Similar Maps Research Articles

Ventricular Arrhythmia Classification Using Similarity Maps and Hierarchical Multi-Stream Deep Learning.

Ventricular arrhythmias are the primary arrhythmias that cause sudden cardiac death. We address the problem of classification between ventricular tachycardia (VT), ventricular fibrillation (VF) and non-ventricular rhythms (NVR). To address the challenging problem of the discrimination between VT and VF, we develop similarity maps - a novel set of features designed to capture regularity within an ECG trace. These similarity maps are combined with features extracted through learnable Parzen band-pass filters and derivative features to discriminate between VT, VF, and NVR. To combine the benefits of these different features, we propose a hierarchical multi-stream ResNet34 architecture. Our empirical results demonstrate that the similarity maps significantly improve the accuracy of distinguishing between VT and VF. Overall, the proposed approach achieves an average class sensitivity of 89.68%, and individual class sensitivities of 81.46% for VT, 89.29% for VF, and 98.28% for NVR. The proposed method achieves a high accuracy of ventricular arrhythmia detection and classification. Correct detection and classification of ventricular fibrillation and ventricular tachycardia are essential for effective intervention and for the development of new therapies and translational medicine.

Similarity joins and clustering for SPARQL

The SPARQL standard provides operators to retrieve exact matches on data, such as graph patterns, filters and grouping. This work proposes and evaluates two new algebraic operators for SPARQL 1.1 that return similarity-based results instead of exact results. First, a similarity join operator is presented, which brings together similar mappings from two sets of solution mappings. Second, a clustering solution modifier is introduced, which instead of grouping solution mappings according to exact values, brings them together by using similarity criteria. For both cases, a variety of algorithms are proposed and analysed, and use-case queries that showcase the relevance and usefulness of the novel operators are presented. For similarity joins, experimental results are provided by comparing different physical operators over a set of real world queries, as well as comparing our implementation to the closest work found in the literature, DBSimJoin, a PostgreSQL extension that supports similarity joins. For clustering, synthetic queries are designed in order to measure the performance of the different algorithms implemented.

Human and hamster sera correlate well in identifying antigenic drift among SARS-CoV-2 variants, including JN.1.

Antigenic assessments of SARS-CoV-2 variants inform decisions to update COVID-19 vaccines. Primary infection sera are often used for assessments, but such sera are rare due to population immunity from SARS-CoV-2 infections and COVID-19 vaccinations. Here, we show that neutralization titers and breadth of matched human and hamster pre-Omicron variant primary infection sera correlate well and generate similar antigenic maps. The hamster antigenic map shows modest antigenic drift among XBB sub-lineage variants, with JN.1 and BA.4/BA.5 variants within the XBB cluster, but with fivefold to sixfold antigenic differences between these variants and XBB.1.5. Compared to sera following only ancestral or bivalent COVID-19 vaccinations, or with post-vaccination infections, XBB.1.5 booster sera had the broadest neutralization against XBB sub-lineage variants, although a fivefold titer difference was still observed between JN.1 and XBB.1.5 variants. These findings suggest that antibody coverage of antigenically divergent JN.1 could be improved with a matched vaccine antigen.IMPORTANCEUpdates to COVID-19 vaccine antigens depend on assessing how much vaccine antigens differ antigenically from newer SARS-CoV-2 variants. Human sera from single variant infections are ideal for discriminating antigenic differences among variants, but such primary infection sera are now rare due to high population immunity. It remains unclear whether sera from experimentally infected animals could substitute for human sera for antigenic assessments. This report shows that neutralization titers of variant-matched human and hamster primary infection sera correlate well and recognize variants similarly, indicating that hamster sera can be a proxy for human sera for antigenic assessments. We further show that human sera following an XBB.1.5 booster vaccine broadly neutralized XBB sub-lineage variants but titers were fivefold lower against the more recent JN.1 variant. These findings support updating the current COVID-19 vaccine variant composition and developing a framework for assessing antigenic differences in future variants using hamster primary infection sera.

Novel CRM cosine similarity mapping strategy for simultaneous in-situ visual profiling lignocellulose in plant cell walls

Confocal Raman microscopy (CRM) is a promising in-situ visual technique that provides detailed insights into multiple lignocellulosic components and structures in plant cell walls at the micro-nano scale. In this study, we propose a novel CRM cosine similarity (CS) mapping strategy for the simultaneous in-situ visual profiling of lignin, cellulose, and hemicellulose in plant cell walls. The main stages of this strategy include: 1) a modified Otsu algorithm for extracting the regions of interest (ROI); 2) a modified subtraction method for cleaning the background signals in the ROI spectra; 3) a lignin signal subtraction method based on the pixel correction factor for eliminating the interference of strong lignin signals with weak cellulose and hemicellulose signals in the Raman full spectra of the cell walls; 4) second-order derivative spectral preprocessing for enhancing the discrimination between the characteristic peaks of cellulose and hemicellulose; 5) a CS mapping algorithm for simultaneous in-situ profiling of lignin, cellulose, and hemicellulose in plant cell walls. The effectiveness of the strategy is verified by characterizing the Brittle Culm1 (BC1) gene-mutant rice stem (IL349-BC1-KO) with known bioinformatics. This approach provides methodological support for in-situ visualization and analysis in fields such as plant or crop science at the micro-nano scale.

Experience of Euclidean geometry sculpts the development and dynamics of rodent hippocampal sequential cell assemblies

Euclidean space is the fabric of the world we live in. Whether and how geometric experience shapes our spatial-temporal representations of the world remained unknown. We deprived male rats of experience with crucial features of Euclidean geometry by rearing them inside spheres, and compared activity of large hippocampal neuronal ensembles during navigation and sleep with that of cuboid cage-reared controls. Sphere-rearing from birth permitted emergence of accurate neuronal ensemble spatial codes and preconfigured and plastic time-compressed neuronal sequences. However, sphere-rearing led to diminished individual place cell tuning, more similar neuronal mapping of different track ends/corners, and impaired pattern separation and plasticity of multiple linear tracks, coupled with reduced preconfigured sleep network repertoires. Subsequent experience with multiple linear environments over four days largely reversed these effects. Thus, early-life experience with Euclidean geometry enriches the hippocampal repertoire of preconfigured neuronal patterns selected toward unique representation and discrimination of multiple linear environments.

A Robust Generalized Zero-Shot Learning Method with Attribute Prototype and Discriminative Attention Mechanism

In the field of Generalized Zero-Shot Learning (GZSL), the challenge lies in learning attribute-based information from seen classes and effectively conveying this knowledge to recognize both seen and unseen categories during the training process. This paper proposes an innovative approach to enhance the generalization ability and efficiency of GZSL models by integrating a Convolutional Block Attention Module (CBAM). The CBAM blends channel-wise and spatial-wise information to emphasize key features, thereby improving the model’s discriminative and localization capabilities. Additionally, the method employs a ResNet101 backbone for systematic image feature extraction, enhanced contrastive learning, and a similarity map generator with attribute prototypes. This comprehensive framework aims to achieve robust visual–semantic embedding for classification tasks. The proposed method demonstrates significant improvements in performance metrics in benchmark datasets, showcasing its potential in advancing GZSL applications.

Dual Channel‐Spatial Self‐Attention Transformer and CNN synergy network for 3D medical image segmentation

Even though the Vision Transformer leverages the self-attention mechanism to capture long-range dependencies, showing significant potential in medical image segmentation, the limited annotations in the image dataset make it difficult for the Transformer model to extract different global features, resulting in attention collapse and generating similar or identical attention maps. Previous studies have attempted to solve the problem by integrating convolutional neural layers into Transformer-based architectures. However, improper integration may lead to the inability of the model to effectively capture local and global information in both spatial and channel dimensions. To address the above issue, we propose a hybrid architecture using the Dual Channel-Spatial Self-Attention Transformer and CNN Synergy Network (DTC-SUNETR) for medical image segmentation. Specifically, we redesigned the self-attention mechanism. A novel Channel-Spatial Self-Attention (CSSA) block is introduced to integrate the enhanced channel and spatial self-attention mechanism to capture the global relationship and local structure among image features. This helps the model to more comprehensively understand the inter-dependencies between different channels and capture the relationships between different pixels, thus enhancing the feature representation of the corresponding dimensions. Simultaneously, it also improves the overall computational efficiency of the network. Extensive experiments on four different medical image segmentation datasets, including Synapse, ACDC, Brain Tumor, and Lung Tumor, demonstrate the superiority of the proposed DTC-SUNETR over state-of-the-art methods.

MMHFND: Fusing Modalities for Multimodal Multiclass Hindi Fake News Detection via Contrastive Learning

Multimodal content contains more deception than unimodal information, causing significant social and economic impacts. Current techniques often focus on single modality, neglecting knowledge fusion. While most studies have concentrated on English fake news detection, this study explores multi-modality for low-resource languages like Hindi. This work introduces the MMHFND model, based on M-CLIP, which uses late fusion for coarse (Fake vs Real) and fine-grained (World vs India vs Politics vs News vs Fact-Check) configurations. We extract deep representations from image and text using image transformer ResNet-50, a BERT-based L3cube-HindRoberta text transformer handling headlines, content, OCR text, and image captions, paired M-CLIP transformers, and an ELA (Error Level Analysis) image forensic method incorporating EfficientNet B0 to analyse multimodal news in Hindi language based on Devanagari script. M-CLIP integrates crossmodal similarity mapping of images and texts with retrieved multimodal features. The extracted features undergo redundancy reduction before being channelled into the final classifier. The MAM (Modality Attention Mechanism) is introduced, which generates weights for each modality individually. The MMHFND model uses a computed modality divergence score to identify dissonance between modalities and a modified contrastive loss on the score. We thoroughly analyse HinFakeNews dataset in a multimodal context, achieving accuracy in coarse and fine-grained configurations. We also undertake an ablation study to evaluate outcomes and explore alternative fusion processes on three different setups. The results show that the MMHFND model effectively detects fake news in Hindi with an accuracy of 0.986, outperforming other existing multimodal approaches.

An Interpretable Multi-Model Machine Learning Approach for Spatial Mapping of Deep-Sea Polymetallic Nodule Occurrences

AbstractHigh-resolution mapping of deep-sea polymetallic nodules is needed (a) to understand the reasons behind their patchy distribution, (b) to associate nodule coverage with benthic fauna occurrences, and (c) to enable an accurate resource estimation and mining path planning. This study used an autonomous underwater vehicle to map 37 km2 of a geomorphologically complex site in the Eastern Clarion–Clipperton Fracture Zone. A multibeam echosounder system (MBES) at 400 kHz and a side scan sonar at 230 kHz were used to investigate the nodule backscatter response. More than 30,000 seafloor images were analyzed to obtain the nodule coverage and train five machine learning (ML) algorithms: generalized linear models, generalized additive models, support vector machines, random forests (RFs) and neural networks (NNs). All models ML yielded similar maps of nodule coverage with differences occurring in the range of predicted values, particularly at parts with irregular topography. RFs had the best fit and NNs had the worst spatial transferability. Attention was given to the interpretability of model outputs using variable importance ranking across all models, partial dependence plots and domain knowledge. The nodule coverage is higher on relatively flat seafloor ( < 3°) with eastward-facing slopes. The most important predictor was the MBES backscatter, particularly from incident angles between 25 and 55°. Bathymetry, slope, and slope orientation were important geomorphological predictors. For the first time, at a water depth of 4500 m, orthophoto-mosaics and image-derived digital elevation models with 2-mm and 5-mm spatial resolutions supported the geomorphological analysis, interpretation of polymetallic nodules occurrences, and backscatter response.

Olivar: towards automated variant aware primer design for multiplex tiled amplicon sequencing of pathogens

Tiled amplicon sequencing has served as an essential tool for tracking the spread and evolution of pathogens. Over 15 million complete SARS-CoV-2 genomes are now publicly available, most sequenced and assembled via tiled amplicon sequencing. While computational tools for tiled amplicon design exist, they require downstream manual optimization both computationally and experimentally, which is slow and costly. Here we present Olivar, a first step towards a fully automated, variant-aware design of tiled amplicons for pathogen genomes. Olivar converts each nucleotide of the target genome into a numeric risk score, capturing undesired sequence features that should be avoided. In a direct comparison with PrimalScheme, we show that Olivar has fewer mismatches overlapping with primers and predicted PCR byproducts. We also compare Olivar head-to-head with ARTIC v4.1, the most widely used primer set for SARS-CoV-2 sequencing, and show Olivar yields similar read mapping rates (~90%) and better coverage to the manually designed ARTIC v4.1 amplicons. We also evaluate Olivar on real wastewater samples and found that Olivar has up to 3-fold higher mapping rates while retaining similar coverage. In summary, Olivar automates and accelerates the generation of tiled amplicons, even in situations of high mutation frequency and/or density. Olivar is available online as a web application at https://olivar.rice.edu and can be installed locally as a command line tool with Bioconda. Source code, installation guide, and usage are available at https://github.com/treangenlab/Olivar.

Parametric response mapping of co-registered intravoxel incoherent motion magnetic resonance imaging and positron emission tomography in locally advanced cervical cancer undergoing concurrent chemoradiation therapy

Background and PurposeIntravoxel-incoherent-motion (IVIM) magnetic-resonance-imaging (MRI) and positron-emission-tomography (PET) have been investigated independently but not voxel-wise to evaluate tumor microenvironment in cervical carcinoma patients. Whether regionally combined information of IVIM and PET offers additional predictive benefit over each modality independently has not been explored. Here, we investigated parametric-response-mapping (PRM) of co-registered PET and IVIM in cervical cancer patients to identify sub-volumes that may predict tumor shrinkage to concurrent-chemoradiation-therapy (CCRT). Materials and MethodsTwenty cervical cancer patients (age: 63[41–85]) were retrospectively evaluated. Diffusion-weighted-images (DWIs) were acquired on 3.0 T MRIs using a free-breathing single-shot-spin echo-planar-imaging (EPI) sequence. Pre- and on-treatment (∼after four-weeks of CCRT) MRI and pre-treatment FDG-PET/CT were acquired. IVIM model-fitting on the DWIs was performed using a Bayesian-fitting simplified two-compartment model. Three-dimensional rigidly-registered maps of PET/CT standardized-uptake-value (SUV) and IVIM diffusion-coefficient (D) and perfusion-fraction (f) were generated. Population-means of PET-SUV, IVIM-D and IVIM-f from pre-treatment-scans were calculated and used to generate PRM via a voxel-wise joint-histogram-analysis to classify voxels as high/low metabolic-activity and with high/low (hi/lo) cellular-density. Similar PRM maps were generated for SUV and f. ResultsTumor-volume (p < 0.001) significantly decreased, while IVIM-f (p = 0.002) and IVIM-D (p = 0.03) significantly increased on-treatment. Pre-treatment tumor-volume (r = -0.45,p = 0.04) and PRM-SUVhiDlo (r = -0.65,p = 0.002) negatively correlated with ΔGTV, while pre-treatment IVIM-D (r = 0.64,p = 0.002), PRM-SUVlofhi (r = 0.52,p = 0.02), and PRM-SUVloDhi (r = 0.74,p < 0.001) positively correlated with ΔGTV. ConclusionIVIM and PET was performed on cervical cancer patients undergoing CCRT and we observed that both IVIM-f and IVIM-D increased during treatment. Additionally, PRM was applied, and sub-volumes were identified that were related to ΔGTV.

Methodology of the national mapping of educational theatre programmes

During the second half of the 2012–13 theatre season, we mapped all then-existing educational theatre practices in Hungary against a standardized set of criteria. We contacted a total of 298 institutions and observed 118 programmes in person. We aimed to map the complete picture, so we involved every programme that fitted our low-threshold criteria and responded to our inquiry. Presumably, we covered the entire Hungarian educational theatre sphere. For each programme, we collected data from six sources: children or youth participating in the programmes; teachers accompanying the participants; organizational contact persons; creators/performers of the programmes; observing of the programmes by independent observers; and secondary research on the internet. In 2023, we repeated part of this data collection. In this article, we present the methodology used for the mapping in detail since we believe it can serve as a model for similar mappings in other countries.

Regional resource provision map: methodology and key approaches

The achievement of sustainable development goals with the help of the implementation of a systematic approach to managing the resource potential of regions through sustainable development goals is one of the actual objectives in regional management. Mapping is known as an approach, which allows combining several data sources with different scaling. This study aims to develop regional resource provision map for creating sustainable development conditions. Multidisciplinary research is a valuable source of this research that allows to unit ESG criteria and their regional commitment through cartographic science tools. The methodology is presented in the form of a sequence of actions to draw up a resource supply map. Using the presented map of Western Kazakhstani region confirms the validity of the scientific and applied methodology. The research outcomes contain proven arguments for the further research based on the issues of constructing integrated resource provision maps for the Kazakhstani regions. Key cartography approaches make it possible to form recommendations for similar maps use in terms of decision-making based on interregional interaction, taking into account resource potential, consisting of natural, labor, financial, and infrastructural capabilities of the regions and environmental risk assessments. Developed recommendations were tested with the help of Microsoft Power BI and SuperMap (laboratory of “Geoinformation Cartography” of Kazakh National University named after al-Farabi Kazakh).

Imaging Based Techniques Combined with Color Measurements for the Enhancement of Medieval Wall Paintings in the Framework of EHEM Project.

(1) Background: This paper illustrates an innovative methodological approach chosen to study and map the colors of the medieval wall painting of Santa Maria Antiqua in the Roman Forum, one of the pilot sites of the EHEM project (Enhancement of Heritage Experiences: The Middle Ages). Digital Layered Models of Architecture and Mural Paintings over Time). (2) Methods: Two methods were employed to gather information about colors and mapping. Specifically, colorimetry was utilized for spot measurements, and hypercolorimetric multispectral imaging (HMI) was employed to map the same colors sampled through colorimetry. (3) Results: Chromatic data for all colors in the wall paintings were obtained in the CIELAB color space. Additionally, chromatic similarity maps were generated using the innovative HMI system, a multispectral imaging technique capable of obtaining color data information through advanced calibration software named SpectraPick® (Version 1.1). This comprehensive approach facilitates a thorough understanding of color characteristics and distribution. (4) Conclusions: The color measurements and mapping represent significant advancements in the interpretation of medieval wall paintings, which are often fragmentary and stratigraphically complex. This research sheds new light on the colors used and enhances our understanding of the original appearance of the iconographic patterns. Furthermore, it enables the reconstruction of colors that closely resemble the originals.

Cross-domain prototype similarity correction for few-shot radar modulation signal recognition

The new classes of radar signals are increasingly difficult to acquire under non-cooperative environments, which makes it difficult to support convolutional neural network training with limited labeled samples. The few-shot learning (FSL) methods have shown great performance in classification with limited labeled samples, but the FSL methods ignore that the class distributions between the new and original tasks are significantly different, resulting in a massive challenge in identifying new radar signals. To solve this problem, a few-shot radar modulation signal recognition method based on cross-domain prototype similarity correction (CDPSC) is proposed. Specifically, a residual feature tokenizer transformer (RFTT) model embedded with a pooling token generation block is designed to focus on the important features and improve the ability to represent samples. Meanwhile, the proposed domain prototype similarity mapping (DPSM) strategy adaptively learns the class mapping, reduces the inter-domain difference through feature distribution alignment, and effectively corrects the target domain prototypes. In addition, we introduce a sample prototype embedding (SPE) strategy in the training phase, which can reduce the intra-class distance and increase the inter-class distance. Experimental results demonstrate that the CDPSC method is superior to typical FSL methods in recognition accuracy under different sample numbers.

Two-dimensional Eclipse Mapping of the Hot-Jupiter WASP-43b with JWST MIRI/LRS

We present eclipse maps of the two-dimensional thermal emission from the dayside of the hot-Jupiter WASP-43b, derived from an observation of a phase curve with the JWST MIRI/LRS instrument. The observed eclipse shapes deviate significantly from those expected for a planet emitting uniformly over its surface. We fit a map to this deviation, constructed from spherical harmonics up to order ℓmax=2 , alongside the planetary, orbital, stellar, and systematic parameters. This yields a map with a meridionally averaged eastward hot-spot shift of (7.75 ± 0.36)°, with no significant degeneracy between the map and the additional parameters. We show the latitudinal and longitudinal contributions of the dayside emission structure to the eclipse shape, finding a latitudinal signal of ∼200 ppm and a longitudinal signal of ∼250 ppm. To investigate the sensitivity of the map to the method, we fix the parameters not used for mapping and derive an “eigenmap” fitted with an optimized number of orthogonal phase curves, which yields a similar map to the ℓmax=2 map. We also fit a map up to ℓmax=3 , which shows a smaller hot-spot shift, with a larger uncertainty. These maps are similar to those produced by atmospheric simulations. We conclude that there is a significant mapping signal which constrains the spherical harmonic components of our model up to ℓmax=2 . Alternative mapping models may derive different structures with smaller-scale features; we suggest that further observations of WASP-43b and other planets will drive the development of more robust methods and more accurate maps.

Developing deep learning-based large-scale organic reaction classification model via sigma-profiles

Advanced technologies like deep learning have accelerated the discovery of novel chemical reactions, especially in the field of organic synthesis. With hundreds of thousands of reactions available for reference, one way to effectively leverage them is by classifying chemical reactions into different clusters based on their specific characteristics, which makes target-guided navigation in the vast chemical space possible. Although previous attempts that apply deep learning to reaction classification tasks have made substantial progress, developing a model with good interpretability as well as high accuracy for large-scale reaction classification tasks remains an open question. In this work, a deep learning-based model for a large-scale reaction classification task is first constructed by utilizing pre-trained BERT and autoencoder. Then, the model is trained under the open-source dataset USPTO_TPL which contains recorded reactions of up to 1000 different types. The multi-classification accuracy of the model on the testing dataset is 99.382%, showing its great potential for practical use. Besides, a reaction similarity map is presented to correlate the reactions in the USPTO_TPL dataset based on their sigma-profile-based statistical features. Finally, representative reactions from the testing dataset are provided to illustrate the model's effectiveness on the reaction classification task.

Shift-insensitive perceptual feature of quadratic sum of gradient magnitude and LoG signals for image quality assessment and image classification

Most existing full-reference (FR) Image quality assessment (IQA) models work in the premise of that the two images should be well registered. Shifting an image would lead to an inaccurate evaluation of image quality, because small spatial shifts are far less noticeable than structural distortion for human observers. To this regard, we propose to study an IQA feature that is shift-insensitive to the basic primitive structure of images, i.e., image edge. According to previous studies, the image gradient magnitude (GM) and the Laplacian of Gaussian (LoG) operator that depict the edge profiles of natural images are highly efficient structural features in IQA tasks. In this paper, we find that the Quadratic sum of the normalized GM and the LoG signals (QGL) has excellent shift-insensitive property in representing image edges after theoretically solving the selection problem of a ratio parameter to balance the GM and LoG signals. Based on the proposed QGL feature, two FR-IQA models can be built directly by measuring the similarity map with mean and standard deviation pooling strategies, named mQGL and sQGL, respectively. Experimental results show that the proposed sQGL and mQGL work robustly on four benchmark IQA databases, and QGL-based models show great shift-insensitive property to spatial translation and image rotation while judging the image quality. In addition, we explore the feasibility of combining QGL feature with deep neural networks, and verify that it can help to promote image pattern recognition in texture classification tasks.

Empowerment Among Adolescent Girls in Nepal: A Concept Mapping Exploratory Study.

The concept of empowerment is challenging to operationalize and measure; it is multidimensional, the outcomes are not always directly observable, and meanings of empowerment are highly contextual and socially and culturally situated. This study aimed to explore perspectives of empowerment among adolescent girls in Nepal to identify statements for inclusion in a context-specific empowerment measure. We used a participatory and mixed method research method called concept mapping in 3 districts in Nepal. Three sequential concept mapping sessions were used to solicit, organize, and process how participants responded to the prompt: "The life of an adolescent girl improves when she has/can…" The Concept Systems Global software was used to manage and analyze the concept mapping brainstorming, sorting, and rating data using established tools, such as multidimensional scaling and cluster analysis. Concept mapping was conducted with 113 participants, including national experts, program staff, adolescents, and their mothers. They identified 105 items that fall into a 4-cluster solution: education and knowledge, decision-making, supports and skills, and physical infrastructure. Rating data uncovered there was some overlap between the top 10 most important items between the national-level experts and other stakeholder groups; however, several components associated with empowerment differed by stakeholder group in terms of importance. This research represents a critical step in exploring definitions of empowerment among adolescent girls in the Nepal context and with expert input. These results led to the development of a contextually specific definition of empowerment. Researchers and practitioners interested in developing context-specific understandings of complex topics that incorporate community voices and perspectives could use a similar concept mapping approach in other countries to explore various topics with diverse populations.

Long-term effects of childhood single-parent family structure on brain connectivity and psychological well-being.

The high and increasing proportion of single-parent families is considered a risk factor associated with various childhood trauma experiences. Consequently, concerns have been raised regarding the potential long-term effects of the childhood single-parent family structure. In this study, we employed advanced magnetic resonance imaging technology, including morphometric similarity mapping, functional connectivity density, and network-based analysis, to investigate brain connectivity and behavioral differences among young adults who were raised in single-parent families. Our study also aimed to explore the relationship between these differences and childhood trauma experiences. The results showed that individuals who grew up in single-parent families exhibited higher levels of anxiety, depression, and harm-avoidant personality. The multimodal MRI analysis further showed differences in regional and network-based connectivity properties in the single-parent family group, including increased functional connectivity density in the left inferior parietal lobule, enhanced cortical structural connectivity between the left isthmus cingulate cortex and peri-calcarine cortex, and an increase in temporal functional connectivity. Moreover, elevated levels of anxiety and depression, along with heightened functional connectivity density in the left inferior parietal lobule and increased temporal functional connectivity, were found to be correlated with a greater number of childhood trauma experiences. Through analyzing multiple data patterns, our study provides objective neuropsychobiological evidence for the enduring impact of childhood single-parent family structure on psychiatric vulnerability in adulthood.