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

Prog Neurobiol. 2025 Mar 11:102748. doi: 10.1016/j.pneurobio.2025.102748. Online ahead of print.

ABSTRACT

Classically, the insula is considered an associative multisensory cortex where emotional awareness emerges through the integration of interoceptive and exteroceptive information, along with autonomic regulation. However, since early intracortical microstimulation (ICMS) studies, the insular cortex has also been conceived as a mosaic of anatomo-functional sectors processing various types of sensory information to generate specific overt behaviors. Based on this, the insula has been subdivided into distinct functional fields: an anterior field associated with oroalimentary behaviors, a middle field involved dorsally in hand movements and ventrally in emotional reactions, and a posterior field engaged in axial and proximal movements. Nevertheless, the anatomo-functional networks through which these fields produce motor behaviors remain largely unknown. To fill this gap in the present study, we investigated the connectivity of the macaque insula using a multimodal approach which combines resting-state fMRI with data from tract-tracing injections in insular functional fields defined by ICMS, as well as in brain areas known to be connected to the insula and characterized by specific somatotopic organization. The results revealed that each insular functional field takes part in distinct somatotopically organized network modulating specific motor or visceromotor behaviors, extending previous models that subdivide the insula primarily based on the types of interoceptive and exteroceptive information it receives. Our findings posit the various insular sectors as interfaces that synthesize diverse interoceptive and exteroceptive inputs into coherent subjective experiences and decision-making processes, within an embodied and enactive framework, that moves beyond the traditional dichotomy between sensory experience and motor behavior.

PMID:40074022 | DOI:10.1016/j.pneurobio.2025.102748

Clin Neurol Neurosurg. 2025 Mar 11;251:108849. doi: 10.1016/j.clineuro.2025.108849. Online ahead of print.

ABSTRACT

OBJECTIVE: Language impairments may mask non-language cognitive deficits in post-stroke aphasia (PSA) patients. Moreover, the underlying neural mechanisms of both language and non-language cognitive impairment remain unclear. This study aimed to investigate the activities and functional abnormalities of local and remote brain regions and their relationship with cognitive function in PSA patients, to provide more effective tips in future clinical therapy.

METHODS: This cross-sectional study included 46 PSA patients and 40 controls, who underwent language and non-language cognitive assessments, and resting-state functional magnetic resonance imaging (rs-fMRI). We then examined the fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and functional connectivity (FC) based on a modest sample size (46 PSA patients and 40 normal controls (NCs)). Independent two-sample t-tests were used to identify differences in these measures between PSA patients and NCs. Moreover, partial correlation analyses were performed to determine the correlation between FC from the affected brain regions and language, and non-language cognitive performance in PSA patients.

RESULTS: This study revealed that both fALFF and ReHo in PSA patients presented significantly lower in the right superior cerebellum, left thalamus, and left middle frontal gyrus, along with increased values in the right superior frontal gyrus (dorsolateral part) (p < 0.05). Notably, decreased FC between the left middle frontal gyrus and orbital part of the left inferior frontal gyrus was significantly associated with both language and non-language cognitive performance (p < 0.05). In addition, PSA patients were further divided into fluent and non-fluent groups. The results revealed that non-fluent patients demonstrated worse overall cognitive functioning, accompanied by reduced FC between the left thalamus and the left supplementary motor area (p < 0.001).

CONCLUSION: This study provides new evidence that abnormal neural activities and functional connectivities within specific brain regions may play crucial roles in language and non-language cognitive function. The underlying mechanisms of non-language cognitive decline accompanied by impaired language function in PSA patients may be a partial overlap between language and cognitive networks. In the future, combining language and non-language functions and designing a comprehensive treatment plan will be the focus of rehabilitation.

PMID:40073749 | DOI:10.1016/j.clineuro.2025.108849

Gigascience. 2025 Jan 6;14:giaf010. doi: 10.1093/gigascience/giaf010.

ABSTRACT

BACKGROUND: The cerebellum is one of the major central nervous structures consistently altered in obesity. Its role in higher cognitive function, parts of which are affected by obesity, is mediated through projections to and from the cerebral cortex. We therefore investigated the relationship between body mass index (BMI) and cerebellocerebral connectivity.

METHODS: We utilized the Human Connectome Project's Young Adults dataset, including functional magnetic resonance imaging (fMRI) and behavioral data, to perform connectome-based predictive modeling (CPM) restricted to cerebellocerebral connectivity of resting-state fMRI and task-based fMRI. We developed a Python-based open-source framework to perform CPM, a data-driven technique with built-in cross-validation to establish brain-behavior relationships. Significance was assessed with permutation analysis.

RESULTS: We found that (i) cerebellocerebral connectivity predicted BMI, (ii) task-general cerebellocerebral connectivity predicted BMI more reliably than resting-state fMRI and individual task-based fMRI separately, (iii) predictive networks derived this way overlapped with established functional brain networks (namely, frontoparietal networks, the somatomotor network, the salience network, and the default mode network), and (iv) we found there was an inverse overlap between networks predictive of BMI and networks predictive of cognitive measures adversely affected by overweight/obesity.

CONCLUSIONS: Our results suggest obesity-specific alterations in cerebellocerebral connectivity, specifically with regard to task execution. With brain areas and brain networks relevant to task performance implicated, these alterations seem to reflect a neurobiological substrate for task performance adversely affected by obesity.

PMID:40072905 | DOI:10.1093/gigascience/giaf010

Cereb Cortex. 2025 Mar 6;35(3):bhaf028. doi: 10.1093/cercor/bhaf028.

ABSTRACT

This study investigates the relationship between resting-state functional magnetic resonance imaging (rs-fMRI) topological properties and synaptic vesicle glycoprotein 2A (SV2A) positron emission tomography (PET) synaptic density (SD) in late-life depression (LLD). 18 LLD patients and 33 healthy controls underwent rs-fMRI, 3D T1-weighted MRI, and 11C-UCB-J PET scans to assess SD. The rs-fMRI data were utilized to construct weighted networks for calculating four global topological metrics, including clustering coefficient, characteristic path length, global efficiency, and small-worldness, and six nodal metrics, including nodal clustering coefficient, nodal characteristic path length, nodal degree, nodal strength, local efficiency, and betweenness centrality. The 11C-UCB-J PET provided standardized uptake value ratios as SD measures. LLD patients exhibited preserved global topological organization, with reduced nodal properties in regions associated with LLD, such as the medial prefrontal cortex (mPFC), and increased nodal properties in the basal ganglia and cerebellar regions. Notably, a negative correlation was observed between betweenness centrality in the mPFC and depressive symptom severity. No significant alterations in SD or associations between rs-fMRI topological properties and SD were found, challenging the hypothesis that SD alterations are the molecular basis for rs-fMRI topological changes in LLD. Our findings suggest other molecular mechanisms may underlie the observed functional connectivity alterations in these patients.

PMID:40072885 | DOI:10.1093/cercor/bhaf028

Curr Opin Psychiatry. 2025 Mar 10. doi: 10.1097/YCO.0000000000001003. Online ahead of print.

ABSTRACT

PURPOSE OF REVIEW: This review examines the literature associating cannabis with schizophrenia, glutamate dysregulation in schizophrenia, and cannabis involvement in glutamate pathways. Cannabis use is widespread among adolescents world-wide and is sold legally in many countries for recreational use in a variety of forms. Most people use it without lasting effects, but a portion of individuals have negative reactions that manifest in acute psychotic symptoms, and in some, symptoms continue even after the use of cannabis has ceased. To date, there is a huge gap in our understanding of why this occurs.

RECENT FINDINGS: Recent studies have focused on abnormalities in the glutamate pathway in schizophrenia, the effect of cannabis on the glutamate system, and the role of glutamate in the brain Default Mode Network.

SUMMARY: Given these observations, we hypothesize that perturbance of glutamate neuronal connectivity by cannabis in the brains of individuals genetically at high risk for psychosis will initiate a schizophrenia-like psychosis. Future studies may tie together these diverse observations by combining magnetic resonance spectroscopy (MRS) and functional magnetic resonance imaging (fMRI) of the default resting state network in patients with new onset schizophrenia who do and do not use cannabis compared with nonpsychotic individuals who do and do not use cannabis.

PMID:40071480 | DOI:10.1097/YCO.0000000000001003

Front Psychol. 2025 Feb 25;16:1492447. doi: 10.3389/fpsyg.2025.1492447. eCollection 2025.

ABSTRACT

BACKGROUND: High-definition-tDCS (HD-tDCS) is a recent technology that allows for localized cortical stimulation, but has not yet been investigated as an augmentative therapy while targeting the left temporoparietal cortex in logopenic variant PPA (lvPPA). The changes in neuronal oscillatory patterns and resting-state functional connectivity in response to HD-tDCS also remains poorly understood.

OBJECTIVE: We sought to investigate the effects of HD-tDCS with phonologic-based language training on language, cognition, and resting-state functional connectivity in lvPPA.

METHODS: We used a double-blind, within-subject, sham-controlled crossover design with a 4-month between-treatment period in four participants with lvPPA. Participants completed language, cognitive assessments, and imaging with magnetoencephalography (MEG) and resting-state functional MRI (fMRI) prior to treatment with either anodal HD-tDCS or sham targeting the left supramarginal gyrus over 10 sessions. Language and cognitive assessments, MEG, and fMRI were repeated after the final session and at 2 months follow-up. Preliminary data on efficacy was evaluated based on relative changes from baseline in language and cognitive scores. Language measures included metrics derived from spontaneous speech from picture description. Changes in resting-state functional connectivity within the phonological network were analyzed using fMRI. Magnitudes of source-level evoked responses and hemispheric laterality indices from language task-based MEG were used to assess changes in cortical engagement induced by HD-tDCS.

RESULTS: All four participants were retained across the 4-month between-treatment period, with satisfactory blinding of participants and investigators throughout the study. Anodal HD-tDCS was well tolerated with a side effect profile that did not extend past the immediate treatment period. No benefit of HD-tDCS over sham on language and cognitive measures was observed in this small sample. Functional imaging results using MEG and fMRI indicated an excitatory effect of anodal HD-tDCS compared to sham and suggested that greater temporoparietal activation and connectivity was positively associated with language outcomes.

CONCLUSION: Anodal HD-tDCS to the inferior parietal cortex combined with language training appears feasible and well tolerated in participants with lvPPA. Language outcomes may be explained by regression to the mean, and to a lesser degree, by ceiling effects and differences in baseline disease severity. The intervention has apparent temporoparietal correlates, and its clinical efficacy should be further studied in larger trials.

CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov, Number NCT03805659.

PMID:40070907 | PMC:PMC11893574 | DOI:10.3389/fpsyg.2025.1492447

Neural Netw. 2025 Feb 28;187:107335. doi: 10.1016/j.neunet.2025.107335. Online ahead of print.

ABSTRACT

Objective classification biomarkers that are developed using resting-state functional magnetic resonance imaging (rs-fMRI) data are expected to contribute to more effective treatment for psychiatric disorders. Unfortunately, no widely accepted biomarkers are available at present, partially because of the large variety of analysis pipelines for their development. In this study, we comprehensively evaluated analysis pipelines using a large-scale, multi-site fMRI dataset for major depressive disorder (MDD). We explored combinations of options in four sub-processes of the analysis pipelines: six types of brain parcellation, four types of functional connectivity (FC) estimations, three types of site-difference harmonization, and five types of machine-learning methods. A total of 360 different MDD classification biomarkers were constructed using the SRPBS dataset acquired with unified protocols (713 participants from four sites) as the discovery dataset, and datasets from other projects acquired with heterogeneous protocols (449 participants from four sites) were used for independent validation. We repeated the procedure after swapping the roles of the two datasets to identify superior pipelines, regardless of the discovery dataset. The classification results of the top 10 biomarkers showed high similarity, and weight similarity was observed between eight of the biomarkers, except for two that used both data-driven parcellation and FC computation. We applied the top 10 pipelines to the datasets of other psychiatric disorders (autism spectrum disorder and schizophrenia), and eight of the biomarkers exhibited sufficient classification performance for both disorders. Our results will be useful for establishing a standardized pipeline for classification biomarkers.

PMID:40068496 | DOI:10.1016/j.neunet.2025.107335