Most recent paper

Neuroinformatics. 2026 Jun 10;24(2):35. doi: 10.1007/s12021-026-09792-3.

ABSTRACT

Dynamic functional connectivity (dFC) analysis in functional magnetic resonance imaging (fMRI) faces a fundamental challenge: conventional sliding-window methods must trade temporal resolution against statistical reliability, while rare transient neural events risk becoming undetectable when included in training data. We introduce HESREN (Hermite-Enhanced Software Reservoir Network), a novel framework integrating echo state networks with derivative-informed Hermite-type neural operators to enable windowless dFC estimation and leakage-free transient detection. HESREN employs a leaky-integrator reservoir that projects multivariate fMRI time series into high-dimensional state spaces, augmented with Gaussian-smoothed temporal derivatives to form enhanced feature vectors encoding value, velocity, and acceleration. Strict temporal partitioning trains all components exclusively on baseline segments while evaluating on complete time series, preserving transient events as out-of-distribution signals. Teacher-student distillation transfers the temporal precision of micro-window connectivity estimates into stable windowless operators via ridge-regularised linear readout; all hyperparameters and initialisation procedures are fully specified to ensure reproducibility. Validation on the NEBULA101 resting-state fMRI dataset across [Formula: see text] participants demonstrates consistent and substantial improvements over conventional methods. Transient event detection achieves AUC[Formula: see text] and average precision AP[Formula: see text], compared to AUC[Formula: see text] for raw-derivative baselines (Wilcoxon [Formula: see text], [Formula: see text], Cohen's [Formula: see text]), with phase-randomised surrogate testing confirming statistical robustness in all participants ([Formula: see text], [Formula: see text] surrogates). Comparison against mainstream dFC alternatives shows that HESREN statistically significant performance gains Gaussian Hidden Markov Models (AUC[Formula: see text]), temporal convolutional networks (AUC[Formula: see text]), LSTM autoregressive predictors (AUC[Formula: see text]), and conventional sliding-window correlation (AUC[Formula: see text]), with all advantages statistically significant ([Formula: see text]). Windowless dFC trajectories attain lag-corrected correlation [Formula: see text] with micro-window teachers while providing 3-[Formula: see text] finer temporal resolution than 25-TR sliding windows. Network-level analysis reveals that HESREN detects transient events an average of 4.5 TR (9 s) earlier than sliding-window methods, selectively amplifies within-language-network coupling by [Formula: see text] and default-mode-network coupling by [Formula: see text] during detected events, and is the only evaluated method to yield a positive network segregation index ([Formula: see text]), consistent with the known modular organisation of resting-state brain networks. HESREN overcomes fundamental limitations of sliding-window dFC through derivative-aware reservoir dynamics, offering a computationally efficient, mathematically principled framework for capturing transient neural reconfigurations with temporal precision previously improved in fMRI connectivity analysis. The modular architecture facilitates adaptation to diverse neuroimaging applications, from basic neuroscience to real-time clinical monitoring systems.

PMID:42268529 | PMC:PMC13253793 | DOI:10.1007/s12021-026-09792-3

Clin Neuroradiol. 2026 Jun 10. doi: 10.1007/s00062-026-01674-0. Online ahead of print.

ABSTRACT

OBJECTIVE: To investigate topological alterations of functional brain networks in patients with post-stroke cognitive impairment (PSCI) using resting-state functional magnetic resonance imaging (rs-fMRI), and to explore the relationship between network organization and post-stroke cognitive performance.

MATERIALS AND METHODS: This study was conducted with a prospective enrollment of 45 patients with ischemic stroke, including 21 patients with PSCI and 24 patients with post-stroke non-cognitive impairment (PSNCI), coupled with the recruitment of 30 age-, sex-, and education-matched healthy controls (HC). All participants underwent brain rs-fMRI and cognitive function assessment. This study further employed comparative analyses to clarify the inter-group differences of global and nodal topological metrics, and the correlation between different brain regions and cognitive scores.

RESULTS: Compared with the HC group, both PSCI and PSNCI groups exhibited significant topological alterations of functional brain networks, including increased characteristic path length (Lp), reduced global efficiency (Eg) and local efficiency (Eloc), and disrupted small-worldness (σ). However, PSCI and PSNCI groups exhibited no significant differences in global network metrics. At the nodal level, PSCI and PSNCI patients showed increased nodal clustering coefficient (NCp) and local efficiency (NLe) in the left medial and paracingulate cortices, left caudate nucleus, and right paracentral lobule. Pearson correlation analysis revealed that Eloc (r = 0.580, P = 0.006), σ (r = 0.513, P = 0.017) and normalized clustering coefficient (γ) (r = 0.581, P = 0.006) were positively correlated with MoCA scores in PSCI group. Clustering coefficient (Cp) (r = -0.492, P = 0.015) was negatively correlated with the MMSE score in PSNCI group.

CONCLUSIONS: The global and node topological properties of the brain networks in patients with PSCI have changed. This is manifested as impaired information transmission efficiency and decreased integration ability in the entire brain. The abnormal global properties are related to cognitive dysfunction, providing valuable insights from the imaging perspective for understanding the neural cognitive mechanism of PSCI.

PMID:42268399 | DOI:10.1007/s00062-026-01674-0

Brain Behav. 2026 Jun;16(6):e71545. doi: 10.1002/brb3.71545.

ABSTRACT

BACKGROUND: Neuropathic pain (NP) is associated with maladaptive functional reorganization of the brain, yet the central mechanisms through which massage therapy exerts its analgesic effects remain poorly understood. This study aimed to investigate the impact of acupoint massage on spontaneous neural activity in a rat model of chronic constriction injury (CCI) of the sciatic nerve using resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS: Male Sprague-Dawley rats (N = 45) were randomly allocated into three groups: control, CCI model, and CCI with massage intervention. The massage group received daily acupoint pressing therapy from postoperative day 4 to day 17. Mechanical paw withdrawal threshold (PWT) and thermal paw withdrawal latency (PWL) were assessed at baseline and on days 4, 7, 10, and 17 post-modeling. rs-fMRI scans were acquired at three time points (pre-modeling, day 7, and day 17) using a 9.4T small-animal MRI system. Whole-brain amplitude of low-frequency fluctuations (ALFF) was analyzed to evaluate spontaneous neural activity.

RESULTS: CCI modeling induced significant alterations in ALFF across multiple brain regions involved in sensory, affective, and cognitive processing, including the amygdala, hippocampus, insular cortex, and somatosensory cortex. Massage intervention produced a significant group × time interaction effect in the left hippocampus (voxel p < 0.005, cluster p < 0.05 FWE corrected). Specifically, at 7 days post-modeling, ALFF values in the massage group were significantly lower than those in the model group (p = 0.0357), indicating early attenuation of CCI-induced hippocampal hyperactivity. Behaviorally, massage intervention significantly elevated PWT and PWL from day 7 onward (p < 0.001), with sustained improvement through day 17.

CONCLUSIONS: Massage therapy alleviates NP through modulation of spontaneous neural activity across multiple brain regions, with dynamic regulation of hippocampal plasticity emerging as a critical central mechanism. The early normalization of hippocampal hyperactivity may serve as a potential neuroimaging biomarker for massage-mediated analgesia and provides experimental evidence supporting the clinical application of massage for NP management.

PMID:42266135 | PMC:PMC13250635 | DOI:10.1002/brb3.71545

J Neurosci Res. 2026 Jun;104(6):e70132. doi: 10.1002/jnr.70132.

ABSTRACT

Although functional connectomics typically relies on resting-state fMRI, its analytical methods have been applied to task fMRI data in the investigation of broader involvements of brain regions even if inactive during a specific task. The purpose of this study is to assess the feasibility of inferring a true resting-state connectivity from task-fMRI data and to investigate the impact of connectomic-based analysis on behavioral trait studies. To this purpose, subjects underwent two visual fMRI tasks. The Blood-Oxygen-Level-Dependent (BOLD) time-series were processed to get both a "task" condition and a "pseudo-resting" condition applying different task regression setups to derive connectomes. Stimulus-classification experiments were conducted to compare "task" and "pseudo-resting" connectomes. Additionally, the influence of task regression was assessed through a classification experiment comparing children with Developmental Dyslexia (DD) and Typical Readers (TR). While task regression successfully removes task-related content from fMRI signals, stimulus information could still be inferred from connectomes, regardless of the preprocessing method used. Furthermore, a Support Vector Machine (SVM) experiment effectively discriminates between DD and TR in both "task" and "pseudo-resting" conditions. The study explored the impact of preprocessing in task fMRI experiments analyzed with connectomics. The ability to classify the stimuli in "pseudo-resting" conditions suggests that connectomes retain task-related signals even after task regression. Discriminative connections vary across tasks, affecting how classifiers differentiate between DD and TR. Despite these task-related differences, preprocessing had no effect on the inference of classification rules, indicating that key features are similarly evaluated in both tasks.

PMID:42265860 | PMC:PMC13250240 | DOI:10.1002/jnr.70132

Brain Topogr. 2026 Jun 10;39(4):64. doi: 10.1007/s10548-026-01221-7.

ABSTRACT

Cortical networks reorganize following early sensory deprivation, yet the relationship between structural architecture and large-scale functional organization remains incompletely understood. We examined connectome organization in 54 congenitally deaf adults and matched hearing controls using resting-state functional MRI, diffusion tensor imaging, and graph-theoretical analysis. Deaf individuals exhibited higher global efficiency and lower local efficiency, indicating a shift toward distributed integration with reduced regional segregation. These effects were most prominent in auditory, multisensory, and associative cortices. Diffusion measures showed reduced fractional anisotropy in auditory pathways, with relatively preserved white-matter organization in visual and parietal regions. At the regional level, functional topology showed coordinated correspondence with local white-matter organization, whereas network-averaged structure-function associations were not significant. Multivariate analyses further indicated structured alignment between structural and functional measures within altered territories. Overall, congenital deafness is associated with large-scale reconfiguration of cortical network topology, accompanied by spatially selective variation in white-matter architecture. These findings suggest that early sensory experience shapes intrinsic connectome organization through coordinated, regionally specific adaptations rather than uniform network change.

PMID:42265444 | DOI:10.1007/s10548-026-01221-7

Commun Biol. 2026 Jun 9. doi: 10.1038/s42003-026-10411-9. Online ahead of print.

ABSTRACT

Most brain-behavior mapping studies rely on resting-state functional connectivity (FC), but this approach has known accuracy limits and can be outperformed by movie-watching FC. Here, we present a novel deep neural network framework to predict cognitive scores and sex from FC during naturalistic movie viewing, and examine how movie content and its ability to synchronize brain activity across individuals relate to prediction performance. We show that FC from movie-watching generally outperforms resting-state FC - even when compared to five times more temporal data - with sensory and higher-order brain networks emerging as the most important for prediction. Using both static and sliding-window dynamic FC approaches, we find that higher cognitive prediction accuracy is positively associated with greater inter-subject synchrony and the duration of human faces and voices in the movies; these effects were not found for sex prediction. This work underscores the promise of naturalistic movie viewing as a powerful tool for probing individual differences in the brain and revealing neural underpinnings of human behavior.

PMID:42265316 | DOI:10.1038/s42003-026-10411-9

Acad Radiol. 2026 Jun 9:S1076-6332(26)00378-8. doi: 10.1016/j.acra.2026.05.004. Online ahead of print.

ABSTRACT

RATIONALE AND OBJECTIVES: Chronic neck pain (CNP) is characterized by persistent pain and disability, often accompanied by alterations in brain structure and function. Although Chinese manual therapy has demonstrated clinical efficacy in relieving CNP, its central neural mechanisms remain poorly understood.

MATERIALS AND METHODS: Thirty patients with CNP and 32 age- and sex-matched healthy controls (HCs) underwent 5.0-T magnetic resonance imaging (MRI) including neurite orientation dispersion and density imaging (NODDI) and resting-state functional MRI(rs-fMRI). Patients received 12 sessions of Chinese manual therapy administered three times per week over a 4-week period. Clinical outcomes were evaluated with the Visual Analog Scale (VAS), Neck Disability Index (NDI_score), and Pain Catastrophizing Scale (PCS). Between-group and within-group differences were analyzed using appropriate parametric or nonparametric tests with false discovery rate correction.

RESULTS: Chinese manual therapy significantly reduced VAS (mean ± SD, -3.18 ± 1.35; p < 0.001), NDI (-7.97 ± 6.20; p < 0.001), and PCS (-4.17 ± 7.80; p =.006) scores, indicating substantial pain relief and functional recovery. At baseline, patients exhibited decreased neurite density index (NDI) and increased orientation dispersion index (ODI) in the right thalamus, left posterior cingulate cortex, and left precuneus (all p < 0.01), along with altered functional connectivity (FC) within thalamocortical and default-mode networks. After treatment, NDI increased and ODI decreased toward HC levels, while thalamus-anterior cingulate hyperconnectivity decreased (ΔFC = -0.10 ± 0.05; p < 0.01) and thalamus-medial superior frontal connectivity increased (ΔFC = 0.09 ± 0.04; p <0.05). Reductions in thalamocortical FC were significantly associated with pain improvement (ΔVAS vs ΔFC, r = 0.73; p <0.05). No adverse events occurred.

CONCLUSION: Chinese manual therapy was associated with significant pain and disability reduction in CNP, accompanied by concurrent microstructural and functional reorganization within thalamic and default-mode network regions. These findings provide neuroimaging evidence supporting a central neural correlate of Chinese manual therapy that extends beyond purely peripheral biomechanical explanations.

PMID:42265017 | DOI:10.1016/j.acra.2026.05.004

J Affect Disord. 2026 Jun 8;412:122087. doi: 10.1016/j.jad.2026.122087. Online ahead of print.

ABSTRACT

BACKGROUND: Major depressive disorder (MDD) and social anxiety disorder (SAD) are prevalent and frequently co-occurring. Few studies have directly compared MDD and SAD using spatially and frequency-resolved resting-state functional connectivity (rsFC). We examined whether rsFC networks show shared and diagnosis-associated features of MDD and SAD.

METHODS: Baseline rsFC from 150 adults (MDD = 60; SAD = 55; healthy controls [HC] = 35) underwent group-information-guided ICA (GIG-ICA). Component spatial maps and frequency spectra were compared across groups with age, sex, and motion covariates. Follow-up regressions related ICA features to Hamilton Depression Rating Scale (HAM-D) and Liebowitz Social Anxiety Scale (LSAS). Robustness analyses evaluated motion, comorbidity, illness/treatment history, sex balance, and preprocessing choices.

RESULTS: Relative to SAD, MDD showed reduced low-frequency rsFC in the salience network (SN; 0.045-0.058 Hz) and superior temporal gyrus (STG; 0.037-0.041, 0.054-0.093 Hz), whereas SAD showed greater fusiform/parahippocampal (FusPHG) spatial-map expression. FusPHG correlated positively with LSAS (β = 0.425, p < .001) and negatively with HAM-D (β = -0.375, p < .001). SN connectivity correlated negatively with HAM-D and LSAS, while STG connectivity correlated negatively with LSAS but not HAM-D. Parsimonious sensitivity models identified FusPHG spatial expression and STG 0.054-0.093 Hz power as the most stable candidate group-level associations; SN effects attenuated after fuller illness-course and lifetime-treatment proxy adjustment.

CONCLUSIONS: MDD and SAD showed candidate spatially and frequency-resolved network differences involving SN, STG, and FusPHG circuits. These preliminary findings indicate SAD and MDD differ in neural pathways linking salience-related control, social-auditory integration, and visual-affective simulation and require replication.

PMID:42264313 | DOI:10.1016/j.jad.2026.122087

Brain Struct Funct. 2026 Jun 9;231(6):82. doi: 10.1007/s00429-026-03137-1.

ABSTRACT

Spatial neglect is a common and disabling consequence of right hemisphere stroke, characterized by a failure to attend to the contralesional left space, and frequently persists into the chronic stage. There is robust evidence on the role of right-hemisphere frontoparietal dysfunction, interhemispheric structural disconnection and maladaptive activity in the left hemisphere in the persistence of neglect. However, the specific impact of right frontoparietal dysfunction on the functional (re)organization of the left, non-lesioned hemisphere remains poorly understood. In this study, we introduce a novel application of functional connectivity gradient analysis to investigate macroscale functional reorganization in the non-lesioned left hemisphere of patients with chronic left spatial neglect. Focusing on resting-state fMRI data, we demonstrate that abnormal segregation patterns in the left frontoparietal and default mode networks are robustly associated with neglect severity and spatial attentional bias. Notably, the gradient capturing the unimodal-to-transmodal hierarchy was associated with neglect severity, and gradients related to the frontoparietal control network were altered in neglect patients. Single-subject analyses confirmed the presence of this pattern in 11 of the 13 patients included in the study. We also show that greater structural integrity of the left inferior fronto-occipital fasciculus (IFOF) is positively associated with these functional dynamics. These findings reveal a previously overlooked aspect of neglect pathophysiology: the maladaptive dominance of the non-lesioned hemisphere's intrinsic architecture. By combining innovative gradient-based metrics with classical lesion approaches, our study offers a new framework for understanding neglect as an emergent property of large-scale network imbalance, with clinical implications for diagnosis and intervention, and theoretical consequences for models of hemispheric asymmetries and conscious access.

PMID:42262587 | DOI:10.1007/s00429-026-03137-1

Menopause. 2026 Jun 9. doi: 10.1097/GME.0000000000002836. Online ahead of print.

ABSTRACT

OBJECTIVE: Our goal was to assess the relationship between menopause stage and resting-state functional connectivity during midlife.

METHODS: Data from the Human Connectome Project-Aging 2.0 release were utilized in this study. Imaging and demographic data of 151 female participants between 40 and 55 years of age were included. To investigate functional connectivity, we utilized Conn Toolbox to assess the strength of functional associations between brain regions at rest at both connection and cluster levels.

RESULTS: Differences in resting-state functional connectivity between the supramarginal gyrus, right anterior division, and right planum temporale at the connection level were identified between participants in the pre-, peri-, and postmenopausal groups when all groups were compared. Further analysis comparing the pre- and postmenopausal groups revealed one cluster of altered resting-state connectivity that was lower in the postmenopausal group compared to the premenopausal group. Regions with altered connectivity included the left and right supramarginal gyrus, the anterior division, and the right and left planum temporale.

CONCLUSIONS: Resting-state functional connectivity differed between menopause stages, highlighting the relationship between menopause and brain functioning during midlife in females. Differences in functional connectivity between pre- and postmenopausal participants suggest that the menopause transition may be relevant to brain functioning during the female aging process.

PMID:42262362 | DOI:10.1097/GME.0000000000002836

J Gerontol B Psychol Sci Soc Sci. 2026 Jun 8:gbag101. doi: 10.1093/geronb/gbag101. Online ahead of print.

ABSTRACT

OBJECTIVES: The neurobiology of subthreshold depression, a prevalent and debilitating condition among older women, remains poorly understood. Neuroticism is a known depression risk factor, yet its role linking brain function to depressive symptoms in this population is understudied. This study investigated neurofunctional alterations in older women with subthreshold depression and tested whether neuroticism statistically explains the link between neural alterations and depressive symptoms.

METHODS: Fifty older women with subthreshold depression and 52 healthy older women controls underwent resting-state fMRI. Amplitude of low-frequency fluctuations (ALFF) and seed-based resting-state functional connectivity (RSFC) were analyzed. Depressive symptoms were assessed using the Geriatric Depression Scale and Center for Epidemiologic Studies Depression Scale, and personality traits with the Big Five Inventory-2. Mediation analyses examined the indirect effects of neuroticism.

RESULTS: Compared to controls, older women with subthreshold depression showed higher depression and neuroticism scores and lower scores on other personality traits. Neuroimaging revealed greater ALFF in the left lateral orbitofrontal cortex (LOFC) and increased RSFC between LOFC and medial OFC (MOFC) in the subthreshold depression group. These neural alterations positively correlated with depressive symptoms across all participants. Notably, only neuroticism correlated with both LOFC ALFF and LOFC-MOFC RSFC, and positively mediated the link between these neural markers and depression symptoms.

DISCUSSION: Older women with subthreshold depression exhibit OFC dysfunction, with neuroticism mediating the link to depressive symptoms. These findings elucidate a neuropsychological pathway linking intrinsic brain function to depressive symptomatology via personality vulnerability, offering potential targets for early identification and intervention in this at-risk population.

PMID:42261263 | DOI:10.1093/geronb/gbag101

Psychol Med. 2026 Jun 9;56:e187. doi: 10.1017/S0033291726104358.

ABSTRACT

BACKGROUND: Functional connectivity (FC) is consistently altered in patients with schizophrenia. The brain's primary inhibitory neurotransmitter, gamma-aminobutyric acid (GABA), and its relationship to FC in psychosis spectrum disorders are under-investigated. The anterior cingulate cortex (ACC) has been implicated in many cognitive functions impaired in psychosis. We hypothesize that the relationships between ACC GABA and FC in key brain networks will be altered in first-episode psychosis (FEP) patients as compared to healthy controls (HC).

METHODS: We used magnetic resonance spectroscopy (MRS) with a MEGA-PRESS sequence to quantify ACC GABA levels in 67 antipsychotic medication-naïve FEP patients and 110 HC. Resting state functional magnetic resonance imaging (fMRI) was used to assess positive and negative FC within the default mode (DMN), salience (SN), dorsal attention (DAN), and executive control (ECN) networks. We used linear regressions to test GABA-FC relationships in each network between groups.

RESULTS: FEP patients had significantly lower GABA levels compared to HC. We also found several clusters in the ECN, DAN, and DMN where FC differed between groups. Ultimately, we found significant GABA-FC group interactions in two ECN clusters and one SN cluster, where GABA and FC were positively correlated in HC but negatively correlated in FEP.

CONCLUSIONS: Our data add to the growing literature supporting GABA's significant role in psychosis spectrum disorders, especially as it relates to FC in key brain networks. Our findings call for further investigation of the mechanisms underlying altered neurometabolic activity and connectivity in psychosis spectrum disorders.

PMID:42261240 | PMC:PMC13247797 | DOI:10.1017/S0033291726104358

Hum Brain Mapp. 2026 Jun 1;47(8):e70559. doi: 10.1002/hbm.70559.

ABSTRACT

The study of brain networks is essential for improving our understanding of how the human brain functions. Functional connectivity (FC) analysis is a widely used approach for studying co-activating patterns among brain regions by estimating their temporal dependencies and constructing an undirected network. Data processing is critical before estimating a subject's functional network, but the absence of a standardized procedure serves as a source of heterogeneity in results, especially in multi-site studies. Commonly studied functional networks include the default mode, sensorimotor, visual, salience, dorsal attention, frontoparietal, and language networks. These networks are stable and still exhibit intrinsic activation when an individual is at rest, making them ideal networks to focus on for studying how processing choices affect the replicability of functional connectivity networks. We use the aforementioned seven networks to assess the impact of various processing choices, including preprocessing pipeline, band-pass filtering, and brain parcellation, on the replicability of functional connectivity estimates for multi-site resting-state fMRI (rs-fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE). Finally, we provide some practical recommendations for how researchers should proceed with processing choices in the face of these effects.

PMID:42260753 | PMC:PMC13247136 | DOI:10.1002/hbm.70559

Sci Data. 2026 Jun 8. doi: 10.1038/s41597-026-07540-5. Online ahead of print.

ABSTRACT

There is an increasing need to integrate multimodal datasets in epilepsy research, particularly to correlate electrophysiology with imaging in patients with refractory epilepsy. We present a multimodal paired 3T and 7T MRI dataset acquired from 30 drug-resistant focal epilepsy patients (18 females, 38.8 ± 11.7 years) who underwent T1-weighted (T1w), T2-weighted (T2w), Fluid Attenuated Inversion Recovery (FLAIR), and resting-state functional MRI (rs-fMRI). In addition to the raw data, we release preprocessed anatomical and functional data, along with various quality control and clinical metadata files. For participants who subsequently underwent intracranial EEG (iEEG) (n = 15), curated ictal and interictal epochs are also included. We demonstrate a potential application of this paired 3T and 7T data by training a deep learning model capable of synthesizing high-field 7T T1w MR images from the 3T equivalents. We anticipate that this dataset will facilitate future multiscale analyses in epilepsy.

PMID:42259832 | DOI:10.1038/s41597-026-07540-5

Biometrics. 2026 Apr 9;82(2):ujag099. doi: 10.1093/biomtc/ujag099.

ABSTRACT

Autism spectrum disorder (ASD) is a neurodevelopmental condition associated with difficulties with social interactions, communication, and restricted or repetitive behaviors. To characterize ASD, investigators often use functional connectivity derived from resting-state functional magnetic resonance imaging of the brain. However, participants' head motion during the scanning session can induce motion artifacts. Many studies remove participants with excessive motion and then estimate the effect of diagnosis on functional connectivity using linear regression. However, participant exclusions and linearity assumptions can cause biases. We propose an estimand that quantifies the difference in average functional connectivity in autistic and non-ASD children while standardizing motion relative to the low motion distribution in scans that pass motion quality control. We introduce a nonparametric estimator for motion control, called Motion Controlled (MoCo), that uses all participants and flexibly models the impacts of motion and other relevant features using an ensemble of machine learning methods. We establish large-sample efficiency and multiple robustness of our proposed estimator. The framework is applied to estimate the difference in functional connectivity between 132 autistic and 245 non-ASD children, of which 34 and 126 pass motion quality control, respectively. MoCo appears to dramatically reduce motion artifacts compared to a standard approach with no participant removal, while more efficiently utilizing participant data and accounting for possible selection biases compared to participant removal.

PMID:42259652 | PMC:PMC13245281 | DOI:10.1093/biomtc/ujag099

Epilepsia. 2026 Jun 8. doi: 10.1002/epi.70323. Online ahead of print.

ABSTRACT

OBJECTIVE: This study was undertaken to reliably estimate individual-specific resting-state cortical networks and determine whether language network topography can predict task-based language dominance in drug-resistant epilepsy.

METHODS: We utilized a multisession hierarchical Bayesian model (MS-HBM) trained on drug-resistant epilepsy patients to map high-quality individual-specific cortical networks in this population (n = 65) with only 6-24 min of resting-state functional magnetic resonance imaging (fMRI). We compared the quality of networks to MS-HBM models trained on healthy participants from the human connectome project (n = 40) and tested the generalizability of the model in an independent cohort of drug-resistant epilepsy participants (n = 26). Resting-state language network topography was then used to predict task-based language dominance.

RESULTS: Ninety-one participants with drug-resistant epilepsy (National Institutes of Health, n = 65; University of Iowa, n = 26) were included: 61 (67.0%) temporal lobe epilepsy, 29 (31.9%) extratemporal lobe epilepsy, and one (1.1%) undetermined seizure onset zone. The mean age was 33.0 ± 11.4 years, and 50 (54.9%) were male. There were 40 healthy participants with a mean age of 29.0 ± 4.0 years, and 16 (40.0%) were male. MS-HBM trained on drug-resistant epilepsy estimated individual-specific networks that more accurately capture cortical functional organization than group-average networks or MS-HBM trained on healthy participants. The trained MS-HBM model generalized to an independent cohort of drug-resistant epilepsy participants with concurrent intracranial electrical stimulation and fMRI. Critically, cortical evoked fMRI activity aligned more closely with individual-specific networks than with group-average networks. Furthermore, individual-specific language network topography significantly predicted task-based language dominance, achieving high accuracy for left (area under the curve [AUC] = .82), bilateral (AUC = .72), and right (AUC = .83) dominance.

SIGNIFICANCE: These results demonstrate that MS-HBM captures functionally meaningful network reorganization in drug-resistant epilepsy and enables accurate, individual-level prediction of language lateralization, with direct implications for presurgical functional mapping.

PMID:42257618 | DOI:10.1002/epi.70323

Open Med (Wars). 2026 Jun 5;21(1):20261455. doi: 10.1515/med-2026-1455. eCollection 2026 Jan.

ABSTRACT

OBJECTIVES: To investigate functional changes of the default mode network (DMN) in resting-state functional magnetic resonance imaging (rsfMRI) of perimenopausal women, and explore the association between these changes and estrogen levels.

METHODS: A total of 16 women in the perimenopausal period and 15 women in the premenopausal period underwent general health status, menopausal rating scale (MRS), and depression screening scale assessment, cognitive function tests (Stroop task) and measurement of sex hormone levels including prolactin (PRL), follicle stimulating hormone (FSH), estradiol (E2), testosterone (T), progesterone (P), and luteinizing hormone (LH). The resting state fMRI data were acquired using a 3.0 T magnetic resonance scanner, and the differences in DMN functional connection between these two groups were evaluated by independent component analysis (ICA). The amplitudes of low frequency fluctuations (ALFF) was analyzed using independently defined regions of interest (ROIs). Correlations between DMN connectivity and E2 were tested with Bonferroni/FDR correction; post hoc power analysis verified sample size adequacy.

RESULTS: Post hoc power analysis showed 89 % power for the primary outcome (DMN connectivity differences). After AlphaSim (cluster ≥74, p<0.05) and threshold-free cluster enhancement (TFCE, FWE p<0.05) correction, perimenopausal women exhibited significantly enhanced DMN connectivity in the bilateral middle frontal gyri (MFG, functional extension nodes of the anterior DMN), left insula, and posterior cingulate cortex (PCC, core hub of the posterior DMN) (all p<0.05). No significant group differences were detected in the canonical medial prefrontal cortex (MPFC) core (MNI coordinates: x=0, y=50, z=20). ALFF values of these regions were also significantly higher in perimenopausal women (all p<0.05). Correlation analysis revealed no significant linear correlations between E2 levels and DMN connectivity of the four regions in perimenopausal women (all p>0.05), while a significant positive correlation between E2 and left insula connectivity was observed in premenopausal women (r=0.489, Bonferroni-corrected p=0.032). No group differences were found in cognitive function (Stroop task: p=0.495-0.519) or PHQ-9 scores (p=0.067); adjusting for these variables did not alter DMN results.

CONCLUSIONS: Estrogen reduction and fluctuation are associated with functional reorganization of DMN-associated regions in perimenopausal women. Enhanced connectivity of the bilateral middle frontal gyri, left insula, and PCC may reflect a compensatory mechanism to maintain cognitive and emotional stability amid E2 decline. These findings provide preliminary neuroimaging evidence for brain function adaptations during the perimenopausal transition, with E2 dynamics (decline and fluctuation) as key drivers.

PMID:42254985 | PMC:PMC13238467 | DOI:10.1515/med-2026-1455

Front Hum Neurosci. 2026 May 21;20:1786568. doi: 10.3389/fnhum.2026.1786568. eCollection 2026.

ABSTRACT

BACKGROUND: The potential negative influences of short video platforms (SVPs) usage on mental health have been attracting increasing attention in recent years. This study aimed to investigate the possible effects of SVP usage on brain functions using the resting-state functional magnetic resonance imaging (fMRI) methods.

METHODS: Resting-state fMRI data were acquired from a total of 55 young healthy adults. Based on self-reported daily usage time of SVPs, these participants were divided into a lower SVP usage (SVP-) group (< 1 h per day, n = 20) and a higher SVP usage (SVP+) group (≥1 h per day, n = 35). Between-group comparisons of functional brain measures were performed across multiple spatial levels.

RESULTS: At the single-edge level, the SVP + group showed significantly increased functional connectivity (FC) across many edges linking most major brain networks, including sensorimotor, visual, auditory, subcortical, default-mode, attention, and cingulo-opercular networks. Network-level analyses confirmed this widespread hyperconnectivity, with particularly robust increases within sensorimotor, auditory, subcortical, and cingulo-opercular networks after multiple comparisons correction. Voxel-wise analyses revealed higher fractional amplitude of low-frequency fluctuations (fALFF) in the left precentral gyrus and lower fALFF in the right frontal lobe in the SVP + group. Global topological analysis indicated that the SVP + group had significantly higher global efficiency, local efficiency, and clustering coefficient, as well as lower characteristic path length, suggesting an altered network topology.

CONCLUSION: This multi-level fMRI study suggests that a relatively higher-intensity SVP use is associated with an altered pattern of brain functional organization, characterized by widespread hyperconnectivity across most major brain networks, localized spontaneous activity alterations in sensorimotor regions, and an altered topology at the global level. These findings highlight the importance of considering potential impacts of SVP usage on brain functioning, and calls for future larger-sample and longitudinal studies to further understand such relationships.

PMID:42253790 | PMC:PMC13233705 | DOI:10.3389/fnhum.2026.1786568

Brain Behav Immun Health. 2026 May 25;54:101274. doi: 10.1016/j.bbih.2026.101274. eCollection 2026 Jul.

ABSTRACT

BACKGROUND: Post-COVID-19 Syndrome (PCS) is characterised by persistent fatigue, cognitive impairments, and affective symptoms, yet its underlying neural mechanisms remain poorly understood. While static neuroimaging studies have identified resting-state connectivity abnormalities in PCS, such approaches fail to capture the brain's dynamic functional organisation. This represents a missed opportunity to understand how alterations in large-scale network interactions may contribute to the fluctuating symptom profile of PCS. Cognitive and emotional processes rely on the brain's capacity to flexibly reconfigure large-scale networks over time; disruptions in this dynamic repertoire may therefore play a role in PCS pathophysiology.

METHODS: Resting-state fMRI data were acquired from 20 individuals with PCS (mean age = 41.8 years, SD = 9.4) and 20 age- and sex-matched healthy controls (mean age = 40.6 years, SD = 8.1) using a multi-echo sequence. Following denoising with multi-echo independent component analysis, we applied Leading Eigenvector Dynamics Analysis (LEiDA) to identify recurrent patterns of whole-brain phase synchrony. The optimal number of dynamic brain states was determined using the Dunn index. For each state, we quantified probability of occurrence, lifetime, and transition probabilities, and mapped spatial topographies onto canonical functional networks. Group differences were assessed using ANCOVAs controlling for age, sex, and handedness. Exploratory associations with clinical symptoms, cognitive performance, and inflammatory markers were examined using both frequentist and Bayesian approaches.

RESULTS: Five recurrent dynamic brain states were identified. Compared with controls, PCS participants showed reduced probability of occurrence and shorter lifetime of a visual/dorsal attention state, alongside increased probability of a limbic/default mode network (DMN) state. PCS was also characterised by tentative reduced transitions between visual/dorsal attention and frontoparietal-DMN states, and increased transitions from somatomotor/visual states toward the limbic-DMN configuration. Exploratory analyses (uncorrected for multiple comparisons) suggested that greater expression of the limbic-DMN state was associated with lower global cognitive performance (MoCA) and higher serum IL-1β levels, although these associations did not survive correction for multiple comparisons.

CONCLUSIONS: PCS is associated with a reorganisation of intrinsic brain dynamics, marked by a shift from externally oriented attentional states toward limbic-DMN configurations and reduced transition flexibility. These findings suggest that PCS may involve alterations in the dynamic balance of large-scale brain systems supporting attention and internally oriented processing. While exploratory, the observed patterns are consistent with a potential link between brain-state dynamics, cognitive function, and inflammatory signalling, and provide a systems-level framework for future studies of post-viral brain dysfunction.

PMID:42253624 | PMC:PMC13234210 | DOI:10.1016/j.bbih.2026.101274

Imaging Neurosci (Camb). 2026 Jun 4;4:IMAG.a.1248. doi: 10.1162/IMAG.a.1248. eCollection 2026.

ABSTRACT

Brain activity unfolds over hierarchical timescales that reflect how brain regions integrate and process information, linking functional and structural organization. While timescale studies are prevalent, existing estimation methods rely on the restrictive assumption of exponentially decaying temporal autocorrelation and only provide point estimates without standard errors, limiting statistical inference. In this paper, we formalize and evaluate two methods for mapping timescales in resting-state fMRI: a time-domain fit of an autoregressive (AR1) model and an autocorrelation-domain fit of an exponential decay model. Rather than assuming exponential autocorrelation decay, we define timescales by projecting the fMRI time series onto these approximating models, requiring only stationarity and mixing conditions while incorporating robust standard errors to account for model misspecification. We introduce theoretical properties of timescale estimators and show parameter recovery in realistic simulations, as well as applications to fMRI from the Human Connectome Project. Comparatively, the time-domain method produces more accurate estimates under model misspecification, remains computationally efficient for high-dimensional fMRI data, and yields maps aligned with known functional brain organization. In this work, we show valid statistical inference on fMRI timescale maps, and provide Python implementations of all methods.

PMID:42253608 | PMC:PMC13237991 | DOI:10.1162/IMAG.a.1248