Prediction of pediatric bipolar disorder using neuroanatomical signatures of the amygdala.

Epidemiological studies have previously established that pediatric bipolar disorder (PBD) has a prevalence rate of approximately 1.0–1.5% in the general population (1-3). Family, twin, and adoption studies have also reported that PBD is highly heritable, with families of bipolar probands having an elevated risk of developing the disorder (4, 5). In addition, nearly one-third of patients with PBD attempt suicide at least once (6), with previous reports indicating that early onset bipolar disorder appears to follow a more severe course with poorer long-term prognosis as compared to late onset bipolar disorder (7, 8). Overall, these facts translate to elevated treatment costs and a high socio-economic burden which underscores the need to elucidate the pathophysiological mechanism of the disorder (6). Converging evidence from both structural and functional neuroimaging studies has consistently implicated the dysfunction of the anterior-limbic system in the pathophysiology of PBD (3, 9-13). The anterior-limbic system consists of heavily interconnected regions which include the amygdala, prefrontal cortex, thalamus, and striatum, which are involved in mood, cognition, and emotion regulation (3, 9, 14-18). Interestingly, experiments from both functional neuroimaging and lesion studies have also established that the amygdala is principally responsible for emotion modulation, such as recognizing fear and anger from facial expressions (3, 10, 19-24). Remarkably, there is also consensus from multiple group-level structural neuroimaging studies on the existence of volumetric reductions in the amygdala of patients with PBD as compared to their typically developing peers (9, 11-13, 17, 25-28). Indeed, this is by far the most replicated finding in PBD structural neuroimaging studies (29). This finding is further highlighted in these subsequent reviews and meta-analyses (3, 10, 30). However, despite these noteworthy advancements in our understanding of PBD, diagnosis, prognosis, and treatment planning still remain subjective—based on signs and symptoms and without any pathognomonic tests. Markedly, an important first step in achieving the goal of deploying pathognomonic tests in diagnosis and prognosis of PBD is being able to predict subjects with PBD at an individual subject level. A majority of previous studies have only been able to identify average group-level neuroanatomical differences between subjects with PBD and healthy controls. As a result, in this study we set out to investigate whether neuroanatomical signatures of the amygdala from T1-weighted neuroimaging scans can objectively predict PBD diagnosis at an individual subject level. Multivariate pattern analysis techniques also known as machine learning techniques have recently been applied in clinical neuroimaging studies to make predictions at an individual subject level with success (22, 31-37). In general, these techniques are implemented in three key stages. First, neuroimaging scan data from several subjects with corresponding diagnostic labels (e.g., 1 = PBD and 2 = healthy controls) are used to train or develop a statistical model. Second, the accuracy of the learnt model is evaluated using novel data not used during the training stage. Third, the model's ability to generalize from novel or unseen data is reported using several statistical measurements such as diagnostic accuracy, specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and a receiver operating characteristic (ROC) curve. Detailed discussions of these methods with respect to clinical neuroimaging data are given elsewhere (22, 31-35, 38, 39). However, in this study, the Elastic Net pattern analysis method (40, 41) was ‘trained’ to make novel predictions on individual subjects. In summary, the main objective of this study was to examine the utility of voxel-based gray matter values from the amygdala to objectively predict individual subjects with PBD or healthy controls. This hypothesis was primarily motivated by the emerging consensus from multiple neuroimaging studies on volumetric reductions in the amygdala of patients with PBD as compared to healthy controls (9, 11-13, 17, 25, 26, 42) as summarized in Table 1. Table 1 A summary of previous studies highlighting structural neuroanatomical abnormalities in the amygdala of patients with pediatric bipolar disorder