Better Biomarkers Based on Quality, Reproducible and Open Science
Mental health is a major public health challenge currently costing trillions of dollars. Neuroscientific approaches are key to understanding the underlying risk factors as well as developing biomarkers and treatments. The rapid adoption of data sharing, and open science practices present an unprecedented opportunity to offer better care at a lower cost for mental health issues. However, the quality and efficacy of these potential biomarkers and treatments is critically dependent on the quality of multiple stages of data science they are based on. These stages include but are not limited to preprocessing, quality control, feature extraction, model building, and performance evaluation. Often overlooked, inaccuracies at these stages can get multiplied to produce suboptimal and/or irreproducible final results (so-called “garbage-in, garbage-out” and butterfly effects). In this talk, I present an outline of few at the Open MINDS lab projects in medical image processing, machine learning and data science to solve these challenges in two key areas: neuroimaging quality control and biomarker performance evaluation. I encourage you to visit the following websites to learn more: niQC SIG and crossinvalidation.com.