This Research Topic, Reviews and Insights in Data-Driven Approaches and Tools, is dedicated to publishing high-quality scholarly review papers on pivotal topics at the intersection of neuroscience and data science. The rapid growth of data in neuroscience has led to the increasing adoption of data-driven approaches and computational tools to analyze complex brain functions. These methods, ranging from machine learning algorithms and statistical models to large-scale brain simulations and high-dimensional neural data analyses, these methods are transforming researchers’ understanding of the brain’s structure and function. As the field continues to evolve, synthesizing current trends and evaluating emerging techniques is essential for guiding future research. It aims to provide a comprehensive overview of the latest computational methodologies, highlight their applications in neuroscience, and offer critical perspectives on their strengths, limitations, and future directions. These reviews will serve as valuable resources for both new and experienced researchers navigating the data-rich landscape of modern neuroscience.
Despite recent advancements in neuroimaging, data collection, and computing has enabled powerful analyses of brain data, but there remains a need for clear, accessible reviews to guide researchers through current techniques and best practices. The goal of this Research Topic is to fill that gap by publishing high-quality review articles that critically evaluate key data-driven methods in computational neuroscience. These reviews will summarize major approaches, highlight applications, and offer perspectives on future directions—helping the field make better use of computational tools to advance brain research.
This Research Topic invites high-quality review articles and perspective pieces focused on data-driven approaches in computational neuroscience. We welcome contributions exploring key methodologies, including machine learning, deep learning, neural decoding, network analysis, dimensionality reduction, and large-scale brain simulations. Reviews may focus on specific tools, comparative evaluations, or methodological best practices, providing critical insights into their applications, limitations, and future potential. Additional topics may encompass the integration of multimodal data, model interpretability, and the role of open science and reproducibility in data-driven research. We are particularly encourage interested in reviews that bridge theory and application, highlight emerging trends, or guide researchers in selecting suitable tools for various types of neural data. Manuscripts should be accessible to both newcomers and experts, serving as a valuable resources for the broader neuroscience community. We strongly encourage interdisciplinary perspectives that integrate neuroscience, computer science, and data analysis.
Topic Editor Antonio Parziale is the co-founder of 'Natural Intelligent Technologies srl'. The other Topic Editors declare no competing interests with regard to the Research Topic subject.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Conceptual Analysis
Data Report
Editorial
FAIR² Data
General Commentary
Hypothesis and Theory
Methods
Mini Review
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
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.
