| Literature DB >> 34350425 |
Ying Huang1,2, Fan Wang1, Zhengwang Wu1, Zengsi Chen3, Han Zhang1, Li Wang1, Weili Lin1, Dinggang Shen1, Gang Li1.
Abstract
Infant cortical surface templates play an essential role in spatial normalization of cortical surfaces across individuals in pediatric neuroimaging analysis. However, existing infant surface templates have two major limitations in functional MRI analysis. First, they are constructed by co-registration of cortical surfaces based on structural attributes, which cannot lead to accurate functional alignment, due to the highly variable relationship between cortical folds and functions. Second, they are constructed by simply averaging co-registered cortical attributes, which is sensitive to registration errors and leads to blurred attribute patterns on templates, thus deteriorating the accuracy in spatial normalization. Therefore, the construction of infant cortical functional templates encoding sharp functional architectures is critical for infant fMRI analysis. To this end, we construct the first set of spatiotemporal infant cortical surface functional templates using Wasserstein barycenter and a state-of-the-art functional feature, namely the gradient density of functional connectivity. To address the first issue, we leverage functional gradient density to drive surface registration to improve inter-individual functional correspondences. To address the second issue, we compute templates based on the Wasserstein barycenter of functional gradient density maps across individuals. The motivation is that Wasserstein barycenter represents a meaningful mean under the Wasserstein distance metric, which takes into account the alignment of local spatial distribution of cortical attributes and thus is robust to registration errors, leading to sharp and detailed patterns on templates. Experiments on a dataset with 207 fMRI scans between 0 and 2 years of age show the validity and accuracy of our constructed infant cortical functional templates.Keywords: Functional templates; Infant; Wasserstein barycenter
Year: 2020 PMID: 34350425 PMCID: PMC8330392 DOI: 10.1007/978-3-030-59728-3_24
Source DB: PubMed Journal: Med Image Comput Comput Assist Interv