Psychophysiological and fMRI neural correlates to stress response: A pilot study

Pruneti, Carlo; Vanello, Nicola; Morese, Rosalba; Gentili, Claudio; Fontana, Federico; Ricciardi, Emiliano; Fante, Chiara; Paterni, Marco; Pietrini, Pietro; Guazzelli, Mario; Landini, Luigi; Ferdeghini, E.M.

doi:10.1016/j.ijpsycho.2008.05.065

Cited by 1 publication

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“…Particularly, artificial intelligence technologies were applied in fMRI research successfully, the fuzzy c-mean clustering [18] [19] and independent component analysis (ICA) [20] [21], and fMRI visualization works were also crucial for fMRI images' processing [22]. Thereafter, neural networks [23], support vector machine [24], and Markov models [25] were applied in classification of fMRI from different zones of human brain. Analysis tools for fMRI were developed to some mathematical issues, such as fuzzy set, local optimization and preserving mappings algorithms [26].…”

Section: Introductionmentioning

confidence: 99%

A Gaussian-mixed Fuzzy Clustering Model on Valence-Arousal-related fMRI Data-Set

Shi¹,

Bash²

2013

APH

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Previous medical experiments illustrated that Valence and Arousal were high corresponded to brain response by amygdala and orbital frontal cortex through observation by functional magnetic resonance imaging (fMRI). In this paper, Valence-Arousal related fMRI data-set were acquired from the picture stimuli experiments, and finally the relative Valence-Arousal feature values for a given word that corresponding to a given picture stimuli were calculated. Gaussian bilateral filter and independent components analysis (ICA) based Gaussian component method were applied for image denosing and segmenting; to construct the timing signals of Valence and Arousal from fMRI data-set separately, expectation maximal of Gaussian mixed model was addressed to calculate the histogram, and furthermore, Otsu curve fitting algorithm was introduced to scale the computational complexity; time series based Valence-Arousal related curve were finally generated. In Valence-Arousal space, a fuzzy c-mean method was applied to get typical point that represented the word relative to the picture. Analyzed results showed the effectiveness of the proposed methods by comparing with other algorithms for feature extracting operations on fMRI data-set including power spectrum density (PSD), spline, shape-preserving and cubic fitting methods.

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