Most recent paper

Front Neurosci. 2025 Feb 26;19:1511654. doi: 10.3389/fnins.2025.1511654. eCollection 2025.

ABSTRACT

BACKGROUND: Chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) is a prevalent condition in urology characterized by chronic pain. The pathogenesis of CP/CPPS remains unclear.

METHODS: We enrolled 45 eligible CP/CPPS patients and 45 healthy volunteers. We evaluated their resting-state fMRI data using a comprehensive set of parameters, such as Regional Homogeneity (ReHo) and Degree Centrality (DC), to detect brain abnormalities and identify potential correlates with the clinical manifestations of CP/CPPS. We further categorized the patients into subgroups according to their scores of NIH-CPSI to elucidate the brain changes associated with differing symptom severities.

RESULTS: Profound alterations in brain function were observed in patients with CP/CPPS. These changes involved multiple brain regions identified by DC analysis, including the right anterior cingulate cortex (ACC), left inferior frontal opercular cortex, left amygdala, right middle frontal cortex, and bilateral insula. ReHo analysis revealed significant changes in the right thalamus, left inferior frontal triangular cortex, right superior temporal pole, left ACC, and right superior frontal cortex (cluster >20 voxels, GRF correction, p < 0.05). Analysis using ReHo and DC revealed that brain alterations associated with varying symptom severities were localized in pain perception and modulation regions. Specifically, the DC values in the right ACC showed a linear correlation with the severity of symptoms measured by the NIH-CPSI (AUC = 0.9654, p < 0.0001).

CONCLUSION: In CP/CPPS, we first discovered differences in brain function among patients with varying degrees of severity. The brain alterations of DC in the right ACC might be a potential biomarker for diagnosing and assessing disease severity.

PMID:40078709 | PMC:PMC11897570 | DOI:10.3389/fnins.2025.1511654

Sci Rep. 2025 Mar 12;15(1):8594. doi: 10.1038/s41598-025-89359-5.

ABSTRACT

Schizophrenia(SZ) classification and treatment response prediction hold substantial clinical application value. However, only a limited number of researchers have exploited the multi-feature information derived from resting-state functional magnetic resonance imaging (rs-fMRI) to achieve short-term drug-treatment SZ classification and treatment response prediction. We developed a multi-feature fusion recursive feature elimination random forest (RFE-RF) approach for SZ classification and treatment response prediction. Initially, we computed multiple features, such as regional homogeneity, fractional amplitude of low-frequency fluctuations, and functional connectivity. Subsequently, the RFE-RF method was employed to conduct SZ classification. Moreover, we utilized the rate of score reduction (RR) of the Positive and Negative Symptom Scale (PANSS) to forecast the treatment response of individual patients. Finally, we identified the neuroimaging biomarkers for SZ classification and drug-treatment response prediction. This method achieved the classification results (accuracy = 91.7%, sensitivity = 90.9%, and specificity = 92.6%), and the abnormalities in the visual and default mode networks emerged as potential neuroimaging biomarkers for differentiating SZ from healthy controls (HC). Additionally, we predicted the drug-treatment response of SZ patients in terms of their total PANSS scores, as well as negative and positive symptom scores after eight weeks of treatment. Specifically, the abnormalities in the visual network, sensorimotor network, and right superior frontal gyrus are crucial biomarkers for the short-term drug-treatment response of negative symptoms in SZ patients. Meanwhile, the abnormalities in the visual and default mode networks serve as important biomarkers of the short-term drug-treatment response of positive symptoms. There findings offer novel insights into the neural mechanisms underlying SZ following eight weeks of short-term drug treatment. With further clinical validation in the future, this research may provide potential biomarkers and intervention targets for personalized treatment of SZ.

PMID:40075170 | DOI:10.1038/s41598-025-89359-5

Sci Rep. 2025 Mar 12;15(1):8524. doi: 10.1038/s41598-025-92316-x.

ABSTRACT

Convergent evidence has suggested that the disruption of either structural connectivity (SC) or functional connectivity (FC) in the brain can lead to various neuropsychiatric disorders. Since changes in SC-FC coupling may be more sensitive than a single modality to detect subtle brain connectivity abnormalities, a few learning-based methods have been proposed to explore the relationship between SC and FC. However, these existing methods still fail to explain the relationship between altered SC-FC coupling and brain disorders. Therefore, in this paper, we explore three types of tree-based ensemble models (i.e., Decision Tree, Random Forest, and Adaptive Boosting) toward counterfactual explanations for SC-FC coupling. Specifically, we first construct SC and FC matrices from preprocessed diffusion-weighted DTI and resting-state functional fMRI data. Then, we quantify the SC-FC coupling strength of each region and convert it into feature vectors. Subsequently, we select SC-FC coupling features that can reflect disease-related information and trained three tree-based models to analyze the predictive role of these coupling features for diseases. Finally, we design a tree ensemble counterfactual explanation model to generate a set of counterfactual examples for patients, thereby assisting the diagnosis of brain diseases by fine-tuning the patient's abnormal SC-FC coupling feature vector. Experimental results on two independent datasets (i.e., epilepsy and schizophrenia) validate the effectiveness of the proposed method. The identified discriminative brain regions and generated counterfactual examples provide new insights for brain disease analysis.

PMID:40075142 | DOI:10.1038/s41598-025-92316-x