Calcarine as a bridge between brain function and structure in irritable bowel syndrome: A multiplex network analysis

Altered brain activity and functional connectivity in psychogenic erectile dysfunction: Combining findings from LOOCV-SVM-RFE and rs-fMRI

Psychogenic erectile dysfunction (pED) is often accompanied by abnormal brain activities. This study aimed to develop an automaticclassifier to distinguish pED from healthy controls (HCs) by identified brain-basedcharacteristics. Resting-state functional magnetic resonance imaging data were acquired from 45 pED patients and 43 HCs. Regional homogeneity (ReHo) and functional connectivity (FC) values were calculated and compared between groups. Moreover, based on altered ReHo and FC values, support vector machine (SVM) classifier, incorporating recursive feature elimination (RFE), an SVM-RFE diagnostic model was established using leave-one-out cross-validation. Patients demonstrated reduced ReHo values in the left middle temporal gyrus (had decreased FC values with the left medial superior frontal gyrus and cuneus), orbital part of inferior frontal gyrus (had decreased FC values within the same region), triangular part of inferior frontal gyrus, anterior cingulate gyrus (had decreased FC values with the left inferior temporal gyrus, anterior cingulate gyrus, cuneus and right supplementary motor area) and middle frontal gyrus. The right calcarine fissure displayed increased ReHo values. The diagnostic model demonstrated excellent performance, achieving an accuracy rate of 90.80%. This study identified altered regional activity and FC in specific brain regions of pED patients, which might be related to the development of pED. The application of machine learning confirmed the distinctive characteristics of these functional changes in the brain. The high accuracy of our diagnostic model suggested a promising direction for developing objective diagnostic tools for psychological disorders.

Study of dynamic brain function in irritable bowel syndrome via Hidden Markov Modeling

Background and purpose

Irritable bowel syndrome (IBS) is a common bowel-brain interaction disorder whose pathogenesis is unclear. Many studies have investigated abnormal changes in brain function in IBS patients. In this study, we analyzed the dynamic changes in brain function in IBS patients using a Hidden Markov Model (HMM).

Methods

Resting-state functional magnetic resonance imaging (rs-fMRI) data and the clinical characteristics of 35 patients with IBS and 31 healthy controls (HCs) were collected. The rs-fMRI data of all participants were analyzed using HMM to identify recurrent brain activity states that evolve over time during the resting state. Additionally, the temporal properties of these HMM states and their correlations with clinical scale scores were examined.

Result

This study utilized the Hidden Markov Model (HMM) method to identify six distinct HMM states. Significant differences in fractional occupancy (FO) and lifetime (LT) were observed in states 5 and 6 between the IBS and HCs. The state transition probabilities differed between IBS and HCs, with an increased probability of transitioning from state 2 to state 6 in IBS patients. The reconfiguration of HMM states over time scales in IBS patients was associated with abnormal activity in the default mode network (DMN), sensorimotor network (SMN), and cingulo-opercular network (CON).

Conclusion

This study offers novel insights into the dynamic reorganization of brain activity patterns in IBS and elucidates potential links between these patterns and IBS-related emotional regulation and symptom experience, thereby contributing to a deeper understanding of the neural mechanisms underlying IBS.

Association of aberrant brain network dynamics with gut microbial composition uncovers disrupted brain-gut-microbiome interactions in irritable bowel syndrome: Preliminary findings

BACKGROUND AND PURPOSE

Growing evidence suggests that abnormalities in brain-gut-microbiome (BGM) interactions are involved in the pathogenesis of irritable bowel syndrome (IBS). Our study aimed to explore alterations in dynamic functional connectivity (DFC), the gut microbiome and the bidirectional interaction in the BGM.

METHODS

Resting-state functional magnetic resonance imaging (rs-fMRI), fecal samples and clinical chacteristics were collected from 33 IBS patients and 32 healthy controls. We performed a systematic DFC analysis on rs-fMRI. The gut microbiome was analyzed by 16S rRNA gene sequencing. Associations between DFC characteristics and microbial alterations were explored.

RESULTS

In the DFC analysis, four dynamic functional states were identified. IBS patients exhibited increased mean dwell and fraction time in State 4, and reduced transitions from State 3 to State 1. Aberrant temporal properties in State 4 were only evident when choosing a short window (36 s or 44 s). Decreased functional connectivity (FC) variability was found in State 1 and State 3 in IBS patients, two of which (independent component [IC]51-IC91, IC46-IC11) showed significant correlations with clinical characteristics. Additionally, we identified nine significantly differential abundances in microbial composition. We also found that IBS-related microbiota were associated with aberrant FC variability, although these exploratory results were obtained at an uncorrected threshold of significance.

CONCLUSIONS

Although future studies are needed to confirm our results, the findings not only provide a new insight into the dysconnectivity hypothesis in IBS from a dynamic perspective, but also establish a possible link between DFC and the gut microbiome, which lays the foundation for future research on disrupted BGM interactions.

Research on Voxel-Based Features Detection and Analysis of Alzheimer’s Disease Using Random Survey Support Vector Machine

Alzheimer’s disease (AD) is a degenerative disease of the central nervous system characterized by memory and cognitive dysfunction, as well as abnormal changes in behavior and personality. The research focused on how machine learning classified AD became a recent hotspot. In this study, we proposed a novel voxel-based feature detection framework for AD. Specifically, using 649 voxel-based morphometry (VBM) methods obtained from MRI in Alzheimer’s Disease Neuroimaging Initiative (ADNI), we proposed a feature detection method according to the Random Survey Support Vector Machines (RS-SVM) and combined the research process based on image-, gene-, and pathway-level analysis for AD prediction. Particularly, we constructed 136, 141, and 113 novel voxel-based features for EMCI (early mild cognitive impairment)-HC (healthy control), LMCI (late mild cognitive impairment)-HC, and AD-HC groups, respectively. We applied linear regression model, least absolute shrinkage and selection operator (Lasso), partial least squares (PLS), SVM, and RS-SVM five methods to test and compare the accuracy of these features in these three groups. The prediction accuracy of the AD-HC group using the RS-SVM method was higher than 90%. In addition, we performed functional analysis of the features to explain the biological significance. The experimental results using five machine learning indicate that the identified features are effective for AD and HC classification, the RS-SVM framework has the best classification accuracy, and our strategy can identify important brain regions for AD.