Discovering cortical sulcal folding patterns in neonates using large-scale dataset.

Area of Science:

• Neuroscience
• Developmental Biology
• Computational Biology

Background:

• Human cerebral cortical folding is complex and varies significantly between individuals.
• Understanding common cortical folding patterns is crucial for studying brain development, cognitive functions, and neurological disorders.
• Neonatal brains offer an ideal model for studying major cortical folding patterns due to minimal postnatal environmental influence.

Purpose of the Study:

• To develop and validate a novel method for discovering common patterns of human cerebral cortical folding.
• To characterize sulcal patterns using the spatial distribution of sulcal pits.
• To group similar cortical folding patterns using a hierarchical clustering approach.

Main Methods:

• A sulcal-pit-based method was proposed, characterizing cortical folding by the spatial distribution of sulcal pits.
• Similarity between sulcal pit distributions was measured using spatial, geometrical, and topological features.
• A comprehensive similarity matrix was constructed by fusing these measurements.
• Hierarchical affinity propagation was used to cluster similar sulcal folding patterns.

Main Results:

• The method was applied to 677 neonatal brains.
• Multiple distinct and meaningful sulcal patterns were identified in key cortical regions.
• The findings highlight consistent major patterns in cortical folding observable even at birth.

Conclusions:

• The proposed sulcal-pit-based method effectively identifies common cortical folding patterns in neonates.
• These identified patterns provide a basis for understanding inter-individual variability in brain structure.
• The findings contribute to the study of genetic influences on cortical development and potential links to brain disorders.