Most recent paper

Commun Biol. 2025 Nov 20;8(1):1619. doi: 10.1038/s42003-025-08972-2.

ABSTRACT

The hippocampus and cerebellum are traditionally considered to support distinct memory systems, yet evidence from nonhuman species indicates a close relationship during spatial-mnemonic behaviour, with hippocampal projections to and from several cerebellar regions. However, little is known about this relationship in humans. To address this, we applied seed-based functional connectivity analysis to resting-state fMRI data from 479 cognitively normal participants aged 18-88 years. We identified significant functional correlations between the hippocampus and widespread areas of cerebellar cortex, particularly lobules HIV, HV, HVI, HVIIA (Crus I and II), HIX, and HX. Moreover, anterior hippocampus showed stronger connectivity with right Crus II, whereas posterior hippocampus was strongly connected to vermal lobule V. Finally, we observed age-related reductions in functional connectivity between the hippocampus and lobules HVI and HV. These findings provide insight into the topography of hippocampal-cerebellar functional organisation in humans and the influence of ageing on this system.

PMID:41266803 | DOI:10.1038/s42003-025-08972-2

Imaging Neurosci (Camb). 2025 Nov 17;3:IMAG.a.1022. doi: 10.1162/IMAG.a.1022. eCollection 2025.

ABSTRACT

Many studies utilize resting-state functional magnetic resonance imaging (rs-fMRI) metrics, such as functional connectivity (FC), to investigate the neuronal underpinnings of autism and identify functional brain networks related to autistic behaviors. However, fMRI indirectly measures neuronal activity by imaging local fluctuations in the blood oxygen level dependent (BOLD) signal, which, in turn, rely on the cerebrovascular system to efficiently direct oxygenated blood flow. Most rs-fMRI studies of autism interpret group differences in FC as autism-related changes in neuronal activity, without considering the underlying vascular function. Yet, atypical cerebrovasculature has been identified in preclinical and post-mortem studies of autism, strongly underscoring the need to characterize cerebrovascular differences to enhance our neurobiological understanding of autism. We evaluated relative cerebrovascular reactivity (rCVR) in autistic and non-autistic children using a novel measure of local brain vasodilatory capacity based on rs-fMRI. We leveraged the cross-sectional Autism Brain Imaging Data Exchange repository to quantify rCVR in 199 non-autistic (74 female) and 95 autistic (16 female) children, 9-12 years old. We identified sex-specific differences in rCVR in autism, particularly in right-frontal brain regions, where rCVR was increased in autistic females compared to non-autistic females. Then, within the same rs-fMRI scans, we demonstrated that rCVR in the right inferior frontal gyrus was positively associated with its FC to regions associated with attentional control in girls, suggesting that cerebrovascular differences may differentially affect FC findings between regions and sexes in children. Our study highlights potential sex differences in cerebrovascular function in autism that enhance our neurobiological understanding of autism and improve interpretations of rs-fMRI findings in children.

PMID:41262555 | PMC:PMC12624364 | DOI:10.1162/IMAG.a.1022

Mol Psychiatry. 2025 Nov 19. doi: 10.1038/s41380-025-03367-5. Online ahead of print.

ABSTRACT

Psychotic disorders, such as schizophrenia and bipolar disorder, pose significant diagnostic challenges with major implications on mental health. The measures of resting-state fMRI spatiotemporal complexity offer a powerful tool for identifying irregularities in brain activity. To capture global brain connectivity, we employed information-theoretic metrics, overcoming the limitations of pairwise correlation analysis approaches. This enables a more comprehensive exploration of higher-order interactions and multiscale intrinsic connectivity networks (ICNs) in the psychotic brain. In this study, we provide converging evidence suggesting that the psychotic brain exhibits states of randomness across both spatial and temporal dimensions. To further investigate these disruptions, we estimated brain network connectivity using redundancy and synergy measures, aiming to assess the integration and segregation of topological information in the psychotic brain. Our findings reveal a disruption in the balance between redundant and synergistic information, a phenomenon we term brainquake in this study, which highlights the instability and disorganization of brain networks in psychosis. Moreover, our exploration of higher-order topological functional connectivity reveals profound disruptions in brain information integration. Aberrant information interactions were observed across both cortical and subcortical ICNs. We specifically identified the most easily affected irregularities in the sensorimotor, visual, temporal, default mode, and fronto-parietal networks, as well as in the hippocampal and amygdalar regions, all of which showed disruptions. These findings underscore the severe impact of psychotic states on multiscale critical brain networks, suggesting a profound alteration in the brain's complexity and organizational states.

PMID:41261142 | DOI:10.1038/s41380-025-03367-5

Behav Brain Res. 2025 Nov 17:115944. doi: 10.1016/j.bbr.2025.115944. Online ahead of print.

ABSTRACT

This study aimed to explore the neurobiology of implicit and explicit suicidal ideation (SI) in depression. Seventy-four patients with major depressive disorder (MDD) along with 74 age- and gender- matched healthy controls were enrolled. The Death/Suicide implicit association test (D/S-IAT), the explicit Beck Scale for Suicidal Ideation (BSSI), and Resting-state functional magnetic resonance imaging (rs-fMRI) scanning were administered. The amplitude of low-frequency fluctuations (ALFF) was calculated and compared between groups to identify brain regions showing spontaneous neural activity related to implicit and explicit SI, and then seed-based functional connectivity (FC) was performed among these regions to reconstruct brain networks related to SI. Behavioral analysis demonstrated higher implicit SI (D values from D/S-IAT) in MDD patients, when compared to HCs，which was also significantly correlated with explicit SI (BSSI scores). Whole brain regression analysis indicated abnormal ALFF in the right postcentral gyrus associated with implicit SI, while ALFF alterations in the left insula, postcentral gyrus, and right middle temporal gyrus (MTG) was associated with explicit SI in MDD. Furthermore, FC analysis revealed increased connectivity between the right postcentral gyrus with the right SFG, MFG, MTG, SPG, insula, and amygdala for implicit SI. Conversely, higher FC between the left insula ROI and left SFG, as well as between the right MTG and left MFG and IPL for explicit SI. These findings suggesting partly overlapped but largely distinct neural basis of the implicit and explicit SI in the brain.

PMID:41260561 | DOI:10.1016/j.bbr.2025.115944

Transl Psychiatry. 2025 Nov 10. doi: 10.1038/s41398-025-03715-7. Online ahead of print.

ABSTRACT

Hemispheric lateralization, recognized as a pivotal feature in both the structural and functional organization of the human brain, may undergo alterations in specific psychiatric disorders. However, the time-varying patterns of hemispheric lateralization in individuals with major depressive disorder (MDD) and the relationship between these patterns and gene expression profiles remain largely unexplored thus far. Using a large multi-site resting-state functional magnetic resonance imaging (rs-fMRI) data encompassing 2611 participants (1660 MDD patients and 1341 healthy controls), we examined MDD-related abnormalities in dynamic laterality and its association with clinical symptoms, meta-analytic cognitive functions, and neurotransmitter receptor profiles, respectively. And the biological basis behind these changes was investigated through gene enrichment analysis and cell-specific analysis. Here we found revealed pronounced fluctuations in lateralization primarily in the regions in default mode network, attention network and control network in MDD patients when compared to healthy controls. In addition, these fluctuations exhibited significant correlations with higher-order cognition terms and the distributions of disease related neurotransmitters. Further, through gene enrichment and cell-specific analysis, we identified a molecular genetic basis for these changes, highlighting synaptic function-related genes and neuronal cells. Collectively, these results demonstrated robust altered brain lateralization patterns in MDD and its molecular genetic basis, providing new clues to understand the pathophysiology of MDD.

PMID:41257981 | DOI:10.1038/s41398-025-03715-7

J Biomed Life Sci. 2025;5(2):122-138. doi: 10.31586/jbls.2025.6208. Epub 2025 Nov 6.

ABSTRACT

BACKGROUND: Resting-state functional MRI (rsfMRI) provides insights into large-scale brain network organization associated with cognitive control, emotion regulation, and attentional processes. The ventral attention network (VAN) is a key salience-driven network that supports attentional re-orienting to behaviorally relevant stimuli. However, little is known about how VAN resting state functional connectivity varies by demographic, socioeconomic, psychosocial, and behavioral factors during early adolescence.

OBJECTIVE: To examine associations between VAN rsfMRI connectivity and multiple demographic, socioeconomic, psychosocial, and behavioral characteristics.

METHODS: Data came from the baseline and early follow-up waves of the Adolescent Brain Cognitive Development (ABCD) Study. The analytic sample included youth with high-quality baseline rsfMRI data and complete socioeconomic and psychosocial measures. The primary outcome was mean resting-state functional connectivity within the VAN across subcortical and cortical regions of interest (ROIs). Bivariate correlations were computed between VAN connectivity and demographic (age, sex, puberty, race/ethnicity), socioeconomic (income, parental education, marital status, neighborhood income), psychosocial (trauma, discrimination, financial difficulty), trait (impulsivity), and behavioral variables (body mass index, depression, suicide, prodromal symptoms, and substance use). Unadjusted bivariate correlations and adjusted logistic regressions were used for data analysis.

RESULTS: VAN connectivity showed small but significant correlations with multiple contextual factors. Higher household income, parental education, and neighborhood affluence were associated with greater connectivity, whereas Black race and Hispanic ethnicity were related to lower connectivity. Youth reporting higher discrimination and financial difficulty exhibited weaker VAN connectivity. Greater VAN connectivity was negatively associated with impulsive reward-driven trait (drive), prodromal symptoms, BMI, and marijuana and alcohol use. Associations between VAN connectivity and suicide, depression, marijuana use, and alcohol use remained significant in age and sex adjusted models.

CONCLUSIONS: VAN connectivity reflects subtle neural correlates of socioeconomic and psychosocial context in early adolescence. Our results underscore the importance of integrating structural and contextual factors in interpreting brain-behavior associations across diverse populations. These findings are suggestive of stable socioeconomic and psychosocial correlates of network efficiency.

PMID:41257054 | PMC:PMC12622571 | DOI:10.31586/jbls.2025.6208

Front Psychiatry. 2025 Nov 3;16:1671813. doi: 10.3389/fpsyt.2025.1671813. eCollection 2025.

ABSTRACT

BACKGROUND: Non-Suicidal Self-Injury (NSSI) is a primary risk factor for suicide, but objective biomarkers to assess this risk are urgently needed. The "prefrontal-limbic dysregulation" model provides a neurobiological framework for self-injurious behaviors. This study aimed to identify resting-state neural markers of suicidal ideation severity in adolescents with NSSI and to build a predictive model for individualized risk assessment.

METHODS: We recruited 64 adolescent psychiatric inpatients with NSSI. Suicidal ideation was measured using the Beck Scale for Suicide Ideation (BSI). Resting-state functional MRI (rs-fMRI) was used to measure spontaneous brain activity via the amplitude of low-frequency fluctuation (ALFF). We performed a whole-brain correlation analysis between ALFF and BSI scores. A support vector regression (SVR) model was then developed using the identified neural feature to predict individual BSI scores.

RESULTS: A significant negative correlation was found between BSI scores and ALFF values in the left Middle Frontal Gyrus (MFG). Lower spontaneous activity in this region was associated with more severe suicidal ideation. The SVR model, based on the left MFG ALFF values, successfully predicted individual BSI scores with significant accuracy (r = 0.492, p < 0.001), a finding confirmed by permutation testing.

CONCLUSION: Diminished resting-state activity in the left MFG is a key neural correlate of suicidal ideation severity in adolescents with NSSI. The functional activity of the left MFG is a promising biomarker for suicide risk assessment and may serve as a potential target for novel neuromodulatory therapies in this high-risk population.

PMID:41256945 | PMC:PMC12620615 | DOI:10.3389/fpsyt.2025.1671813

bioRxiv [Preprint]. 2025 Sep 29:2025.09.26.678805. doi: 10.1101/2025.09.26.678805.

ABSTRACT

Accumulation of amyloid-beta plaques and disruption of intrinsic brain networks are two important characteristics of Alzheimer's disease (AD), yet the relationship between amyloid accumulation and network dysfunction remains unclear. In this study, we integrated [18F]Florbetapir PET and resting-state fMRI (rsfMRI) derived Functional Network Connectivity (FNC) from 552 temporally matched longitudinal PET-rsfMRI sessions across 395 participants spanning Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and AD stages. With a model order of 11, joint Independent Component Analysis (jICA) was applied to the fused PET-FNC data, identifying 11 stable components, of which 9 PET-derived components corresponded to previously characterized brain regions or networks. The multimodal analysis revealed disease progression markers, including (1) a pattern of reduced subject loadings across clinical stages (CN > MCI > AD) in white matter and cerebellar regions, reflecting structural degeneration; (2) increased amyloid accumulation in affected individuals in grey matter regions, particularly in frontal, sensorimotor, extended hippocampal, and default mode network (DMN) regions, accompanied by functional connectivity alterations that reflected both compensatory and disruptive network dynamics. We identified PET-derived components that captured distinct stages of disease progression, with the DMN component emerging as a late-stage biomarker and a white matter component showing early-stage changes with limited progression thereafter. Additionally, several components showed significant variation in loadings between APOE ε 4 carriers and non-carriers, linking the multimodal signatures to a well-established genetic risk factor for AD.

PMID:41256650 | PMC:PMC12621773 | DOI:10.1101/2025.09.26.678805

bioRxiv [Preprint]. 2025 Oct 3:2025.10.02.679992. doi: 10.1101/2025.10.02.679992.

ABSTRACT

Evaluating consciousness levels after coma remains clinically challenging, and probing the brain's functional hierarchy offers model-based biomarkers of brain states. We characterize the hierarchy loss in disorders of consciousness (DoC) via departures from non-equilibrium dynamics. Irreversible, directed interactions are indexed by deviation from the fluctuation- dissipation theorem (FDT), computed from individualized whole-brain models fit to fMRI from controls and patients in minimally conscious state (MCS) or unresponsive wakefulness syndrome (UWS). Global and resting-state network dynamics in DoC were closer to equilibrium than in controls, decreasing stepwise with decreasing levels of consciousness. Mapping site-specific hierarchical drive over the system revealed disruptions within default-mode network components (e.g., medial and dorsolateral superior frontal gyrus) and subcortical hubs (e.g., thalamus, pallidum and putamen) differentiating between all groups. Recovery of near-control hierarchy in the visual network differentiated MCS from UWS, whereas multiple limbic areas showed similar abnormalities across both DoC groups. Together, these results identify non-equilibrium dynamics as a signature of conscious capacity and stablish FDT deviation as a principled, model-based hierarchy measure that can be operationalised for clinical stratification and monitoring, opening avenues for targeted in silico intervention planing.

PMID:41256538 | PMC:PMC12621754 | DOI:10.1101/2025.10.02.679992

bioRxiv [Preprint]. 2025 Oct 14:2025.10.02.679969. doi: 10.1101/2025.10.02.679969.

ABSTRACT

The spatial distribution of tau pathology, the core driver of neurodegeneration in Alzheimer's disease (AD), varies markedly across individuals. While tau is thought to spread along brain networks, the role of inter-individual variability in shaping these patterns remains underexplored. Using resting-state fMRI and tau- PET from 805 participants across the AD continuum, we studied whether subject-specific functional connectivity (FC) profiles enhance the characterization of tau deposition patterns. A hybrid approach integrating individual and group- average FC outperformed both alone, particularly in symptomatic individuals and at finer spatial resolutions, the latter underscoring a critical but often overlooked role of spatial scale. Individualized FC also better captured individual tau topographies than canonical tau-PET maps derived from cohort-level data. These effects were specific to tau, and not seen for β-amyloid, and their predictive power increased with spatial granularity. Furthermore, baseline FC also predicted future tau accumulation at the individual level, supporting its prognostic value. Together, these findings provide strong evidence that individual functional brain architecture shapes tau propagation in humans, supporting the network spread hypothesis by showing that variability in connectivity translates into heterogeneity in tau distribution. This work advances biological understanding of tau propagation in AD, highlighting functional connectivity as a mechanistic substrate that supports prognostic assessment of tau trajectories.

PMID:41256509 | PMC:PMC12621874 | DOI:10.1101/2025.10.02.679969

bioRxiv [Preprint]. 2025 Oct 11:2025.09.30.679686. doi: 10.1101/2025.09.30.679686.

ABSTRACT

Adaptive cognition relies on brain activity that is both flexible and resilient to noise, a property we term functional inertia. Conventional dynamic fMRI metrics treat networks as memoryless and cannot capture the persistence that makes some states fleeting and others entrenched. We introduce the functional inertia index (FII), the first index to quantify temporal momentum by measuring the force required to deviate from a brain's long-running trajectory. Applied to resting-state fMRI from a multisite schizophrenia cohort, FII revealed distinct recurrent states, with prolonged residence in a high-inertia plateau predicting greater symptom severity. This effect was mediated by whole-brain FII, which also showed a positive relationship with cognition in patients but a negative relationship in controls, revealing a dissociation between adaptive and maladaptive rigidity. At the regional level, FII unifies two long-standing observations in schizophrenia: excessive rigidity in associative hubs and pathological volatility in sensory pathways, situating both within a single inertial framework and offering a candidate dynamic biomarker.

PMID:41256393 | PMC:PMC12621831 | DOI:10.1101/2025.09.30.679686

Sci Rep. 2025 Nov 18;15(1):40421. doi: 10.1038/s41598-025-18146-z.

ABSTRACT

About 30% of temporal lobe epilepsy (TLE) cases are negative on MRI, so quantitative diagnosis based on clinical symptoms becomes challenging. There is an urgent need for an accurate and reliable method to differentiate patients with MRI-negative TLE from healthy individuals. This study aimed to explore the use of machine learning methods to diagnose MRI-negative TLE patients based on single and combined resting-state fMRI (rs-fMRI) metrics. This study investigates the diagnostic implications of using both singular and composite resting-state fMRI (rs-fMRI) indices in patients with MRI-negative TLE. We carried out a retrospective analysis of the clinical data and rs-fMRI data of 90 patients with MRI-negative TLE and 90 healthy controls (HCs). Next, the participants were divided into a training set and a test set at 8:2. Functional indices extracted from each brain region included degree centrality (DC), voxel-mirrored homotopic connectivity (VMHC), regional homogeneity (ReHo), fractional amplitude of low-frequency fluctuations (fALFF), and amplitude of low-frequency fluctuations (ALFF). A two-sample t-test was utilized to select significant voxels. After this, classification models based on individual rs-fMRI indices and combined rs-fMRI indices were constructed using ML algorithms such as support vector machines (SVM), random forests (RF), and logistic regression (LR) on the training set. Model performance was evaluated using metrics such as specificity, the area under the receiver operating characteristic curve (AUC), sensitivity, and accuracy, and validations were performed on the test set. Lastly, the feature contribution was assessed using Shapley Additive explanations (SHAP) values. The SVM model employing a combination of rs-fMRI functional indices had optimal performance. On the test set, this model achieved an AUC of 0.89, with an accuracy rate of 82%, where the ALFF values from the cerebellum contributed most significantly to the model. In contrast, ML models based on individual rs-fMRI indices demonstrated inferior classification performance, whereas the RF model using the DC index had the lowest accuracy of 47% on the test set. The SVM model combining the fMRI indices has the greatest potential to distinguish between MRI-negative temporal lobe epilepsy patients and healthy individuals, suggesting a complementary role for the classification of resting-state fMRI indices.

PMID:41253869 | DOI:10.1038/s41598-025-18146-z

NPJ Parkinsons Dis. 2025 Nov 18;11(1):319. doi: 10.1038/s41531-025-01168-9.

ABSTRACT

Apathy is a prevalent non-motor symptom in Parkinson's disease (PD) that negatively impacts quality of life. Impaired self-awareness of apathy (ISA-a) further impacts patient care by limiting engagement. While apathy has been associated with reduced fronto-striatal functional connectivity (FC), the neural basis of ISA-a remains unclear. We examined ISA-a in 52 individuals and the neural basis of ISA-a in 35 individuals with PD using a dimensional approach (i.e., initiation, executive, and emotional apathy) and resting-state fMRI (3T scanner). Apathetic PD patients (42%) showed poorer self-awareness than non-apathetic peers. Apathetic PD patients showed a trend towards reduced FC between the left anterior cingulate cortex (ACC) and the left nucleus accumbens (NAcc). A trend for ISA-a in the emotional domain showed altered FC between the left NAcc and orbitofrontal cortices, and the right ACC and right anterior insular cortex. These findings suggest potential neural mechanisms underlying apathy and ISA-a to be studied in larger populations.

PMID:41253797 | DOI:10.1038/s41531-025-01168-9

Transl Psychiatry. 2025 Nov 18;15(1):484. doi: 10.1038/s41398-025-03670-3.

ABSTRACT

Narcolepsy type 1 (NT1) is characterized by sleep-onset rapid eye movement periods (SOREMPs), reflecting dysregulated rapid eye movement (REM) sleep control. Treatment response variability in SOREMP persistence remains poorly understood, particularly regarding hypothalamic functional connectivity (FC) and depressive symptoms. This study investigated clinical, polysomnographic, and neuroimaging differences between NT1 patients with low (0-1) versus high (≥2) post-treatment SOREMPs, and explored whether hypothalamic FC mediates the relationship between depressive symptoms and SOREMPs outcomes. One hundred ten NT1 patients were categorized into low (n = 62) and high (n = 48) post-treatment SOREMPs groups. Demographic, clinical variables (symptoms and questionnaires), and polysomnography (PSG)/multiple sleep latency test (MSLT) parameters were compared. Resting-state fMRI assessed hypothalamic FC with whole-brain regions. LASSO regression modeled associations between FC, sleep latency, and clinical variables, while mediation analysis tested hypothalamic pathways as mediators of depression-SOREMP relationships. High post-treatment SOREMPs patients exhibited shorter pre/post-treatment REM sleep latency, lower post-treatment wakefulness index, and higher depressive symptom prevalence compared to low SOREMPs patients. Hypothalamic FC differed significantly between groups: low SOREMPs patients showed enhanced connectivity in right medial hypothalamus-right thalamus/left precuneus, left medial hypothalamus-left inferior parietal lobule (IPL), and right lateral hypothalamus-left IPL pathways, but reduced connectivity in left lateral hypothalamus-right insula/left anterior cingulate cortex pathways (p < 0.05, GRF-corrected). LASSO regression identified left medial hypothalamus-left IPL FC as a significant predictor of post-treatment MSLT mean sleep latency (β = 0.272, p = 0.001), alongside age (β = -0.256, p = 0.002) and pre-treatment sleep latency (β = 0.392, p < 0.001). Mediation analysis revealed complete mediation by two hypothalamic pathways: depressive symptoms predicted reduced right lateral hypothalamus-left IPL FC (indirect effect: 0.15-1.05), associated with fewer SOREMPs, and increased left lateral hypothalamus-right insula FC (indirect effect: 0.08-1.14), associated with more SOREMPs. Hypothalamic-parietal/insular FC abnormalities link depressive symptoms to post-treatment SOREMP variability in NT1, with specific pathways mediating opposing effects on REM sleep regulation. These findings highlight hypothalamic connectivity as a critical neural substrate for treatment response, integrating sleep-wake and emotional processing networks. Targeting these pathways may improve personalized management for NT1 patients with comorbid depression and treatment-resistant SOREMPs.

PMID:41253778 | DOI:10.1038/s41398-025-03670-3

Schizophr Res. 2025 Nov 17;287:37-45. doi: 10.1016/j.schres.2025.11.007. Online ahead of print.

ABSTRACT

Schizophrenia is a complex neurodevelopmental disorder characterized by widespread functional dysconnectivities across the brain. While disturbed temporal dynamics have been reported in schizophrenia, the information flow involving both temporal and spatial dynamics remains unclear. To capture spatio-temporal transition of brain information and to investigate these processes from a neurodevelopmental perspective, we collected resting-state functional MRI (rs-fMRI) data from 86 early-onset schizophrenia (EOS) patients (onset before age 18) and 91 demographically matched typically developing (TD) controls. We employed a non-homogeneous Markov model (NHMM) on dynamic functional connectivities derived from fMRI data. By means of transition probabilities, we modeled the switching of information flow in brain functional networks over time. Stationary probability vectors were used to describe the information convergence distribution of each network, while optimal reachable steps were used to characterize inter-network transmission efficiency. Compared to controls, EOS patients showed significantly increased stationary transition probabilities in the ventral attention network (VAN) and the dorsal attention network (DAN) but decreased probabilities in the default mode network (DMN). In terms of the dynamic interaction characteristics between networks, patients showed increased optimal reachable steps relative to controls, particularly in the VAN-DMN pathway. By integrating NHMM with neuroimaging data, this study revealed VAN- and DMN-involved information transition abnormalities in the early stage of schizophrenia spatio-temporal dynamics, offering novel insights into the developmental pathophysiology of the disorder. Our approach thus provides a novel analytical framework for quantifying spatio-temporal brain dynamics in neurodevelopmental disorders.

PMID:41253019 | DOI:10.1016/j.schres.2025.11.007

Cephalalgia. 2025 Nov;45(11):3331024251396102. doi: 10.1177/03331024251396102. Epub 2025 Nov 18.

ABSTRACT

BackgroundMenstrually-related migraine (MRM) is a subtype of migraine associated with the ovarian cycle that imposes a significant burden on female patients. Although MRM and non-menstrual migraine (NMM) differ in clinical presentation and treatment response, their distinct neural mechanisms remain unclear. Emerging evidence suggests that alterations in intrinsic functional connectivity (FC) within and between large-scale brain networks may underlie the phenotypic heterogeneity of migraine subtypes. This study investigated FC alterations between patients with MRM and NMM, explored their correlations with clinical characteristics, and assessed the preliminary utility of FC in subtype differentiation.MethodsResting-state functional magnetic resonance imaging (MRI) with independent component analysis was used to examine whole-brain FC in 50 patients with MRM, 50 with NMM and 50 age-balanced healthy controls (HC). We analyzed within- and between-network connectivity across major resting-state networks, including the frontoparietal, default mode, salience and dorsal attention networks, and applied logistic regression to test whether FC values could classify migraine subtypes. Correlation analyses were further performed between FC measures and clinical indices, including disease duration, headache frequency, visual analog scale scores and Headache Impact Test (HIT-6) scores.ResultsBoth MRM and NMM groups showed weaker within-network connectivity compared to HCs, primarily in the right frontoparietal, default mode and salience networks. Compared with NMM, the MRM group exhibited significantly stronger connectivity in the left frontoparietal network and weaker between-network connectivity between the dorsal attention and default mode networks. In the women with migraine, FC within the dorsal attention network (DAN) was negatively correlated with disease duration (r = -0.200, p = 0.046) and HIT-6 score (r = -0.183, p = 0.049). Furthermore, FC between the DAN and auditory network was inversely associated with disease duration (r = -0.225, p = 0.025). The logistic regression model achieved an area under the receiver operating characteristic curve of 0.73 (sensitivity = 0.70; specificity = 0.64) in distinguishing MRM from NMM.ConclusionsOur findings reveal both shared and distinct alterations in large-scale brain networks in MRM and NMM, potentially explaining differences in clinical presentation and treatment response. This enhanced understanding of migraine pathophysiology supports the development of subtype-specific diagnostic tools and targeted therapies and underscores the value of resting-state fMRI as a non-invasive tool for migraine phenotyping and personalized care.Registration NumberChiCTR2200065586.

PMID:41252278 | DOI:10.1177/03331024251396102

J Neurosci. 2025 Nov 17:e0577252025. doi: 10.1523/JNEUROSCI.0577-25.2025. Online ahead of print.

ABSTRACT

Sensorimotor and cognitive abilities undergo substantial changes throughout the human lifespan, but the corresponding changes in the functional properties of cortical networks remain poorly understood. This can be studied using temporal and spatial scales of functional magnetic resonance imaging (fMRI) signals, which provide a robust description of the topological structure and temporal dynamics of neural activity. For example, timescales of resting-state fMRI signals parsimoniously predict a significant amount of the individual variability in functional connectivity networks identified in adult human brains. In the present study, we quantified and compared temporal and spatial scales in resting-state fMRI data collected from 2,352 subjects of either sex between the ages of 5 and 100 in Developmental, Young Adult, and Aging datasets from the Human Connectome Project. For most cortical regions, we found that both temporal and spatial scales decreased with age throughout the lifespan, with the visual cortex and the limbic network consistently showing the largest and smallest scales, respectively. For some prefrontal regions, however, these two scales displayed non-monotonic trajectories and peaked around the same time during adolescence and decreased throughout the rest of the lifespan. We also found that cortical myelination increased monotonically throughout the lifespan, and its rate of change was significantly correlated with the changes in both temporal and spatial scales across different cortical regions in adulthood. These findings suggest that temporal and spatial scales in fMRI signals, as well as cortical myelination, are closely coordinated during both development and aging.Significance Statement Temporal and spatial scales of resting-state cortical activity in humans measured by fMRI largely decreased throughout the lifespan, except that for some regions in the prefrontal cortex they peaked similarly during adolescence. In addition, whereas cortical myelination consistently increased throughout the lifespan, its variation across different cortical networks and the rate of age-related changes were correlated with the dynamics of temporal and spatial scales of rs-fMRI activity, suggesting that the spatio-temporal scales of cortical activity and cortical myelination might be co-regulated during development and aging.

PMID:41249059 | DOI:10.1523/JNEUROSCI.0577-25.2025

Am J Physiol Regul Integr Comp Physiol. 2025 Nov 17. doi: 10.1152/ajpregu.00211.2025. Online ahead of print.

ABSTRACT

In preclinical models, the organum vasculosum of the lamina terminalis (OVLT) and subfornical organ (SFO) sense changes in serum sodium chloride (NaCl) concentration and mediate NaCl-induced changes in sympathetic nerve activity, vasopressin (AVP), thirst, and blood pressure (BP). In humans, brain imaging studies have shown that acute hypernatremia alters the activity or functional connectivity of the SFO and OVLT. However, no studies have investigated whether there are sex differences in central NaCl sensing in humans, which could underlie sex differences in neurohumoral responses to hypernatremia. Therefore, the purpose of this study was to test the hypothesis that acute relative hypernatremia would increase resting-state functional connectivity between NaCl-sensing brain regions and that these responses would be greater in men. Thirty-two young adults (17 men/15 women) underwent resting-state functional magnetic resonance imaging (fMRI) at baseline and during a 30-minute intravenous hypertonic saline infusion. We performed a seed-to-seed functional connectivity analysis. Despite similar increases in serum sodium, thirst, systolic BP, and plasma AVP between the sexes, there was a time*sex interaction (p<0.001) on SFO-OVLT functional connectivity, as SFO-OVLT functional connectivity increased in men during the late phase (15-30 minutes) of the hypertonic saline infusion (z-scores: baseline=0.21±0.20, late phase=0.29±0.21; p=0.04), but decreased in women (z-scores: baseline=0.27±0.17, late phase=0.15±0.18; p=0.004). Collectively, these results suggest that the functional coupling of the SFO and OVLT, which regulate sympathoexcitation and BP during acute hypernatremia, may be modulated by sex.

PMID:41247769 | DOI:10.1152/ajpregu.00211.2025

J Neurol. 2025 Nov 17;272(12):765. doi: 10.1007/s00415-025-13506-1.

ABSTRACT

OBJECTIVES: Cognitive decline in Parkinson's disease (PD) is closely associated with degeneration of the cholinergic system; however, the stage-dependent reorganization of cholinergic networks remains poorly understood. This study aimed to delineate alterations in cholinergic connectivity across the spectrum of cognitive impairment in PD patients.

METHODS: We enrolled 211 PD patients-classified as PD with normal cognition (PD-NC, n = 91), mild cognitive impairment (PD-MCI, n = 79), or dementia (PDD, n = 41)-and 71 healthy controls (HCs). Cholinergic functional networks were reconstructed by mapping predefined cholinergic subnetwork maps onto individual resting-state functional MRI data to derive subject-specific functional connectivity matrices. Graph theoretical measures were applied to quantify global and local topological characteristics. In addition, voxel-based morphometry (VBM) was used to assess group differences in cholinergic nuclei volumes. Furthermore, correlation and mediation analyses were conducted to explore the relationship between network disruption and cognitive performance.

RESULTS: PD patients showed stage-dependent alterations in cholinergic network topology, with increased shortest path length (Lp) and global efficiency in the Ch1-3 pathway and reduced clustering coefficient, gamma, Lp, and sigma in the medial Ch4 pathway (p < 0.05). Regionally, right hippocampal nodal centrality (Ch1-3) and inferior occipital gyrus/local efficiency (Ch4 lateral capsular division) were reduced in PDD, while posterior orbital part of the right medial superior frontal gyrus (medial Ch4) degree centrality increased. Medial Ch4 topological brain metrics correlated with global cognition and key domains, whereas metrics of Ch4 lateral capsular division pathway related to visuospatial and language performance. Structurally, compared to HCs, Ch4 volume loss occurred in PD-NC and PD-MCI groups, while Ch5-6 atrophy was specific in PDD group. Mediation analysis confirmed that medial Ch4 Lp mediated the effect of disease stage on global cognition.

CONCLUSIONS: This study provides new insights into the stage-specific disruption of cholinergic network topology and structural atrophy in PD, demonstrating that Ch4 nucleus degeneration is critically associated with stage-dependent network dysfunction and domain-specific cognitive impairment, thereby offering cholinergic network biomarkers as potential tools for stratifying cognitive stages.

PMID:41247531 | DOI:10.1007/s00415-025-13506-1

Front Neurol. 2025 Oct 30;16:1660039. doi: 10.3389/fneur.2025.1660039. eCollection 2025.

ABSTRACT

BACKGROUND: Blepharospasm, characterized by involuntary contractions of the orbicularis oculi muscles, significantly impairs the quality of life. Its pathophysiology remains unclear. Inter-hemispheric cooperation is a prominent feature of the human brain. This study utilizes resting-state functional magnetic resonance imaging (rs-fMRI) to explore inter-hemispheric functional cooperation in blepharospasm patients by examining connectivity between functionally homotopic voxels (CFH), aiming to identify neural disruptions associated with the disorder.

METHODS: We recruited 30 patients with blepharospasm and 30 age-, sex-, and education-matched healthy controls. All participants underwent rs-fMRI scanning. CFH maps were generated for each participant to quantify inter-hemispheric connectivity at the voxel level. Group differences were assessed, and partial correlation analyses were performed in the patient group to examine the relationship between aberrant CFH values and clinical variables.

RESULTS: Compared to healthy controls, patients with blepharospasm showed significantly increased CFH in the left putamen and left precentral gyrus. However, these aberrant CFH values did not significantly correlate with clinical variables, including disease duration or total Jankovic Rating Scale (JRS) scores and its subscales.

CONCLUSIONS: This study identifies increased inter-hemispheric functional connectivity (FC) within key motor-related brain regions in blepharospasm. The observed hyperconnectivity in the putamen and precentral gyrus may reflect a compensatory neural mechanism to counteract motor dysfunction. These findings provide novel insights into the pathophysiology of blepharospasm and suggest that modulating inter-hemispheric communication may be a potential therapeutic target.

PMID:41245859 | PMC:PMC12611749 | DOI:10.3389/fneur.2025.1660039