Abstract
In fMRI the key problem of data analysis is to detect the weak BOLD signal component (about 2–5%) in the MR signal. Standard approaches, that typically use cross-correlation analysis or statistical parametric mapping, imply a presumptive knowledge of the expected stimulus-response pattern, which is not available in spontaneous events like hallucinations, sleep, or epileptic seizures. To evidence the possibility of analyzing these events by means of fMRI, we investigated a computational approach based on a self-organizing neural network (Neural Gas) that detects timedependent alterations in the regional intensity of the functional signal.
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© 1999 Springer-Verlag London Limited
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Frisone, F. et al. (1999). A Neural Network approach to detect functional MRI signal. In: Marinaro, M., Tagliaferri, R. (eds) Neural Nets WIRN Vietri-99. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0877-1_9
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DOI: https://doi.org/10.1007/978-1-4471-0877-1_9
Publisher Name: Springer, London
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