Micaela V McCall1, Patricio Riva-Posse1, Steven J Garlow2, Helen S Mayberg3, Andrea L Crowell1. 1. Emory University School of Medicine, Department of Psychiatry and Behavioral Sciences. 2. University of Wisconsin School of Medicine and Public Health. 3. Icahn School of Medicine at Mount Sinai, Center for Advanced Circuit Therapeutics.
Abstract
BACKGROUND: Traditional rating scales for depression rely heavily on patient self-report, and lack detailed measurement of non-verbal behavior. However, there is evidence that depression is associated with distinct non-verbal behaviors, assessment of which may provide useful information about recovery. This study examines non-verbal behavior in a sample of patients receiving Deep Brain Stimulation (DBS) treatment of depression, with the purpose to investigate the relationship between non-verbal behaviors and reported symptom severity. METHODS: Videotaped clinical interviews of twelve patients participating in a study of DBS for treatment-resistant depression were analyzed at three time points (before treatment and after 3 months and 6 months of treatment), using an ethogram to assess the frequencies of 42 non-verbal behaviors. The Beck Depression Inventory (BDI) and Hamilton Depression Rating Scale (HDRS-17) were also collected at all time points. RESULTS: Factor analysis grouped non-verbal behaviors into three factors: react, engage/fidget, and disengage. Two-way repeated measures ANOVA showed that scores on the three factors change differently from each other over time. Mixed effects modelling assessed the relationship between BDI score and frequency of non-verbal behaviors, and provided evidence that the frequency of behaviors related to reactivity and engagement increase as BDI score decreases. LIMITATIONS: This study assesses a narrow sample of patients with a distinct clinical profile at limited time points. CONCLUSIONS: Non-verbal behavior provides information about clinical states and may be reliably quantified using ethograms. Non-verbal behavior may provide distinct information compared to self-report.
BACKGROUND: Traditional rating scales for depression rely heavily on patient self-report, and lack detailed measurement of non-verbal behavior. However, there is evidence that depression is associated with distinct non-verbal behaviors, assessment of which may provide useful information about recovery. This study examines non-verbal behavior in a sample of patients receiving Deep Brain Stimulation (DBS) treatment of depression, with the purpose to investigate the relationship between non-verbal behaviors and reported symptom severity. METHODS: Videotaped clinical interviews of twelve patients participating in a study of DBS for treatment-resistant depression were analyzed at three time points (before treatment and after 3 months and 6 months of treatment), using an ethogram to assess the frequencies of 42 non-verbal behaviors. The Beck Depression Inventory (BDI) and Hamilton Depression Rating Scale (HDRS-17) were also collected at all time points. RESULTS: Factor analysis grouped non-verbal behaviors into three factors: react, engage/fidget, and disengage. Two-way repeated measures ANOVA showed that scores on the three factors change differently from each other over time. Mixed effects modelling assessed the relationship between BDI score and frequency of non-verbal behaviors, and provided evidence that the frequency of behaviors related to reactivity and engagement increase as BDI score decreases. LIMITATIONS: This study assesses a narrow sample of patients with a distinct clinical profile at limited time points. CONCLUSIONS: Non-verbal behavior provides information about clinical states and may be reliably quantified using ethograms. Non-verbal behavior may provide distinct information compared to self-report.
Authors: Ashan Veerakumar; Vineet Tiruvadi; Bryan Howell; Allison C Waters; Andrea L Crowell; Bradley Voytek; Patricio Riva-Posse; Lydia Denison; Justin K Rajendra; Johnathan A Edwards; Kelly R Bijanki; Ki Sueng Choi; Helen S Mayberg Journal: J Neurophysiol Date: 2019-07-17 Impact factor: 2.714
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