Most recent paper

Psychiatry Res Neuroimaging. 2026 Aug 17;363:112304. doi: 10.1016/j.pscychresns.2026.112304. Online ahead of print.

ABSTRACT

The pathological mechanisms of depression are not yet fully clear, and the causative factors remain somewhat ambiguous. Clinical diagnosis of depression is often challenging and complex, frequently leading to misdiagnosis and missed diagnoses. Combining deep learning with resting-state fMRI can quantify the degree of abnormal brain function caused by depression and automatically screen for discriminative features that aid in the classification and identification of depression, which may serve as hypothesis-generating discriminative features within the current dataset, providing candidate neuroimaging signatures that warrant further investigation. This paper proposes a cross-site fMRI data analysis framwork for depression. First, it contains a graph deep learning-based auxiliary diagnostic method, which fully leverages the topological structure of brain networks to achieve higher classification accuracy compared to existing models, with interpretable results. Building on this network, the framework also contains a domain-adversarial-based cross-site semi-supervised transfer method is proposed, making full use of multi-site data to analyze depression-related brain networks and ROIs. Finally, based on cross site data, the distribution of brain networks and brain regions was discussed. The research findings are consistent with existing studies, confirming the reliability of this method. Furthermore, we validated the cross-dataset generalizability of our framework on an independent OpenNeuro dataset, where adversarial transfer consistently outperformed direct transfer, demonstrating the potential of our approach to generalize beyond the original consortium.

PMID:42617283 | DOI:10.1016/j.pscychresns.2026.112304

Lasers Med Sci. 2026 Aug 19;41(1):197. doi: 10.1007/s10103-026-04992-4.

ABSTRACT

Purpose previous studies have suggested that laser acupuncture (LA) may influence autonomic nervous system (ANS) activity and brain functional connectivity (FC); however, the underlying neural mechanisms and potential sex-dependent responses remain to be further investigated. Using resting-state functional magnetic resonance imaging (fMRI), we examined the effects of LA at different energy densities (EDs), hypothesizing that varying dosages elicit differential brain responses influenced by biological sex. Methods we enrolled 60 healthy adults of both sexes aged 18-55 years with a BMI of 18.5-24 kg/m². Participants were randomized into three groups (n = 20 each) receiving LA at EDs of 0, 7.96, or 23.87 J/cm², with male/female distributions of 7/13, 7/13, and 9/11, respectively. Bilateral PC6 (Neiguan) was stimulated, and two 500-second resting-state fMRI scans were acquired before and after LA to analyze FC between ANS-related brainstem regions and other brain areas by sex. Results female-specific analyses more closely resembled previously reported sex-independent results, indicating females as the primary contributing subgroup. Accordingly, in females, LA at 23.87 J/cm² enhanced FC between the rostral ventrolateral medulla and orbitofrontal cortex (OFC), while reducing FC between the caudal ventrolateral medulla (CVLM), nucleus of the solitary tract/nucleus ambiguus, and dorsal motor nucleus of the vagus with the sensorimotor cortex (SMC). In males, 23.87 and 7.96 J/cm² LA decreased FC between the CVLM and OFC, with no significant changes between all regions of interest (ROIs) and SMC. Conclusion different EDs of LA elicited distinct FC changes in males and females between ROIs and other brain areas, while PRV findings showed limited evidence of sex-dependent autonomic modulation.

PMID:42616158 | DOI:10.1007/s10103-026-04992-4

Sci Rep. 2026 Aug 18;16(1):24914. doi: 10.1038/s41598-026-63059-0.

ABSTRACT

College students often struggle to regulate emotions under changing academic and social stress, highlighting the need for interventions targeting adaptive emotion regulation (ER). Here, we tested a 5-week online intervention designed to enhance ER and cognitive flexibility in college students. Participants (N = 39) were pseudo-randomly assigned to the experimental (N = 20) or control (N = 19) group, with higher distress individuals preferentially assigned to the experimental condition. Behavioral, self-report, eye-tracking, and brain imaging measures were collected before and after the training. Compared to controls, the experimental group participants showed reduced emotional reactivity to negative stimuli and experiences, along with reduced visual attention to emotionally salient regions. They also showed improvements in the use of attention strategies, positive refocusing, and perceived cognitive control. In the imaging subsample (N = 23; Experimental: N = 15; Control: N = 8), these improvements were accompanied by changes in resting-state functional connectivity, including reduced coupling between affective and higher-order systems and increased efficiency of attentional control and self-referential pathways. These results suggest that training promoted more efficient and flexible interactions among emotion, attention, and cognitive control regions, providing a neural mechanism for the observed behavioral benefits. Overall, the findings support the intervention as a promising approach for improving adaptive ER and resilience during emerging adulthood.

PMID:42613380 | DOI:10.1038/s41598-026-63059-0

Transl Psychiatry. 2026 Aug 18;16(1):417. doi: 10.1038/s41398-026-04392-w.

ABSTRACT

Rumination has been associated with aberrant dynamics of a coactivation pattern (CAP) comprising the default mode (DMN) and frontoparietal (FPN) networks. Ketamine exerts rapid antidepressant effects and may influence these dynamics via glutamatergic mechanisms. In a randomized, double-blind, placebo-controlled fMRI study, we investigated ketamine's effects on dynamic CAPs associated with rumination and examined whether inhibition of glutamatergic release through lamotrigine attenuates these effects. Seventy-five healthy adults were randomized to placebo-placebo, placebo-ketamine, or lamotrigine-ketamine treatment. Resting-state fMRI was acquired at baseline, during ketamine/placebo infusion, and 24 h post-infusion. Whole-brain CAP analysis identified seven recurring network configurations. Occurrence rates were examined for group differences, while controlling for age, sex, and drug plasma concentrations. Rumination was assessed using a validated self-report questionnaire. A hybrid DMN + FPN CAP showed a positive association with rumination at baseline. Ketamine acutely reduced the occurrence rate of this hybrid CAP compared to placebo, with larger decreases in individuals reporting higher rumination. These effects were transient, returning to baseline after 24 h. Exploratorily, ketamine also reduced engagement of a canonical somatomotor CAP during infusion. Lamotrigine pretreatment nominally attenuated ketamine-induced changes across analyses. Ketamine transiently alters dynamic brain states implicated in rumination and somatosensory processing, and preliminary evidence suggests partial glutamatergic mediation. These findings provide insights into ketamine's mechanism of action.

PMID:42613319 | DOI:10.1038/s41398-026-04392-w

J Affect Disord. 2026 Aug 18:122395. doi: 10.1016/j.jad.2026.122395. Online ahead of print.

ABSTRACT

OBJECTIVE: Post-stroke depression (PSD) is under-recognized and associated with poorer rehabilitation outcomes and quality of life. We characterized PSD-related alterations in large-scale brain network dynamics and evaluated their cross-center classification performance.

METHODS: This dual-center cross-sectional study included 300 patients with post-stroke motor impairment: 100 with and 100 without PSD in the development cohort, and 50 per group in the external validation cohort. Resting-state functional MRI, sliding-window dynamic functional connectivity, and clustering across K = 2-8 state resolutions were combined with nested cross-validation, nine machine-learning classifiers, and SHAP interpretation. Sensitivity analysis excluded users of antidepressants, anxiolytics, or sedative-hypnotics.

RESULTS: The five-state solution provided the best balance of discrimination and interpretability. The support vector machine achieved internally cross-validated and external validation AUCs of 0.838 and 0.784, respectively. Higher proportion and longer dwell time of a globally integrated state, lower proportion and dwell time of a stable modular state, and higher self-transition probability of a low-integration state shifted classification toward PSD. After medication users were excluded, the artificial neural network under the five-state solution achieved internally cross-validated and external validation AUCs of 0.798 and 0.793, respectively; a higher proportion of the globally integrated state also remained among the most informative features.

CONCLUSIONS: PSD was associated with altered temporal organization and reduced stability of large-scale functional networks. Dynamic state features may serve as interpretable candidate imaging markers for PSD classification, but longitudinal multicenter validation is required before clinical application.

PMID:42612921 | DOI:10.1016/j.jad.2026.122395

Sleep Med. 2026 Aug 12;148:109214. doi: 10.1016/j.sleep.2026.109214. Online ahead of print.

ABSTRACT

Insomnia disorder is clinically heterogeneous, with variation in objective sleep duration, sleep perception, cognitive-emotional features, and spontaneous neural activity. We examined whether insomnia with normal sleep duration (INSD) has a distinct behavioral and neural profile relative to insomnia with short sleep duration (ISSD). In 233 participants (100 healthy controls, 67 INSD, 66 ISSD), we assessed objective and diary-based sleep, presleep arousal, and emotion regulation; resting-state fMRI was additionally acquired in a subset of 133 patients with insomnia. Analyses included group comparisons, phenotype-specific associations, moderated mediation, voxel-wise z-standardized amplitude of low-frequency fluctuation (zALFF), and exploratory brain-behavior partial least squares (PLS). Both insomnia groups reported poorer subjective sleep than controls. As expected from the phenotype definitions, INSD showed greater sleep misperception than ISSD; the novel findings were that INSD also showed greater presleep cognitive arousal and brooding, distinct regional zALFF alterations, and a conditional indirect association linking brooding to sleep misperception via presleep cognitive arousal that was evident in INSD but not ISSD. Compared with ISSD, INSD showed higher zALFF in the left insula, left hippocampus, and right precuneus, and lower zALFF in the right middle occipital gyrus. Exploratory PLS linked this INSD-like zALFF pattern to a behavioral profile dominated by sleep misperception and presleep cognitive arousal, mainly at the phenotype level. These findings suggest that INSD involves convergent cognitive-emotional and neural features related to sleep misperception, while causal and individual-prediction claims remain premature.

PMID:42612566 | DOI:10.1016/j.sleep.2026.109214

Brain Topogr. 2026 Aug 18;39(5):90. doi: 10.1007/s10548-026-01246-y.

ABSTRACT

Parkinson's disease (PD) disrupts intrinsic brain networks that support motor and cognitive functions. Using resting-state fMRI from 138 PD patients and 54 controls, we combined independent component analysis (ICA) with nonlinear functional connectivity (FC) based on distance correlation. Compared with conventional Pearson-based measures, nonlinear FC revealed stronger and spatially distinct connectivity patterns, especially in parietal and sensorimotor regions. Group comparisons showed robust differences (p < 1×10⁻⁷), with four ICA networks (Components 4, 10, 19, and 20) displaying consistent alterations. Importantly, hyperconnectivity within a posterior midline network (Component 10), overlapping with regions commonly associated with the default mode network, correlated positively with Hoehn & Yahr stage (r = 0.51, p = 0.022), linking network reorganization to clinical severity. These findings demonstrate that nonlinear FC enhances sensitivity to PD-related network alterations, while spatial features highlight clinically relevant biomarkers. By integrating advanced connectivity metrics with data-driven network analysis, our study contributes to the methodological development of resting-state fMRI and its translational application to PD.

PMID:42611380 | DOI:10.1007/s10548-026-01246-y

Eur Arch Psychiatry Clin Neurosci. 2026 Aug 17. doi: 10.1007/s00406-026-02351-6. Online ahead of print.

ABSTRACT

OBJECTIVE: This study aimed to investigate the whole-brain functional connectivity (FC) patterns of goal-directed/habitual systems in patients with obsessive-compulsive disorder (OCD), and to explore their exploratory classification potential and spatial associations with gene expression profiles.

METHODS: Resting-state functional magnetic resonance imaging (fMRI) data and clinical variables were collected from 60 patients with OCD and 60 matched healthy controls (HCs). Seed-to-whole-brain FC analyses were conducted using the regions of interest (ROIs) implicated in goal-directed/habitual systems. Pearson correlation analyses were conducted to assess the associations between altered FCs and clinical characteristics. An exploratory support vector machine (SVM) analysis was utilized to evaluate whether altered FC patterns could distinguish patients with OCD from HCs, and neuroimaging-transcriptomic analysis was performed to investigate the spatial association between aberrant FCs and gene expression profiles.

RESULTS: Compared to HCs, patients with OCD exhibited decreased FCs between goal-directed system and the default mode network (DMN) and central executive network (CEN), as well as between habitual system and salience network (SN). Conversely, increased FCs were observed between habitual system and DMN. No significant correlations were observed between altered FCs and clinical characteristics after multiple-comparison correction. Exploratory SVM analysis indicated that altered FC patterns of goal-directed system showed higher classification performance (accuracy = 90.83%, AUC = 0.951) than those of the habitual system (accuracy = 79.17%, AUC = 0.867). Imaging-transcriptomic analysis revealed that regional FC alterations were spatially associated with gene (i.e., PPARD and SLC25A12), which were enriched in transmembrane ion transport and metal ion homeostasis.

CONCLUSION: OCD exhibited altered FCs patterns between goal-directed/habitual systems and the triple networks at rest. These findings may provide more information to understand the pathology of OCD.

PMID:42608583 | DOI:10.1007/s00406-026-02351-6

J Neurosci. 2026 Aug 17:e0274262026. doi: 10.1523/JNEUROSCI.0274-26.2026. Online ahead of print.

ABSTRACT

Anhedonia reflects a transdiagnostic deficit in a range of processes that impact reward and motivation. While human neuroimaging has mainly focused on striatal-related alterations in anhedonia, animal models suggest hippocampal [HPC] novelty processing regulates mesolimbic dopamine activity, implicating mesolimbic-HPC alterations in anhedonia. Childhood trauma, which disproportionately impacts HPC structure and function, may exacerbate this vulnerability. The present study of 37 males and 55 females examined whether HPC alterations interact with childhood trauma to predict anhedonia in humans. Using fMRI in a sample enriched for anhedonia, we assessed three HPC-related processes: resting-state connectivity with mesolimbic targets in the ventral tegmental area [VTA] and nucleus accumbens [NAc], task-based HPC novelty response, and task-based HPC modulation of VTA activation reflecting novelty-evoked facilitation of target detection. Significant interactions emerged for anticipatory anhedonia: reduced HPC-NAc connectivity, reduced novelty response, and weaker HPC→VTA modulation were each associated with greater anticipatory anhedonia among individuals with high childhood trauma. Moreover, LASSO regression confirmed these interactions as unique predictors. These findings suggest that early life adversity interacts with alterations in HPC-mesolimbic signaling to contribute to individual differences in anhedonia, highlighting the HPC as a potential target of motivation-related deficits in striatum.Significance Statement The ability to process rewarding stimuli and initiate goal-directed behavior is critical for human functioning. While human neuroimaging has highlighted the role of striatal function in these processes, animal evidence suggests that hippocampal modulation of mesolimbic dopamine signaling is critical for motivated behavior. The present study translates this framework to humans, demonstrating that hippocampal-mesolimbic functional integrity interacts with childhood trauma to predict individual differences in anticipatory anhedonia (i.e., impairments in one's ability to anticipate and pursue rewards). These findings implicate the hippocampus as a potential upstream contributor to motivational deficits and highlight early life stress as a key context in which mesolimbic circuit dysfunction becomes behaviorally relevant.

PMID:42608204 | DOI:10.1523/JNEUROSCI.0274-26.2026

J Affect Disord. 2026 Aug 17:122394. doi: 10.1016/j.jad.2026.122394. Online ahead of print.

ABSTRACT

BACKGROUND: Early-life exposure to chronic and unpredictable stress is a major risk factor for affective disorders, yet the neurodevelopmental mechanisms linking such exposure to emotional vulnerability remain unclear. War-related environments represent an extreme form of environmental unpredictability and may shape how children's attachment-related characteristics relate to the organization of cognitive-limbic brain systems. Here we test whether war-related exposure moderates this brain-behavior association, rather than producing uniform group-level differences.

METHODS: Seventy-nine children (42 war-exposed; 37 non-exposed) underwent behavioral assessment and resting-state fMRI. Executive functions (EF) were assessed via parent report, attachment-related characteristics via the Experiences in Close Relationships questionnaire, and war exposure via a structured exposure index. Functional connectivity analyses focused on large-scale executive-control networks and limbic circuits.

RESULTS: The war-exposed and non-exposed groups did not differ significantly in attachment-related characteristics, and group differences in parent-reported EF were no longer significant after adjustment for scan timing and nonverbal ability. At the neural level, the two groups showed divergent patterns of association between attachment-related characteristics and limbic connectivity. Critically, attachment-related difficulties were associated with distinct connectivity patterns depending on exposure status: in the war-exposed group, greater attachment-related anxiety and avoidance were associated with reduced cingulate-amygdala connectivity, whereas in the non-exposed group the same characteristics were associated with reduced amygdala-hippocampal connectivity. For large-scale networks, the direction of the attachment-connectivity association shifted across exposure levels, from negative at lower exposure to positive at higher exposure; these within-level simple slopes did not survive correction for multiple comparisons and are interpreted with caution.

CONCLUSIONS: War-related exposure was associated with distinct patterns of association between children's attachment-related characteristics and cognitive-limbic connectivity, rather than with uniform group-level differences in attachment or connectivity. These findings provide preliminary evidence that environmental adversity may moderate brain-behavior coupling in childhood; longitudinal studies with validated exposure and symptom measures are needed to clarify causal mechanisms.

PMID:42607893 | DOI:10.1016/j.jad.2026.122394

Brain Inform. 2026 Jul 26;13(1):37. doi: 10.1186/s40708-026-00324-y.

ABSTRACT

This study introduces a graph theory-based machine learning framework to analyze functional brain networks in Multiple Sclerosis (MS) patients during working memory tasks. Using fMRI data from 22 participants (8 MS patients/14 controls) performing a Persian n-back task, we constructed functional connectivity networks with an automated anatomical labeling atlas (116 regions). Novel methodological contributions include: a proportional thresholding approach (5-80% connectivity strength) to optimize network analysis, and extraction of six graph-theoretic features (degree, clustering coefficient, betweenness/eigenvector/page-rank/k-coreness centrality) for classification. Machine learning models (SVM, decision trees, kNN) were trained on task-specific networks (1-back, 2-back, 3-back), with leave-one-subject-out validation. The 3-back task (high cognitive load) yielded superior classification (95.5% accuracy) using SVM with degree, k-coreness, and eigenvector centrality features, outperforming traditional resting-state approaches. Key innovations include identification of the cerebellum (1-back) and orbitofrontal cortex (3-back) as novel discriminative hubs, and demonstration that task difficulty modulates network separability. Results indicate MS patients exhibit impaired high-load connectivity patterns, with the proposed framework showing potential as an auxiliary diagnostic tool. The study provides both a technical blueprint for task-based network analysis and clinically relevant insights into MS-related cognitive impairment, bridging engineering methodologies with neurological applications.

PMID:42606763 | DOI:10.1186/s40708-026-00324-y

Biol Psychiatry Glob Open Sci. 2026 Jul 8;6(5):100787. doi: 10.1016/j.bpsgos.2026.100787. eCollection 2026 Sep.

ABSTRACT

BACKGROUND: Prevailing theories propose that autism is characterized by local cortical overconnectivity and long-range underconnectivity, but empirical evidence remains mixed.

METHODS: Here, we applied the turbulence dynamics framework to the ABIDE (Autism Brain Imaging Data Exchange) dataset (N = 1009) to examine how functional synchronization profiles dynamically change over time and across the cortex over different spatial scales.

RESULTS: Autistic individuals showed increased short-range and reduced long-range functional synchronization variability over time, as well as reduced synchronization strength across all spatial scales. Synchronization also decayed more rapidly with distance and exerted weaker influence across scales in autism. These distance-specific alterations suggest that local hyperconnectivity may be associated with turbulent synchronization dynamics that fail to propagate coherently across the cortex, resulting in an overly rigid brain organization at longer distances. Mapping these effects onto the sensorimotor-association cortical gradient revealed increased variability in sensorimotor regions and decreased variability in the association cortex.

CONCLUSIONS: Together, we found evidence of disturbances in functional synchronization dynamics at different spatial scales and along hierarchical brain gradients in autistic individuals. These results consolidate ideas about dynamic functional connectomic organization in autism and situate these alterations along hierarchical brain gradients that are closely linked to neurodevelopmental processes.

PMID:42602560 | PMC:PMC13475223 | DOI:10.1016/j.bpsgos.2026.100787

Front Hum Neurosci. 2026 Jul 31;20:1753838. doi: 10.3389/fnhum.2026.1753838. eCollection 2026.

ABSTRACT

BACKGROUND: Previous work has shown that endurance training is associated with structural and functional plasticity in prefrontal, hippocampal, cerebellar and network level regions using both cross sectional and longitudinal designs. The present study extends this foundation by examining how key endurance indicators relate to multimodal neural characteristics across different training levels.

METHODS: Thirty-two male participants were classified into high level endurance athletes, moderately trained runners and healthy active controls. All participants completed structural MRI and resting state functional MRI together with graded exercise testing to assess maximal oxygen consumption, relative maximal oxygen consumption, lactate threshold and individual lactate threshold (ILT). Gray matter volume, fractional amplitude of low frequency fluctuations and degree centrality were extracted from predefined regions of interest. Correlation analyses were first conducted in the full sample and within subgroups, followed by false discovery rate correction within imaging modalities. Associations surviving FDR correction were further evaluated using Spearman rank correlations, bootstrap confidence intervals for subgroup findings, and covariate-adjusted partial correlations as sensitivity analyses.

RESULTS: Across all participants, incremental exercise-capacity indicators were associated with multimodal neural characteristics in prefrontal, premotor, precuneus, temporal, and posterior cerebellar regions, with additional negative associations observed in the thalamus and inferior cerebellum in the uncorrected analyses. After FDR correction, the surviving associations became more selective. Subgroup analyses suggested descriptively different association patterns across training levels, but these subgroup findings were less stable in sensitivity analyses and should therefore be interpreted cautiously.

CONCLUSION: Incremental exercise-capacity indicators were associated with distributed multimodal brain characteristics, and several core associations remained robust after multiple-comparison correction and supplementary analyses. The subgroup results suggested stage-related differences in brain-physiology coupling, particularly a broader pattern in moderately trained individuals and a more focal prefrontal-associated profile in high-level athletes, although these findings still require cautious interpretation.

PMID:42602297 | PMC:PMC13473415 | DOI:10.3389/fnhum.2026.1753838

Medicine (Baltimore). 2026 Aug 14;105(33):e50185. doi: 10.1097/MD.0000000000050185.

ABSTRACT

BACKGROUND: Depression poses a major global health burden. Many patients exhibit treatment resistance or experience intolerable side effects with standard therapies. Acupuncture shows promise as an adjunctive therapy for depression, but its neurobiological mechanisms remain incompletely understood.

METHODS: We conducted a coordinate-based meta-analysis to identify consistent patterns of acupuncture-induced neuroplastic changes in patients with major depressive disorder, quantified via resting-state functional magnetic resonance imaging measures of regional homogeneity and amplitude of low-frequency fluctuations (ALFF/fractional ALFF). We synthesized data from 7 randomized controlled trials involving 357 patients with major depressive disorder, comparing acupuncture-based interventions (manual acupuncture, electroacupuncture, or transcutaneous auricular vagus nerve stimulation) against sham acupuncture, conventional treatment, or treatment-as-usual. The seed-based d mapping with permutation of subject images algorithm was used for the imaging meta-analysis, and a leave-one-out sensitivity analysis was performed to assess the robustness of the pooled findings to intervention heterogeneity.

RESULTS: Acupuncture induced significant regional homogeneity /ALFF increases within the right cingulum and right caudate nucleus. In contrast, conventional treatments primarily increased activity in the left hippocampus and right middle occipital gyrus. Meta-regression linked clinical improvement specifically to plasticity changes in the left cerebellum and revealed a dose-dependent relationship between the number of acupuncture sessions and modulation of the left amygdala. Clinically, acupuncture produced significantly greater reductions in depression and anxiety scores than control conditions.

CONCLUSION: Our findings indicate that acupuncture-based interventions elicit a distinct pattern of neuroplastic remodeling, targeting key regions involved in emotion regulation and reward processing, which contrasts with the hippocampal-occipital effects of conventional treatments. The correlation between neuroplastic changes in affective circuits and clinical improvement supports a potential mechanism for acupuncture's efficacy in alleviating core depressive symptoms such as anhedonia and emotional dysregulation. Limitations include the small number of included trials, heterogeneity in acupuncture protocols and resting-state functional magnetic resonance imaging metrics, absence of long-term follow-up data, and methodological caveats inherent to coordinate-based meta-analysis. Future research requires larger, standardized trials with follow-up assessments and integration of neuroimaging with molecular biomarkers to advance personalized neuromodulation strategies for depression.

PMID:42601716 | DOI:10.1097/MD.0000000000050185

Geroscience. 2026 Aug 14. doi: 10.1007/s11357-026-02449-8. Online ahead of print.

ABSTRACT

Understanding how brain connectivity reorganizes with age is essential for characterizing healthy aging. While static functional connectivity (sFC) has revealed broad age-related shifts in network segregation and integration, recent work underscores the need to examine how these patterns dynamically fluctuate over time to better understand cognitive decline and resilience in aging. The present resting-state fMRI study was conducted to enhance comprehension of the temporal variability and dynamics of the brain's functional architecture in the context of the aging process. To this end, dynamic functional connectivity (dFC) was extracted in 817 older adults between 55 and 85 years from the 1000BRAINS study. Using a sliding window and clustering approach, we identified four recurring dFC states and quantified temporal metrics, e.g., mean dwell time, or number of transitions. Overall, aging was associated with slower and less flexible network dynamics: integrative states became less frequent, whereas segregated states dominated, reflecting reduced inter-network communication, although the effect size was relatively small. Age-stratified analyses yielded two novel insights, thereby suggesting a refinement of the so far established theories of aging and giving rise to a new model of age-related differences in functional connectivity: First, trajectories of dedifferentiation and segregation shown from younger to older adults seem to stabilize during the transition from mid-to-old age and turn into a process of re-segregation and overcompensation during older-old ages, challenging the assumption of a monotonic increase in integration across the lifespan. Secondly, opposing links between dFC and cognitive performance have been identified, with greater network integration supporting better cognitive performance in mid-to-old adults but poorer cognitive performance in older-old adults. This suggests a shift from dedifferentiated to re-segregated connectivity with advancing age. Sex-stratified modeling further demonstrated stronger age-related reductions in flexibility among females, indicating that pooled analyses might obscure systematic sex-specific dynamics and highlighting divergent adaptive and vulnerability profiles across aging. The findings of this study emphasize the necessity of accounting for temporal variability in age-related changes of functional brain connectivity when studying aging, specifically during advanced age, with the aim of enhancing comprehension of cognitive variability.

PMID:42601544 | DOI:10.1007/s11357-026-02449-8

Behav Brain Res. 2026 Aug 14:116425. doi: 10.1016/j.bbr.2026.116425. Online ahead of print.

ABSTRACT

Problematic online behaviors often span multiple digital platforms, yet previous research has focused on single forms of problematic use. It remains unclear whether distinct cross-platform behavioral profiles are associated with differential reward and punishment sensitivity and alterations in brain network topology. This study combined latent profile analysis with resting-state functional magnetic resonance imaging to identify latent risk profiles of cross-platform problematic online behaviors among Chinese university students and examine their motivational and neural correlates. In Study 1, 947 students completed measures of problematic usage of smartphones, video gaming, and social networking, and reward/punishment sensitivity. Latent profile analysis identified three graded profiles: Low-Risk, Medium-Risk, and High-Risk. In Study 2, a stratified subsample of 150 participants underwent resting-state functional magnetic resonance imaging; whole-brain eigenvector centrality (EC) indexed hub-like network properties. The Medium-Risk and High-Risk groups showed higher reward sensitivity than the Low-Risk group, with no difference between at-risk groups. Punishment sensitivity increased stepwise across profiles. Neuroimaging analyses revealed reduced EC in the left superior frontal gyrus and left precuneus in both at-risk groups relative to the Low-Risk group. Lower EC in these regions was associated with greater punishment sensitivity, whereas reward sensitivity showed no significant association with EC. These findings suggest that punishment sensitivity is not only a psychological marker of POB severity but is also associated with reduced hub centrality in the left superior frontal gyrus and left precuneus, indicating a potential neural correlate of heightened sensitivity to negative outcomes.

PMID:42600894 | DOI:10.1016/j.bbr.2026.116425

J Affect Disord. 2026 Aug 14:122383. doi: 10.1016/j.jad.2026.122383. Online ahead of print.

ABSTRACT

BACKGROUND: Orbitofrontal cortex (OFC) dysfunction contributes to major depressive disorder (MDD), particularly through disrupted network interactions. Current studies lack whole-brain coverage and fine-grained OFC mapping. We employed OFC parcellation and multi-metric connectivity to comprehensively profile OFC-related network disturbances in MDD.

METHODS: OFC was parcellated into medial (MOFC), anterior middle (aMidOFC), posterior middle (pMidOFC), and lateral (LOFC) subregions via community analysis of resting-state fMRI from 586 healthy individuals. Using these subregions and whole OFC as seeds, we computed whole-brain functional connectivity (FC), total interdependence (TI), and Granger causality (GC) from 83 MDD patients and 80 healthy controls. Group differences were evaluated, and a support vector machine integrated multi-metric indicators to identify key network alterations.

RESULTS: FC revealed altered coordination between OFC subregions and distributed networks in MDD. GC and TI showed imbalanced anterior/posterior default mode network (DMN) interactions with right MOFC, LOFC, and pMidOFC; enhanced ventral attention network influence to left MOFC and pMidOFC; increased right MOFC and left pMidOFC interactions with sensorimotor and visual systems; and reduced dorsal anterior cingulate cortex input to left LOFC. Machine learning identified that alterations involving bilateral LOFC, MOFC, and left pMidOFC with DMN, ventral attention network, and dACC contributed most to classification.

CONCLUSION: These findings reveal widespread OFC-subnetwork dysregulation in MDD, reflecting impaired coordination with distributed neural systems that may underlie the disorder's pathophysiology.

PMID:42600783 | DOI:10.1016/j.jad.2026.122383

Neurosci Lett. 2026 Aug 14:138708. doi: 10.1016/j.neulet.2026.138708. Online ahead of print.

ABSTRACT

BACKGROUND: Major depressive disorder (MDD) is highly prevalent and often complicated by anxiety symptoms, yet the neurobiological features associated with anxious depression remain incompletely characterized. The amygdala-prefrontal cortex (PFC) circuit, central to affect regulation, has been implicated in anxious depressive symptoms.

METHODS: We recruited 40 MDD patients and 69 healthy controls (HCs) from Tianjin Anding Hospital. All participants underwent resting-state functional magnetic resonance imaging(fMRI) and clinical assessments. Seed-based functional connectivity (FC) analyses were performed using the bilateral amygdala based on the Automated Anatomical Labeling (AAL) atlas. Group comparisons, partial correlations, and regression analyses tested associations between amygdala-based FC and HDRS-17 symptom dimensions, with subgroup analyses distinguishing MDD patients with and without anxiety.

RESULTS: Compared with HCs, MDD patients exhibited reduced FC between the right amygdala and prefrontal cortex, including the right middle frontal gyrus (MFG) and the left inferior frontal gyrus, triangular part (IFGtri) (GRF: voxel-level p < 0.001, cluster-level p < 0.05). Within the MDD group, higher right amygdala-MFG FC values were associated with HDRS-17 symptoms, including total scores and depressive and somatic symptom dimensions (r = 0.393, p = 0.020; r = 0.360, p = 0.033; r = 0.351, p = 0.039, uncorrected p). In exploratory subgroup analyses, right amygdala-MFG FC showed a positive association with HDRS-17 scores in MDD patients with anxiety (R2 = 0.280, F = 8.957, p = 0.006, Beta = 0.529, t = 2.993, p = 0.006); however, the FC × anxiety subgroup interaction did not reach statistical significance.

CONCLUSION: Our findings identify disrupted right amygdala-MFG connectivity as a potential neural correlate of symptom heterogeneity in MDD, particularly in patients with anxiety symptoms.

PMID:42600677 | DOI:10.1016/j.neulet.2026.138708

Brain Imaging Behav. 2026 Aug 14;20(4):121. doi: 10.1007/s11682-026-01192-0.

ABSTRACT

To investigate alterations in the amplitude of low-frequency fluctuations (ALFF) in herpes simplex encephalitis (HSE) patients using resting-state functional magnetic resonance imaging (rs-fMRI) and evaluate their associations with anxiety symptoms. In this single-center cohort study, 45 HSE patients and 46 healthy controls (HC) were recruited from the First Affiliated Hospital of Zhejiang University School of Medicine, China. HSE was confirmed by clinical features and positive herpes simplex virus (HSV) DNA in cerebrospinal fluid via polymerase chain reaction. Participants underwent rs-fMRI scans within one week of ICU admission. ALFF was calculated using REST software; group differences were assessed with voxel-wise two-sample t-tests (covariates: age, sex, education, framewise displacement; whole-brain voxel-wise FDR-corrected P < 0.05) and validated nonparametrically. Associations between ALFF and anxiety (Hamilton Anxiety Rating Scale, HAMA) were examined via general linear models, controlling for age, sex, hypertension, and education, with Spearman's correlations for robustness. Compared to HC, HSE patients exhibited increased ALFF in the left precuneus and right putamen, alongside decreased ALFF in the right angular gyrus and right caudate nucleus (whole-brain voxel-wise FDR-corrected P < 0.05). These changes correlated significantly with HAMA scores: positive associations in hyperactive regions (left precuneus: β = 0.558, 95% CI: 0.312 to 0.805, p < 0.001; right putamen: β = 0.511, 95% CI: 0.254 to 0.768, p < 0.001) and negative associations in hypoactive regions (right angular gyrus: β = -0.517, 95% CI: -0.773 to -0.261, p < 0.001; right caudate: β = -0.556, 95% CI: -0.804 to -0.307, p < 0.001). No significant correlations emerged with depression scores (Hamilton Depression Rating Scale). HSE induces region-specific ALFF disruptions, characterized by hyperactivity in the precuneus and putamen together with hypoactivity in the angular gyrus and caudate within limbic-paralimbic networks. These alterations show strong associations with anxiety severity, suggesting ALFF as a potential noninvasive biomarker for HSE-related functional and neuropsychiatric impairments. Larger longitudinal studies are warranted to elucidate underlying mechanisms and therapeutic implications.

PMID:42599335 | DOI:10.1007/s11682-026-01192-0

iScience. 2026 Aug 3;29(8):117015. doi: 10.1016/j.isci.2026.117015. eCollection 2026 Aug 21.

ABSTRACT

Long-term exposure to a high-altitude (HA) hypoxic environment induces cognitive impairments, yet the underlying temporal mechanisms remain elusive. This longitudinal study investigated brain functional alterations associated with cognitive changes in 49 college freshmen relocated from sea level to Tibet, with comprehensive cognitive assessments and magnetic resonance imaging (MRI) at baseline and 2- and 4-year follow-ups. Resting-state fMRI quantified changes in regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF)/fractional ALFF (fALFF), static functional connectivity (sFC), and dynamic FC (dFC). Behavioral data confirmed persistent cognitive deficits, while neuroimaging analyses revealed biphasic patterns (initial suppression then partial/full recovery) in ReHo, ALFF/fALFF, and sFC. Notably, dFC variability in the right orbital middle frontal gyrus (ORBmid.R) and Heschl's gyrus (HES.R) increased at 2 years and remained elevated, with this alteration strongly correlated with cognitive changes. Our findings highlight that elevated dFC variability in two brain regions is a key contributor to chronic hypoxia-induced cognitive impairments.

PMID:42598111 | PMC:PMC13470281 | DOI:10.1016/j.isci.2026.117015