SAN: mitigating spatial covariance heterogeneity in cortical thickness data collected from multiple scanners or sites.

ABSTRACT: In neuroimaging studies, combining data collected from multiple study sites or scanners is becoming common to increase the reproducibility of scientific discoveries. At the same time, unwanted variations arise by using different scanners (inter-scanner biases), which need to be corrected before downstream analyses to facilitate replicable research and prevent spurious findings. While statistical harmonization methods such as ComBat have become popular in mitigating inter-scanner biases in neuroimaging, recent methodological advances have shown that harmonizing heterogeneous covariances results in higher data quality. In vertex-level cortical thickness data, heterogeneity in spatial autocorrelation is a critical factor that affects covariance heterogeneity. Our work proposes a new statistic

SUBMITTER: Zhang R 

PROVIDER: S-EPMC10723364 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature