About this Research Topic
Attention is a cognitive process that involves concentration and memory to prioritize task-relevant information required to develop a goal. Attention can be exogenous or endogenous. In the first case, there exists a stimulus that captures brain resources for processing and responding to it. In the second case, the subject voluntarily devotes its resources to the development of a task, e.g., performing mathematical operations.
Brain activity related to attention produce the temporal and spatial dynamics. For instance, beta rhythm and frontal lobes evidence the most significant changes when associated with being focused and analytic. Further, frontal lobes, responsible for immediate and sustained attention, identify problems and send them to the parietal lobe generating the frontoparietal attention network (FPAN). In the case of visual attention, the neural activity of the FPAN consists of regions within the superior parietal lobe, the intraparietal sulcus, the frontal eye field, and the supplementary eye field.
There are several real-world applications taking advantage of the brain responses to attention tasks. In the clinical domain, the recording of responses to an infrequent stimulus within a trend of repetitive ones composes the well-known "oddball" paradigms, widely used for the diagnosis of brain disorders and for the follow-up of neuro-rehabilitation therapies. For developing brain-computer interfaces, stimulus-based paradigms that indicate the task to perform generate the brain signals used to train classification machines. For neuro-psychologist, the study of activated brain regions and their connections improves the understanding of how the brain works under tasks as reading in the mother language or reading in a foreign language.
We invite researchers to submit their original research articles on theoretical studies, methodological development, and computational effort related to the neuroscience of attention tasks. The scope of this Research Topic encompasses, but is not limited to, articles on 1) construction of brain imaging datasets for attention tasks and experimental paradigms; 2) processing of signals for brain-computer interfaces; 3) use of attention task for computer-aided diagnosis of mental disorders; and 4) applications of pattern recognition and artificial intelligence methods on attention-related brain signals.
Brain activity related to attention produce the temporal and spatial dynamics. For instance, beta rhythm and frontal lobes evidence the most significant changes when associated with being focused and analytic. Further, frontal lobes, responsible for immediate and sustained attention, identify problems and send them to the parietal lobe generating the frontoparietal attention network (FPAN). In the case of visual attention, the neural activity of the FPAN consists of regions within the superior parietal lobe, the intraparietal sulcus, the frontal eye field, and the supplementary eye field.
There are several real-world applications taking advantage of the brain responses to attention tasks. In the clinical domain, the recording of responses to an infrequent stimulus within a trend of repetitive ones composes the well-known "oddball" paradigms, widely used for the diagnosis of brain disorders and for the follow-up of neuro-rehabilitation therapies. For developing brain-computer interfaces, stimulus-based paradigms that indicate the task to perform generate the brain signals used to train classification machines. For neuro-psychologist, the study of activated brain regions and their connections improves the understanding of how the brain works under tasks as reading in the mother language or reading in a foreign language.
We invite researchers to submit their original research articles on theoretical studies, methodological development, and computational effort related to the neuroscience of attention tasks. The scope of this Research Topic encompasses, but is not limited to, articles on 1) construction of brain imaging datasets for attention tasks and experimental paradigms; 2) processing of signals for brain-computer interfaces; 3) use of attention task for computer-aided diagnosis of mental disorders; and 4) applications of pattern recognition and artificial intelligence methods on attention-related brain signals.
Keywords: temporal dynamics, spatial dynamics, computer-aided diagnosis, brain-computer interfaces, brain imaging
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