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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

Mov Disord. 2025 Sep 1. doi: 10.1002/mds.70025. Online ahead of print.

ABSTRACT

BACKGROUND: Isolated rapid eye movement (REM) sleep behavior disorder (iRBD) is a prodromal manifestation of synucleinopathies and provides a critical window to identify early markers of progression to Parkinson's disease (PD) and dementia with Lewy bodies (DLB). Time-averaged (static) and time-varying (dynamic) functional connectivity between large-scale brain networks may sensitively capture early pathophysiological changes and offer prognostic value beyond structural imaging.

OBJECTIVES: To use functional magnetic resonance imaging (fMRI) on a longitudinal iRBD cohort to assess alterations in static and dynamic functional connectivity and explore their relationship with disease conversion and regional neurotransmitter density.

METHODS: Static and dynamic resting state fMRI and clinical testing were acquired from 41 iRBD participants and 38 healthy controls, with 21 iRBD participants undergoing repeated scanning.

RESULTS: Cross-sectional analysis revealed reduced static connectivity within the visual network and a shift toward a more segregated functional architecture in iRBD. Longitudinally, a further increase in segregation was observed, characterized by heightened modularity and reduced intermodular connectivity. These changes were accompanied by static connectivity disruptions in somatomotor and attentional networks, particularly pronounced in patients who converted to DLB. Regions showing the greatest connectivity decline overlapped with areas rich in cholinergic and noradrenergic transporters, suggesting early neuromodulatory dysfunction as a potential driver.

CONCLUSIONS: Our findings reveal progressive functional segregation and widespread disrupted static connectivity of resting-state networks in iRBD. These results identify imaging biomarkers of disease progression, describe likely neurotransmitter associations, and support the implementation of fMRI as a sensitive tool for detecting early neurobiological signatures of synucleinopathies. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

PMID:40891082 | DOI:10.1002/mds.70025

Bipolar Disord. 2025 Sep 1. doi: 10.1111/bdi.70056. Online ahead of print.

ABSTRACT

OBJECTIVE: Accumulative research indicates key roles of the peripheral inflammation system and hippocampal function in major mood disorders. The complement system modulates inflammatory function and is abnormal in mood disorders, but its precise neural pathway remains unclear. This study investigates the interrelations among complement component 3 (C3) levels, hippocampal function, and mood symptoms among offspring of bipolar disorder (BD) parents who carry familial risk of mood disorders.

METHOD: We recruited unaffected BD offspring with (symptomatic offspring, SO, N = 31) or without (asymptomatic offspring, AO, N = 39) subthreshold symptoms, and matched healthy controls (HC, N = 41). Peripheral C3 levels were measured, and resting-state fMRI was conducted to assess hippocampal functional connectivity (FC). Spectral dynamic causal modeling (spDCM) was conducted to verify the directionality of the hippocampal FC.

RESULTS: The SO group exhibited significantly lower peripheral C3 levels (F2,108 = 23.651, p < 0.001) and reduced left hippocampus-left cerebellum FC (F2,108 = 8.541, p < 0.001) compared to both the AO and HC groups. Furthermore, the left hippocampus-left cerebellum FC partially mediated the relationship between C3 levels and depressive symptoms in the SO group (bootstrapping 95% CI = -4.1168 to -0.1569), but not in AO (bootstrapping 95% CI = -0.3479 to 0.1317) or HC (bootstrapping 95% CI = -0.3297 to 0.0885). The left hippocampus-left cerebellum FC was bidirectional in all 3 groups.

CONCLUSION: Our findings indicate a C3-hippocampus-depressive symptom pathway might underpin the particular high vulnerability of individuals with both familial and symptomatic risks of mood disorders. This evidence provides new neuroinflammatory markers and targets for early identification and intervention of these individuals.

PMID:40891026 | DOI:10.1111/bdi.70056

Sci Rep. 2025 Sep 1;15(1):32133. doi: 10.1038/s41598-025-18023-9.

ABSTRACT

Local segregation and global integration are the fundamnetal organizational principles of human brain. It is unknown how subcortex configures itself with respect to the segregation and integration dynamics at rest. Using resting state functional MRI data of 92 healthy adult participants, we revealed three non-overlapping segregated communities in subcortex, confining anatomically to thalamus, basal ganglia, and subcortical limbic structures, termed as subcortical networks. Further using network science, we analysed the topology of subcortex and found about 80% of subcortical regions acting as hubs, connecting with other cortical as well as subcortical communities. Next, using statistical modelling, we determined the role of subcortex (both at region-level and network-level) in cortical information integration and found multiple, widespread cortical regions (networks) converging onto individual subcortical regions (networks) (a many-to-one mapping). Individual subcortical networks showed varied extent of convergence, broadly from primary and association networks in cortex. We found functional diversity of cortex to be the major driving factor behind cortical convergence within subcortex and that the absence of subcortical regions significantly impacted the information transmission efficiency within the cortico-subcortical converging organization. Overall, our results provide insights into the subcortical organization at rest and underscore the subcortical contributions in shaping the large-scale brain dynamics.

PMID:40890479 | DOI:10.1038/s41598-025-18023-9

Brain Imaging Behav. 2025 Sep 2. doi: 10.1007/s11682-025-01034-5. Online ahead of print.

ABSTRACT

The structural and functional characteristics of hippocampal subfields have been extensively studied in dementia, with findings indicating stronger associations with cognitive performance than those observed in the whole hippocampus (HP). However, the impact of post-stroke dementia (PSD) on the structural and functional connectivity between hippocampal subfields and cortical regions remains unclear. The objective of this study is to examine alterations in the functional and structural connectivity between hippocampal subfields and cortical regions in PSD. We collected resting-state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data from 24 PSD patients, 36 post-stroke non-demented (PSND) patients, and 21 normal controls (NC). These data were used to estimate fractional amplitude of low-frequency fluctuations (fALFF), fractional anisotropy (FA), and diffusivity maps in the hippocampal subfields. Additionally, we constructed functional and structural connectivity matrices between hippocampal subfields and cortical regions for each participant, highlighting group-specific connectivity alterations. Statistical analyses were conducted using a linear mixed model to compare group differences and assess the relationship between MRI measures and clinical evaluations. Our results revealed distinct PSD-related changes in functional connectivity, particularly with the temporal-occipital cortex, within hippocampal subfields compared to the whole HP. Notably, different subfields contributed differently to connectivity changes within the entire HP. Furthermore, we identified positive correlations between diffusivity in the bilateral hippocampal tails and illness duration in PSND patients, which were not observed in PSD. These findings highlight the significant impact of PSD on hippocampal subfields, with subfield analysis offering new insights into the underlying mechanisms of PSD.

PMID:40890395 | DOI:10.1007/s11682-025-01034-5

Trends Neurosci Educ. 2025 Sep;40:100262. doi: 10.1016/j.tine.2025.100262. Epub 2025 Jun 18.

ABSTRACT

the aim of this study was to investigate whole-brain functional connectivity patterns of the reward circuitry and executive control network, and their associations with growth mindset of intelligence in adolescents METHODS: we investigated seed-based functional connectivity of three pre-defined seeds, the caudate and putamen (reward circuitry), and dorsal anterior cingulate cortex (dACC; executive control region) in 59 adolescents between 13-16 years old. Growth mindset was used as covariate in the seed-based analysis RESULTS: our findings revealed the expected whole-brain functional connectivity patterns of the three pre-defined seeds. In contrast to the literature, none of these functional connectivity patterns between the seeds and all other voxels of the brain were related to growth mindset CONCLUSION: the current study suggests that the neural representation of a growth mindset is not consistently observed in resting-state neural connectivity and might depend on contextual or cultural differences.

PMID:40889826 | DOI:10.1016/j.tine.2025.100262

Psychiatry Res Neuroimaging. 2025 Aug 26;353:112059. doi: 10.1016/j.pscychresns.2025.112059. Online ahead of print.

ABSTRACT

OBJECTIVE: Major depressive disorder (MDD) has become the second largest risk factor affecting human health, with a progress in its treatment especially non-pharmacological therapies. The loving-kindness meditation (LKM) has been introduced to depression but is not popular due to requirement on awareness and concentration, and its utilization in clinical MDD is absent as well as exploration on neural mechanism. This study aims to develop a more feasible novel therapy-loving-kindness meditation integrating cognition and behavior (LKM-CB), examine its effect on clinical depression, and further explore its neural mechanism by multimodal neuroimaging.

METHOD: In study 1, the knowledge about love and the behavior of love were integrated into the LKM to form a LKM-CB, to better activate patients with cognitive and behavioral approach. It was further utilized to 30 MDD patients (31 controls). Study 2 further explored the neural mechanism behind the LKM-CB with 16 MDD patients, who underwent a structural MRI, resting-state fMRI, and reward card-guessing task fMRI before and after the LKM-CB.

RESULTS: Study 1 developed a novel 8-week LKM-CB and found that compared with control group, LKM-CB significantly improved clinical depression in intervention group. Study 2 further showed that after LKM-CB intervention, patients showed lower activation in frontal-striatum especially middle orbito-frontal cortex (OFC) and anterior cingulate (AC) and insula for win and neutral outcome and anticipation following a loss feedback, while they showed higher activation in frontal-striatum including medial/middle-OFC and hippocampus for loss outcome and anticipation following a win feedback. Similar increased ALFF activation and grey matter in frontal cortex was also found. In contrast, patients showed higher activation in non-reward temporal-occipital cortex for loss and neutral outcome and anticipation following a loss feedback, while they showed decreased temporal-occipital ALFF activation and grey matter.

CONCLUSIONS: This study develops a novel LKM-CB, which is effective in improving clinical depression. After the LKM-CB, there is dissociation in the neural reward activation pattern between reward anticipation (hyperactivation) and reward outcome (hypoactivation), and a hypoactivation in non-reward temporal-occipital cortex. This study provides a new feasible LKM-CB for non-pharmacological therapy of MDD, and to our knowledge, this is the first study to explore the neural mechanism behind the efficacy of LKM in depression therapy.

PMID:40889474 | DOI:10.1016/j.pscychresns.2025.112059

Neuroimage. 2025 Aug 29:121422. doi: 10.1016/j.neuroimage.2025.121422. Online ahead of print.

ABSTRACT

Graph neural networks (GNN) have emerged as a popular tool for modelling functional magnetic resonance imaging (fMRI) datasets. Many recent studies have reported significant improvements in disorder classification performance via more sophisticated GNN designs and highlighted salient features that could be potential biomarkers of the disorder. However, existing methods of evaluating their robustness are often limited to cross-referencing with existing literature, which is a subjective and inconsistent process. In this review, we provide an overview of how GNN and model explainability techniques (specifically, feature attributors) have been applied to fMRI datasets for disorder prediction tasks, with an emphasis on evaluating the robustness of potential biomarkers produced by these feature attributors for psychiatric disorders. Then, 65 studies using GNNs that reported potential fMRI biomarkers for psychiatric disorders (attention-deficit hyperactivity disorder, autism spectrum disorder, major depressive disorder, schizophrenia) published before 9 October 2024 were identified from 2 online databases (Scopus, PubMed). We found that while most studies have performant models, salient features highlighted in these studies (as determined by feature attribution scores) vary greatly across studies on the same disorder. Reproducibility of biomarkers is only limited to a small subset at the level of regions and few transdiagnostic biomarkers were identified. To address these issues, we suggest establishing new standards that are based on objective evaluation metrics to determine the robustness of these potential biomarkers. We further highlight gaps in the existing literature and put together a prediction-attribution-evaluation framework that could set the foundations for future research on discovering robust biomarkers of psychiatric disorders via GNNs.

PMID:40886780 | DOI:10.1016/j.neuroimage.2025.121422

Dialogues Clin Neurosci. 2025 Dec;27(1):265-275. doi: 10.1080/19585969.2025.2550953. Epub 2025 Aug 31.

ABSTRACT

BACKGROUND: Non suicidal self injury (NSSI) is a public health concern, and its prevalence has increased significantly following the COVID-19 pandemic. Despite its rising incidence, the neurobiological mechanisms underlying NSSI behaviour in adolescents remain poorly understood.

METHODS: A sample of 89 adolescents (46 NSSI positive, 43 NSSI negative) aged 15.39 ± 1.77 years was recruited from clinical settings. NSSI behaviour and psychological resilience were evaluated. Resting-state functional magnetic resonance imaging (Rs-fMRI) was conducted to examine brain connectivity patterns. Data analysis incorporated descriptive and inferential statistics, as well as support vector machine algorithms, to identify the neural correlates of NSSI and resilience.

RESULTS: The NSSI positive group had significantly lower resilience scores (M = 23.41, SD = 7.95). Connectivity between the sensorimotor and limbic networks was negatively associated with NSSI (r = -0.222, p < 0.05), while connectivity between the sensorimotor and subcortical networks showed a positive association (r = 0.201, p < 0.05). Stronger connectivity between dorsal attention and default mode networks indirectly reduced NSSI by enhancing psychological resilience, highlighting resilience as a critical protective factor.

CONCLUSION: These findings underscore the importance of targeting specific brain connectivity patterns and enhancing psychological resilience as crucial components of neurobiologically informed interventions.

PMID:40886727 | DOI:10.1080/19585969.2025.2550953

Clin Neurol Neurosurg. 2025 Aug 28;258:109127. doi: 10.1016/j.clineuro.2025.109127. Online ahead of print.

ABSTRACT

BACKGROUND: Resting-state functional MRI (rs-fMRI) has advanced our understanding of trigeminal neuralgia (TN), but the neural distinctions between its classical (CTN) and idiopathic (ITN) subtypes are poorly understood. This study aims to investigate differential brain activity and connectivity patterns between CTN and ITN to elucidate their underlying central mechanisms and identify potential neuroimaging biomarkers.

METHODS: This prospective study included rs-fMRI data from 139 TN patients (84 CTN, 55 ITN) and 49 matched healthy controls (HCs). We analyzed the amplitude of low-frequency fluctuations (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), and functional connectivity (FC). Group comparisons were performed using two-sample t-tests with cluster-level family-wise error (FWE) correction. Correlations between imaging metrics and clinical variables were assessed.

RESULTS: Compared to HCs, TN patients exhibited decreased ReHo in the right fusiform gyrus and increased ReHo in the right thalamus. Subtype analysis revealed that ITN patients, compared to CTN, showed significantly increased ALFF in the right hippocampus and decreased fALFF in the bilateral postcentral gyrus. Clinically, ReHo in the right fusiform gyrus negatively correlated with pain intensity (VAS; r = -0.255, p = 0.002), while right thalamic ReHo showed a positive correlation (r = 0.208, p = 0.014). In the CTN subgroup, connectivity between the left supramarginal gyrus and right perigenual cingulate gyrus was inversely correlated with disease duration (r = -0.267, p = 0.014).

CONCLUSION: Our findings reveal divergent rs-fMRI profiles for TN patients versus HCs, and notably, between CTN and ITN subtypes. These distinctions, particularly the hippocampal hyperactivity in ITN, suggest different central pathophysiological mechanisms. These quantifiable neurofunctional alterations may serve as potential biomarkers to differentiate TN subtypes and guide personalized therapeutic strategies.

PMID:40886511 | DOI:10.1016/j.clineuro.2025.109127

Brain Lang. 2025 Aug 30;270:105637. doi: 10.1016/j.bandl.2025.105637. Online ahead of print.

ABSTRACT

Developmental Language Disorder (DLD) is a common neurodevelopmental condition characterized by not only significant difficulty with language learning, comprehension, and expression but also with executive, procedural and/or motor functions. The understanding of the brain abnormalities in DLD remains largely unclear and functional MRI (fMRI) studies have largely focused on the language network. Using resting-state fMRI, we investigated whole-brain functional connectivity (FC) in 22 children with DLD and 23 with typical language development (TD), aged 7-to-13-years. Using a non-parametric network-based statistics approach, we found that children with DLD had an extensive network of lower FC across the whole brain, compared to the TD children. In particular, the sensorimotor (SM), cognitive control (CC) and default-mode (DM) networks included the largest amounts of altered FC. In detail, FC links within the DM network and between the SM and DM networks, and between the SM and CC networks were the most altered. No FC was found to be significantly higher in the children with DLD than in their peers with TD. To our knowledge, this is the first investigation of resting-state FC in children with DLD, showing widespread functional brain abnormalities that are not limited to the language network, but rather involve networks supporting other cognitive and motor functions. Such extensive functional abnormalities offer a potential explanation for the other cognitive and motor impairments characterizing DLD.

PMID:40886422 | DOI:10.1016/j.bandl.2025.105637