnostic and prognostic information. Behavioral assessment remains the gold standard for assessing consciousness but previous studies have shown a high rate of misdiagnosis. This study aimed to investigate the usefulness of electroencephalography (EEG) entropy measurements in differentiating unconscious (coma or vegetative) from minimally conscious patients. Left fronto-temporal EEG recordings (10-minute resting state epochs) were prospectively obtained in 56 patients and 16 age-matched healthy volunteers. Patients were assessed in the acute (≤1 month post-injury; n=29) or chronic (>1 month post-injury; n=27) stage. The etiology was traumatic in 23 patients. Automated online EEG entropy calculations (providing an arbitrary value ranging from 0 to 91) were compared with behavioral assessments (Coma Recovery Scale-Revised) and outcome. EEG entropy correlated with Coma Recovery Scale total scores (r=0.49). Mean EEG entropy values were higher in minimally conscious (73±19; mean and standard deviation) than in vegetative/unresponsive wakefulness syndrome patients (45±28). Receiver operating characteristic analysis revealed an entropy cut-off value of 52 differentiating acute unconscious from minimally conscious patients (sensitivity 89% and specificity 90%). In chronic patients, entropy measurements offered no reliable diagnostic information. EEG entropy measurements did not allow prediction of outcome. User-independent time-frequency balanced spectral EEG entropy measurements seem to constitute an interesting diagnostic - albeit not prognostic - tool for assessing neural network complexity in disorders of consciousness in the acute setting. Future studies are needed before using this tool in routine clinical practice, and these should seek to improve automated EEG quantification paradigms in order to reduce the remaining false negative and false positive findings.

Breakdown of within- and between-network resting state functional magnetic resonance imaging connectivity during propofol-induced loss of consciousness.

BACKGROUND: Mechanisms of anesthesia-induced loss of consciousness remain poorly understood. Resting-state functional magnetic resonance imaging allows investigating whole-brain connectivity changes during pharmacological modulation of the level of consciousness. METHODS: Low-frequency spontaneous blood oxygen level-dependent fluctuations were measured in 19 healthy volunteers during wakefulness, mild sedation, deep sedation with clinical unconsciousness, and subsequent recovery of consciousness. RESULTS: Propofol-induced decrease in consciousness linearly correlates with decreased corticocortical and thalamocortical connectivity in frontoparietal networks (i.e., default- and executive-control networks). Furthermore, during propofol-induced unconsciousness, a negative correlation was identified between thalamic and cortical activity in these networks. Finally, negative correlations between default network and lateral frontoparietal cortices activity, present during wakefulness, decreased proportionally to propofol-induced loss of consciousness. In contrast, connectivity was globally preserved in low-level sensory cortices, (i.e., in auditory and visual networks across sedation stages). This was paired with preserved thalamocortical connectivity in these networks. Rather, waning of consciousness was associated with a loss of cross-modal interactions between visual and auditory networks. CONCLUSIONS: Our results shed light on the functional significance of spontaneous brain activity fluctuations observed in functional magnetic resonance imaging. They suggest that propofol-induced unconsciousness could be linked to a breakdown of cerebral temporal architecture that modifies both within- and between-network connectivity and thus prevents communication between low-level sensory and higher-order frontoparietal cortices, thought to be necessary for perception of external stimuli. They emphasize the importance of thalamocortical connectivity in higher-order cognitive brain networks in the genesis of conscious perception.

Neuroimaging after coma.

Following coma, some patients will recover wakefulness without signs of consciousness (only showing reflex movements, i.e., the vegetative state) or may show non-reflex movements but remain without functional communication (i.e., the minimally conscious state). Currently, there remains a high rate of misdiagnosis of the vegetative state (Schnakers et. al. BMC Neurol, 9:35, 8) and the clinical and electrophysiological markers of outcome from the vegetative and minimally conscious states remain unsatisfactory. This should incite clinicians to use multimodal assessment to detect objective signs of consciousness and validate para-clinical prognostic markers in these challenging patients. This review will focus on advanced magnetic resonance imaging (MRI) techniques such as magnetic resonance spectroscopy, diffusion tensor imaging, and functional MRI (fMRI studies in both "activation" and "resting state" conditions) that were recently introduced in the assessment of patients with chronic disorders of consciousness.

Mental imagery for brain-computer interface control and communication in non-responsive individuals.

BACKGROUND: People who survive severe brain damage may eventually develop a prolonged consciousness disorder. Others can regain full consciousness but remain unable to speak or move because of the severity of the lesions, as for those with locked-in syndrome (LIS). Brain-computer interface techniques can be useful to disentangle these states by detecting neurophysiological correlates of conscious processing of information to enable communication with these individuals after the diagnosis. OBJECTIVE: The goal of our study was to evaluate with a user-centered design approach the usability of a mental imagery task to detect signs of voluntary information processing and enabling communication in a group of severely disabled individuals. METHODS: Five individuals with LIS participated in the study. Participants were instructed to imagine hand, arm or feet movements during electroencephalography (EEG) to detect patterns of event-related synchronization/desynchronization associated with each task. After the user-centered design, usability was evaluated (i.e., efficiency, effectiveness and satisfaction). RESULTS: Two participants achieved significant levels of accuracy in 2 different tasks. The associated workload and levels of satisfaction perceived by the users were moderate and were mainly related to the time demand of the task. CONCLUSION: Results showed lack of effectiveness of the task to detect voluntary brain activity and thus detect consciousness or communicate with non-responsive individuals. The application must be modified to be sufficiently satisfying for the intended end-users and suggestions are made in this regard.

Reproducible grey matter patterns index a multivariate, global alteration of brain structure in schizophrenia and bipolar disorder.

Schizophrenia is a severe mental disorder characterized by numerous subtle changes in brain structure and function. Machine learning allows exploring the utility of combining structural and functional brain magnetic resonance imaging (MRI) measures for diagnostic application, but this approach has been hampered by sample size limitations and lack of differential diagnostic data. Here, we performed a multi-site machine learning analysis to explore brain structural patterns of T1 MRI data in 2668 individuals with schizophrenia, bipolar disorder or attention-deficit/ hyperactivity disorder, and healthy controls. We found reproducible changes of structural parameters in schizophrenia that yielded a classification accuracy of up to 76% and provided discrimination from ADHD, through it lacked specificity against bipolar disorder. The observed changes largely indexed distributed grey matter alterations that could be represented through a combination of several global brain-structural parameters. This multi-site machine learning study identified a brain-structural signature that could reproducibly differentiate schizophrenia patients from controls, but lacked specificity against bipolar disorder. While this currently limits the clinical utility of the identified signature, the present study highlights that the underlying alterations index substantial global grey matter changes in psychotic disorders, reflecting the biological similarity of these conditions, and provide a roadmap for future exploration of brain structural alterations in psychiatric patients.