Mismatch negativity to patient's own name in chronic disorder of consciousness

Laboratory for Higher Brain Functions, Institute of Psychology, Chinese Academy of Sciences, 4A Datun Road, Chaoyang District, Beijing 100101, China.
Neuroscience Letters (Impact Factor: 2.03). 10/2008; 448(1):24-8. DOI: 10.1016/j.neulet.2008.10.029
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Previous studies implicated potential value of mismatch negativity (MMN) in predicting recovery of consciousness in patients with disorders of consciousness (DOC). We have adopted a novel MMN evoked by subject's own name (SON), a self-referential stimulus thought to be powerful in evoking residual brain activity, and examined the correlation between the MMN and recovery of consciousness in patients with chronic (>1 month) DOC. Twelve patients and 12 age-matched healthy controls were investigated. The patients were diagnosed as coma (n=4), vegetative state (VS, n=6), and minimally conscious state (MCS, n=2), mainly based on the JFK Coma Recovery Scale-Revised. The SON-evoked MMN (SON-MMN) was present in seven patients. Critically, the presence of SON-MMN was significantly correlated with recovery of consciousness. While four of the five patients (three VS and two coma) showing SON-MMN changed to MCS 3 months later, the rest of the patients (three VS and two coma) without SON-MMN failed to show any clinical improvement. Our study thus illustrates that the subject's own name is effective in evoking MMN in patients with DOC, and that SON-MMN has potential prognostic values in predicting recovery of consciousness.

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    • "A full P300 response consists of two underlying components (P3a and P3b), which are thought to reflect attention switching and subsequent context updating (Polich, 2007). Memory: Several groups have utilized name stimuli in an oddball paradigm to evoke responses indicative of own name recognition in brain-injured patients (Cavinato et al., 2011; Fischer et al., 2008, 2010; Marosi et al., 1993; Mazzini et al., 2001; Perrin et al., 2006; Qin et al., 2008; Schnakers et al., 2008). When the subject's own name is presented with low probability in a sequence of unfamiliar names, recognition of one's own name is associated with a P300 (Perrin et al., 1999, 2006). "
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    ABSTRACT: Event-related potentials (ERPs) may provide a non-invasive index of brain function for a range of clinical applications. However, as a lab-based technique, ERPs are limited by technical challenges that prevent full integration into clinical settings. To translate ERP capabilities from the lab to clinical applications, we have developed methods like the Halifax Consciousness Scanner (HCS). HCS is essentially a rapid, automated ERP evaluation of brain functional status. The present study describes the ERP components evoked from auditory tones and speech stimuli. ERP results were obtained using a 5-minute test in 100 healthy individuals. The HCS sequence was designed to evoke the N100, the mismatch negativity (MMN), P300, the early negative enhancement (ENE), and the N400. These components reflected sensation, perception, attention, memory, and language perception, respectively. Component detection was examined at group and individual levels, and evaluated across both statistical and classification approaches. All ERP components were robustly detected at the group level. At the individual level, nonparametric statistical analyses showed reduced accuracy relative to support vector (SVM) machine classification, particularly for speech-based ERPs. Optimized SVM results were MMN: 95.6%; P300: 99.0%; ENE: 91.8%; and N400: 92.3%. A spectrum of individual-level ERPs can be obtained in a very short time. Machine learning classification improved detection accuracy across a large healthy control sample. Translating ERPs into clinical applications is increasingly possible at the individual-level. Copyright © 2015. Published by Elsevier B.V.
    Journal of Neuroscience Methods 04/2015; DOI:10.1016/j.jneumeth.2015.02.008 · 2.05 Impact Factor
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    • "Another line of research has studied neural response to sensory stimulation in DOC patients. The presence of a mismatch-negativity EEG component, indicating neural detection of deviant stimuli, has been demonstrated in DOC patients and shown to coincide with the return of functional communication (Kane et al. 1993; Fischer et al. 1999, 2010; Kotchoubey et al. 2003; Wijnen et al. 2007; Qin et al. 2008). The P300 EEG component, elicited to infrequent stimuli as well as stimuli relevant to a particular participant or task, has likewise been demonstrated in DOC populations and often tracks patient recovery (Kotchoubey et al. 2001; Cavinato et al. 2009; Faugeras et al. 2012). "
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    ABSTRACT: Background It is well established that some patients who are diagnosed as being in a vegetative state or a minimally conscious state show reliable signs of volition that may only be detected by measuring neural responses. A pertinent question is whether these patients are capable of higher cognitive processes.Methods Here, we develop a series of EEG paradigms that probe several core aspects of cognition at the bedside without the need for motor responses and explore the sensitivity of this approach in a group of healthy controls.ResultsUsing analysis of ERPs alone, this method can determine with high reliability whether individual participants are able to attend a stimulus stream, maintain items in working memory, or solve complex grammatical reasoning problems.Conclusion We suggest that this approach could form the basis of a brain-based battery for assessing higher cognition in patients with severe motor impairments or disorders of consciousness.
    Brain and Behavior 03/2015; 5(6). DOI:10.1002/brb3.336 · 2.24 Impact Factor
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    • "In our paradigm, the differences between local standard and local deviant stimuli were similar in all attentional conditions and essentially driven by local transitional probabilities. As we already mentioned, previous studies showed that the MMN can be observed in inattentive conditions [11], [12], [13], [14] or in non-conscious patients [12], [13], [15], [16], [17], [18], [19], [20], [21]. However, there is also evidence that the MMN can be modulated by several factors such as attention for instance [11], [14], [62], [63] or by the history of trials [64], [65], [66]. "
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    ABSTRACT: How does the human brain extract regularities from its environment? There is evidence that short range or ‘local’ regularities (within seconds) are automatically detected by the brain while long range or ‘global’ regularities (over tens of seconds or more) require conscious awareness. In the present experiment, we asked whether participants’ attention was needed to acquire such auditory regularities, to detect their violation or both. We designed a paradigm in which participants listened to predictable sounds. Subjects could be distracted by a visual task at two moments: when they were first exposed to a regularity or when they detected violations of this regularity. MEG recordings revealed that early brain responses (100-130 ms) to violations of short range regularities were unaffected by visual distraction and driven essentially by local transitional probabilities. Based on global workspace theory and prior results, we expected that visual distraction would eliminate the long range global effect, but unexpectedly, we found the contrary, i.e. late brain responses (300-600 ms) to violations of long range regularities on audio-visual trials but not on auditory only trials. Further analyses showed that, in fact, visual distraction was incomplete and that auditory and visual stimuli interfered in both directions. Our results show that conscious, attentive subjects can learn the long range dependencies present in auditory stimuli even while performing a visual task on synchronous visual stimuli. Furthermore, they acquire a complex regularity and end up making different predictions for the very same stimulus depending on the context (i.e. absence or presence of visual stimuli). These results suggest that while short-range regularity detection is driven by local transitional probabilities between stimuli, the human brain detects and stores long-range regularities in a highly flexible, context dependent manner.
    PLoS ONE 09/2014; In Press(9). DOI:10.1371/journal.pone.0107227 · 3.23 Impact Factor
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