Multimodal Data Processing in Neuroscience and Perception Science: Advances, Challenges, and Applications

Neuroscience and perception science are rapidly evolving fields that increasingly rely on the integration of multimodal data to better understand the complexity of brain function and sensory processing. Traditional single-modality approaches, such as isolated neuroimaging or electrophysiological measurements, can only provide partial perspectives on the intricate dynamics underlying perception and cognition. Recent years have witnessed a paradigm shift, driven by the realization that combining diverse data sources—ranging from neuroimaging and electrophysiology to behavioral and computational modeling—can reveal the multilayered interactions that shape human experience. Notably, in perception science, the integration of visual, auditory, tactile, and other sensory modalities is essential to advancing our understanding of how the brain interprets and responds to a constantly changing environment.

Despite the promise of multimodal integration, significant challenges remain in both technical and theoretical domains. Cutting-edge advances in machine learning and artificial intelligence now enable researchers to analyze high-dimensional, heterogeneous datasets—facilitating the discovery of patterns that were previously hidden in single-modality studies. Methods such as deep learning, Bayesian models, and multimodal fusion frameworks are being developed to address persistent issues like noise, temporal misalignment, and interpretability. The field is also seeing transformative impacts in clinical neuroscience, where multimodal data processing supports improved diagnosis and treatment strategies for disorders such as autism, schizophrenia, and Alzheimer’s disease, as well as the development of next-generation brain–computer interfaces. However, barriers still exist in the standardization of methods, the scalability of computational tools, and the meaningful integration of disparate data types.

This Research Topic aims to catalyze progress by highlighting innovative solutions and practical applications in multimodal data processing, with a special focus on perception science and its intersection with neuroscience. We seek to address core challenges related to integrating and interpreting diverse datasets and to discuss the opportunities that arise from interdisciplinary collaboration. Contributions may address fundamental methodological advancements, novel computational or experimental paradigms, translational clinical studies, or theoretical perspectives that drive the field forward. Our objective is to build a comprehensive understanding of perception and cognition by bridging computational, experimental, and applied approaches.

This Research Topic will consider a range of studies and perspectives that address the integration of multimodal data within neuroscience and perception science, without extending into unrelated domains such as purely molecular neuroscience or abstract theoretical modeling. Relevant themes include, but are not limited to:

- Fusion of multimodal neuroimaging and electrophysiological data

- Computational models and machine learning for multisensory integration

- Advances in deep learning, Bayesian modeling, and multimodal fusion frameworks

- Clinical applications: diagnosis, monitoring, and treatment of neurological disorders

- Brain–computer interfaces leveraging multimodal data

- Strategies for addressing noise, uncertainty, and alignment in heterogeneous datasets

- Case studies and methodological innovations in perception science