Improvement of Functional Brain Parcellation Using Machine Learning Techniques

Functional brain parcellation is fundamental to understanding large-scale neural organization and improving downstream neuroimaging analyses. Traditional atlas-based and classical clustering approaches, while widely used, often fail to capture complex, non-linear functional connectivity patterns and inter-individual variability present in high-dimensional resting-state fMRI data. This study proposes a machine learning–driven framework to enhance functional brain parcellation using large-scale datasets, including those from the Human Connectome Project. The methodology integrates advanced preprocessing, feature extraction from functional connectivity matrices, and multiple machine learning models, including unsupervised clustering, deep learning architectures, and graph-based neural networks. Model performance was evaluated using functional homogeneity metrics (Silhouette Score, Davies–Bouldin Index), spatial consistency measures (Dice Similarity Coefficient), and predictive utility in cognitive outcome modeling. Results demonstrate that the proposed framework outperformed traditional atlas-based, spectral clustering, and graph modularity approaches. The machine learning model achieved a higher mean silhouette score (0.61), a lower Davies–Bouldin Index (0.94), and improved Dice similarity (0.76), indicating enhanced intra-parcel coherence and inter-subject reproducibility. Furthermore, predictive modeling accuracy improved by 9–14%, with an 11% reduction in mean absolute error compared to atlas-based features. Statistical testing confirmed that these improvements were significant (p < 0.05). The findings suggest that machine learning techniques substantially improve the accuracy, stability, and practical utility of functional brain parcellation. By capturing non-linear connectivity structures and adapting to individual variability, the proposed framework advances computational brain mapping and supports more reliable neuroimaging analyses for research and potential clinical applications.