A protocol for a multicenter randomized and personalized controlled trial using rTMS in patients with disorders of consciousness.

Background: Improving the functional recovery of patients with DoC remains one of the greatest challenges of the field. Different theories exist about the role of the anterior (prefrontal areas) versus posterior (parietal areas) parts of the brain as hotspots for the recovery of consciousness. Repetitive transcranial magnetic stimulation (rTMS) is a powerful non-invasive brain stimulation technique for the treatment of DoC. However, a direct comparison of the effect of TMS treatment on the front versus the back of the brain has yet to be performed. In this study, we aim to assess the short- and long-term effects of frontal and parietal rTMS on DoC recovery and characterize responders phenotypically. Methods/design: Ninety patients with subacute and prolonged DoC will be included in a two-part multicenter prospective study. In the first phase (randomized controlled trial, RCT), patients will undergo four rTMS sessions in a crossover design over 10 days, targeting (i) the left dorsolateral prefrontal cortex (DLPFC) and (ii) the left angular gyrus (AG), as well as (iii & iv) their sham alternatives. In the second phase (longitudinal personalized trial), patients will receive personalized stimulations for 20 working days targeting the brain area that showed the best results in the RCT and will be randomly assigned to either active or sham intervention. The effects of rTMS on neurobehavioral and neurophysiological functioning in patients with DoC will be evaluated using clinical biomarkers of responsiveness (i.e., the Coma Recovery Scale-Revised; CRS-R), and electrophysiological biomarkers (e.g., power spectra, functional and effective connectivity, perturbational complexity index before and after intervention). Functional long-term outcomes will be assessed at 3 and 6 months post-intervention. Adverse events will be recorded during the treatment phase. Discussion: This study seeks to identify which brain region (front or back) is best to stimulate for the treatment of patients with DoC using rTMS, and to characterize the neural correlates of its action regarding recovery of consciousness and functional outcome. In addition, we will define the responders' profile based on patients' characteristics and functional impairments; and develop biomarkers of responsiveness using EEG analysis according to the clinical responsiveness to the treatment. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT04401319, Clinicaltrials.gov, n° NCT04401319.

Sham-controlled randomized multicentre trial of transcranial direct current stimulation for prolonged disorders of consciousness.

BACKGROUND AND PURPOSE: Transcranial direct current stimulation (tDCS) has been shown to improve signs of consciousness in a subset of patients with disorders of consciousness (DoC). However, no multicentre study confirmed its efficacy when applied during rehabilitation. In this randomized controlled double-blind study, the effects of tDCS whilst patients were in rehabilitation were tested at the group level and according to their diagnosis and aetiology to better target DoC patients who might repond to tDCS. METHODS: Patients received 2 mA tDCS or sham applied over the left prefrontal cortex for 4 weeks. Behavioural assessments were performed weekly and up to 3 months' follow-up. Analyses were conducted at the group and subgroup levels based on the diagnosis (minimally conscious state [MCS] and unresponsive wakefulness syndrome) and the aetiology (traumatic or non-traumatic). Interim analyses were planned to continue or stop the trial. RESULTS: The trial was stopped for futility when 62 patients from 10 centres were enrolled (44 ± 14 years, 37 ± 24.5 weeks post-injury, 18 women, 32 MCS, 39 non-traumatic). Whilst, at the group level, no treatment effect was found, the subgroup analyses at 3 months' follow-up revealed a significant improvement for patients in MCS and with traumatic aetiology. CONCLUSIONS: Transcranial direct current stimulation during rehabilitation does not seem to enhance patients' recovery. However, diagnosis and aetiology appear to be important factors leading to a response to the treatment. These findings bring novel insights into possible cortical plasticity changes in DoC patients given these differential results according to the subgroups of patients.

Stability of mental motor-imagery classification in EEG depends on the choice of classifier model and experiment design, but not on signal preprocessing.

Introduction: Modern consciousness research has developed diagnostic tests to improve the diagnostic accuracy of different states of consciousness via electroencephalography (EEG)-based mental motor imagery (MI), which is still challenging and lacks a consensus on how to best analyse MI EEG-data. An optimally designed and analyzed paradigm must detect command-following in all healthy individuals, before it can be applied in patients, e.g., for the diagnosis of disorders of consciousness (DOC). Methods: We investigated the effects of two important steps in the raw signal preprocessing on predicting participant performance (F1) and machine-learning classifier performance (area-under-curve, AUC) in eight healthy individuals, that are based solely on MI using high-density EEG (HD-EEG): artifact correction (manual correction with vs. without Independent Component Analysis [ICA]), region of interest (ROI; motor area vs. whole brain), and machine-learning algorithm (support-vector machine [SVM] vs. k-nearest neighbor [KNN]). Results: Results revealed no significant effects of artifact correction and ROI on predicting participant performance (F1) and classifier performance (AUC) scores (all ps > 0.05) in the SVM classification model. In the KNN model, ROI had a significant influence on the classifier performance [F(1,8.939) = 7.585, p = 0.023]. There was no evidence for artifact correction and ROI selection changing the prediction of participants performance and classifier performance in EEG-based mental MI if using SVM-based classification (71-100% correct classifications across different signal preprocessing methods). The variance in the prediction of participant performance was significantly higher when the experiment started with a resting-state compared to a mental MI task block [X2(1) = 5.849, p = 0.016]. Discussion: Overall, we could show that classification is stable across different modes of EEG signal preprocessing when using SVM models. Exploratory analysis gave a hint toward potential effects of the sequence of task execution on the prediction of participant performance, which should be taken into account in future studies.

Informal Caregivers of Patients with Disorders of Consciousness: a Qualitative Study of Communication Experiences and Information Needs with Physicians.

Due to improvements in medicine, the figures of patients with disorders of consciousness (DoC) are increasing. Diagnostics of DoC and prognostication of rehabilitation outcome is challenging but necessary to evaluate recovery potential and to decide on treatment options. Such decisions should be made by doctors and patients' surrogates based on medico-ethical principles. Meeting information needs and communicating effectively with caregivers as the patients´ most common surrogate-decision makers is crucial, and challenging when novel tech-nologies are introduced. This qualitative study aims to explore information needs of informal DoC caregivers, how they manage the obtained information and their perceptions and experiences with caregiver-physician communication in facilities that implemented innovative neurodiagnostics studies. In 2021, we conducted semi-structured interviews with nine caregivers of clinically stable DoC patients in two rehabilitation centers in Italy and Germany. Participants were selected based on consecutive purposeful sampling. Caregivers were recruited at the facilities after written informed consent. All interviews were recorded, transcribed verbatim and translated. For analysis, we used reflexive thematic analysis according to Braun & Clarke (2006). Caregivers experienced the conversations emotionally, generally based on the value of the information provided. They reported to seek positive information, comfort and empathy with-in the communication of results of examinations. They needed detailed information to gain a deep understanding and a clear picture of their loved-one's condition. The results suggest a mismatch between the perspectives of caregivers and the perspectives of medical profession-als, and stress the need for more elaborate approaches to the communication of results of neu-rodiagnostics studies. Supplementary Information: The online version contains supplementary material available at 10.1007/s12152-022-09503-0.

Transcranial Pulsed-Current Stimulation versus Transcranial Direct Current Stimulation in Patients with Disorders of Consciousness: A Pilot, Sham-Controlled Cross-Over Double-Blind Study.

Transcranial direct-current stimulation (tDCS) over the prefrontal cortex can improve signs of consciousness in patients in a minimally conscious state. Transcranial pulsed-current stimulation (tPCS) over the mastoids can modulate brain activity and connectivity in healthy controls. This study investigated the feasibility of tPCS as a therapeutic tool in patients with disorders of consciousness (DoC) and compared its neurophysiological and behavioral effects with prefrontal tDCS. This pilot study was a randomized, double-blind sham-controlled clinical trial with three sessions: bi-mastoid tPCS, prefrontal tDCS, and sham. Electroencephalography (EEG) and behavioral assessments were collected before and after each stimulation session. Post minus pre differences were compared using Kruskal-Wallis and Wilcoxon signed-rank tests. Twelve patients with DoC were included in the study (eight females, four traumatic brain injury, 50.3 ± 14 y.o., 8.8 ± 10.5 months post-injury). We did not observe any side-effects following tPCS, nor tDCS, and confirmed their feasibility and safety. We did not find a significant effect of the stimulation on EEG nor behavioral outcomes for tPCS. However, consistent with prior findings, our exploratory analyses suggest that tDCS induces behavioral improvements and an increase in theta frontal functional connectivity.

Spontaneous eye blinking as a diagnostic marker in prolonged disorders of consciousness.

Clinical diagnosis of patients with prolonged disorders of consciousness is very challenging. As spontaneous eye blink rate (EBR) is reliably correlated with cognitive activity in healthy individuals, we investigated whether EBR could serve as a marker of patients' level of consciousness. We assessed ten patients in prolonged Vegetative State/Unresponsive Wakefulness Syndrome (VS/UWS; three females; mean age = 50.3 ± 17.8 years) and fourteen patients in Minimally Conscious State (MCS; three females; mean age = 52.9 ± 17.5 years) at their admission to a rehabilitation unit after the acute phase. During two separate 3-min rest conditions, we recorded patients' EBR by integrating on-line visual and off-line electro-oculographic count. We also assessed EBR during two auditory oddball tasks, i.e. passive listening and active counting of target tones in a sub-group of patients. EBR was significantly higher in MCS than in VS/UWS; moreover, EBR positively correlated with a validated index of responsiveness derived from the Coma Recovery Scale-Revised. Patients' mean EBR showed no significant differences within sessions and across experimental conditions of the oddball task, in both VS/UWS and MCS. Our findings suggest that, at least in the post-acute phase, observing patients' EBR for 3 min at rest could help to discriminate between VS/UWS and MCS, improving accuracy of clinical diagnosis.

Outcome prediction with serial neuron-specific enolase and machine learning in anoxic-ischaemic disorders of consciousness.

BACKGROUND: The continuation of life-sustaining therapy in critical care patients with anoxic-ischemic disorders of consciousness (AI-DOC) depends on prognostic tests such as serum neuron-specific enolase (NSE) concentration levels. OBJECTIVES: To apply predictive models using machine learning methods to examine, one year after onset, the prognostic power of serial measurements of NSE in patients with AI-DOC. To compare the discriminative accuracy of this method to both standard single-day, absolute, and difference-between-days, relative NSE levels. METHODS: Classification algorithms were implemented and K-nearest neighbours (KNN) imputation was used to avoid complete case elimination of patients with missing NSE values. Non-imputed measurements from Day 0 to Day 6 were used for single day and difference-between-days. RESULTS: The naive Bayes classifier on imputed serial NSE measurements returned an AUC of (0.81±0.07) for n=126 patients (100 poor outcome). This was greater than logistic regression (0.73±0.08) and all other classifiers. Naive Bayes gave a specificity and sensitivity of 96% and 49%, respectively, for an (uncalibrated) probability decision threshold of 90%. The maximum AUC for a single day was Day 3 (0.75) for a subset of n=79 (61 poor outcome) patients, and for differences between Day 1 and Day 4 (0.81) for a subset of n=46 (39 poor outcome) patients. CONCLUSION: Imputation avoided the elimination of patients with missing data and naive Bayes outperformed all other classifiers. Machine learning algorithms could detect automatically discriminatory features and the overall predictive power increased from standard methods due to the larger data set. CODE AVAILABILITY: Data analysis code is available under GNU at: https://github.com/emilymuller1991/outcome_prediction_nse.

Consciousness Indexing and Outcome Prediction with Resting-State EEG in Severe Disorders of Consciousness.

We applied the following methods to resting-state EEG data from patients with disorders of consciousness (DOC) for consciousness indexing and outcome prediction: microstates, entropy (i.e. approximate, permutation), power in alpha and delta frequency bands, and connectivity (i.e. weighted symbolic mutual information, symbolic transfer entropy, complex network analysis). Patients with unresponsive wakefulness syndrome (UWS) and patients in a minimally conscious state (MCS) were classified into these two categories by fitting and testing a generalised linear model. We aimed subsequently to develop an automated system for outcome prediction in severe DOC by selecting an optimal subset of features using sequential floating forward selection (SFFS). The two outcome categories were defined as UWS or dead, and MCS or emerged from MCS. Percentage of time spent in microstate D in the alpha frequency band performed best at distinguishing MCS from UWS patients. The average clustering coefficient obtained from thresholding beta coherence performed best at predicting outcome. The optimal subset of features selected with SFFS consisted of the frequency of microstate A in the 2-20 Hz frequency band, path length obtained from thresholding alpha coherence, and average path length obtained from thresholding alpha coherence. Combining these features seemed to afford high prediction power. Python and MATLAB toolboxes for the above calculations are freely available under the GNU public license for non-commercial use ( https://qeeg.wordpress.com ).

Jigsaw Puzzles As Cognitive Enrichment (PACE) - the effect of solving jigsaw puzzles on global visuospatial cognition in adults 50 years of age and older: study protocol for a randomized controlled trial.

BACKGROUND: Neurocognitive disorders are an important societal challenge and the need for early prevention is increasingly recognized. Meta-analyses show beneficial effects of cognitive activities on cognition. However, high financial costs, low intrinsic motivation, logistic challenges of group-based activities, or the need to operate digital devices prevent their widespread application in clinical practice. Solving jigsaw puzzles is a cognitive activity without these hindering characteristics, but cognitive effects have not been investigated yet. With this study, we aim to evaluate the effect of solving jigsaw puzzles on visuospatial cognition, daily functioning, and psychological outcomes. METHODS: The pre-posttest, assessor-blinded study will include 100 cognitively healthy adults 50 years of age or older, who will be randomly assigned to a jigsaw puzzle group or a cognitive health counseling group. Within the 5-week intervention period, participants in the jigsaw puzzle group will engage in 30 days of solving jigsaw puzzles for at least 1 h per day and additionally receive cognitive health counseling. The cognitive health counseling group will receive the same counseling intervention but no jigsaw puzzles. The primary outcome, global visuospatial cognition, will depict the average of the z-standardized performance scores in visuospatial tests of perception, constructional praxis, mental rotation, processing speed, flexibility, working memory, reasoning, and episodic memory. As secondary outcomes, we will assess the eight cognitive abilities, objective and subjective visuospatial daily functioning, psychological well-being, general self-efficacy, and perceived stress. The primary data analysis will be based on mixed-effects models in an intention-to-treat approach. DISCUSSION: Solving jigsaw puzzles is a low-cost, intrinsically motivating, cognitive leisure activity, which can be executed alone or with others and without the need to operate a digital device. In the case of positive results, these characteristics allow an easy implementation of solving jigsaw puzzles in clinical practice as a way to improve visuospatial functioning. Whether cognitive impairment and loss of independence in everyday functioning might be prevented or delayed in the long run has to be examined in future studies. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02667314 . Registered on 27 January 2016.