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| . Wavelet-based localization of oscillatory and synchronous sources in Brainstorm | . '''Wavelet-based localization of oscillatory and synchronous sources in Brainstorm''' . ''Brain Entropy in space and time (BEst) is a software toolbox dedicated to localizing the neural sources of electromagnetic activity recorded over the scalp. It implements an inverse problem solving technique based on the statistical entropy, called Maximum Entropy on the Mean (MEM, Amblard et al., 2004). In addition, BEst also implements time-frequency decomposition tools based on discrete and continuous wavelets. In conjunction with the MEM, these spectral decompositions are used to localize specifically neural sources of oscillatory activity (Lina et al., 2012) and also networks of synchronous sources (Zerouali et al., 2011).''<<BR>>''BEst is distributed as part of BrainStorm software to facilitate analysis of physiological time series. This presentation emphasizes the main functionalities of the toolbox, illustrated with results from real data from a sleep protocol recorded in MEG. We localize the neural sources of sleep spindles using the MEM, then we show how time-frequency decompositions improve spatial accuracy of these reconstructions to provide better resolved images of the functional networks at play during sleep spindles.'' [read more] |
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| . ''Understanding the nature of human consciousness is one of the fundamental goals of psychological research. Recent studies of consciousness and learning suggest that learning the relationship between two temporally separated events requires the individual to hold one event in working memory, and that this maintenance requires conscious awareness. In trace fear conditioning a neutral stimulus (CS) predicts the occurrence of an aversive outcome (UCS) after a brief stimulus free period (trace interval). This type of learning requires the amygdala. Given that this structure is also sensitive to face stimuli, we hypothesized that amygdala activity may be capable of maintaining a representation of a face CS during a brief trace interval.'' [read more] | . ''Understanding the nature of human consciousness is one of the fundamental goals of psychological research. Recent studies of consciousness and learning suggest that learning the relationship between two temporally separated events requires the individual to hold one event in working memory, and that this maintenance requires conscious awareness. In trace fear conditioning a neutral stimulus (CS) predicts the occurrence of an aversive outcome (UCS) after a brief stimulus free period (trace interval). This type of learning requires the amygdala. Given that this structure is also sensitive to face stimuli, we hypothesized that amygdala activity may be capable of maintaining a representation of a face CS during a brief trace interval.''[[attachment:balderston-biomag_12_abstract.pdf|[read more]]] |
1st Brainstorm User Symposium
Paris, August 31 2012 (10am -11:30am):
a selection of recent research from Brainstorm users
This event is part of Brainstorm's Training Session (satellite of Biomag2012)
Katia Lehongre (Ecole Normale Supérieure, Paris, France)
- Altered low-γ sampling in auditory cortex accounts for the three main facets of dyslexia.
Younes Zerouali (Electrical Engineering Department, Ecole de Technologie Superieure, Montreal, Canada)
Wavelet-based localization of oscillatory and synchronous sources in Brainstorm
Brain Entropy in space and time (BEst) is a software toolbox dedicated to localizing the neural sources of electromagnetic activity recorded over the scalp. It implements an inverse problem solving technique based on the statistical entropy, called Maximum Entropy on the Mean (MEM, Amblard et al., 2004). In addition, BEst also implements time-frequency decomposition tools based on discrete and continuous wavelets. In conjunction with the MEM, these spectral decompositions are used to localize specifically neural sources of oscillatory activity (Lina et al., 2012) and also networks of synchronous sources (Zerouali et al., 2011).
BEst is distributed as part of BrainStorm software to facilitate analysis of physiological time series. This presentation emphasizes the main functionalities of the toolbox, illustrated with results from real data from a sleep protocol recorded in MEG. We localize the neural sources of sleep spindles using the MEM, then we show how time-frequency decompositions improve spatial accuracy of these reconstructions to provide better resolved images of the functional networks at play during sleep spindles. [read more]
Nicholas Balderston (University of Wisconsin, Milwaukee)
Rapid Amygdala Responses during Trace Fear cConditioning without Awareness
Understanding the nature of human consciousness is one of the fundamental goals of psychological research. Recent studies of consciousness and learning suggest that learning the relationship between two temporally separated events requires the individual to hold one event in working memory, and that this maintenance requires conscious awareness. In trace fear conditioning a neutral stimulus (CS) predicts the occurrence of an aversive outcome (UCS) after a brief stimulus free period (trace interval). This type of learning requires the amygdala. Given that this structure is also sensitive to face stimuli, we hypothesized that amygdala activity may be capable of maintaining a representation of a face CS during a brief trace interval.[read more]
Ghislaine Dehaene (CEA-Neurospin, Paris, France)
- Studies of Infant's Cognition with EEG
EEG remains one of the easiest and most robust techniques to study infants' cognition. However, this infant age presents several particularities increasing the difficulties of EEG studies relative to adults (movement artefacts, immaturity and small size of the brain structures, fontanella in skull, etc..). I will present several studies in speech perception showing the advantages of EEG to decipher infant's cognition and how Brainstorm brings numerous advantages when we deal with a difficult population, such as infants.