The Development and External Validation of Artificial Intelligence-Driven MRI-Based Models to Improve Prediction of Lesion-Specific Extraprostatic Extension in Patients with Prostate Cancer.

Adequate detection of the histopathological extraprostatic extension (EPE) of prostate cancer (PCa) remains a challenge using conventional radiomics on 3 Tesla multiparametric magnetic resonance imaging (3T mpMRI). This study focuses on the assessment of artificial intelligence (AI)-driven models with innovative MRI radiomics in predicting EPE of prostate cancer (PCa) at a lesion-specific level. With a dataset encompassing 994 lesions from 794 PCa patients who underwent robot-assisted radical prostatectomy (RARP) at two Dutch hospitals, the study establishes and validates three classification models. The models were validated on an internal validation cohort of 162 lesions and an external validation cohort of 189 lesions in terms of discrimination, calibration, net benefit, and comparison to radiology reporting. Notably, the achieved AUCs ranged from 0.86 to 0.91 at the lesion-specific level, demonstrating the superior accuracy of the random forest model over conventional radiological reporting. At the external test cohort, the random forest model was the best-calibrated model and demonstrated a significantly higher accuracy compared to radiological reporting (83% vs. 67%, p = 0.02). In conclusion, an AI-powered model that includes both existing and novel MRI radiomics improves the detection of lesion-specific EPE in prostate cancer.

Self-processing in coma, unresponsive wakefulness syndrome and minimally conscious state.

Introduction: Behavioral and cerebral dissociation has been now clearly established in some patients with acquired disorders of consciousness (DoC). Altogether, these studies mainly focused on the preservation of high-level cognitive markers in prolonged DoC, but did not specifically investigate lower but key-cognitive functions to consciousness emergence, such as the ability to take a first-person perspective, notably at the acute stage of coma. We made the hypothesis that the preservation of self-recognition (i) is independent of the behavioral impairment of consciousness, and (ii) can reflect the ability to recover consciousness. Methods: Hence, using bedside Electroencephalography (EEG) recordings, we acquired, in a large cohort of 129 severely brain damaged patients, the brain response to the passive listening of the subject's own name (SON) and unfamiliar other first names (OFN). One hundred and twelve of them (mean age ± SD = 46 ± 18.3 years, sex ratio M/F: 71/41) could be analyzed for the detection of an individual and significant discriminative P3 event-related brain response to the SON as compared to OFN ('SON effect', primary endpoint assessed by temporal clustering permutation tests). Results: Patients were either coma (n = 38), unresponsive wakefulness syndrome (UWS, n = 30) or minimally conscious state (MCS, n = 44), according to the revised version of the Coma Recovery Scale (CRS-R). Overall, 33 DoC patients (29%) evoked a 'SON effect'. This electrophysiological index was similar between coma (29%), MCS (23%) and UWS (34%) patients (p = 0.61). MCS patients at the time of enrolment were more likely to emerged from MCS (EMCS) at 6 months than coma and UWS patients (p = 0.013 for comparison between groups). Among the 72 survivors' patients with event-related responses recorded within 3 months after brain injury, 75% of the 16 patients with a SON effect were EMCS at 6 months, while 59% of the 56 patients without a SON effect evolved to this favorable behavioral outcome. Discussion: About 30% of severely brain-damaged patients suffering from DoC are capable to process salient self-referential auditory stimuli, even in case of absence of behavioral detection of self-conscious processing. We suggest that self-recognition covert brain ability could be an index of consciousness recovery, and thus could help to predict good outcome.

Impairment of central language processing in critically ill coronavirus disease 2019 patients with delirium.

Accumulating evidence indicates that coronavirus disease 2019 is a major cause of delirium. Given the global dimension of the current pandemic and the fact that delirium is a strong predictor of cognitive decline for critically ill patients, this raises concerns regarding the neurological cost of coronavirus disease 2019. Currently, there is a major knowledge gap related to the covert yet potentially incapacitating higher-order cognitive impairment underpinning coronavirus disease 2019 related delirium. The aim of the current study was to analyse the electrophysiological signatures of language processing in coronavirus disease 2019 patients with delirium by using a specifically designed multidimensional auditory event-related potential battery to probe hierarchical cognitive processes, including self-processing (P300) and semantic/lexical priming (N400). Clinical variables and electrophysiological data were prospectively collected in controls subjects (n = 14) and in critically ill coronavirus disease 2019 patients with (n = 19) and without (n = 22) delirium. The time from intensive care unit admission to first clinical sign of delirium was of 8 (3.5-20) days, and the delirium lasted for 7 (4.5-9.5) days. Overall, we have specifically identified in coronavirus disease 2019 patients with delirium, both a preservation of low-level central auditory processing (N100 and P200) and a coherent ensemble of covert higher-order cognitive dysfunctions encompassing self-related processing (P300) and sematic/lexical language priming (N400) (spatial-temporal clustering, P-cluster ≤ 0.05). We suggest that our results shed new light on the neuropsychological underpinnings of coronavirus disease 2019 related delirium, and may constitute a valuable method for patient's bedside diagnosis and monitoring in this clinically challenging setting.

Virtually spatialized sounds enhance auditory processing in healthy participants and patients with a disorder of consciousness.

Neuroscientific and clinical studies on auditory perception often use headphones to limit sound interference. In these conditions, sounds are perceived as internalized because they lack the sound-attributes that normally occur with a sound produced from a point in space around the listener. Without the spatial attention mechanisms that occur with localized sounds, auditory functional assessments could thus be underestimated. We hypothesize that adding virtually externalization and localization cues to sounds through headphones enhance sound discrimination in both healthy participants and patients with a disorder of consciousness (DOC). Hd-EEG was analyzed in 14 healthy participants and 18 patients while they listened to self-relevant and irrelevant stimuli in two forms: diotic (classic sound presentation with an "internalized" feeling) and convolved with a binaural room impulse response (to create an "externalized" feeling). Convolution enhanced the brains' discriminative response as well as the processing of irrelevant sounds itself, in both healthy participants and DOC patients. For the healthy participants, these effects could be associated with enhanced activation of both the dorsal (where/how) and ventral (what) auditory streams, suggesting that spatial attributes support speech discrimination. Thus, virtually spatialized sounds might "call attention to the outside world" and improve the sensitivity of assessment of brain function in DOC patients.

Sharing an Open Stimulation System for Auditory EEG Experiments Using Python, Raspberry Pi, and HifiBerry.

In auditory behavioral and EEG experiments, the variability of stimulation solutions, for both software and hardware, adds unnecessary technical constraints. Currently, there is no easy to use, inexpensive, and shareable solution that could improve collaborations and data comparisons across different sites and contexts. This article outlines a system composed by a Raspberry Pi coupled with Python programming and associated with a HifiBerry sound card. We compare its sound performances with those of a wide variety of materials and configurations. This solution achieves the high timing accuracy and sound quality important in auditory cognition experiments, while being simple to use and open source. The present system shows high performances and results along with excellent feedback from users. It is inexpensive, easy to build, share, and improve on. Working with such low-cost, powerful, and collaborative hardware and software tools allows people to create their own specific, adapted, and shareable system that can be standardized across different collaborative sites, while being extremely simple and robust in use.

An Echo of Consciousness: Brain Function During Preferred Music.

Background: Given its emotional and autobiographical properties, music appears as a potential aid in diagnostic assessment and therapeutics in patients with disorders of consciousness (DOCs). Several studies have shown boosting effects on behavior and brain functioning when patients were exposed to (their preferred) music. Objective: The aim of this study was to investigate if these effects on the brain's spontaneous activity are dependent on the level of consciousness. Methods: Nine postcomatose patients and eight healthy control subjects were assessed using functional magnetic resonance imaging in two conditions: rest (without stimulation) and music. All patients presented at least an auditory startle, thereby suggesting the presence of residual auditory function. A gradient of consciousness (from unresponsive wakefulness syndrome to healthy subjects) was formulated to evaluate consciousness-level-dependent increases in brain activity and connectivity. Network-based functional connectivity assessed auditory, default-mode, frontoparietal, and music-evoked emotion networks. Furthermore, exploratory voxel-to-voxel analyses were performed at the whole brain level using intrinsic connectivity contrast and (fractional) amplitude of low-frequency fluctuations. Results: Stronger consciousness-level-dependent increases within network-to-voxel analysis of connectivity were found in the frontoparietal network with the precuneus during music stimulation compared with rest. Voxel-to-voxel analyses showed stronger increases of intrinsic connectivity in the music condition compared with rest in regions previously related to music processing. There were consistently more regions with increased connectivity during the main effect of music compared with rest. These increases of connectivity during music were observed in brain regions involved in consciousness, language, emotion, and memory processing. Conclusions: Our results show that music seems to trigger more substantial connectivity changes compared with rest, pointing toward the potential ability of music to stimulate patients' brain function. Further research should focus on effects of music in general, its specific acoustical features, and the effects of simple auditory stimuli, as well as the possible therapeutic and diagnostic effects of music in well-controlled clinical trials. Impact statement Due to its emotional and autobiographical properties, music is a particularly salient stimulus. A few studies using musical stimuli have shown promising results in terms of behavioral responsiveness in patients with disorders of consciousness (Magee, 2005; Raglio et al., 2014; Verger et al., 2014). Our study confirms the presence of a beneficial effect of music on brain connectivity in these severely brain-injured patients, which moreover seems to be dependent on the level of consciousness. Our findings therefore strengthen the hypothesis that music could play a role from a diagnostic and therapeutic standpoint in this population, paving the way for future well-controlled clinical trials.

Detecting Brain Activity Following a Verbal Command in Patients With Disorders of Consciousness.

BACKGROUND: The accurate assessment of patients with disorders of consciousness (DOC) is a challenge to most experienced clinicians. As a potential clinical tool, functional magnetic resonance imaging (fMRI) could detect residual awareness without the need for the patients' actual motor responses. METHODS: We adopted a simple active fMRI motor paradigm (hand raising) to detect residual awareness in these patients. Twenty-nine patients were recruited. They met the diagnosis of minimally conscious state (MCS) (male = 6, female = 2; n = 8), vegetative state/unresponsive wakefulness syndrome (VS/UWS) (male = 17, female = 4; n = 21). RESULTS: We analyzed the command-following responses for robust evidence of statistically reliable markers of motor execution, similar to those found in 15 healthy controls. Of the 29 patients, four (two MCS, two VS/UWS) could adjust their brain activity to the "hand-raising" command, and they showed activation in motor-related regions (which could not be discovered in the own-name task). CONCLUSION: Longitudinal behavioral assessments showed that, of these four patients, two in a VS/UWS recovered to MCS and one from MCS recovered to MCS+ (i.e., showed command following). In patients with no response to hand raising task, six VS/UWS and three MCS ones showed recovery in follow-up procedure. The simple active fMRI "hand-raising" task can elicit brain activation in patients with DOC, similar to those observed in healthy volunteers. Activity of the motor-related network may be taken as an indicator of high-level cognition that cannot be discerned through conventional behavioral assessment.

A Heartbeat Away From Consciousness: Heart Rate Variability Entropy Can Discriminate Disorders of Consciousness and Is Correlated With Resting-State fMRI Brain Connectivity of the Central Autonomic Network.

Background: Disorders of consciousness are challenging to diagnose, with inconsistent behavioral responses, motor and cognitive disabilities, leading to approximately 40% misdiagnoses. Heart rate variability (HRV) reflects the complexity of the heart-brain two-way dynamic interactions. HRV entropy analysis quantifies the unpredictability and complexity of the heart rate beats intervals. We here investigate the complexity index (CI), a score of HRV complexity by aggregating the non-linear multi-scale entropies over a range of time scales, and its discriminative power in chronic patients with unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS), and its relation to brain functional connectivity. Methods: We investigated the CI in short (CIs) and long (CIl) time scales in 14 UWS and 16 MCS sedated. CI for MCS and UWS groups were compared using a Mann-Whitney exact test. Spearman's correlation tests were conducted between the Coma Recovery Scale-revised (CRS-R) and both CI. Discriminative power of both CI was assessed with One-R machine learning model. Correlation between CI and brain connectivity (detected with functional magnetic resonance imagery using seed-based and hypothesis-free intrinsic connectivity) was investigated using a linear regression in a subgroup of 10 UWS and 11 MCS patients with sufficient image quality. Results: Higher CIs and CIl values were observed in MCS compared to UWS. Positive correlations were found between CRS-R and both CI. The One-R classifier selected CIl as the best discriminator between UWS and MCS with 90% accuracy, 7% false positive and 13% false negative rates after a 10-fold cross-validation test. Positive correlations were observed between both CI and the recovery of functional connectivity of brain areas belonging to the central autonomic networks (CAN). Conclusion: CI of MCS compared to UWS patients has high discriminative power and low false negative rate at one third of the estimated human assessors' misdiagnosis, providing an easy, inexpensive and non-invasive diagnostic tool. CI reflects functional connectivity changes in the CAN, suggesting that CI can provide an indirect way to screen and monitor connectivity changes in this neural system. Future studies should assess the extent of CI's predictive power in a larger cohort of patients and prognostic power in acute patients.

Clinical subcategorization of minimally conscious state according to resting functional connectivity.

Patients in minimally conscious state (MCS) have been subcategorized in MCS plus and MCS minus, based on command-following, intelligible verbalization or intentional communication. We here aimed to better characterize the functional neuroanatomy of MCS based on this clinical subcategorization by means of resting state functional magnetic resonance imaging (fMRI). Resting state fMRI was acquired in 292 MCS patients and a seed-based analysis was conducted on a convenience sample of 10 MCS plus patients, 9 MCS minus patients and 35 healthy subjects. We investigated the left and right frontoparietal networks (FPN), auditory network, default mode network (DMN), thalamocortical connectivity and DMN between-network anticorrelations. We also employed an analysis based on regions of interest (ROI) to examine interhemispheric connectivity and investigated intergroup differences in gray/white matter volume by means of voxel-based morphometry. We found a higher connectivity in MCS plus as compared to MCS minus in the left FPN, specifically between the left dorso-lateral prefrontal cortex and left temporo-occipital fusiform cortex. No differences between patient groups were observed in the auditory network, right FPN, DMN, thalamocortical and interhemispheric connectivity, between-network anticorrelations and gray/white matter volume. Our preliminary group-level results suggest that the clinical subcategorization of MCS may involve functional connectivity differences in a language-related executive control network. MCS plus and minus patients are seemingly not differentiated by networks associated to auditory processing, perception of surroundings and internal awareness/self-mentation, nor by interhemispheric integration and structural brain damage.

Electrodermal reactivity to emotional stimuli in healthy subjects and patients with disorders of consciousness.

OBJECTIVES: After a coma, one major challenge is the detection of awareness in patients with disorders of consciousness. In some patients, the only manifestation indicative of awareness is an appropriate emotional response. Preferred music is a powerful medium to elicit emotions and autobiographical memory. Furthermore, music has been shown to improve cognitive functions both in healthy subjects and patients with neurological impairment. We hypothesized that signs of awareness could be enhanced in some patients with disorders of consciousness under appropriate emotional stimulation such as preferred music and also probably preferred odors. METHODS: To investigate an objective, easily recordable marker of emotions at the patients' bedside, electrodermal activity (skin conductance level, SCL) was assessed with stimulations in auditory and olfactory modalities, notably with preferred music, neutral sound, preferred odors, and neutral odors. The study was conducted in 11 patients with disorders of consciousness (DOC) and 7 healthy participants. RESULTS: In healthy subjects, the mean amplitude of the SCL was increased during exposure to preferred music as compared to neutral sounds (respectively: 0.00037±0.0004 vs. - 0.00004±0.00019µS). No significant difference between conditions was detected in patients. CONCLUSION: The results of this study suggest that electrodermal activity could be a useful marker of emotions induced by music in healthy controls. However, it failed to show any significant difference between conditions in patients with DOC.

Personalized objects can optimize the diagnosis of EMCS in the assessment of functional object use in the CRS-R: a double blind, randomized clinical trial.

BACKGROUND: Behavioral assessment has been acted as the gold standard for the diagnosis of disorders of consciousness (DOC) patients. The item "Functional Object Use" in the motor function sub-scale in the Coma Recovery Scale-Revised (CRS-R) is a key item in differentiating between minimally conscious state (MCS) and emergence from MCS (EMCS). However, previous studies suggested that certain specific stimuli, especially something self-relevant can affect DOC patients' scores of behavioral assessment scale. So, we attempted to find out if personalized objects can improve the diagnosis of EMCS in the assessment of Functional Object Use by comparing the use of patients' favorite objects and other common objects in MCS patients. METHODS: Twenty-one post-comatose patients diagnosed as MCS were prospectively included. The item "Functional Object Use" was assessed by using personalized objects (e.g., cigarette, paper) and non-personalized objects, which were presented in a random order. The rest assessments were performed following the standard protocol of the CRS-R. The differences between functional uses of the two types of objects were analyzed by the McNemar test. RESULTS: The incidence of Functional Object Use was significantly higher using personalized objects than non-personalized objects in the CRS-R. Five out of the 21 MCS studied patients, who were assessed with non-personalized objects, were re-diagnosed as EMCS with personalized objects (χ2 = 5, df = 1, p < 0.05). CONCLUSIONS: Personalized objects employed here seem to be more effective to elicit patients' responses as compared to non-personalized objects during the assessment of Functional Object Use in DOC patients. TRIAL REGISTRATION: Clinical Trials.gov: NCT02988206 ; Date of registration: 2016/12/12.

Resistance to eye opening in patients with disorders of consciousness.

INTRODUCTION: Resistance to eye opening (REO) is a commonly encountered phenomenon in clinical practice. We aim to investigate whether REO is a sign of consciousness or a reflex in severely brain-injured patients. METHODS: We recorded REO in chronic patients with disorders of consciousness during a multimodal diagnostic assessment. REO evaluations were performed daily in each patient and clinical diagnosis of unresponsive wakefulness syndrome (UWS), minimally conscious state with (MCS+) or without (MCS-) preserved language processing was made using the Coma Recovery Scale-Revised (CRS-R). RESULTS: Out of 150 consecutive patients, 79 patients fit inclusion criteria. REO was seen in 19 patients (24.1%). At the group level, there was a significant relationship between the presence of REO and the level of consciousness. We also observed a difference in the repeatability of REO between patients in UWS, MCS- and MCS+. Out of 23 patients in UWS, six showed REO, in whom five showed atypical brain patterns activation. CONCLUSION: Our findings suggest a voluntary basis for REO and stress the need for multiple serial assessments of REO in these patients, especially since most patients show fluctuating levels of consciousness.

Regional brain volumetry and brain function in severely brain-injured patients.

OBJECTIVE: The relationship between residual brain tissue in patients with disorders of consciousness (DOC) and the clinical condition is unclear. This observational study aimed to quantify gray (GM) and white matter (WM) atrophy in states of (altered) consciousness. METHODS: Structural T1-weighted magnetic resonance images were processed for 102 severely brain-injured and 52 healthy subjects. Regional brain volume was quantified for 158 (sub)cortical regions using Freesurfer. The relationship between regional brain volume and clinical characteristics of patients with DOC and conscious brain-injured patients was assessed using a linear mixed-effects model. Classification of patients with unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) using regional volumetric information was performed and compared to classification using cerebral glucose uptake from fluorodeoxyglucose positron emission tomography. For validation, the T1-based classifier was tested on independent datasets. RESULTS: Patients were characterized by smaller regional brain volumes than healthy subjects. Atrophy occurred faster in UWS compared to MCS (GM) and conscious (GM and WM) patients. Classification was successful (misclassification with leave-one-out cross-validation between 2% and 13%) and generalized to the independent data set with an area under the receiver operator curve of 79% (95% confidence interval [CI; 67-91.5]) for GM and 70% (95% CI [55.6-85.4]) for WM. INTERPRETATION: Brain volumetry at the single-subject level reveals that regions in the default mode network and subcortical gray matter regions, as well as white matter regions involved in long range connectivity, are most important to distinguish levels of consciousness. Our findings suggest that changes of brain structure provide information in addition to the assessment of functional neuroimaging and thus should be evaluated as well. Ann Neurol 2018;83:842-853.

Prevalence of coma-recovery scale-revised signs of consciousness in patients in minimally conscious state.

Different behavioural signs of consciousness can distinguish patients with an unresponsive wakefulness syndrome from patients in minimally conscious state (MCS). The Coma Recovery Scale-Revised (CRS-R) is the most sensitive scale to differentiate the different altered states of consciousness and eleven items detect the MCS. The aim of this study is to document the prevalence of these items. We analysed behavioural assessments of 282 patients diagnosed in MCS based on the CRS-R. Results showed that some items are particularly frequent among patients in MCS, namely fixation, visual pursuit, and reproducible movement to command, which were observed in more than 50% of patients. These responses were also the most probably observed items when the patients only showed one sign of consciousness. On the other hand, some items were rarely or never observed alone, e.g., object localisation (reaching), object manipulation, intelligible verbalisation, and object recognition. The results also showed that limiting the CRS-R assessment to the five most frequently observed items (i.e., fixation, visual pursuit, reproducible movement to command, automatic motor response and localisation to noxious stimulation) detected 99% of the patients in MCS. If clinicians have only limited time to assess patients with disorders of consciousness, we suggest to evaluate at least these five items of the CRS-R.

Global structural integrity and effective connectivity in patients with disorders of consciousness.

BACKGROUND: Previous studies have separately reported impaired functional, structural, and effective connectivity in patients with disorders of consciousness (DOC). The perturbational complexity index (PCI) is a transcranial magnetic stimulation (TMS) derived marker of effective connectivity. The global fractional anisotropy (FA) is a marker of structural integrity. Little is known about how these parameters are related to each other. OBJECTIVE: We aimed at testing the relationship between structural integrity and effective connectivity. METHODS: We assessed 23 patients with severe brain injury more than 4 weeks post-onset, leading to DOC or locked-in syndrome, and 14 healthy subjects. We calculated PCI using repeated single pulse TMS coupled with high-density electroencephalography, and used it as a surrogate of effective connectivity. Structural integrity was measured using the global FA, derived from diffusion weighted imaging. We used linear regression modelling to test our hypothesis, and computed the correlation between PCI and FA in different groups. RESULTS: Global FA could predict 74% of PCI variance in the whole sample and 56% in the patients' group. No other predictors (age, gender, time since onset, behavioural score) improved the models. FA and PCI were correlated in the whole population (r = 0.86, p < 0.0001), the patients, and the healthy subjects subgroups. CONCLUSION: We here demonstrated that effective connectivity correlates with structural integrity in brain-injured patients. Increased structural damage level decreases effective connectivity, which could prevent the emergence of consciousness.

Multifaceted brain networks reconfiguration in disorders of consciousness uncovered by co-activation patterns.

INTRODUCTION: Given that recent research has shown that functional connectivity is not a static phenomenon, we aim to investigate the dynamic properties of the default mode network's (DMN) connectivity in patients with disorders of consciousness. METHODS: Resting-state fMRI volumes of a convenience sample of 17 patients in unresponsive wakefulness syndrome (UWS) and controls were reduced to a spatiotemporal point process by selecting critical time points in the posterior cingulate cortex (PCC). Spatial clustering was performed on the extracted PCC time frames to obtain 8 different co-activation patterns (CAPs). We investigated spatial connectivity patterns positively and negatively correlated with PCC using both CAPs and standard stationary method. We calculated CAPs occurrences and the total number of frames. RESULTS: Compared to controls, patients showed (i) decreased within-network positive correlations and between-network negative correlations, (ii) emergence of "pathological" within-network negative correlations and between-network positive correlations (better defined with CAPs), and (iii) "pathological" increases in within-network positive correlations and between-network negative correlations (only detectable using CAPs). Patients showed decreased occurrence of DMN-like CAPs (1-2) compared to controls. No between-group differences were observed in the total number of frames CONCLUSION: CAPs reveal at a more fine-grained level the multifaceted spatial connectivity reconfiguration following the DMN disruption in UWS patients, which is more complex than previously thought and suggests alternative anatomical substrates for consciousness. BOLD fluctuations do not seem to differ between patients and controls, suggesting that BOLD response represents an intrinsic feature of the signal, and therefore that spatial configuration is more important for consciousness than BOLD activation itself. Hum Brain Mapp 39:89-103, 2018. © 2017 Wiley Periodicals, Inc.

The repetition of behavioral assessments in diagnosis of disorders of consciousness.

OBJECTIVE: To determine whether repeated examinations using the Coma Recovery Scale-Revised (CRS-R) have an impact on diagnostic accuracy of patients with disorders of consciousness and to provide guidelines regarding the number of assessments required for obtaining a reliable diagnosis. METHODS: One hundred twenty-three adult patients with chronic disorders of consciousness were referred to our tertiary center. They were assessed at least six times with the CRS-R within a 10-day period. Clinical diagnoses based on one, two, three, four, and five Coma Recovery Scale-Revised assessments were compared with a reference diagnosis (ie, the highest behavioral diagnosis obtained after six evaluations) using nonparametric statistics. Results were considered significant at p < 0.05 corrected for multiple comparisons. RESULTS: The number of assessments had a significant effect on the clinical diagnosis. Up to the fourth examination, the diagnosis was still statistically different from the reference diagnosis based on six CRS-R assessments. Compared to this reference diagnosis, the first evaluation led to 36% of misdiagnoses. INTERPRETATION: The number of CRS-R assessments has an impact on the clinical diagnosis of patients with chronic disorders of consciousness. Up to the fourth examinations, behavioral fluctuations may still impact the diagnostic accuracy. We here suggest performing at least five assessments in each patient with disorders of consciousness within a short time interval (eg, 2 weeks) to reduce misdiagnosis. Ann Neurol 2017;81:883-889 Ann Neurol 2017;81:883-889.

A method for independent component graph analysis of resting-state fMRI.

INTRODUCTION: Independent component analysis (ICA) has been extensively used for reducing task-free BOLD fMRI recordings into spatial maps and their associated time-courses. The spatially identified independent components can be considered as intrinsic connectivity networks (ICNs) of non-contiguous regions. To date, the spatial patterns of the networks have been analyzed with techniques developed for volumetric data. OBJECTIVE: Here, we detail a graph building technique that allows these ICNs to be analyzed with graph theory. METHODS: First, ICA was performed at the single-subject level in 15 healthy volunteers using a 3T MRI scanner. The identification of nine networks was performed by a multiple-template matching procedure and a subsequent component classification based on the network "neuronal" properties. Second, for each of the identified networks, the nodes were defined as 1,015 anatomically parcellated regions. Third, between-node functional connectivity was established by building edge weights for each networks. Group-level graph analysis was finally performed for each network and compared to the classical network. RESULTS: Network graph comparison between the classically constructed network and the nine networks showed significant differences in the auditory and visual medial networks with regard to the average degree and the number of edges, while the visual lateral network showed a significant difference in the small-worldness. CONCLUSIONS: This novel approach permits us to take advantage of the well-recognized power of ICA in BOLD signal decomposition and, at the same time, to make use of well-established graph measures to evaluate connectivity differences. Moreover, by providing a graph for each separate network, it can offer the possibility to extract graph measures in a specific way for each network. This increased specificity could be relevant for studying pathological brain activity or altered states of consciousness as induced by anesthesia or sleep, where specific networks are known to be altered in different strength.