Search Results (592)

Background: Accurate and early diagnosis of autism spectrum disorder (ASD) is essential for timely intervention. Structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) provide complementary insights into brain structure and function. Most deep learning approaches rely on a single modality, limiting their ability to capture cross-modal relationships. Methods: We propose DAGMNet, a dual-branch attention-pruned graph neural network for ASD prediction that integrates sMRI, fMRI, and phenotypic data. The framework employs modality-specific feature extraction to preserve unique structural and functional characteristics, an attention-based cross-modal fusion module to model inter-modality complementarity, and a phenotype-pruned dynamic graph learning module with adaptive graph construction for personalized diagnosis. Results: Evaluated on the ABIDE-I dataset, DAGMNet achieves an accuracy of 91.59% and an AUC of 96.80%, outperforming several state-of-the-art baselines. To validate the method’s generalizability, we also validate it on ADNI datasets from other degenerative diseases and achieve good results. Conclusions: By effectively fusing multimodal neuroimaging and phenotypic information, DAGMNet enhances cross-modal representation learning and improves diagnostic accuracy. To further assist clinical decision making, we conduct biomarker detection analysis to provide region-level explanations of our model’s decisions. Full article

Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by heterogeneous behavioral and neurological patterns, complicating timely and accurate diagnosis. Behavioral datasets are commonly used to diagnose ASD. In clinical practice, it is difficult to identify ASD because of the complexity of the behavioral symptoms, overlap of neurological disorders, and individual heterogeneity. Correct and timely identification is dependent on the presence of skilled professionals to perform thorough neurological examinations. Nevertheless, with developments in deep learning techniques, the diagnostic process can be significantly improved by automatically identifying and automatically classifying patterns of ASD-related behaviors and neuroimaging features. Method: This study introduces a novel multimodal diagnostic paradigm that combines structured behavioral phenotypes and structural magnetic resonance imaging (sMRI) into an interpretable and personalized framework. A Generalized Additive Model with Interactions (GAMI-Net) is used to process behavioral data for transparent embedding of clinical phenotypes. Structural brain characteristics are extracted via a hybrid CNN–GNN model, which retains voxel-level patterns and region-based connectivity through the Harvard–Oxford atlas. The embeddings are then fused using an Autoencoder, compressing cross-modal data into a common latent space. A Hyper Network-based MLP classifier produces subject-specific weights to make the final classification. Results: On the held-out test set drawn from the ABIDE-I dataset, a 20% split with about 247 subjects, the constructed system achieved an accuracy of 99.40%, precision of 100%, recall of 98.84%, an F1-score of 99.42%, and an ROC-AUC of 99.99%. For another test of generalizability, five-fold stratified cross-validation on the entire dataset yielded a mean accuracy of 98.56%, an F1-score of 98.61%, precision of 98.13%, recall of 99.12%, and an ROC-AUC of 99.62%. Conclusions: These results suggest that interpretable and personalized multimodal fusion can be useful in aiding practitioners in performing effective and accurate ASD diagnosis. Nevertheless, as the test was performed on stratified cross-validation and a single held-out split, future research should seek to validate the framework on larger, multi-site datasets and different partitioning schemes to guarantee robustness over heterogeneous populations. Full article

Hypertrophic cardiomyopathy (HCM) is a cardiac disorder characterized by unexplained left ventricular hypertrophy and a clinical presentation that is heterogeneous, ranging from asymptomatic cases to sudden cardiac death (SCD). The condition’s complex pathophysiology encompasses myocyte disarray, fibrosis, and impaired cellular metabolism. Advancements in non-invasive cardiac imaging, notably echocardiography and cardiac magnetic resonance (CMR), have led to substantial progress in the domains of early diagnosis, phenotypic characterization, and risk stratification. Echocardiography is the preferred diagnostic modality, as it provides a comprehensive evaluation of ventricular hypertrophy patterns, left ventricular outflow tract (LVOT) obstruction, mitral valve abnormalities, left atrial size, and diastolic function. Novel techniques, such as speckle-tracking strain imaging, have emerged as means to detect subclinical myocardial dysfunction and to provide significant prognostic information. Cine-CMR sequences, tissue characterization with late gadolinium enhancement, and quantitative techniques such as strain imaging have been shown to enhance diagnostic precision and prognostic evaluation. The integration of multimodality imaging has been demonstrated to enhance the management of patients with HCM, both in the short term and in the long term, by facilitating individualized monitoring. This review summarizes the role of cardiac imaging in the comprehensive evaluation of HCM, emphasizing the impact of these methods on diagnosis, risk assessment, and personalized patient care, particularly in challenging clinical settings, such as cases of athlete’s heart and pathological ventricular hypertrophy. Full article

Iron is critical for brain development, metabolism, and function; however, dysregulated iron disposition contributes to neurological diseases. Many neuroimaging techniques have enabled detection of iron susceptibility, and quantitative susceptibility mapping (QSM) offers a sensitive magnetic resonance imaging (MRI) technique for quantifying brain iron. To elucidate the functional role of cerebral iron and clarify the utility of QSM in establishing iron as a potential biomarker, this review synthesizes cellular and regional behaviours of iron from physiological aging to disease conditions, with a focus on neurodegeneration such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and multiple sclerosis (MS), as well as cerebral small vessel disease (CSVD) as cerebrovascular manifestation. Distinct patterns of iron distribution in deep gray matter and selective cortical regions are associated with motor and cognitive impairment, while the interaction between iron, vascular integrity, and glial function further stresses its pathological relevance. QSM of iron may, thereby, serve as a marker to monitor iron-related disease progression and facilitate intervention. Temporal dynamics of iron in brain pathology remain underexplored, and we emphasized the need for longitudinal mapping and multi-modality biomarker integration. Establishing iron as a clinically relevant imaging biomarker requires continued investigation into its topographical, molecular, and functional correlates across aging and disease trajectories. Full article

To address the problem of low-frequency broadband vibration and noise encountered in engineering, a method for expanding the low-frequency band gap of locally resonant acoustic metamaterials is proposed based on the multi-mode coupling effect. A computational method for the band gap characteristics of second-order multi-mode acoustic metamaterials has been derived. By incorporating the vibrational modes obtained from band structure calculations, a systematic investigation of the formation mechanisms of multiple band gaps was conducted, revealing that the emergence of these multiple band gaps stems from the coupled resonance between elastic waves and distinct vibrational modes of the local resonator units. Furthermore, the influence of design parameter variations on the bandgap was investigated, and the strategy of realizing low-frequency multi-order bandgaps by increasing the order of local resonance units was examined. Finally, vibration tests were conducted on the second-, third-, and fourth-order multi-mode coupled acoustic metamaterials. The results demonstrated that these materials exhibit an expanded vibration band gap within the low-frequency range, and the measured frequency response aligns closely with the theoretical calculations. This type of acoustic metamaterial offers viable applicability for controlling low-frequency broadband vibrations. Full article

Borderline Personality Disorder (BPD) is marked by emotional dysregulation, instability in self-image and relationships, and high impulsivity. While functional magnetic resonance imaging (fMRI) studies have provided valuable insights into the disorder’s neural correlates, electroencephalography (EEG) may capture real-time brain activity changes relevant to BPD’s rapid emotional shifts. This review summarizes findings from studies investigating resting state and task-based EEG in individuals with BPD, highlighting common neurophysiological markers and their clinical implications. A targeted literature search (1980–2025) was conducted across databases, including PubMed, Google Scholar, and Cochrane. The search terms combined “EEG” or “electroencephalography” with “borderline personality disorder” or “BPD”. Clinical trials and case reports published in English were included if they recorded and analyzed EEG activity in BPD. A total of 24 studies met the inclusion criteria. Findings indicate that individuals with BPD often show patterns consistent with chronic hyperarousal (e.g., reduced alpha power and increased slow-wave activity) and difficulties shifting between vigilance states. Studies examining frontal EEG asymmetry reported varying results—some linked left-frontal activity to heightened hostility, while others found correlations between right-frontal shifts and dissociation. Childhood trauma, mentalization deficits, and dissociative symptoms were frequently predicted or correlated with EEG anomalies, underscoring the impact of adverse experiences on neural regulation—however, substantial heterogeneity in methods, small sample sizes, and comorbid conditions limited study comparability. Overall, EEG research supports the notion of altered arousal and emotion regulation circuits in BPD. While no single EEG marker uniformly defines the disorder, patterns such as reduced alpha power, increased theta/delta activity, and shifting frontal asymmetries converge with core BPD features of emotional lability and interpersonal hypersensitivity. More extensive, standardized, and multimodal investigations are needed to establish more reliable EEG biomarkers and elucidate how early trauma and dissociation shape BPD’s neurophysiological profile. Full article

Traditional culture plays a vital role in shaping national identity and emotional belonging, making it imperative to explore innovative strategies for its digital preservation and engagement. This study investigates how interaction design in cultural digital games influences users’ emotional experiences and cultural understanding. Centering on the Chinese intangible cultural heritage puppet manipulation, we developed an interactive cultural game with three modes: gesture-based interaction via Leap Motion, keyboard control, and passive video viewing. A multimodal evaluation framework was employed, integrating subjective questionnaires with physiological indicators, including Functional Near-Infrared Spectroscopy (fNIRS), infrared thermography (IRT), and electrodermal activity (EDA), to assess users’ emotional responses, immersion, and perception of cultural content. Results demonstrated that gesture-based interaction, which aligns closely with the embodied cultural behavior of puppet manipulation, significantly enhanced users’ emotional engagement and cultural comprehension compared to the other two modes. Moreover, fNIRS data revealed broader activation in brain regions associated with emotion regulation and cognitive control during gesture interaction. These findings underscore the importance of culturally congruent interaction design in enhancing user experience and emotional resonance in digital cultural applications. This study provides empirical evidence supporting the integration of cultural context into interaction strategies, offering valuable insights for the development of emotionally immersive systems for intangible cultural heritage preservation. Full article

Chronic pain is a dynamic, brain-wide condition that eludes effective management by conventional, static treatment approaches. Transcutaneous Electrical Nerve Stimulation (TENS), traditionally perceived as a simple and generic modality, is on the verge of a significant transformation. Guided by advances in brain-state decoding and adaptive algorithms, TENS can evolve into a precision neuromodulation system tailored to individual needs. By integrating multimodal neuroimaging—including the spatial resolution of functional magnetic resonance imaging (fMRI), the temporal sensitivity of an Electroencephalogram (EEG), and the ecological validity of functional near-infrared spectroscopy (fNIRS)—with real-time machine learning, we envision a paradigm shift from fixed stimulation protocols to personalized, closed-loop modulation. This comprehensive review outlines a translational framework to reengineer TENS from an open-loop device into a responsive, intelligent therapeutic platform. We examine the underlying neurophysiological mechanisms, artificial intelligence (AI)-driven infrastructures, and ethical considerations essential for implementing this vision in clinical practice—not only for chronic pain management but also for broader neuroadaptive healthcare applications. Full article

The integration of organoids with biosensors serves as a miniaturized model of human physiology and diseases, significantly transforming the research frameworks surrounding drug development, toxicity testing, and personalized medicine. This review aims to provide a comprehensive framework for researchers to identify suitable technical approaches and to promote the advancement of organoid sensing towards enhanced biomimicry and intelligence. To this end, several primary methods for technology integration are systematically outlined and compared, which include microfluidic integrated systems, microelectrode array (MEA)-based electrophysiological recording systems, optical sensing systems, mechanical force sensing technologies, field-effect transistor (FET)-based sensing techniques, biohybrid systems based on synthetic biology tools, and label-free technologies, including impedance, surface plasmon resonance (SPR), and mass spectrometry imaging. Through multimodal collaboration such as the combination of MEA for recording electrical signals from cardiac organoids with micropillar arrays for monitoring contractile force, these technologies can overcome the limitations inherent in singular sensing modalities and enable a comprehensive analysis of the dynamic responses of organoids. Furthermore, this review discusses strategies for integrating strategies of multimodal sensing approaches (e.g., the combination of microfluidics with MEA and optical methods) and highlights future challenges related to sensor implantation in vascularized organoids, signal stability during long-term culture, and the standardization of clinical translation. Full article

Brain natriuretic peptide (BNP) is an important biomarker for the diagnosis and prediction of chronic heart failure (CHF). Aiming at the problems of the low sensitivity and poor portability of traditional BNP detection methods, this study proposes a Surface-enhanced Raman-scattering (SERS) fiber-optic sensor based on a multimode fiber (MMF)–no core fiber (NCF) structure. The sensor achieves BNP detection by significantly amplifying the Raman signal of the toluidine blue (TB) marker through the synergistic effect of NCF’s unique optical transmission modes and localized surface plasmon resonance (LSPR). To optimize the sensor performance, we first investigated the effect of the NCF length on the Raman signal, using Rhodamine 6G (R6G), and determined the optimal structural parameters. Combined with the microfluidic chip integration technology, the antibody–BNP–antibody sandwich structure was adopted, and TB was used as the Raman label to realize the quantitative detection of BNP. Experimental results demonstrate that the detection limit of the sensor is lower than the clinical diagnostic threshold and exhibits stability. The sensor sensitivity can be adjusted by regulating the laser power. With its stability and high portability, this sensor provides a new solution for the early diagnosis of heart failure and demonstrates broad application prospects in biomarker detection. Full article

Amid growing demands for heritage conservation and precision urban governance, this study proposes a multimodal framework to analyze tourist perception and behavior in Macau’s Historic Centre. We integrate geotagged social media images and text, ultra-wideband (UWB) pedestrian trajectories, and a LiDAR-derived 3D digital twin to examine the interplay among spatial configuration, movement, and affect. Visual content in tourist photos is classified with You Only Look Once (YOLOv8), and sentiment polarity in Weibo posts is estimated with a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model. UWB data provide fine-grained trajectories, and all modalities are georeferenced within the digital twin. Results indicate that iconic landmarks concentrate visual attention, pedestrian density, and positive sentiment, whereas peripheral sites show lower footfall yet strong emotional resonance. We further identify three coupling typologies that differentiate tourist experiences across spatial contexts. The study advances multimodal research on historic urban centers by delivering a reproducible framework that aligns image, text, and trajectory data to extract microscale patterns. Theoretically, it elucidates how spatial configuration, movement intensity, and affective expression co-produce experiential quality. Using Macau’s Historic Centre as an empirical testbed, the findings inform heritage revitalization, wayfinding, and crowd-management strategies. Full article

Eosinophilic myocarditis (EM) is a rare and potentially fatal form of acute myocarditis. Currently, no validated diagnostic criteria exist, and definitive diagnosis relies on endomyocardial biopsy (EMB) not devoid of periprocedural complications. This review aims to explore how a multimodality imaging approach can support early diagnosis, reduce reliance on EMB, enable risk stratification and monitor the response to anti-inflammatory therapy. In particular, while echocardiography provides rapid and useful information in suspected EM, cardiac magnetic resonance (CMR) remains the non-invasive gold standard for diagnosis due to its ability to provide accurate tissue characterization. Moreover, positron emission tomography/computed tomography (PET/CT) and cardiac CT (CCT) may offer valuable insights, particularly when echocardiographic image quality is poor or CMR is contraindicated or unavailable. Based on our experience and current literature, an optimal multimodality imaging approach should reserve EMB only for high-risk or inconclusive cases. Furthermore, this strategy offers complementary information, supporting clinical decisions and optimizing long-term outcomes. Full article

In their simplest form, bulk acoustic wave (BAW) devices consist of a piezoelectric crystal between two electrodes that transduce the material’s vibrations into electrical signals. They are adopted in frequency control and metrology, with well-established standards at frequencies of 5 MHz and above. Their use as a resonant-mass strain antenna for high-frequency gravitational waves has been recently proposed (Goryachev and Tobar, 2014). The estimated power spectral density sensitivity at the resonant frequencies is of the order of ${10}^{-21}\mathrm{strain}/\sqrt{\mathrm{Hz}}$. In this paper, after introducing the science opportunity and potential of gravitational wave detection with BAWs, we describe the two-stage BAUSCIA project plan to build a multimode antenna based on commercial BAWs, followed by an optimized array of custom BAWs. We show that commercially available BAWs already provide sensitivity comparable to current experiments around 10 MHz. Finally, we outline options for optimization of custom devices to improve sensitivity in an unexplored region, probe multiple frequencies between 0.1 and 10 MHz, and target specific signals, such as post-merger emission from neutron stars or emission from various dark matter candidates. Full article

Accurate segmentation of deep brain structures is critical for preoperative planning in such neurosurgical procedures as Deep Brain Stimulation (DBS). Previous research has showcased successful pipelines for segmentation from T1-weighted (T1w) Magnetic Resonance Imaging (MRI) data. Nevertheless, the role of T2-weighted (T2w) MRI data has been underexploited so far. This study proposes and evaluates a fully automated deep learning pipeline based on nnU-Net for the segmentation of eight clinically relevant deep brain structures. A heterogeneous dataset has been prepared by gathering 325 paired T1w and T2w MRI scans from eight publicly available sources, which have been annotated by means of an atlas-based registration approach. Three 3D nnU-Net models—unimodal T1w, unimodal T2w, and multimodal (encompassing both T1w and T2w)—have been trained and compared by using 5-fold cross-validation and a separate test set. The outcomes prove that the multimodal model consistently outperforms the T2w unimodal model and achieves comparable performance with the T1w unimodal model. On our dataset, all proposed models significantly exceed the performance of the state-of-the-art DBSegment tool. These findings underscore the value of multimodal MRI in enhancing deep brain segmentation and offer a robust framework for accurate delineation of subcortical targets in both research and clinical settings. Full article

Functional mitral regurgitation (FMR) is a common condition with significant prognostic implications, primarily driven by left atrial or ventricular remodeling secondary to ischemic or non-ischemic cardiomyopathies. While guideline-directed medical therapy (GDMT) remains the cornerstone of management, reducing mitral regurgitation severity in up to 40–45% of cases, additional interventions are often necessary. In patients where atrial fibrillation (AF) or ventricular dyssynchrony due to abnormal electrical conduction contributes to disease progression, guideline-directed AF management or cardiac resynchronization therapy plays a pivotal role. For those with persistent moderate to severe MR and unresolved symptoms despite optimal GDMT, percutaneous intervention may be warranted, provided specific clinical and echocardiographic criteria are met. This review highlights a precision-medicine approach to patient selection for transcatheter treatment of functional mitral regurgitation (FMR), emphasizing the integration of clinical characteristics with advanced multimodal imaging, including echocardiography, cardiac magnetic resonance, and computed tomography. In anatomically or clinically complex cases, complementary use of these imaging modalities is essential to ensure accurate phenotyping and procedural planning. Once a suitable candidate for percutaneous intervention has been identified, we provide a detailed overview of current transcatheter strategies, with a focus on device selection tailored to anatomical and pathophysiological features. Finally, we discuss emerging technologies and evolving therapeutic paradigms that are shaping the future of individualized FMR management. Full article