RESUMEN
Previous research has indicated that the left dorsolateral prefrontal cortex (DLPFC) exerts an influence on attentional bias toward visual emotional information. However, it remains unclear whether the left DLPFC also play an important role in attentional bias toward natural emotional sounds. The current research employed the emotional spatial cueing paradigm, incorporating natural emotional sounds of considerable ecological validity as auditory cues. Additionally, high-definition transcranial direct current stimulation (HD-tDCS) was utilized to examine the impact of left dorsolateral prefrontal cortex (DLPFC) on attentional bias and its subcomponents, namely attentional engagement and attentional disengagement. The results showed that (1) compared to sham condition, anodal HD-tDCS over the left DLPFC reduced the attentional bias toward positive and negative sounds; (2) anodal HD-tDCS over the left DLPFC reduced the attentional engagement toward positive and negative sounds, whereas it did not affect attentional disengagement away from natural emotional sounds. Taken together, the present study has shown that left DLPFC, which was closely related with the top-down attention regulatory function, plays an important role in auditory emotional attentional bias.
RESUMEN
Background: Artificial intelligence (AI) technology has made breakthroughs in spinal cord neural injury and restoration in recent years. It has a positive impact on clinical treatment. This study explores AI research's progress and hotspots in spinal cord neural injury and restoration. It also analyzes research shortcomings related to this area and proposes potential solutions. Methods: We used CiteSpace 6.1.R6 and VOSviewer 1.6.19 to research WOS articles on AI research in spinal cord neural injury and restoration. Results: A total of 1,502 articles were screened, in which the United States dominated; Kadone, Hideki (13 articles, University of Tsukuba, JAPAN) was the author with the highest number of publications; ARCH PHYS MED REHAB (IF = 4.3) was the most cited journal, and topics included molecular biology, immunology, neurology, sports, among other related areas. Conclusion: We pinpointed three research hotspots for AI research in spinal cord neural injury and restoration: (1) intelligent robots and limb exoskeletons to assist rehabilitation training; (2) brain-computer interfaces; and (3) neuromodulation and noninvasive electrical stimulation. In addition, many new hotspots were discussed: (1) starting with image segmentation models based on convolutional neural networks; (2) the use of AI to fabricate polymeric biomaterials to provide the microenvironment required for neural stem cell-derived neural network tissues; (3) AI survival prediction tools, and transcription factor regulatory networks in the field of genetics were discussed. Although AI research in spinal cord neural injury and restoration has many benefits, the technology has several limitations (data and ethical issues). The data-gathering problem should be addressed in future research, which requires a significant sample of quality clinical data to build valid AI models. At the same time, research on genomics and other mechanisms in this field is fragile. In the future, machine learning techniques, such as AI survival prediction tools and transcription factor regulatory networks, can be utilized for studies related to the up-regulation of regeneration-related genes and the production of structural proteins for axonal growth.