SUpporting well-being through PEeR-Befriending (SUPERB) trial: an exploration of fidelity in peer-befriending for people with aphasia

Assessing the evolution of severely brain-injured patients with disorders of consciousness (DOC) with current tools like the Glasgow Outcome Scale-Extended (GOS-E) remains a challenge. At the bedside, the most reliable diagnostic tool is currently the Coma Recovery Scale-Revised. The CRS-R distinguishes patients with unresponsive wakefulness syndrome (UWS) from patients in minimally conscious state (MCS) and patients who have emerged from MCS (EMCS). This international multi-centric study aims to validate a phone outcome questionnaire (POQ) based on the CRS-R and compare it to the CRS-R performed at the bedside and to the GOS-E which evaluates the level of disability and assigns patient’s in outcomes categories. The POQ will allow clinicians to probe the evolution of patient’s state of consciousness based on caregivers feedback. This research project is part of the International Brain Injury Association, Disorders of Consciousness-Special Interest Group (DOCSIG) and DOCMA consortium

Single tDCS session of motor cortex in patients with disorders of consciousness: a pilot study

peer reviewedPrimary Objective: Patients with disorders of consciousness (DOC) face a lack of treatments and risk ofmisdiagnosis, potentially due to motor impairment. Transcranial direct current stimulation (tDCS)showed promising results over the prefrontal cortex in DOC and over the primary motor cortex (M1)in stroke. Tis pilot study aimed at evaluating the behavioral effects of M1 tDCS in patients with DOC.Research Design: In this randomized double-blind sham-controlled crossover trial, we included 10patients (49 ± 22 years, 7 ± 13 months since injury, 4 unresponsive wakefulness syndrome, 6 minimallyconscious state, 5 traumatic etiologies).Methods and Procedures: One session of tDCS (2 mA for 20 min) and one session of sham tDCS wereapplied over M1 in a randomized order with a washout period of minimum 24 h and behavioral effectswere assessed using the CRS-R. At the group level, no treatment effect was identified on the total score(p= .55) and on the motor subscale (p= .75). Two patients responded to tDCS by showing a new sign ofconsciousness (visual pursuit and object localization).Conclusions: One session of M1 tDCS failed to improve behavioral responsiveness in patients with DOC.Other application strategies should be tested

From unconscious to conscious: a spectrum of states

peer reviewe

Un protocole IRM 3T pour la recherche et la pratique clinique en moins de 30 minutes

In the past decades, non-invasive neuroimaging allowed significant progress in the understanding of all neuroscientific domains from neuroanatomy to neurofunctional connectomes. Magnetic resonance imagery (MRI) is an ubiquitous tool nowadays, being available in most hospitals, which allows a wide array of imaging contrasts, from structural anatomy and connectivity, to functional connectivity and blood flow imaging. Although MRI is widely used for both clinical and research purposes, the relative protocols are often very different, to fulfill different and seemingly irreconcilable needs: clinical pertinence and time efficiency in the clinical setting with a total acquisition time often strictly restricted below 30 minutes per patient slot, whereas research MRI has more flexibility and more cutting-edge needs, with total acquisition time reaching up to 2h for a single subject, allowing to acquire complex sequences like multi-shell DTI. Furthermore, the targeted populations are often fundamentally different: uncooperative or motor uncontrolling patients for clinical are quite common, inducing motion artifacts, patient discomfort or even panic if the acquisition is too long, whereas research often is done on healthy volunteers who well understand the study instructions and can remain still for a long period of time. During this session, we will present a new 3T MRI protocol that can be used for both clinical and research purposes, and which has been successfully applied on uncollaborative and very motion prone patients with disorders of consciousness. This protocol is acquired in less than 30 minutes, and can thus be implemented on a clinical machine. The acquisition speed also reduces motion artifacts. We will demonstrate how we implemented and cursorily analyzed the sequences of this protocol, including multi-shell DTI for structural connectivity without T1 constraints, FLAWS MP2RAGE for physiologically segmented grey and white matter without computional approximations and sub-second EPI BOLD for dynamic functional connectivity analyses, as well as SWI MIP and FLAIR for clinical purposes or lesional studies. Furthermore, meta-protocol procedures will be described to support and enhance acquisitions in uncooperative patients, such as the use of innovative 3D axis motion reducing pillows like the Pearltec MultiPad and the importance of protocol programming such as sequence ordering and name changing on if-conditions. We hope this state-of-the-art protocol will allow clinicians and researchers alike to consider new opportunities in the optimization of MRI protocols as a mean to push beyond this seeming dichotomy

Residual implicit versus explicit language abilities in patients with disorders of consciousness

peer reviewe

Behavioral signs of recovery from unresponsive wakefulness syndrome to emergence of minimally conscious state after severe brain injury

[EN] Precise description of behavioral signs denoting transition from unresponsive wakefulness syndrome/vegetative state (UWS/VS) to minimally conscious state (MCS) or emergence from MCS after severe brain injury is crucial for prognostic purposes. A few studies have attempted this goal but involved either non-standardized instruments, limited temporal accuracy or samples, or focused on (sub)acute patients. The objective of this study was to describe the behavioral signs that led to a change of diagnosis, as well as the factors influencing this transition, in a large sample of patients with chronic disorders of consciousness after severe brain injury. In this retrospective cohort study, 185 patients in UWS/VS or MCS were assessed with the Coma Recovery Scale Revised (CRS-R) five times within the two weeks following their admission to a neurorehabilitation center and then weekly until emergence from MCS, discharge or death. Of these 185 patients, 33 patients in UWS/VS and 45 patients in MCS transitioned to another state. Transition to MCS was mostly denoted by one behavioral sign (72%), predominantly visual fixation (57%), followed by localization to noxious stimulation (27%), visual pursuit (21%) and object manipulation (12%), and could be predicted by etiology, time post-injury and age. Emergence from MCS was characterized by one sign in 64% of patients and by two signs (functional communication and objects use) in the remaining cases, and could be predicted by time post-injury and number of behavioral signs at admission. Clinicians should be therefore advised to pay particular attention to visual and motor subscales of the CRS-R to detect behavioral recovery.This work was supported by the European Union's Horizon 2020 research and innovation programme under the Marie Sk¿odowska-Curie (Grant Agreement No. 778234) and by Conselleria de Educación, Investigación, Cultura y Deporte of Generalitat Valenciana (Project SEJI/2019/017)Carrière, M.; Llorens Rodríguez, R.; Navarro, MD.; Olaya, J.; Ferri, J.; Noé, E. (2022). Behavioral signs of recovery from unresponsive wakefulness syndrome to emergence of minimally conscious state after severe brain injury. Annals of Physical and Rehabilitation Medicine. 65(2):1-7. https://doi.org/10.1016/j.rehab.2021.101534S1765

Brain functional network segregation and integration in patients with disorders of consciousness

Introduction: The brain regulates information flow by balancing integration and segregation of networks to facilitate flexible cognition and behavior. However, it is unclear how this mechanism manifests during loss of consciousness [1-3]. In this study, we studied brain network segregation and integration using resting state functional magnetic resonance imaging (fMRI) data to assess brain networks in patients with disorders of consciousness. Methods: Fifty-four patients with disorders of consciousness (24 unresponsive wakefulness syndrome (UWS) (M:F=16:8; mean age= 45±13), 30 minimally conscious state (MCS) (M:F=23:7; mean age= 36±14) and 30 age- and gender-matched healthy controls underwent fMRI. The resting-state MRI data were acquired during wakefulness with eyes closed using a 3 Tesla MRI scanner. Additionally a T1-weighted, structural imaging was performed for anatomical coregistration. First, the fMRI data were pre-processed for realignment, co-registration, segmentation, normalization, head motion regressed out and 0.01-0.1Hz band pass filtered. Data were then parcellated in 256 brain regions (ROIs) using Shen functional atlas from [4]. The connectivity matrix was computed using Pearson correlation. Graph theory connectivity was carried out to measure brain network topological properties in terms of network segregation and integration by computing binarized undirected connectivity matrix. Normalized clustering coefficients were computed as measures of network segregation while normalized participation coefficients were computed as measures of network integration [3]. Through integrated nodal graph measures, individual networks (such as default mode, frontoparietal, auditory, salience, subcortical and cerebellum networks) were also computed to study which networks were predominantly affected [3]. To enable comparison of network properties across groups, we used sparsity-based threshold (S) to avoid spurious results. To prevent biases associated with a single threshold, we determined a range of sparsity (0.06 ≤ S ≤ 0.5, with an increment of 0.025), which avoids excess network fragmentation at sparser thresholds. The between group differences for global (i.e., whole brain) and individual networks were computed with unpaired t-test with FDR correction for multiple comparison [5-6]. Finally the network segregation and integration mean values were correlated with Coma Recovery Scale-Revised (CRS-R) modified score [7]. Results: Patients in UWS had decreased participation coefficients (network integration) compared to those in MCS (effect size= -0.44, p<0.0001) and controls (effect size= -0.63, p<0.0001). Patients in MCS had significant decreased participation coefficients compared to controls (effect size= -0.37, p<0.001). On the other hand, patients in UWS had significant increased clustering coefficient (network segregation) compared to those in MCS (effect size= 0.39, p= <0.001) and controls (effect size= 0.63, p<0.0001). Patients in MCS had significant increased clustering coefficients compared to controls (effect size= 0.03, p<0.01). This decreased participation coefficient and increased clustering coefficient were noted predominantly observed in the frontoparietal and subcortical networks. Conclusions: Patients with disorders of consciousness present decreased in network integration and increased in network segregation. Notably, fragmentation of network integration is observed in patients in unaware patients (UWS), which indicates impaired information flow in the brain modules, especially in the frontoparietal and subcortical networks. This introduces a potential measure to classify patients with disorders of consciousness, which could ultimately be used for clinical diagnosis. Reference: 1. Fukushima, M., (2018). Structure–function relationships during segregated and integrated network states of human brain functional connectivity. Brain Structure and Function, 223(3), 1091-1106. 2. Deco, G., (2015). Rethinking segregation and integration: contributions of whole-brain modelling. Nature Reviews Neuroscience, 16(7), 430. 3. Keerativittayayut, R., (2018). Large-scale network integration in the human brain tracks temporal uctuations in memory encoding performance. eLife, 7, e32696. 4. Finn, E. S., (2015). Functional connectome ngerprinting: identifying individuals using patterns of brain connectivity. Nature neuroscience, 18(11), 1664. 5. Holla, B., (2017). Disrupted resting brain graph measures in individuals at high risk for alcoholism. Psychiatry Research: Neuroimaging, 265, 54-64. 6. Chennu, S., (2017). Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness. Brain, 140(8), 2120-2132. 7. Demertzi, A., (2015). Intrinsic functional connectivity differentiates minimally conscious from unresponsive patients. Brain, 138(9), 2619-2631.Multimodal Neuroimaging in alter state of Consciousnes