Predictive Clinical Neuroscience Portal (PCNportal): instant online access to research-grade normative models for clinical neuroscientists. [version 1; peer review: 2 approved]
Predictive Clinical Neuroscience Portal (PCNportal): instant online access to research-grade normative models for clinical neuroscientists. [version 1; peer review: 2 approved]
Blog Article
Background: The neurobiology of mental disorders remains poorly understood despite substantial scientific efforts, due to large clinical heterogeneity and to a lack of tools suitable to map individual variability.Normative modeling is one recently successful framework that can address these problems by comparing individuals to a reference population.The methodological underpinnings of normative modelling are, however, relatively complex and computationally expensive.Our research group has developed the python-based normative modelling package Predictive Clinical Neuroscience toolkit (PCNtoolkit) which provides access to many validated algorithms for normative modelling.PCNtoolkit has since proven to be a strong foundation for large scale normative modelling, diegojavierfares.com but still requires significant computation power, time and technical expertise to develop.
Methods: To address these problems, we introduce PCNportal.PCNportal is an online platform integrated with PCNtoolkit that offers access to pre-trained research-grade normative models estimated on tens of thousands of participants, without the need for computation power or programming abilities.PCNportal is an easy-to-use web interface that is highly scalable to large user bases as necessary.Finally, we demonstrate how the resulting normalized deviation scores can be used in a clinical application through a schizophrenia classification task applied to cortical thickness and volumetric data from the longitudinal Northwestern University Schizophrenia Data and Software Tool (NUSDAST) dataset.Results: At each longitudinal timepoint, the transferred normative models achieved a mean[std.
dev.] explained variance of 9.4[8.8]%, 9.2[9.
2]%, 5.6[7.4]% respectively in the control group and 4.7[5.5]%, 6.
0[6.2]%, 4.2[6.9]% in the schizophrenia group.Diagnostic classifiers achieved AUC of 0.
78, 0.76 and 0.71 the gel bottle audrey respectively.Conclusions: This replicates the utility of normative models for diagnostic classification of schizophrenia and showcases the use of PCNportal for clinical neuroimaging.By facilitating and speeding up research with high-quality normative models, this work contributes to research in inter-individual variability, clinical heterogeneity and precision medicine.