Most recent paper

Prog Neuropsychopharmacol Biol Psychiatry. 2025 Mar 12:111326. doi: 10.1016/j.pnpbp.2025.111326. Online ahead of print.

ABSTRACT

Maladaptive brain plasticity has been reported in chronic pain (CP) conditions, though it remains unclear if there are common alterations across pathologies. Therefore, we systematically synthesized literature comparing resting-state functional magnetic resonance imaging (rs-fMRI) in CP patients and healthy controls (HC), and meta-analyzed data whenever applicable. Separate meta-analyses were performed for each method - (fractional) amplitude of low-frequency fluctuations (fALFF, ALFF), regional homogeneity (ReHo), seed-based connectivity (according to the seed) and independent component analysis (according to the network). In qualitative synthesis, sensory-discriminative pain processing - thalamus, insula, temporal and sensory cortices - and cognitive and emotional processing - cingulate, prefrontal and parietal cortices and precuneus - regions concentrated CP/HC differences. Meta-analyses revealed decreased ALFF and increased ReHo in the precuneus, increased fALFF in the left posterior insula and disrupted within- and cross-network connectivity of default mode network (DMN) nodes, as well as altered connectivity in top-down pain modulation pathways. Specifically, it showed decreased anterior and increased posterior components' representation within DMN, enhanced connectivity between the medial prefrontal cortex (mPFC, part of the DMN) and anterior insula (part of the salience network), and decreased mPFC connectivity with the periaqueductal gray matter (PAG). Collectively, results suggest that CP disrupts the natural functional organization of the brain, particularly impacting DMN nodes (mPFC and precuneus), insula and top-town pain modulation circuits.

PMID:40086716 | DOI:10.1016/j.pnpbp.2025.111326

Dev Cogn Neurosci. 2025 Mar 4;73:101541. doi: 10.1016/j.dcn.2025.101541. Online ahead of print.

ABSTRACT

Adolescence is a key period for the maturation of cognitive control during which cortical circuitry is refined through processes such as synaptic pruning, but how these refinements modulate local functional dynamics to support cognition remains only partially characterized. Here, we used data from a longitudinal, adolescent cohort (N = 134 individuals ages 10-31 years, N = 202 total sessions) that completed MRI scans at ultra-high field (7 Tesla). We used resting state fMRI data to compute surface-based regional homogeneity (ReHo)-a measure of time-dependent correlations in fMRI activity between a vertex and its immediate neighbors-as an index of local functional connectivity across the cortex. We found widespread decreases in ReHo, suggesting increasing heterogeneity and specialization of functional circuits through adolescence. Decreases in ReHo included a spatial component which overlapped with sensorimotor and cingulo-opercular networks, in which ReHo decreases were associated with developmental stabilization of working memory performance. We show that decreases in ReHo are associated with higher intrinsic coding dimensionality, demonstrating how functional specialization of these circuits may confer computational benefits by facilitating increased capacity for encoding information. These results suggest a remodeling of cortical activity in adolescence through which local functional circuits become increasingly specialized, higher-dimensional, and more capable of supporting adult-like cognitive functioning.

PMID:40086409 | DOI:10.1016/j.dcn.2025.101541

Imaging Neurosci (Camb). 2024 May 10;2:1-20. doi: 10.1162/imag_a_00165. eCollection 2024 May 1.

ABSTRACT

The precise network topology of functional brain systems is highly specific to individuals and undergoes dramatic changes during critical periods of development. Large amounts of high-quality resting state data are required to investigate these individual differences, but are difficult to obtain in early infancy. Using the template matching method, we generated a set of infant network templates to use as priors for individualized functional resting-state network mapping in two independent neonatal datasets with extended acquisition of resting-state functional MRI (fMRI) data. We show that template matching detects all major adult resting-state networks in individual infants and that the topology of these resting-state network maps is individual-specific. Interestingly, there was no plateau in within-subject network map similarity with up to 25 minutes of resting-state data, suggesting that the amount and/or quality of infant data required to achieve stable or high-precision network maps is higher than adults. These findings are a critical step towards personalized precision functional brain mapping in infants, which opens new avenues for clinical applicability of resting-state fMRI and potential for robust prediction of how early functional connectivity patterns relate to subsequent behavioral phenotypes and health outcomes.

PMID:40083644 | PMC:PMC11899874 | DOI:10.1162/imag_a_00165

J Obstet Gynaecol. 2025 Dec;45(1):2472767. doi: 10.1080/01443615.2025.2472767. Epub 2025 Mar 14.

ABSTRACT

BACKGROUND: Chronic pelvic pain is a substantial clinical challenge that profoundly impacts quality of life for many women. The Neuro Emotional Technique (NET) is a novel mind-body intervention designed to attenuate emotional arousal of distressing thoughts and pain. This study evaluated functional connectivity changes in key areas of the brain in patients with chronic pelvic pain receiving the NET intervention. The goal was to assess whether the NET intervention was associated with functional connectivity (FC) changes in the brain related to reductions in emotional distress and pain, particularly in the limbic areas, sensory/pain regions, and cerebellum.

METHODS: This is a prospectively designed study that included twenty-six patients with a diagnosis of chronic pelvic pain who were randomised to either the NET intervention or a waitlist control. To evaluate the primary outcome of neurophysiological effects, all participants received resting state functional blood oxygen level dependent (BOLD) magnetic resonance imaging (rs-fMRI) before and after the NET intervention or waitlist control period. Pain, mood, anxiety, and quality of life also were assessed.

RESULTS: Compared to the control group, the NET group demonstrated significant improvements in pain interference and pain intensity, and in emotional measures such anxiety and depression. Functional connectivity in the NET group compared to controls, was significantly decreased in the amygdala, cerebellum, and postcentral gyrus. There were also significant correlations between FC changes and changes in clinical measures.

CONCLUSIONS: This study is an initial step towards describing a neurological signature of reducing emotional distress in women with chronic pelvic pain. Specifically, FC changes between the cerebellum and the amygdala and sensory areas appears to be associated with a reduction in pain and the effects of that pain. Future, larger clinical trials are warranted to further evaluate these mechanisms and NET as a potential therapeutic intervention in patients with chronic pelvic pain.

PMID:40083279 | DOI:10.1080/01443615.2025.2472767

Biostatistics. 2024 Dec 31;26(1):kxaf004. doi: 10.1093/biostatistics/kxaf004.

ABSTRACT

This study introduces a mediation analysis framework when the mediator is a graph. A Gaussian covariance graph model is assumed for graph presentation. Causal estimands and assumptions are discussed under this presentation. With a covariance matrix as the mediator, a low-rank representation is introduced and parametric mediation models are considered under the structural equation modeling framework. Assuming Gaussian random errors, likelihood-based estimators are introduced to simultaneously identify the low-rank representation and causal parameters. An efficient computational algorithm is proposed and asymptotic properties of the estimators are investigated. Via simulation studies, the performance of the proposed approach is evaluated. Applying to a resting-state fMRI study, a brain network is identified within which functional connectivity mediates the sex difference in the performance of a motor task.

PMID:40083191 | DOI:10.1093/biostatistics/kxaf004

Neuropsychopharmacology. 2025 Mar 13. doi: 10.1038/s41386-025-02083-6. Online ahead of print.

ABSTRACT

Individual differences in brain intrinsic functional connectivity (FC) and reactivity to nicotine cues are linked to variability in clinical outcomes in nicotine dependence. However, the relative contributions and potential interdependencies of these brain imaging-derived phenotypes in the context of craving and nicotine dependence are unclear. Moreover, it is unknown whether these relationships differ in individuals who smoke versus vape nicotine. To investigate these questions, eighty-six individuals who use nicotine daily (n = 67 smoking, n = 19 vaping) completed either a smoking or vaping cue-reactivity task and a resting-state scan during functional magnetic resonance imaging (fMRI). Validating the efficacy of the smoking and vaping tasks, both cohorts displayed robust reactivity to nicotine versus neutral cues in the default mode network (DMN) and the anterior insula (AI), a primary node of the salience network (SN), which did not habituate over time. In the smoking and vaping groups, lower prefrontal reactivity to nicotine versus neutral cues and greater resting-state FC between nodes of the SN and DMN were associated with higher cue-induced craving. Moreover, we found that the former partially mediated the latter, suggesting a mechanism in which high resting SN-DMN connectivity increases craving susceptibility partly via a constraining effect on regulatory prefrontal reactivity to cues. These relationships were not impacted by group, suggesting that links between brain function and craving are similar regardless of smoking or vaping nicotine.

PMID:40082646 | DOI:10.1038/s41386-025-02083-6

Transl Psychiatry. 2025 Mar 13;15(1):80. doi: 10.1038/s41398-025-03301-x.

ABSTRACT

Social Anxiety Disorder (SAD) involves fear of negative evaluation and social avoidance, impacting quality of life. Early life adversities (ELA) are recognized as risk factors for SAD. Previous research indicated inconsistent alterations in resting state functional connectivity (RSFC) in SAD, particularly in the prefrontal cortex and precuneus. This study investigated the interaction between SAD and ELA at the RSFC level. Functional magnetic resonance imaging (fMRI) was conducted on 120 participants (aged 19-48). Four groups were formed: low/ high ELA controls (n = 49, n = 22) and low/ high ELA SAD participants (n = 30, n = 19). Seed-based correlation analyses (SCA) and multi-voxel pattern analysis (MVPA) were applied. A network in which ELA moderates the neural correlates of SAD during the resting state was identified, involving key nodes like the subgenual anterior cingulate cortex, left middle frontal gyrus, and an area in the calcarine fissure/precuneus. Five distinct interaction patterns of SAD and ELA were observed, showcasing opposite RSFC patterns in individuals with SAD based on ELA experience. Results remained significant when controlled for general anxiety and depression measures. Emotional aspects of ELA played a significant role in these interactions. These findings stress the necessity of considering primarily emotional ELA as covariate in neuroimaging studies investigating SAD and potentially also other psychiatric disorders, addressing inconsistencies in prior research. The left middle frontal gyrus emerges as a link in the SAD-ELA interaction during resting state and anxiety-relevant stimulation. Longitudinal studies, starting from childhood, are needed to understand ELA's impact on brain function and to identify potential neuromarkers for SAD predisposition post-ELA exposure.

PMID:40082409 | DOI:10.1038/s41398-025-03301-x

AJNR Am J Neuroradiol. 2025 Mar 13. doi: 10.3174/ajnr.A8612. Online ahead of print.

ABSTRACT

BACKGROUND AND PURPOSE: Idiopathic generalized epilepsy (IGE) accounts for approximately 20% of epilepsy cases. Characterized by generalized spike-wave discharge, IGE is increasingly recognized as a network disorder with potential metabolic underpinnings. This study leverages the advantages of simultaneous PET/MRI, which enables the concurrent acquisition of MRI and PET data, to integrate structural connectivity (SC), functional connectivity (FC), and glucose metabolism into a unified framework. This study aims to elucidate the multimodal abnormalities of the neocortex in IGE, to analyze the correlations between these abnormalities and clinical presentations, and to investigate the interactions among different imaging modalities.

MATERIALS AND METHODS: Twenty-one patients with IGE and 34 healthy controls (HCs) were recruited. Simultaneous PET/MRI scans were performed, incorporating DTI, resting-state fMRI, and [18F]FDG-PET. DTI generated a neocortical connectivity blueprint, while resting-state fMRI provided a whole-brain connectivity matrix. [18F]FDG-PET data were processed to obtain standardized uptake value ratios (SUVRs). Multivariate distance matrix regression was used to identify abnormal neocortical regions in SC and FC. Differences in SUVRs were identified by using least absolute shrinkage and selection operator regression. Statistical analyses, including t tests, linear models, mediation analysis, and Pearson correlations, were conducted to compare values of each technique between groups and explore relationships with clinical features.

RESULTS: SC abnormalities were primarily found in the limbic (40% of all abnormal neocortical regions) and visual networks (31%), while FC abnormalities were mostly in the default mode network (DMN, 45%). Metabolic abnormalities were predominantly in the frontoparietal (26%) and somatomotor (22%) networks. SC in the limbic was positively correlated with onset age, while seizure frequency was negative correlated with DMN FC and positively correlated with frontoparietal metabolism. Mediation analysis showed that DMN FC mediated the relationship between limbic SC and frontoparietal and somatomotor metabolism.

CONCLUSIONS: A multimodal approach reveals distinct and interrelated abnormalities in IGE, with different modalities reflecting various aspects of the disease, thus enhancing our understanding of its complex mechanisms. This integrative analysis could inform more effective treatments.

PMID:40081849 | DOI:10.3174/ajnr.A8612

AJNR Am J Neuroradiol. 2025 Mar 12:ajnr.A8737. doi: 10.3174/ajnr.A8737. Online ahead of print.

ABSTRACT

BACKGROUND AND PURPOSE: This study evaluated preoperative alterations and postoperative reorganization of the joint language-memory network (LMN) from the perspective of resting-state functional and structural connectivity in Temporal lobe epilepsy (TLE). Graph theory and machine learning approaches were employed to explore automatic lateralization.

MATERIALS AND METHODS: Resting-state fMRI and DTI data were obtained from 20 healthy subjects and 35 patients with TLE. Functional and structural connectivity were calculated within the LMN before and after temporal lobectomy. ANOVA was performed to identify significant connectivity differences between groups. Four local graph measures were extracted from functional and structural connectivity matrices. Standard feature selection techniques and genetic algorithm (GA) methods were applied to select the optimal features. Subsequently, the K-nearest neighbor, support vector machine (SVM), Naive Bayes, and logistic regression classification methods were used to classify healthy controls (HCs) and pre-surgical TLE groups, as well as pre-surgical left TLE (LTLE) and right TLE (RTLE) groups. Also, relationships between psychological scores and the selected features were evaluated using a linear regression method.

RESULTS: The results demonstrated increased functional and decreased structural connectivity in TLE patients before surgery. After surgery, significant connections revealed reduced functional connectivity and increased structural connectivity in TLE patients. Functional analysis identified the left parahippocampal region in LTLE and the right temporal regions in RTLE as key areas. Structural connectivity analysis showed that memory-related areas in the bilateral occipital region and the left language-related area were the origins of alterations. The GA method achieved the highest classification performance using SVM for fMRI and DTI graph measures, with accuracy rates of 97% and 88% for distinguishing LTLE from RTLE, and 93% and 87% for distinguishing TLE from HC, respectively. Moreover, a significant relationship was observed between the best-selected features and memory-assisted cognitive tests.

CONCLUSIONS: Pre-surgical functional hyperconnectivity and post-surgical hypoconnectivity and also newly observed bilateral postsurgical structural connectivity, highlighting functional and structural alterations in the LMN network. Additionally, the study underscores the potential of machine learning for TLE diagnosis and lateralization. A limited sample size, particularly in the postsurgical group was one of the constraints of this study.

ABBREVIATIONS: TLE=Temporal lobe epilepsy; LMN=Language-memory network; GA=Genetic algorithm; HC=Healthy controls; LTLE=Left TLE; RTLE=Right TLE; AUC=Area under the curve.

PMID:40081848 | DOI:10.3174/ajnr.A8737

J Affect Disord. 2025 Mar 11:S0165-0327(25)00395-7. doi: 10.1016/j.jad.2025.03.057. Online ahead of print.

ABSTRACT

BACKGROUND: Major Depressive Disorder (MDD) and Bipolar Disorder (BD) exhibit overlapping depressive symptoms, complicating their differentiation in clinical practice. Traditional neuroimaging studies have focused on specific regions of interest, but few have employed whole-brain analyses like regional homogeneity (ReHo). This study aims to differentiate MDD from BD by identifying key brain regions with abnormal ReHo and using advanced machine learning techniques to improve diagnostic accuracy.

METHODS: A total of 63 BD patients, 65 MDD patients, and 70 healthy controls were recruited from the Shanghai Mental Health Center. Resting-state functional MRI (rs-fMRI) was used to analyze ReHo across the brain. We applied Support Vector Machine (SVM) and SVM-Recursive Feature Elimination (SVM-RFE), a robust machine learning model known for its high precision in feature selection and classification, to identify critical brain regions that could serve as biomarkers for distinguishing BD from MDD. SVM-RFE allows for the recursive removal of non-informative features, enhancing the model's ability to accurately classify patients. Correlations between ReHo values and clinical scores were also evaluated.

RESULTS: ReHo analysis revealed significant differences in several brain regions. The study results revealed that, compared to healthy controls, both BD and MDD patients exhibited reduced ReHo in the superior parietal gyrus. Additionally, MDD patients showed decreased ReHo values in the Right Lenticular nucleus, putamen (PUT.R), Right Angular gyrus (ANG.R), and Left Superior occipital gyrus (SOG.L). Compared to the MDD group, BD patients exhibited increased ReHo values in the Left Inferior occipital gyrus (IOG.L). In BD patients only, the reduction in ReHo values in the right superior parietal gyrus and the right angular gyrus was positively correlated with Hamilton Depression Scale (HAMD) scores. SVM-RFE identified the IOG.L, SOG.L, and PUT.R as the most critical features, achieving an area under the curve (AUC) of 0.872, with high sensitivity and specificity in distinguishing BD from MDD.

CONCLUSION: This study demonstrates that BD and MDD patients exhibit distinct patterns of regional brain activity, particularly in the occipital and parietal regions. The combination of ReHo analysis and SVM-RFE provides a powerful approach for identifying potential biomarkers, with the left inferior occipital gyrus, left superior occipital gyrus, and right putamen emerging as key differentiating regions. These findings offer valuable insights for improving the diagnostic accuracy between BD and MDD, contributing to more targeted treatment strategies.

PMID:40081596 | DOI:10.1016/j.jad.2025.03.057

Brain Res Bull. 2025 Mar 11:111294. doi: 10.1016/j.brainresbull.2025.111294. Online ahead of print.

ABSTRACT

BACKGROUND: Resting-state functional magnetic resonance imaging (fMRI) studies have shown abnormal functional connectivity (FC) of the insula (INS) in patients with musculoskeletal pain (MSP). However, there is a lack of consistency in previous studies, which is an obstacle to understanding the underlying neuropathology of MSP.

METHOD: Seven databases, including PubMed, Web of Science, the Cochrane Library, Embase, China National Knowledge Infrastructure (CNKI), Wanfang Database, and Chongqing VIP, were systematically searched from inception to 15 May 2024. The meta-analysis of the aberrant INS-based FC in MSP patients was performed using the anisotropic effect-size signed differential mapping (AES-SDM).

RESULTS: A total of eleven neuroimaging studies with 276 patients and 253 HCs were included in the meta-analysis. The results indicate that MSP patients have increased FC between INS and the right median cingulate gyri, right inferior frontal gyrus, right paracentral lobule, and right supplementary motor area, and decreased FC between INS and the left precuneus and left angular gyrus. Heterogeneity and sensitivity analysis showed that most of the results of INS-based FC were highly reproducible and robust. Meta-regression analysis showed that revealed a negative association between the Visual Analog Scale (VAS) score and the reduction in FC between the INS and the left precuneus.

CONCLUSION: The meta-analysis reveals that patients with MSP show abnormal FC between the INS and multiple brain regions, which are involved in emotional, cognitive, sensory, visuospatial and motor regulation of pain. These findings provide important insights into the underlying neuropathological mechanisms of musculoskeletal disorders.

PMID:40081505 | DOI:10.1016/j.brainresbull.2025.111294

Dev Cogn Neurosci. 2025 Mar 6;73:101543. doi: 10.1016/j.dcn.2025.101543. Online ahead of print.

ABSTRACT

Adolescence is a period of growth in cognitive performance and functioning. Recently, data-driven measures of brain-age gap, which can index cognitive decline in older populations, have been utilized in adolescent data with mixed findings. Instead of using a data-driven approach, here we assess the maturation status of the brain functional landscape in early adolescence by directly comparing an individual's resting-state functional connectivity (rsFC) to the canonical early-life and adulthood communities. Specifically, we hypothesized that the degree to which a youth's connectome is better captured by adult networks compared to infant/toddler networks is predictive of their cognitive development. To test this hypothesis across individuals and longitudinally, we utilized the Adolescent Brain Cognitive Development (ABCD) Study at baseline (9-10 years; n = 6469) and 2-year-follow-up (Y2: 11-12 years; n = 5060). Adjusted for demographic factors, our anchored rsFC score (AFC) was associated with better task performance both across and within participants. AFC was related to age and aging across youth, and change in AFC statistically mediated the age-related change in task performance. In conclusion, we showed that a model-fitting-free index of the brain at rest that is anchored to both adult and baby connectivity landscapes predicts cognitive performance and development in youth.

PMID:40080996 | DOI:10.1016/j.dcn.2025.101543

Brain Behav. 2025 Mar;15(3):e70414. doi: 10.1002/brb3.70414.

ABSTRACT

OBJECTIVE: The pathophysiological mechanism of migraine is still not clear. Thus, this study aimed to evaluate the changes in effective connectivity (EC) in the brain functional network underlying migraine and its association with clinical measures of migraine.

BACKGROUND: Fifty patients with episodic migraine without aura (MwoA) and 48 healthy controls (HCs) were enrolled in this study. Spontaneous activity in the brain was evaluated using the degree centrality (DC) method, and the brain regions with obvious signal differences between the two groups were taken as seed points for whole brain Granger causality analysis (GCA) analysis. The values of the brain regions with differences in DC and GCA were extracted and correlated with clinical measures of migraine.

RESULTS: Compared to the HCs, the MwoA patients showed decreased DC in the left inferior temporal gyrus (ITG) and increased DC in the right precuneus and exhibited significantly decreased EC from the left ITG to the left inferior parietal gyrus and right inferior occipital gyrus (IOG) as well as significantly increased EC from the left postcentral gyrus and left cerebellum posterior lobe to the left ITG. Moreover, decreased EC from the left thalamus to the right precuneus was found in the MwoA patients compared to the HCs. The DC values in the right precuneus were significantly negatively correlated with the duration of headache. Additionally, we found a significantly positive correlation between the Migraine Disability Assessment questionnaire score and the EC from the left ITG to the right IOG, as well as between the intensity of headache and the EC from the left thalamus to the right precuneus.

CONCLUSIONS: This study found changes in the EC of the brain functional network underlying migraine and their associations with migraine-related parameters. These findings are helpful for understanding the pathophysiological mechanism in migraine patients.

PMID:40079637 | DOI:10.1002/brb3.70414

J Cereb Blood Flow Metab. 2025 Mar 13:271678X251325399. doi: 10.1177/0271678X251325399. Online ahead of print.

ABSTRACT

Cerebrovascular reactivity (CVR) and cerebral pulsatility (CP) are important indicators of cerebrovascular health, which are associated with physical activity (PA). While sex differences influence the impact of PA on cerebrovascular health, sex-specific effects of PA intensity and dose on CP and CVR remains unknown. This study aimed to evaluate the sex-specific effects of self-reported PA dose and intensity on CVR and CP. The Human Connectome Project - Aging dataset was used, including 626 participants (350 females, 276 males) aged 36-85. The effect of menopausal status was also assessed. Resting state fMRI data was used to estimate both CVR and CP. Weekly self-reported PA was quantified as metabolic equivalent of task. Females presented a unique non-linear relationship between relative CVR and total PA in the cerebral cortex. Females and menopausal subgroups revealed negative linear relationships with total and walking PA in occipital and cingulate regions. Males exhibited negative linear relationships between total and vigorous PA and CVR in parietal and cingulate regions. Postmenopausal females showed greater reductions across more regions in CP than other groups. Overall, males and females appear to benefit from different amounts and intensities of PA, with menopause status influencing the effect of PA on cerebrovascular health.

PMID:40079560 | DOI:10.1177/0271678X251325399

Brain Connect. 2025 Mar 12. doi: 10.1089/brain.2024.0056. Online ahead of print.

ABSTRACT

Background: The brain's complex functionality emerges from network interactions that go beyond dyadic connections, with higher-order interactions significantly contributing to this complexity. Homotopic functional connectivity (HoFC) is a key neurophysiological characteristic of the human brain, reflecting synchronized activity between corresponding regions in the brain's hemispheres. Materials and Methods: Using resting-state functional magnetic resonance imaging data from the Human Connectome Project, we evaluate dyadic and higher-order interactions of three functional connectivity (FC) parameterizations-bivariate correlation, partial correlation, and tangent space embedding-in their effectiveness at capturing HoFC through the inter-hemispheric analogy test. Results: Higher-order feature vectors are generated through node2vec, a random walk-based node embedding technique applied to FC networks. Our results show that higher-order feature vectors derived from partial correlation most effectively represent HoFC, while tangent space embedding performs best for dyadic interactions. Discussion: These findings validate HoFC and underscore the importance of the FC construction method in capturing intrinsic characteristics of the human brain.

PMID:40079154 | DOI:10.1089/brain.2024.0056

Front Neurosci. 2025 Feb 26;19:1519870. doi: 10.3389/fnins.2025.1519870. eCollection 2025.

ABSTRACT

INTRODUCTION: In aviation, exceptional visual perception is crucial for pilots to monitor flight instruments and respond swiftly to deviations, as well as make rapid judgments regarding environmental changes, ensuring aviation safety. However, existing research on pilots' visual perception has predominantly focused on behavioral observations, with limited exploration of the neurophysiological mechanisms involved.

METHODS: This study aimed to investigate the brain activity associated with the visual perception capabilities of flight cadets. Data were collected from 25 flying cadets and 24 ground students under two conditions: a resting-state functional magnetic resonance imaging session conducted in 2022 and a change-detection task. The data were analyzed using RESTplus software.

RESULTS: The analysis revealed that degree centrality values in the right precuneus and left lingual gyrus showed significantly positive correlations with task reaction time and accuracy, respectively, in the pilot group. These brain regions were found to be significantly associated with the visual perception abilities of the pilots.

DISCUSSION: The findings suggest that alterations in the left precuneus and right lingual gyrus in pilots are linked to their visual perception capabilities, which may play a crucial role in mission performance. These results provide a foundation for improving flight training programs and selecting suitable flight trainees based on neurophysiological markers of visual perception.

PMID:40078710 | PMC:PMC11897578 | DOI:10.3389/fnins.2025.1519870

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