Lithium use associated with symptom severity in comorbid bipolar disorder I and migraine.

INTRODUCTION: Bipolar disorder (BD) and migraine headaches are frequently comorbid. The common etiological features are unknown, however cortical hyperexcitability (EEG) of migraines, and the report of hyperexcitability in pluripotent stem cell-derived neurons from lithium responsive BD subjects offers a physiological hypothesis of excitable neurons linking these disorders. However, clinical studies suggest that a history of migraine is associated with higher rates of relapse in those with BD taking lithium. Lithium use and history of migraine in this prospective longitudinal study of BD find that lithium use is associated with a greater symptom severity in BD. METHODS: Data on longitudinal outcome from 538 patients with BD I were categorized according to treatment with lithium and comorbidity with migraine. Clinical outcome measures on depression, mania, and quality of life over the most recent 2-year period compared the BD and BD/migraine cohort according to lithium treatment status. RESULTS: A history of migraines was associated with worse clinical outcomes of depression (p = .002), mania (p = .005), and mental and physical quality of life (p = .004 and p = .005, respectively), independent of lithium use. The BD/migraine cohort treated with lithium was associated with worse symptoms of mania, whereas those without migraine and lithium use were associated with milder manic symptoms (p = .026). CONCLUSIONS: Herein, we replicate the relatively worse outcome in BD with comorbid migraine. We find evidence to suggest that lithium use is associated with more severe symptoms of mania among those with BD and a history of migraine and conclude that lithium is contraindicated in BD comorbid with migraine.

Giving Voice to Vulnerable Children: Machine Learning Analysis of Speech Detects Anxiety and Depression in Early Childhood.

Childhood anxiety and depression often go undiagnosed. If left untreated these conditions, collectively known as internalizing disorders, are associated with long-term negative outcomes including substance abuse and increased risk for suicide. This paper presents a new approach for identifying young children with internalizing disorders using a 3-min speech task. We show that machine learning analysis of audio data from the task can be used to identify children with an internalizing disorder with 80% accuracy (54% sensitivity, 93% specificity). The speech features most discriminative of internalizing disorder are analyzed in detail, showing that affected children exhibit especially low-pitch voices, with repeatable speech inflections and content, and high-pitched response to surprising stimuli relative to controls. This new tool is shown to outperform clinical thresholds on parent-reported child symptoms, which identify children with an internalizing disorder with lower accuracy (67-77% versus 80%), and similar specificity (85-100% versus 93%), and sensitivity (0-58% versus 54%) in this sample. These results point toward the future use of this approach for screening children for internalizing disorders so that interventions can be deployed when they have the highest chance for long-term success.