Joint prediction of longitudinal development of cortical surfaces and white matter fibers from neonatal MRI

Abstract

The human brain can be modeled as multiple interrelated shapes (or a multishape), each for characterizing one aspect of the brain, such as the cortex and white matter pathways. Predicting the developing multishape is a very challenging task due to the contrasting nature of the developmental trajectories of the constituent shapes: smooth for the cortical surface and non-smooth for white matter tracts due to changes such as bifurcation. We recently addressed this problem and proposed an approach for predicting the multishape developmental spatiotemporal trajectories of infant brains based only on neonatal MRI data using a set of geometric, dynamic, and fiber-to-surface connectivity features. In this paper, we propose two key innovations to further improve the prediction of multishape evolution. First, for a more accurate cortical surface prediction, instead of simply relying on one neonatal atlas to guide the prediction of the multishape, we propose to use multiple neonatal atlases to build a spatially heterogeneous atlas using the multidirectional varifold representation. This individualizes the atlas by locally maximizing its similarity to the testing baseline cortical shape for each cortical region, thereby better representing the baseline testing cortical surface, which founds the multishape prediction process. Second, for temporally consistent fiber prediction, we propose to reliably estimate spatiotemporal connectivity features using low-rank tensor completion, thereby capturing the variability and richness of the temporal development of fibers. Experimental results confirm that the proposed variants significantly improve the prediction performance of our original multishape prediction framework for both cortical surfaces and fiber tracts shape at 3, 6, and 9 months of age. Our pioneering model will pave the way for learning how to predict the evolution of anatomical shapes with abnormal changes. Ultimately, devising accurate shape evolution prediction models that can help quantify and predict the severity of a brain disorder as it progresses will be of great aid in individualized treatment planning.

Resting-state functional MRI studies on infant brains: A decade of gap-filling efforts

“…Based on dFC, the flexibility of functional network affiliations (Li et al, 2015b;Liu and Duyn, 2013), the statuses of the brain functional networks and their dwelling time (Damaraju et al, 2014a), occurring frequency (Abrol et al, 2017), and transition probability (Allen et al, 2014) could be extracted and adopted as informative features for early detection. Finally, with more longitudinal FC becoming available, the development of 4D prediction methods that jointly consider the longitudinal information to achieve better classification performance are greatly needed (Meng et al, 2016;Rekik et al, 2017).…”

Joint Prediction and Classification of Brain Image Evolution Trajectories from Baseline Brain Image with Application to Early Dementia

“…We also note that our framework leverages information from only brain MR images, however brain dementia also atrophies the cortical surface [13]. Hence, based on the seminal shape evolution learning models for predicting infant cortical development from a single timepoint [14] and inspired from the joint shape-image regression model proposed in [15], we aim to build a unified model which simultaneously predicts cortical shape and brain image evolution trajectories for a more accurate early diagnosis from baseline data.…”

Infant Brain Development Prediction With Latent Partial Multi-View Representation Learning

“…The rationality of using this scheme for infant brain parcellation lies in the fact that all major gyral and sulcal folds are established at term birth and are stable during postnatal brain development [57]. For example, this parcellation scheme has been successfully adopted in infant studies [23], [58]. However, leveraging infant-specific parcellation schemes could potentially further improve the performance.…”

“…In this model, the cerebral cortex is modeled as a deformable elastoplasticity surface driven via a growth model. The second line of research aims to predict the longitudinal postnatal development of cortical features (e.g., cortical thickness maps) [8], [9] or white matter fibers after term birth [23]. On the one hand, all the above mentioned models focus on modeling the longitudinal dynamic development of infant brain MR images after term birth, rather than relating the infant brain development scores (e.g., these five cognitive scores mentioned above) and the longitudinal neuroimages.…”