Psychiatry - Research Publications

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    Emotional reactivity following surgery to the prefrontal cortex
    Jenkins, LM ; Andrewes, DG ; Nicholas, CL ; Drummond, KJ ; Moffat, BA ; Phal, PM ; Desmond, P (WILEY, 2018-03)
    We aimed to elicit emotion in patients with surgically circumscribed lesions of the prefrontal cortex (PFC) in order to elucidate the precise functional roles in emotion processing of the discrete subregions comprising the ventromedial PFC, including the medial PFC and orbitofrontal cortex (OFC). Three components of emotional reactivity were measured: subjective experience, behaviour, and physiological response. These included measures of self-reported emotion, observer-rated facial expression of emotion and measurements of heart rate and heart rate variability (HRV) during film viewing, and a measure of subjective emotional change since surgery. Patients with lesions to the ventromedial PFC demonstrated significant differences compared with controls in HRV during the film clips, suggesting a shift to greater dominance of sympathetic input. In contrast, patients with lesions restricted to the OFC showed significant differences in HRV suggesting reduced sympathetic input. They also showed less facial expression of emotion during positive film clips, and reported more subjective emotional change since surgery compared with controls. This human lesion study is important for refining theoretical models of emotion processing by the ventromedial PFC, which until now have primarily been based on anatomical connectivity, animal lesion, and human functional neuroimaging research. Such theories have implications for the treatment of a wide variety of emotional disorders.
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    Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging
    Petrov, D ; Gutman, BA ; Yu, S-HJ ; Alpert, K ; Zavaliangos-Petropulu, A ; Isaev, D ; Turner, JA ; van Erp, TGM ; Wang, L ; Schmaal, L ; Veltman, D ; Thompson, PM ; Wang, Q ; Shi, Y ; Suk, HI ; Suzuki, K (SPRINGER INTERNATIONAL PUBLISHING AG, 2017)
    As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.