Multimodal Brain Data Integration and Computational Modeling

The integration of diverse brain imaging modalities and neurobiological data into comprehensive computational models represents a burgeoning frontier in neuroscience. Techniques such as fMRI, EEG, PET, and MEG each provide unique insights into brain activity, while genetic, behavioural, and clinical data add further dimensions to our understanding. The challenge lies in effectively combining these heterogeneous data sources to form holistic, multi-faceted representations of brain function. Successful integration not only advances our theoretical understanding but also paves the way for practical applications in diagnosing, monitoring, and treating neurological and psychiatric disorders.

The primary objective of this Research Topic is to explore innovative approaches and solutions for integrating multimodal brain imaging and neurobiological data into scalable computational models. These models have the potential to predict disease progression, treatment responses, and cognitive outcomes. By addressing this objective, the Research Topic seeks to confront several key questions:

-How can diverse imaging modalities be effectively combined to offer a more comprehensive view of brain activity?
-What role do machine learning and AI play in the fusion and analysis of these complex datasets?
-How can integrated computational models enhance our understanding of cognitive functions such as memory, attention, and decision-making?
-What novel insights can these models provide into the neural mechanisms underlying disorders like Alzheimer’s disease, epilepsy, and depression?

To achieve these goals, we welcome contributions that demonstrate practical applications in both cognitive and clinical neuroscience, focus on personalized medicine and precision neuroscience, and align with current technological advancements.

This Research Topic invites a wide range of manuscripts focusing on the following themes:
Integration of Multimodal Neuroimaging Data:
-Combining fMRI, EEG, PET, MEG, etc.
-Including genetic, behavioral, and clinical data.

Computational Modeling of Brain Function:
-Predicting disease progression and treatment response.
-Exploring cognitive outcomes and neural mechanisms.

Machine Learning and AI in Multimodal Data Fusion:
-Advanced tools and methods for data analysis.
-Extracting meaningful insights from complex datasets.

Applications in Cognitive and Clinical Neuroscience:
-Models for studying memory, attention, and decision-making.
-Investigating neurological and psychiatric disorders.

Types of manuscripts we are interested in include original research papers, methodological papers, review articles, and short communication papers. We encourage interdisciplinary collaboration involving neuroscientists, data scientists, clinicians, and professionals in related fields. This subtheme aims to be at the cutting edge of neuroscience research, offering valuable insights for both researchers and clinicians.

Keywords: Neuroimaging, Computational Models, Machine Learning, Data Integration, Neuroscience Disorders

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.