The human connectome: new approaches to mapping neural networks - Kozlovska , G.O.; Demchenko , K.O.; Hrytsenko , A.O. (Морфологія / Morphologia / Morfologìâ, 2025)
Background. The study of the human connectome, that is, the comprehensive map of structural and functional connections of the brain, is one of the key areas of modern neuroscience. Understanding the organization of neural networks opens new perspectives for diagnosing and treating neurological and psychiatric disorders, as well as for developing innovative approaches in neurotechnology. Recent decades have been characterized by the rapid advancement of imaging methods and computer modeling, which significantly expand our knowledge of brain network architecture. Objective. The aim of this article is to analyze modern methods of mapping the human connectome and to determine their advantages, limitations, and prospects for development. Methods. A review of publications in PubMed, Scopus, and Web of Science over the past two decades was conducted, focusing on structural and functional connectomics. Particular attention was paid to diffusion tensor imaging (DTI), functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), electroencephalography (EEG), as well as new approaches that combine multimodal techniques with artificial intelligence algorithms. Results. Structural connectomics is primarily based on DTI and tractography, which allow visualization of the brain’s major pathways, though with limited accuracy for smaller fibers. Functional connectomics relies on fMRI, EEG, and MEG, which capture synchronization of activity across brain regions in real time. The integration of structural and functional data provides a more complete picture of brain function. A promising direction is the application of artificial intelligence for analyzing large datasets, which enables the discovery of new patterns in neural network connectivity. Conclusions. Modern methods of human connectome mapping provide the foundation for a deeper understanding of the brain’s neural organization and hold significant potential in clinical neuroscience. Further development of multimodal technologies and machine learning algorithms will contribute to the creation of more accurate connectome models, which will help optimize diagnosis, prognosis, and treatment of nervous system disorders.