Modeling functional difference between gyri and sulci within intrinsic connectivity networks.

Area of Science:

• Neuroimaging
• Cognitive Neuroscience
• Computational Neuroscience

Background:

• Cortical folding patterns (gyri and sulci) are crucial for brain function.
• Previous studies focused on large-scale relationships, potentially missing network-specific differences.
• Functional brain networks are fundamental units of cognitive processing.

Purpose of the Study:

• To investigate the localized functional differences between gyri and sulci within intrinsic connectivity networks (ICNs).
• To introduce a novel deep learning model for analyzing these differences.
• To explore the functional heterogeneity of cortical folding patterns across different brain networks.

Main Methods:

• Developed a novel intrinsic connectivity network (ICN)-guided pooling-trimmed convolutional neural network (I-ptFCN).
• Applied the I-ptFCN model to task-based functional magnetic resonance imaging (fMRI) data from the Human Connectome Project.
• Analyzed classification accuracy of gyral and sulcal fMRI signals across various ICNs.

Main Results:

• Significant variations in classification accuracy of gyral and sulcal signals were observed across different ICNs.
• This indicates functional heterogeneity of cortical folding patterns within distinct brain networks.
• Sulcal signals exhibited heterogeneous frequency features across ICNs, unlike homogeneous gyral features.

Conclusions:

• Cortical folding patterns exhibit functional heterogeneity across different intrinsic connectivity networks.
• Sulci, rather than gyri, appear to be the primary contributors to this observed functional heterogeneity.
• These findings provide new insights into the functional roles of gyri and sulci in specialized brain networks.