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Soc Cogn Affect Neurosci. 2025 Sep 4:nsaf087. doi: 10.1093/scan/nsaf087. Online ahead of print.

ABSTRACT

Sensitivity to the social environment is linked to suicidal ideation in adolescence, and little research has examined variance in neural functioning that may underlie this sensitivity and increase risk. Neural-based pathways to suicidal ideation are likely mediated by subjective experiences of the social environment. Loneliness is associated with both salience network connectivity and suicidal ideation. This longitudinal study tested whether greater salience network functional integration (ie, global efficiency) in early adolescence, which may underlie hypervigilance to social experiences, predicts risk for future suicidal ideation via loneliness. Participants (N = 96; Mage=12.94) completed a fMRI scan to measure resting-state salience network functional integration. Loneliness, suicidal ideation, and a sociometric measure of adolescents' real-world peer environment were assessed over several years. Greater salience network global efficiency was associated with suicidal ideation two years later via higher levels of loneliness approximately one year later, particularly for girls. Across boys and girls, the effect of salience network global efficiency on loneliness appeared stronger for youth experiencing relatively larger decreases in peer acceptance over the prior year. While findings should be interpreted as preliminary given the sample size, they suggest a possible social-developmental pathway from early-adolescent salience network integration to future vulnerability for loneliness and suicidal thinking.

PMID:40905679 | DOI:10.1093/scan/nsaf087

Front Neurol. 2025 Aug 19;16:1608371. doi: 10.3389/fneur.2025.1608371. eCollection 2025.

ABSTRACT

BACKGROUND: Ulcerative colitis (UC), a chronic inflammatory bowel disease (IBD), is linked to neuropsychiatric comorbidities and changes in brain connectivity through the brain-gut axis. Resting-state functional MRI (RS-fMRI) offers a non-invasive approach to examining these neural alterations; however, no comprehensive review has compiled findings specific to UC.

OBJECTIVE: This review summarizes RS-fMRI studies to characterize functional connectivity (FC) alterations and methodological approaches in UC patients compared to healthy controls (HCs) and other inflammatory bowel disease (IBD) subtypes.

METHODS: Literature searches were performed in Ovid, PubMed, Google Scholar, and EMBASE (up to July 2025) using keywords: resting-state fMRI, RS-FMRI, ulcerative colitis, and UC. Few studies meeting the inclusion criteria (human participants, UC diagnosis, RS-fMRI analysis) were reviewed.

KEY FINDINGS: Seven studies were included in this review. UC patients show disrupted FC in the salience, cerebellar, visual, default mode, and dorsal attention networks. Reduced hippocampal activity is linked to working memory deficits, while increased FC in corticolimbic areas (e.g., caudate anterior, cingulate) correlates with active inflammation. Grey matter volume decreases in cerebellar regions and increases in parahippocampal regions. Sex-specific differences in FC are observed, especially in the visual and attention networks. Altered FC patterns are associated with the severity of anxiety, depression, and stress. UC exhibits distinct neural signatures compared to CD.

IMPLICATIONS: RS-fMRI uncovers UC-specific neural phenotypes, advancing the mechanistic understanding of brain-gut interactions. These findings highlight potential biomarkers for neuropsychiatric comorbidities and support the use of integrated fMRI in clinical assessments. Future research should focus on longitudinal studies, larger cohorts, and AI-enhanced analytics to clarify causality and identify therapeutic targets.

PMID:40904825 | PMC:PMC12402898 | DOI:10.3389/fneur.2025.1608371

JMIR Med Inform. 2025 Sep 3;13:e72155. doi: 10.2196/72155.

ABSTRACT

BACKGROUND: Deep learning has demonstrated significant potential in advancing computer-aided diagnosis for neuropsychiatric disorders, such as migraine, enabling patient-specific diagnosis at an individual level. However, despite the superior accuracy of deep learning models, the interpretability of image classification models remains limited. Their black-box nature continues to pose a major obstacle in clinical applications, hindering biomarker discovery and personalized treatment.

OBJECTIVE: This study aims to investigate explainable artificial intelligence (XAI) techniques combined with multiple functional magnetic resonance imaging (fMRI) indicators to (1) compare their efficacy in migraine classification, (2) identify optimal model-indicator pairings, and (3) evaluate XAI's potential in clinical diagnostics by localizing discriminative brain regions.

METHODS: We analyzed resting-state fMRI data from 64 participants, including 21 (33%) patients with migraine without aura, 15 (23%) patients with migraine with aura, and 28 (44%) healthy controls. Three fMRI metrics-amplitude of low-frequency fluctuation, regional homogeneity, and regional functional connectivity strength (RFCS)-were extracted and classified using GoogleNet, ResNet18, and Vision Transformer. For comprehensive model comparison, conventional machine learning methods, including support vector machine and random forest, were also used as benchmarks. Model performance was evaluated through accuracy and area under the curve metrics, while activation heat maps were generated via gradient-weighted class activation mapping for convolutional neural networks and self-attention mechanisms for Vision Transformer.

RESULTS: The GoogleNet model combined with RFCS indicators achieved the best classification performance, with an accuracy of >98.44% and an area under the receiver operating characteristic curve of 0.99 for the test set. In addition, among the 3 indicators, the RFCS indicator improved accuracy by approximately 8% compared with the amplitude of low-frequency fluctuation. Brain activation heat maps generated by XAI technology revealed that the precuneus and cuneus were the most discriminative brain regions, with slight activation also observed in the frontal gyrus.

CONCLUSIONS: The use of XAI technology combined with brain region features provides visual explanations for the progression of migraine in patients. Understanding the decision-making process of the network has significant potential for clinical diagnosis of migraines, offering promising applications in enhancing diagnostic accuracy and aiding in the development of new diagnostic techniques.

PMID:40903006 | DOI:10.2196/72155

Front Neurol. 2025 Aug 18;16:1622274. doi: 10.3389/fneur.2025.1622274. eCollection 2025.

ABSTRACT

Cerebral small vessel disease (CSVD) has recently garnered extensive attention owing to its significant disease burden, insidious onset, and the absence of effective specific treatments. Poor lifestyle habits and chronic diseases are closely linked to its occurrence and development, eventually resulting in cognitive dysfunction. Therefore, improvement of lifestyle, stable blood pressure, effective glucose lowering, low-salt and low-fat diet, smoking cessation, moderate exercise and adequate sleep are the keys to preventing cognitive dysfunction in cerebral small-vessel disease. Early prevention and intervention are of significant clinical importance and social value, particularly as CSVD represents a major contributor to cognitive dysfunction in approximately 40 million elderly individuals worldwide. This comprehensive review integrates findings across four functional MRI techniques-diffusion tensor imaging (DTI), resting-state functional MRI (rs-fMRI), magnetic resonance spectroscopy (MRS), and arterial spin labeling (ASL)-to provide a holistic framework connecting structural abnormalities with functional deficits in CSVD. This paper aimed to cover four aspects: an overview of CSVD, the correlation between the clinical manifestations of CSVD and cognitive dysfunction, the neuroradiological features of CSVD, and the application of functional magnetic resonance imaging (fMRI) in CSVD patients with cognitive dysfunction. The integration of these complementary techniques offers unprecedented insights into disease mechanisms, enabling improved early diagnosis, establishment of reliable imaging biomarkers for monitoring disease progression, and development of tailored therapeutic strategies to slow or prevent cognitive decline in affected individuals.

PMID:40901662 | PMC:PMC12400868 | DOI:10.3389/fneur.2025.1622274

Dev Psychobiol. 2025 Sep;67(5):e70080. doi: 10.1002/dev.70080.

ABSTRACT

Early adversity is a well-established risk factor for psychopathology in youth. Contemporary taxonomies of adversity seek to distill the diverse stressors children face into meaningful categories of experience to enable more precise prediction of risk; however, few studies have tested these models using data-driven approaches in well-characterized, longitudinal samples. Here, we examined the latent structure of early stress across diverse domains of exposure, tested differential associations with psychopathology in adolescence, and investigated frontolimbic functional connectivity as a potential mediator. In a sample of 168 youth (Mage = 11.36), factor analyses identified two latent stress factors at baseline-"Parenting" and "Deprivation & Unpredictability"-and a single "Psychopathology" factor extracted from measures of mental health obtained 2 years later. While adverse parenting predicted greater psychopathology, exposure to threat emerged as the strongest predictor of adolescent mental health problems. High-dimensional regularized mediation analyses revealed that frontolimbic functional connectivity mediated the association between Threat and Psychopathology in girls but not in boys. These findings suggest that widely used dimensional models overlook key aspects of adversity, including sex-linked asymmetries across neurodevelopment and the distinct role of parenting-related stress. Refining adversity taxonomies across diverse samples and stress domains is crucial to advancing targeted interventions for youth mental health.

PMID:40898734 | DOI:10.1002/dev.70080

BMC Neurosci. 2025 Sep 2;26(1):57. doi: 10.1186/s12868-025-00979-z.

NO ABSTRACT

PMID:40898026 | DOI:10.1186/s12868-025-00979-z

Brain Res Bull. 2025 Aug 31:111534. doi: 10.1016/j.brainresbull.2025.111534. Online ahead of print.

ABSTRACT

Emotion regulation is crucial for maintaining normal social interactions and individual psychological health. Using transcranial magnetic stimulation (TMS) to modulate emotional regulation may be a powerful method for neurological or psychiatric disorders. However, TMS efficacy varies between protocols and individuals, with the brain's state during treatment being an often-overlooked factor. This study aimed to explore the influence of emotional brain state on TMS effects. Ninety-nine healthy participants were randomly assigned to three groups: one watched neutral film clips and received active TMS (neutral group), while the other two watched sadness film clips and received either active or sham TMS (sad and sham groups, respectively). The amplitude of low-frequency fluctuations (ALFF) and functional connectivity (FC) were investigated using resting-state functional magnetic resonance imaging. Compared with the neutral group, the sad group showed different changes in neural activity (as measured by ALFF) in the right superior occipital gyrus and right middle frontal gyrus after TMS. In the neutral group, the ALFF change in the right superior occipital gyrus was correlated with the baseline FC between this region and the TMS target. Additionally, changes in neural activity in the right superior occipital gyrus and right middle frontal gyrus were related to changes in depression scale scores in the sad group. These findings may suggest that TMS during different emotional states can induce state-dependent alterations in neural activity. By combining emotional induction, TMS, and fMRI, this study offers a unique perspective on state-dependent effects and may improve TMS treatment outcomes.

PMID:40897293 | DOI:10.1016/j.brainresbull.2025.111534

Brain Cogn. 2025 Sep 1;189:106352. doi: 10.1016/j.bandc.2025.106352. Online ahead of print.

ABSTRACT

Rumination is closely associated with social anxiety and is considered a key cognitive factor in its onset and persistence. Both processes engage brain functions related to self-referential cognition and emotional regulation; however, the neural pathways linking rumination and social anxiety remain incompletely understood. Using resting-state neuroimaging data from 470 participants, we conducted voxel-based functional connectivity analysis focusing on the ventromedial prefrontal cortex (vmPFC), a key region implicated in self-referential processing and affective regulation. Results showed that functional connectivity between the anterior vmPFC and the left inferior frontal gyrus (IFG) and the right superior frontal gyrus (SFG) was significantly associated with both rumination and social anxiety, and mediated their association. Notably, functional connectivity related to social anxiety was primarily observed in the anterior rather than the posterior vmPFC, suggesting that social anxiety may be closely linked to heightened sensitivity to social value and reward cues. This study reveals the central role of the vmPFC in integrating self-related cognition and emotion regulation, demonstrating how its functional connectivity mediates the influence of rumination on social anxiety, thereby deepening our understanding of the neural mechanisms underlying social anxiety.

PMID:40897111 | DOI:10.1016/j.bandc.2025.106352

Front Psychiatry. 2025 Aug 14;16:1622162. doi: 10.3389/fpsyt.2025.1622162. eCollection 2025.

ABSTRACT

BACKGROUND: The causal or direct connectivity alterations of triple network including salience network (SN), central executive network (CEN), and default mode network (DMN) in tobacco use disorder (TUD) and the neurobiological features associated with smoking motivation are still unclear, which hampered the development of a targeted intervention for TUD.

METHOD: We recruited 93 male smokers and 55 male non-smokers and obtained their resting-state functional MRI (rs-fMRI) and smoking-related clinical scales. We applied dynamic causal modeling (DCM) to rs-fMRI to characterize changes of effective connectivity (EC) among seven major hubs from triple networks in TUD. Leave-one-out (LOO) cross-validation was used to investigate whether the altered EC could predict the smoking motivations (evaluated by Russell Reason for Smoking Questionnaire).

RESULTS: Compared with the control group, the TUD group displayed inhibitory extrinsic effective connectivity within SN. The abnormal ECs between networks were mainly characterized by uncoordinated switching between DMN and ECN activities in TUD individuals, with insula acting as a causal hub in this process. Moreover, increased EC from the right dorsolateral prefrontal cortex (R-DLPFC) and medial prefrontal cortex (MPFC) could predict the smoking motivations related to physical dependence.

CONCLUSIONS: This study revealed aberrant causal connectivity in triple network and clarified the potential neural mechanism of smoking behavior driven by physical dependence. These findings suggested that a network-derived indicator could be a potential bio-marker of TUD and help to identify the heterogeneity in the motivation of smoking behavior.

PMID:40896217 | PMC:PMC12392163 | DOI:10.3389/fpsyt.2025.1622162

Biol Psychiatry Glob Open Sci. 2025 Jul 8;5(6):100562. doi: 10.1016/j.bpsgos.2025.100562. eCollection 2025 Nov.

ABSTRACT

BACKGROUND: Specific phobia (SP) is a prevalent mental disorder for which exposure-based treatments are the most effective. Little is known about the intrinsic functional connectivity of SP and its modification by treatment. While previous studies were limited to a priori-defined brain regions, we used connectome-wide analyses to capture the full extent of altered functional connectivity.

METHODS: We used functional magnetic resonance imaging in combination with hypothesis-free, data-driven functional connectivity multivariate pattern analysis (fc-MVPA) to identify differences between 72 individuals with SP and a nonphobic control group (CG) (n = 82). The SP group then received a one-session exposure treatment and was scanned again 9 weeks later on average.

RESULTS: fc-MVPA identified the largest differences between the SP group and CG in sensorimotor regions, with lower connectivity to temporal nodes of the default network and anticorrelations with the frontoparietal network in the SP group compared with the CG. Stronger connectivity in the pretreatment compared with the posttreatment condition was evident in visual regions, while anticorrelations with the frontoparietal network were reduced. Post hoc comparisons showed that the connectivity strengths of the SP group after treatment between almost all identified nodes of both contrasts (SP vs. CG and pre vs. post) were comparable to those of the CG at baseline.

CONCLUSIONS: Given the known functions of the identified networks, it is possible that the changes in connectivity measured after treatment indicate improved action control, enabled by more accurate prediction of the environment and stronger coupling of perceptual and action regions with higher-order control regions.

PMID:40895837 | PMC:PMC12390938 | DOI:10.1016/j.bpsgos.2025.100562

Depress Anxiety. 2025 Aug 22;2025:9062022. doi: 10.1155/da/9062022. eCollection 2025.

ABSTRACT

Major depressive disorder (MDD) and schizophrenia (SZ) are among the most debilitating psychiatric disorders, characterized by widespread disruptions in large-scale brain networks. However, the commonalities and distinctions in their large-scale network distributions remain unclear. The present study aimed to leverage advanced deep learning techniques to identify these common and distinct patterns, providing insights into the shared and disorder-specific neural mechanisms underlying MDD and SZ. Recent advances in graph neural networks (GNNs) offer a powerful framework for analyzing brain connectivity patterns, enabling automated learning of complex, high-dimensional network features. In this study, we applied state-of-art GNN architectures to classify MDD and SZ patients from healthy controls (HCs), respectively, using a multisite resting-state fMRI dataset. The attention-based hierarchical pooling GNN (SAGPool) model achieved the highest performance, with mean accuracies of 71.50% for MDD and 75.65% for SZ classification. Using a perturbation-based explainability method, we identified prominent functional connections driving model decisions, revealing distinct patterns of the large-scale network disruption across disorders. In MDD, alterations were dominantly observed in the default mode network (DMN), whereas SZ exhibited prominent alterations in the ventral attention network (VAN). Notably, specific functional connections identified by our model showed significant correlations with clinical symptoms, particularly positive and general symptoms measured by the positive and negative syndrome scale (PANSS) in SZ patients. Our findings demonstrate the utility of GNNs for uncovering complex connectivity patterns in psychiatric disorders and provide novel insights into the distinct neural mechanisms underlying MDD and SZ. These results highlight the potential of graph-based models as tools for both diagnostic classification and biomarker discovery in psychiatric research.

PMID:40895757 | PMC:PMC12396894 | DOI:10.1155/da/9062022

Front Mol Neurosci. 2025 Aug 14;18:1676946. doi: 10.3389/fnmol.2025.1676946. eCollection 2025.

NO ABSTRACT

PMID:40894855 | PMC:PMC12391148 | DOI:10.3389/fnmol.2025.1676946

bioRxiv [Preprint]. 2025 Aug 22:2023.09.16.558065. doi: 10.1101/2023.09.16.558065.

ABSTRACT

Functional connectivity (FC) has been invaluable for understanding the brain's communication network, with strong potential for enhanced FC approaches to yield additional insights. Unlike with the fMRI field-standard method of pairwise correlation, theory suggests that partial correlation can estimate FC without confounded and indirect connections. However, partial correlation FC can also display low repeat reliability, impairing the accuracy of individual estimates. We hypothesized that reliability would be increased by adding regularization, which can reduce overfitting to noise in regression-based approaches like partial correlation. We therefore tested several regularized alternatives - graphical lasso, graphical ridge, and principal component regression - against unregularized partial and pairwise correlation, applying them to empirical resting-state fMRI and simulated data. As hypothesized, regularization vastly improved reliability, quantified using between-session similarity and intraclass correlation. This enhanced reliability then granted substantially more accurate individual FC estimates when validated against structural connectivity (empirical data) and ground truth networks (simulations). Graphical lasso showed especially high accuracy among regularized approaches, seemingly by maintaining more valid underlying network structures. We additionally found graphical lasso to be robust to noise levels, data quantity, and subject motion - common fMRI error sources. Lastly, we demonstrated that resting-state graphical lasso FC can effectively predict fMRI task activations and individual differences in behavior, further establishing its reliability, external validity, and ability to characterize task-related functionality. We recommend graphical lasso or similar regularized methods for calculating FC, as they can yield more valid estimates of unconfounded connectivity than field-standard pairwise correlation, while overcoming the poor reliability of unregularized partial correlation.

PMID:40894659 | PMC:PMC12393294 | DOI:10.1101/2023.09.16.558065

medRxiv [Preprint]. 2025 Aug 19:2025.08.15.25332894. doi: 10.1101/2025.08.15.25332894.

ABSTRACT

OBJECTIVE: Conduct a mega-analysis of two complementary measures of resting-state functional magnetic resonance imaging (rsfMRI) dynamics--amplitude of low-frequency fluctuation (ALFF) and low-frequency spectral entropy (lfSE)--in a transdiagnostic mood and psychosis-spectrum sample to evaluate group differences and clinical symptom associations.

DESIGN: ALFF and lfSE were calculated at the node-level by filtering data from 0.01 Hz to 0.08 Hz, regressing demographic variables, and harmonizing sites. Group differences were assessed using the Wilcoxon signed test. Symptom associations were evaluated with Spearman's rho. Analyses were conducted at both whole-brain and network levels, with sensitivity analyses to evaluate the impact of frequency brands.

SETTING: Four open-source case-control datasets with resting-state functional magnetic resonance imaging were used: the Center for Biomedical Research Excellence, the Human Connectome Project for Early Psychosis, the Strategic Research Program for Brain Sciences, and the UCLA Consortium for Neuropsychiatric Phenomics.

PARTICIPANTS: Included participants had a mood disorder (bipolar, dysthymia, or major depressive disorder, n=228), a psychosis-spectrum disorder (early psychosis or schizophrenia spectrum disorder, n=318), or a healthy control (n=535).

MAIN OUTCOMES AND MEASURES: To identify transdiagnostic group differences and to evaluate mood and psychosis symptom associations using ALFF and lfSE.

RESULTS: ALFF in psychosis-spectrum was significantly lower than mood disorders and controls (q's<0.001) at the whole-brain and network levels. lfSE in controls was significantly lower than both psychosis-spectrum and mood disorders at the whole-brain and network levels (q's<0.001). Whole-brain ALFF is positively associated with mood symptoms (rho=0.27, p<0.05). Whole-brain lfSE is negatively associated with positive (rho=-0.13, p<0.05) and mood (rho=-0.38, p<0.01) symptoms. Across frequency analyses, mood disorders exhibited greater sensitivity to group differences and symptom associations.

CONCLUSIONS AND RELEVANCE: Widespread, global differences in ALFF and lfSE underly transdiagnostic spectra of psychosis-spectrum and mood disorders. lfSE may be applicable for wider use in fMRI. Spectral measures of brain dynamics may represent transdiagnostic markers of mental health.

PMID:40894152 | PMC:PMC12393583 | DOI:10.1101/2025.08.15.25332894

Quant Imaging Med Surg. 2025 Sep 1;15(9):8265-8281. doi: 10.21037/qims-2024-2572. Epub 2025 Aug 13.

ABSTRACT

BACKGROUND: Abdominal pain is a prevalent and debilitating manifestation of Crohn's disease (CD) that significantly impacts the lives of those affected. The neurological pathways responsible for abdominal pain in patients with CD remain unidentified. Therefore, the purpose of this study was to characterize the structural alterations in the brain and associated functional connectivity (FC) in patients with CD and abdominal pain.

METHODS: The data for three-dimensional T1-weighted and resting-state functional magnetic resonance imaging (fMRI) were gathered from 23 patients with CD and abdominal pain (pain CD), 24 patients with CD but without abdominal pain (nonpain CD), and 25 healthy controls (HCs). Differences in gray-matter volume (GMV) and FC between the pain CD group, nonpain CD group, and HCs were evaluated via analysis of covariance. Biased correlation analyses were employed to evaluate the association of variations in GMV and FC with clinical measures.

RESULTS: Voxel-based morphometry analysis revealed that the pain CD group exhibited changes in GMV in the right anterior cingulate cortex (ACC) and orbitofrontal regions, including the orbital parts of the superior frontal gyri, middle frontal gyri (ORBmid), and inferior frontal gyri, as compared to both the HC and nonpain CD groups. Additionally, compared to the HC group, the nonpain CD group showed increased GMV in the bilateral hippocampus. FC analysis showed that the pain CD group had enhanced FC between the right ACC and the default mode network (DMN), particularly with the parahippocampal gyrus (PHG), Rolandic operculum, and postcentral gyrus, as compared to the nonpain CD group. Furthermore, compared to both the nonpain CD and HC groups, pain CD group exhibited increased FC between the left ORBmid and key pain-processing hubs, including the left thalamus, left ACC, and right middle frontal gyrus (MFG). Notably, the FC between the ACC and PHG was negatively correlated with Beck Depression Inventory score (r=-0.548; P=0.019). The FC between the left ORBmid and the right MFG showed a significant negative correlation with Pain Sensitivity Questionnaire score (r=-0.495; P=0.037).

CONCLUSIONS: Our results suggest that pain may differentially affect brain morphology and function in patients with CD, particularly involving the ACC and orbitofrontal cortex. Specifically, increased FC between the ACC and DMN, as well as orbitofrontal-thalamic circuits, provide novel imaging evidence for the neural mechanisms underlying visceral pain in CD.

PMID:40893572 | PMC:PMC12397677 | DOI:10.21037/qims-2024-2572

Quant Imaging Med Surg. 2025 Sep 1;15(9):7820-7832. doi: 10.21037/qims-24-1321. Epub 2025 Aug 15.

ABSTRACT

BACKGROUND: Chemotherapy-related cognitive impairments (CRCIs) are frequently reported by patients with non-small cell lung cancer (NSCLC) following chemotherapy treatment. Studies have revealed that cognitive impairment may be linked to abnormal spontaneous neuronal activity and changes in cerebral blood flow (CBF). However, the specific impact of neurovascular coupling (NVC) alterations on patients who have undergone chemotherapy has not been clarified. The aim of this study was to examine the variations in NVC in patients with lung cancer postchemotherapy and to determine potential correlations between these NVC alterations and neurocognitive dysfunction.

METHODS: A sample of 43 patients with NSCLC was recruited, including 20 patients treated with chemotherapy [CT(+)] and 23 chemotherapy-naïve [CT(-)] individuals who underwent pseudocontinuous arterial spin labeling (pCASL) scans and resting-state functional magnetic resonance imaging (rs-fMRI), along with neurocognitive evaluations. Global and regional NVC indices were assessed according to correlation coefficients and the ratios between CBF and neuronal activity-derived metrics, including the amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo). Statistical analyses were conducted to calculate the difference between groups and characterize relationships between alterations in global and regional NVC and cognitive performance.

RESULTS: In comparison to the CT(-) group, the CT(+) group exhibited significantly lower coupling strength for global CBF-ALFF and CBF-ReHo correlations (P<0.05). Regionally, the CT(+) group demonstrated a decreased CBF:ALFF ratio in the right middle temporal gyrus (MTG) and left middle frontal gyrus (MFG), as well as an increased CBF:ALFF ratio in the left thalamus and left parahippocampal region. Furthermore, the CT(+) group had higher CBF:ReHo ratios in the left precuneus, right central operculum, right inferior parietal lobule, and right superior occipital gyrus but lower CBF:ReHo ratios in the left inferior frontal gyrus and right MFG (false-discovery rate-corrected P value <0.05). Notably, there was a negative correlation observed between Montreal Cognitive Assessment scores and memory scores and the CBF:ALFF ratios in the right MFG and left parahippocampal region.

CONCLUSIONS: This research offers comprehensive insights into the neurological foundations of CRCI. The application of multimodal neuroimaging analyses combining rs-fMRI and pCASL may uncover the induction of neurovascular decoupling in lung cancer patients undergoing chemotherapy.

PMID:40893533 | PMC:PMC12397636 | DOI:10.21037/qims-24-1321

Quant Imaging Med Surg. 2025 Sep 1;15(9):7989-8007. doi: 10.21037/qims-2025-290. Epub 2025 Aug 19.

ABSTRACT

BACKGROUND: People living with human immunodeficiency virus (PLWH) are at risk of human immunodeficiency virus (HIV)-associated neurocognitive disorders (HAND). The mildest disease stage of HAND is asymptomatic neurocognitive impairment (ANI), and the accurate diagnosis of this stage can facilitate timely clinical interventions. The aim of this study was to mine features related to the diagnosis of ANI based on resting-state functional magnetic resonance imaging (rs-fMRI) and to establish classification models.

METHODS: A total of 74 patients with 74 ANI and 78 with PLWH but no neurocognitive disorders (PWND) were enrolled. Basic clinical, T1-weighted imaging, and rs-fMRI data were obtained. The rs-fMRI signal values and radiomics features of 116 brain regions designated by the Anatomical Automatic Labeling template were collected, and the features were selected via the least absolute shrinkage and selection operator. rs-fMRI, radiomics, and combined models were constructed with five machine learning classifiers, respectively. Model performance was evaluated via the mean area under the curve (AUC), accuracy, sensitivity, and specificity.

RESULTS: Twenty-one rs-fMRI signal values and 28 radiomics features were selected to construct models. The performance of the combined models was exceptional, with the standout random forest (RF) model delivering an AUC value of 0.902 [95% confidence interval (CI): 0.813-0.990] in the validation set and 1.000 (95% CI: 1.000-1.000) in the training set. Further analysis of the 49 features revealed significantly overlapping brain regions for both feature types. Three key features demonstrating significant differences between ANI and PWND were identified (all P values <0.001). These features correlated with cognitive test performance (r>0.3).

CONCLUSIONS: The RF combined model exhibited high classification performance in ANI, enabling objective and reliable individual diagnosis in clinical practice. It thus represents a novel method for characterizing the brain functional impairment and pathophysiology of patients with ANI. Greater attention should be paid to the frontoparietal and striatum in the research and clinical work related to ANI.

PMID:40893529 | PMC:PMC12397634 | DOI:10.21037/qims-2025-290

Quant Imaging Med Surg. 2025 Sep 1;15(9):8505-8514. doi: 10.21037/qims-24-1680. Epub 2025 Aug 19.

ABSTRACT

BACKGROUND: Chronic rhinosinusitis (CRS) is a chronic inflammatory disorder of the paranasal sinus and nasal cavity. Previous studies have demonstrated that patients with CRS have an increased risk of emotional and cognitive disorders. Although neuroimaging studies have identified brain alterations in CRS, the specific etiology of these neurological changes remains unclear. This study thus examined the abnormal brain networks in patients with CRS through use of a voxel-wise degree centrality (DC) approach.

METHODS: In this cross-sectional study, 26 patients with CRS and 38 healthy controls (HCs) were enrolled for resting-state functional magnetic resonance imaging (rs-fMRI) scans. The DC value was calculated and correlated with clinical symptoms and with anxiety and depression scores in the CRS group. Moreover, receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic utility of DC in distinguishing patients from HCs.

RESULTS: Compared with HCs, patients with CRS had decreased DC values in the right precuneus and increased DC values in the left inferior temporal gyrus (ITG) (P<0.05, false-discovery rate corrected). In addition, a positive correlation was identified between the DC values in the left ITG and disease duration (R=0.5317; P=0.0052). ROC curves analysis indicated that the DC values in the right precuneus [area under the curve (AUC) =0.7945] and left ITG (AUC =0.7915) had significant diagnostic accuracy, indicating their potential utility as imaging biomarkers for CRS.

CONCLUSIONS: Altered DC in the right precuneus and the left ITG may play important roles in the pathological changes underlying CRS-related brain dysfunction.

PMID:40893504 | PMC:PMC12397695 | DOI:10.21037/qims-24-1680

Neuroimage Rep. 2025 Aug 25;5(3):100285. doi: 10.1016/j.ynirp.2025.100285. eCollection 2025 Sep.

ABSTRACT

People with obesity tend to have altered functional connectivity of reward-related areas in the brain, contributing to overeating and weight gain. The gut-brain axis may function as a mediating factor, with gut-derived short-chain fatty acids (SCFAs) as possible intermediates in the relationship between microbiota and functional connectivity. We investigated the influence of SCFA turnover on resting state functional connectivity in healthy individuals with extremely high and extremely low levels of intestinal SCFA turnover. In this study, we included individuals with low or high intestinal SCFA turnover (estimated by fecal concentration of the butyryl-coenzyme A (CoA)-transferase (ButCoA) gene). Resting state functional magnetic resonance imaging (rs-fMRI) was used to assess functional connectivity of eight regions of interest (ROIs) either directly involved in the mesolimbic dopaminergic network (amygdala, hippocampus, caudate nucleus, putamen and nucleus accumbens) or primary projection regions of this network (middle frontal gyrus, superior frontal gyrus, insula). Functional connectivity was assessed using connectivity strength and eigenvector centrality. No differences in connectivity strength or eigenvector centrality were observed between the high and the low ButCoA group in any of our ROIs, suggesting SCFA turnover is not associated with resting state functional connectivity of central reward-related areas. Although previous studies provide evidence for an association between gut microbiota and resting state functional connectivity of reward-related areas, our findings do not support the hypothesis that this relationship is mediated by SCFAs. This suggests the existence of alternative mechanisms via which the intestinal microbiota may affect appetite, beyond local SCFA production.

PMID:40893427 | PMC:PMC12398794 | DOI:10.1016/j.ynirp.2025.100285

PLoS One. 2025 Sep 2;20(9):e0328219. doi: 10.1371/journal.pone.0328219. eCollection 2025.

ABSTRACT

The study of disorders of consciousness (DoC) is very complex because patients suffer from a wide variety of lesions, affected brain mechanisms, different severity of symptoms, and are unable to communicate. Combining neuroimaging data and mathematical modeling can help us quantify and better describe some of these alterations. The goal of this study is to provide a new analysis and modeling pipeline for fMRI data leading to new diagnosis and prognosis biomarkers at the individual patient level. To do so, we project patients' fMRI data into a low-dimension latent-space. We define the latent space's dimension as the smallest dimension able to maintain the complexity, non-linearities, and information carried by the data, according to different criteria that we detail in the first part. This dimensionality reduction procedure then allows us to build biologically inspired latent whole-brain models that can be calibrated at the single-patient level. In particular, we propose a new model inspired by the regulation of neuronal activity by astrocytes in the brain. This modeling procedure leads to two types of model-based biomarkers (MBBs) that provide novel insight at different levels: (1) the connectivity matrices bring us information about the severity of the patient's diagnosis, and, (2) the local node parameters correlate to the patient's etiology, age and prognosis. Altogether, this study offers a new data processing framework for resting-state fMRI which provides crucial information regarding DoC patients diagnosis and prognosis. Finally, this analysis pipeline could be applied to other neurological conditions.

PMID:40892891 | DOI:10.1371/journal.pone.0328219