Advanced Computational Tools for Mapping the Multidimensional Architecture of the Brain

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About this Research Topic

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Background

The brain is an extremely complex system operating in multiple domains and scales. During the last decades, advances in neuroscience research have paved the way to exponentially expand our knowledge on the structure of neuronal architecture, the functional organization of cerebral processes, and the hierarchical and parallel processing dynamics of brain activity. At the same time, these studies have stimulated the formulation of new questions on the elusive nature of the intricate interactions at microscopic, mesoscopic, and macroscopic levels.

Three canonical dimensions are usually considered in functional neuroimaging studies: time, frequency, and space. The fusion of multimodal information provided by different neuroimaging techniques has enlarged this analysis framework, expanding the view of the brain as a complex, nonlinear, dynamic, and highly interconnected system in which the information from structure, function, and cognition must be combined to obtain an integrated vision of the whole system. Multidimensional brain representations are playing a crucial role to gain further insight into the different organization scales of the human brain. Hence, the combination or fusion of information provided by diverse neural representations and different neuroimaging techniques (CT, EEG, fUS, MEG, MRI, NIRS, PET, among others) becomes essential to glimpse the multidimensional architecture of the human brain. This framework is continuously expanding, adding new perspectives of analysis to further understand the complex relationships between molecular, metabolic, and neuroelectrical brain mechanisms. Recently introduced new computational tools and high-performance processing techniques are opening the neuroscience field, encouraging the integration of information at different levels, together with new representations of structural and functional brain organization.

This Research Topic aims to bring together researchers with an interest in novel multidimensional representations of brain activity, those working on the integration of information from different neuroimaging modalities, and computational neuroscience researchers; likewise, this Research Topic aims to stimulate collaboration between scientists in these fields. We welcome submissions of original research papers that can help to expand our comprehension on fundamental aspects of the brain, as well as from different brain disorders and conditions, using novel methodological approaches able to extract and combine information from the multiple dimensions that can be identified in a complex, nonlinear, dynamic, and highly interconnected system: the brain.

We are looking for contributions that cover, but are not limited to, the following topics:
• New algorithms and methodologies to extract information from multi-dimensional representations of the brain.
• Development and application of techniques for the fusion of multimodal information coming from different neuroimaging techniques.
• Characterization and modeling of the multi-level interactions between brain structure, neural function, and higher cognitive functions.
• Novel computational methods to integrate brain information at different levels.
• Exploration and identification of the multidimensional brain mechanisms in different disorders and conditions.

Conflict of interest:
Topic Editor, Dr. Shigihara is leading a joint Research Project with RICOH. All other Topic Editors declare no competing interests regarding this Research Topic subject.

Keywords: Multidimensional brain representations, Multimodal analysis, Brain architecture, Brain dynamics, Integration of brain-related information, Multi-level interactions

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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