Author Correction: Resolving tissue complexity by multimodal spatial omics modeling with MISO
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Multimodal insights: enhancing cultural promotion through analysis of Saudi Arabian audiovisual productions
This research explores the application of Dicerto’s (2018) multimodal pragmatic model in analyzing Arabic audiovisual productions for translation purposes, focusing on enhancing cultural promotion. Employing a qualitative descriptive analysis approach, the study examines samples from Saudi productions that promote tourism, mainly focusing on Saudi coffee and its cultural traditions to enlighten foreign visitors about Saudi culture. The analysis reveals that Dicerto’s model provides a clear framework for achieving semantic fidelity in translation, ensuring that the translated text closely resembles its original in interpretative richness. Central to this framework is the principle of optimal relevance, wherein the sender intends the message to be maximally pertinent to the audience, thereby justifying the recipient’s cognitive effort in processing it and facilitating access to the sender’s intentions. This research sheds light on the effectiveness of applying multimodal analysis models in cultural promotion efforts through audiovisual productions, particularly in Saudi Arabian tourism promotion.
Innovating beyond electrophysiology through multimodal neural interfaces
Neural circuits distributed across different brain regions mediate how neural information is processed and integrated, resulting in complex cognitive capabilities and behaviour. To understand dynamics and interactions of neural circuits, it is crucial to capture the complete spectrum of neural activity, ranging from the fast action potentials of individual neurons to the population dynamics driven by slow brain-wide oscillations. In this Review, we discuss how advances in electrical and optical recording technologies, coupled with the emergence of machine learning methodologies, present a unique opportunity to unravel the complex dynamics of the brain. Although great progress has been made in both electrical and optical neural recording technologies, these alone fail to provide a comprehensive picture of the neuronal activity with high spatiotemporal resolution. To address this challenge, multimodal experiments integrating the complementary advantages of different techniques hold great promise. However, they are still hindered by the absence of multimodal data analysis methods capable of providing unified and interpretable explanations of the complex neural dynamics distinctly encoded in these modalities. Combining multimodal studies with advanced data analysis methods will offer novel perspectives to address unresolved questions in basic neuroscience and to develop treatments for various neurological disorders.
A multimodal neural signature of face processing in autism within the fusiform gyrus
Atypical face processing is commonly reported in autism. Its neural correlates have been explored extensively across single neuroimaging modalities within key regions of the face processing network, such as the fusiform gyrus (FFG). Nonetheless, it is poorly understood how variation in brain anatomy and function jointly impacts face processing and social functioning. Here we leveraged a large multimodal sample to study the cross-modal signature of face processing within the FFG across four imaging modalities (structural magnetic resonance imaging (MRI), resting-state functional magnetic resonance imaging, task-functional magnetic resonance imaging and electroencephalography) in 204 autistic and nonautistic individuals aged 7–30 years (case–control design). We combined two methodological innovations—normative modeling and linked independent component analysis—to integrate individual-level deviations across modalities and assessed how multimodal components differentiated groups and informed social functioning in autism. Groups differed significantly in a multimodal component driven by bilateral resting-state functional MRI, bilateral structure, right task-functional MRI and left electroencephalography loadings in face-selective and retinotopic FFG. Multimodal components outperformed unimodal ones in differentiating groups. In autistic individuals, multimodal components were associated with cognitive and clinical features linked to social, but not nonsocial, functioning. These findings underscore the importance of elucidating multimodal neural associations of social functioning in autism, offering potential for the identification of mechanistic and prognostic biomarkers.
A machine learning approach to leveraging electronic health records for enhanced omics analysis
Omics studies produce a large number of measurements, enabling the development, validation and interpretation of systems-level biological models. Large cohorts are required to power these complex models; yet, the cohort size remains limited due to clinical and budgetary constraints. We introduce clinical and omics multimodal analysis enhanced with transfer learning (COMET), a machine learning framework that incorporates large, observational electronic health record databases and transfer learning to improve the analysis of small datasets from omics studies. By pretraining on electronic health record data and adaptively blending both early and late fusion strategies, COMET overcomes the limitations of existing multimodal machine learning methods. Using two independent datasets, we showed that COMET improved the predictive modelling performance and biological discovery compared with the analysis of omics data with traditional methods. By incorporating electronic health record data into omics analyses, COMET enables more precise patient classifications, beyond the simplistic binary reduction to cases and controls. This framework can be broadly applied to the analysis of multimodal omics studies and reveals more powerful biological insights from limited cohort sizes.
Stromal architecture and fibroblast subpopulations with opposing effects on outcomes in hepatocellular carcinoma
Dissecting the spatial heterogeneity of cancer-associated fibroblasts (CAFs) is vital for understanding tumor biology and therapeutic design. By combining pathological image analysis with spatial proteomics, we revealed two stromal archetypes in hepatocellular carcinoma (HCC) with different biological functions and extracellular matrix compositions. Using paired single-cell RNA and epigenomic sequencing with Stereo-seq, we revealed two fibroblast subsets CAF-FAP and CAF-C7, whose spatial enrichment strongly correlated with the two stromal archetypes and opposing patient prognosis. We discovered two functional units, one is the intratumor inflammatory hub featured by CAF-FAP plus CD8_PDCD1 proximity and the other is the marginal wound-healing hub with CAF-C7 plus Macrophage_SPP1 co-localization. Inhibiting CAF-FAP combined with anti-PD-1 in orthotopic HCC models led to improved tumor regression than either monotherapy. Collectively, our findings suggest stroma-targeted strategies for HCC based on defined stromal archetypes, raising the concept that CAFs change their transcriptional program and intercellular crosstalk according to the spatial context.
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