Regional homogeneity of intrinsic brain activity related to the main alexithymia dimensions

Background Alexithymia is a multidimensional personality construct.Objective This study aims to investigate the neuronal correlates of each alexithymia dimension by examining the regional homogeneity(ReHo) of intrinsic brain activity in a resting situation.Methods From university freshmen, students with alexithymia and non-alexlthymia were recruited. Their alexithymic traits were assessed using the Toronto Alexithymia Scale-20. The ReHo was examined using a resting-state functional MRI approach.Results This study suggests significant gro叩 differences in ReHo in multiple brain regions distributed in the frontal lobe, parietal lobe, temporal lobe, occipital lobe and insular cortex. However, only the ReHo in the insula was positively associated with difficulty identifying feelings, a main dimension of alexithymia. The ReHo in the lingual gyrus,precentral gyrus and postcentral gyrus was positively associated with difficulty describing feelings in participants with alexithymia. Lastly, the ReHo in the right dorsomedial prefrontal cortex(DMPFC_R) was negatively related to the externally oriented thinking style of participants with alexithymia.Conclusion In conclusion, these results suggest that the main dimensions of alexithymia are correlated with specific brain regions' function, and the role of the insula,lingual gyrus, precentral gyrus, postcentral gyrus and DMPFC_R in the neuropathology of alexithymia should be further investigated.

SEMILINEAR ELLIPTIC EQUATIONS ON FRACTAL SETS

In this article, the authors consider a class of semilinear elliptic equations on fractal sets under some new conditions, which are more weaker than those in usual cases. The authors get the non-trivial and non-negative solution of the zero boundary Dirichlet problem using Mountain Pass Lemma.

Method for denoising and reconstructing radar HRRP using modified sparse auto-encoder

A high resolution range profile(HRRP) is a summation vector of the sub-echoes of the target scattering points acquired by a wide-band radar.Generally, HRRPs obtained in a noncooperative complex electromagnetic environment are contaminated by strong noise.Effective pre-processing of the HRRP data can greatly improve the accuracy of target recognition.In this paper, a denoising and reconstruction method for HRRP is proposed based on a Modified Sparse Auto-Encoder, which is a representative non-linear model.To better reconstruct the HRRP, a sparse constraint is added to the proposed model and the sparse coefficient is calculated based on the intrinsic dimension of HRRP.The denoising of the HRRP is performed by adding random noise to the input HRRP data during the training process and fine-tuning the weight matrix through singular-value decomposition.The results of simulations showed that the proposed method can both reconstruct the signal with fidelity and suppress noise effectively, significantly outperforming other methods, especially in low Signal-to-Noise Ratio conditions.

Feedforward Control of a 3-D Physiological Articulatory Model for Vowel Production

A three-dimensional （3-D） physiological articulatory model was developed to account for the biomechanical properties of the speech organs in speech production. Control of the model to investigate the mechanism of speech production requires an efficient control module to estimate muscle activation patterns, which is used to manipulate the 3-D physiological articulatory model, according to the desired articulatory posture. For this purpose, a feedforward control strategy was developed by mapping the articulatory target to the corresponding muscle activation pattern via the intrinsic representation of vowel articulation. In this process, the articulatory postures are first mapped to the corresponding intrinsic representations; then, the articulatory postures are clustered in the intrinsic representations space and a nonlinear function is approximated for each cluster to map the intrinsic representation of vowel articulation to the muscle activation pattern by using general regression neural networks （GRNN）. The results show that the feedforward control module is able to manipulate the 3-D physiological articulatory model for vowel production with high accuracy both acoustically and articulatorily.