Near-Death Experiences : Real or imagined memories?

Poster (2010, September)

Resting-state auditory network in tinnitus: a fMRI study

Poster (2010, June)

Brain-computer interface in disorders of consciousness: answering simple questions with a P3 speller

Poster (2010, June)

Objective: In the recovery from coma, the acquisition of command following represents an important milestone, indicating emergence from the vegetative state (Schnakers et al., 2009). In some patients ... [more ▼]

Objective: In the recovery from coma, the acquisition of command following represents an important milestone, indicating emergence from the vegetative state (Schnakers et al., 2009). In some patients, recovery of consciousness may precede motor recovery. Brain-computer interfaces (BCI) might permit these patients to show non-motor dependent signs of awareness and in a next step might enable communication. This study aimed at testing to what extent an EEG-based BCI could help detecting signs of awareness and communication in disorders of consciousness. We employed a P300 based BCI where healthy volunteers and patients with locked-in syndrome and in a minimally conscious state were asked to answer yes or no to simple questions by paying attention to one out of four auditorily presented stimuli (‘yes’, ‘no’, ‘stop’, ‘go’). Methods: We studied 13 patients with a minimally conscious state (MCS, 5 TBI – 8 anoxic, mean time post injury 70±109 months; mean age 42 ± 21) and 2 in pseudo-coma or locked in syndrome (LIS; brainstem stroke, time post injury 26 and 46 months; aged 63 and 29)) and 16 healthy controls (aged 45±19). Patients were evaluated using the Coma Recovery Scale Revised (CRS-R). An auditory P300 four choice speller paradigm (Furdea et al., 2009) based on the BCI2000 system (Schalk et al., 2004) was used. 16-Channel EEG was recorded using a g.tec USBAmp amplifier. A trial constituted of 15 presentation of four sounds the order of presentation being pseudo-randomized (sound duration: ~400ms; inter-stimulus interval: ~600ms). After a training session of 4 trials, patients and healthy subjects were required to answer 10 or 12 questions, respectively. Questions were of the following kind: “Is your name Quentin?”, “Is your mother’s name Dorothée?”. A stepwise linear discriminant analysis based on the training session was used to classify the data and to provide online feedback. Offline, all training and testing sequences were pooled. Sequences with artifacts were discarded and a leave-one-out approach was used to classify the data. Results: Healthy subjects presented a mean correct response rate of 73% online and 93% offline. Note that online classification failed for one control subject due to a presumed change in cognitive strategy between training and testing sessions. LIS patients showed a correct response rate of 30 and 60% (online) and 36 and 79% (offline). Three MCS patients had a correct response rate of ≥50% offline (10, 18, 0% online and 50, 53, 57% offline). Two of these three patients did not show any command following at the bedside. The 10 remaining MCS cases showed online and offline correct answers <50% (mean 33±9% online and 25±13% offline). Conclusion: Our auditory P300-based BCI permitted functional interactive communication in 15/16 controls (online) and in all offline. Our data obtained in patients with locked-in syndrome and disorders of consciousness demonstrate the potential clinical usefulness of the technique following coma but also show lower accuracy in patients as compared to healthy volunteers. This might be due to fluctuating attentional levels and exhaustibility in the MCS and to the suboptimal EEG recording quality due to movement, ocular and respiration artifacts in these challenging patients. Further algorithmic developments should include automatic artifact detection and single trial classification. Despite the need for further improvement in BCI devices adapted to post-coma patients, our results already indicate that MCS patients without any clinical sign of command-following can employ a yes-no speller offering the hope of functional interactive communication and a possibility for decision making and autonomy. Bibliography Furdea A, Halder S, Krusienski DJ, Bross D, Nijboer F, Birbaumer N, Kübler A, 2009, An auditory oddball (P300) spelling system for brain-computer interfaces, Psychophysiology. May; 46(3):617-25. Schalk G., McFarland D.J., Hinterberger T., Birbaumer N., and Wolpaw J.R. 2004, BCI2000: A General-Purpose Brain-Computer Interface (BCI) System, IEEE Trans Biomed Eng, 51(6). Schnakers C, Vanhaudenhuyse A, Giacino J, Ventura; Boly M, Majerus S, Moonen G, Laureys S, 2009, Diagnostic accuracy of the vegetative and minimally conscious state: Clinical consensus versus standardized neurobehavioral assessment, BMC Neurology, 9 (35). [less ▲]

Near-death experiences: real or imagined?

Conference (2010, June)

Willful Modulation of Brain Activity in Disorders of Consciousness

in New England Journal of Medicine [=NEJM] (2010)

Coma et états apparentés

Scientific conference (2010)

The neural correlate of consciousness: lessons from coma related states

Scientific conference (2010)

Disorders of consciousness: Moving from passive to resting state and active paradigms

in Cognitive Neuroscience (2010), 1(1), 193203

Following coma, some patients will recover wakefulness without signs of consciousness (i.e., vegetative state) or may show nonreflexive movements but with no ability for functional communication (i.e ... [more ▼]

Following coma, some patients will recover wakefulness without signs of consciousness (i.e., vegetative state) or may show nonreflexive movements but with no ability for functional communication (i.e., minimally conscious state). Currently, there remains a high rate of misdiagnosis of the vegetative state. The increasing use of fMRI and EEG tools permits the clinical characterization of these patients to be improved. We first discuss “resting metabolism” and “passive activation” paradigms, used in neuroimaging and evoked potential studies, which merely identify neural activation reflecting “automatic” processing—that is, occurring without the patient’s willful intervention. Secondly, we present an alternative approach consisting of instructing subjects to imagine well-defined sensorymotor or cognitive-mental actions. This strategy reflects volitional neural activation and, hence, witnesses awareness. Finally, we present results on blood-oxgen-level-dependent “default mode network”/resting state studies that might be a promising tool in the diagnosis of these challenging patients. [less ▲]

La Sensory Modality Assessment and Rehabilitation Technique (SMART) : une echelle comportementale d'evaluation et de revalidation pour des etats alteres de conscience.

in Revue Neurologique (2010), 166(8-9), 675-82

INTRODUCTION: Difficulties in detecting bedside signs of consciousness in non-communicative patients still lead to a high rate of misdiagnosis illustrating the need to employ standardized behavioral ... [more ▼]

INTRODUCTION: Difficulties in detecting bedside signs of consciousness in non-communicative patients still lead to a high rate of misdiagnosis illustrating the need to employ standardized behavioral assessment scales. STATE OF ART: The Sensory Modality Assessment and Rehabilitation Technique (SMART) is a behavioral assessment scale of consciousness that assesses responses to multimodal sensory stimulation in disorders of consciousness. These stimulations can also be considered to have therapeutic value. PERSPECTIVES: We here review the different components and use of the SMART assessment and discuss its validity, reliability, and robustness in clinical practice. The scale has a high intra- and inter-observer reliability thanks to a detailed procedure description. However, in the absence of objective gold standards in the assessment of consciousness, it is currently difficult to make strong claims about its validity. A comparison between SMART and other standardized and validated coma-scales is proposed. CONCLUSION: In our view, SMART is an interesting tool for monitoring patients with altered states of consciousness subsequent to coma. Currently, we await studies on its concurrent validity as compared to other validated behavioral assessment scales and on the effect of SMART stimulations on patient outcome. [less ▲]

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

in Brain : A Journal of Neurology (2010), 133(Pt 1), 161-71

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 ... [more ▼]

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. [less ▲]