Meta-analysis in human neuroimaging: computational modeling of large-scale databases

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Data Science

Background:

• Spatial normalization, establishing standardized anatomical coordinates, has been a cornerstone of human neuroimaging for three decades.
• This foundational technique has spurred methodological advancements, creating a robust and extensive scientific literature.

Purpose of the Study:

• To review the evolution and impact of spatial normalization in neuroimaging research.
• To highlight the development and capabilities of advanced meta-analytic techniques applied to neuroimaging data.

Main Methods:

• Review of methodological advancements in spatial normalization over 30 years.
• Analysis of the growth and utilization of large-scale neuroimaging databases.
• Examination of the progression of coordinate-based meta-analytic methods.

Main Results:

• Spatial normalization has enabled a rigorous and large-scale body of neuroimaging research.
• Large online databases now aggregate neuroimaging findings and metadata.
• Advanced meta-analytic methods can now derive coactivation-based connectivity and functional parcellation from massive datasets.

Conclusions:

• Coordinate-based meta-analysis of human neuroimaging data is at the forefront of computational neurobiology.
• The integration of spatial normalization, large databases, and advanced analytics drives novel insights into brain function.
• Future research will likely focus on complex network models and large-scale data integration.