title: Estimating false positives and negatives in brain networks.
abstract: The human brain is a complex network of anatomically segregated regions interconnected by white matter pathways, known as the human connectome. Diffusion tensor imaging can be used to reconstruct this structural brain network in vivo and noninvasively. However, due to a wide variety of influences, both false positive and false negative connections may occur. By choosing a 'group threshold', brain networks of multiple subjects can be combined into a single reconstruction, affecting the occurrence of these false positives and negatives. In this case, only connections that are detected in a large enough percentage of the subjects, specified by the group threshold, are considered to be present. Although this group threshold has a substantial impact on the resulting reconstruction and subsequent analyses, it is often chosen intuitively. Here, we introduce a model to estimate how the choice of group threshold influences the presence of false positives and negatives. Based on our findings, group thresholds should preferably be chosen between 30% and 90%. Our results further suggest that a group threshold of circa 60% is a suitable setting, providing a good balance between the elimination of false positives and false negatives.
authors.affiliation: Department of Psychiatry, Rudolf Magnus Institute, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands. m.a.dereus-4@umcutrecht.nl
authors.forename: Marcel A
authors.initials: MA
authors.lastname: de Reus
authors.forename: Martijn P
authors.initials: MP
authors.lastname: van den Heuvel
mesh_terms: D063132:Connectome; D056324:Diffusion Tensor Imaging; D005188:False Negative Reactions; D005189:False Positive Reactions; D006801:Humans; D007091:Image Processing, Computer-Assisted; D015233:Models, Statistical; D009415:Nerve Net