Functional Networks Research Articles

Distinct brain network structure of mesial temporal lobe epilepsy compared to that of neocortical epilepsy: functional MRI study with surgically treated patients.

Mesial temporal lobe epilepsy (mTLE) and neocortical epilepsy (NE) have different anatomo-clinical characteristics. The authors hypothesized that this may be reflected in the different functional connectivity of the epileptogenic zone between mTLE and NE. The authors, therefore, examined preoperative resting-state functional connectivity MRI with regional global connectivity (rGC) analysis for surgically treated patients. The goal of this study was to detect the different functional networks associated with the epileptogenic zone between mTLE and NE. Twenty-four patients (12 with mTLE and 12 with NE) who underwent surgery were included in the study. All patients received resting-state functional MRI preoperatively. The mean rGC and number of high-rGC or low-rGC voxels were calculated using preoperative MRI in various regions of interest including the resected area. The ratio of mean rGC in the resected area to that of the whole brain was significantly lower in mTLE patients than in NE patients. Mean rGC was significantly lower than that of the contralateral counterpart of the resected area in mTLE patients, although it was not significantly different in NE patients. Low rGC was more frequently observed in the resected area in mTLE patients than NE patients, and high rGC more frequently observed in NE than mTLE. Multivariate analysis showed that the etiology of hippocampal sclerosis, the ratio of mean rGC in the resected area to that in the whole brain, and the ratio of the number of low- and high-rGC voxels relative to the whole brain were significant factors to distinguish mTLE from NE. The authors revealed a distinct brain network structure between mTLE and NE based on rGC analysis with resting-state functional MRI. The authors' unique functional connectivity analysis may be helpful for providing landmarks for lateralization or epileptogenic zones in mTLE and NE.

Aberrant Dynamic Network Connectivity Changes in Comorbid Depression and Overweight/Obesity: Insights From the Triple Network Model.

The interaction between major depressive disorder (MDD) and overweight/obesity has received considerable attention owing to its widespread occurrence and the intricate biopsychological implications involved. Despite extensive research, the neural mechanisms underlying these comorbid conditions, particularly in terms of functional network connectivity (FNC), are still not well understood. This study aimed to clarify these mechanisms by utilizing resting-state functional magnetic resonance imaging (rs-fMRI) to examine both static and dynamic FNC. We analyzed data from 57 patients with both MDD and overweight/obesity (MDD-OW), 57 MDD patients of normal weight (MDD-NW), and 44 healthy controls, using techniques such as independent component analysis, sliding window analysis, K-means clustering, and graph theory. In contrast to static FNC, which showed no significant differences, dynamic FNC analysis identified four consistent states across all participants. Both MDD groups demonstrated reduced flexibility in functional coordination among these states and decreased nodal characteristics within the salience network. Notably, the MDD-OW group displayed enhanced dynamic FNC between the default mode network (DMN) and the executive control network (ECN) during certain states, which was inversely associated with the severity of depressive symptoms. These results highlight the importance of altered dynamic connectivity patterns in individuals with MDD and concurrent overweight/obesity, especially between the DMN and ECN, suggesting their potential utility as biomarkers for depressive states. This research contributes to our understanding of how comorbid overweight/obesity affects brain network dynamics in depressive disorders and provides a basis for targeted therapeutic strategies.

A library of lineage-specific driver lines connects developing neuronal circuits to behavior in the Drosophila Ventral Nerve Cord.

Understanding the developmental trajectories of neuronal lineages is crucial for elucidating how they are assembled into functional neural networks. Studies investigating the nervous system development in model animals have focused only on a few regions of the Central Nervous System due to the limited availability of genetic drivers to target these regions throughout development and adult life. This hindered our understanding of how distinct neuronal lineages come together to form neuronal circuits during development. Here, we present a split-GAL4 library composed of driver lines, which we generated via editing the endogenous locus of the lineage specific transcription factors and demonstrate that we can use the elements of this library to specifically target the majority of individual neuronal lineages in the Drosophila ventral nerve cord (VNC) across development and adulthood. Using these genetic lines, we found striking morphological changes of neuronal processes within a lineage during metamorphosis. We also showed how neurochemical features can be quickly assessed for a class of neurons expressing a specific gene. Lastly, we documented behaviors elicited in response to optogenetic activation of individual neuronal lineages and generated a comprehensive lineage-behavior map of the entire fly VNC. Looking forward, this lineage-specific driver library will provide genetic handles to address the questions emerging from the analysis of the recent VNC connectomics and transcriptomics datasets.

In silico screening of phytochemicals against chromatin modifier, SETD7 for remodeling of the immunosuppressive tumor microenvironment in renal cancer.

The tumor microenvironment and immune evasion function in a complex cellular network profoundly challenge the clinical outcome of promising therapies. Our recently published study reported that the subset of genes upregulated in ccRCC due to H3K4me1 and DNA demethylation potentially leads to an immunosuppressive environment. Thus, modulating H3K4me1 chromatin modifier SETD7 with a natural inhibitor in combination with immunotherapy might improve the immune landscape for a better therapeutic outcome.The present study was conducted via virtual screening and MD simulation using compounds from the literature, IMPPAT, and SuperNatural database. The phytochemical IMPHY002979 showed better binding affinity and lower energy than the reported R-PFI-2 and cyproheptadine inhibitors. The phytochemicals interact with the SET domain through H-bonding, as confirmed by MD simulation and molecular interaction analysis. Further, the compound was assessed using ADME parameters and free energy estimation, showing better pharmacokinetic properties. Therefore, the non-accessibility of the histone methyltransferase activity domain of SET7 with IMPHY002979 can downregulate H3K4me1 and, thereby, the expression of genes potentially responsible for immunosuppressive TME. Thus, patient stratification based on molecular markers for immunotherapy and combining epigenetic modulators with therapeutic drugs will improve the efficacy of immunotherapy in ccRCC.

Digital-twin-based active input refinement for insertion loss estimation and QoT optimization in C and C + L networks

Quality of transmission (QoT) prediction is a fundamental function in optical networks. It is typically embedded within a digital twin and used for operational tasks, including service establishment, service rerouting, and (per-channel or per-amplifier) power management to optimize the working point of services and hence to maximize their capacity. Inaccuracy in QoT prediction results in additional, unwanted design margins. A key contributor to QoT inaccuracy is the uncertain knowledge of fiber insertion loss, e.g., the attenuation due to connector losses at the beginning or at the end of each fiber span, as such loss cannot be directly monitored. Indeed, insertion losses drive the choice of the launch power in fiber spans, which in turn drive key physical effects, including the Kerr and stimulated Raman scattering (SRS) effects, which affect services’ QoT. It is thus important to estimate (and detect possibly anomalous) fiber insertion losses at each span. We thereby propose a novel active input refinement (AIR) technique using active probing to estimate insertion losses in C and C + L systems. Here, active probing consists of adjusting amplifier gains span by span to slightly alter SRS. The amount of adjustment must be sufficient to be measurable (such that insertion losses can be inferred from the measures) but small enough to have a negligible impact on running services in a live network. The method is validated by simulations on a European network with 30 optical multiplex sections (OMSs) in C and C + L configurations and by lab experiments on a C-band network, demonstrating that AIR significantly improves insertion loss estimation, network QoT optimization, and QoT prediction compared with other state-of-the-art monitoring techniques. This work underscores the critical role of accurate estimation of QoT inputs in enhancing optical network performance.

Non-Susceptibility Gene Variants in Head and Neck Paragangliomas

Head and neck paragangliomas (HNPGLs) are rare neoplasms that, along with pheochromocytomas and extra-adrenal paragangliomas, are associated with inherited mutations in at least 12 susceptibility genes in approximately 40% of cases. However, due to the rarity of HNPGLs, only a series of small-scale studies and individual cases have reported mutations in additional genes that may be involved in tumorigenesis. Consequently, numerous disease-causing mutations and genes responsible for the pathogenesis of HNPGLs remain poorly investigated. The aim of this study was to gain a deeper understanding of the genetic basis of HNPGLs by focusing on variants in genes that were not previously identified as well-known drivers. A whole-exome data analysis was conducted on a representative set of 152 HNPGLs. In 30% of the tumors examined, 53 potentially deleterious variants were identified in 36 different genes. The analysis identified pathogenic or likely pathogenic variants in the ARNT, IDH2, L2HGDH, MYH3, PIK3CA, and TERT genes. A functional network analysis of the mutated genes revealed numerous associations and a list of metabolic pathways (e.g., the TCA cycle, carbon metabolism, pyruvate metabolism, etc.) and signaling pathways (e.g., HIF1, PI3K-Akt, FoxO, AMPK, MAPK, etc.) that may play an important role in the development of HNPGLs. The identified range of genetic alterations affecting multiple genes and, potentially, influencing diverse cellular pathways provides an enhanced molecular genetic characterization of HNPGLs.

Abnormal stability of dynamic functional architecture in drug‐naïve children with attention‐deficit/hyperactivity disorder

Background and aimsAttention-deficit/hyperactivity disorder (ADHD) is most commonly diagnosed neurodevelopmental disorder in childhood, characterized by developmentally inappropriate inattention and/or hyperactivity/impulsivity symptoms. Static and dynamic functional connectivity (FC) studies have revealed brain dysfunction in ADHD. However, few studies have estimated the stability of dynamic functional architecture of children with ADHD. The present study attempted to identify the functional stability (FS) abnormalities associated with ADHD in drug‐naïve children.Materials and methodsThe resting-state fMRI of 42 children with ADHD and 30 healthy controls (HCs) were collected. Using the sliding window approach, FS of each voxel was obtained by measuring the concordance of dynamic FC over time. Further, the seed based dynamic FC (dFC) was conducted to explore the specific brain regions with dFC alteration related to these brain regions with altered FS. Then, the inter-group comparison and correlation analysis were performed.ResultsWe found that children with ADHD exhibited (1) decreased FS in the bilateral superior frontal gyrus (SFG) and increased FS in the right middle temporal gyrus (MTG), which both belong to the default mode network (DMN); (2) increased dFC between the bilateral SFG of DMN and the left insula of salience networks (SN) (GRF, voxel-wise p < 0.001, cluster-wise p < 0.05); (3) decreased dFC between the right MTG and the left cerebellum posterior lobe, and (3) worse performance in the Stroop test that significantly correlate with decreased FS in the bilateral SFG (p = 0.043, FDR corrected).ConclusionsOur findings showed that the abnormal functional architecture involved the DMN (the bilateral SFG and right MTG) and SN (left insula) regions in children with ADHD. This preliminary study provides novel insight into the dynamic brain functional networks in ADHD.

Multimodal predictive modeling: Scalable imaging informed approaches to predict future brain health

Background:Predicting future brain health is a complex endeavor that often requires integrating diverse data sources. The neural patterns and interactions identified through neuroimaging serve as the fundamental basis and early indicators that precede the manifestation of observable behaviors or psychological states. New Method:In this work, we introduce a multimodal predictive modeling approach that leverages an imaging-informed methodology to gain insights into future behavioral outcomes. We employed three methodologies for evaluation: an assessment-only approach using support vector regression (SVR), a neuroimaging-only approach using random forest (RF), and an image-assisted method integrating the static functional network connectivity (sFNC) matrix from resting-state functional magnetic resonance imaging (rs-fMRI) alongside assessments. The image-assisted approach utilized a partially conditional variational autoencoder (PCVAE) to predict brain health constructs in future visits from the behavioral data alone. Results:Our performance evaluation indicates that the image-assisted method excels in handling conditional information to predict brain health constructs in subsequent visits and their longitudinal changes. These results suggest that during the training stage, the PCVAE model effectively captures relevant information from neuroimaging data, thereby potentially improving accuracy in making future predictions using only assessment data. Comparison with Existing Methods:The proposed image-assisted method outperforms traditional assessment-only and neuroimaging-only approaches by effectively integrating neuroimaging data with assessment factors. Conclusion:This study underscores the potential of neuroimaging-informed predictive modeling to advance our comprehension of the complex relationships between cognitive performance and neural connectivity.

Functional network disruption in cognitively unimpaired autosomal dominant Alzheimer’s disease: a magnetoencephalography study

Functional network disruption in cognitively unimpaired autosomal dominant Alzheimer’s disease: a magnetoencephalography study

Disruption of macroscale functional network organisation in patients with frontotemporal dementia.

Neurodegenerative dementias have a profound impact on higher-order cognitive and behavioural functions. Investigating macroscale functional networks through cortical gradients provides valuable insights into the neurodegenerative dementia process and overall brain function. This approach allows for the exploration of unimodal-multimodal differentiation and the intricate interplay between functional brain networks. We applied cortical gradients mapping to resting-state functional MRI data of patients with frontotemporal dementia (FTD) (behavioural-bvFTD, non-fluent and semantic) and healthy controls. In healthy controls, the principal gradient maximally distinguished sensorimotor from default-mode network (DMN) and the secondary gradient visual from salience network (SN). In all FTD variants, the principal gradient's unimodal-multimodal differentiation was disrupted. The secondary gradient, however, showed widespread disruptions impacting the interactions among all networks specifically in bvFTD, while semantic and non-fluent variants exhibited more focal alterations in limbic and sensorimotor networks. Additionally, the visual network showed responsive and/or compensatory changes in all patients. Importantly, these disruptions extended beyond atrophy distribution and related to symptomatology in patients with bvFTD. In conclusion, optimal brain function requires networks to operate in a segregated yet collaborative manner. In FTD, our findings indicate a collapse and loss of differentiation between networks not solely explained by atrophy. These specific cortical gradients' fingerprints could serve as a functional signature for identifying early changes in neurodegenerative diseases or potential compensatory processes.

Preconfigured cortico-thalamic neural dynamics constrain movement-associated thalamic activity

Neural preconfigured activity patterns (nPAPs), conceptualized as organized activity parcellated into groups of neurons, have been proposed as building blocks for cognitive and sensory processing. However, their existence and function in motor networks have been scarcely studied. Here, we explore the possibility that nPAPs are present in the motor thalamus (VL/VM) and their potential contribution to motor-related activity. To this end, we developed a preparation where VL/VM multiunitary activity could be robustly recorded in mouse behavior evoked by primary motor cortex (M1) optogenetic stimulation and forelimb movements. VL/VM-evoked activity was organized as rigid stereotypical activity patterns at the single and population levels. These activity patterns were unable to dynamically adapt to different temporal architectures of M1 stimulation. Moreover, they were experience-independent, present in virtually all animals, and pairs of neurons with high correlations during M1-stimulation also presented higher correlations during spontaneous activity, confirming their preconfigured nature. Finally, subpopulations expressing specific M1-evoked patterns also displayed specific movement-related patterns. Our data demonstrate that the behaviorally related identity of specific neural subpopulations is tightly linked to nPAPs.

Heterogeneous virus classification using a functional deep learning model based on transmission electron microscopy images

Viruses are submicroscopic agents that can infect other lifeforms and use their hosts’ cells to replicate themselves. Despite having simplistic genetic structures among all living beings, viruses are highly adaptable, resilient, and capable of causing severe complications in their hosts’ bodies. Due to their multiple transmission pathways, high contagion rate, and lethality, viruses pose the biggest biological threat both animal and plant species face. It is often challenging to promptly detect a virus in a host and accurately determine its type using manual examination techniques. However, computer-based automatic diagnosis methods, especially the ones using Transmission Electron Microscopy (TEM) images, have proven effective in instant virus identification. Using TEM images collected from a recent dataset, this article proposes a deep learning-based classification model to identify the virus type within those images. The methodology of this study includes two coherent image processing techniques to reduce the noise present in raw microscopy images and a functional Convolutional Neural Network (CNN) model for classification. Experimental results show that it can differentiate among 14 types of viruses with a maximum of 97.44% classification accuracy and F1-score, which asserts the effectiveness and reliability of the proposed method. Implementing this scheme will impart a fast and dependable virus identification scheme subsidiary to the thorough diagnostic procedures.

Claustrum modulation drives altered prefrontal cortex dynamics and connectivity

This study delves into the claustrum’s role in modulating spontaneous and sensory-evoked network activity across cortical regions. Using mesoscale calcium imaging and Gi and Gq DREADDs in anesthetized mice, we show that decreasing claustral activity enhances prefrontal cortical activity, while activation reduces prefrontal cortical activity. This claustrum modulation also caused changes to the brain’s large-scale functional networks, emphasizing the claustrum’s ability to influence long-range functional connectivity in the cortex. Claustrum inhibition increased the local coupling between frontal cortex areas, but reduced the correlation between anterior medial regions and lateral/posterior regions, while also enhancing sensory-evoked responses in the visual cortex. These findings indicate the claustrum can participate in orchestrating neural communication across cortical regions through modulation of prefrontal cortical activity. These insights deepen our understanding of the claustrum’s impact on prefrontal connectivity, large-scale network dynamics, and sensory processing, positioning the claustrum as a key node modulating large-scale cortical dynamics.

Rapid post-earthquake damage assessment of building portfolios through deep learning-based component-level image recognition

Frequent seismic events significantly heighten the likelihood of the building structure being impacted throughout its operational lifecycle. Seismic effects on these structures result in a notable reduction in structural safety and serviceability. Enabling the post-earthquake recovery of building structures necessitates a precise and swift assessment of earthquake-induced damage. The conventional method for assessing building damage involves collecting data through close manual observation or contact inspection. Although it can obtain relatively accurate results by combining evaluation specifications, it is time-consuming and labour-intensive. Deep learning (DL) is data-driven and employs computational methods for data processing. The image classification function of convolutional neural network (CNN) in DL brings great convenience to image data processing. It has been applied to various aspects of structural health monitoring/post-disaster assessment. However, most of the current studies focus on the component level and lack of a comprehensive perspective on the whole structure, which is not conducive to the judgment of the overall condition. Consequently, this paper proposes a rapid assessment method of the overall damage level of post-earthquake buildings based on component images and DL. Two component-level image classification models, component type and damage level, are firstly trained. Then, the images of the buildings to be evaluated after the earthquake are collected and classified. Combined with the weights of different component types and damage levels proposed, the overall condition scores and grades of the structures can be obtained by weighted calculation. The proposed method realizes an overall evaluation of the structural damage condition after seismic events, and further extends to the building portfolios, providing actionable guidance for subsequent personnel and fund allocation, rescue, and maintenance measures. Due to limitations in the dataset, such as the potential biases in the training dataset, the trained model is not perfect and faces challenges in distinguishing between minor and moderate damage. In the future, the dataset should update and ideally cover a wide range of building types, component types, and damage levels to ensure the model's robustness and applicability to various real-world scenarios.

The cell-type underpinnings of the human functional cortical connectome.

The functional properties of the human brain arise, in part, from the vast assortment of cell types that pattern the cerebral cortex. The cortical sheet can be broadly divided into distinct networks, which are embedded into processing streams, or gradients, that extend from unimodal systems through higher-order association territories. Here using microarray data from the Allen Human Brain Atlas and single-nucleus RNA-sequencing data from multiple cortical territories, we demonstrate that cell-type distributions are spatially coupled to the functional organization of cortex, as estimated through functional magnetic resonance imaging. Differentially enriched cells follow the spatial topography of both functional gradients and associated large-scale networks. Distinct cellular fingerprints were evident across networks, and a classifier trained on postmortem cell-type distributions was able to predict the functional network allegiance of cortical tissue samples. These data indicate that the in vivo organization of the cortical sheet is reflected in the spatial variability of its cellular composition.

Model of the HVC neural network as a song motor in zebra finch.

The nucleus HVC within the avian song system produces crystalized instructions which lead to precise, learned vocalization in zebra finches (Taeniopygia guttata). This paper proposes a model of the HVC neural network based on the physiological properties of individual HVC neurons, their synaptic interactions calibrated by experimental measurements, as well as the synaptic signal into this region which triggers song production. This neural network model comprises of two major neural populations in this area: neurons projecting to the nucleus RA and interneurons. Each single neuron model of HVCRA is constructed with conductance-based ion currents of fast Na+ and K+ and a leak channel, while the interneuron model includes extra transient Ca2+ current and hyperpolarization-activated inward current. The synaptic dynamics is formed with simulated delivered neurotransmitter pulses from presynaptic cells and neurotransmitter receptor opening rates of postsynaptic neurons. We show that this network model qualitatively exhibits observed electrophysiological behaviors of neurons independent or in the network, as well as the importance of bidirectional interactions between the HVCRA neuron and the HVCI neuron. We also simulate the pulse input from A11 neuron group to HVC. This signal successfully suppresses the interneuron, which leads to sequential firing of projection neurons that matches measured burst onset, duration, and spike quantities during the zebra finch motif. The result provides a biophysically based model characterizing the dynamics and functions of the HVC neural network as a song motor, and offers a reference for synaptic coupling strength in the avian brain.

Screening and verification of differentially expressed serum proteins in children with severe adenovirus pneumonia.

Human adenoviruses are prevalent pathogens that cause severe acute respiratory infections. The clinical presentation of the adenoviral pneumonia is varied; in severe cases, they may cause systemic multi-system damages. Currently, early clinical differential diagnosis is difficult under the existing testing methods, the study identified potential biomarkers by screening and validating differentially expressed proteins (DEPs), and aimed at distinguishing between severe and non-severe adenovirus pneumonia in children aged <14 years. DEPs were identified using data-independent acquisition (DIA) quantitative proteomics technology, and potential biomarkers were further validated using an enzyme-linked immunosorbent assay (ELISA). Twenty-seven identical DEPs were found in patients with severe adenovirus pneumonia. Among these, 10 were downregulated, and 17 were upregulated. In the protein-protein interaction network, five proteins were located at the center of the functional network. Among these, E-selectin showed significantly higher serum expression levels in the severe adenoviral pneumonia group than in adenoviral pneumonia and control groups (p < 0.001). ELISA results were consistent with the proteomic analyses. The receiver operating characteristic (ROC) curve for E-selectin revealed a sensitivity of 79.31% and a specificity of 96.55%, with an area under the curve (AUC) of 0.92. E-selectin has potential as a novel biomarker for severe adenoviral pneumonia, and offers insights for improved diagnosis and clinical management.

Resting state functional connectivity in adolescents with substance use disorder and their unaffected siblings

We aimed to examine resting-state functional connectivity (rsFC) in adolescents with substance use disorder (SUD) and their unaffected biological siblings (SIB), relative to typically-developing controls (TDC) in order to identify alterations in functional network organization that may be associated with the familial risk for SUD. Resting-state functional magnetic resonance imaging analysis included 20 adolescents with SUD, 20 SIB, and 18 TDC. Network‐based analysis revealed that adolescents with SUD had significantly both weaker and higher rsFC compared to TDC mainly within the default-mode network (DMN) and between the DMN, fronto-parietal (FPN) and salience networks. In addition, adolescents with SUD showed lower rsFC between the visual network and other functional networks. Although the SIB group did not differ from TDC in the whole brain analysis, they showed lower rsFC within DMN and also between the visual network and other large-scale networks as well as higher rsFC between DMN and FPN compared to TDC in connections found to be abnormal in SUD group. Our results suggest that lower rsFC within DMN and higher rsFC between the DMN with FPN which were evident both in SUD and in SIB groups, and might be related to the familial predisposition for SUD.

Tumor microtubes: A new potential therapeutic target for high-grade gliomas.

High-grade infiltrating gliomas are highly aggressive and fatal brain tumors that present significant challenges for research and treatment due to their complex microenvironment and tissue structure. Recent discovery of tumor microtubes (TMs) has provided new insights into how high-grade gliomas develop in the brain and resist treatment. TMs are unique, ultra-long, and highly functional membrane protrusions that form multicellular networks and play crucial roles in glioma invasiveness, drug resistance, recurrence, and heterogeneity. This review focuses on the different roles that TMs play in glioma cell communication, material transport, and tumor cell behavior. Specifically, non-connecting TMs primarily promote glioma invasiveness, likely related to their role in enhancing cell motility. On the other hand, interconnecting TMs form functional and communication networks by connecting with surrounding astrocytes and neurons, thereby promoting glioma malignancy. We summarize the factors that influence the formation of TMs in gliomas and current strategies targeting TMs. As the understanding of TMs advances, we are closer to uncovering whether they might be the long-sought Achilles' heel of treatment-resistant gliomas. By delving deeper into TMs research, we hope to develop more effective therapeutic strategies for patients with malignant gliomas.

Combined Mendelian randomization and quantitative proteomics analysis to study the influence of thyroid dysfunction on acute ischemic stroke

AbstractAcute ischemic stroke (AIS) is characterized by high morbidity and mortality, making it crucial to identify the risk factors that influence its occurrence and prognosis. Although individuals with thyroid dysfunction exhibit altered stroke patterns, evidence from observational studies remains inconsistent. Herein, we investigated the influence of thyroid dysfunction on stroke progression and prognosis. We combined Mendelian randomization (MR) and tandem mass tag (TMT)‐based quantitative proteomics analysis to study the influence of thyroid dysfunction on AIS. Differentially expression proteins (DEPs) were subsequently identified, functional enrichment analysis was performed, and a protein–protein interaction (PPI) network was constructed. Protein alterations were further validated by western blot. MR analysis revealed a causal association between thyroid disorders and ischemic stroke. DEP analysis identified 38 downregulated proteins and five upregulated proteins. Functional enrichment analysis and PPI network construction highlighted the importance of immune response activation and acute phase pathways, along with the suppression of focal adhesion, regulation of the actin cytoskeleton, and platelet activation pathways. Vasodilator‐stimulated phosphoprotein, MYL12B, MYL6, and TPM4 were identified as key DEPs significantly associated with pathological pathways and were verified by western blot. The identification of these key proteins and pathways provides new perspectives for investigating the progression and prognosis of AIS.