A Neural Network approach to detect functional MRI signal
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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