Audrey Vanhaudenhuyse

Willful Modulation of Brain Activity in Disorders of Consciousness

BACKGROUNDnThe differential diagnosis of disorders of consciousness is challenging. The rate of misdiagnosis is approximately 40%, and new methods are required to complement bedside testing, particularly if the patients capacity to show behavioral signs of awareness is diminished.nnnMETHODSnAt two major referral centers in Cambridge, United Kingdom, and Liege, Belgium, we performed a study involving 54 patients with disorders of consciousness. We used functional magnetic resonance imaging (MRI) to assess each patients ability to generate willful, neuroanatomically specific, blood-oxygenation-level-dependent responses during two established mental-imagery tasks. A technique was then developed to determine whether such tasks could be used to communicate yes-or-no answers to simple questions.nnnRESULTSnOf the 54 patients enrolled in the study, 5 were able to willfully modulate their brain activity. In three of these patients, additional bedside testing revealed some sign of awareness, but in the other two patients, no voluntary behavior could be detected by means of clinical assessment. One patient was able to use our technique to answer yes or no to questions during functional MRI; however, it remained impossible to establish any form of communication at the bedside.nnnCONCLUSIONSnThese results show that a small proportion of patients in a vegetative or minimally conscious state have brain activation reflecting some awareness and cognition. Careful clinical examination will result in reclassification of the state of consciousness in some of these patients. This technique may be useful in establishing basic communication with patients who appear to be unresponsive.

Improving EEG-BCI analysis for low certainty subjects by using dictionary learning

The diagnosis of patients with Disorders Of Consciousness represents a challenge in the clinical routine. Recently, Brain Computer Interfaces based in Electroencephalography (EEG-BCI) have been used to detect signs of consciousness in these patients. This approach allows to discover brain responses to command following. Nevertheless, a reliable BCI strategy must to be able to determine the commands with high levels of certainty. Current results reported in the literature evidence that about 25% of the subjects in which BCI is used may have low performances, near to the chance level, even in collaborating subjects. In this work, we propose a novel approach based on dictionary learning representations aimed to improve performance in low certainty subjects. We propose to introduce an intermediate representation scheme, based on sparse dictionaries, before feature selection step. Our main assumption is that by using these representations we can capture more efficiently the EEG signal structure for subjects responses. The results show that using the new representation the weighted average performance in command following outcome the previews proposed methods of 64.8% to 67.9%. Higher improvements in performance were obtained for low certainly subjects. Our results suggests that this approach may improve BCI-EEG performance in low certainly subjects.