Single-Subject Anxiety Treatment Outcome Prediction using Functional Neuroimaging

Abstract

The possibility of individualized treatment prediction has profound implications for the development of personalized interventions for patients with anxiety disorders. Here we utilize random forest classification and pre-treatment functional magnetic resonance imaging (fMRI) data from individuals with generalized anxiety disorder (GAD) and panic disorder (PD) to generate individual subject treatment outcome predictions. Before cognitive behavioral therapy (CBT), 48 adults (25 GAD and 23 PD) reduced (via cognitive reappraisal) or maintained their emotional responses to negative images during fMRI scanning. CBT responder status was predicted using activations from 70 anatomically defined regions. The final random forest model included 10 predictors contributing most to classification accuracy. A similar analysis was conducted using the clinical and demographic variables. Activations in the hippocampus during maintenance and anterior insula, superior temporal, supramarginal, and superior frontal gyri during reappraisal were among the best predictors, with greater activation in responders than non-responders. The final fMRI-based model yielded 79% accuracy, with good sensitivity (0.86), specificity (0.68), and positive and negative likelihood ratios (2.73, 0.20). Clinical and demographic variables yielded poorer accuracy (69%), sensitivity (0.79), specificity (0.53), and likelihood ratios (1.67, 0.39). This is the first use of random forest models to predict treatment outcome from pre-treatment neuroimaging data in psychiatry. Together, random forest models and fMRI can provide single-subject predictions with good test characteristics. Moreover, activation patterns are consistent with the notion that greater activation in cortico-limbic circuitry predicts better CBT response in GAD and PD.

The role of expectancy and proactive control in stress regulation: A neurocognitive framework for regulation expectation

“…Finally, we would note that, although our framework is mainly focused on depression, it is important to keep in mind that many different disorders such as anxiety (Ball, Ramsawh, Campbell-Sills, Paulus & Stein, 2013) and substance abuse (Gowin, Mackey & Paulus, 2013) involve the same neurocircuits with the DLPFC playing a key NEUROGNITIVE FRAMEWORK REGULATION EXPECTATION 35 role. Depression is also a disorder that is frequently comorbid with a broad range of clinical conditions.…”

Neural activity during object perception in schizophrenia patients is associated with illness duration and affective symptoms

“…Further, anxiety has been associated with decreased activation (Ball et al, 2014;Brown et al, 2015) and functional connectivity (Modi et al, 2015) of the supramarginal gyrus in individuals with high levels of anxiety. Our data suggest that these brain abnormalities associated with affective symptoms are also present in patients with schizophrenia.…”

Prediction as a Humanitarian and Pragmatic Contribution from Human Cognitive Neuroscience

“…A study of patients with generalized anxiety disorder or panic disorder aimed to develop measures that might be sensitive to single-subject responses to treatment (Ball et al, 2013). Patients saw negative scenes and either maintained or reduced (via reappraisal) their emotional response to the scenes.…”