Most recent paper

Alzheimers Dement. 2026 Jun;22(6):e71547. doi: 10.1002/alz.71547.

ABSTRACT

INTRODUCTION: The brain is a complex dynamical system, influenced by arousal state. Cortical synchrony supports information processing and is disrupted in Alzheimer's disease (AD). Locus coeruleus (LC) integrity and pupillometry index arousal system structure and function.

METHODS: Sixty-four AD and 26 controls underwent resting-state pupillometry-fMRI. Neuromelanin MRI and Addenbrooke's Cognitive Examination were conducted. Mean and standard deviation of blood oxygen level dependent (BOLD) phase coherence yielded synchrony and metastability, respectively. Leading Eigenvector Dynamics Analysis (LEiDA) produced coherence-based states.

RESULTS: AD had reduced global synchrony [b = -0.90, p < 0.001], metastability [b = -0.61, p < 0.01], LEiDA "global coherence state" occupancy [b = -0.06, p < 0.01], and LC integrity [b = -0.37, p = 0.01]. Synchrony [b = 0.19, p = 0.01] and LC integrity [b = 0.17, p < 0.01] related to cognition and one another [b = 0.27, p = 0.01]. Pupil-linked arousal correlated with synchrony and global coherence state maintenance.

DISCUSSION: In health, cortical activity shows widespread but dynamic synchrony across regions to meet changing demands. In AD, arousal dysfunction appears to disrupt these dynamics, impacting cognition.

PMID:42273876 | PMC:PMC13254816 | DOI:10.1002/alz.71547

Front Psychiatry. 2026 May 26;17:1835812. doi: 10.3389/fpsyt.2026.1835812. eCollection 2026.

ABSTRACT

OBJECTIVE: To investigate the anterior cingulate cortex (ACC) resting-state functional connectivity patterns in OCD patients who have not yet received therapy and analyze how they relate to the intensity of their clinical symptoms.

METHODS: Resting-state fMRI data were acquired from 46 medication-naïve participants with OCD and 33 demographically comparable neurotypical control subjects. The region of focus for the seed-based whole-brain functional connectivity investigation was bilateral ACC. Relationships between aberrant connections and clinical characteristics measured by the Y-BOCS, HAMD-17, and HAMA were investigated using Pearson's correlation coefficient and partial correlation analysis.

RESULTS: Key findings (OCD patients vs. healthy controls): Increased functional connectivity (FC) in OCD patients: Right insula (Brodmann area 48), Right hippocampus (BA 20), Right fusiform gyrus (BA 37); Decreased FC involving the anterior cingulate cortex (ACC) with: Right supplementary motor area (SMA, BA 32), Left inferior frontal gyrus (IFG, BA 47) (AlphaSim corrected, P < 0.05). Clinical association: There was a significant positive relationship between Y-BOCS total scores (a measure of OCD symptom severity) and FC strength linking the left IFG to the ACC (r = 0.351, P = 0.017). In practical terms, greater symptom severity is associated with stronger coupling between these two regions. No other clear brain-behavior relationships were found in the other regions examined.

CONCLUSION: Treatment-naïve OCD patients demonstrate distinct ACC functional connectivity alterations involving cognitive control, motor planning, and limbic processing regions. The specific association between left inferior frontal gyrus (IFG)-ACC connectivity and symptom severity suggests that this pathway may serve as a neurobiological marker for OCD pathophysiology.

PMID:42273591 | PMC:PMC13248406 | DOI:10.3389/fpsyt.2026.1835812

Quant Imaging Med Surg. 2026 Jun 1;16(6):492. doi: 10.21037/qims-2026-1-0066. Epub 2026 May 13.

ABSTRACT

BACKGROUND: Mild cognitive impairment (MCI) is a transitional stage between subjective cognitive decline and Alzheimer's dementia, representing a critical window for intervention. We characterize the small-world properties of brain networks in MCI to identify sensitive biomarkers for early detection and assessment.

METHODS: Thirty-one patients diagnosed with MCI were recruited as the experimental group, while 30 healthy elderly individuals served as the normal control (NC) group. Based on diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI), small-world properties of the brain networks were observed using graph theory analysis. Global and nodal properties were computed to assess differences in brain network topology.

RESULTS: Both structural and functional brain networks in the MCI and NC groups exhibited small-world properties (σ>1), and significant differences were noted in nodal properties such as nodal efficiency, nodal degree centrality, and nodal shortest path length (P<0.05). Importantly, these nodal properties in brain regions were significantly correlated with Montreal Cognitive Assessment (MoCA) scores in patients with MCI (P<0.05).

CONCLUSIONS: Patients with MCI exhibit small-world properties in their brain networks, suggesting preserved efficiency of information transfer. Node property metrics in regions such as the posterior cingulate cortex, prefrontal cortex, and occipital lobe are promising biomarkers for early detection of MCI.

PMID:42273161 | PMC:PMC13247931 | DOI:10.21037/qims-2026-1-0066

J Appl Stat. 2025 Nov 23;53(8):1515-1537. doi: 10.1080/02664763.2025.2567979. eCollection 2026.

ABSTRACT

The classification of resting-state functional magnetic resonance imaging (rs-fMRI) data presents unique challenges in the detection and diagnosis of neuropsychiatric disorders such as Attention-Deficit/Hyperactivity Disorder (ADHD). Traditional classification approaches often prove inadequate when handling complex spatiotemporal patterns and high-dimensional fMRI data, particularly when significant variations exist both between and within diagnostic groups. To address these limitations, we introduce a novel classification framework that integrates three complementary analytical components: elastic registration for curve alignment, geometric curve length computation for capturing signal variability, and sparse principal component analysis for dimensionality reduction. Extensive simulation studies show that our proposed method significantly outperforms existing approaches, especially in scenarios where groups exhibit distinct variation patterns rather than mean differences in their functional curves. When applied to the ADHD-200 dataset, our method achieves classification accuracy rates substantially exceeding conventional approaches. The proposed framework's ability to capture subtle variability differences while maintaining computational efficiency makes it particularly valuable for biomarker discovery and clinical applications in neuropsychiatric research. Our approach's focus on signal variability rather than mean activation patterns offers new insights into the dynamic nature of brain activity differences in ADHD and provides a promising foundation for analyzing other neurological conditions.

PMID:42272799 | PMC:PMC13248495 | DOI:10.1080/02664763.2025.2567979

Imaging Neurosci (Camb). 2026 Jun 8;4:IMAG.a.1268. doi: 10.1162/IMAG.a.1268. eCollection 2026.

ABSTRACT

Much of a typical individual's mental life is characterized by spontaneous thoughts that occur independently of external stimuli. In prior studies, ongoing mental experiences and their neural correlates have been captured using thought probes presented at random intervals during functional magnetic resonance imaging (fMRI). However, this approach results in temporally imprecise estimates of brain activity relative to the arising of mental experience. In this preregistered, proof-of-concept study, we aimed to improve temporal precision using a novel method termed real-time fMRI-triggered experience sampling (rt-fMRI-ES). We analyzed blood-oxygenation level-dependent signals in real time during a wakeful resting state (n = 60) to trigger thought probes from spontaneous activations within two regions: the dorsal anterior insular cortex (daIC; a key region within salience network) and posteromedial cortex (PMC; a key region within default mode network). We tested two preregistered hypotheses: (H1) Ratings of arousal time-locked to daIC-activation trials are higher than ratings time-locked to non-daIC-activation trials; (H2) Ratings of external attention time locked to PMC-activation trials are lower than ratings time-locked to non-PMC-activation trials. After applying preregistered exclusion criteria, 42 participants (1243 trials) and 49 participants (1429 trials) were included in H1 and H2 analyses, respectively. We did not find evidence in support of H1, but we did find evidence in support of H2, as external-attention ratings were significantly lower for trials triggered by PMC activation than other trial types. Taken together, we successfully developed and validated the rt-fMRI-ES method, offering a novel technique to efficiently capture spontaneous thoughts based on ongoing neural activity. Preregistered Stage 1 Recommendation: https://osf.io/sd4hu (Date of in-principle acceptance: July 24, 2024).

PMID:42272744 | PMC:PMC13248897 | DOI:10.1162/IMAG.a.1268

Brain Connect. 2026 Jun 11:21580014261455378. doi: 10.1177/21580014261455378. Online ahead of print.

ABSTRACT

Background: Tinnitus is an auditory phantom perception in the absence of any corresponding acoustic stimulus whose pathophysiology remains poorly understood. This study aimed to investigate alterations in the functional organization of the brain in individuals with tinnitus using resting-state functional magnetic resonance imaging (rs-fMRI) and graph theory analysis.Methods: We conducted a study including 44 individuals with tinnitus and 32 healthy controls. Using rs-fMRI and graph theory measures, we characterized whole-brain topological properties, including network segregation, integration, small-worldness, and global efficiency. In addition, regional segregation and integration were assessed using clustering coefficient and participation coefficient analyses to identify alterations in brain hub regions.Results: Our findings revealed altered topological properties in the tinnitus brain, particularly in the balance between cerebral segregation and integration, leading to deviations from optimal small-world architecture. We also observed alterations in the topology of specific auditory and nonauditory brain regions associated with phantom sound perception. Notably, patients with tinnitus exhibited a decreased nodal participation coefficient in the thalamus, suggesting reduced connectivity between this region and different functional modules as well as long-range connections.Conclusions: These results suggest that tinnitus is associated with alterations in the functional organization of the brain, leading to disrupted information processing and sensory integration.

PMID:42272341 | DOI:10.1177/21580014261455378

BMC Psychiatry. 2026 Jun 10. doi: 10.1186/s12888-026-08267-8. Online ahead of print.

ABSTRACT

BACKGROUND: Major depressive disorder (MDD) is a prevalent psychiatric condition; however, candidate neural substrates related to resilience to MDD remain unclear.

METHODS: Resting-state functional magnetic resonance imaging (fMRI) data were collected from 113 patients with MDD, 36 unaffected siblings, and 81 healthy controls (HCs). First, degree centrality (DC) analysis was performed to identify group differences. Next, regions showing significant DC differences were used as seeds for whole-brain functional connectivity (FC) analyses. Finally, associations between these significant brain regions and depressive symptom severity were examined separately within each participant group.

RESULTS: DC in the medial frontal gyrus and the right precentral gyrus, as well as FC between the medial frontal gyrus and the right postcentral gyrus, exhibited the pattern: patients with MDD < HCs < unaffected siblings. Correlation analyses revealed no significant relationships between depressive symptom severity and DC or FC values.

CONCLUSIONS: Unaffected siblings exhibited both the highest DC and the strongest FC related to the medial frontal gyrus. These neuroimaging findings provide further insight into the role of the medial frontal gyrus in potential resilience mechanisms and suggest potential directions for the prevention and treatment of MDD.

PMID:42271284 | DOI:10.1186/s12888-026-08267-8

J Headache Pain. 2026 Jun 10. doi: 10.1186/s10194-026-02409-9. Online ahead of print.

ABSTRACT

BACKGROUND: Elucidating functional brain dynamics under different blood-brain barrier (BBB) permeabilities may resolve the enigmatic pathophysiology of reversible cerebral vasoconstriction (RCVS); however, relevant investigations are lacking. We aimed determine the relationship between brain functional dynamics and BBB permeability in patients with RCVS.

METHODS: We prospectively recruited RCVS patients and healthy controls (HCs) from November 2016 to January 2023 in Headache Center in a tertiary medical center (> 3000 beds) and adjacent communities. RCVS patients who were diagnosed according to the International Headache Society criteria, and age- and sex-matched HCs were enrolled. Normalized entropy, derived from resting-state fMRI (rs-fMRI), was compared at the network and parcel levels between RCVS patients with and without BBB disruption. Dynamic contrast-enhanced MRI (DCE-MRI)-derived Ktrans BBB permeability and ultrasonographic findings were analyzed.

RESULTS: In total, 188 subjects (100 RCVS + 88 HCs) were enrolled. Compared with the HCs, the RCVS patients had greater entropy (p = 0.014), which was greater in the RCVS patients without than in those with (adjusted p = 0.040) BBB disruption. Compared with HCs, patients without BBB disruption had greater entropy between 60 and 90 days after headache onset (adjusted p = 0.006). No significant differences in entropy were noted between disease stages in BBB-disrupted patients. Parcels within subnetworks of the Default mode network (Default A and C) exhibited higher entropy in patients without BBB disruption. In specific anatomical locations, entropy values were negatively correlated with ultrasonographic Lindegaard index vasoconstriction severity (p = 4.3 × 10- 6; within the Default A network) and DCE-MRI Ktrans BBB permeability (p = 0.005; within the Default C network).

CONCLUSION: The differential changes in normalized entropy suggest that increased rs-fMRI signal complexity may reflect a compensatory functional response to abrupt vasoconstriction. In contrast, the loss of entropy fluctuations in the context of severe BBB disruption indicates a state of impaired cerebral autoregulation in patients with RCVS. The compensatory capacity decreased as vasoconstriction or BBB disruption exacerbated.

PMID:42271210 | DOI:10.1186/s10194-026-02409-9

Alcohol Alcohol. 2026 May 13;61(4):agag035. doi: 10.1093/alcalc/agag035.

ABSTRACT

BACKGROUND: Despite evidence-based pharmacological and behavioural interventions, alcohol use disorder (AUD) is associated with highly variable treatment outcomes. Functional magnetic resonance imaging (fMRI) may identify neural markers that predict treatment response, ultimately supporting a precision medicine approach to AUD.

OBJECTIVES: This systematic review synthesized evidence on fMRI predictors of treatment outcomes in individuals with AUD, evaluated methodological consistency, and identified gaps to guide biomarker development.

METHODS: A comprehensive search of PubMed/MEDLINE, Embase, and PsycINFO combined terms related to fMRI, AUD, and treatment outcomes. Eligible studies included participants with AUD receiving pharmacological, behavioural, or neuromodulatory interventions with fMRI measures collected before or early in treatment to predict clinical outcomes. Screening and extraction were conducted in duplicate using Covidence, and study quality was assessed with the Grading of Recommendations Assessment, Development, and Evaluation framework.

RESULTS: Of 342 records, 15 studies met the inclusion criteria. Most used alcohol cue reactivity tasks (k = 11), with others using resting-state fMRI (k = 2), a monetary reward task (k = 1), or an alcohol-specific Go/No-Go task (k = 1). Pharmacological treatments were most common (k = 8), followed by behavioural therapies (k = 6) and one neuromodulation trial. Across paradigms, neural activity in the ventral striatum, orbitofrontal cortex, and anterior cingulate cortex commonly predicted outcomes. Greater prefrontal engagement predicted improvement, while heightened striatal cue reactivity predicted relapse. Resting-state findings suggested reduced reward- and stress-network connectivity corresponded with better outcomes. Across studies, however, considerable heterogeneity and inconsistency were present and sample sizes tended to be small.

CONCLUSIONS: Evidence implicates frontostriatal and salience circuitry in predicting AUD treatment outcomes, but inconsistency and underpowered studies limit firm conclusions. Larger longitudinal studies are needed to robustly validate clinically useful biomarkers.

PMID:42268834 | PMC:PMC13253039 | DOI:10.1093/alcalc/agag035

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