Most recent paper

Neurobiol Lang (Camb). 2026 Jan 13;7:NOL.a.208. doi: 10.1162/NOL.a.208. eCollection 2026.

ABSTRACT

Age-related declines in cognitive function are often accompanied by changes in brain activity and network organization. This study investigated the relationship between resting state brain activity and age-related differences in speech production. We hypothesized that older adults would exhibit altered functional connectivity and activation intensity, correlating with reduced speech quality. Resting state functional MRI data were collected and a composite measure of speech complexity and fluency was calculated from younger and older adults. Results revealed significantly worse speech performance in older adults, accompanied by less segregated whole-brain networks, reduced amplitude of low-frequency fluctuations, and more heterogeneous brain states. Univariate regression analyses indicated stronger brain-behavior relationships in younger adults, while multivariate regression analyses revealed that age-related differences in resting state brain state patterns critically relate to speech production differences. Notably, the language network remained relatively stable with age, whereas whole-brain status became very important for speech performance in older adults. These findings suggest that resting state brain activity, particularly whole brain network characteristics, may serve as a stable biomarker of age-related changes in speech production.

PMID:41822136 | PMC:PMC12978676 | DOI:10.1162/NOL.a.208

Int J Neurosci. 2026 Mar 12:1-24. doi: 10.1080/00207454.2026.2644508. Online ahead of print.

ABSTRACT

PURPOSE: To have an insight into language-related functional connectivity in post-stroke aphasia (PSA) from graph theory measurements when performing an ability-matched auditory-verbal task fMRI.

METHODS: Fifty-seven PSA patients were stratified into high-level (n = 22) and low-level (n = 35) groups using an ability-matched auditory-verbal fMRI paradigm. Functional connectivity was modeled via ROI-to-ROI generalized psychophysiological interactions, from which graph metrics for predefined language nodes were extracted. Network measure differences were assessed via ANCOVA, followed by binary classification with nested cross-validation. Performance (accuracy, sensitivity, specificity, AUC) and model interpretability (SHAP) were evaluated for the best-performing model.

RESULTS: Random Forest classification reached a significant AUC of 0.671 (p= 0.035, 95%CI [0.512, 0.816]) and an accuracy of 0.667, outperforming other models in analyzing task-embedded resting-state data. Notably, the model demonstrated high sensitivity (0.800) in identifying task levels. SHAP analysis revealed that the left temporo-occipital inferior temporal gyrus (toITG_L) and the right posterior supramarginal gyrus (pSMG_R) were the most influential predictors. High-level task was characterized by increased Local Efficiency in the bilateral pSMG and decreased Global Efficiency in the toITG_L.

CONCLUSIONS: Our findings suggest that the high-level group relies on a synergistic interaction between the ventral stream (toITG) and the dorsal stream (pSMG). The shift toward increased local specialization, particularly the compensatory recruitment of the right pSMG, highlights a critical neural modularity strategy for functional recovery. These results suggest the feasibility of integrating graph metrics with interpretable machine learning, offering preliminary insights that could support the development of objective tools for monitoring aphasia rehabilitation.

PMID:41820794 | DOI:10.1080/00207454.2026.2644508

Mol Psychiatry. 2026 Mar 12. doi: 10.1038/s41380-026-03504-8. Online ahead of print.

ABSTRACT

Social cognitive deficits are common and impact functional outcomes in people with schizophrenia spectrum disorders (SSDs). Functional brain networks during task and rest show complex relationships with cognition. We aimed to identify relationships between social and non-social cognitive performance and functional connectivity during social processing and at rest across individuals with SSDs and healthy controls. Adults (N = 352; 195 SSDs, 157 controls) completed functional magnetic resonance imaging (fMRI) during the Empathic Accuracy (EA) task and rest, and cognitive assessments. Partial least squares correlation was used to identify latent dimensions (LDs) capturing multivariate relationships between functional connectivity and cognitive measures, evaluated using permutation testing, bootstrapping, and cross-validation. Two significant EA LDs were identified, explaining 73.6 and 9.1% of the variance. EA LD1 captured an association between better performance across cognitive measures and positive connectivity across networks implicated in processing dynamic multimodal and social stimuli. EA LD2 reflected an association between worse EA task performance and stronger positive connectivity between networks implicated in language and social processing. One significant resting-state LD was identified, explaining 85.6% of the variance and capturing an association between better overall cognition and visual, somatomotor, and subcortical connectivity, driven more by the SSD group. Overlapping and divergent connectivity patterns appear to covary with cognitive performance during social processing versus rest across SSDs and healthy controls. Our results support the value of task-based fMRI to identify dimensional functional connectivity patterns associated with particular social cognitive abilities, whereas resting-state connectivity may reflect broader relationships with cognition.

PMID:41820637 | DOI:10.1038/s41380-026-03504-8

Magn Reson Med. 2026 Mar 12. doi: 10.1002/mrm.70338. Online ahead of print.

ABSTRACT

PURPOSE: Development and evaluation of a double asymmetric spin echo planar imaging (dASE-EPI) sequence to balance sensitivity to blood oxygenation level-dependent (BOLD) contrast with mitigation of susceptibility-induced intravoxel spin dephasing in ultra high-field rodent brain imaging.

METHODS: A dASE-EPI pulse sequence was implemented and resting-state BOLD fMRI was acquired in the rat brain. Data acquired with dASE-EPI were compared to conventional gradient-recalled EPI. Functional connectivity analyses were performed to assess detection of established networks and to evaluate signal recovery in regions affected by susceptibility (e.g., amygdala, hypothalamic nuclei).

RESULTS: dASE-EPI was found to provide BOLD sensitivity that was non-inferior to conventional GRE-EPI while substantially reducing susceptibility-related signal loss. Established functional networks including the bilateral insular, bilateral sensory, and default mode network were reliably detected with dASE-EPI. Functional connectivity of the amygdala, which was obscured in GRE-EPI, was recoverable with the proposed sequence.

CONCLUSION: The proposed dASE-EPI pulse sequence is a viable solution when studying brain regions which suffer from severe susceptibility artifacts.

PMID:41820226 | DOI:10.1002/mrm.70338

Integr Med Res. 2026 Mar;15(1):101250. doi: 10.1016/j.imr.2025.101250. Epub 2025 Aug 30.

ABSTRACT

BACKGROUND: The aim of this study is to explore how electroacupuncture at the Shenmen (HT7) and Neiguan (PC6) acupoints can improve chronic partial sleep deprivation(CPSD) by regulating brain function, and to elucidate its potential neural mechanisms using resting state Blood Oxygen Level-Dependent functional magnetic resonance imaging (BOLD-fMRI).

METHODS: 43 CPSD participants and 48 healthy controls (HC) were recruited and underwent neuropsychological assessments before electroacupuncture. 3.0T BOLD-fMRI scans were conducted before and after receiving bilateral electroacupuncture at HT7 and PC6. Amplitude of low-frequency fluctuation (ALFF) regional homogeneity (ReHo) values and functional connectivity were analyzed between two groups before and after electroacupuncture.

RESULTS: CPSD participants showed prolonged reaction time (RT), increased omission rate (OR), and decreased accuracy (ACC) compared to HC. Significant differences (P < 0.05) in ALFF, ReHo, and functional connectivity were observed between groups before and after electroacupuncture, particularly in the default mode network (DMN) and limbic system. ALFF in the right parahippocampal gyrus positively correlated with ACC (r = 0.637, P = 0.001) and negatively with OR (r = -0.427, P = 0.047). ReHo in the left superior frontal gyrus negatively correlated with RT (r = -0.514, P = 0.014).

CONCLUSION: CPSD disrupts functional brain activity, while electroacupuncture at HT7 and PC6 modulates resting-state brain function, offering neuroimaging insights into its potential mechanisms for treating emotional and cognitive impairments in CPSD.

PMID:41816267 | PMC:PMC12974194 | DOI:10.1016/j.imr.2025.101250

Quant Imaging Med Surg. 2026 Mar 1;16(3):251. doi: 10.21037/qims-2025-aw-2110. Epub 2026 Feb 11.

ABSTRACT

BACKGROUND: Human immunodeficiency virus (HIV) infection can lead to HIV-associated neurocognitive disorders (HAND), among which asymptomatic neurocognitive impairment (ANI) represents a critical stage for early intervention. However, neuroimaging biomarkers with high sensitivity and specificity for ANI are lacking. The neurovascular coupling (NVC) characteristic in ANI remains unclear. This study aimed to investigate changes in cerebral blood flow (CBF), functional connectivity strength (FCS), and their coupling in patients with ANI under both resting-state and movie-watching conditions, and to evaluate the discriminative performance of multimodal neuroimaging indicators for ANI.

METHODS: This study enrolled 75 participants with HIV, including 41 with ANI and 34 who were cognitively normal (CN). All participants underwent multimodal magnetic resonance imaging (MRI), including T1-weighted imaging, arterial spin labeling (ASL), resting-state and movie-watching-state functional MRI (fMRI). CBF, FCS, and CBF-FCS coupling coefficients were calculated. Between-group differences were assessed using independent-samples t-tests, with adjustments for age and years of education, and multiple-comparison correction where applicable. Correlation analyses were conducted to explore their associations with cognitive and clinical indicators. Three machine learning (ML) models [K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM)] with leave-one-out cross-validation were constructed to evaluate the classification performance of multimodal neuroimaging metrics for ANI, and SHapley Additive exPlanations (SHAP) were applied to quantify feature importance.

RESULTS: The ANI group exhibited abnormal CBF in multiple brain regions and abnormal FCS in both resting-state and movie-watching-state. At the whole-brain level, the CBF-FCS coupling reversed from weakly positive in the CN participants (resting-state: r=0.0348; movie-watching-state: r=0.0364) to weakly negative in the ANI participants (resting-state: r=-0.0283; movie-watching-state: r=-0.0354), and the coupling coefficients were significantly reduced in the ANI participants compared to the CN participants (resting-state: P=0.004; movie-watching-state: P<0.001). Among the ML models, the full multimodal feature set achieved optimal classification performance [KNN: area under the curve (AUC) =0.957; accuracy =0.890; sensitivity =0.980; specificity =0.790], and the movie-based combination "CBF + movie-FCS + movie CBF-FCS coupling" showed consistently high performance across the models (AUC =0.929-0.962). SHAP indicated that the movie-watching-state NVC contributed the most prominently to the prediction of ANI.

CONCLUSIONS: Patients with ANI exhibit abnormal CBF, FCS, and NVC. Compared with the resting-state paradigm, the movie paradigm was more sensitive in detecting neural functional abnormalities. The integration of multimodal neuroimaging indicators showed promising discriminative performance for ANI classification. NVC decoupling may represent a candidate neuroimaging marker of early ANI-related brain alterations and warrants longitudinal validation.

PMID:41816068 | PMC:PMC12971328 | DOI:10.21037/qims-2025-aw-2110

Quant Imaging Med Surg. 2026 Mar 1;16(3):201. doi: 10.21037/qims-2025-2070. Epub 2026 Feb 11.

ABSTRACT

BACKGROUND: Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms, yet its stage-dependent neurobiological mechanisms remain incompletely understood. Multimodal magnetic resonance imaging (MRI) offers a noninvasive approach to investigate both functional and structural alterations across disease stages. Therefore, this study aimed to characterize stage-dependent functional and iron-related brain alterations in PD using multimodal MRI and to explore their associations with motor severity.

METHODS: We enrolled 104 PD patients, stratified into early-stage (n=49) and advanced-stage (n=55) based on Hoehn and Yahr (H&Y) score, along with 53 age- and sex- matched healthy controls. Quantitative susceptibility mapping (QSM) quantified iron deposition in the substantia nigra (SN) and globus pallidus (GP), while resting-state functional magnetic resonance imaging (rs-fMRI) was used to assess functional alterations using regional homogeneity (ReHo) and fractional amplitude of low-frequency fluctuations (fALFF). Group comparisons were conducted using one-way analysis of variance (ANOVA) with post-hoc tests, and voxel-wise analyses were corrected using cluster-level false discovery rate (FDR, P<0.05). Correlation analyses were performed to evaluate associations between imaging metrics and Unified Parkinson's Disease Rating Scale part III (UPDRS-III) motor scores.

RESULTS: Compared with healthy controls, both early and advanced-stage PD patients showed significantly increased iron deposition in the bilateral SN and GP (all P<0.05), with higher QSM values in advanced-stage PD than in early-stage PD (all P<0.01). Iron deposition in these regions was positively correlated with motor severity assessed by UPDRS-III scores (all P<0.001). Early-stage PD primarily exhibited abnormal fALFF and ReHo in visual-related regions, whereas advanced-stage PD showed more widespread involvement of the basal ganglia-thalamocortical motor circuit, frontoparietal regions, and limbic structures. Functional alterations in motor-related regions were significantly associated with UPDRS-III scores (all P<0.001), while ReHo changes in limbic regions were correlated with cognitive performance (Mini-Mental State Examination, MMSE; P<0.001).

CONCLUSIONS: Building on established evidence that PD involves progressive iron deposition in the SN and GP and widespread neural network dysfunction, our multimodal MRI findings demonstrate that integrating ReHo, fALFF, and QSM provides a framework for characterizing stage-specific pathophysiological changes and support their potential as biomarkers for early diagnosis, disease staging, and therapeutic development.

PMID:41816040 | PMC:PMC12971341 | DOI:10.21037/qims-2025-2070

Neuroimage Clin. 2026 Mar 4;50:103981. doi: 10.1016/j.nicl.2026.103981. Online ahead of print.

ABSTRACT

OBJECTIVE: This study aimed to investigate the therapeutic effects and neural mechanisms of high-frequency neuro-navigated repetitive transcranial magnetic stimulation (nrTMS) targeting the hand knob in glioma patients with postoperative motor deficits, using functional gradient analysis to characterize cortical reorganization.

METHODS: Thirty patients with postoperative motor deficits were randomized to receive nrTMS or sham stimulation targeting the ipsilateral hand knob. Motor function was assessed using Fugl-Meyer Assessment (FMA) and muscle strength. Resting-state fMRI was acquired to compute principal functional gradients. Control/tumor, nrTMS/sham, and Pre-TMS/Post-TMS gradient changes were analyzed. Correlation and regression analyses related to motor recovery were performed.

RESULTS: The nrTMS group showed significantly greater improvement in muscle strength (Post-treatment: nrTMS: 3.533 ± 0.720, Sham: 2.067 ± 0.572, p = 0.019, d = 1.082; 3-month follow-up: nrTMS: 4.600 ± 0.408, Sham: 3.733 ± 0.609, p = 0.035, d = 1.012). Gradient analysis revealed increased sensorimotor network (SMN) gradient scores following nrTMS (Pre-TMS: -0.707 ± 0.108; Post-TMS: -0.636 ± 0.077; p = 0.016), and HH_SomMot_22 within upper limb motor cortex is most strongly correlated with motor recovery.

CONCLUSIONS: High-frequency nrTMS targeting the hand knob accelerated the motor recovery. Gradient analysis findings provide novel insights into therapeutic mechanisms of nrTMS and underscore the value of the hand knob as a stimulation target.

PMID:41812281 | DOI:10.1016/j.nicl.2026.103981

Hum Brain Mapp. 2026 Mar;47(4):e70469. doi: 10.1002/hbm.70469.

ABSTRACT

Accounting for interindividual variability in brain function is key to precision medicine. Here, by considering functional interindividual variability as meaningful data rather than noise, we introduce VarCoNet, an enhanced self-supervised framework for robust functional connectome (FC) extraction from resting-state fMRI (rs-fMRI) data. VarCoNet employs self-supervised contrastive learning to exploit inherent functional interindividual variability, serving as a brain function encoder that generates FC embeddings readily applicable to downstream tasks even in the absence of labeled data. Contrastive learning is facilitated by a novel augmentation strategy based on segmenting rs-fMRI signals. At its core, VarCoNet integrates a 1D-convolutional neural network (CNN) with a Transformer encoder for advanced time-series processing, enhanced with robust Bayesian hyperparameter optimization. Our VarCoNet framework is evaluated on two downstream tasks: (i) subject fingerprinting, using rs-fMRI data from the Human Connectome Project (2117 recordings), and (ii) autism spectrum disorder (ASD) classification, using rs-fMRI data from the Autism Brain Imaging Data Exchange (ABIDE) I (995 recordings) and II (730 recordings) datasets. Using different brain parcellations, our extensive testing against state-of-the-art methods, including 13 deep learning methods, demonstrates VarCoNet's superiority, robustness, interpretability, and generalizability, achieving up to 98% subject fingerprinting accuracy and an area under the curve (AUC) of 72.6% for ASD classification. Overall, VarCoNet provides a versatile and robust framework for FC analysis in rs-fMRI.

PMID:41810518 | DOI:10.1002/hbm.70469

ArXiv [Preprint]. 2026 Feb 25:arXiv:2510.03306v2.

ABSTRACT

Current atlas-based approaches to brain network analysis rely heavily on standardized anatomical or connectivity-driven brain atlases. However, these fixed atlases often introduce significant limitations, such as spatial misalignment across individuals, functional heterogeneity within predefined regions, and atlas-selection biases, collectively undermining the reliability and interpretability of the derived brain networks. To address these challenges, we propose a novel atlas-free brain network transformer (atlas-free BNT) that leverages individualized brain parcellations derived directly from subject-specific resting-state fMRI data. Our approach computes ROI-to-voxel connectivity features in a standardized voxel-based feature space, which are subsequently processed using the BNT architecture to produce comparable subject-level embeddings. Experimental evaluations on sex classification and brain-connectome age prediction tasks demonstrate that our atlas-free BNT consistently outperforms state-of-the-art atlas-based methods, including elastic net, BrainGNN, Graphormer and the original BNT. Our atlas-free approach significantly improves the precision, robustness, and generalizability of brain network analyses. This advancement holds great potential to enhance neuroimaging biomarkers and clinical diagnostic tools for personalized precision medicine. Reproducible code is available at https://github.com/shuai-huang/atlas_free_bnt.

PMID:41810022 | PMC:PMC12970342

Neuroimage Rep. 2026 Mar 4;6(1):100335. doi: 10.1016/j.ynirp.2026.100335. eCollection 2026 Mar.

ABSTRACT

BACKGROUND: Military members often report concussion-like symptoms from repetitive low-level blast (LLB) exposure, defined as overpressure from outgoing munitions like rifles and explosive breaching. Typically, the early stages of concussion and other neurological conditions (e.g., Alzheimer's Disease) lead to hyperconnectivity which is a transient and adaptive brain response to strengthen and establish neural connections to restore brain function. Over time, however, chronic hyperconnectivity can contribute to neurodegeneration. To determine whether LLB exposure also exhibits this connectivity trajectory, this study investigated the neural signature of LLB at two time points: At the chronic stage extrapolated from the duration of an individual's occupational career, and following a recent and concentrated blast regimen.

METHODS: Forty-six military breachers and snipers underwent a resting state functional magnetic resonance imaging brain scan before and after a training course. Graph theory was used to study the whole-brain network, cross-validated by a principal components analysis (PCA) conducted post-hoc. The pre-course scan was analyzed separately to examine the neural effects of chronic LLB exposure. The pre- and post-course scans were compared to examine the neural effects of recent blast exposure. Military controls without occupational breaching and/or sniping experience underwent the same protocol.

RESULTS: At pre-course, breachers and snipers exhibited hyperconnectivity compared to controls. However, after undergoing a recent LLB regimen, only breachers showed hypoconnectivity post-course relative to pre-course compared to controls.

CONCLUSION: The mechanism of repeated LLB overpressure and its associated neural response to this exposure appear to be specific to this condition. Characterizing LLB exposure can help refine assessment and treatment.

PMID:41809238 | PMC:PMC12969305 | DOI:10.1016/j.ynirp.2026.100335

Front Neurol. 2026 Feb 23;17:1771144. doi: 10.3389/fneur.2026.1771144. eCollection 2026.

ABSTRACT

INTRODUCTION: Isolated cervical dystonia (ICD) is the most common focal dystonia, characterized by involuntary neck muscle contractions leading to abnormal head postures and nonmotor symptoms such as anxiety. Although structural and functional brain alterations have been reported, findings remain inconsistent, and the neurobiological mechanisms underlying motor and nonmotor symptoms remain incompletely understood.

METHODS: Thirty-five ICD patients and twenty-eight matched healthy controls underwent structural MRI and resting-state fMRI. Voxel-based morphometry was used to assess gray matter volume (GMV) differences. Seed-based resting-state functional connectivity (rsFC) analyses were performed using regions with significant structural alterations. Partial correlation and mediation analyses examined associations among brain measures, motor severity, and mood symptoms.

RESULTS: ICD patients showed reduced GMV in the left paracentral lobule (PCL) and right middle temporal gyrus (MTG). The left PCL exhibited altered connectivity with prefrontal, temporal, and thalamic regions, indicating disruption of cerebello-thalamo-cortical pathways. The right MTG showed decreased connectivity with the left temporal pole and increased connectivity with the right middle frontal gyrus, suggesting compensatory mechanisms for cognitive processing. GMV reduction in the left PCL significantly mediated the relationship between ICD status and anxiety symptoms.

DISCUSSION: These findings support ICD as a network disorder involving both motor and cognitive-affective circuits. Structural alterations in the PCL and MTG and their connectivity patterns may underlie motor dysfunction and nonmotor symptoms such as anxiety. Multimodal neuroimaging biomarkers may help guide targeted therapeutic interventions and improve clinical outcomes in ICD.

PMID:41809195 | PMC:PMC12967988 | DOI:10.3389/fneur.2026.1771144

bioRxiv [Preprint]. 2026 Feb 23:2026.02.21.707183. doi: 10.64898/2026.02.21.707183.

ABSTRACT

The cerebral cortex constrains its spontaneous activity to a low-dimensional manifold learnable from resting state functional magnetic resonance imaging (fMRI) data. However, it remains unclear whether this intrinsic manifold also captures cortical responses to complex, naturalistic stimuli. To test this, we pretrained a deep variational autoencoder model on 3-Tesla resting-state fMRI data to learn the latent structure of spontaneous activity, and then applied this model without finetuning to 7-Tesla fMRI data acquired during movie-watching. Despite the different field strengths, the model generalized robustly from resting to movie-watching states. The latent representation of stimulus-evoked responses was confined to a subspace that occupied about 13% of the latent space spanned by spontaneous activity, demonstrating that task-related neural responses do not require a distinct representational space. By representing cortical dynamics as an evolving latent trajectory, we found striking differences across individuals or between brain states. During movie watching, the velocity of the latent trajectory provided a reliable marker of cortical engagement, and its temporal structure was highly reliable and sensitive to naturalistic events. These findings suggest that the intrinsic manifold of spontaneous activity forms a full reservoir of cortical states that the brain can differentially engage when interacting with the external environment.

PMID:41808985 | PMC:PMC12970341 | DOI:10.64898/2026.02.21.707183

Dev Cogn Neurosci. 2026 Mar 6;79:101704. doi: 10.1016/j.dcn.2026.101704. Online ahead of print.

ABSTRACT

The human brain undergoes the most rapid maturation across the lifespan from the fetal stage through early childhood. Diffusion magnetic resonance imaging (dMRI) and resting-state functional MRI (rs-fMRI) enable noninvasive mapping of the emerging brain structural and functional connections, which can subsequently be examined using various network-based analytic approaches to characterize how brain network topology evolves during development. Here, we review developmental connectome studies spanning mid-gestation to early childhood using dMRI and rs-fMRI. During this critical early period, unique and dynamic short- and long-range connectivity changes continually reshape the brain connectome. Structural and functional brain networks achieve highly efficient topological architectures in early life, with small-world organization emerging prenatally and adult-like hub distributions observed at birth. Importantly, early connectome development is characterized by a shift from segregation to integration, facilitated by initially faster growth of short-range connections followed by the later strengthening of long-range connections, and demonstrates a hierarchical axis from primary to higher-order regions. Structural connectome maturation is underpinned by the microstructural enhancement of certain white matter fibers and the pruning of others, while functional network emergence is supported by increased cerebral blood flow. Moreover, neurodevelopmental disorders such as autism and schizophrenia are associated with aberrant patterns of hyper- and hypo-connectivity, respectively, and exhibit atypical maturation of brain connectivity, underscoring the need for a developmental perspective. Collectively, this review outlines the spatiotemporal principles of early connectome development, discusses key challenges and methodological considerations in studying the baby brain connectome, setting the stage for understanding aberrant brain development during this vulnerable period.

PMID:41807894 | DOI:10.1016/j.dcn.2026.101704

Proc Natl Acad Sci U S A. 2026 Mar 17;123(11):e2528913123. doi: 10.1073/pnas.2528913123. Epub 2026 Mar 10.

ABSTRACT

Infraslow (<0.1 Hz) global brain activity, quantified by the global mean blood-oxygenation-level-dependent (gBOLD) signal in resting-state functional magnetic resonance imaging (fMRI), is elevated during sleep and coupled to cerebrospinal fluid (CSF) dynamics, a key pathway for the brain waste clearance implicated in neurodegenerative disorders such as Alzheimer's disease. However, the effect of sleep deprivation on gBOLD activity and its interaction with aging remain poorly understood. Using a rigorously controlled in-laboratory total sleep deprivation (TSD) protocol, we demonstrate that TSD significantly increases both the gBOLD signal amplitude and its coupling with CSF flow, suggesting a compensatory mechanism that may enhance glymphatic clearance following acute sleep loss. Notably, these TSD-induced enhancements exhibit robust age dependency, with markedly attenuated responses in midlife adults (40 to 50 y). The absence of this compensatory mechanism in midlife may exacerbate age-related impairments in neurotoxic clearance and increase dementia susceptibility, thereby offering mechanistic insights into the nexus between sleep disruption, aging, and neurodegeneration.

PMID:41805579 | DOI:10.1073/pnas.2528913123

Proc Natl Acad Sci U S A. 2026 Mar 17;123(11):e2531921123. doi: 10.1073/pnas.2531921123. Epub 2026 Mar 10.

ABSTRACT

A high risk of relapse triggered by craving during abstinence remains a main challenge in opioid addiction treatment. Multiple brain regions have been implicated in opioid craving, but the brain-wide neural mechanisms underlying this process remain poorly understood. Using resting-state fMRI and connectome-based predictive modeling, we identified a whole-brain connectome that predicted the time-dependent increases (incubation) in oxycodone craving in individual rats after voluntary abstinence induced by exposure to an electric barrier. Incubation of oxycodone craving was operationally defined as the increase in nonreinforced lever pressing during relapse tests from early (day 1) to late (day 15) abstinence (incubation score). We found that changes in whole-brain functional connectivity during abstinence, but not during oxycodone self-administration, predicted the incubation score. Greater decreases in functional connectivity were associated with higher incubation scores. The predictive connectome involved complex interactions across multiple brain systems, including frontal-striatal, frontal-insula, insula-striatal, and hippocampal and sensorimotor circuits. To test causality of the predictive connectome, we examined the effect of pharmacological inactivation of dorsomedial striatum (DMS), which significantly decreased oxycodone seeking after electric barrier-induced abstinence. DMS inactivation increased connectivity strength within the predictive connectome, supporting a causal role of this connectome in incubation of oxycodone craving. The predictive connectome did not predict food-reward seeking after electric barrier-induced abstinence, indicating specificity to oxycodone craving. Our findings identify a brain-wide connectome marker that predicts individual differences in the incubation of opioid craving and provide potential targets for developing personalized interventions and monitoring therapeutic outcomes in opioid addiction treatment.

PMID:41805566 | DOI:10.1073/pnas.2531921123

Sci Rep. 2026 Mar 9. doi: 10.1038/s41598-026-42000-5. Online ahead of print.

NO ABSTRACT

PMID:41803387 | DOI:10.1038/s41598-026-42000-5

J Neurosci. 2026 Mar 9:e1846252026. doi: 10.1523/JNEUROSCI.1846-25.2026. Online ahead of print.

ABSTRACT

Congenital heart disease (CHD) affects approximately 1% of live births in the United States and is the most prevalent congenital disorder. Despite advances in neonatal cardiovascular surgery improving survival, neurodevelopmental impairments remain prevalent, impacting motor skills, social behavior, and executive function. Motor deficits and long-term challenges in emotional regulation and memory are particularly common. Recent research using resting-state functional MRI (rs-fMRI) has revealed disorganized brain networks in newborns with CHD. However, those few prior rs-fMRI studies examining the impact of CHD have relied on predefined brain parcellations to compare group-level connectivity, limiting the ability to capture spatial alterations in neonatal brain networks in CHD. Understanding these network-level changes is critical for elucidating mechanisms of neurodevelopmental impairment and identifying early biomarkers of risk. To address these gaps, our study introduces two conceptual advances: 1) a data-driven approach to investigate atypical brain network development in high-risk CHD and 2) the use of a population-sized, independent dataset of healthy newborns to derive a normative set of neonatal brain networks. By analyzing a large rs-fMRI of human newborns (N=448; 219 females and 229 males), we identify atypical brain activity in the sensorimotor and limbic networks of newborns with complex CHD. Notably, before cardiovascular surgery, these networks are split into left and right hemispheric subnetworks. Postoperatively, these components coalesce into a singular, symmetric pattern resembling networks observed in healthy neonates. Our study highlights the potential of rs-fMRI to detect subtle, early functional disruptions in CHD and may inform future biomarkers of neurodevelopmental risk.Significant Statement Congenital heart disease, the most common congenital disorder, affects 1% of live births and is associated with persistent neurodevelopmental impairments despite improved surgical survival. These deficits, including motor, socio-emotional, and cognitive challenges, may stem from early brain network disruptions. Prior resting-state fMRI studies in CHD relied on predefined parcellations, limiting detection of subtle spatial alterations. In this study, we used a data-driven approach and leveraged an independent normative dataset to define resting-state networks. Comparing CHD patients and healthy controls against these independently derived networks, we reveal atypical sensorimotor and limbic network organization preoperatively, which normalizes post-surgery. These findings highlight the potential of rs-fMRI to identify early biomarkers of neurodevelopmental risk and guide targeted interventions in this high-risk population.

PMID:41802866 | DOI:10.1523/JNEUROSCI.1846-25.2026

Neuropsychologia. 2026 Mar 7:109429. doi: 10.1016/j.neuropsychologia.2026.109429. Online ahead of print.

ABSTRACT

Valence bias (VB) refers to an individual's tendency to consistently interpret emotionally ambiguous stimuli as either positive or negative. As such bias may be closely linked to mental health outcomes such as anxiety and depression, investigating its underlying neural mechanisms hold important clinical relevance. Here, we obtained VB scores through a robust performance-based behavioral measure and sought to examine whether the whole-brain resting-state functional connectome could be leveraged to predict VB using connectome-based predictive modeling (CPM). Results highlighted a functional network model that could significantly predict individual VB. Specifically, our analyses revealed distributed patterns of connectivity in brain regions that support functions related to emotion regulation, cognitive control, and perceptual/emotional processing. These regions contained several key nodes - the amygdala, dorsal anterior cingulate cortex, and frontal operculum - that demonstrated predictive value for VB. Extending prior findings linking VB to functional brain organization, our findings demonstrated that VB can be predicted from large-scale functional brain regions using CPM, with several key nodes emerging as particularly influential, and further generalized these findings to a Korean adult sample. As VB reflects an individual's past experiences and interpretive tendencies, understanding the neural underpinnings of VB could assist in identifying potential neurobiological markers of vulnerability and resilience.

PMID:41802588 | DOI:10.1016/j.neuropsychologia.2026.109429

Behav Brain Res. 2026 Mar 7:116150. doi: 10.1016/j.bbr.2026.116150. Online ahead of print.

ABSTRACT

Although resting-state thought content and brain activity have been extensively studied, large-scale covariation between these domains remains poorly understood. In this study, we applied fully data-driven canonical correlation analysis (CCA) to resting-state fMRI and Amsterdam Resting-State Questionnaire (ARSQ) data from 1,616 healthy adults to investigate shared and sex-differentiated brain-behavior associations. For males, the first canonical correlation was 0.358 (p = 0.003, FDR-corrected p = 0.030), and for females, it was 0.331 (p < 0.001, FDR-corrected p < 0.001), with the first canonical variate explaining the largest proportion of covariance between ARSQ dimensions and network connectivity. Structure correlations revealed that "Sleepiness" was most strongly associated with Default Mode Network (DMN) connectivity in both sexes (male r = 0.764, p < 0.001; female r = 0.899, p < 0.001). Sex-differentiated patterns were observed in higher-order networks, including fronto-parietal, cingulo-opercular, and cerebellar networks. These results indicate a shared DMN-sleepiness coupling across sexes, alongside sex-specific associations in other large-scale networks. Our findings provide quantitative evidence for the neural embodiment of resting-state cognition and highlight the importance of considering both shared and sex-differentiated network in functional connectivity research.

PMID:41802498 | DOI:10.1016/j.bbr.2026.116150