Functional connectivity derived from functional MRI reflects the neuronal synchronization between brain regions anchored through an anatomical infrastructure of white matter pathways that can be computed from diffusion MRI. The inter-relation between the structure-function topography is of high interest in disentangling the complexities of the large-scale dynamics of the brain. Moreover, the deviation of structure-function coupling during development, aging, and pathology is intriguing.
Structure-functional association between various regions of the brain still remains unknown. Multimodal fusion or multi-modal connectomics is a rising area that aims at capitalizing joint information among structural and functional modalities by combining them in order to model and predict brain functioning arising from underlying brain structure. The fast-paced technological advancements in the field of machine learning based on artificial neural networks are demonstrating great promise in modeling brain function. Furthermore, a multimodal fusion approach coupled with deep learning techniques holds immense potential in unearthing missing links in complex mental illnesses.
Our Research Topic focuses on incorporating state-of-the-art quantitative techniques to translate the relation between structural and functional connectivity, or jointly analyze structure and function to understand the aberrations in neurological diseases and neuropsychiatric disorders. Specifically, the following points define the scope of this article collection:
• Technological advancements in translating structure-function relation;
• Technological advancements in joint structure-function analysis;
• Application of multigraph techniques, graph-based embedding techniques, deep models for the analysis of clinical population or to answer neuroscientific questions.
Functional connectivity derived from functional MRI reflects the neuronal synchronization between brain regions anchored through an anatomical infrastructure of white matter pathways that can be computed from diffusion MRI. The inter-relation between the structure-function topography is of high interest in disentangling the complexities of the large-scale dynamics of the brain. Moreover, the deviation of structure-function coupling during development, aging, and pathology is intriguing.
Structure-functional association between various regions of the brain still remains unknown. Multimodal fusion or multi-modal connectomics is a rising area that aims at capitalizing joint information among structural and functional modalities by combining them in order to model and predict brain functioning arising from underlying brain structure. The fast-paced technological advancements in the field of machine learning based on artificial neural networks are demonstrating great promise in modeling brain function. Furthermore, a multimodal fusion approach coupled with deep learning techniques holds immense potential in unearthing missing links in complex mental illnesses.
Our Research Topic focuses on incorporating state-of-the-art quantitative techniques to translate the relation between structural and functional connectivity, or jointly analyze structure and function to understand the aberrations in neurological diseases and neuropsychiatric disorders. Specifically, the following points define the scope of this article collection:
• Technological advancements in translating structure-function relation;
• Technological advancements in joint structure-function analysis;
• Application of multigraph techniques, graph-based embedding techniques, deep models for the analysis of clinical population or to answer neuroscientific questions.