Most recent paper

Neuropsychiatr Dis Treat. 2026 Jun 13;22:601136. doi: 10.2147/NDT.S601136. eCollection 2026.

ABSTRACT

PURPOSE: Alzheimer's disease (AD) is increasingly prevalent, yet how acupuncture modulates cognitive brain networks remains unclear. We used resting-state fMRI (rs-fMRI) to examine whether acupuncture prescription regulates effective connectivity within the frontoparietal network (FPN) in AD.

PATIENTS AND METHODS: Sixty AD patients were randomized to donepezil alone (drug group) or acupuncture plus donepezil (acupuncture group) for 6 weeks (n=30/group). Seven healthy controls were scanned once. Global cognition was assessed with MoCA-B. Independent component analysis identified the FPN, and Granger causality analysis quantified directed effective connectivity before and after treatment.

RESULTS: Both interventions improved MoCA-B (P<0.05), with larger gains in the acupuncture group (P<0.05). Relative to controls, AD showed FPN disruption with compensatory reorganization. Decreased connectivity was observed from the left middle temporal gyrus (MTG) to the right inferior parietal lobule (IPL), and from the left median cingulate/paracingulate gyri (P<0.05). Increased connectivity emerged from the right IPL and left cingulate/paracingulate to the left MTG, and from the right IPL to the left medial frontal gyrus (orbital part) (P<0.05). The right IPL and left MTG were core FPN nodes. Post-treatment, the drug group showed reduced right IPL→left orbital medial frontal connectivity, whereas the acupuncture group showed reduced right IPL→left MTG connectivity (P<0.05). Between-group comparisons indicated acupuncture-specific modulation of right insula→left MTG and left precuneus→left MTG connectivity (P<0.05).

CONCLUSION: Acupuncture combined with donepezil provides superior cognitive benefits and selectively reshapes directed FPN interactions, supporting a network-level mechanism involving frontal-parietal-temporal integration.

PMID:42318047 | PMC:PMC13274761 | DOI:10.2147/NDT.S601136

Imaging Neurosci (Camb). 2026 Jun 16;4:IMAG.a.1275. doi: 10.1162/IMAG.a.1275. eCollection 2026.

ABSTRACT

Intrinsic brain activity is characterized by pervasive long-range temporal correlations. While these scale-invariant dynamics are a fundamental hallmark of brain function, their implications for individual-level metabolic regulation remain poorly understood. Here, we address this gap by integrating resting-state functional Magnetic Resonance Imaging (fMRI) and dynamic [18F]FDG Positron Emission Tomography (PET) data acquired from the same cohort of participants. We uncover a systematic relationship between long-range temporal correlations, quantified via the Hurst exponent, and glucose metabolism. Our findings reveal that persistent temporal dependencies are associated with a measurable metabolic cost, with brains exhibiting higher long-range temporal correlations incurring greater energetic demands. Full kinetic modeling of the [18F]FDG PET data traces this association specifically to intracellular glucose phosphorylation, pointing to a direct link with neuronal energy metabolism. Beyond glucose metabolism, we also show that these dynamics are likely supported by continuous biosynthetic processes, such as protein synthesis, which are critical for neural circuit maintenance and remodeling. Overall, our results suggest that a significant fraction of the brain's so-called "Dark Energy" may be linked to spontaneous long-range temporal correlations.

PMID:42318034 | PMC:PMC13274566 | DOI:10.1162/IMAG.a.1275

Imaging Neurosci (Camb). 2026 Jun 16;4:IMAG.a.1271. doi: 10.1162/IMAG.a.1271. eCollection 2026.

ABSTRACT

Bereavement is a near-universal experience in late life, yet only some older adults develop prolonged grief disorder (PGD). While most transition from acute grief (AG) to integrated (adaptive) grief, the neurobiological substrates underlying divergent trajectories are unclear. Emotion regulation dysfunction is hypothesized to play a central role in PGD pathogenesis, but longitudinal neuroimaging data in bereaved adults are lacking. This study aims to identify functional brain circuit measures of emotional regulation that predict pathological versus adaptive grief trajectories, aligning with the Research Domain Criteria (RDoc) framework for the Negative Valence System (Loss) construct. This single-site, 1-year longitudinal study aims to enroll 170 adults aged 50-89 years: 115 with AG and 55 age- and gender-equated non-bereaved participants. Participants will complete comprehensive psychiatric, neuropsychological, and psychosocial assessments, alongside neuroimaging both at study baseline and after 12 months. Functional neuroimaging includes resting-state fMRI, a face-shape matching task probing emotion processing, and a stop-signal task probing inhibitory control. Functional neuroimaging data are acquired using a harmonized Human Connectome Project protocol on a GE Signa Premier 3T MRI scanner. We present a comprehensive overview of the eligibility criteria, clinical study procedures, and neuroimaging protocol. Baseline findings from 103 AG and 40 non-bereaved participants thus far enrolled show that the groups are demographically matched and provide high-quality neuroimaging data and robust task performance. This study is among the first longitudinal neuroimaging investigations of AG in older adults and may identify early biomarkers of PGD risk, potentially guiding precision prevention and intervention strategies for bereaved older adults.

PMID:42318033 | PMC:PMC13274567 | DOI:10.1162/IMAG.a.1271

Brain Behav. 2026 Jun;16(6):e71530. doi: 10.1002/brb3.71530.

ABSTRACT

BACKGROUND: Partial sleep deprivation (PSD) poses health risks to the night-shift workers (NSW), but its underlying impacts on local brain function remain underexplored. Brain entropy (BEN), a nonlinear dynamic metric, has emerged as a novel parameter of choice in probing the temporal irregularity of brain activity, thus may offer new insights into the characterization of the brain dysfunction following sleep deprivation.

METHODS: Seventy-eight female medical NSWs and 30 non-NSW healthy controls (HC) were recruited in this study. Psychomotor vigilance tasks (PVT) and resting-state fMRI (rs-fMRI) were sequentially conducted at three conditions for NSWs: baseline (prior to NSW), PSD (immediately after NSW), and recovery (after 3-5 days of regular sleep). Nine NSWs were excluded due to consecutive or intermittent sleep duration exceeding 6 h on the night-shift day, or significant head motion in the rs-fMRI scans, resulting in 69 NSWs in the following analysis. Static and dynamic sample entropy (SampEn) metrics were calculated for BEN quantification. An L1-regularized logistic regression (LR) model was constructed based on baseline SampEn metrics to distinguish PSD-vulnerable (N = 35) and PSD-resistant (N = 34) individuals with their PVT performances as the classification reference.

RESULTS: Compared to HCs, SampEn of young female NSWs reduced mainly in the occipital and temporal cortices, and associated with poor sleep quality. But these inter-group differences did not survive the strict multiple comparison correction. Both static and dynamic SampEn metrics of NSWs were significantly altered following acute PSD, and largely returned to the baseline after sleep recovery. The SampEn-based LR model achieved a classification accuracy value of 78.26% to distinguish PSD-vulnerable and PSD-resistant individuals.

CONCLUSIONS: One-night of acute PSD in shift work leads to reduced but largely reversible BEN, whereas long-term occupational chronic PSD tends to reduce the BEN in distributed primary cortices and correlates with the poor sleep quality in young female NSWs. The BEN metrics could serve as a potential biomarker to identify individual susceptibility to PSD, which may contribute to the optimization of rotating-shift schedules.

PMID:42317125 | DOI:10.1002/brb3.71530

Psychiatry Investig. 2026 Jun;23(6):790-796. doi: 10.30773/pi.2025.0436. Epub 2026 Jun 8.

ABSTRACT

OBJECTIVE: Children's cognitive function is undergoing great dynamic changes with age. This study aims to explore the characteristics of changes in children's cognitive function before and after 10 years, and discover brain areas where brain function has undergone significant changes.

METHODS: In 2008, functional magnetic resonance imaging (fMRI) data were collected from 30 students aged 7-12 from ordinary primary schools in Changzhou. In 2017, fMRI data of 30 primary school students matched for age and sex were collected again on the same MRI machine using the same parameters. Amplitude of low frequency fluctuation (ALFF) and degree centrality (DC) values were calculated respectively for the two sets of data and analyzed by paired t-test.

RESULTS: The brain areas with ALFF values higher than those of 10 years after 10 years were left posterior cerebellar lobe, right posterior cerebellar lobe, and left middle occipital gyrus; the brain areas with ALFF values lower than those of 10 years after 10 years were left hippocampus, left inferior frontal gyrus, right inferior frontal gyrus, and left medial frontal gyrus. The left superior occipital gyrus was found in the brain area with a higher DC value after 10 years and the left inferior parietal lobe was found in the brain area with a lower DC value after 10 years.

CONCLUSION: After 10 years, children's overall attention, memory, and cognition related brain areas are developing. Some brain areas show improvement in brain function, and some brain areas play a role more efficiently. This study provides a basis for predicting the development of brain function in school-age children.

PMID:42316463 | DOI:10.30773/pi.2025.0436

Nutr Res. 2026 May 25;152:1-10. doi: 10.1016/j.nutres.2026.05.005. Online ahead of print.

ABSTRACT

Maternal nutrition plays a major role in neurodevelopment. This study hypothesized that maternal egg yolk supplementation will influence offspring choline concentrations, and these changes will be associated with resting-state network (RSN) activation and monoamine neurochemistry at weaning. Sows were fed a control (CON; n = 6) or an egg yolk-supplemented diet (EGG; n = 5) from gestation day 70 to weaning at postnatal day 21, and piglet liver and plasma were collected (CON, n = 24; EGG, n = 20) for choline and metabolite quantification at weaning. Maternal egg yolk supplementation increased liver choline and methionine concentrations in piglets (p < .05). Given our previous findings that egg yolk supplementation elevated executive control and cerebellar functional activation, the associations between metabolites and RSNs were examined. Liver choline was correlated with executive (r = 0.4584) and auditory network activation (r = -0.4751), while plasma choline was correlated with visual network activation (r = 0.5295) (p < .05). As we previously reported that maternal egg yolk supplementation altered piglet monoamine homeostasis, its links to choline and metabolite concentrations were assessed. Liver choline was associated with anterior hippocampal norepinephrine (r = 0.3275) and cerebellar serotonin metabolism (r = 0.3043) and liver TMAO was associated with anterior hippocampal norepinephrine (r = 0.3896), while plasma choline was linked to caudate serotonin (r = 0.3902) and dopamine (r = -0.3763) metabolism (p < .05). Overall, maternal egg yolk supplementation altered offspring choline and metabolite status, which was associated with RSNs and monoamines. These findings broaden our understanding of how one-carbon metabolite status may relate to neurochemical and functional brain outcomes in offspring.

PMID:42314521 | DOI:10.1016/j.nutres.2026.05.005

Affect Sci. 2026 Apr 21;7(2):153-165. doi: 10.1007/s42761-025-00347-4. eCollection 2026 Jun.

ABSTRACT

The onset of depression and depressive symptoms spikes during adolescence, and the prevalence of depression in adolescents appears to be increasing over time (Daly, 2022). In previous research, we found that greater integration of an allostatic interoceptive system (AIS), a brain system involved in the predictive regulation of the body, prospectively predicted more depressive symptoms in adolescents two years later, mediated by a greater tendency to ruminate (Frye et al., 2025). This pattern of brain organization may reflect excessive internal focus. Here, we examine the potential moderating effect of emotional awareness on the relationship between AIS integration and prospective depression symptoms. Specifically, using data from a larger longitudinal study of adolescents beginning in 6th -8th grade, we test whether trait emotional clarity or emotional attention moderates the relationship between AIS global efficiency during a resting-state fMRI scan and prospective depressive symptoms assessed an average of two years later (N = 117, 55% female, M initial age= 12.99, M age at follow-up= 14.74). We found that for adolescents who paid little attention to their emotions, greater AIS integration was related to lower prospective depressive symptoms, whereas for adolescents who paid above-average attention to their emotions, greater AIS integration was related to greater prospective depressive symptoms. These findings contribute to an emerging understanding of the role of the allostatic-interoceptive system in depression in adolescents by showing that the relationship between system integration and prospective depressive symptoms differs depending on adolescents' emotional attention.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42761-025-00347-4.

PMID:42311803 | PMC:PMC13269600 | DOI:10.1007/s42761-025-00347-4

Front Immunol. 2026 Jun 2;17:1724276. doi: 10.3389/fimmu.2026.1724276. eCollection 2026.

ABSTRACT

INTRODUCTION: Shift work sleep disorder (SWSD) is prevalent among female nurses and is associated with significant health morbidities. While inflammation is implicated in SWSD, how it relates to brain network alterations and clinical symptoms remains underexplored. This study aimed to investigate the associations among peripheral inflammation, brain functional network topological disruptions, clinical symptoms, and transcriptomic signatures in female nurses with SWSD.

METHODS: Fifty female nurses with SWSD and 50 healthy daytime-working controls (HCs) comparable in age and education underwent clinical assessments, quantification of peripheral inflammatory markers, and resting-state functional magnetic resonance imaging (rs-fMRI). Graph theory was applied to rs-fMRI data to assess brain network topology. Mediation analyses were used to evaluate the pathways linking inflammation, network topology, and symptoms. Imaging transcriptomics, leveraging the Allen Human Brain Atlas, was used to identify gene expression patterns correlated with network alterations. Machine learning models were employed to assess the utility of these multimodal features in classifying SWSD.

RESULTS: Compared with HCs, nurses with SWSD exhibited immune dysregulation (elevated levels of interferon α (IFN-α), IFN-γ, interleukin 4 (IL-4), IL-5, IL-17A, and particularly IL-6). Graph analysis revealed altered global network topology (reduced global efficiency and small-worldness, increased local efficiency, clustering coefficient, and characteristic path length) alongside significant nodal changes, notably increased local efficiency and clustering coefficient in the left medial superior frontal gyrus (SFGmed.L). These topological alterations were significantly correlated with the severity of clinical symptoms. Mediation analyses indicated that global small-worldness mediated the relationship between IL-6 levels and poor sleep quality, whereas the local efficiency of SFGmed.L mediated the associations between IFN-γ levels and anxiety and cognitive performance. The support vector classifier model accurately differentiated nurses with SWSD from HCs (accuracy: 90%). Imaging transcriptomics identified spatial gene-expression patterns associated with altered nodal topology, particularly involving genes related to cytokine signaling and cellular regulation.

DISCUSSION: Our findings suggest that systemic inflammation is associated with characteristic brain functional network disruptions in female nurses with SWSD, and that these disruptions are associated with clinical symptoms. These inflammation-related neurobiological alterations, together with spatially associated transcriptomic signatures, provide novel insights into SWSD pathophysiology and may help identify potential biomarkers and therapeutic targets.

PMID:42311681 | PMC:PMC13269080 | DOI:10.3389/fimmu.2026.1724276

BMC Psychiatry. 2026 Jun 17. doi: 10.1186/s12888-026-08304-6. Online ahead of print.

ABSTRACT

BACKGROUND: Insomnia disorder with comorbid anxiety is a prevalent clinical challenge. Although transcranial direct current stimulation (tDCS) is a promising noninvasive neuromodulatory approach, its effects on glymphatic-related neurovascular-cerebrospinal fluid (CSF) coupling in insomnia remain unclear. This study investigated the effects of tDCS on global blood oxygen level-dependent (gBOLD)-CSF coupling using resting-state functional magnetic resonance imaging (rs-fMRI) in patients with insomnia disorder and comorbid anxiety.

METHODS: We conducted a 2-week, double-blind, randomized, sham-controlled trial. Patients with insomnia disorder and comorbid anxiety were randomized to receive either active or sham tDCS. The intervention consisted of 14 consecutive daily 20-min sessions delivered at 1.1 mA, targeting the left forehead and right dorsolateral prefrontal cortex. Healthy controls were frequency-matched to the overall patient cohort by age and sex. The Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), 14-item Hamilton Anxiety Rating Scale (HAMA-14), 17-item Hamilton Depression Rating Scale, Epworth Sleepiness Scale, 20-item Multidimensional Fatigue Inventory, polysomnography (PSG), and rs-fMRI were assessed at baseline, and patients were reassessed after treatment. The primary outcome was the post-intervention between-group difference in gBOLD-CSF coupling; clinical scales and PSG parameters were secondary outcomes.

RESULTS: Sixty patients with insomnia disorder and comorbid anxiety and 30 healthy controls completed baseline assessments. Compared with healthy controls, patients showed significantly lower gBOLD-CSF coupling (P < 0.001, PFDR = 0.002). Coupling strength was negatively correlated with ISI, PSQI, HAMA-14 scores, and arousal index, and positively correlated with NREM stage 3 (N3) sleep duration in patients. Multivariate regression identified N3 sleep duration as an independent predictor of gBOLD-CSF coupling (β = 0.328, P = 0.005, PFDR = 0.040). After therapy, the active tDCS group showed a greater increase in gBOLD-CSF coupling than the sham tDCS group (effect size: 0.035, 95% CI: 0.011 to 0.058; Pinteraction = 0.005, PFDR = 0.014). Adverse events were mild and comparable between groups.

CONCLUSIONS: Patients with insomnia disorder and comorbid anxiety showed reduced gBOLD-CSF coupling during wakeful rs-fMRI, which was associated with subjective sleep quality, insomnia severity, anxiety level, arousal index, and N3 sleep duration. Active tDCS enhanced gBOLD-CSF coupling compared with sham stimulation, suggesting that its therapeutic effects may involve modulation of neurovascular-CSF dynamics alongside improvements in sleep and anxiety symptoms.

CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov ID NCT07340268, retrospectively registered on January, 13, 2026.

PMID:42310658 | DOI:10.1186/s12888-026-08304-6

J Geriatr Psychiatry Neurol. 2026 Jun 17:8919887261462178. doi: 10.1177/08919887261462178. Online ahead of print.

ABSTRACT

IntroductionCognitive rehabilitation (CR) enhances the autonomy of patients with Alzheimer's disease. Their daily activities are likely dependent on attention networks.MethodThis pilot resting-state fMRI study investigated the cerebral correlates of CR in participants with mild Alzheimer disease (n = 22), compared to a control intervention in patients (n = 21) and in healthy participants (n = 27). Connectivity changes between dorsal and ventral attention networks were expected after 3 months of rehabilitation.ResultsA mixed ANOVA comparing pre- and post-intervention data across groups revealed increased connectivity between the dorsal and the ventral attention network following CR (FDR-corrected P = .0072). A post hoc correlation analysis of post-intervention data in the CR group showed that greater autonomy in daily activities was associated with stronger functional relationship between the two attention networks (FDR-corrected P = .0001).ConclusionEnhanced connectivity between attention networks may be a characteristic of CR benefits in individuals with mild Alzheimer disease.

PMID:42308323 | DOI:10.1177/08919887261462178

Brain Topogr. 2026 Jun 17;39(4):69. doi: 10.1007/s10548-026-01223-5.

ABSTRACT

The Default Mode Network was a key finding for cognitive neuroscience, but being the result of a data-driven analysis of resting-state fMRI data, its psychological and clinical implications have been difficult to elucidate. This is because in the resting-state paradigm we cannot directly correlate an observable specific task with specific brain connectivity patterns, and therefore inferences about the relationship between particular cognitive domains and the resting-state networks are limited. A similar problem arises when trying to link the network with personality traits: the DMN, as other intrinsic networks, is not a simple metric to compare with the results of a psychological test, but a complex composite of spatio-temporal features. Although over the last two decades several research works have provided insights about these relationships, we still lack a consensus on the methodology that best captures these interactions. In this context, we propose an alternative method to model the psycho-physiological relationships of the resting state components with behavioral data, based on the dimensionality reduction of an extensive psychological evaluation and the spatial dimension of the intrinsic connectivity components. Our results show that the connectivity networks are low to moderately related with behavioral and personality traits, or at least this relation is not in a direct way. This integration of neuroimaging and psychological assessment data creates valuable pathways for cognitive neuroscience, potentially revealing with precision how intrinsic brain network organization relates to individual variations in cognitive functioning and personality dimensions.

PMID:42307681 | DOI:10.1007/s10548-026-01223-5

Apert Neuro. 2026;6(SI 1). doi: 10.52294/001c.155279. Epub 2026 Feb 13.

ABSTRACT

We present an open, vendor-neutral BOLD SMS-EPI protocol tailored for multi-site fMRI studies, intended as a drop-in replacement for conventional vendor-specific acquisition and reconstruction pipelines. Built on Pulseq-an emerging standard for cross-platform MRI pulse sequence development-our protocol ensures identical SMS-EPI pulse sequences and image reconstruction across scanner vendors. This provides, for the first time, known and consistent experimental conditions across sites and scanner software versions. We begin by reviewing the current capabilities of the Pulseq framework, including vendor support and safety considerations. We then detail our SMS-EPI implementation and demonstrate its performance using resting-state fMRI pilot data from healthy volunteers, showing reduced site variance compared to corresponding vendor protocols on Siemens and GE scanners. To support adoption, we provide practical resources to help researchers integrate Pulseq fMRI into their studies, including example text for grant proposals and IRB submissions. These resources are freely available at https://github.com/HarmonizedMRI/Functional. Our vision is for Pulseq fMRI to become the standard for multi-site research, enabling more reproducible science and serving as a reference for the development of novel acquisition and reconstruction methods.

PMID:42306264 | PMC:PMC13268408 | DOI:10.52294/001c.155279

Epilepsy Behav Rep. 2026 May 19;34:100874. doi: 10.1016/j.ebr.2026.100874. eCollection 2026 Jun.

ABSTRACT

PURPOSE: To characterize the epileptic network and postoperative network remodeling in a rare case of right cuneus meningioangiomatosis (MA) with electro-clinical discordance, we integrated electroencephalography (EEG), fluorodeoxyglucose positron emission tomography (FDG-PET), and resting-state fMRI (rs-fMRI).

METHODS: A 16-year-old male with drug-resistant focal impaired-awareness seizures and a right cuneus lesion underwent preoperative multimodal evaluation. Preoperative video-EEG, magnetic resonance imaging (MRI)/computed tomography (CT), and FDG-PET were obtained; gross total resection confirmed MA. Pre- and postoperative rs-fMRI were processed in DPABI. We computed lesion-seed functional connectivity (FC), fractional amplitude of low-frequency fluctuation (fALFF), Regional homogeneity (ReHo), and weighted degree centrality (DC; r > 0.25), then generated post-pre difference maps using Fisher z-transformed data and retained the top 5% of absolute voxel changes (cluster size >50 voxels).

RESULTS: Ictal EEG showed onset in the right parieto-occipital region with rapid spread to right temporal and frontal areas. FDG-PET revealed focal hypometabolism in the right cuneus and remote hypometabolism in the right mesial temporal and left central regions. Postoperatively, lesion-based FC decreased in bilateral primary sensorimotor, early visual, and superior temporal/opercular cortices, but increased in the ipsilesional dorsal occipito-parietal network. fALFF and ReHo decreased widely in peri-Rolandic and occipital regions; fALFF and DC increased in medial/superior frontal cortex and bilateral precuneus; DC decreased in inferior frontal opercular/triangular areas.

CONCLUSION: Multimodal findings localized the seizure onset to the right posterior region with widespread propagation. Postoperative rs-fMRI showed network changes consistent with partial normalization, paralleling sustained seizure freedom-though causality cannot be inferred from a single case.

PMID:42306261 | PMC:PMC13266237 | DOI:10.1016/j.ebr.2026.100874

Mindfulness (N Y). 2026 Jun 4. doi: 10.1007/s12671-026-02885-9. Online ahead of print.

ABSTRACT

OBJECTIVES: Meditation training can influence various self-related traits, such as self-judgment (SJ), self-kindness (SK), rumination (RUM) and reflection (REF), but little is known about the underlying neural mechanisms. This study aimed to elucidate the neural correlates of changes in traits of SJ, SK, RUM, and REF in response to mindfulness meditation training.

METHOD: Secondary data analyses were conducted on pre- and post-intervention questionnaires and resting state fMRI data collected in a prior mechanistic neuroimaging Randomized Controlled Trial (RCT) that compared 8 weeks of Mindfulness-Based Stress Reduction (MBSR, n=39) to an active control program of Stress Management Education (SME) (n=25) among healthy adults. Voxelwise Amplitudes of Low Frequency Fluctuations (ALFF) were calculated from resting state fMRI as a measurement of magnitudes of spontaneous brain activity.

RESULTS: Linear mixed effects model analyses found no significant group by time interaction effects with any of the outcome measures. The MBSR group had significant improvements in all four trait measures, whereas the control group only had significant improvements in SJ and SK (p < 0.05). Linear regression analyses were conducted to predict voxelwise ALFF changes (ΔALFF) using questionnaire score changes (Δ). Within the MBSR group, ΔSK was positively associated with ΔALFF at the right dorsolateral prefrontal cortex (DLPFC), while ΔRUM and ΔREF respectively had negative and positive associations with ΔALFF in the Temporal Parietal Junction (TPJ). Different neural correlates were observed in the control group.

CONCLUSIONS: These findings suggest that meditation training can influence self-related traits through adaptive changes in brain regions involved in executive functioning (DLPFC) and empathy (TPJ).

PREREGISTRATION: The original RCT was registered as NCT01488422 at ClinicalTrials.gov, accessible at: https://clinicaltrials.gov/study/NCT01488422.

PMID:42306255 | PMC:PMC13268588 | DOI:10.1007/s12671-026-02885-9

iScience. 2026 Jun 4;29(6):116173. doi: 10.1016/j.isci.2026.116173. eCollection 2026 Jun 19.

ABSTRACT

Brain graph classification from resting-state fMRI (rs-fMRI) can support the identification of neurological conditions and inform personalized analysis. Here, we present a hierarchical sparse spatiotemporal graph neural network (STGNN)-GLNSTGNN-to address sparse feature selection in spatiotemporal brain graph classification. We evaluated GLNSTGNN on two rs-fMRI datasets comprising 1,956 participants with 200 regions of interest (ROIs) and 12 subnetworks after standardized preprocessing. GLNSTGNN applies GroupLassoNet-based hierarchical sparsity to select informative features, while combining spatial graph convolution on a fixed functional connectivity adjacency with temporal convolution on time-varying BOLD signals to capture spatial dependencies and temporal dynamics. Across multiple baselines, GLNSTGNN showed improved discriminative performance and consistent ROI selection, supporting interpretable subnetwork-level patterns. These results suggest that integrating hierarchical sparsity with spatiotemporal graph learning can provide a practical framework for robust and interpretable brain graph classification.

PMID:42305596 | PMC:PMC13266135 | DOI:10.1016/j.isci.2026.116173

Prog Neuropsychopharmacol Biol Psychiatry. 2026 Jun 16:111789. doi: 10.1016/j.pnpbp.2026.111789. Online ahead of print.

ABSTRACT

BACKGROUND: Previous research on brain network topology in Tobacco Use Disorder (TUD) has been inconsistent, likely due to overlooking the heterogeneity of addiction severity. Consequently, how these topological alterations manifest across different smoking severities is not fully understood.

METHODS: Resting-state functional magnetic resonance imaging (fMRI) and clinical data were collected from 102 males (24 heavy smokers, 36 light smokers, 42 healthy controls). Based on the fMRI data, we computed global graph metrics and applied Network-Based Statistics (NBS) analysis to identify abnormal subnetworks, and performed correlation analyses between global graph metrics and clinical scales. A Support Vector Machine (SVM) classifier was constructed using functional connectivity (FC) strength, graph metrics, and multi-scale fusion features to discriminate the severity of smoking at the individual level within a rigorous repeated stratified nested cross-validation framework.

RESULTS: Graph-theory analysis revealed that patients with Tobacco Use Disorder (TUD) exhibited significant large-scale topological reorganization, characterized by increased global integration, indexed by higher global efficiency (Eglob), and reduced local segregation, indexed by a lower clustering coefficient (Cp). Network-Based Statistics (NBS) identified two distinct subnetworks showing reduced connectivity in the TUD group, primarily involving the default mode, limbic, and sensorimotor networks. Subgroup analyses further demonstrated a severity-dependent pattern of network alterations. Light smokers showed increased Eglob with relatively preserved Cp, whereas heavy smokers exhibited a significant reduction in Cp accompanied by more extensive disruption of connectivity across distributed higher-order networks. In contrast, no significant subnetworks were detected in the light smoker group. Importantly, correlation analysis revealed a significant negative association between Cp and Fagerström Test for Nicotine Dependence (FTND) scores. Finally, a multi-scale machine learning model integrating network features and evaluated using 10 × 5 repeated stratified nested cross-validation achieved optimal classification of smoking severity (ensemble AUC = 0.8380), outperforming models based on single-modality features.

CONCLUSION: TUD involves a severity-dependent pathophysiological gradient, which may reflect a hypothesized shift from compensatory strengthening of global integration in light smokers to advanced-severity weakening of local network organization in heavy smokers. The severity-dependent reduction in local segregation, together with widespread hypoconnectivity, suggests multi-scale network disruption. These multi-scale functional network features provide preliminary evidence supporting their potential utility for severity stratification in TUD.

PMID:42303076 | DOI:10.1016/j.pnpbp.2026.111789

Behav Brain Res. 2026 Jun 16:116316. doi: 10.1016/j.bbr.2026.116316. Online ahead of print.

ABSTRACT

Non-suicidal self-injury (NSSI) is highly prevalent in adolescents with major depressive disorder (MDD), elevating suicide risk and indicating poor prognosis. While neuroimaging studies have examined NSSI and MDD separately, systematic reviews focusing on major depressive disorder with non-suicidal self-injury (nsMDD) remain scarce. In accordance with PRISMA guidelines, this study systematically reviewed 24 neuroimaging studies (2013 - 2025) involving 2,379 participants (mean age ≤23 years) from PubMed, PsycInfo, and Embase. Using the Newcastle-Ottawa scale for quality assessment, we found 20 functional magnetic resonance imaging (resting-state fMRI and task fMRI) and 4 structural magnetic resonance imaging (MRI) studies. Key findings identified distinct neural signatures specific to nsMDD that differentiate it from depression without NSSI, including: suppression of the frontal gyrus, reduced volume of the putamen, abnormal activation of the lingual gyrus, midline cortical structures, and the prefrontal-limbic-mesencephalic circuit. In terms of neural networks, the default mode network exhibits enhanced connectivity with other neural networks, while the frontoparietal network shows abnormal suppression. Despite limitations including cross-sectional designs, gender imbalance, and scarce multimodal data, these findings provide a basis for targeted intervention that require longitudinal validation of their clinical utility.

PMID:42302888 | DOI:10.1016/j.bbr.2026.116316

Neuroimage. 2026 Jun 16:122055. doi: 10.1016/j.neuroimage.2026.122055. Online ahead of print.

ABSTRACT

Functional connectivity changes in neurodegeneration involve not only regional disconnection but also scale-specific reorganization of brain networks. We introduce the Multiscale Spectral Gaussian Filtering (MSGauF) framework, which transforms each subject's Laplacian spectrum into a data-driven coordinate system for connectivity analysis. MSGauF defines adaptive frequency bands from spectral changepoints, and within each band, normalized similarity measures enable sign-separated, cluster-level permutation testing without requiring eigenvector alignment. Simulations show improved precision when connectivity changes are confined to specific spectral ranges. Applied to resting-state fMRI from Alzheimer's and Parkinson's cohorts, the method revealed distinct spectral signatures of disease. In Alzheimer's disease, connectivity shifted from large-scale attenuation in mild impairment to fine-scale polarity reversal, reflecting disrupted long-range inhibition and local hyperexcitability. In Parkinson's disease, polarity was preserved but spectrally compressed, indicating reduced flexibility and rigid synchronization of local circuits. These findings highlight the advantages of frequency-resolved analysis in revealing structured, multiscale connectivity changes that are missed by conventional broadband approaches.

PMID:42302882 | DOI:10.1016/j.neuroimage.2026.122055

Eur Child Adolesc Psychiatry. 2026 Jun 16. doi: 10.1007/s00787-026-03089-1. Online ahead of print.

ABSTRACT

Communication deficits in Autism Spectrum Disorder (ASD) involve impairments in both verbal and nonverbal domains, potentially associated with altered brain network connectivity related to language, attention, and social cognition systems. This study investigated functional connectivity patterns among the Default Mode Network (DMN), Salience Network (SN), Dorsal Attention Network (DAN), and Language Network (LN) in male children with ASD using resting-state fMRI data and Principal Component Analysis (PCA) to define sample-specific regions of interest. The sample included 53 males with ASD and 27 typically developing controls aged 5 to 12 years. Group comparisons revealed underconnectivity between the SN and LN (p = 0.003, beta = -0.49; p = 0.04, beta = -0.38) and overconnectivity between the DMN and LN in the ASD group (p = 0.003, beta = 0.5; p = 0.02, beta = 0.4). Crucially, further analyses showed that impairments in verbal (p = 0.016, beta = -0.45; p = 0.02, beta = -0.46) and nonverbal (p = 0.03; beta = -0.42) communication in ASD were associated with reduced connectivity within the DAN and between the DAN and SN, rather than with the LN. These findings suggest that communication difficulties in ASD have a stronger attentional basis linked to disruptions in sustained and switching attention mechanisms, as opposed to isolated language network dysfunctions. Our results underscore the importance of considering the integrated functioning of attentional and social brain networks to better understand the neural substrates of communication challenges in ASD.

PMID:42301279 | DOI:10.1007/s00787-026-03089-1

Diabetes Metab Syndr Obes. 2026 Jun 10;19:592789. doi: 10.2147/DMSO.S592789. eCollection 2026.

ABSTRACT

PURPOSE: Obesity can exacerbate metabolic dysfunction in patients with type 2 diabetes mellitus (T2DM), and the degree of various injuries to our body cannot be fully controlled. However, research on early brain function changes in overweight/obese patients with T2DM is not yet perfect. Herein, we studied the topological structure of brain networks and cognitive function alterations in newly-onset overweight/obese T2DM patients.

METHODS: To investigate the changes in topological structure, we collected clinical data, cognitive scales, and resting-state functional magnetic resonance imaging (fMRI) data from 32 patients with newly-onset overweight/obese T2DM and 27 normal controls (NCs). The topological structure of the constructed network, derived from preprocessed fMRI data, was extracted and analyzed using graph theory methods by GRETNA. The relationships between changes in topological structure and clinical data as well as neurocognitive test scores in patients with newly-onset overweight/obese T2DM were analyzed.

RESULTS: Within the set threshold range, both newly-onset T2DM and NC groups had global small-world (δ) values >1.1, but the T2DM group showed lower δ values at each threshold. Compared to NC, T2DM had decreased δ (P = 0.0423, T = -2.079), global efficiency (Eglob) (P = 0.0312, T = -2.212), and increased characteristic path length (Lp) (P = 0.0253, T = 2.301). After multiple comparison corrections, local topological metrics showed no significant inter-group differences. For the sake of prudence, we employed a significance reference threshold of p < 0.005 for uncorrected analyses. Through exploratory investigation, we identified that, without multiple comparison corrections, there were inter-group nodal differences in specific brain regions for the metrics including nodal degree centrality (Dc), nodal clustering coefficient (NCp), and nodal local efficiency (NLe). Network-based statistic found no aberrant connections (P > 0.05). In the T2DN group, a positive correlation was observed between the Dc of left inferior parietal angular gyrus and BMI (P < 0.05).

CONCLUSION: The functional networks of patients with newly-onset T2DM have changes in network efficiency and brain region function. Brain function damage may exist in the early stage of overweight/obese T2DM. This study provides a new thought for the neurobiological mechanism of early brain function damage in overweight/obese T2DM. It is worth noting that the findings of this study suggest that the combined metabolic burden resulting from the coexistence of T2DM and overweight/obesity may jointly participate in mediating alterations in brain networks. However, constrained by the study design, this study failed to independently distinguish the specific effects of obesity. In the future, it is still necessary to conduct refined analyses stratified by BMI to further disentangle the independent and interactive effects of obesity and hyperglycemia on brain structure and function.

PMID:42299365 | PMC:PMC13264985 | DOI:10.2147/DMSO.S592789