The goal of this study was to investigate the relationship between fluid intelligence (gF) and the pattern of the functional characteristics in the resting state in adults using multivariate pattern analysis. Resting-state functional images from 100 participants in the Human Connectome Project data set were analyzed. The amplitude of low-frequency fluctuation (ALFF) was first calculated, and a support vector regression approach was used to identify the association with gF. To discover whether the connectivity of the gF-associated areas was also related to gF, we further checked the seed-based functional connectivity using the seeds from the ALFF. The ALFF showed that gF was correlated with the left anterior cingulate cortex, which is involved in high cognitive control processes. The functional connectivity showed that the connection between the right prefrontal cortex (Brodmann area 8) and the left anterior cingulate cortex could predict gF. The multivariate pattern analysis result indicated that the brain functional activity and functional integrity that we identified have the potential to become an objective biomarker for evaluating individual differences in gF.

We propose a novel medical image fusion scheme based on the statistical dependencies between coefficients in the nonsubsampled contourlet transform (NSCT) domain, in which the probability density function of the NSCT coefficients is concisely fitted using generalized Gaussian density (GGD), as well as the similarity measurement of two subbands is accurately computed by Jensen-Shannon divergence of two GGDs. To preserve more useful information from source images, the new fusion rules are developed to combine the subbands with the varied frequencies. That is, the low frequency subbands are fused by utilizing two activity measures based on the regional standard deviation and Shannon entropy and the high frequency subbands are merged together via weight maps which are determined by the saliency values of pixels. The experimental results demonstrate that the proposed method significantly outperforms the conventional NSCT based medical image fusion approaches in both visual perception and evaluation indices.

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