Abstract 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 generalisation, 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.
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.
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.
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.
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.
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.
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: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 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 glymphactic 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.
Backgrounds: The overlapping symptoms between bipolar disorder (BD) in the depressive period and major depressive disorder (MDD) pose a substantial challenge in diagnosis and treatment. A prevailing hypothesis suggests that mood dysregulation may be linked to impairments in the dopamine reward system in the brain, but the underlying neurocomputational differences between BD and MDD remain elusive. In this study, we investigate whether atypical reward processing affects subjective mood in adolescents with BD and MDD. Our findings aim 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 (N = 25) or MDD (N = 20) were asked to complete 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 fluctuation in subjective mood during the gambling task.Results: We found that adolescents with BD exhibited a greater behavioral propensity for uncertain options, as compared to those with MDD. Computational modeling and mediation analysis suggest a triple relationship between RPE-mood association, decision rationality, and symptom severity. Using fMRI, we further observed distinct brain response patterns in a distributed mood regulation network between adolescents with BD and MDD.Conclusions: Our findings highlight the critical role of RPE in mood regulation and suggest more potential engagement of the model-free control system in BD as compared to MDD. These results provide new insights into the diagnosis and rehabilitation of BD and MDD in adolescents.
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 internetwork 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.
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 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.
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 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 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.
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.
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.
