Computational neuroanatomy of baby brains: A review

Area of Science:

• Neuroscience
• Medical Imaging
• Computational Biology

Background:

• The first postnatal years are critical for human brain development, characterized by rapid structural, functional, and connectivity changes.
• Non-invasive infant brain MRI offers opportunities to study early brain development, but presents unique processing challenges compared to adult MRI.
• Existing computational tools for adult brain MRI are often unsuitable for infant brain images due to reduced contrast and dynamic changes.

Purpose of the Study:

• To provide a comprehensive review of state-of-the-art computational methods for infant brain MRI processing and analysis.
• To summarize publicly available infant-dedicated resources for neuroimaging research.
• To discuss current limitations and suggest future research directions in infant computational neuroanatomy.

Main Methods:

• Systematic review of computational neuroanatomy methods tailored for infant brain MRI.
• Analysis of challenges specific to infant brain image processing (e.g., contrast, intensity variations, dynamic changes).
• Compilation of infant-specific resources including datasets, software tools, challenges, and brain atlases.

Main Results:

• Identification and categorization of numerous infant-tailored computational methods for MRI processing.
• Summary of key infant brain MRI datasets, computational tools, grand challenges, and atlases.
• Overview of advancements in understanding early postnatal brain development through these methods.

Conclusions:

• Specialized computational methods are essential for accurate infant brain MRI analysis.
• The availability of dedicated resources facilitates research in early brain development.
• Further research is needed to address current limitations and advance infant computational neuroanatomy.