Most recent paper

Front Neurosci. 2025 Oct 15;19:1639838. doi: 10.3389/fnins.2025.1639838. eCollection 2025.

ABSTRACT

INTRODUCTION: Anodal direct current stimulation (a-tDCS) of the visual cortex is a potential rehabilitation tool for vision disorders such as amblyopia and macular degeneration. However, the underlying neural mechanisms are currently unknown. When applied to the human motor cortex, a-tDCS reduces the concentration of gamma-aminobutyric acid (GABA), an inhibitory neurotransmitter that modulates neuroplasticity. Our primary aim was to assess whether the same a-tDCS paradigm alters local GABA concentration when applied to the healthy primary visual cortex. We also measured the effect of a-tDCS on visual cortex resting-state connectivity and sought to replicate reported observations of an association between visual cortex GABA concentration and the dynamics of binocular rivalry.

METHODS: Fourteen participants with normal vision completed two brain imaging sessions at least 48 hours apart. In each session, binocular rivalry dynamics, primary visual cortex GABA and glutamate-glutamine (Glx) concentrations (via magnetic resonance spectroscopy (MRS)) and resting-state functional connectivity (via task-free fMRI) were measured at baseline. Real or sham a-tDCS (20 min, 2mA) was then applied to the visual cortex in a randomized sequence followed by a second set of MRS and fMRI measurements.

RESULTS: No between-session effects of a-tDCS on GABA or Glx concentration or resting-state functional connectivity were observed. A pre-planned within-session analysis revealed a significant increase in Glx following a-tDCS that did not withstand multiple comparisons correction. No consistent relationships between binocular rivalry dynamics and GABA concentration were apparent.

DISCUSSION: Together, our results suggest that a-tDCS effects on the visual cortex may differ from the GABA-associated mechanism in motor cortex.

PMID:41169746 | PMC:PMC12570334 | DOI:10.3389/fnins.2025.1639838

Brain Commun. 2025 Oct 29;7(5):fcaf343. doi: 10.1093/braincomms/fcaf343. eCollection 2025.

ABSTRACT

The treatment of chronic pain represents a widespread clinical challenge. Current approaches to network-based mapping of the cerebral cortex have the potential to localize chronic pain in the brain. In an effort to further characterize the dynamical brain networks, or the 'dynome' in the setting of chronic pain, we performed a Coordinate-Based Meta-Analysis of resting-state functional Magnetic Resonance Imaging studies on chronic pain to create a multinetwork dynome of chronic pain. A cluster-level analysis generated seven statistically significant activation likelihood estimates (ALEs): one for chronic pain as a whole dynome, three for chronic pain conditions, and three for chronic pain mechanisms. Chronic pain is a complex disease process involving tripartite network dysfunction encompassing the Default Mode Network, Central Executive Network and Salience Network. Chronic visceral pain was distinct from chronic headache and chronic musculoskeletal pain, and chronic pain mechanisms have the potential to share common cortical network rearrangements with their respective chronic pain conditions. Collectively, this work represents the first anatomically specific network-based cortical map of chronic pain, with representation of disease-specific and mechanism-specific disruptions in cortical function.

PMID:41169268 | PMC:PMC12569763 | DOI:10.1093/braincomms/fcaf343

BMC Psychiatry. 2025 Oct 30;25(1):1040. doi: 10.1186/s12888-025-07483-y.

ABSTRACT

BACKGROUND: Suicide thoughts and behaviors (STB), including suicidal ideation (SI) and suicide attempts (SA), are significant concerns in major depressive disorder (MDD), yet their neurobiological mechanisms remain poorly understood. This study aims to identify key regional brain activity and connectivity abnormalities associated with STB in MDD by combining a meta-analysis of regional brain activity comparing MDD patients with STB to non-STB controls (both MDD without STB and healthy controls) and an exploratory functional connectivity (FC) analysis in an independent sample of MDD patients.

METHODS: A meta-analysis employing Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) software identified regional brain abnormalities. Studies included were those comparing MDD patients with STB to non-STB controls, employing resting-state fMRI with whole-brain analyses, using ALFF, fALFF, and ReHo metrics. The identified abnormal brain regions were used as regions of interest (ROIs) for FC analyses in 57 first-episode, drug-naive MDD patients.

RESULTS: The meta-analysis included 12 studies (13 datasets) comprising 555 MDD patients with STB and 430 non-STB controls. Compared to non-STB controls, MDD-STB patients showed increased activity in the right middle occipital gyrus (MOG) and right inferior frontal gyrus, triangular part (IFGtriang), while decreased activity in right precuneus. Subgroup analysis of SA revealed increased activity in the left angular gyrus in MDD patients with SA, compared to non-SA controls. SI subgroup analysis and two medication status subgroup analyses showed no significant results. In independent sample, FC analysis yielded two significant FCs after Bonferroni correction. Correlation analysis showed a negative association between right MOG-IFGtriang FC and most severe SI scores measured by the Beck Scale for Suicidal Ideation (P = 0.04), though it was non-significant after correction.

CONCLUSIONS: These findings provide novel insights into the neural mechanisms of STB in MDD, identifying specific brain regions and FC patterns associated with STB. These results align with prior studies, highlighting the role of visual processing and cognitive control regions in STB. By combining a meta-analysis of regional abnormalities with an exploratory FC analysis, this study offers a comprehensive understanding of the brain networks implicated in STB and suggests potential targets for future interventions.

PMID:41168736 | DOI:10.1186/s12888-025-07483-y

Nat Commun. 2025 Oct 30;16(1):9359. doi: 10.1038/s41467-025-64988-6.

ABSTRACT

The microbiome-gut-brain-axis plays a critical role in mental health. However, research linking the microbiome to brain function is limited, particularly during development, when tremendous plasticity occurs and many mental health issues, like depression and anxiety, initially manifest. Further complicating attempts to understand interactions between the brain and microbiome is the complex and multidimensional nature of both systems. In the current observational study (N = 55), we use sparse partial least squares to identify linear combinations of brain networks (brain signatures) derived from resting state fMRI scans at age 6 years that maximally covary with internalizing symptoms at age 7.5 years, before identifying microbe abundances (microbial profiles) derived from 16S rRNA sequencing of stool samples at age 2 years that maximally covary with those brain signatures. Finally, we test whether any early microbial profiles are indirectly associated with later internalizing symptoms via the brain signatures, highlighting potential microbial programming effects. We find that microbes in the Clostridiales order and Lachnospiraceae family are associated with internalizing symptoms in middle childhood through connectivity alterations within emotion-related brain networks.

PMID:41168153 | DOI:10.1038/s41467-025-64988-6

Biol Psychiatry Cogn Neurosci Neuroimaging. 2025 Oct 28:S2451-9022(25)00325-8. doi: 10.1016/j.bpsc.2025.10.010. Online ahead of print.

ABSTRACT

BACKGROUND: Neuroimaging studies of functional neurological disorder (FND), a core neuropsychiatric condition, often rely on discrete connections or parcellations that may obscure the brain's functional network architecture. This study applied a gradient-based approach to examine macroscale cortical organization in FND.

METHODS: We analyzed resting-state functional magnetic resonance imaging (fMRI) data from 64 patients with mixed FND (FND-mixed), 61 age- and sex-matched healthy controls (HCs), and 62 psychiatric controls (PCs) matched on age, sex, depression, anxiety, and post-traumatic stress disorder (PTSD) severity. Functional connectivity gradients were computed to capture dominant axes of cortical organization. Between-group comparisons were conducted for the top three gradients, and associations with symptom severity were investigated. Subtype-specific patterns in functional motor disorder (n=49) and functional seizure (n=24) were also examined. Analyses controlled for age, sex, antidepressant use, and head motion, and were post-hoc adjusted for depression, anxiety, and PTSD-severity, and for childhood maltreatment.

RESULTS: The FND-mixed group showed alterations across all three gradients relative to HCs and PCs. Gradient 1 revealed increased values in sensorimotor regions, reflecting a shift toward more association-like connectivity. Gradient 2 showed altered differentiation between sensory systems. Gradient 3 exhibited reduced functional separation between representational and modulatory regions, with prominent shifts in the anterior cingulate cortex. Several regions displaying between-group differences also showed correlations with FND and somatic symptom severity. Exploratory analyses revealed overlapping and distinct patterns across subtypes vs. controls.

CONCLUSIONS: We provide novel evidence of atypical hierarchical brain organization in FND, highlighting gradient-based approaches for identifying mechanistically-relevant altered functional brain organization.

PMID:41167519 | DOI:10.1016/j.bpsc.2025.10.010

Biomed Eng Online. 2025 Oct 29;24(1):125. doi: 10.1186/s12938-025-01458-6.

ABSTRACT

INTRODUCTION: Epilepsy due to hypothalamic hamartoma (HH) is associated with epileptic encephalopathy and often requires surgical intervention, as medications are ineffective at reducing the seizures. However, the first step of disentangling the impact of the surgery on the broader whole-brain networks, a biomarker of encephalopathy compared to controls, is not quantified. Subtle pre- and post-operative networks can elude conventional rs-fMRI analysis.

METHODS: We retrospectively analyzed rs-fMRI from 56 HH patients scanned before and 6 months after surgery. We developed a two-stage contrastive learning-based algorithm to classify the motor, vision, language, frontal, and temporal networks as pre- vs post-operative. In stage one, a multimodal contrastive encoder jointly ingests 3D spatial Independent Component Analysis (ICA) maps and their corresponding 1D temporal ICA time series to learn embeddings that distinguish pre-operative from post-operative states for each network while separating embeddings of different networks. In stage two, a lightweight classifier refines these embeddings, augmented by original ICA inputs, to classify each network as pre-operative or post-operative.

RESULTS: Visualization of the learned feature space with t-SNE revealed clear separation by pre- vs post-surgical condition across all five networks. Across networks, mean accuracy ranged from 0.85 to 0.90, sensitivity from 0.79 to 0.90, specificity from 0.87 to 0.93, F1-score from 0.83 to 0.90 and AUC from 0.90 to 0.94 in stratified cross validation.

CONCLUSIONS: Contrastive learning can sensitively detect functional shifts in critical cortical networks that previous traditional analyses may overlook. These findings inform broader shifts in whole-brain network functioning following effective HH surgery and establish a featurewise distinction between preoperative and postoperative states, motivating future studies that compare HH patients to healthy controls to quantify network recovery.

PMID:41163168 | DOI:10.1186/s12938-025-01458-6

Front Neurol. 2025 Oct 13;16:1655478. doi: 10.3389/fneur.2025.1655478. eCollection 2025.

ABSTRACT

BACKGROUND: Acupuncture is a widely used complementary therapy; however, the central mechanisms underlying its effects, particularly how stimulation at different specific acupoints modulates brain function in distinct or common ways, remain poorly understood. This gap persists due to a lack of large-sample, systematic comparative studies under a unified experimental paradigm. Task-based and resting-state functional magnetic resonance imaging (fMRI) offer powerful tools to capture both the instant and sustained neural responses to acupuncture.

METHODS: We designed a randomized, single-blind, controlled trial. To achieve high statistical power and generalizability, 250 healthy participants will be enrolled. Each participant will undergo acupuncture at one of seven predefined acupoints (verum) and its corresponding non-acupoint (sham control) in two separate sessions, with a 1-week interval. Each session includes: (1) resting-state fMRI before and after needle manipulation, and (2) task-fMRI during the manipulation. The primary outcomes are fMRI-derived brain activity and functional connectivity patterns. Blinding assessment and the Modified Massachusetts General Hospital Acupuncture Sensation Scale-Chinese version (C-MMASS) will be collected to evaluate the credibility of sham control and the Deqi sensation.

DISCUSSION: This study is novel in its comprehensive approach to mapping the neural correlates of multiple acupoints within a single, rigorous design. We anticipate that our results will provide the first systematic characterization of the "acupoint-brain functional network" map, elucidating both common activation patterns across acupoints and acupoint-specific differential responses. This will significantly contribute to understanding the functional neuroanatomy of acupuncture and provide high-level evidence for its mechanism of action, ultimately helping to bridge the gap between traditional practice and modern neuroscience.

CONCLUSION: The findings of this trial are expected to establish a robust empirical foundation for the neural basis of acupuncture, offering insights that could validate clinical practice and guide future target-specific acupuncture applications.

CLINICAL TRIAL REGISTRATION: Identifier ITMCTR2025000066.

PMID:41159193 | PMC:PMC12554436 | DOI:10.3389/fneur.2025.1655478

Addict Behav Rep. 2025 Oct 17;22:100637. doi: 10.1016/j.abrep.2025.100637. eCollection 2025 Dec.

ABSTRACT

BACKGROUND: Internet Gaming Disorder (IGD), recognized as a non-substance addictive behavior, has been incorporated into the diagnostic frameworks of DSM-5 and ICD-11. Its high prevalence rate (approximately 10 %) and widespread harmful consequences position it as a significant global public health challenge. Functional Magnetic Resonance Imaging (fMRI) technology offers a powerful tool for elucidating the neural mechanisms underlying addictive behaviors. This is particularly relevant during adolescence, a period characterized by high neurobiological plasticity, enabling a deeper understanding of the neurobiological basis of IGD.

OBJECTIVE: This review aims to systematically synthesize the application of fMRI techniques in adolescent IGD research. It integrates empirical findings from resting-state fMRI (rs-fMRI), task-based fMRI (tb-fMRI), and Diffusion Tensor Imaging (DTI) to clarify IGD-associated abnormalities in brain function and structure, and explores their potential for clinical translation.

METHODS: Relevant literature published between January 2015 and February 2025 was retrieved from PubMed, Web of Science, and Science Direct databases using keyword searches. Twenty-one studies meeting the inclusion criteria (employing fMRI techniques, utilizing defined IGD diagnostic criteria, focusing on adolescent samples aged 10-20 years) were selected. The risk of bias for included studies was assessed using a standardized tool. The analysis encompassed investigations of resting-state functional connectivity (FC), task-based activation patterns, and white matter microstructure.

CONCLUSION: Adolescents with IGD exhibit functional dysregulation within the prefrontal-striatal circuit, hyperactivation of the reward system, and white matter microstructural impairments. These neural abnormalities are closely associated with behavioral disinhibition and cognitive deficits. fMRI research provides a neuroimaging foundation for the objective diagnosis and targeted intervention of IGD. Future research necessitates the integration of multimodal data to optimize clinical applications.

PMID:41158443 | PMC:PMC12554800 | DOI:10.1016/j.abrep.2025.100637

Brain Res Bull. 2025 Oct 26:111604. doi: 10.1016/j.brainresbull.2025.111604. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Oxytocin (OT), a hormone and neurotransmitter, plays a critical role in human behavior and cognition, including social bonding, reproduction, and stress response. Intranasal OT administration may enhance brain function, offering therapeutic benefits for socioemotional deficits. This study explores OT's effects on the Papez circuit in younger and older adults using functional magnetic resonance imaging (fMRI) and advanced data analysis, focusing on emotion management, learning, attention, and memory.

METHOD: Eighty-seven healthy participants (both young and older adults) underwent resting-state fMRI after receiving OT or placebo nasal sprays. Seed-to-voxel and region of interest (ROI-to-ROI) analyses were used to investigate the effects of OT on functional connectivity within the Papez network. Graph theory analysis evaluated topological properties of brain networks and identified connectivity differences between the OT and placebo groups.

RESULT: Advanced analysis revealed significant differences in brain activity and connectivity within the Papez circuit between OT and placebo groups. Graph theoretical analysis showed statistically significant differences in graph measures for the thalamus, posterior cingulate (PC), anterior cingulate (AC), and parahippocampus.

CONCLUSION: Our findings enhance understanding of brain activation changes following intranasal OT administration, offering valuable insights for pharmacists, psychiatrists, and psychologists. The results highlight OT's potential as a therapeutic tool for diagnosing and treating psychiatric disorders.

PMID:41151702 | DOI:10.1016/j.brainresbull.2025.111604

Cereb Cortex. 2025 Oct 2;35(10):bhaf296. doi: 10.1093/cercor/bhaf296.

ABSTRACT

Previous studies have shown that individuals with internet gaming disorder (IGD) and tobacco use disorder (TUD) represent nonsubstance and substance-related addictions, respectively. Identifying neuroimaging differences is essential for detecting and intervening in these disorders. 44 IGD participants, 73 TUD participants, and 33 healthy controls (HCs) were scanned with resting-state fMRI (rs-fMRI). We used independent component analysis (ICA) to identify regions of interest and compared the functional connectivity between groups using a false discovery rate correction. Rs-fMRI revealed increased functional connectivity in the precuneus cortex, left postcentral gyrus, and right superior frontal gyrus in the dorsal attention network (DAN) in the IGD group in contrast to HC group. In the TUD group, increased functional connectivity was observed in the superior frontal gyrus, supplementary motor cortex, right precentral gyrus, cingulate gyrus, anterior division, postcentral gyrus, thalamus r, and thalamus I. In contrast, decreased functional connectivity was observed in the right lateral occipital cortex (the inferior and superior division), right occipital fusiform gyrus, and the right occipital pole. Our results indicate distinct alterations in the DAN associated with IGD and TUD, suggesting divergent mechanisms in behavioral versus substance-related addictions.

PMID:41148227 | DOI:10.1093/cercor/bhaf296

Eur J Neurosci. 2025 Oct;62(8):e70289. doi: 10.1111/ejn.70289.

ABSTRACT

Psychogenic erectile dysfunction (pED) is a prevalent form of erectile dysfunction in younger men and is strongly linked to mood disturbances such as anxiety and depression. While previous functional connectivity studies have reported abnormal interactions in cortical and subcortical networks, the causal architecture of the reward circuit in pED remains poorly understood. In this study, we applied spectral dynamic causal modeling (spDCM) to resting-state fMRI data from 27 patients with pED and 31 matched healthy controls to investigate alterations in effective connectivity within the reward circuit. At the group level, parametric empirical Bayes and Bayesian model reduction identified 14 connections that survived a stringent posterior probability threshold (PP > 0.99). Correlation analyses revealed three robust associations after false discovery rate correction: stronger vmPFC-PCC connectivity was negatively correlated with erectile function, while reduced LAI-RAI connectivity and increased LAI self-connectivity were associated with depressive symptoms. These findings suggest that abnormal interactions among default mode, salience, and reward networks may underlie both sexual dysfunction and comorbid mood disturbances in pED. Our results highlight candidate neural pathways that may inform mechanistic understanding and guide future therapeutic strategies, while emphasizing the need for replication in larger samples.

PMID:41146601 | DOI:10.1111/ejn.70289

Netw Neurosci. 2025 Sep 22;9(3):1154-1175. doi: 10.1162/netn_a_00459. eCollection 2025.

ABSTRACT

Resting-state functional connectivity (RSFC) methods are the most widely applied tools in the network neurosciences, but their reliability remains an active area of study. We use back-to-back 10-min resting-state scans in a healthy aging (n = 41) and traumatic brain injury (TBI) sample (n = 45) composed of older adults to assess the replicability of RSFC using a "mini" multiverse approach. The goal was to evaluate the reproducibility of commonly used graph metrics and determine if aging and moderate-severe TBI influences RSFC reliability using intraclass correlation coefficients (ICCs). There is clear evidence for reliable results in aging and TBI. Global network metrics such as within-network connectivity and segregation were most reliable whereas other whole-brain connectivity estimates (e.g., clustering coefficient, eigenvector centrality) were least reliable. Analysis of canonical networks revealed the default mode and salience networks as most reliable. There was a notable influence of motion scrubbing on ICCs, with diminished reliability proportional to the number of volumes removed. Choice of brain atlas had a modest effect on findings. Overall, RSFC reproducibility is preserved in older adults and after significant neurological compromise. We also identify a subset of graph metrics and canonical networks with promising reliability.

PMID:41142954 | PMC:PMC12548667 | DOI:10.1162/netn_a_00459

Netw Neurosci. 2025 Sep 19;9(3):1039-1064. doi: 10.1162/netn_a_00452. eCollection 2025.

ABSTRACT

Intrinsic brain dynamics play a fundamental role in cognitive function, but their development is incompletely understood. We investigated pubertal changes in temporal fluctuations of intrinsic network topologies (focusing on the strongest connections and coordination patterns) and signals, in an early longitudinal sample from the Adolescent Brain Cognitive Development (ABCD) study, with resting-state fMRI (n = 4,099 at baseline; n = 3,376 at follow-up [median age = 10.0 (1.1) and 12.0 (1.1) years; n = 2,116 with both assessments]). Reproducible, inverse associations between low-frequency signal and topological fluctuations were estimated (p < 0.05, β = -0.20 to -0.02, 95% confidence interval (CI) = [-0.23, -0.001]). Signal (but not topological) fluctuations increased in somatomotor and prefrontal areas with pubertal stage (p < 0.03, β = 0.06-0.07, 95% CI = [0.03, 0.11]), but decreased in orbitofrontal, insular, and cingulate cortices, as well as cerebellum, hippocampus, amygdala, and thalamus (p < 0.05, β = -0.09 to -0.03, 95% CI = [-0.15, -0.001]). Higher temporal signal and topological variability in spatially distributed regions were estimated in girls. In racial/ethnic minorities, several associations between signal and topological fluctuations were in the opposite direction of those in the entire sample, suggesting potential racial differences. Our findings indicate that during puberty, intrinsic signal dynamics change significantly in developed and developing brain regions, but their strongest coordination patterns may already be sufficiently developed and remain temporally consistent.

PMID:41142950 | PMC:PMC12548668 | DOI:10.1162/netn_a_00452

Netw Neurosci. 2025 Sep 19;9(3):1065-1086. doi: 10.1162/NETN.a.22. eCollection 2025.

ABSTRACT

Brain structure-function coupling has been studied in health and disease by many different researchers in recent years. Most of the studies have estimated functional connectivity matrices as correlation coefficients between different brain areas, despite well-known disadvantages compared with partial correlation connectivity matrices. Indeed, partial correlation represents a more sensible model for structural connectivity since, under a Gaussian approximation, it accounts only for direct dependencies between brain areas. Motivated by this and following previous results by different authors, we investigate structure-function coupling using partial correlation matrices of functional magnetic resonance imaging brain activity time series under various regularization (also known as noise-cleaning) algorithms. We find that, across different algorithms and conditions, partial correlation provides a higher match with structural connectivity retrieved from density-weighted imaging data than standard correlation, and this occurs at both subject and population levels. Importantly, we also show that regularization and thresholding are crucial for this match to emerge. Finally, we assess neurogenetic associations in relation to structure-function coupling, which presents promising opportunities to further advance research in the field of network neuroscience, particularly concerning brain disorders.

PMID:41142949 | PMC:PMC12548666 | DOI:10.1162/NETN.a.22

Int J Clin Health Psychol. 2025 Oct-Dec;25(4):100639. doi: 10.1016/j.ijchp.2025.100639. Epub 2025 Oct 15.

ABSTRACT

PURPOSES: To explore if pretreatment brain function/structure connectome could explain the response to a psychosocial intervention on resilience in breast cancer.

METHODS: Between February 2018 and October 2021, women newly diagnosed with breast cancer were retrospectively enrolled from the Be Resilient to Breast Cancer (BRBC) trial and received a supportive-expressive therapy intervention. Baseline Resting-state Functional Magnetic Resonance Imaging (rs-fMRI) combined with Diffusion Tensor Imaging (DTI) were administered and resilience was scored by 10-item Resilience Scale specific to Cancer (RS-SC-10) at baseline and after the intervention (6 months). Response to the supportive intervention on resilience was defined as > 0.5 standard deviation (SD) improvement at 6 months compared to baseline mean resilience score.

RESULTS: A total of 105 patients received intervention. At 6 months, the resilience score improved in 62.9 % (N = 66), defined as the Response group. Amygdala (53 %) and Hippocampus (15 %) in rs-fMRI and CorpusCallosum_ForcepsMinor (96 %) in DTI were recognized as the main significant brain regions associated with treatment response.

CONCLUSION: These preliminary data suggest that neuro-markers of brain function/structure connectome from MR imaging might be useful in evaluating response to behavioral interventions on resilience.

PMID:41142584 | PMC:PMC12550284 | DOI:10.1016/j.ijchp.2025.100639

Front Neurosci. 2025 Oct 10;19:1672129. doi: 10.3389/fnins.2025.1672129. eCollection 2025.

ABSTRACT

Spatial group independent component analysis (sgr-ICA) is widely used in resting-state fMRI to identify intrinsic connectivity networks (ICNs). While lower-order decompositions reveal large-scale networks, higher-order models provide finer granularity but have been limited by small sample sizes. In this study, we applied sgr-ICA with 500 components to more than 100,000 subjects with rsfMRI to generate a robust fine-grained ICN template. Using this template, we examined whole brain functional network connectivity (FNC) in 502 individuals with schizophrenia and 640 typical controls and compared the findings with a lower order multiscale template. The 500-component template yielded a large set of reliable ICNs, particularly in the cerebellar and paralimbic regions, and revealed schizophrenia-related dysconnectivity patterns that were not detected at larger spatial scales. Specifically, we observed hypoconnectivity between the cerebellar and subcortical domains (basal ganglia and thalamus) and hyperconnectivity between the cerebellar domain and the visual, sensorimotor and higher cognitive domains. These results demonstrate that very high-order ICA can capture distinct fine-grained ICNs, improving the detection of disease-related connectivity differences and enriching current multiscale ICN templates. The derived ICNs can serve as a valuable reference for future studies and potentially enhance the clinical utility of rsfMRI in psychiatric research.

PMID:41141424 | PMC:PMC12549557 | DOI:10.3389/fnins.2025.1672129

Brain Commun. 2025 Oct 24;7(5):fcaf346. doi: 10.1093/braincomms/fcaf346. eCollection 2025.

ABSTRACT

Glioma patients frequently suffer from cognitive impairments after surgery, but predicting these impairments preoperatively at an individual level remains challenging. Cognitive functions are increasingly studied from a network perspective, where an important role is played by the Default Mode Network (DMN) and Frontoparietal Network (FPN). Hypothesizing that postsurgical cognitive impairments arise from structural network vulnerabilities, we trained models using presurgical structural connectivity of DMN and FPN regions to predict postsurgical cognitive impairment. We obtained individualized structural connectomes in 63 glioma patients (grades II-IV) who underwent diffusion-weighted MRI before surgery (T0) and neuropsychological screening 3 months after surgery (T3) and, for a small majority, adjuvant treatment. Random forest classifiers were trained on a combination of baseline (sociodemographic and clinical), tumour location and structural network variables available before surgery to predict postsurgical cognitive impairment in individual patients. Classifier performance was measured as area under curve of the receiver operating characteristic (AUC-ROC), testing statistical significance via permutation testing. Predictor importance was calculated post-hoc using Shapley additive explanations for trees. Postsurgical impairment was predicted by baseline variables available at T0 (AUC = 0.69, P = 0.011), presurgical DMN degrees (AUC = 0.73, P = 0.001), presurgical FPN degrees (AUC = 0.73, P = 0.001) and combinations of network and baseline variables (AUC = 0.75, P < 0.001; AUC = 0.76, P < 0.001 for DMN and FPN, respectively), but not by tumour location only (AUC = 0.62, P = 0.068). The combination of baseline variables, DMN degrees and FPN degrees (AUC = 0.76, P < 0.001) did not improve results. Importantly, models including network variables performed better than models using baseline or tumour location variables only. The most important predictors of postsurgical cognitive impairment were older age and low connectivity of the left lateral superior frontal gyrus (DMN), right pars opercularis (FPN) and bilateral middle frontal gyrus (DMN). This study represents a step towards preoperative prediction of postsurgical cognitive impairments in individual glioma patients. Our results underscore the importance of the DMN and FPN for cognition and suggest a biomarker for cognitive resilience to damage from treatment. The success of our model illustrates the utility of individual structural connectomes for studying cognitive impairment. Future expansions, e.g. incorporating resting-state fMRI, could improve our model. Ultimately, a sufficiently accurate model could be applied in neurosurgical planning by assessing a patient's risk of postsurgical impairment from presurgical information only, improving counselling of glioma patients regarding surgical expectations.

PMID:41140809 | PMC:PMC12550501 | DOI:10.1093/braincomms/fcaf346

Front Neuroimaging. 2025 Oct 10;4:1650987. doi: 10.3389/fnimg.2025.1650987. eCollection 2025.

ABSTRACT

INTRODUCTION: Schizophrenia is extremely heterogenous, and the underlying brain mechanisms are not fully understood. Many attempts have been made to substantiate and delineate the relationship between schizophrenia and the brain through unbiased exploratory investigations of resting-state functional magnetic resonance imaging (rs-fMRI). The results of numerous data-driven rs-fMRI studies have converged in support of the disconnection hypothesis framework, reporting aberrant connectivity in cortical-subcortical-cerebellar circuitry. However, this model is vague and underspecified, encompassing a vast array of findings across studies. It is necessary to further refine this model to identify consistent patterns and establish stable imaging markers of schizophrenia and psychosis. The organizational structure of the NeuroMark atlas is especially well-equipped for describing functional units derived through independent component analysis (ICA) and uniting findings across studies utilizing data-driven whole-brain functional connectivity (FC) to characterize schizophrenia and psychosis.

METHODS: Toward this goal, a systematic literature review was conducted on primary empirical articles published in English in peer-reviewed journals between January 2019-February 2025 which utilized cortical-subcortical-cerebellar terminology to describe schizophrenia-control comparisons of whole-brain FC in human rs-fMRI. The electronic databases utilized included Google scholar, PubMed, and APA PsycInfo, and search terms included ("schizophrenia" OR "psychosis") AND "resting-state fMRI" AND ("cortical-subcortical-cerebellar" OR "cerebello-thalamo-cortical").

RESULTS: Ten studies were identified and NeuroMark nomenclature was utilized to describe findings within a common reference space. The most consistent patterns included cerebellar-thalamic hypoconnectivity, cerebellar-cortical (sensorimotor & insular-temporal) hyperconnectivity, subcortical (basal ganglia and thalamic)-cortical (sensorimotor, temporoparietal, insular-temporal, occipitotemporal, and occipital) hyperconnectivity, and cortical-cortical (insular-temporal and occipitotemporal) hypoconnectivity.

DISCUSSION: Patterns implicating prefrontal cortex are largely inconsistent across studies and may not be effective targets for establishing stable imaging markers based on static FC in rs-fMRI. Instead, adapting new analytical strategies, or focusing on nodes in the cerebellum, thalamus, and primary motor and sensory cortex may prove to be a more effective approach.

PMID:41140643 | PMC:PMC12549315 | DOI:10.3389/fnimg.2025.1650987

Neuroimage. 2025 Oct 23:121554. doi: 10.1016/j.neuroimage.2025.121554. Online ahead of print.

ABSTRACT

The increasing scale and complexity of neuroimaging datasets aggregated from multiple study sites present substantial analytic challenges, as existing statistical analysis tools struggle to handle missing voxel-data, suffer from limited computational speed and inefficient memory allocation, and are restricted in the types of statistical designs they are able to model. We introduce Image-Based Meta- & Mega-Analysis (IBMMA), a novel software package implemented in R and Python that provides a unified framework for analyzing diverse neuroimaging features, efficiently handles large-scale datasets through parallel processing, offers flexible statistical modeling options, and properly manages missing voxel-data commonly encountered in multi-site studies. IBMMA successfully analyzed a large-n dataset of several thousand participants and revealed findings in brain regions that some traditional software overlooked due to missing voxel-data resulting in gaps in brain coverage. IBMMA has the potential to accelerate discoveries in neuroscience and enhance the clinical utility of neuroimaging findings.

PMID:41138791 | DOI:10.1016/j.neuroimage.2025.121554

Addiction. 2025 Oct 25. doi: 10.1111/add.70219. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: Internet gaming addiction (IGA) is associated with altered reward/punishment sensitivity and risky decision-making. Nevertheless, the underlying neural mechanisms of such changes remain poorly understood. This study examined behavioral and neural predictors of IGA tendency with multiple datasets.

DESIGN: Observational study.

SETTING AND PARTICIPANTS: A total of 1142 university students [360 males and 782 females, mean (standard deviation) age of 18.75 (1.67) years] participated in the behavior-brain cross-sectional dataset (BBC). A subset of 303 BBC participants [71 males and 232 females, baseline mean age of 18.84 (1.72) years] participated in the behavior longitudinal dataset (BL).

MEASUREMENTS: The Sensitivity to Punishment and Sensitivity to Reward Questionnaire (SPSRQ) assessed sensitivity to reward and punishment stimuli. The Internet Game Addiction Questionnaire assessed levels of addiction symptoms in the context of internet games. The Iowa Gambling Task (IGT) assessed risky decision-making behavior. Resting-state functional magnetic resonance imaging (MRI) data were preprocessed using standard pipelines and analyzed based on Yeo's seven-network parcellation template, with particular focus on the Limbic Network (LN) and its functional connectivity patterns. Statistical analyses included Spearman correlation, structural equation modeling and cross-lagged panel models.

FINDINGS: Cross-sectional analyses revealed that the IGT net score (NS) was negatively associated with reward sensitivity (RS, rho = -0.181, P = 0.022), which was positively associated with punishment sensitivity (PS, rho = 0.125, P < 0.001). PS positively predicted IGA tendency (β = 0.180, P < 0.001). Additionally, LN strength exhibited a positive correlation with RS (rho = 0.077, P < 0.001) and a negative correlation with PS (rho = -0.045, P = 0.090). Moreover, the functional connectivity strength between LN and other functional networks was positively associated with RS. Longitudinal analyses demonstrated that (1) the IGT net score at the first time point (T1) negatively predicted RS at the second time point (T2, β = -0.123, P = 0.031), (2) RS at T1 positively predicted IGA tendency at T2 (β = 0.100, P = 0.019), (3) PS at T1 negatively predicted RS at T2 (β = 0.085, P = 0.056) and (4) LN strength at T1 directly predicted RS and PS at T1 (RS: β = 0.126, P = 0.027; PS: β = -0.104, P = 0.064), as well as RS at T2 (β = 0.079, P = 0.080).

CONCLUSION: Internet gaming activity net score appears to be negatively correlated with reward sensitivity. Punishment sensitivity appears to be positively correlated with tendency toward internet gaming activity. There appears to be a positive correlation between reward sensitivity and punishment sensitivity.

PMID:41137797 | DOI:10.1111/add.70219