Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience

Abstract

Neuroimaging has greatly enhanced the cognitive neuroscience understanding of the human brain and its variation across individuals (neurodiversity) in both health and disease. Such progress has not yet, however, propelled changes in educational or medical practices that improve people's lives. We review neuroimaging findings in which initial brain measures (neuromarkers) are correlated with or predict future education, learning, and performance in children and adults; criminality; health-related behaviors; and responses to pharmacological or behavioral treatments. Neuromarkers often provide better predictions (neuroprognosis), alone or in combination with other measures, than traditional behavioral measures. With further advances in study designs and analyses, neuromarkers may offer opportunities to personalize educational and clinical practices that lead to better outcomes for people.

Figure 1. Three stages of predictive model identification

1) Discovery Phase. Explore and evaluate associations between baseline neuromarkers and behavioral outcomes. 2) Cross-Validation Phase. A cross-validation routine is used to separate data into training and test sets. The model is built using training data and tested on out-of-sample test data. Upon successful evaluation of the performance of the model and features, all data are used to build a prediction model. 3) Generalization Phase. A prediction model built via cross-validation is applied to a new data set. The new data are then used to update the model.

Figure 2. Functional and Structural Brain Measures Predicting Educational Outcomes

(A–B) fMRI predictor of reading gains in dyslexia. (A) Greater activation for a phonological task in right inferior frontal gyrus (Rt IFG) predicted (B) greater gains in reading 2.5 years later in dyslexic children; each red circle is an individual (based on Hoeft et al., 2011). (C–D) MRI predictor of math tutoring gains in students. (C) Greater grey-matter volume of right (R) hippocampus predicted (D) greater performance gains in students after 8 weeks of tutoring; each blue circle is an individual (from Supekar, 2013).

Figure 3. Functional Brain Measure Predicting A Clinical Outcome

Prior to treatment, patients with social anxiety disorder who exhibited greater posterior activation (left panel) for angry relative to neutral facial expressions had better clinical response to cognitive behavioral therapy (CBT) than patients who exhibited lesser activation (right panel) (based on Doehrmann et al., 2013).

Figure 4. Treatment-Specific Biomarker Candidates for Treatment of Depression

Mean regional activity values for remitters and nonresponders segregated by treatment (either Escitalopram given as escitalopram oxalate or cognitive behavioral therapy (CBT)) are plotted for the 6 regions showing a significant treatment × outcome analysis of variance interaction effect. Regional metabolic activity values are displayed as region/whole-brain metabolism converted to z scores. From McGrath et al., 2013b.