Major depressive disorder (MDD) is common and disabling, yet reported brain structural differences vary across studies. Here we performed a large vertex-wise (point-by-point) meta-analysis of cortical thickness and surface area using harmonized magnetic resonance imaging processing across 64 cohorts from the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) MDD and Depression Imaging Research Consortium (DIRECT) consortia (5,736 patients; 6,538 controls). We show significantly lower cortical thickness in patients with MDD in multiple brain regions, including the inferior parietal, lateral occipital, superior parietal, medial and lateral orbitofrontal, anterior and posterior cingulate, and precentral gyri, with cortical surface area showing no significant differences. Effects were most pronounced in adults with acute depression, whereas adolescents showed no significant case-control differences. Antidepressant medication use at scanning was associated with more extensive thinning, although effect sizes remained modest (mostly |Cohen's d| < 0.20). This high-resolution, globally generalizable map can support studies of mechanisms and help evaluate structural markers of the clinical course and treatment response.

BACKGROUND AND HYPOTHESIS:Visual hallucinations (VH), a key symptom in neurodegenerative and psychiatric disorders, are associated with a more severe psychopathological profile and less favorable outcome. Neuroimaging research has revealed widespread brain regions involved in VH, echoing the updated notion that neuropsychiatric symptoms correspond more closely to interconnected brain networks than to single brain regions. However, there is still a dearth of studies examining brain network localization of VH. STUDY DESIGN:We initially identified brain structural and functional alterations specific to VH from 21 published neuroimaging studies with 418 VH and 522 non-VH individuals. By applying novel functional connectivity network mapping to large-scale discovery (n = 1113) and validation (n = 1093) resting-state functional magnetic resonance imaging datasets, we mapped these affected brain locations to 2 specific networks. STUDY RESULTS:The VH structural alteration network comprised a broadly distributed set of brain regions principally implicating the frontoparietal and dorsal attention networks. The VH functional alteration network also consisted of widely distributed brain areas predominantly involving the ventral attention and frontoparietal networks. CONCLUSIONS:Our findings may not only draw a more refined picture of the neurobiology of VH from a network perspective, but also potentially contribute to more targeted and effective treatment for VH.

BACKGROUND:Prior neuroimaging studies and meta-analyses investigating brain correlates of placebo analgesia (PA) have yielded neuroanatomically heterogeneous findings, which may be reconciled from a connectomics perspective. The objective of this study was to examine network localization of brain functional alterations related to PA. METHODS:We initially identified PA-induced brain activation alterations (hyper-activation and hypo-activation separately) during experimental pain from 29 published studies with 674 individuals. By combining these implicated dysfunctional brain regions with large-scale discovery (N = 1113) and validation (N = 1093) resting-state functional magnetic resonance imaging datasets, we then employed a novel functional connectivity network mapping approach to construct PA hyper-activation and hypo-activation networks, respectively. RESULTS:The PA hyper-activation network manifested as a pattern of circumscribed brain regions mainly involving the limbic, default, and frontoparietal networks. By contrast, the PA hypo-activation network comprised a broadly distributed set of brain regions primarily implicating the ventral attention, somatomotor, and subcortical networks. CONCLUSIONS:Our findings regarding the brain network representations of PA may contribute to a deeper understanding of its action mechanisms and provide a neural framework that may inform future clinical translation.

Cortical morphological alteration patterns differ between adolescent and adult psychiatric disorders. However, the biological factors contributing to the divergence are unclear. Cortical thickness (CT) alterations in adolescents and adults with attention deficit hyperactivity disorder (ADHD), bipolar disorder (BD), major depressive disorder (MDD), and obsessive-compulsive disorder (OCD) were derived from the ENIGMA. We examined whether the structural connectome constrains disease-related CT alterations, followed by identifying likely epicenter regions and testing the hub vulnerability hypothesis. Using neurotransmitter, transcriptome, and mitochondria atlases, we furthermore investigated the neurochemical basis, genetic architecture, and molecular energetic landscape related to the CT alterations. Results showed that the structural connectome constrained CT alterations in adult psychiatric disorders rather than their adolescent counterparts. The epicenters were largely consistent in adolescents and adults for ADHD and MDD, while divergent for BD and OCD. The demonstration of CT alterations in adolescent BD, adult BD, and adult OCD as a function of connectome degree centrality was consistent with the hub vulnerability hypothesis. We also found distinct neurotransmitter systems linked to CT alterations in psychiatric adolescents and adults. Transcriptomic contextualization showed that CT alterations in adult ADHD, adult MDD, and adolescent OCD were related to genes involving essential components of the cerebral cortex, signal pathway, and nervous system development, while those in adolescent and adult BD to synapse and catabolic process. Additionally, mitochondrial features were associated with CT alterations in almost all conditions. Our findings may elucidate the biological factors associated with the differential cortical abnormalities between psychiatric adolescents and adults.

The widespread use of smartphones, particularly among young adults, has raised concerns about their impact on cognitive functioning, leading to a growing interest in understanding the neurobiological underpinnings of problematic smartphone use (PSU). Despite evidence linking PSU to negative cognitive and emotional outcomes, the neurobiological mechanisms underlying cognitive impairments in PSU individuals remain underexplored. This study aimed to investigate the relationship between cognitive fatigue (CF) and brain activity in individuals with PSU. Eighty-one healthy adults underwent functional magnetic resonance imaging (fMRI) while performing cognitively demanding tasks designed to induce CF. Brain activation patterns, functional connectivity, and correlations with behavioral performance and self-reported fatigue were analyzed using brain imaging analyses. The PSU group showed significant activation in the ventromedial prefrontal cortex (vmPFC) compared to the non-problematic smartphone use (nonPSU) group, and vmPFC activity was positively correlated with fatigue scores. In addition, there was also an increase in functional connectivity between the vmPFC and left middle frontal gyrus (MFG) in the PSU group. Correlations between task performance and activation in the nucleus accumbens (NAcc) and middle cingulate cortex (MCC) further indicated the engagement of compensatory mechanisms related to reward sensitivity and cognitive control. These results define specific neural markers of cognitive fatigue in people with PSU, indicating that increased activity and connectivity in key brain areas require greater cognitive resources to maintain functioning. Therefore, this highlights the need for targeted interventions to minimize cognitive fatigue and mitigate the neurocognitive impact of PSU.

Elucidating how resting-state functional connectivity relates to task-evoked neural activation is an important topic in systems neuroscience. We analyzed task-based and resting-state functional magnetic resonance imaging data from 1,005 participants from the Human Connectome Project. On the basis of connectome-constrained predictive modeling, we calculated a neural activation constraint index (NACI) to assess the extent to which intrinsic functional connectome architecture constrains task-evoked neural activation. NACIs showed task-dependent variations, indicating differential constraint effects of the intrinsic functional connectome across distinct tasks. Spectral clustering based on the NACI classified participants into the high- and low-constraint groups. The high-constraint group exhibited superior cognitive functions in several domains and better performance across multiple tasks. Higher NACIs from working memory, language, and relational tasks correlated with greater cognitive functions and better task performance. These findings support the neurobiological and behavioral relevance of NACI and suggest its utility for characterizing individual differences in functional brain organization.

Background Gut microbial dysbiosis and inflammation have been implicated in the pathophysiology of major depressive disorder (MDD). However, no attempts have been made to comprehensively investigate the potential relationship between gut microbiota, inflammation, brain function, and clinical features in MDD. Methods We conducted an integrative multi-omics study to examine the multi-dimensional differences in gut microbiome, inflammatory cytokines, brain functional connectivity, and clinical features between 60 MDD patients and 70 healthy controls. Furthermore, the potential associations between these multi-omics alterations were assessed using correlation and serial mediation analyses. Results MDD patients exhibited both depleted beneficial gut microbes and enriched detrimental bacteria, elevated interleukin-1 beta (IL-1β) level, a mix of decreased and increased functional connectivity of multiple brain regions. More important, we found that decreased abundance of anti-inflammatory bacterium (i.e., Blautia) led to increased IL-1β level, which in turn resulted in functional abnormalities in fronto-parietal regions that were associated with clinical symptoms and executive dysfunction. Conclusion Our findings may corroborate the gut microbiota-inflammation-brain axis hypothesis in depression, as well as highlight the potential use of targeting gut microbiota as anti-inflammatory intervention strategies in the prevention or treatment for MDD patients.

Background Considerable effort has been devoted to investigate the neuroimaging correlates and predictors of antidepressant response to ketamine, yet inconsistency in the location and nature of the regional brain effects makes it difficult to unify this research. Despite the revolutionary notion that psychiatric therapeutics show network-level brain representations, investigations into network localization of brain functional effects of ketamine treatment are still lacking.Methods We initially identified the locations of longitudinal brain functional alterations (increase and decrease separately) induced by ketamine treatment from 16 published studies with 508 depressed patients. By integrating these affected brain locations with large-scale functional MRI datasets from 1113 healthy and 255 depressed individuals, we then leveraged a novel functional connectivity network mapping approach to construct ketamine-induced hyper-functional and hypo-functional networks respectively.Results The hyper-functional network mainly involved the subcortical (caudate nucleus and thalamus) and default (medial prefrontal cortex) networks, while its hypo-functional counterpart predominantly implicated the limbic (temporal pole), subcortical (hippocampus and amygdala), and default (lateral temporal cortex) networks.Conclusion Our findings may shed light on the neurobiological effects of ketamine from a network perspective, which might represent a crucial step toward fostering the clinical application of ketamine in antidepressant treatment.

Background: Major depressive disorder (MDD) is increasingly recognized as a highly heterogeneous disorder. Although the person-based similarity index (PBSI) provides a useful framework for characterizing individualized brain structural similarity, existing studies in MDD remain limited by either small samples or a lack of integration across different morphological features. Methods: We used structural MRI data from 1442 patients with MDD and 1277 healthy controls to calculate PBSI scores of cortical morphology measures based on cortical thickness (CT), cortical volume (CV), cortical surface area (SA), and sulcal depth (SD). Group comparisons of whole-brain PBSI and regional contributions to PBSI scores were then performed, and a subgroup analysis in 243 first-episode, drug-naive (FEDN) patients with MDD was further conducted. Results: Patients with MDD showed significant alterations in PBSI. Specifically, PBSI scores were significantly reduced for CT, CV, and SD, whereas no significant group difference was observed for SA in the main analysis. Analyses of regional contributions to PBSI further revealed significant between-group differences across multiple cortical regions. These alterations were mainly distributed in the default mode, ventral attention, and visual networks for CT; in the default mode, ventral attention, sensorimotor, and visual networks for CV; and in the default mode, dorsal attention, frontoparietal, and sensorimotor networks for SD. Similar patterns were also observed in the FEDN MDD subgroup. Conclusions: These findings provide neurobiological evidence for the marked structural heterogeneity of MDD and highlight the potential of PBSI as an individualized neuroimaging marker for more precise diagnosis and personalized intervention.

Background Rumination, a recurrent and passive focus of thoughts on depressed mood and its possible causes and consequences, constitutes a characteristic feature of major depressive disorder (MDD). Despite considerable recent efforts to map neuropsychiatric symptoms to specific brain networks, little attention has been paid to network localization of rumination. Methods We initially conducted a systematic review of 49 published neuroimaging studies to identify rumination-related brain structural and functional alterations. Subsequently, by integrating these affected brain locations with large-scale discovery (1113 healthy individuals) and validation (1093 healthy individuals and 255 MDD patients) resting-state functional magnetic resonance imaging datasets, we applied novel functional connectivity network mapping to construct 3 rumination networks corresponding to different imaging modalities. Results The rumination gray matter volume abnormality network comprised widely distributed brain regions, primarily involving the ventral attention, subcortical, frontoparietal, limbic, somatomotor, and dorsal attention networks. The resting-state activity abnormality network mainly implicated the default network. The task-induced activation abnormality network was similar to the gray matter volume abnormality network, but the spatial extent was much smaller, chiefly involving the ventral attention and somatomotor networks. Conclusion Our findings help establish an integrative framework that may explain the neurobiology of rumination from a network perspective, laying the foundation for developing reliable biomarkers and targeted treatments for rumination.

Background Reduced sleep efficiency (SE) is common in Major Depressive Disorder (MDD) and correlates with cognitive deficits. However, the underlying neurobiological mechanisms remain inadequately explained. Methods Resting-state functional magnetic resonance imaging, event-related potentials (P3a and P3b), and polysomnography data were collected from 102 MDD patients into Low SE group (SE < 90%, n = 54) and Normal SE group (SE ≥ 90%, n = 48). Brain function was evaluated using amplitude of low-frequency fluctuation (ALFF) and seed-based functional connectivity (FC). Analyses included group comparisons, correlations, and mediation models. Results Compared to the Normal SE patients, the Low SE patients showed prolonged P3a and P3b latencies, reduced ALFF in the right cuneus and left precuneus, increased ALFF in the right nucleus accumbens, and attenuated FC between the right cuneus and right orbital part of the inferior frontal gyrus and between the right cuneus and left medial superior frontal gyrus. Correlation analyses further revealed negative associations between P3a/P3b latencies and SE. The FC between the right cuneus and right orbital part of the inferior frontal gyrus was positively associated with SE, but was negatively associated with P3a/P3b latencies. Importantly, mediation analysis revealed that within the tested model, this FC statistically fully mediated the association between SE and P3a latency and partially mediated the association between SE and P3b latency. Conclusion Our findings suggest that reduced SE may cause cognitive dysfunction in MDD via disrupting the fronto-visual circuit connectivity, which may expose this circuit as a promising potential therapeutic target for ameliorating sleep-related cognitive deficits in patients with MDD.

BACKGROUND:Considerable neuroimaging effort has been dedicated to investigate the neural correlates of episodic memory, but the micro-scale molecular mechanisms that underlie the macro-scale neuroimaging correlates of episodic memory are still unclear. METHODS:Resting-state functional MRI data were obtained from a large cohort of 510 healthy young adults to calculate regional homogeneity (ReHo) to reflect spontaneous intrinsic brain activity. We then explored the relationship between California Verbal Learning Test-Ⅱ performance and ReHo across participants to delineate the neural substrates of episodic memory. Finally, we conducted the spatial relationship analyses between the neural correlates with gene expression and neurotransmitter atlases to further explore their potential genetic architecture and neurochemical underpinnings. RESULTS:Our analysis revealed a significant negative correlation between episodic memory and ReHo in the bilateral precuneus. Additional spatial correlation analyses revealed that the identified neural correlates of episodic memory were associated with expression of gene categories predominantly implicating signal transduction, immune system process, cellular metabolic process and anatomical structure development, as well as were linked to serotonin transporter. CONCLUSIONS:These findings may not only offer novel insights into the molecular substrates underlying the neural basis of episodic memory, but also help inform prevention and intervention strategies for individuals in at-risk and early phases of dementia.

Anhedonia, encompassing a broad spectrum of deficits in reward processing, is highly prevalent in major depressive disorder (MDD) and constitutes one of its core symptoms. While substantial progress has recently been made in mapping neuropsychiatric symptoms to specific brain networks, focused efforts to examine network localization of anhedonia are limited. We initially synthesized extant neuroimaging literature to identify brain locations with structural or functional alterations related to anhedonia. By integrating these affected brain locations with large-scale discovery (1113 healthy individuals) and validation (1093 healthy individuals and 255 MDD patients) resting-state functional magnetic resonance imaging datasets, we then applied novel functional connectivity network mapping to construct an anhedonia network. The anhedonia network was composed of the dorsal anterior cingulate cortex, insula, lateral prefrontal cortex, and striatum, principally implicating the canonical ventral attention and subcortical networks. Further analyses revealed that the trait and state anhedonia networks preferentially involved the default and limbic networks respectively, in addition to the commonly affected ventral attention and subcortical networks. Our findings may not only advance the understanding of the neurobiology underlying anhedonia from a network perspective, but also potentially contribute to more targeted and effective intervention strategies for anhedonia.

INTRODUCTION Individuals with similar white matter hyperintensities (WMH) burden show heterogeneous cognitive outcomes, yet the biological mechanisms underlying this variability remain incompletely understood.METHODS We integrated 16S rDNA sequencing, untargeted metabolomics, and multi-modal magnetic resonance imaging (MRI) to comprehensively characterize gut microbiome, plasma metabolome, and brain glymphatic function in 56 healthy controls, 40 WMH with normal cognition (WMH-NC), and 49 WMH with cognitive impairment (WMH-CI).RESULTS Group comparisons revealed differences in six bacterial genera, three plasma metabolites, and five glymphatic markers across three groups, with Acetivibrio, 1,5-naphthalenediamine, beta-uridine, free water fraction within the white matter, and index of diffusivity along the perivascular spaces (ALPS index) showing differences between WMH-CI and WMH-NC. Correlation and mediation analyses demonstrated associations between microbiota and cognition, mediated by tetradecyldiethanolamine and ALPS index.DISCUSSION These findings provide preliminary insights into plausible microbiota-metabolites-glymphatic function-cognition associations in WMH, potentially informing more targeted interventions for vascular cognitive impairment.

Functional magnetic resonance imaging (fMRI) allows real-time observation of brain activity through blood oxygen level-dependent (BOLD) signals and is extensively used in studies related to sex classification, age estimation, behavioral measurements prediction, and mental disorder diagnosis. However, the application of deep learning techniques to brain fMRI analysis is hindered by the small sample size of fMRI datasets. Transfer learning offers a solution to this problem, but most existing approaches are designed for large-scale 2D natural images. The heterogeneity between 4D fMRI data and 2D natural images makes direct model transfer infeasible. This study proposes a novel geometric mapping-based fMRI transfer learning method that enables transfer learning from 2D natural images to 4D fMRI brain images, bridging the transfer learning gap between fMRI data and natural images. The proposed Multi-scale Multi-domain Feature Aggregation (MMFA) module extracts effective aggregated features and reduces the dimensionality of fMRI data to 3D space. By treating the cerebral cortex as a folded Riemannian manifold in 3D space and mapping it into 2D space using surface geometric mapping, we make the transfer learning from 2D natural images to 4D brain images possible. Moreover, the topological relationships of the cerebral cortex are maintained with our method, and calculations are performed along the Riemannian manifold of the brain, effectively addressing signal interference problems. The experimental results based on the Human Connectome Project (HCP) dataset demonstrate the effectiveness of the proposed method. Our method achieved state-of-the-art performance in sex classification, age estimation, and behavioral measurement prediction tasks. Moreover, we propose a cascaded transfer learning approach for depression diagnosis, and proved its effectiveness on 23 depression datasets. In summary, the proposed fMRI transfer learning method, which accounts for the structural characteristics of the brain, is promising for applying transfer learning from natural images to brain fMRI images, significantly enhancing the performance in various fMRI analysis tasks.

Major depressive disorder (MDD) imposes significant global health burdens, yet its underlying neural mechanisms remain elusive. Traditional static functional metrics inadequately capture the brain’s dynamic nature, motivating the exploration of dynamic functional metrics to understand both the temporal and spatial reconfigurations of brain networks in MDD. Leveraging the Depression Imaging Research Consortium (DIRECT) dataset, this study conducted vertex-wise dynamic analyses in a large cohort of MDD patients (n = 1660) and healthy controls (n = 1341). We identified significant alterations in temporal stability across the brain, with MDD patients exhibiting increased stability in higher-order association areas (e.g., frontoparietal and default mode networks) and decreased stability in primary sensory-motor regions. Among the regions showing altered temporal stability, brain-symptom relationships were further explored. We identified a set of brain regions including the superior frontal gyrus, postcentral gyrus and superior insular sulcus, which were potentially involved in the common abnormal dFC network and associated with insomnia, feelings of guilt, and insight symptoms in MDD. By incorporating advanced vertex-wise dynamic functional analyses and a large sample size, this study provides insights into the neural mechanisms of MDD, emphasizing the value of dynamic approaches for identifying biomarkers. Future longitudinal and task-based studies are promising to elucidate causal relationships and refine personalized therapeutic interventions targeting specific dynamic dysfunctions in MDD.

To address the clinical difficulty of differentiating Generalized Anxiety Disorder (GAD) from Major Depressive Disorder (MDD), this study aimed to create a machine learning framework with strong interpretability, leveraging a comprehensive suite of resting-state functional MRI features to enhance potential relevance. A comprehensive set of functional measures, encompassing both local neural activity and global functional connectivity, was extracted as neuroimaging features from resting-state fMRI data. These data were obtained from 91 patients with Generalized Anxiety Disorder (GAD), 94 with Major Depressive Disorder (MDD), and 71 healthy controls (HCs). Five machine learning algorithms were used for classification, with performance estimated within a rigorous nested cross-validation framework. Then, partial correlations were conducted to assess the associations between top-ranked contributive neuroimaging features and symptom severity. For the discrimination tasks, the machine learning models achieved area under the curve (AUC) values of 0.783 (GAD vs. MDD), 0.824 (GAD vs. HCs), and 0.867 (MDD vs. HCs), demonstrating moderate classification performance. Feature importance analysis revealed that precuneus function served as a prominently differential neurobiological signature and it showed opposing relationships with the severity of anxiety (positive correlation) and depression (negative correlation) across different diagnostic categories. These findings could guide the creation of a computational framework based on neuroimaging to effectively differentiate GAD from MDD. This is underscored by the pivotal role that the precuneus appears to play in the neurobiological processes associated with symptoms of both disorders. This is a non-interventional study with a control group. Clinical trial registration is not applicable.

Postoperative depression adversely influences breast cancer patients' clinical outcomes. Our prior study demonstrated that intraoperative esketamine ameliorated postoperative depression in breast cancer patients, yet the underlying neural mechanism remains incompletely understood. We performed a double-blind randomized controlled trial in 35 breast cancer patients with preoperative depressive symptoms, who were randomly given intraoperative esketamine 0.25 mg·kg⁻¹ (n = 18) or saline placebo (n = 17) over the initial 40 min of anesthesia. Resting-state functional magnetic resonance imaging data were collected at preoperative baseline and postoperative day 1 follow-up to calculate brain functional network measures. In contrast to no significant change in the placebo group, the esketamine group showed increased degree centrality of the left inferior frontal gyrus, opercular part from baseline to follow-up, which was related to improvement in depressive symptoms. Additionally, we found significant associations of baseline network measures at the global, nodal, and edge levels with short-term and long-term improvements in depressive symptoms following esketamine administration. These findings may not only provide novel insights into the neural mechanism by which esketamine exerts its antidepressant efficacy during the perioperative period, but also highlight the prospect of functional network measures as useful predictors of antidepressant response to esketamine in patients with breast cancer.

Background While the relationships between somatic movement, mental well-being, and brain health have been well established, the causal nature and underlying mechanisms of such associations remain incompletely understood.Methods By applying multi-stage Mendelian randomization to multi-source summary data derived from genome-wide association studies, we examined the causal effects of 4 somatic movement measures on 2 mental well-being indices and 13 types of brain structures, followed by testing the mediating roles of brain structures in accounting for the causal associations between somatic movement and mental well-being.Results Two-sample Mendelian randomization revealed that more physical activity was causally associated with greater mental well-being (life satisfaction and positive affect), while more sedentary behavior (longer leisure screen time and more sedentary behavior at work) with lower mental well-being. With respect to brain structures, sedentary behavior was causally linked to decreased volume, surface area, and local gyrification index in distributed cortical regions. Remarkably, decreased surface area of the piriform cortex was found to mediate the causal associations between sedentary behavior and lower mental well-being.Conclusions Our findings not only complement and extend earlier reports on the associations of somatic movement with mental well-being and brain health by further resolving the causality but also help elucidate the neural mechanisms by which sedentary behavior adversely affects mental well-being.

Thrombotic molecular markers include the thrombin-antithrombin complex (TAT), plasmin inhibitor-plasmin complex (PIC), thrombomodulin (TM), and tissue plasminogen activator-plasminogen activator inhibitor-1 complex (t-PAIC). These molecular markers facilitate the early assessment of coagulation and fibrinolysis system functions, as well as vascular endothelial injury; however, their clinical application following intravenous thrombolysis for acute ischemic stroke (AIS) remains unclear. Therefore, our study aims to evaluate the dynamic levels of these novel thrombosis-related molecular markers within 24 h after intravenous thrombolysis in patients with AIS and analyze their relationship with patients outcome. We conducted a retrospective cohort study based on the data of 77 patients with AIS who underwent alteplase intravenous thrombolysis between November 2022 and February 2024. Thrombotic molecular markers were evaluated at four time points: prior to thrombolysis, 1 h after thrombolysis, 6 h after thrombolysis, and 24 h after thrombolysis. Based on the modified Rankin scale (mRS) score at day 90 post-discharge, patients were categorized into the excellent outcome group (mRS ≤ 1) and the non-excellent outcome group (mRS > 1). Stepwise multivariate logistic regression was employed to analyze the association between 90-day functional outcomes and the measured variables. The area under the receiver operating characteristic curve (AUC) was utilized to assess the predictive ability of novel thrombotic markers. Our study demonstrated that, compared to the non-excellent outcome group, the excellent outcome group exhibited significant lower serum TAT levels both prior to thrombolysis and at 6 h post-thrombolysis (all p < 0.05), and serum TM and t-PAIC levels were also significantly elevated in the excellent outcome group at 1 h, 6 h, and 24 h following thrombolysis (all p < 0.05). Stepwise logistic regression analysis indicated that increased serum TM and t-PAIC levels at 24 h post-thrombolysis were protective factors for a excellent 90-day outcome. The AUC values of TM, t-PAIC, and TM combined with t-PAIC for predicting 90-day functional outcomes were 0.918 (sensitivity 80.0