Most recent paper

Hum Brain Mapp. 2026 Oct 1;47(14):e70641. doi: 10.1002/hbm.70641.

ABSTRACT

Functional magnetic resonance imaging (fMRI) time series are known to exhibit long-range temporal dependencies that challenge traditional modeling approaches. In this study, we propose a novel computational pipeline to characterize and interpret these dependencies using a long-memory (LM) framework, which captures the slow, power-law decay of autocorrelation in resting-state fMRI (rs-fMRI) signals. The pipeline involves voxelwise estimation of LM parameters via a wavelet-based Bayesian method, yielding spatial maps that reflect temporal dependence across the brain. These maps are then projected onto a lower-dimensional space via a composite basis and are then related to individual-level covariates through group-level regression. We applied this framework to the ADHD-200 dataset. Analysis identified a significant positive association between age and the long-memory parameter in the hippocampus after adjusting for ADHD symptom severity and medication status. After accounting for acquisition site, the estimated effect sizes were attenuated and no longer reached statistical significance, although the spatial pattern was largely preserved, with the strongest estimated effects remaining localized to the same anatomical regions. These findings suggest a relationship between long-range temporal dependence and developmental changes in memory-related brain regions while highlighting the influence of acquisition-site variability on effect magnitude and statistical significance. Overall, the proposed methodology enables detailed mapping of intrinsic temporal dynamics in rs-fMRI and offers new insights into the relationship between functional signal memory and brain development.

PMID:42754916 | DOI:10.1002/hbm.70641

Int J Neural Syst. 2027 Jan;37(1):2750008. doi: 10.1142/S0129065727500080.

ABSTRACT

The global signal (GS) represents the broad variations in neural activity throughout the brain. Recent research has identified changes in the GS of functional magnetic resonance imaging (fMRI) in schizophrenia, challenging the traditional view of GS as mere noise that is typically discarded during data preprocessing. However, there has been no comprehensive characteristic analysis of GS in schizophrenia. This study recruited 100 patients with schizophrenia and 113 healthy subjects to perform resting-state fMRI. The averaged gray matter fMRI signal is denoted as GS. The GS topography was constructed by calculating Pearson correlation (i.e. GSCORR) between the GS and time series of each gray matter voxel. Furthermore, the relevance between GS topography and the function network features was constructed according to graph theory. Finally, we implemented an integrated analytical framework combining independent component analysis and multiple linear regression to quantify the contributions of resting-state brain networks to the spatiotemporal characteristics of the GS. The GSCORR of schizophrenia decreased in the bilateral insula and exhibited a significant negative association with disease duration. The increased GSCORR in the thalamus and default mode network (DMN) showed a positive correlation with the scale scores. We found that global properties are represented by GS topography in schizophrenia. In addition, the linear characterization results of GS showed that the component contribution decreased at the primary sensory network but increased at the high-order associated network. Our results further demonstrated that GS contains brain features associated with schizophrenia, which would help to understand the neural mechanisms underlying the psychopathological symptoms of schizophrenia.

PMID:42754391 | DOI:10.1142/S0129065727500080

Neuroimage Clin. 2026 Sep 12;52:104061. doi: 10.1016/j.nicl.2026.104061. Online ahead of print.

ABSTRACT

The two hemispheres of the brain are strongly connected both structurally and functionally, and disruptions to interhemispheric communication can have significant cognitive and behavioral consequences. While alterations to interhemispheric structural and/or functional connectivity have been reported in many neurological conditions, this issue has been scarcely addressed in Primary Progressive Aphasia (PPA). PPA is a neurogenerative syndrome primarily involving (left-)lateralized gray matter atrophy and typically categorized into three variants - logopenic, non-fluent, and semantic. Here we examined interhemispheric, homotopic structural and functional connectivity in 72 individuals from the three variants and age-matched controls. Structural connectivity (SC) was calculated from diffusion-weighted MRI and functional connectivity (FC) from resting-state fMRI. Both resulting connectomes were based on the same atlas of homotopic parcels. We asked: 1) Are patterns of abnormal homotopic SC and/or FC shared across PPA variants? 2) How are the FC changes related to underlying structural changes to white and gray matter? We found, first, that average homotopic SC was reduced in all variants and was related to greater behavioral disease severity. By contrast, for homotopic FC, the three variants displayed distinct patterns. Second, direct FC-SC comparisons established that, across variants, the FC and SC changes were distinct from one another and were largely dissociated from gray matter atrophy. In sum, the structural connectivity between the two hemispheres weakens in PPA with disease progression. However, the uncoupling of FC from underlying structural changes indicates that these changes reflect complex patterns of functional re-organization of interhemispheric communication in response to the disease.

PMID:42753639 | DOI:10.1016/j.nicl.2026.104061

Psychiatry Res Neuroimaging. 2026 Sep 14;364:112334. doi: 10.1016/j.pscychresns.2026.112334. Online ahead of print.

ABSTRACT

Panic disorder (PD) is a psychiatric condition marked by recurring, unexpected panic attacks characterized by intense fear and rapid-onset physiological symptoms. Functional magnetic resonance imaging (fMRI) in individuals with PD has revealed abnormalities in several brain regions associated with fear processing, emotional regulation, and interoception. A systematic search was conducted in PubMed and Embase to review the fMRI changes associated with PD published until April 2024. Eighteen studies were included, primarily employing resting-state fMRI and examining functional connectivity. Consistent findings included amygdala and insula hyperactivity and reduced connectivity within the default mode network, particularly in the posterior cingulate cortex and precuneus. Altered connectivity patterns were also observed in the prefrontal cortex, anterior cingulate cortex, and hippocampus. Task-based fMRI studies revealed increased activity in fear-related regions that correlated with clinical measures of PD severity. This review consolidates the evidence of neural network disruptions in PD, emphasizing dysregulated fear processing, emotional regulation, and self-referential thinking. These findings highlight the potential of fMRI to elucidate the pathophysiology of PD and inform targeted interventions. However, the limited number of available studies, small sample sizes, and methodological heterogeneity, particularly among task-based fMRI studies, warrant cautious interpretation.

PMID:42753522 | DOI:10.1016/j.pscychresns.2026.112334

Philos Trans R Soc Lond B Biol Sci. 2026 Sep 17;381(1958):2024-0472.1. doi: 10.1098/rstb.2024.0472.

ABSTRACT

Recent advances in neuroimaging have significantly reshaped our understanding of functional neurological disorder (FND), shifting the focus from structural damage to dysfunctional brain networks. Building on Charcot's early intuition of a 'functional lesion', this review traces the evolution of neuroimaging research in FND, from its historical roots to current developments. We synthesize findings from structural and functional imaging studies, highlighting alterations in brain regions involved in motor control, self-agency and emotion regulation. Task-based fMRI studies have shown atypical activation in areas such as the supplementary motor area, dorsolateral prefrontal cortex and temporo-parietal junction, supporting the notion of impaired voluntary motor control. Additionally, changes in the amygdala and periaqueductal grey point to the role of emotional dysregulation. Structural imaging reveals grey and white matter alterations, while machine learning applied to resting-state connectivity shows promise for biomarker development. Emerging evidence also suggests that genetic and epigenetic factors-particularly within oxytocinergic and serotonergic systems-may contribute to individual vulnerability. We argue for a multi-modal approach integrating neuroimaging, genetics and computational methods to advance both the diagnosis and treatment of FND. This article is part of the discussion meeting issue 'Digital healthcare for the management of functional neurological disorders'.

PMID:42750448 | DOI:10.1098/rstb.2024.0472

J Affect Disord. 2026 Sep 16:122515. doi: 10.1016/j.jad.2026.122515. Online ahead of print.

ABSTRACT

Accelerated intermittent theta burst stimulation (aiTBS) is an effective neuromodulatory intervention for treatment resistant depression (TRD). aiTBS delivers repetitive transcranial magnetic stimulation sessions over a compressed timeframe and can be individualized to specific locations using resting-state functional magnetic resonance imaging (rs-fMRI) to target pathological cortical networks or individualized in time using electroencephalography (EEG) to determine optimal participant-specific stimulation frequencies. In this randomized, placebo-controlled trial, we tested these forms of personalization against sham stimulation. Fifty-one patients with TRD were randomized to one of three arms; 43 completed the protocol per pre-specified criteria and assigned to one of three arms: (1) fMRI-connectivity-individualized aiTBS; (2) frequency- and connectivity-individualized aiTBS; or (3) sham aiTBS. Participants received 10 aiTBS sessions per day for five consecutive days with clinical assessments at baseline, and 1 and 4 weeks post-treatment. The primary outcome was change in depression severity on the Montgomery-Åsberg Depression Rating Scale from baseline to 1 week post-treatment. Anhedonia severity measured with the Dimensional Anhedonia Rating Scale was a secondary outcome. No significant differences were observed between active and sham arms on primary MADRS or secondary DARS endpoints for either the intent-to-treat or per-protocol sample. Remission and response rates also did not differ between arms. aiTBS individualized using either rs-fMRI or EEG and rs-fMRI failed to yield significant antidepressant benefits over sham stimulation, with relatively muted overall therapeutic benefits, underscoring the need for further clinical trials evaluating the efficacy of personalized aiTBS in TRD.

PMID:42749167 | DOI:10.1016/j.jad.2026.122515

Neuroscience. 2026 Sep 16:S0306-4522(26)00626-3. doi: 10.1016/j.neuroscience.2026.09.015. Online ahead of print.

ABSTRACT

Mild cognitive impairment (MCI) is an important prodromal stage of Alzheimer's disease, and its early identification is critical for risk assessment and timely intervention. Resting-state functional magnetic resonance imaging (rs-fMRI) can noninvasively characterize brain functional activity and connectivity. However, most existing methods rely on static second-order functional connectivity, limiting their ability to capture dynamic coordination and high-order interactions among multiple brain regions. To address these limitations, we propose a brain functional network analysis framework integrating multi-scale temporal feature fusion with adaptive high-order hypergraph convolution. Specifically, the multi-scale temporal fusion module captures functional dynamics at different temporal resolutions, while the adaptive hypergraph convolutional network learns high-order associations among brain regions in an end-to-end manner. Spatiotemporal feature encoding is further incorporated to facilitate automatic MCI identification. The proposed framework was evaluated using rs-fMRI data from the ADNI dataset, with regional BOLD time series extracted based on the AAL atlas. Under five-fold cross-validation, the proposed method achieved an accuracy of 82.66%, a sensitivity of 82.07%, and a specificity of 83.07% on ADNI-2, while maintaining an accuracy of 81.50% on ADNI-3, outperforming existing methods on both datasets. Further analysis identified abnormal high-order coordination patterns involving the default mode network, limbic system, and hippocampus-related memory circuits in patients with MCI. These findings provide methodological support for the early identification of MCI and the discovery of potential neuroimaging biomarkers.

PMID:42749078 | DOI:10.1016/j.neuroscience.2026.09.015

Neural Netw. 2026 Sep 12;205(Pt C):109616. doi: 10.1016/j.neunet.2026.109616. Online ahead of print.

ABSTRACT

Major depressive disorder (MDD) is a brain disorder characterized by substantial individual heterogeneity, and its accurate diagnosis remains challenging in clinical practice. Graph neural networks applied to functional brain networks (FBNs) constructed from resting-state functional magnetic resonance imaging (rs-fMRI) data have shown promise for MDD diagnosis. However, in multicenter settings, site differences are often entangled with disease-related information, which not only interferes with the model's learning of disease patterns but also compromises the reliable modeling of topological patterns in individual FBNs, thereby limiting identification performance. To address this problem, we propose a disentangled population-guided graph neural network with dynamic refinement (DPGR-Net) for MDD identification. Specifically, DPGR-Net first constructs individual FBN from rs-fMRI data and learns discriminative subject-level brain graph representations. To mitigate the interference of site effects, we disentangle disease-related associations from site-related variations at the population level and impose an independence constraint to learn more stable cross-site disease-discriminative representations. On this basis, we innovatively design a population-guided dynamic optimization mechanism for individual brain graphs, which feeds the stable disease-discriminative information learned at the population level back into the structural updating of individual brain graphs, thereby enhancing disease-related connections while suppressing site-related noisy connections. Experimental results on the public REST-meta-MDD dataset show that the proposed method achieves an ACC of 92.37% and outperforms existing state-of-the-art methods. Furthermore, DPGR-Net identifies discriminative brain regions and aberrant functional connectivity patterns associated with MDD, thereby advancing the interpretability and clinical utility of rs-fMRI for MDD diagnosis.

PMID:42748805 | DOI:10.1016/j.neunet.2026.109616

Psychiatry Res Neuroimaging. 2026 Sep 10;364:112327. doi: 10.1016/j.pscychresns.2026.112327. Online ahead of print.

ABSTRACT

BACKGROUND: Generalized anxiety disorder (GAD) and major depressive disorder (MDD) show substantial clinical overlap, yet their shared and disorder-specific intrinsic functional abnormalities remain unclear. This study examined regional brain activity and whole-brain functional integration in GAD and MDD using resting-state fMRI.

METHODS: Forty-two GAD patients, 43 MDD patients, and 83 healthy controls underwent resting-state fMRI. ALFF and FCS were calculated to assess regional spontaneous activity and whole-brain functional integration, respectively. Group differences were tested using voxel-wise ANCOVA controlling for age, sex, education, and frame-wise displacement. Partial correlations examined associations with clinical symptoms.

RESULTS: Both patient groups showed decreased ALFF in a cluster spanning the left precuneus and right cuneus, with lower ALFF in GAD than in MDD. Both groups also showed decreased FCS in the bilateral lingual gyri, right middle occipital gyrus, and left postcentral gyrus, and increased FCS in the right inferior frontal gyrus. GAD showed lower FCS in the left lingual gyrus than MDD.

CONCLUSIONS: GAD and MDD showed shared functional alterations in posterior cortical, visual, somatosensory, and frontal regions, while GAD exhibited lower ALFF and FCS in specific posterior cortical regions than MDD.

PMID:42748768 | DOI:10.1016/j.pscychresns.2026.112327

Cereb Cortex. 2026 Sep 1;36(9):bhag148. doi: 10.1093/cercor/bhag148.

ABSTRACT

Obsessive-compulsive disorder (OCD) emerging during adolescence coincides with a critical period of white matter maturation, yet the structural, functional, and molecular characteristics of white matter abnormalities remain incompletely understood. We acquired multimodal magnetic resonance imaging data from 40 adolescents with OCD and 40 healthy controls and quantified fractional anisotropy (FA) and fractional amplitude of low-frequency fluctuations (fALFF) along white matter tracts using Automated Fiber Quantification. Group comparisons, clinical correlation analyses, support vector machine classification, and molecular association analyses were performed. Adolescents with OCD exhibited increased FA in the bilateral anterior thalamic radiation (ATR) and left corticospinal tract, together with increased fALFF in the right ATR and right cingulum bundle (cingulate segment). Greater functional activity within the cingulum bundle was associated with lower symptom severity. A multimodal model integrating FA and fALFF achieved better classification performance than either modality alone. Molecular analyses linked white matter structural alterations to dopaminergic and cholinergic neurotransmitter systems, while transcriptomic analyses implicated neurodevelopmental and synaptic processes as well as excitatory and inhibitory neuronal cell types. These findings demonstrate convergent structural and functional alterations of white matter in adolescent OCD and suggest that their associated molecular signatures may provide insights into the biological context of these imaging abnormalities.

PMID:42747911 | DOI:10.1093/cercor/bhag148

Neuropsychiatr Dis Treat. 2026 Sep 11;22:630533. doi: 10.2147/NDT.S630533. eCollection 2026.

ABSTRACT

OBJECTIVE: In the era of combination antiretroviral therapy (cART), asymptomatic neurocognitive impairment (ANI) has become a common manifestation of HIV-associated neurocognitive disorders (HAND). Because ANI occurs without overt functional impairment, it may be overlooked in routine clinical practice despite being associated with an increased risk of subsequent symptomatic cognitive decline. This study aimed to develop a classification model for HIV-ANI by integrating node-level radiomic features derived from structural and functional MRI within a machine-learning framework, and to investigate the potential association between abnormalities in core cognitive network nodes and large-scale brain network dysfunction.

MATERIALS AND METHODS: A total of 67 individuals with HIV-associated asymptomatic neurocognitive impairment (HIV-ANI) and 70 cognitively intact individuals with HIV (HIV-IC) were included. Core regions of the default mode network (DMN), central executive network (CEN), and salience network (SN) were defined as regions of interest (ROIs). Node-level radiomic features were extracted from 3D T1-weighted imaging, amplitude of low-frequency fluctuation (ALFF), and regional homogeneity (ReHo) maps derived from resting-state fMRI. Unimodal, multimodal, and imaging-clinical fusion models were constructed using ElasticNet-regularized logistic regression after a stratified 7:3 training-test split. SHapley Additive exPlanations (SHAP) and a nomogram were used for model interpretation and individualized risk prediction.

RESULTS: The imaging-clinical fusion model achieved the highest AUC in the test set (0.735), with numerically better discriminative performance than the unimodal models. Functional features, particularly ReHo-derived features, contributed substantially to model prediction. SHAP analysis identified the medial prefrontal cortex, anterior and posterior cingulate cortices, and insula as key predictive regions. Clinical factors, including years of education and duration of HIV infection, also contributed to model performance.

CONCLUSION: Structural and functional alterations are detectable within core cognitive network nodes at the HIV-ANI stage. The integration of multimodal MRI radiomic features with clinical factors showed potential for identifying HIV-ANI and provided preliminary evidence that node-level abnormalities may be associated with broader network dysfunction in early HAND. Further validation in larger, independent cohorts is warranted.

PMID:42746602 | PMC:PMC13577179 | DOI:10.2147/NDT.S630533

Front Neurol. 2026 Sep 1;17:1867157. doi: 10.3389/fneur.2026.1867157. eCollection 2026.

ABSTRACT

BACKGROUND: Progressive cognitive impairment in vascular dementia (VaD) is increasingly linked to brain network disruption, or "disconnection syndrome." Although Compound Congrong Yizhi Capsule (CCYC), a marketed Chinese patent medicine approved in China for vascular cognitive disorders, has been studied in terms of therapeutic efficacy and molecular targets, these approaches do not directly explain how this multi-target intervention modulates brain functional organization. Resting-state functional magnetic resonance imaging (rs-fMRI) enables in vivo characterization of BOLD-based brain network functional phenotypes. Here, we used rs-fMRI to investigate the effects of CCYC in a rat model of VaD.

METHODS: VaD was induced by permanent bilateral common carotid artery occlusion (BCCAO) in rats. CCYC was administered by gavage for 6 weeks. Spatial navigation and memory retention were assessed using the Morris water maze (MWM). Resting-state fMRI was acquired on a 9.4 T scanner and analyzed using regional activity metrics, behavioral correlations, functional connectivity, and graph theoretical analyses.

RESULTS: VaD rats showed abnormal regional activity in the frontal cortex, retrosplenial cortex, and hippocampal system, which were associated with impaired MWM performance. They also exhibited disrupted connectivity within the fronto-retrosplenial-hippocampal memory circuit, particularly between prefrontal/cingulate and retrosplenial-hippocampal regions. CCYC treatment was associated with partial normalization of these regional and circuit-level abnormalities. Graph theoretical analyses further showed partial improvement in whole-brain topological organization and nodal properties after CCYC treatment. These findings do not establish anatomical restoration of the underlying white matter tracts.

CONCLUSION: CCYC may ameliorate VaD by modulating a systems-level neuroimaging phenotype centered on the fronto-retrosplenial-hippocampal memory circuit.

PMID:42745929 | PMC:PMC13574697 | DOI:10.3389/fneur.2026.1867157

Front Neurol. 2026 Sep 1;17:1870552. doi: 10.3389/fneur.2026.1870552. eCollection 2026.

ABSTRACT

OBJECTIVE: Dysfunction of the default mode network (DMN) is strongly linked to pain processing, as well as onset and progression of chronic pain disorders; however, its specific role in the pathogenesis of tension-type headache (TTH) remains incompletely understood. This study aims to explore the network homogeneity (NH) of the DMN in the pathophysiological mechanisms underlying TTH.

METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI) and clinical headache scale assessments were conducted on 68 patients with TTH and 63 healthy controls (HCs). Independent component analysis and NH analysis were used to evaluate the neuroimaging data.

RESULTS: Patients with TTH exhibited decreased NH values in the right posterior cingulate cortex (PCC) and increased NH values in the right medial superior frontal gyrus (SFGmed), bilateral angular gyrus (AG), and left precuneus (PCu), compared to HCs. Additionally, the NH values of right AG, left AG and right PCC were positively correlated with Headache Impact Test-6 (HIT-6), Migraine Disability Assessment Test (MIDAS) scores and headache intensity. Furthermore, support vector machine (SVM) analysis revealed that NH values in the right PCC could distinguish TTH patients from HCs with an accuracy of 75.57%, sensitivity of 84.13%, specificity of 70.59%, and an AUC of 0.7156.

CONCLUSION: This study demonstrates altered network homogeneity (NH) within the DMN in patients with TTH. Furthermore, the NH value of the right PCC showed discriminative potential to distinguish TTH patients from healthy controls via SVM classification.

PMID:42745836 | PMC:PMC13574691 | DOI:10.3389/fneur.2026.1870552

J Affect Disord. 2026 Sep 15:122497. doi: 10.1016/j.jad.2026.122497. Online ahead of print.

ABSTRACT

BACKGROUND: Major depressive disorder (MDD) is a highly heterogeneous affective disorder, and the identification of reliable neuroimaging markers remains challenging. Resting-state functional magnetic resonance imaging offers a promising approach for characterizing altered brain functional organization in MDD. However, most existing studies model subjects independently and do not sufficiently consider inter-subject heterogeneity, particularly in multisite datasets.

METHODS: We developed an interpretable brain region-aware multimodal graph neural network (BRM-GNN) for rs-fMRI-based MDD identification using the multisite REST-meta-MDD dataset. After quality control, 1586 participants from 16 centers were included, comprising 821 MDD patients and 765 healthy controls. At the individual level, temporal features were learned from raw blood oxygen level-dependent signals and combined with functional connectivity information to characterize subject-specific brain networks. A multi-scale pooling fusion module was introduced to preserve local and global topological information while identifying disease-relevant brain regions. At the population level, a sex-aware heterogeneous population graph was constructed to model inter-subject relationships by integrating imaging and non-imaging information.

RESULTS: Compared with existing graph-based methods, BRM-GNN achieved better identification performance while retaining neurobiological interpretability. The most informative regions were mainly located in prefrontal, cingulate, subcortical, and motor-related areas, suggesting that MDD may involve abnormalities in neural systems related to emotion regulation, reward processing, cognitive control, and psychomotor function.

CONCLUSION: Our findings suggest that integrating subject-specific brain network features with sex-aware inter-subject relationships may improve multisite rs-fMRI-based MDD identification. This framework also highlights biologically plausible brain regions associated with MDD and may support future research on neuroimaging-informed biomarkers in affective disorders.

PMID:42744142 | DOI:10.1016/j.jad.2026.122497

Eur Radiol Exp. 2026 Sep 15;10(1):131. doi: 10.1186/s41747-026-00788-x.

ABSTRACT

OBJECTIVE: Abnormal thalamo-cerebral and thalamo-cerebellar interactions are implicated in idiopathic generalized epilepsy (IGE), but their influence on thalamic functional hierarchy remains unclear. This study aimed to characterize thalamic functional hierarchy across varying connectivity strengths in IGE.

MATERIALS AND METHODS: Resting-state functional magnetic resonance imaging (fMRI) data were collected for 144 IGE patients and 222 healthy controls. The voxel-wise thalamo-cerebral and thalamo-cerebellar functional connectivity (FC) was first computed. Connectome gradient mapping was applied separately to anticorrelated and correlated connectivity strengths for both thalamo-cerebral and thalamo-cerebellar pathways. Furthermore, the first three thalamic gradient features were investigated using eccentricity.

RESULTS: In both healthy controls and patients with IGE, the thalamus predominantly coupled with low-order unimodal networks under anticorrelated conditions, and with high-order cognitive networks under correlated conditions. Moreover, connectome-dependent alterations were further observed in the limbic network, attentional network, and cerebellar systems in IGE. Additionally, thalamic gradients in IGE showed marked distributional abnormalities across a broad range of connectivity strengths. Notably, both thalamo-cerebral and thalamo-cerebellar eccentricity revealed shared increases and decreases within motor and cognitive thalamic nuclei, together with additional pathway-specific abnormalities unique to the thalamo-cerebellar system. These dispersion abnormalities were associated with poorer working memory performance in IGE, with three significant thalamic clusters identified (all p ≤ 0.008).

CONCLUSION: These fMRI findings demonstrate disruption of the thalamic functional hierarchy, manifested as nucleus-specific alterations in integration and segregation in IGE. Collectively, this study provides novel insights into thalamic functional abnormalities in epilepsy.

KEY POINTS: Question The hierarchical disruption of thalamo-cerebral and thalamo-cerebellar pathways in idiopathic generalized epilepsy and its clinical significance remains unclear. Findings Both pathways show altered functional hierarchy, with eccentricity changes in specific thalamic nuclei linked to slower working memory performance in idiopathic generalized epilepsy. Relevance statement This study revealed nucleus-specific integration-segregation disruptions in IGE that correlated with poorer working memory performance, providing an important window into thalamic dysfunction mechanisms.

PMID:42742800 | DOI:10.1186/s41747-026-00788-x

Neuroinformatics. 2026 Sep 15;24(3):60. doi: 10.1007/s12021-026-09816-y.

ABSTRACT

Functional connectivity (FC) analysis using resting-state fMRI is widely applied to study brain organization in autism spectrum disorder (ASD). However, variability in FC estimation, limited sample sizes, and high-dimensional feature spaces often produce unstable and over-optimistic machine-learning results, highlighting the need for rigorous methodological benchmarking. We propose a neuroinformatics framework for systematic evaluation of multiple FC measures under small-sample rs-fMRI conditions. Data were analyzed across children, adolescents, and adults. Group independent component analysis followed by dual regression extracted subject-specific network time series. Five FC measures-full correlation, partial correlation, bivariate Granger causality, coherence, and mutual information-captured linear, nonlinear, time-, and frequency-domain interactions. To ensure leakage-aware evaluation, feature selection was performed strictly within training folds using leave-one-out cross-validation, and multiple machine-learning classifiers were used as standardized evaluation tools. Performance of FC measures varied across developmental stages. Linear connectivity measures showed more stable behavior in childhood, nonlinear information-theoretic measures were most informative in adolescence, and frequency-domain measures demonstrated stronger performance in adulthood. Unlike studies focused primarily on diagnostic accuracy, this work emphasizes comparative methodological evaluation and explicitly addresses data leakage and overfitting through strict cross-validation and training-only feature selection. FC-based machine-learning outcomes are strongly influenced by developmental stage and methodological choice. The proposed leakage-aware framework provides a practical benchmark for evaluating FC measures in small-sample rs-fMRI Studies and supports more reliable neuroimaging machine-learning research.

PMID:42742790 | DOI:10.1007/s12021-026-09816-y

Eur Child Adolesc Psychiatry. 2026 Sep 15. doi: 10.1007/s00787-026-03194-1. Online ahead of print.

ABSTRACT

Tourette Syndrome (TS) is a neurodevelopmental disorder characterized by motor/vocal tics and premonitory urges. The triple network model (TNM)-including the salience network (SAN), frontoparietal network (FPN, also known as control network, Con), and default mode network (DMN)-provides a framework for understanding large-scale brain dysfunction. However, the TNM's role in the "urge-to-tic" mechanism in pediatric TS remains unclear. This study aimed to characterize network-level functional connectivity (FC) within the TNM in pediatric TS, examine its associations with clinical symptoms, and propose a refined neurobiological hypothesis for the urge-to-tic process. We enrolled 68 children with TS and 65 matched controls. Resting-state fMRI was used to quantify FC within the TNM. Clinical severity was assessed using the Yale Global Tic Severity Scale (YGTSS), Premonitory Urge for Tics Scale (PUTS), and Sensory Phenomenon Assessment Scale (SPAS). Group differences in FC were identified, and random forest (RF) regression models were employed to predict symptom severity based on FC within the TNM. Children with TS exhibited significant inter-network FC alterations, specifically involving increased connectivity between the left DMN and bilateral SAN. While YGTSS and SPAS score were significantly associated with TNM connectivity, no significant correlation was found with PUTS scores. The RF models successfully predicted YGTSS (R2 = 0.169) and SPAS scores (R2 = 0.139), with the left DMN-right SAN circuit emerging as the most robust predictor. TNM dysconnectivity is a core neural signature of pediatric TS, highlighting the TNM as a crucial framework for understanding the pathophysiology of the "urge-to-tic" process.

PMID:42742677 | DOI:10.1007/s00787-026-03194-1

Front Neurol. 2026 Aug 31;17:1898658. doi: 10.3389/fneur.2026.1898658. eCollection 2026.

ABSTRACT

OBJECTIVE: To investigate alterations in resting-state functional connectivity (rs-FC) and the standard deviation of dynamic functional connectivity (dFCstd) of the bilateral insula in patients with migraine without aura (MwoA), and to examine their associations with clinical symptoms.

METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI) was conducted on 45 patients with MwoA and 45 healthy controls (HCs). The left and right insulae were defined as seed regions according to the AAL116 atlas. rs-FC and dFCstd were calculated between each insular seed and the remaining 115 brain regions, with dFCstd defined as the standard deviation of Fisher-z transformed connectivity estimates across sliding windows. Group differences were evaluated with general linear models adjusted for age and sex, with false discovery rate (FDR) correction applied for multiple comparisons. Associations between altered functional connectivity and clinical measures were further examined within the MwoA group.

RESULTS: Patients with MwoA had higher rs-FC than HCs for the left and right insulae with the right hippocampus. Dynamic connectivity analysis identified lower dFCstd involving the left insula and ipsilateral caudate nucleus and higher dFCstd involving the left insula and ipsilateral superior frontal gyrus, orbital part. All group differences survived FDR correction. Exploratory correlation analyses showed that several altered connectivity measures were associated with monthly migraine attack frequency, visual analog scale (VAS) scores, and Self-Rating Depression Scale (SDS) standard scores.

CONCLUSION: Combined rs-FC and dFCstd analyses revealed altered functional connectivity involving the insula in patients with MwoA. These connectivity patterns may reflect disturbances in neural circuits supporting pain processing, emotion regulation, and cognitive control, providing complementary imaging evidence for brain network dysfunction in MwoA.

PMID:42741022 | PMC:PMC13572113 | DOI:10.3389/fneur.2026.1898658

Brain Behav. 2026 Sep;16(9):e71764. doi: 10.1002/brb3.71764.

ABSTRACT

BACKGROUND: Neurovascular coupling (NVC) is essential for regulating brain metabolism and energy supply and may be altered under acute stress. This study aimed to investigate how acute stress induced by dobutamine affects NVC.

MATERIALS AND METHODS: This study enrolled 38 healthy participants undergoing a resting-state fMRI scan and an arterial spin labeling MRI scan prior to and during a stress test induced by dobutamine. Amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and cerebral blood flow (CBF) were calculated. Acrossvoxel CBF-ReHo and CBF-ALFF correlations were analyzed to evaluate the NVC within the whole gray matter, and the regional NVC of the brain was assessed with the CBF/ReHo and CBF/ALFF ratios.

RESULTS: The blood pressures at the ventricle systolic state (SBP) and diastolic state (DBP) were significantly increased after the infusion of dobutamine. The CBF/ReHo ratios in the postcentral cortex and the middle cingulate cortex under stress were significantly higher and lower, respectively, than the CBF/ReHo ratios in the postcentral cortex and the middle cingulate cortex under the resting state. Logistic regression analysis showed that DBP in the resting state (odds ratio [OR] 0.817, 95% confidence interval [CI] 0.692-0.965, p = 0.017) and SBP in the stress state (OR 1.218, 95% CI 1.067-1.390, p = 0.004) were significantly related to the increase in CBF/ReHo ratios in the left postcentral cortex.

CONCLUSION: Dobutamine-induced acute stress modulated NVC dynamics. Participants with lower DBP in the resting state and higher SBP during acute stress were more prone to have increased NVC induced by stress.

RELEVANCE STATEMENT: Acute cardiovascular stress induced by dobutamine altered neurovascular coupling dynamics, and individuals with lower resting diastolic pressure and greater systolic responses showed stronger stress-related NVC enhancement.

PMID:42740572 | DOI:10.1002/brb3.71764

Psychol Med. 2026 Sep 15;56:e272. doi: 10.1017/S0033291726105388.

ABSTRACT

BACKGROUND: Posttraumatic stress disorder (PTSD) exhibits disrupted functional organization of brain networks involving sensory and association cortices. Connectome studies reveal a principal unimodal-to-transmodal gradient in the brain functional network that supports the spectrum from sensation to cognition. However, the disruption of the connectome gradient in PTSD and its associated transcriptional and neurotransmitter patterns remains poorly understood.

METHODS: Using resting-state functional magnetic resonance imaging data from a cohort of 86 non-comorbid adult patients with PTSD caused by an earthquake and 86 trauma-exposed non-PTSD (TENP) controls who experienced the same disaster, we examined PTSD-related changes in the principal connectome gradient, their links to symptom severity and sex/age interactions, and brain-wide associations between gradient changes and microscale transcriptomic and neurotransmitter maps and macroscale cognitive function.

RESULTS: Compared with TENP controls, PTSD exhibited global topographic abnormalities in the principal unimodal-to-transmodal gradient, and focal aberrance mainly in the unimodal (visual network) and transmodal systems (limbic, ventral attention, and frontoparietal networks). Sex had no significant effects, while gradient decrease in the subcortical network is more prominent in adolescents with PTSD than adults. The PTSD-related gradients colocalized with brain-wide gene expression enriched in mitochondria and DNA/RNA metabolic process and corresponded with the distribution of dopamine, opioid, noradrenaline, and cholinergic neurotransmitter systems.

CONCLUSIONS: These findings highlight the dysfunctional connectome hierarchy in PTSD and its association with transcriptomic and molecular profiles, suggesting that early traumatization may more significantly impact the development of subcortical network. This work provides insight into the neurobiological basis of PTSD and indicates potential biomarkers for prediction and prevention.

PMID:42740407 | DOI:10.1017/S0033291726105388