Background There are notable sex differences in the symptoms and treatment response of late-life depression (LLD); however, the underlying static and dynamic abnormalities in brain function that may drive these disparities remain unclear. This study was to investigate sex-specific aberrant brain activity in LLD.

Methods We recruited 75 LLD patients and 164 healthy controls (HCs). Static and dynamic metrics of amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and functional connectivity (FC) were compared across four groups (LLD-female, LLD-male, HC-female, and HC-male). Correlation and moderation analyses were then used to examine whether sex moderated the associations between brain activity, cognitive impairment, and depressive symptoms.

Results First, significant interaction effects between diagnosis (LLD vs. HCs) and sex were found for ALFF in the left paracentral lobule, ReHo in the right superior temporal gyrus, and static FC (sFC) between the right superior temporal gyrus and left middle frontal gyrus. Second, in LLD-female, ReHo (right superior temporal gyrus) and sFC (right superior temporal gyrus–left middle frontal gyrus) correlated with weight, and ALFF (left paracentral lobule) correlated with visuospatial skills. Third, sex significantly moderated the relationships between ReHo (right superior temporal gyrus) and cognition, ALFF (left paracentral lobule) and depressive symptoms, and sFC (right superior temporal gyrus–left middle frontal gyrus) and depressive symptoms in the LLD group.

Conclusion Our study highlights sex differences in static brain activity related to cognitive impairment and depressive symptoms in LLD, indicating sex-specific neurobiological underpinnings for this disorder.

Although repetitive transcranial magnetic stimulation (rTMS) has been widely used in the treatment of post-stroke hemiparesis, its efficacy in restoring fine motor function of the hand remains limited, largely because of a lack of specificity in target selection. This study presents the case of a 61-year-old female chronic stroke patient who showed significant improvement in hand fine motor function following an 8-week intervention using function-specific rTMS guided by functional magnetic resonance imaging (fMRI). The patient previously experienced multiple lacunar infarctions in the bilateral basal ganglia and centrum semiovale and received the intervention 4 years after the stroke. Following treatment, substantial improvements were observed in fine motor function, with electrophysiological and MRI findings indicating a trend toward restored interhemispheric functional balance. This case highlights a novel therapeutic approach for patients who have shown limited responses to conventional rehabilitation.

Background Major depressive disorder (MDD) is associated with dysregulation of γ-aminobutyric acid (GABA) and glutamate (Glu) neurotransmission in the prefrontal cortex. Proton magnetic resonance spectroscopy (¹H-MRS) enables non-invasive in vivo quantification of GABA and Glx (glutamate + glutamine) levels. This study investigated neurochemical characteristics of the bilateral dorsolateral prefrontal cortex (DLPFC) in first-episode adolescent MDD (FEA-MDD) and repetitive transcranial magnetic stimulation (rTMS)'s impact on these changes.

Methods 42 drug-naïve FEA-MDD patients underwent bilateral DLPFC MRS scans before and after rTMS, with 42 healthy controls (HCs) as baseline. All participants were right-handed. The Mescher–Garwood point-resolved spectroscopy (MEGA-PRESS) protocol detected GABA⁺ (GABA plus macromolecules and high carnosine) and Glx levels, processed via Gannet software.

Results FEA-MDD patients exhibited significantly lower GABA⁺ and higher Glx levels in the left DLPFC than HCs; in the right DLPFC, no significant difference in GABA⁺ levels was observed, though Glx levels were elevated. After rTMS treatment, GABA⁺ levels in the left DLPFC increased significantly, whereas Glx levels showed a non-significant decreasing trend. Additionally, HCs had no hemispheric differences, while in FEA-MDD, the left DLPFC showed lower GABA⁺ and Glx levels compared to the right. We also found that in the left DLPFC, baseline GABA⁺ levels were negatively correlated with Hamilton Depression Scale (HAMD) scores; Glx levels showed positive correlations with scores on the Ruminative Response Scale (RRS), Self-Rating Depression Scale (SDS), and Self-Esteem Scale (SES).

Conclusions FEA-MDD involves prefrontal GABA⁺/Glx dysregulation, and rTMS may aid in restoring neurotransmitter balance within the DLPFC. This study adds to the expanding body of evidence supporting the application of targeted neurochemical modulation in the treatment of FEA-MDD, while also providing insights into potential intervention mechanisms.

We read with great interest the study by Tang et al. ( 2025 ), which investigated how growing up without siblings (GWS) affects adult brain structure, function, and behavior using a large-scale dataset. Their findings challenge long-standing stereotypes about only-children by reporting positive associations between sibling absence and neurocognitive outcomes as well as mental health indicators. Importantly, their work went beyond simple association analysis by incorporating proximal environmental exposures (PEEs), including multiple socioeconomic and family environmental factors, which they showed mediated most of the observed brain–behavioral associations. This multidimensional design represents a major step forward in understanding how social structure interacts with neurodevelopment. However, we argue that the interpretation of these findings requires careful consideration of the historical and demographic context of the sample, specifically the heterogeneity in the enforcement of China’s one-child policy (1979–2015). Rather than representing a random distribution of family sizes, sibling status in China during this period was strongly structured by socioeconomic and geographic factors. Urban families, particularly those employed in government or state-owned sectors, faced strict enforcement, making them far more likely to have only one child (Hesketh et al., 2005; Li et al., 2008). In contrast, rural families and informal labor populations had greater flexibility in practice, especially when the firstborn was female (Hesketh et al., 2005; Li et al., 2008). As a result, being an “only child” during the period of the one-child policy was not simply a family preference or social circumstance, but rather it was often an outcome shaped by state regulation and socioeconomic opportunity. Tang et al. ( 2025 ) themselves reported robust group differences between only children and those with siblings in family socioeconomic status (SES) indicators, including parental education and occupation, income-to-needs ratios, family resources, and particularly urbanicity scores (pre-adult urban living). These factors formed core components of the PEE latent variables (family SES and city-level SES) and were shown to significantly mediate the associations between GWS and brain and behavioral outcomes. In fact, their own causal mediation analysis demonstrated that a large proportion of the variance initially attributed to GWS was explained by SES-linked environments. This is not a peripheral result; instead it is central to the interpretation of their study. SES-related environments are not simply mediators of sibling effects but also shape the likelihood of being an only child in the first place due to policy-dependent enforcement. Thus, SES cannot be treated exclusively as an intermediate pathway; it may also function as an antecedent determinant of sibling status (Fig. 1). Tang and colleagues acknowledge one key limitation: they were unable to collect data on why participants were only-children. This prevents disentangling policy-related, socioeconomic, cultural, and personal motivations underlying family size. Acknowledging this limitation is important, as it shifts the interpretation of their findings away from individual or familial choice and towards historically constrained demographic structure. To isolate the developmental effects of GWS more rigorously from simple SES-related influences (Yaple and Yu, 2020), future studies could benefit from several methodological extensions. For example, stratified analyses across urban and rural cohorts, or across birth cohorts differing in policy exposure, would help reduce SES confounding by creating more comparable subgroups. Natural experiment approaches exploiting regional variation in policy enforcement or implementation intensity could further strengthen causal inference. In addition, sensitivity analyses testing the robustness of GWS effects after jointly residualizing multiple SES indicators would help assess the extent to which results depend on SES adjustment strategies. Finally, while the authors discussed potential policy implications, caution is needed to translating these findings to contemporary demographic policy. China, now facing declining birth rates, has shifted from decades of birth restriction to encouraging larger families. In this context, findings about only-child development may be misinterpreted as evidence against the value of sibling relationships (Whiteman et al., 2011; McHale et al., 2012) (see also (Rolan et al., 2018). Instead, both Tang et al.’s ( 2025 ) data and our argument support a different message: developmental outcomes are shaped not by sibling number alone, but by the socioeconomic and caregiving environments in which children grow up. Future research should continue to disentangle these intertwined influences to inform family and education policy more responsibly.

We aimed to evaluate how the AIR™Recon DL algorithm influences magentic resonance imaging (MRI) quality and quantitative brain morphometry relative to conventional reconstruction (CR). Seventy-four healthy adults underwent 3D T1-weighted MRI reconstructed with CR and AIR™Recon DL. Image quality was rated by two neuroradiologists (κ = 0.74–0.97). Voxel-based morphometry assessed total, gray matter (GM), white matter (WM), and cerebrospinal (CSF) volumes; surface-based morphometry analyzed cortical thickness, sulcal depth, fractal dimension, and gyrification across 148 regions. Hippocampal volumes were extracted using the Neuromorphometrics atlas. Reconstruction times were compared. AIR™Recon DL significantly improved image quality (reduced noise and artifacts, P < 0.001) but introduced systematic morphometric shifts-smaller total and WM volumes, larger GM and CSF volumes, and widespread regional thickness increases (effect sizes d ≈ 0.3–0.5). Hippocampal volumes increased bilaterally (ΔL = + 0.15 mL, + 3.97%; ΔR = + 0.15 mL, + 3.88%; both P < 0.05). Mean reconstruction time was longer for deep learning-based reconstruction (11.6 ± 1.6 s) than CR (9.9 ± 1.4 s; Δ = + 1.7 s, P < 0.001). AIR™Recon DL enhances image quality but causes modest, systematic volumetric biases. Harmonizing reconstruction methods is essential for reliable morphometric comparisons in neuropsychiatric imaging.

Emotion regulation (ER) research has, for decades, focused on mental health outcomes, such as emotional recovery and well-being reinstatement, in behavioral, physiological, and neural measures across different regulatory strategies. Although important, the practical significance of ER research should not only be limited to mental health, but also needs to consider aiding people’s real-time adaptive behavior, to meet varying environmental demands or goals flexibly. In this paper, we propose an idea of ER adaptiveness that pays equal attention to both mental health outcomes, and how an ER strategy may be used to facilitate functional adaptiveness in meeting distinct goals. For instance, research of ER adaptiveness needs to highlight how to design regulatory strategies for the purpose of promoting cognitive, behavior, or social functions (nonaffective goal) in addition to that of affective wellbeing, and how to help a learned strategy work flexibly in changing contexts (affective goal). Lastly, taking application in sport psychology for example, we propose potential directions of how ER adaptiveness research may help participants to improve motor performance in competitive sports.

Background Chronic insomnia disorder (CID) is associated with disrupted functional brain networks, yet prior research has focused primarily on group-level analyses. This study employed personalized functional network mapping to identify connectivity abnormalities in CID.

Methods Resting-state functional magentic resonance imaging (rs-fMRI) data were collected from 86 CID patients and 38 good sleeper controls (GSCs). Using non-negative matrix factorization (NMF), we derived individualized large-scale brain networks for each participant to uncover subject-specific connectivity changes in CID. We also constructed functional network connectivity (FNC) matrices using Pearson correlation coefficients and compared global and local graph-theory metrics across groups based on these individualized networks.

Results FNC analysis revealed significant differences between CID patients and GSCs within the default mode network (DMN), ventral attention network, visual network (VIS), and other key brain regions. CID exhibited altered global network topology and significant differences in local topological properties. At the global level, CID demonstrated significantly higher small-worldness (Sigma) and normalized clustering coefficient (Gamma). At the nodal level, CID showed increased local efficiency and clustering coefficient, as well as decreased nodal efficiency in the DMN, along with increased degree centrality in the VIS.

Conclusion By focusing on individualized functional connectivity, this approach reveals unique “fingerprint” alterations in CID. These findings provide novel insights into CID’s neurobiological mechanisms and underscore the value of personalized network approaches for understanding and treating sleep disorders.

Background Late-life depression (LLD) and odor identification (OI) dysfunction are risk factors for dementia, but the underlying neural mechanisms remain unclear. This study investigated dynamic functional connectivity (dFC) in olfactory brain regions of LLD patients with and without OI dysfunction and examined how dFC moderates the OI–cognition link.

Methods Resting-state functional magnetic resonance imaging data were acquired from 51 LLD patients with OI deficits (LLD-OID), 59 LLD patients without deficits (LLD-noOID), and 51 healthy controls (HC). A sliding-window approach (50 TR width, 1 TR step) was used to estimate dFC variability between the orbitofrontal cortex (OFC) and the whole brain. Bayesian regression and moderation analyses assessed associations among OFC dFC, OI scores, and cognitive measures. Results were robust across window sizes.

Results Compared to LLD-noOID and HC, LLD-OID showed decreased OFC–left inferior frontal gyrus dFC variability (P < 0.01) and increased OFC–right middle frontal gyrus (MFG) variability (P < 0.001). Higher OFC–MFG variability was associated with worse OI and cognitive performance and significantly moderated the OI–global cognition relationship (β = 1.06, P = 0.027, 95% CI [0.12, 2.0]). No group differences were found in primary olfactory regions.

Conclusion LLD patients with OI dysfunction exhibited more disrupted dFC in secondary olfactory regions compared with those without OI dysfunction. Dynamic OFC–MFG disconnectivity may index vulnerability to cognitive decline and dementia risk in LLD patients.

Background Although language is traditionally regarded as unique to humans and predominantly left-lateralized in the brain, the dynamic interplay between cerebral hemispheres during language processing remains poorly understood.

Methods Using 400 functional magnetic resonance imaging scans acquired with a 7T scanner under diverse narrative stimuli, this study examined whole-brain functional dynamic lateralization patterns during Chinese language processing and explored potential sex differences.

Results We identified two distinct dynamic lateralization states. While core language regions consistently showed left-lateralization, other brain regions displayed reversed lateralization. These two states-characterized by higher-level functional regions lateralizing either left or right-corresponded to the processing of rational and emotional content, respectively. Notably, males showed a stronger tendency toward the former state, whereas females inclined toward the latter, particularly during the processing of rational content. Genetic analyses further suggested that sex differences in these lateralization states may be influenced by sex hormones.

Conclusion This study offers novel insights into the dynamic organization of cerebral lateralization during Chinese language processing.

Psychiatric disorders are complex, disabling conditions that continue to rely on subjective diagnostic criteria due to the absence of objective biological markers. Neuroradiology has become a critical discipline for examining the structural, functional, and biochemical underpinnings of these disorders through advanced brain imaging. This review synthesizes findings from five major psychiatric conditions including major depressive disorder, schizophrenia, autism spectrum disorder, obsessive-compulsive disorder, and generalized anxiety disorder, and briefly discusses the behavioral variant of Alzheimer’s disease, a variant with neuropsychological overlay, across multiple imaging modalities, including structural magnetic resonance imaging (MRI), diffusion tensor imaging, functional MRI, magnetic resonance spectroscopy, functional near-infrared spectroscopy, and positron emission tomography. We present a comparative overview of cross-condition and modality-specific findings, highlighting converging disruptions in frontolimbic and temporoparietal circuits, alongside unique neurobiological features in each disorder. We also acknowledge key confounds such as medication effects, comorbidities, and methodological variability that limit direct transdiagnostic inference. We further discuss methodological limitations, emerging trends such as multimodal integration and machine learning, and future directions for translating imaging data into clinically meaningful biomarkers.

Background Previous studies have reported accelerated brain aging in individuals with major depressive disorder (MDD) compared to healthy controls. However, these findings are based primarily on cross-sectional data, limiting dynamic association between brain aging and MDD. Here, we examined the relationship between brain aging and MDD progression by focusing on subthreshold depression, a prodromal stage of MDD, and aimed to determine whether quantitative markers of brain aging exhibit a stable association with disease progression.

Methods Using neuroimaging data from the UK Biobank and a support vector regression (SVR) model, we predicted brain age in individuals who exhibited subthreshold depressive symptoms at baseline but showed divergent mental status at follow-up, and then conducted between-group comparisons. Logistic regression was then applied to assess whether brain-predicted age difference (Brain-PAD) stably associates with the progression of subthreshold depression after adjusting for covariates.

Results Individuals with subthreshold depression showed a higher risk of progression to MDD compared to healthy controls. Those whose condition worsened from subthreshold depression to MDD exhibited greater brain aging than those who remained subthreshold or recovered. Importantly, Brain-PAD remained significantly and stably associated with this progression after controlling for sex, ethnicity, lifestyle, and socioeconomic factors.

Conclusions This study supports an association between brain aging and MDD progression and demonstrates a robust association between an increased Brain-PAD and the conversion from subthreshold depression to MDD. These findings enhance our understanding of MDD’s developmental trajectory and suggest that Brain-PAD may facilitate early detection and intervention targeting brain aging.

Background While internet gaming disorder (IGD) correlates with regional brain responses and functional connectivity, the brain network architecture underlying addiction severity remains poorly characterized.

Methods Using resting-state functional magentic resonance imaging data and addiction severity metrics from 586 participants (443 IGD, 143 recreational game users), we employed connectome-based predictive modeling (CPM) with leave-one-out cross-validation to identify neural networks predictive of IGD severity. The resulting network was evaluated for replicability in independent datasets, with key predictive networks and nodes further analyzed.

Results CPM identified a replicable addiction severity network. CPM significantly predicted individual gaming addiction scores (r = 0.19, P < 0.001), with features selected using a threshold of P < 0.01. Predictive power primarily derived from internet-work connectivity linking the subcortical, subvisual, and frontoparietal networks. Validation in independent data showed a directional trend (r = 0.17, P = 0.011).

Conclusions Individual variability in subcortical–subvisual–frontoparietal network connectivity predicts IGD addiction severity, highlighting these circuits as potential targets for neuromodulation interventions.

Cognitive disorders, with dementia as a primary exemplar, present profound diagnostic and therapeutic challenges due to their complex pathologies and heterogeneous presentations. Artificial intelligence (AI), particularly when applied to multimodal neuroimaging and clinical data, offers a powerful approach to advancing precision medicine in this domain. This comprehensive review first examines foundational AI algorithms, including artificial neural networks for feature extraction, multimodal fusion strategies (e.g. early, intermediate, and late fusion) for data integration, and explainable AI (XAI) techniques to enhance clinical transparency. The core focus is on the application of these multimodal AI frameworks across the dementia care continuum, encompassing improved differential diagnosis, early detection through presymptomatic biomarkers, development of predictive models for disease progression, and optimization of patient stratification for clinical trials. Despite significant advances, persistent challenges remain, including limited generalizability across populations and protocols, data scarcity for non-Alzheimer’s dementias and prodromal stages-exacerbated by demographic biases-and barriers to interpretability. We discuss solutions such as federated learning for privacy-preserving data sharing and advanced XAI techniques. Finally, we outline pivotal future directions, including intelligent sensor fusion for discovering novel early biomarkers, hybrid AI architectures combining generative and discriminative models, innovations for handling missing modalities, and robust multicenter data integration frameworks. By synthesizing these advances, this review highlights the role of multimodal AI in advancing precise diagnosis, early prediction, and therapeutic development for neurodegenerative and vascular cognitive disorders, while identifying key translational challenges for precision medicine.

Background Schizophrenia is a severe psychiatric disorder characterized by cognitive deficits as well as positive and negative symptoms. It is considered a disorder of widespread network dysconnectivity, including aberrant connectivity between the thalamus and the visual pathway. However, the relationships between the thalamus and various regions of the dorsal and ventral visual pathways in schizophrenia, and how the thalamus affects interactions among these visual regions, remain unclear.

Methods Resting-state functional magnetic resonance imaging, task-state functional magnetic resonance imaging, and diffusion tensor imaging data were acquired to examine the neural activity within the thalamus and the visual pathway, along with the relationships between them (i.e. functional connectivity, structural connectivity, and structure–function coupling). We also correlated the altered imaging parameters with clinical characteristics. Furthermore, based on previous molecular imaging in healthy controls, we explored the spatial associations between altered imaging parameters and receptor/transporter distributions.

Results We found significantly decreased neural activity and widespread altered thalamo-visual pathway connectivity in both dorsal and ventral pathways in schizophrenia patients. Moreover, schizophrenia patients exhibited altered mediation effects within the thalamo-dorsal visual pathway, involving MT, V1, V2, and V3. Abnormal neural activity and connectivity were related to disease duration and positive symptom severity. Altered neural activity of MT was correlated with the density of multiple neurotransmitters.

Conclusions Our findings further expand our understanding of thalamo-visual pathway dysconnectivity and primary information-processing deficits in schizophrenia, which may be related to clinical symptoms. Our findings may provide more potential insights for non-invasive intervention treatments.

Background Major depressive disorder (MDD) in adolescents and young adults is increasingly prevalent, yet accurate diagnosis remains challenging due to the limitations of conventional neuroimaging metrics. Traditional resting-state functional magnetic resonance imaging (rs-fMRI) measures such as amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional connectivity density (FCD) primarily capture static aspects of brain activity and may overlook critical neural dynamics. Brain entropy (BEN), which quantifies temporal irregularity in rs-fMRI signals, may offer a complementary approach to better characterize neural alterations in MDD.

Methods We analyzed multimodal rs-fMRI data from 204 individuals aged 12–24 years (119 with MDD and 85 healthy controls). BEN was computed alongside ALFF, ReHo, and FCD to extract region-wise features across the brain. A support vector machine with recursive feature elimination (SVM-RFE) was used to classify MDD and healthy controls based on various feature combinations. Classification performance was evaluated using repeated cross-validation and permutation testing. Additionally, partial Spearman correlations were performed between selected brain features and clinical measures including depression severity, childhood trauma, sleep quality, and cognitive control.

Results Models incorporating BEN consistently outperformed those using traditional rs-fMRI features alone. The combination of BEN, ALFF, and FCD achieved the highest classification accuracy (AUC = 0.877, permutation test P < 0.001). The most frequently selected brain regions contributing to MDD classification included the putamen, paracentral lobule, cuneus, middle frontal gyrus, and rectus. BEN features also showed preliminary correlations with clinical variables such as childhood trauma and sleep quality, suggesting functional relevance.

Conclusions This study demonstrates that BEN provides complementary diagnostic information to traditional rs-fMRI features in classifying adolescent and young adult MDD. BEN-related alterations in brain activity may reflect underlying neurobiological disruptions and show potential as a functional neuroimaging biomarker for depression during a critical stage of brain development.

Mental disorders refer to abnormal states that affect an individual’s thinking, emotion, behavior, and perception, and are generally associated with dysregulation of brain function. They exhibit genetic heterogeneity as well as multi-level structural and functional abnormalities of the brain. Magnetic resonance imaging has been widely applied to detect macroscopic neurophenotypic alterations in patients with psychiatric disorders; however, it remains limited in directly revealing the underlying molecular and cellular mechanisms. Imaging transcriptomics, by integrating whole-brain gene expression atlases with neuroimaging features, offers a novel paradigm for exploring the associations between microscopic genetic expression and macroscopic neuroimaging phenotypes. This review systematically summarizes the methodological framework of imaging transcriptomic association studies and highlights recent advances in their application to psychiatric disorders such as depression. A growing body of evidence has revealed spatial coupling between structural and functional abnormalities in disease-related brain regions and gene expression in synaptic transmission, ion channels, neurodevelopment, and immune signaling. Imaging transcriptomics not only facilitates a multiscale understanding of the pathophysiological mechanisms of psychiatric disorders but also provides potential pathways for disease classification, targeted intervention, and precision diagnosis and treatment. Future research should further promote the integration of longitudinal imaging omics and spatial transcriptomic data to construct translatable multimodal models, thereby accelerating the translation of psychiatric neuroimaging from mechanistic research to clinical application.

Autism spectrum disorder (ASD) involves alterations in social communication and restricted, repetitive behaviors. Emerging evidence highlights atypical self-awareness as a key factor in ASD-related social impairments. However, the neural mechanisms underlying differences in self-processing remain fragmented. This systematic review synthesizes findings from 49 functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) studies (2000–2025) to explore self-related brain networks in ASD, based on the hierarchical self-model comprising interoceptive, exteroceptive, and mental levels. Across all three levels, consistent atypicalities were observed in ASD. The interoceptive level (insula, thalamus) showed altered functional connectivity (FC) and gray matter density, associated with atypical bodily and affective self-awareness. The exteroceptive level, which includes the medial prefrontal cortex (mPFC), temporoparietal junction (TPJ), and premotor cortex (PMC), exhibited reduced long-range FC and local coherence, potentially reflecting atypical self–other differentiation and communication. The mental level, involving the anterior and posterior cingulate cortices (ACC and PCC), revealed decreased FC and interhemispheric coherence, implicating atypical reflective self-processing. Disrupted cross-level interactions further suggest a breakdown in hierarchical self-integration. These findings emphasize the importance of self-related network alterations in ASD and support their inclusion in neurocognitive models of autism.

Background The glymphatic system plays a critical role in cerebral waste clearance and has been implicated in neurodegenerative and cerebrovascular disorders. However, normative variations in glymphatic structure and function across age, sex, and hemispheric organization in healthy adults remain incompletely characterized.

Methods Using ultra-high-field 7.0T MRI, we quantitatively assessed structural and functional glymphatic markers in 80 healthy adults. Perivascular space (PVS) number and volume were measured in the basal ganglia (BG) and midbrain (Mid), and glymphatic function was evaluated using the diffusion tensor imaging-analysis along the perivascular space (DTI-ALPS) index. Participants were stratified by age and sex. False discovery rate (FDR) correction was applied to control for multiple comparisons.

Results Robust age-related effects were observed. Older participants exhibited significantly greater PVS burden in the BG and Mid and lower right-hemispheric DTI-ALPS indices compared with younger adults. Age correlated positively with PVS number and left BG PVS volume, and negatively with the right DTI-ALPS index. Several sex effects did not survive FDR correction but showed moderate-to-large effect sizes in age-stratified analyses, suggesting potential sex-by-age interactions. Hemispheric analyses revealed a consistent right-dominant PVS burden in the midbrain, evident across age groups, whereas BG PVS burden and DTI-ALPS indices were largely symmetric.

Conclusion These findings demonstrate that glymphatic structure and function in healthy adults are strongly influenced by aging, exhibit age-dependent sex-related trends, and show region-specific hemispheric asymmetry. This study provides high-resolution normative reference data for glymphatic imaging and informs future investigations of early or subclinical glymphatic dysfunction.

Background The human brain is intrinsically organized as canonical functional networks with distinct spatial topographies. While precision functional mapping studies have delineated individualized topographies of single networks, the spatial coordination among these networks and its developmental origin remains largely unknown.

Methods Utilizing three well-established task-free functional magnetic resonance imaging (fMRI) datasets encompassing both conventional and densely sampled scans across neonatal and adult cohorts, we proposed functional topography covariance analysis (FOCA), a novel framework that quantifies convergent and divergent spatial alignments across individualized functional networks and further delineated their internetwork relationships, neurobiological basis, ontogenetic layouts, and cognitive outcomes.

Results In adults, FOCA consistently revealed self-clustered and gradient-distributed functional hierarchies characterized by convergent couplings within primary systems and divergent couplings in higher-order systems. Such pattern was well predicted by fundamental neurobiological attributes, especially aerobic glycolysis. In a large public neonatal cohort, FOCA matrix exhibited adult-inverted hierarchical couplings and prominent changes in auditory and action-mode networks, driven primarily by redistributions of negative couplings. Moreover, neonatal FOCA profiles in the primary visual system significantly predicted neurodevelopmental outcomes at 18 months. Finally, compared with conventional functional connectivity, FOCA demonstrated greater robustness to the global signal and higher sensitivity to the maturation of negative couplings.

Conclusions These findings highlight the critical role of negative functional connectivity and deepen our understanding of the cooperative–competitive interactions among functional systems and their developmental origins.

Background Anxiety and depression share many symptoms, frequently leading to diagnostic uncertainty. They are also the most common co-occurring psychiatric disorders. Standard clinical evaluations typically diagnose each condition categorically rather than assessing their relative severity when both are present. Prior imaging studies largely concentrated on delineating similarities and distinctions between the two.

Methods To address this gap, we propose a novel noisy label learning method to investigate the biotypes and relevant neural characteristics among anxiety, depression, and their comorbidity. Our approach constructs a robust classification model to address potential diagnostic confusion among anxiety, depression, and comorbidity using a noisy label learning strategy, and employs the trained model to identify the anxiety and depression biotypes, as well as comorbidity biotypes characterized by differing degrees of anxiety and depression predominance.

Results Using brain functional network connectivity (FNC) from 502 depression patients, 245 anxiety patients, 177 comorbid patients with both anxiety and depression, and 500 healthy controls, we not only identify four distinct biotypes, but also reveal neural linkages between comorbid biotypes and depression and anxiety biotypes. Compared to the depression biotype (Biotype 1), the anxiety biotype (Biotype 2), and the comorbid biotype with an anxiety predominance (Biotype 3–2) demonstrate comparable alterations across seven FNCs, mainly including the connections between the cognitive control and visual domains. Relative to the anxiety biotype (Biotype 2), the depression biotype (Biotype 1), and the comorbid biotype with a depression predominance (Biotype 3–1) exhibit similar connectivity profiles across 16 FNCs, primarily involving connections within the cognitive control domain, as well as between the sensorimotor and visual domains. Furthermore, biotypes are characterized by distinct patterns of behavioral symptoms that are consistent with their underlying neural relationships.

Conclusions In summary, we propose a neuroimage-based model that addresses the diagnostic ambiguity between anxiety and depression and derive data-driven subtypes for promoting the precise diagnosis.

Background The overlapping symptoms between bipolar disorder (BD) and major depressive disorder (MDD) pose a challenge in diagnosis and treatment. A prevailing hypothesis suggests that mood dysregulation may be linked to impairments in the reward system, but the neurocomputational differences between BD and MDD remain elusive. This study investigates whether atypical reward processing affects subjective mood in adolescents with BD and MDD. Our research aims to elucidate the behavioral and neural differences between the two groups, facilitating more accurate and timely diagnosis and intervention.

Methods Forty-five adolescents (aged ≤ 19 years) diagnosed with BD-II in depressed mood states (N = 25) or MDD (N = 20) completed a risky gambling task while their brain responses were recorded using functional magnetic resonance imaging (fMRI). Several computational models were constructed to uncover the associations between various reward components (e.g. reward prediction errors, RPE) and trial-wise fluctuations in subjective mood during the task.

Results Adolescents with BD exhibited a lower best choice rate and a higher uncertain choice rate compared to those with MDD. Computational modeling and mediation analysis suggested a tripartite mediating relationship between RPE-mood association, decision rationality, and symptom severity. Using fMRI, we observed significant RPE-related activation in the ventral striatum, which showed a slight positive correlation with the RPE-mood association. We also noted subtle differences in several brain regions (i.e. medial orbitofrontal cortex) between the BD and MDD groups. These differences were further associated with manic symptoms.

Conclusion Decision rationality mediated the association between RPE-mood association and symptom severity. Relative to adolescents with MDD, those with BD showed decreased decision rationality, along with modest but distinct reward-related neural patterns on fMRI. These findings highlight the crucial role of reward processing in mood regulation and provide preliminary neurocomputational evidence that may inform future diagnostic biomarker development.

Cognitive reserve (CR) refers to the capacity of the brain to sustain cognitive performance despite age-related changes or pathophysiological conditions. Task-based functional magnetic resonance imaging (tb-fMRI) has been instrumental in exploring its neural correlates. CR is traditionally assessed through socio-behavioral proxies such as education, intelligence quotient, or composite indices. However, these proxies vary considerably in definition and application, posing challenges regarding their validity and consistency across experiments. This systematic review and meta-analysis examined whether these proxies are associated with consistent brain activation patterns in healthy adults. A literature search identified 12 eligible tb-fMRI experiments (n = 802 participants) reporting whole-brain CR-related activation. A coordinate-based meta-analysis using permutation of subject images-signed differential mapping (PSI-SDM) was conducted to assess consistent activation patterns across experiments, complemented by meta-regression analyses to examine whether differences in proxy type accounted for inter-study variability. The PSI-SDM meta-analysis yielded no significant clusters of activation. A qualitative synthesis of individual experiments further highlighted a lack of topographical consistency. These findings indicate that widely used socio-behavioral proxies are not associated with detectable, reproducible convergent patterns in tb-fMRI, revealing a measurement gap in current CR research. This null convergence likely reflects methodological heterogeneity and conceptual inconsistency in the literature, rather than the absence of CR-related neural mechanisms.

Ma Liyang’s team conducted a systematic investigation into the neurochemical underpinnings of first-episode adolescent major depressive disorder (FEA-MDD) and the mechanism of action of repetitive transcranial magnetic stimulation (rTMS). By recruiting a well-characterized cohort of medication-naive adolescents and using proton magnetic resonance spectroscopy (¹H-MRS) with the MEGA-PRESS (Mescher–Garwood Point-RESolved Spectroscopy) sequence, this study addresses a critical gap in our understanding of how rTMS acts on the adolescent brain in depression. The findings revealed prominent neurochemical dysregulation in the left dorsolateral prefrontal cortex (left-DLPFC), specifically reduced levels of the γ-aminobutyric acid complex (GABA+) and elevated levels of the glutamate–glutamine complex (Glx). Notably, these abnormalities were partially normalized in patients who responded to rTMS, providing preliminary neurobiological support for left-DLPFC excitatory/inhibitory (E/I) imbalance in adolescent depression and its modulation by rTMS.

Background In recent years, neuroimaging has emerged as a pivotal tool for investigating neurological and psychiatric disorders. Although numerous studies have demonstrated the diagnostic value of imaging in neurodegenerative diseases, including Parkinson’s disease (PD), there remains a scarcity of comprehensive review articles that consolidate this expanding field. This study aims to address that gap by employing bibliometric analysis to outline current research trends and predict future directions.

Methods A total of 4794 publications issued between 2015 and 2024 were retrieved from the Web of Science Core Collection database. Using CiteSpace, we conducted quantitative analyses of publications, authors, institutions, journals, countries, keywords, citations, and references. The results were visualized via network maps.

Results The analysis included 4794 publications contributed by 631 authors from 430 institutions across 81 countries. China and the USA were the most productive countries, followed by Germany. University College London was the most prolific institution and served as a key hub in inter-institutional collaborations. Phil Hyu Lee was identified as the leading author, and Movement Disorders was the most influential journal, while Parkinsonism & Related Disorders published the highest number of articles within the dataset.

Conclusion Current research is increasingly focused on leveraging imaging techniques to detect early pathological changes in PD, such as dopaminergic neuron loss, abnormal protein deposition, and altered brain connectivity. These efforts aim to identify accurate diagnostic biomarkers and elucidate underlying disease mechanisms. This bibliometric analysis offers a comprehensive overview of the applications of neuroimaging in PD research over the past decade and may help inform future studies and foster international collaboration efforts.

Background The hierarchical functional structure reflects the process of integrating primary information to form higher-order cognition in the human brain. Schizophrenia, as a severe chronic psychiatric illness, has been demonstrated to exhibit an abnormal cerebral functional hierarchical structure. Although repetitive transcranial magnetic stimulation (rTMS) of the dorsolateral prefrontal cortex has shown therapeutic efficacy on schizophrenia, the mechanism of rTMS in schizophrenia is still unclear. Thus, this study attempted to reveal the modulation of rTMS on the cerebral functional hierarchy and its potential molecular mechanism in schizophrenia.

Methods A longitudinal study was performed on 53 patients with schizophrenia (randomly assigned to the rTMS group or drug treatment group). Additionally, 24 age- and gender-matched healthy adults were recruited. Functional gradient analysis was executed to depict the hierarchical organization of schizophrenia. The alteration of the functional gradients at baseline and after 4 weeks of treatment was evaluated in schizophrenia to detect the effect of rTMS on the cerebral hierarchical structure.

Results We found rTMS alleviated the compression of the fronto-parietal network, the core node of the salience network, and the bilateral middle temporal areas in schizophrenia. Leveraging human brain gene expression data, we identified a spatial correlation between the expression of schizophrenia-related genes and impaired functional gradients, and further revealed a close association of the modulatory effects of rTMS with pathways related to neuroplasticity and neuroimmune processes. Furthermore, the alterations of gradient induced by rTMS were associated with the alleviation of clinical symptoms in schizophrenia, suggesting that the altered hierarchical structure may play an important role in the treatment of schizophrenia.

Conclusions Our findings indicate that the alteration of cerebral hierarchy induced by rTMS contributes to clinical symptom improvement in schizophrenia and provides new evidence for the efficacy of rTMS in the treatment of schizophrenia.

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

The investigation by Yu et al. represents a substantive contribution to the neurobiology of major depressive disorder (MDD) by transcending the conventional focus on white matter microstructure to characterize its intrinsic functional architecture (Yu et al., 2025). The authors provide robust evidence for a reconfiguration of the hierarchical organization within the white matter functional connectome in MDD, specifically a contraction of the principal superficial-to-deep gradient, which is further associated with clinical symptom burden. This work signifies an important conceptual evolution, framing depression not merely in terms of focal deficits but as a disorder of large-scale network integration and hierarchical segregation, extending gradient-based insights from prior gray-matter studies while building on the existing evidence of connectome-gradient dysfunction in gray matter (Xiaet al., 2022). The implications of these findings, alongside certain methodological considerations, merit detailed scholarly reflection. Our commentary weighs the strength of this inference, identifies the methodological choices that matter most, and outlines how the approach can be tested and extended.

Introduction Parental socioeconomic status (SES) is associated with children’s brain development and mental health but does not fully capture variation in caregiving environments. Household structure, including the number of caregivers in the home, may also shape children’s experiences through differences in supervision, support, and daily demands. Because household structure and SES are closely linked, it remains unclear whether differences in child outcomes reflect economic conditions, caregiving context, or both. This study examined whether household structure is associated with psychopathology symptoms and brain structure after accounting for SES.

Methods Participants included 7890 children (ages 9–10) from the Adolescent Brain Cognitive Development Study living in households with one or two caregivers. Associations between household structure, psychopathology, and gray matter volume were examined while controlling for age, sex, site, family income, and parental education. Sensitivity analyses included additional caregiving and mental health factors. Mediation analyses tested whether total cortical and subcortical volumes were associated with the relationship between household structure and psychopathology. For all analyses, multiple comparisons were controlled using false discovery rate correction.

Results Households with two caregivers had, on average, higher income and parental education. After adjustment for SES and covariates, differences were observed in externalizing behaviors and attention-related symptoms. Small but widespread differences in regional brain volumes were also observed and remained robust in sensitivity analyses. Total cortical and subcortical volumes were associated with both household structure and behavioral outcomes, with small but significant indirect effects indicating significant mediation.

Discussion Although effect sizes were small, findings suggest that aspects of caregiving context captured by household structure are associated with child development above and beyond SES. Brain structure differences may represent one pathway linking caregiving context to behavioral outcomes, though effects were modest. Further work is needed to identify specific, modifiable processes underlying these associations.

Mitochondrial dysfunction and neuroinflammation are two fundamental pathological processes underlying the development of psychiatric disorders. These processes are interconnected and may jointly contribute to disease progression. This article systematically reviews the progress of multimodal imaging approaches for assessing mitochondrial function, including magnetic resonance spectroscopy, PET tracers targeting mitochondrial complexes, MRI-based cerebral metabolic rate of oxygen, and emerging techniques such as T1ρ imaging and manganese-enhanced MRI, as well as neuroinflammation, including translocator protein (TSPO) PET, monoamine oxidase B PET, novel tracers targeting C3aR/IL-1β, and diffusion imaging techniques reflecting white matter microstructural alterations. These approaches have been applied to various psychiatric disorders, including schizophrenia, major depressive disorder, bipolar disorder, post-traumatic stress disorder, autism spectrum disorder, sleep disorders, and substance use disorders. Based on imaging evidence across different psychiatric disorders and cohorts, this review summarizes three transdiagnostic imaging features: impaired mitochondrial energy metabolism, alterations in glial cell immune status, and white matter microstructural alterations associated with neuroinflammation. However, although individual imaging techniques have independently identified mitochondrial dysfunction and neuroinflammation, few studies have applied dual-target imaging within the same research cohort. The direct relationship between these processes remains unverified byin vivo studies. This review outlines the shared imaging features of various psychiatric disorders and examines the utility of multimodal imaging techniques in investigating mitochondrial and neuroinflammatory pathologies. It aims to provide new insights into the diagnosis and treatment of psychiatric disorders.

Background Transcranial Magnetic Stimulation targeting the dorsolateral prefrontal cortex (DLPFC) is a therapeutic approach for treating depressive disorder. However, traditional anatomical methods often fail to localize a treatment target. The functional connectivity method represented by the Stanford Neuromodulation Therapy (SNT) has been proposed and demonstrated efficacy. A step of SNT is to achieve DLPFC target localization using resting-state functional magnetic resonance imaging (rs-fMRI) data, based on measurement of the negative correlation coefficient between the functional connectivity of DLPFC and the subgenual anterior cingulate cortex (sgACC).

Methods This study has three goals. Firstly, to apply the SNT protocol to investigate variability in target localization in four groups (Human Connectome Project [HCP], China Human Connectome Project [CHCP], major depressive disorder [MDD] from Huaxi Depression Imaging Cohort Study [HX-DICS], and healthy controls [HC] from HX-DICS) from three databases. Secondly, to investigate potential differences in how the DPABI and FSL rs-fMRI pre-processing pipelines, particularly the spatial smoothing, denoising, and temporal filtering steps, affect the localization of individualized DLPFC targets. Thirdly, to assess the effect of rs-fMRI scan duration (ranging from 6 to 30 min) on target localization.

Results Significant inter-subject variability in DLPFC coordinates was found, emphasizing the necessity of individualized targeting. DPABI and FSL pre-processing produced significant differences in target coordinates, which exceeded 1 cm, with mean Euclidean distances ranging from 1.45 to 3.82 cm across cohorts, and individual maximum deviations reaching 6.14 cm. FSL pipeline consistently produced higher negative correlation coefficients than DPABI. Scan duration analysis indicated that after 12 min, the stability of individual target positions no longer improved significantly with prolonged scanning time, suggesting that approximately 12 min may provide sufficiently reliable data for target computation.

Conclusion Taken together, findings demonstrate that advancing precision neuromodulation therapy requires optimized data acquisition, standardized workflows, software-specific validation, and optimum scan duration.

Background: Adolescence is a critical period for brain network remodeling and the onset of major depressive disorder (MDD); however, white matter (WM) functional topology in adolescent MDD remains underexplored. Given that WM functional signals reflect meaningful neural activity and are disrupted in psychiatric disorders, this study aimed to characterize WM functional connectome alterations in adolescents with MDD and examine their clinical associations.

Methods: Resting-state fMRI data were obtained from a cohort of adolescents with MDD (n = 320) and healthy controls (HCs, n = 144), as well as from an independent replication cohort. Following the construction of thresholded WM functional networks, graph-theoretical analyses were used to calculate global topological properties. Canonical correlation analysis (CCA) was used to examine associations between topology and clinical symptoms, while exploratory classification assessed their discriminative information and generalizability. Furthermore, subgroup analyses were conducted to evaluate the effects of a history of suicide attempt, non-suicidal self-injury, childhood trauma, and sex.

Results: Compared with HCs, adolescent MDD exhibited significant reductions in the clustering coefficient, characteristic path length, and local efficiency. CCA identified distinct covariation patterns: reduced global integration was linked to severe suicidal ideation and depressed mood, while impaired local segregation was associated with vegetative symptoms such as weight loss and insomnia. Subgroup analyses revealed significant sexual dimorphism, with male patients demonstrating more severe topological impairments than females. A similar pattern was observed in the independent replication cohort. The classification analysis achieved above-chance accuracy (69.6 and 60% in the two cohorts).

Conclusions: Our results reveal a topologically shifted WM functional connectome structure in adolescent MDD, providing new clues to aid in understanding the pathophysiology of its pathophysiology.

Background Eye gaze provides crucial insights into perceptual and cognitive processes during naturalistic movie viewing, yet concurrent eye tracking is often unavailable in functional MRI (fMRI) research. While deep learning models can estimate gaze directly from fMRI eyeball signals, their out-of-the-box generalizability across heterogeneous datasets requires empirical evaluation.

Methods We applied a specific pre-trained model from the DeepMReye framework in a zero-shot setting (without dataset-specific fine-tuning) to estimate gaze during movie watching across three independent fMRI datasets. Model accuracy was evaluated against camera-based eye-tracking data and via inter-subject correlations. Furthermore, we derived eye-movement-related time series from the predicted gaze signals to map their associated brain activation.

Results At the individual level, predicted gaze showed modest correspondence with measured ground-truth data (r ≈ 0.24–0.37), yielding brain activation maps largely restricted to the visual cortex. In contrast, group-averaged gaze predictions exhibited substantially higher reliability (r ≈ 0.73–0.84). First-level general linear models (GLMs) derived from group-averaged predictions successfully revealed widespread activation across established oculomotor control regions, including the frontal and parietal eye fields. Exploratory analyses of age-related effects on gaze prediction and brain activity yielded inconsistent results across datasets.

Conclusions Under a zero-shot implementation, the pre-trained model exhibits limitations for individual-level inference, likely reflecting the absence of dataset-specific training. However, group-averaged fMRI-based gaze estimates successfully capture shared viewing behaviors and robustly support the investigation of eye-movement-related brain activity. These findings inform the appropriate use of fMRI-based gaze decoding for naturalistic neuroimaging datasets lacking ground-truth eye-tracking logs.

Background Suggestibility refers to an individual’s tendency to accept, believe, and act on information or ideas presented by others with limited critical evaluation or independent judgment. It reflects the extent to which a person’s behaviors, choices, thoughts, and memories can be shaped by external prompts, leading questions, or social influences. Despite its relevance to human behaviors, its neuropsychological basis remains poorly understood. In this study, we examined the behavioral, structural, and functional neural correlates of suggestibility in the domains of working memory and attentional control.

Methods We employed the validated Multidimensional Iowa Suggestibility Scale (MISS) to measure the suggestibility of 42 healthy female participants. They were classified into the high suggestibility (HS) (n = 21) and low suggestibility (LS) (n = 21) groups based on their MISS scores. All participants underwent fMRI while performing working memory (N-back) and attentional control (Flanker) tasks, in addition to structural and resting-state scans.

Results Behaviorally, the HS group exhibited reduced reaction time interference in the Flanker task, suggesting enhanced attentional control. Voxel-based morphometry revealed greater gray matter volume in the right superior frontal gyrus and left postcentral gyrus among HS participants. Task-based activation analyses showed increased activation in frontal, parietal, and temporal regions in the HS group. Resting-state regions of interest (ROI)-to-ROI connectivity analysis revealed a significant increase in connectivity between the salience network (right supramarginal gyrus) and default mode network (right lateral parietal cortex) in the HS group.

Discussion Our findings indicate that high and low levels of suggestibility are associated with distinct patterns of intrinsic functional connectivity. Also, suggestibility modulates attentional control, brain structure, and intrinsic functional connectivity. These findings provide important translational insights for applying suggestibility in therapeutic contexts. Future research should continue to elucidate the mechanisms underlying suggestibility and explore how these traits may be leveraged to enhance interventions across diverse populations.

This is a correction to: Ruoxi Lu, Jianyu Li, Yiran Li, Xinglin Zeng, Yan Guo, Danian Li, Ying Cui, Xinyu Liang, Hanyue Zhang, Jing Wang, Baohua Cheng, Yujie Liu, Ze Wang, Shijun Qiu, Brain entropy as a biomarker of major depression in adolescents and young adults: insights from multimodal resting-state functional magnetic resonance imaging, Psychoradiology, Volume 6, 2026, kkag009, https://doi.org/10.1093/psyrad/kkag009. In the originally published version of this manuscript, the word ‘magnetic’ was misspelled in the title. This error has been corrected.

Psychology has accumulated numerous ‘X-anxieties’-math, test, foreign-language, public-speaking, driving, health, and others-each with its own scale and specialist literature. Yet these experiences often share a phenomenological core of anticipatory worry, bodily arousal, catastrophic simulation, and avoidance. This review considers whether many such labels index context-bound expressions of common threat-regulation processes rather than independent mechanisms. A mechanism-first account is developed from experience to process to biological implementation: threat appraisal under uncertainty, interoceptive inference, and attentional control are proposed as content-general operations through which diverse cues recruit a conserved defensive architecture. Human neuroimaging, psychophysiology, and comparative evidence are broadly compatible with this account but do not establish mechanistic identity. In particular, non-significant cross-domain differences cannot demonstrate equivalence without formal equivalence testing or Bayesian evidence, and the diphasic autonomic response in blood–injection–injury phobia constitutes a genuine boundary case, suggesting that a shared appraisal core may feed more than one effector profile. The shared-process account is therefore treated as a parsimonious, falsifiable hypothesis. It generates predictions and boundary conditions for cross-context neural generalization, autonomic equivalence, reliable cognitive transfer, and latent structure, together with designs capable of distinguishing common processes from domain-specific mechanisms. Classifying anxieties by process rather than label could improve measurement, sharpen mechanistic tests, and guide transdiagnostic interventions while avoiding premature reification of context-specific constructs.

Background Elite racket sports demand visuomotor responses at the millisecond level, requiring rapid prioritization of task-relevant information while suppressing competing or redundant signals. Such perceptual filtering is reflected in spatial suppression, which declines with aging and in clinical cognitive disorders. This study investigated whether visual spatial suppression in expert table tennis players (TTPs) was linked to visuomotor reaction time, and whether prefrontal–visuomotor dynamics were associated with this link.

Methods We examined 28 expert TTPs and 31 healthy controls using 7-Tesla MRI and psychophysical methods. Specifically, behavioral measures included visual spatial suppression and visuomotor reaction time (RT); an attentional cueing paradigm was used to quantify criterion shifts. Brain measures included gray matter volume (GMV) and static versus dynamic functional coupling between the right inferior frontal gyrus (rIFG) and visuomotor cortex.

Results TTPs showed faster visuomotor reaction times and stronger spatial suppression than healthy controls (P < 0.001 and P = 0.007, respectively). Within TTPs, faster RTs were associated with stronger suppression. Structurally, GMV in rIFG tracked suppression and attentional criterion shifts (c: 0.371 ± 0.086 vs 0.034 ± 0.079; P = 0.006). Functionally, dynamic (but not static) coupling between rIFG and the left postcentral gyrus (lPoCG) was associated with suppression strength (large-grating thresholds, partialr = 0.595, P = 0.002) and visuomotor RT (partial r = −0.436, P = 0.033) and statistically mediated the relationship between suppression and RTs.

Conclusion This study demonstrates the association between visuomotor reaction speed and spatial suppression in TTPs, and identifies rIFG–lPoCG dynamics as a systems-level index linking these perceptual and behavioral advantages. These cross-sectional findings suggest that perceptual filtering and rapid visuomotor behavior are correlated in expert performance.