The Vegetative State (VS) is a severe disorder of consciousness in which patients are awake but display no signs of awareness. Yet, recent functional magnetic resonance imaging (fMRI) studies have demonstrated evidence for covert awareness in VS patients by recording specific brain activations during a cognitive task. However, the possible existence of incommunicable subjective emotional experiences in VS patients remains largely unexplored. This study aimed to probe the question of whether VS patients retain a brain ability to selectively process external stimuli according to their emotional value and look for evidence of covert emotional awareness in patients.
The need to infer brain states in a data driven approach is crucial for BCI applications as well as for neuroscience research. In this work we present a novel classification framework based on Regularized Linear Regression classifier constructed from time-frequency decomposition of an EEG (electro-encephalography) signal. The regression is then used to derive a model of frequency distributions that identifies brain states. The process of classifier construction, preprocessing and selection of optimal regularization parameter by means of cross-validation is presented and discussed. The framework and the feature selection technique are demonstrated on EEG data recorded from 10 healthy subjects while requested to open and close their eyes every 30 s. This paradigm is well known in inducing Alpha power modulations that differ from low power (during eyes opened) to high (during eyes closed). The classifier was trained to infer eyes opened or eyes closed states and achieved higher than 90% classification accuracy. Furthermore, our findings reveal interesting patterns of relations between experimental conditions, EEG frequencies, regularization parameters and classifier choice. This viable tool enables identification of the most contributing frequency bands to any given brain state and their optimal combination in inferring this state. These features allow for much greater detail than the standard Fourier Transform power analysis, making it an essential method for both BCI proposes and neuroimaging research.
Mind-wandering (MW) is among the most robust and permanent expressions of human conscious awareness, classically regarded by philosophers, clinicians, and scientists as a core element of an intact sense of self. Nevertheless, the scientific exploration of MW poses unique challenges; MW is by nature a spontaneous, off task, internal mental process which is often unaware and usually difficult to control, document or replicate. Consequently, there is a lack of accepted modus operandi for exploring MW in a laboratory setup, leading to a relatively small amount of studies regarding the neural basis of MW. In order to facilitate scientific examination of MW the current review categorizes recent literature into five suggested strategies. Each strategy represents a different methodology of MW research within functional neuroimaging paradigms. Particular attention is paid to resting-state brain activity and to the "default-mode" network. Since the default network is known to exert high activity levels during off-task conditions, it stands out as a compelling candidate for a neuro-biological account of mind-wandering, in itself a rest-based phenomenon. By summarizing the results within and across strategies we suggest further insights into the neural basis and adaptive value of MW, a truly intriguing and unique human experience.
A leftward bias is well known in humans and animals, and commonly related to the right hemisphere dominance for spatial attention. Our previous fMRI study suggested that this bias is mediated by faster conduction from the right to left parietal cortices, than the reverse (Siman-Tov et al., 2007). However, the limited temporal resolution of fMRI and evidence on the critical involvement of sub-cortical regions in orienting of spatial attention suggested further investigation of the leftward bias using multi-scale measurement. In this simultaneous EEG-fMRI study, healthy participants were presented with face pictures in either the right or left visual fields while performing a central fixation task. Temporo-occipital event related potentials, time-locked to the stimulus onset, showed an association between faster conduction from the right to the left hemisphere and higher fMRI activation in the left pulvinar nucleus following left visual field stimulation. This combined-modal finding provides original evidence of the involvement of sub-cortical central attention-related regions in the leftward bias. This assertion was further strengthened by a DCM analysis designated at cortical (i.e., inferior parietal sulcus; IPS) and sub-cortical (pulvinar nucleus) attention-related nodes that revealed: 1. Stronger inter-hemispheric connections from the right to left than vice versa, already at the pulvinar level. 2. Stronger connections within the right than the left hemisphere, from the pulvinar to the IPS. This multi-level neural superiority can guide future efforts in alleviating attention deficits by focusing on improving network connectivity.
The unique role of the EEG alpha rhythm in different states of cortical activity is still debated. The main theories regarding alpha function posit either sensory processing or attention allocation as the main processes governing its modulation. Closing and opening eyes, a well-known manipulation of the alpha rhythm, could be regarded as attention allocation from inward to outward focus though during light is also accompanied by visual change. To disentangle the effects of attention allocation and sensory visual input on alpha modulation, 14 healthy subjects were asked to open and close their eyes during conditions of light and of complete darkness while simultaneous recordings of EEG and fMRI were acquired. Thus, during complete darkness the eyes-open condition is not related to visual input but only to attention allocation, allowing direct examination of its role in alpha modulation. A data-driven ridge regression classifier was applied to the EEG data in order to ascertain the contribution of the alpha rhythm to eyes-open/eyes-closed inference in both lighting conditions. Classifier results revealed significant alpha contribution during both light and dark conditions, suggesting that alpha rhythm modulation is closely linked to the change in the direction of attention regardless of the presence of visual sensory input. Furthermore, fMRI activation maps derived from an alpha modulation time-course during the complete darkness condition exhibited a right frontal cortical network associated with attention allocation. These findings support the importance of top-down processes such as attention allocation to alpha rhythm modulation, possibly as a prerequisite to its known bottom-up processing of sensory input.
Related JoVE Video
Journal of Visualized Experiments
What is Visualize?
JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.
How does it work?
We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.
Video X seems to be unrelated to Abstract Y...
In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.