We review the research progress on noninvasive neural regulatory systems through system design and theoretical guidance. We provide an overview of the development history of noninvasive neuromodulation technology, focusing on system design. We also discuss typical cases of neuromodulation that use modern noninvasive electrical stimulation and the main limitations associated with this technology. In addition, we propose a closed-loop system design solution of the “time domain”, “space domain”, and “multi-electrode combination”. For theoretical guidance, this paper provides an overview of the “digital brain” development process used for noninvasive electrical-stimulation-targeted modeling and the development of “digital human” programs in various countries. We also summarize the core problems of the existing “digital brain” used for noninvasive electrical-stimulation-targeted modeling according to the existing achievements and propose segmenting the tissue. For this, the tissue parameters of a multimodal image obtained from a fresh cadaver were considered as an index. The digital projection of the multimodal image of the brain of a living individual was implemented, following which the segmented tissues could be reconstructed to obtain a “digital twin brain” model with personalized tissue structure differences. The “closed-loop system” and “personalized digital twin brain” not only enable the noninvasive electrical stimulation of neuromodulation to achieve the visualization of the results and adaptive regulation of the stimulation parameters but also enable the system to have individual differences and more accurate stimulation.

Schizophrenia, a common mental disorder, has a tremendous impact on the health and economy of people worldwide. Evidence suggests that the microbial-gut-brain axis is an important pathway for the interaction between the gut microbiome and the development of schizophrenia. What is not clear is how changes in the gut microbiota composition and structure during antipsychotic treatment improve the symptoms of schizophrenia. In this study, 25 patients with schizophrenia were recruited. Their fecal samples were collected before and after hospital treatment for 14–19 days. The composition and structure of the intestinal microbiota were evaluated by 16S rRNA sequencing analysis, and the results showed significant differences in fecal microbiota before and after treatment. Firmicutes (relative abundances of 82.60 and 86.64%) and Gemminger (relative abundances of 14.17 and 13.57%) were the first dominant species at the phylum and genus levels, respectively. The random forest algorithm and co-occurrence network analysis demonstrated that intestinal flora (especially the core species ASV57) could be used as biomarkers to distinguish different clinical states and match treatment regimens accordingly. In addition, after fecal microbiota transplantation, antibiotic-treated recipient mice showed multiple behavioral improvements. These included decreased psychomotor hyperactivity, increased social interaction, and memory. In conclusion, this study suggests that differences in the composition and structure of gut microbiota after treatment are associated with the development and severity of schizophrenia. Results may provide a potential target for the treatment of this disorder.

Goal: Idiopathic generalized epilepsy (IGE) represents generalized spike-wave discharges (GSWD) and distributed changes in thalamocortical circuit. The purpose of this paper is to investigate how the ongoing alpha oscillation acts upon the local temporal dynamics and spatial hyperconnectivity during epileptic state. Methods: We evaluated the spatiotemporal regulation of alpha oscillations in epileptic state based on simultaneous EEG-fMRI recordings in 45 IGE patients. The alpha-BOLD temporal consistency, as well as the effect of alpha power windows on dynamic functional connectivity strength (dFCS) was analyzed. Then, stable synchronization networks during GSWD were constructed, and the spatial covariation with alpha-based network integration was investigated. Results: Increased temporal covariation was demonstrated between alpha power and BOLD fluctuations in thalamus and distributed cortical regions in IGE. High alpha power had inhibition effect on dFCS in healthy controls, while in epilepsy, high alpha windows arose along with the enhancement of dFCS in thalamus, caudate and some default mode network (DMN) regions. Moreover, synchronization networks in GSWD-before, GSWD-onset and GSWD-after stages were demonstrated, and the connectivity strength in prominent hub nodes (precuneus, thalamus) was associated with the spatially disturbed alpha-based network integration. Conclusion: The results indicated the spatiotemporal regulation of alpha in epilepsy by means of increased power and decreased coherence communication. It provided links between alpha rhythm and the altered temporal dynamics, as well as the hyperconnectivity in thalamus-DMN circuit. Significance: The combination between neural oscillations and epileptic representations may be of clinical importance in terms of seizure prediction and non-invasive interventions.

Echo signals in different regions in the k-space of magnetic resonance imaging (MRI) data possess different amplitudes. The signal-to-noise ratio (SNR) of a received signal can be improved by differentially setting the receiving gain (RG) parameter in different areas of the k-space. Previously, the k-space data splicing method and the gain normalization implementation method were not specifically investigated; however, this study focuses on this aspect. Specifically, to improve the SNR, three RGs and MRI scans are herein designed for each gain parameter using the gradient echo sequence to obtain one group of k-space data. Subsequently, the three groups of experimental k-space data obtained using MRI scans are spliced into one group of k-space data. For the splicing process, a method for gain and phase correction and compensation is developed that normalizes different RG parameters in the k-space. The experimental results indicate that the developed methods improve the SNR by 5–13%. When the RGs are set to other combinations, the k-space data splicing and gain normalization methods presented in this paper are still applicable.

Based on the microbiota–gut–brain axis (MGBA) hypothesis, probiotics play an increasingly important role in treating various psychiatric disorders. Schizophrenia (SCZ) is a common mental disease with a complex pathogenesis and is challenging to treat. Although studies have elucidated the mechanisms associated with the interactions between the microbiota–gut–brain axis and SCZ, few have specifically used probiotics as a therapeutic intervention for SCZ. Accordingly, the current study determines whether L. johnsonii YH1136 effectively prevents SCZ-like behavior in mice and identifies the associated key microbes and metabolites. An SCZ mouse model was established by intraperitoneal injection of MK-801; L. johnsonii YH1136 was administered via oral gavage. L. johnsonii YH1136 significantly improves abnormal behaviors, including psychomotor hyperactivity and sociability and alleviates aberrant enzyme expression associated with tryptophan metabolism in SCZ mice. Additionally, L. johnsonii YH1136 upregulates hippocampal brain-derived neurotrophic factor (BDNF) levels while downregulating tryptophan 2,3-dioxygenase (TDO2), indoleamine-pyrrole 2,3-dioxygenase 1 (IDO1), kynurenine aminotransferase 1 (KAT1). Subsequent 16S rRNA sequencing of intestinal contents suggests that L. johnsonii YH1136 modulates the gut flora structure and composition by increasing the relative abundance of Lactobacillus and decreasing Dubosiella in SCZ mice. N-acetylneuraminic acid and hypoxanthine are the key serum metabolites mediating the interaction between the MGBA and SCZ. These results partially reveal the mechanism underlying the effects of L. johnsonii YH1136 on SCZ-like behavior in mice, supporting the development of therapeutic L. johnsonii probiotic formulations against SCZ.