Most recent paper

Brain Topogr. 2026 Jun 23;39(5):71. doi: 10.1007/s10548-026-01228-0.

ABSTRACT

Brain aging is characterized by complex alterations in both anatomical structure and neural function. While the interdependence between structural connectivity (SC) and functional connectivity (FC) is well-established, the patterns of structural-functional coupling (SFC) during aging remain largely unexplored, despite being crucial for elucidating the neural mechanisms of age-related changes. Moreover, traditional resting-state fMRI studies have predominantly focused on linear correlations, often overlooking nonlinear causal interactions that may play a pivotal role in the aging brain. To address this, we employed a Nonlinear Granger Causality (NGC) model to investigate SFC at the whole-brain level. The study included 227 healthy participants, stratified into a young group (20-35 years, [Formula: see text]) and an older group (59-77 years, [Formula: see text]), with further subgrouping by sex. We analyzed SFC from both static and dynamic perspectives at regional and subnetwork levels. Our results demonstrated that the young group exhibited significantly stronger NGC-based SFC compared to the sex-matched older group. Additionally, males displayed a higher proportion of strong SFC connections than age-matched females. Notably, a widespread age-related decline in nonlinear causal coupling was observed across both regional and subnetwork scales, particularly within networks governing cognitive control and attention. Furthermore, dynamic analyses across sliding windows confirmed the persistence of these aging patterns throughout the scanning duration, despite increased temporal variability observed in the elderly. This study underscores the importance of incorporating nonlinear causal relationships into brain network research, as this approach offers deeper insights into the potential mechanisms underlying age-related cognitive decline and neurodegenerative processes.

PMID:42334649 | DOI:10.1007/s10548-026-01228-0

Acta Neurochir (Wien). 2026 Jun 23. doi: 10.1007/s00701-026-06926-z. Online ahead of print.

ABSTRACT

BACKGROUND: Cognitive impairment following skull base meningioma resection remains poorly understood. This prospective study aimed to investigate the cognitive function alterations and resting-state fMRI (rs-fMRI) characteristics in patients undergoing extradural subtemporal approach meningioma resection.

METHODS: This study enrolled 23 right-handed primary petroclival meningioma patients from June 2024 to November 2024. Participants underwent cognitive assessments combined with rs-fMRI scans at three time points (Time1: one day before surgery; Time2: one week after surgery; and Time3: three months after surgery), utilizing comprehensive test scales including the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Symbol Digit Modalities Test (SDMT) and Trail Making Test (TMT-A & TMT-B). Alterations of low-frequency fluctuations (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), and functional connectivity (FC) were calculated. Patient demographic characteristics, medical records, and neuroimaging data were systematically collected and analyzed. Independent samples t-tests, Mann-Whitney U tests, Fisher's exact tests and paired samples t-tests were used in statistical analysis.

RESULTS: 13 patients were in the non-dominant side (NDS) group, and 10 patients were in the dominant side (DS) group. There were no statistically significant differences in preoperative baseline (Time1) clinical characteristics and preoperative cognitive assessment outcomes between the two groups. Patients in the DS group showed marked decline in MMSE (mean Δ = -6.4 points, corrected p = 0.009), MoCA (mean Δ = -5.1 points, corrected p = 0.047), and SDMT scores (mean Δ = -17.8 points, corrected p = 0.003), along with prolonged TMT-A completion times (mean Δ = + 32.7 s, corrected p = 0.017) at Time2. Six patients (60%) showed significant cognitive decline, while four patients (40%) maintained cognitive performance stable. Patients in the NDS group showed no significant postoperative cognitive decline. The rs-fMRI analysis revealed three characteristic alterations in cognition impaired patients: decreased ALFF in left thalamus, reduced ReHo in left middle temporal gyrus and lowered fALFF in left middle frontal gyrus (all p < 0.05). Significant weakening of functional connectivity between these regions and cerebellar networks were observed, and they show marked improvement in parallel with cognitive recovery at three months postoperatively.

CONCLUSIONS: Patients undergoing dominant hemisphere extradural subtemporal approach for petroclival meningioma resection showed a propensity for transient postoperative declines in verbal memory, orientation, and executive function. These cognitive changes were associated with reduced spontaneous cortical activity and attenuated functional connectivity within cerebello-cortical and cerebello-subcortical networks. Both cognitive performance and these neural alterations demonstrated improvement at the three-month postoperative follow-up, these preliminary observations await further validation in larger independent cohorts.

PMID:42334641 | DOI:10.1007/s00701-026-06926-z

BMC Med Imaging. 2026 Jun 22. doi: 10.1186/s12880-026-02514-w. Online ahead of print.

ABSTRACT

BACKGROUND: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized primarily by social communication deficits and repetitive stereotyped behaviors. Its objective diagnosis has long relied on clinical scale assessments, lacking automated tools based on brain imaging.

METHOD: This study proposes an ASD auxiliary diagnostic framework integrating conditional generative adversarial network (conditional GAN, cGAN) data augmentation, multimodal feature fusion, and explainable deep learning, based on the ABIDE I/II multi-center public datasets. First, functional connectivity matrices of AAL-116 brain regions were extracted from resting-state functional magnetic resonance imaging (rs-fMRI), and cortical morphological features were derived from structural magnetic resonance imaging (sMRI). Multi-site scanning biases were corrected using the ComBat method. On this basis, minority class samples were augmented using class-conditional GAN, followed by multimodal information fusion via a dual-branch encoder and cross-attention mechanism, ultimately outputting classification decisions between ASD and typically developing (TD) subjects.

RESULTS: Experimental results demonstrate that, under stratified five-fold cross-validation, the proposed method achieved an AUC of 0.871 ± 0.016 and a balanced accuracy of 0.797 ± 0.012 on the full multimodal sample set, representing improvements of 13.2% and 9.2% over single sMRI and rs-fMRI modalities, respectively. Leave-one-site-out (LOSO) cross-validation yielded an average AUC of 0.783 ± 0.041, validating the model's cross-center generalization capability.

CONCLUSION: Explainability analysis based on Integrated Gradients revealed that the default mode network and social brain regions are key decision bases for distinguishing ASD from TD, highly consistent with existing neurobiological evidence.

CLINICAL TRIAL: Not applicable.

PMID:42332578 | DOI:10.1186/s12880-026-02514-w

J Invest Dermatol. 2026 Jun 22:S0022-202X(26)02624-2. doi: 10.1016/j.jid.2026.05.035. Online ahead of print.

NO ABSTRACT

PMID:42331309 | DOI:10.1016/j.jid.2026.05.035

Biol Psychiatry Cogn Neurosci Neuroimaging. 2026 Jun 22:S2451-9022(26)00179-5. doi: 10.1016/j.bpsc.2026.06.004. Online ahead of print.

ABSTRACT

BACKGROUND: Estradiol (E2) is associated with enhanced alcohol-related reward. Childhood maltreatment may potentiate its effects, particularly in women. There is a gap in the literature exploring the mechanistic effects of E on alcohol use and reward neurocircuitry, particularly in trauma-exposed women. We hypothesized that women would show an increase in alcohol use and craving immediately before ovulation, exacerbated by childhood maltreatment, and stronger effects of exogenous E2 on reward circuits.

METHODS: Participants were n=91 largely trauma-exposed, naturally cycling women characterized by low alcohol use and craving, recruited from the greater Atlanta area, enrolled in a randomized double-blind within-subjects crossover clinical trial of transdermal E2 as part of the Grady Trauma Project. Participants rated their alcohol use and craving nightly over the course of a full menstrual cycle, using ecological momentary assessment (1,765 responses). Interacting effects of childhood maltreatment and menstrual cycle phase on alcohol use and craving were modeled using zero-inflated Poisson regressions. A subset was then randomized to receive transdermal E2 or placebo during resting state fMRI and crossed over to the other condition on a subsequent cycle (n=49).

RESULTS: Findings supported a preovulatory increase in alcohol craving (β=0.63, p=0.03). An increase around ovulation was blunted in women with higher childhood maltreatment, among whom ovulatory alcohol craving decreased (β=-0.05, p=0.02). Exogenous E2 administration produced changes in resting-state functional connectivity between the VTA and insula among participants who exhibited cycle-dependent alcohol craving.

CONCLUSIONS: Findings suggest that the endogenous preovulatory E2 peak enhances alcohol craving, but that childhood maltreatment may blunt this effect. Neuroendocrine Risk for PTSD in Women, https://clinicaltrials.gov/study/NCT03973229, NCT03973229.

PMID:42331099 | DOI:10.1016/j.bpsc.2026.06.004

Neuron. 2026 Jun 22:S0896-6273(26)00412-5. doi: 10.1016/j.neuron.2026.05.020. Online ahead of print.

ABSTRACT

Precision functional mapping (PFM) enables the individual-level characterization of brain network organization but requires substantially more and higher-quality fMRI data than is standard. Despite the growing use of PFM, the objective criteria for data sufficiency and the quality needed to ensure interpretable and replicable individual-level results remain unclear. Here, we introduce the network similarity index (NSI), an objective measure of the extent to which functional connectivity (FC) patterns express the large-scale network structure required for PFM. The NSI captures low-spatial-frequency, coherent network organization and denoising fidelity, and it aligns closely with blinded expert assessments of PFM usability. The NSI also accounts for the variability in the rate at which FC becomes reliable across individuals. This NeuroResource provides an open source framework for NSI-based data quality evaluation and models linking NSI values with expert-judged PFM suitability. This framework can inform expected returns from additional data collection, thus enabling principled decisions about data sufficiency and replication in precision fMRI research.

PMID:42330956 | DOI:10.1016/j.neuron.2026.05.020

Dev Cogn Neurosci. 2026 Jun 17;80:101764. doi: 10.1016/j.dcn.2026.101764. Online ahead of print.

ABSTRACT

PURPOSE: Brain development is accompanied by gradual refinement of large-scale functional communication. Recent work indicates that functional connectivity can be characterized by both linear and nonlinear dependencies, raising the possibility that maturation involves a shift toward more linearly organized coupling. Whether this developmental trajectory differs in autism spectrum disorder (ASD) is not yet clear.

METHODS: We analyzed resting-state fMRI data from the ABIDE cohort, including 188 males with ASD and 216 age-matched typically developing (TD) males. The ASD group had a mean age of 15.66 ± 66.13 years (range = 6-35), and the TD group had a mean age of 15.71 ± 5.97 years (range = 7-35). After standard preprocessing, we estimated linear and nonlinear connectivity for each participant and derived an index reflecting the extent to which nonlinear interactions were accounted for by linear coupling. This index was examined at the whole-brain scale and within canonical large-scale networks defined by the Yeo 7-network framework. Group differences and age-related effects were assessed, with multiple-comparison correction applied to network-level analyses.

RESULTS: At the whole-brain level, ASD participants showed significantly lower R² values than TD participants. At the network level, significant between-group differences were observed in the visual, somatomotor, dorsal attention, ventral attention, and default mode networks. Across the typically developing group, the index increased significantly with age, suggesting a gradual strengthening of linear structure in functional interactions during maturation. In contrast, the ASD group showed only a weak relationship with age. At the network level, age-related associations were more widespread in the TD group than in the ASD group. In TD, significant positive age associations were observed in multiple networks, whereas in ASD they were limited to fewer systems. The clearest between-group inconsistency in age-related pattern was observed in the limbic network.

CONCLUSION: These findings support the idea that normative brain development is associated with increasing linear organization of functional interactions, and suggest that this pattern is attenuated or altered in males with ASD. The proposed index quantifying the goodness of fit of the linear model predicting distance-correlation-based functional connectivity (FC) from Pearson-correlation-based FC and may provide an operational summary for characterizing atypical maturation of large-scale FC patterns.

PMID:42330879 | DOI:10.1016/j.dcn.2026.101764

Psychoradiology. 2026 Jun 3;6:kkag020. doi: 10.1093/psyrad/kkag020. eCollection 2026.

ABSTRACT

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder whose core pathological mechanism is deeply rooted in the dysfunction of the cortico-striato-thalamo-cortical circuit. This review summarizes recent advances in magnetic resonance imaging (MRI) studies investigating thalamic abnormalities in children with ADHD, highlighting multidimensional pathological changes within the thalamus. Structurally, children with ADHD exhibit delayed overall thalamic volume development and significant atrophy in specific subregions, such as the ventral anterior nucleus, mediodorsal nucleus, and pulvinar. Regarding white matter microstructure, projection pathways from the thalamus to regions such as the motor cortex and striatum are impaired. Both resting-state and task-fMRI consistently demonstrate weakened cortical connectivity between the thalamus and both the default mode and dorsal attention networks. Furthermore, current evidence suggests that mainstream pharmacological treatments, particularly methylphenidate and atomoxetine, effectively promote structural remodeling and the "normalization" of network functions in the aberrant thalamus. Additionally, multidimensional neuroimaging metrics of the thalamus demonstrate substantial potential as objective biomarkers for assessing genetic risk, classifying clinical subtypes, and predicting treatment outcomes. Ultimately, this review emphasizes that the thalamus is not merely a passive sensory relay station, but an active gatekeeper regulating cognition and behavior. Its structural and functional abnormalities constitute a central hub in the pathogenesis of ADHD. Supported by future large-scale machine learning and longitudinal studies, the thalamus holds promise as a crucial biomarker for the precise diagnosis and personalized targeted intervention of ADHD.

PMID:42328622 | PMC:PMC13280544 | DOI:10.1093/psyrad/kkag020

bioRxiv [Preprint]. 2026 Jun 11:2026.06.10.731401. doi: 10.64898/2026.06.10.731401.

ABSTRACT

Brain regions exhibit dynamic yet highly coordinated activity patterns that form large-scale functional networks measurable through resting-state correlations. While their association with fluctuating activity may intuitively suggest functional networks to be temporally transient and dependent on state, a growing body of literature suggests that they are person-specific and stable across days. If truly person-specific, then functional networks should preserve unique characteristics over extended periods. To test this hypothesis, we collected longitudinal precision fMRI data (≥60 minutes per participant per time point) from 10 healthy young adults across 1-3 year intervals, as well as three adults over 8-13 years. We further replicated findings in the MyConnectome dataset and its 10-year follow-up. Functional network organization-when sufficient per-participant data were collected- remained largely stable within individuals over prolonged periods of up to 13 years, suggesting that individualized brain organization constitutes persistent features of personal identity that may be supported by homeostatic mechanisms.

SIGNIFICANCE: While many aspects of brain function are portrayed as dynamic and malleable, our study provides evidence for the long-term preservation of functional organization of the healthy young adult brain. Whole-brain functional organization exhibited unique individual characteristics that were preserved for years, even over a decade. The stability of such individualized neural architecture suggests that the brain encodes features of personhood that may be invariant across contexts and temporal fluctuations. This observation reframes our understanding of brain resilience and plasticity by offering new insights into the extent that homeostatic mechanisms of brain organization can withstand stressors and environmental changes through time to maintain a constant functional organization. These insights pave the way for a more comprehensive understanding of the neural mechanisms behind this stability, with the possibility of pinpointing critical markers of healthy brain function and its disruptions.

PMID:42327182 | PMC:PMC13277913 | DOI:10.64898/2026.06.10.731401

bioRxiv [Preprint]. 2026 Jun 10:2026.06.05.730513. doi: 10.64898/2026.06.05.730513.

ABSTRACT

Understanding how functional brain networks in resting state configurations reorganize to perform cognitive tasks is critical for uncovering the nature and mechanisms underlying network dysconnectivity in psychiatric disorders. We applied energy landscape analysis (ELA), a statistical physics-based computational approach, to functional MRI data from 23 adolescent-onset schizophrenia (AOS) and 44 healthy control (HC) subjects, acquired during rest followed by executive function task. ELA maps brain activity into distinct network states and quantifies how the brain transitions among them, capturing differences in stability of network states and transition complexity across conditions. AOS and HC showed markedly different condition-dependent patterns of brain state organization and dynamics. At rest, AOS exhibited reduced dynamical complexity compared to HC (7 vs. 14 stable states) that reversed during the task with more than 2-fold increase in accessible but rarely occupied brain states, while HC showed an opposite pattern. These results suggest that cognitive demands unmask latent fragmentation of the energy landscape, comprising a proliferation of accessible but rarely occupied states not apparent at rest, in AOS. State occupancy analysis revealed a small number of dominant states accounting for the majority of brain activity time, with AOS showing greater persistence in the fully-active DMN state during task performance compared to HC. These findings suggest that the rest-to-task transition features fundamentally different neural dynamics in AOS compared to HC. Combined analysis of resting fMRI and task-induced brain dynamics revealed neural factors that may contribute to cognitive dysfunction and psychiatric symptoms in schizophrenia, with important implications for development of biomarkers and treatment targets.

PMID:42327046 | PMC:PMC13277904 | DOI:10.64898/2026.06.05.730513

Imaging Neurosci (Camb). 2026 Jun 17;4:IMAG.a.1254. doi: 10.1162/IMAG.a.1254. eCollection 2026.

ABSTRACT

Converging evidence from studies on brain network "fingerprinting" and precision functional mapping suggest that brain networks are highly individualized in functionally meaningful ways. Concurrently with a growth in studies on this topic, there has been a rise in interest on dynamics (approximately second-to-second changes) in brain networks within scan sessions. While analyses of traditional static networks have increasingly grown towards emphasizing the importance of individual differences in brain network topology, studies of dynamic networks typically follow methodology that require brain states to be considered at a group level. Recent studies have begun to assess the individuality of recurring dynamic brain "states". In this work, we extend this recent work by exploring the extent to which functional connectivity fingerprinting is feasible at single-frame temporal resolution. We estimate connectivity at individual volumes using phase coherence. We find that the identity of participants can be classified based on single volumes given sufficient database scan data and that having more highly parcellated atlases facilitates identification. Finally, we find that tasks can be identified more readily within subjects than between subjects. We conclude that participant identity may be an important driver of observed single-volume connectivity patterns. Further, the single-volume neural correlates of a task appear to be more consistent within subjects than between subjects. This highlights the importance of considering individual variability in studies of brain network dynamics.

PMID:42326560 | PMC:PMC13277782 | DOI:10.1162/IMAG.a.1254

Front Neurosci. 2026 Jun 4;20:1815703. doi: 10.3389/fnins.2026.1815703. eCollection 2026.

ABSTRACT

Autonomous sensory meridian response (ASMR) is a tingling sensation that originates in the occipital region and spreads along the neck and spine, elicited by specific audiovisual stimuli known as ASMR triggers. The characteristics of ASMR-related changes in brain activity relative to other external stimuli, and whether these changes are specific to ASMR, remain unclear. The aim of this study is to compare changes in functional connectivity during exposure to ASMR triggers and classical music, and to clarify the changes in connectivity that are more strongly associated with ASMR trigger listening. Forty-eight healthy adults without a history of psychiatric disorders underwent functional MRI under three conditions: resting state without auditory stimulation, listening to ASMR triggers, and listening to classical music. Functional connectivity during the ASMR and classical-music conditions was assessed relative to the resting state. As a result, functional connectivity between the medial prefrontal cortex and the right lateral parietal cortex increased during ASMR trigger listening compared to rest. Relative to classical music listening, ASMR trigger listening increased functional connectivity between the medial prefrontal cortex and the right lateral parietal cortex, between the left anterior insula and left supramarginal gyrus, and between the right rostral prefrontal cortex and the anterior cingulate cortex. No significant changes in functional connectivity were observed during classical music listening alone. These findings suggest that, compared with classical music listening, ASMR trigger listening is associated with stronger functional connectivity between specific ROI pairs involved in self-referential/internal evaluative processing and sensory integration.

PMID:42325953 | PMC:PMC13275351 | DOI:10.3389/fnins.2026.1815703

J Neurophysiol. 2026 Jun 21. doi: 10.1152/jn.00238.2026. Online ahead of print.

ABSTRACT

Inflammation is associated with detrimental health outcomes including deleterious effects on the brain. We have recently introduced a new measure of brain function reflecting the moment-to-moment change, or turnover, in the resting state blood oxygen level dependent (BOLD) signal, or TBOLD. In previous studies, increase of TBOLD with age was found to be associated with concomitant decrease in brain volume, whereas both changes were absent in individuals carrying the neuroprotective Human Leukocyte Antigen (HLA) allele DRB1*13:02. Given that TBOLD reflects neurovascular coupling and that neuroinflammation is involved in brain aging, we hypothesized that TBOLD could reflect neuroinflammation. Here, we tested this hypothesis by assessing the association of TBOLD with known inflammatory markers in a large sample of 1390 healthy participants from the Human Connectome Project on Aging/Aging Adult Brain Connectome (AABC). We found a highly significant, positive association between TBOLD and the inflammatory marker C reactive protein (CRP). Furthermore, we found a highly significant association between TBOLD and interleukin 6 (IL-6) in a smaller group of 97 participants for whom IL-6 measurements were available. These findings document significant positive associations of TBOD to systemic inflammatory markers, lending support to hypothesis that TBOLD may serve as a proxy for neuroinflammation.

PMID:42324453 | DOI:10.1152/jn.00238.2026

Prog Neuropsychopharmacol Biol Psychiatry. 2026 Jun 20:111800. doi: 10.1016/j.pnpbp.2026.111800. Online ahead of print.

ABSTRACT

Aberrant resting-state functional connectivity (rsFC) within neurocognitive networks is a hallmark of alcohol use disorder (AUD). However, the spatial architecture of the central autonomic network (CAN)-the vital neural substrate governing cardiovascular and homeostatic control-and its association with heart rate variability (HRV) remain poorly characterised. We investigated group differences in CAN-related rsFC and its statistical relationship with HRV metrics in 112 young adults (54 AUD, 58 healthy controls [HC]) using resting-state fMRI and separate, HRV assessments. Seed-based analyses targeted core cortical CAN nodes. Young adults with AUD exhibited a profound pattern of functional connectivity alterations within the CAN compared with HCs. Specifically, the AUD group demonstrated attenuated long-range connectivity between CAN seeds and temporo-frontal regions, alongside heightened, pathologically constrained intra-network connectivity involving the insula and brainstem. In HCs, resting HRV indices correlated positively with rsFC across widely distributed cortical and cerebellar systems, reflecting a robust baseline association between central CAN integrity and autonomic output. Conversely, the AUD group exhibited an attenuated and highly segregated pattern of relationships, where the expected association between CAN network architecture and resting HRV features was markedly diminished. These findings demonstrate that early-stage AUD is characterised by a fundamental spatial reorganization of the CAN and a significant blunting of brain-autonomic relationships. This neuro-autonomic alteration may represent a distinctive neuroimaging trait marker of chronic autonomic dysregulation, highlighting the critical need for early detection and targeted management of AUD in young adults.

PMID:42323184 | DOI:10.1016/j.pnpbp.2026.111800

Brain Behav Immun. 2026 Jun 20:106880. doi: 10.1016/j.bbi.2026.106880. Online ahead of print.

ABSTRACT

Systemic inflammation is increasingly recognised as a key modulator of brain function, yet its impact on large-scale brain network topology remains incompletely understood. In particular, it is unclear whether inflammation alters the balance between functional segregation and integration, as captured by small-world organisation, and whether such effects are better detected using dynamic rather than static connectivity analyses. In this study, we conducted a secondary analysis of a previously collected dataset to examine the effects of experimentally induced inflammation on static and dynamic small-world topology using resting-state fMRI and graph-theoretical methods. Eighteen healthy male participants completed a double-blind, placebo-controlled, randomised crossover trial involving typhoid vaccination, a well-established model of low-grade systemic inflammation. Small-worldness was quantified across a fixed density range for both static and dynamic functional connectivity. No significant differences were observed in static small-worldness between conditions. In contrast, dynamic analyses revealed a significant reduction in mean small-worldness following vaccination. This effect was primarily driven by reduced clustering coefficient, with no change in characteristic path length, indicating a selective alteration in local segregation while preserving global integration. Dynamic state analysis identified two recurring connectivity states with distinct topological profiles. Although differences in fractional occupancy and dwell time were not statistically significant, participants showed a consistent tendency to spend less time in the high small-worldness state following inflammation. These preliminary findings indicate that inflammation modulates the temporal organisation of brain networks in ways not detectable using static approaches and are consistent with a metabolically efficient reconfiguration of network topology under inflammatory challenge.

PMID:42323056 | DOI:10.1016/j.bbi.2026.106880

Biol Psychiatry Cogn Neurosci Neuroimaging. 2026 Jun 20:S2451-9022(26)00180-1. doi: 10.1016/j.bpsc.2026.06.005. Online ahead of print.

ABSTRACT

BACKGROUND: In early development autism can be stratified into subtypes differentiated by non-core language, intellectual, motor, and adaptive functioning features. In toddlerhood, these subtypes show different genomic patterning effects on brain structure and function that follow early primary axes of neurodevelopmental organization. This leads to the hypothesis that early cortical patterning differences may continue to be evident between subtypes in later development within hierarchical organization along the sensorimotor-association (S-A) axis.

METHODS: Standardized phenotypic measures from the National Institute of Mental Health Data Archive (NDA; n=419) were used in unsupervised data-driven clustering analyses to build a phenotypic stratification model based on language, intellectual, and adaptive functioning (LIAF) features. This stratification model was then applied to independent multi-site resting state fMRI (rsfMRI) datasets (autism n=174; typically-developing (TD) n = 185) to test for subtype differences in global functional connectivity. Mass univariate and multivariate patterning analyses were used to test for differences between TD and autism subtypes.

RESULTS: Clustering revealed 2 phenotypically distinct autism subtypes (high versus low) in late childhood to adulthood that generalize in independent data with 92% accuracy. Autism was associated with hyper-connectivity in association areas alongside hypo-connectivity in sensorimotor areas. However, these differences were most pronounced in the TD versus autism low LIAF group comparison and resemble patterning effects that follow the S-A axis.

CONCLUSIONS: These findings suggest that cortical patterning of global functional connectivity along the S-A axis is different in autism relative to TD and may be relatively more pronounced in autism with low LIAF features.

PMID:42323050 | DOI:10.1016/j.bpsc.2026.06.005

Psychiatry Res Neuroimaging. 2026 Jun 15;362:112274. doi: 10.1016/j.pscychresns.2026.112274. Online ahead of print.

ABSTRACT

Individuals with schizophrenia (SZ) experience significantly greater obesity rates compared to the general population. The underlying biological mechanisms, however, remain poorly understood. Although brain functional connectivity (FC) has been associated with obesity in the general population, its role in obesity in SZ is largely unexplored. As such, this study examined FC contributions to adiposity in participants with SZ (n = 46) and an adiposity-matched comparison group without SZ (n = 46) as they completed resting-state functional magnetic resonance imaging in fasted and fed states. A machine learning approach identified FC patterns associated with adiposity (percent body fat), with model performance evaluated on an independent test set. In both fasted and fed states, brain regions involved in reward and eating behaviors contributed to adiposity prediction in both groups. While fasted, predictive connections in SZ largely involved limbic and sensorimotor networks, whereas comparison group networks were more varied. In the fed state, SZ predictive features included visual, default mode, and executive control networks, while comparison group connections involved limbic, sensorimotor, salience, and executive control networks. Findings may suggest shared, as well as diagnosis- and state-specific, FC patterns associated with adiposity in SZ, which may help inform future development of obesity-related interventions in this high-risk population.

PMID:42322676 | DOI:10.1016/j.pscychresns.2026.112274

Geroscience. 2026 Jun 20. doi: 10.1007/s11357-026-02333-5. Online ahead of print.

ABSTRACT

Alzheimer's disease (AD) involves early disruptions in brain connectivity, yet how AD risk markers relate to resting-state dynamic functional connectivity (rs-dFC) remains unclear. We examined associations of AD pathology, genetic risk, and blood-based biomarkers (BBMs) of neurodegeneration with local and distant rs-dFC in cognitively normal older adults and explored links with cognitive performance. Baseline data from 86 cognitively normal older adults in the AGUEDA trial (NCT05186090) were analyzed. Participants underwent amyloid-beta (Aβ)-PET, APOE4 genotyping, and plasma BBM quantification (Aβ42/40, BD-tau, GFAP, NfL, p-tau181, and p-tau217). rs-fMRI was used to compute voxel-wise local and distant rs-dFC using a stepwise connectivity framework. Models tested associations between AD markers and rs-dFC adjusting for age, sex, and education; secondary models examined extracted cluster values and six cognitive domains. Aβ-positive individuals and APOE4 carriers showed lower local connectivity in frontal regions. APOE4 carriers also exhibited higher distant connectivity in the superior motor area, inferior frontal gyrus, and anterior insula. Among BBMs, only NfL was associated with both lower local (insula and cingulate) and higher distant (precuneus, putamen, thalamus, supramarginal, and superior motor area) connectivity. Secondary analyses suggested additional cluster-cognition associations. Across AD risk markers, higher risk (Aβ-positivity, APOE4 carriage, and higher NfL) converged on a consistent rs-dFC signature characterized by reduced local and increased distant connectivity. This local-to-distant shift may serve as a candidate functional marker of early AD-related network vulnerability in cognitively normal aging and could support risk monitoring and stratification pending longitudinal validation.

PMID:42322546 | DOI:10.1007/s11357-026-02333-5

Radiol Med. 2026 Jun 20. doi: 10.1007/s11547-026-02245-6. Online ahead of print.

ABSTRACT

BACKGROUND: Brain tumors impair brain function both locally and across distant regions by disrupting network connectivity, contributing to cognitive deficits and triggering compensatory plasticity. Traditional resting-state fMRI methods average brain activity over time, missing transient, dynamic events critical to cognition. Co-Activation Pattern (CAP) analysis captures these brief brain states, enabling quantification of state engagement and duration.

PURPOSE: To investigate alterations in transient brain states in patients with brain tumors using CAP analysis of resting-state fMRI, and to assess whether these changes reflect modified engagement of cognitive states compared to healthy controls.

MATERIALS AND METHODS: This retrospective cross-sectional study included 106 patients with left-hemispheric brain tumors (72 high-grade gliomas, 19 low-grade gliomas, 15 metastases; mean age 61.15 ± 8.95 years; 43 women) and 100 age-matched healthy controls. Resting-state fMRI data were analyzed using a seed-free clustering method (TbCAPs toolbox) to extract CAPs. CAPs were first identified in controls and then matched to patients via spatial similarity. Each CAP was assigned to a canonical brain network using the GIFT toolbox. Dynamic metrics computed included: persistence (duration of a CAP), transitions (switching frequency), in-degree, and out-degree. Group comparisons used two-tailed t-tests with Benjamini-Hochberg correction (p < 0.05).

RESULTS: Six CAPs were identified. Compared to controls, patients showed significantly increased transitions, in-degree, and out-degree, and decreased persistence in CAPs linked to the default mode and executive control networks (all p < 0.01), suggesting more frequent but less stable engagement. Visuospatial network CAPs demonstrated lower transition, in/out-degree and persistence in patients (p < 0.05). No significant differences were observed among tumor types.

CONCLUSION: Patients with brain tumors display altered CAP dynamics involving higher-order cognitive networks and perceptual networks. These alterations may reflect the combined effects of tumor-related network damage and potential adaptive reorganization, with potential implications for functional preoperative planning and prognosis. However, in the absence of direct clinical or neuropsychological correlations, these interpretations remain hypothesis-generating. The findings are also limited by the retrospective cross-sectional design preventing causal or longitudinal interpretation, and the restriction to left-hemispheric tumors. Future studies integrating CAP dynamics with cognitive and clinical outcomes will be necessary to determine whether these changes reflect compensatory functional reorganization in brain tumor patients.

PMID:42322516 | DOI:10.1007/s11547-026-02245-6

J Headache Pain. 2026 Jun 19. doi: 10.1186/s10194-026-02427-7. Online ahead of print.

ABSTRACT

BACKGROUND: Chronic migraine (CM) carries high disability, yet interictal MRI phenotypes that distinguish CM from episodic migraine (EM), particularly in medication-overuse headache (MOH)-inclusive cohorts, remain incompletely characterized. We tested whether periventricular diffusivity (PVeD), an indirect MRI marker of tissue-water properties at the ventricular-parenchymal interface, is associated with CM and headache burden.

METHODS: In this cross-sectional 3.0-T multimodal MRI study, 289 adults with complete structural MRI, diffusion MRI, and resting-state fMRI were analyzed: 67 healthy controls, 162 EM, and 60 CM. MOH was diagnosed and retained in the CM group. MRI was acquired during verified interictal evening sessions; participants with headache within 24 hours before or after MRI were excluded. The primary contrast was age- and sex-adjusted CM versus EM for PVeD. Secondary analyses examined MOH strata, headache-frequency strata, partial Spearman correlations, Firth logistic regression for CM status, and HIT-6 models.

RESULTS: Age- and sex-adjusted PVeD was lower in CM than EM (adjusted difference, -0.011; 95% CI, -0.017 to -0.006; standardized difference, -0.71; p < 0.001) and healthy controls (-0.014; 95% CI, -0.021 to -0.007; standardized difference, -0.88; p < 0.001), with no EM-control difference. Both CM participants with and without MOH had lower PVeD than EM. In migraine participants, lower PVeD correlated with higher headache frequency, HIT-6, and MIDAS and with larger choroid plexus volume and smaller thalamic, nucleus accumbens, and putaminal volumes. In the expanded clinical-plus-MRI Firth model, each 1-SD higher PVeD was associated with lower CM odds (OR, 0.59; 95% CI, 0.38 to 0.89; p = 0.011). Higher PVeD was associated with lower HIT-6 after adjustment for headache frequency or MOH.

CONCLUSIONS: CM was associated with lower PVeD at the ventricular-parenchymal interface, and lower PVeD was linked to patient-centered burden in this MOH-inclusive cohort. PVeD is not yet a diagnostic or treatment-selection biomarker, but may help define an MRI phenotype for future chronification-risk and treatment-response studies after longitudinal and external validation.

PMID:42321653 | DOI:10.1186/s10194-026-02427-7