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This chapter explores how artificial intelligence enhances multimodal neuroimaging analysis. These approaches transform our understanding and diagnosis of neurological disorders by integrating complementary information from diverse imaging modalities. These include structural MRI, functional MRI, PET, diffusion tensor imaging, and electrophysiology. AI approaches systematically overcome fundamental limitations of traditional neuroimaging interpretation. These limitations include the curse of dimensionality, reliance on group-level rather than individual predictions, and inability to capture complex distributed brain patterns. Key AI solutions include automatic feature learning through deep learning, sophisticated dimension reduction techniques, and multivariate pattern recognition that reveals subtle abnormalities across brain networks. The technical foundation encompasses traditional integration strategies, machine learning fusion approaches, and advanced preprocessing challenges including spatial registration and cross-modal harmonization. A comprehensive dimension reduction pipeline is presented, progressing from high-dimensional raw data to clinically meaningful biomarkers through staged linear and nonlinear techniques. This chapter’s centerpiece case study on Long COVID fatigue illustrates these principles in practice, demonstrating how multimodal integration of PET and MRI data identifies frontal-striatal-thalamic network dysfunction with 92% classification accuracy and explains 65% of symptom variance—a 25% improvement over single-modality approaches. Future directions include integration with genomic data and wearable monitoring, advanced AI architectures like graph neural networks and multimodal transformers, and clinical translation challenges involving standardization, interpretable AI, and scalable implementation. This chapter concludes that multimodal analysis represents a paradigm shift from isolated brain measurements toward integrated models that capture the multifaceted nature of neurological disorders, enabling more personalized diagnostic and treatment approaches.

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Multimodal Analysis and Neurological Disorders

  • 章节

  • 发表日期：

    2025年

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  • Thorsten Rudroff

摘要

This chapter explores how artificial intelligence enhances multimodal neuroimaging analysis. These approaches transform our understanding and diagnosis of neurological disorders by integrating complementary information from diverse imaging modalities. These include structural MRI, functional MRI, PET, diffusion tensor imaging, and electrophysiology. AI approaches systematically overcome fundamental limitations of traditional neuroimaging interpretation. These limitations include the curse of dimensionality, reliance on group-level rather than individual predictions, and inability to capture complex distributed brain patterns. Key AI solutions include automatic feature learning through deep learning, sophisticated dimension reduction techniques, and multivariate pattern recognition that reveals subtle abnormalities across brain networks. The technical foundation encompasses traditional integration strategies, machine learning fusion approaches, and advanced preprocessing challenges including spatial registration and cross-modal harmonization. A comprehensive dimension reduction pipeline is presented, progressing from high-dimensional raw data to clinically meaningful biomarkers through staged linear and nonlinear techniques. This chapter’s centerpiece case study on Long COVID fatigue illustrates these principles in practice, demonstrating how multimodal integration of PET and MRI data identifies frontal-striatal-thalamic network dysfunction with 92% classification accuracy and explains 65% of symptom variance—a 25% improvement over single-modality approaches. Future directions include integration with genomic data and wearable monitoring, advanced AI architectures like graph neural networks and multimodal transformers, and clinical translation challenges involving standardization, interpretable AI, and scalable implementation. This chapter concludes that multimodal analysis represents a paradigm shift from isolated brain measurements toward integrated models that capture the multifaceted nature of neurological disorders, enabling more personalized diagnostic and treatment approaches.