PPA: Principal parcellation analysis for brain connectomes and multiple traits

The Shape of the Brain's Connections Is Predictive of Cognitive Performance: An Explainable Machine Learning Study

The shape of the brain's white matter connections is relatively unexplored in diffusion magnetic resonance imaging (dMRI) tractography analysis. While it is known that tract shape varies in populations and across the human lifespan, it is unknown if the variability in dMRI tractography-derived shape may relate to the brain's functional variability across individuals. This work explores the potential of leveraging tractography fiber cluster shape measures to predict subject-specific cognitive performance. We implement two machine learning models (1D-CNN and Least Absolute Shrinkage and Selection Operator [LASSO]) to predict individual cognitive performance scores. We study a large-scale database from the Human Connectome Project Young Adult study (n = 1065). We apply an atlas-based fiber cluster parcellation (953 fiber clusters) to the dMRI tractography of each individual. We compute 15 shape, microstructure, and connectivity features for each fiber cluster. Using these features as input, we train a total of 210 models (using fivefold cross-validation) to predict 7 different NIH Toolbox cognitive performance assessments. We apply an explainable AI technique, SHapley Additive exPlanations (SHAP), to assess the importance of each fiber cluster for prediction. Our results demonstrate that fiber cluster shape measures are predictive of individual cognitive performance. The studied shape measures, such as irregularity, diameter, total surface area, volume, and branch volume, are generally as effective for prediction as traditional microstructure and connectivity measures. The 1D-CNN model generally outperforms the LASSO method for prediction. Further interpretation and analysis using SHAP values from the 1D-CNN suggest that fiber clusters with features highly predictive of cognitive ability are widespread throughout the brain, including fiber clusters from the superficial association, deep association, cerebellar, striatal, and projection pathways. This study demonstrates the strong potential of shape descriptors to enhance the study of the brain's white matter and its relationship to cognitive function.

TractGraphFormer: Anatomically informed hybrid graph CNN-transformer network for interpretable sex and age prediction from diffusion MRI tractography

The relationship between brain connections and non-imaging phenotypes is increasingly studied using deep neural networks. However, the local and global properties of the brain's white matter networks are often overlooked in convolutional network design. We introduce TractGraphFormer, a hybrid Graph CNN-Transformer deep learning framework tailored for diffusion MRI tractography. This model leverages local anatomical characteristics and global feature dependencies of white matter structures. The Graph CNN module captures white matter geometry and grey matter connectivity to aggregate local features from anatomically similar white matter connections, while the Transformer module uses self-attention to enhance global information learning. Additionally, TractGraphFormer includes an attention module for interpreting predictive white matter connections. We apply TractGraphFormer to tasks of sex and age prediction. TractGraphFormer shows strong performance in large datasets of children (n = 9345) and young adults (n = 1065). Overall, our approach suggests that widespread connections in the WM are predictive of the sex and age of an individual. For each prediction task, consistent predictive anatomical tracts are identified across the two datasets. The proposed approach highlights the potential of integrating local anatomical information and global feature dependencies to improve prediction performance in machine learning with diffusion MRI tractography.