Multiscale network dynamics between heart rate and locomotor activity are altered in schizophrenia

• Type of Material: Article
• Authors: Erik Reinertsen, Georgia Institute of Technology; Supreeth P. Shashikumar, Emory University; Amit Shah, Emory University; Shamim Nemati, Emory University; Gari Clifford, Emory University
• Language: English
• Date: 2018-11-01
• Publisher: IOP PUBLISHING LTD
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2018 Institute of Physics and Engineering in Medicine.
• Final Published Version (URL): http://dx.doi.org/10.1088/1361-6579/aae1ed
• Title of Journal or Parent Work: PHYSIOLOGICAL MEASUREMENT
• Volume: 39
• Issue: 11
• Start Page: 115001
• End Page: 115001
• Grant/Funding Information: We acknowledge the support of the National Science Foundation Award 1636933, National Institutes of Health (Grants K23 HL127251, P50 HL117929, K01 ES025445, and R01 HL136205).
• Keywords: schizophrenia; complexity; RATE-VARIABILITY; Engineering, Biomedical; mental health; machine learning; Physiology; Life Sciences & Biomedicine; Engineering; Biophysics; atrial fibrillation; Science & Technology; Technology; network physiology; information theory
• Research Categories: Biology, Physiology; Biophysics, Medical

Abstract

Objective: Changes in heart rate (HR) and locomotor activity reflect changes in autonomic physiology, behavior, and mood. These systems may involve interrelated neural circuits that are altered in psychiatric illness, yet their interactions are poorly understood. We hypothesized interactions between HR and locomotor activity could be used to discriminate patients with schizophrenia from controls, and would be less able to discriminate non-psychiatric patients from controls. Approach: HR and locomotor activity were recorded via wearable patches in 16 patients with schizophrenia and 19 healthy controls. Measures of signal complexity and interactions were calculated over multiple time scales, including sample entropy, mutual information, and transfer entropy. A support vector machine was trained on these features to discriminate patients from controls. Additionally, time series were converted into a network with nodes comprised of HR and locomotor activity states, and edges representing state transitions. Graph properties were used as features. Leave-one-out cross validation was performed. To compare against non-psychiatric illness, the same approach was repeated in 41 patients with atrial fibrillation (AFib) and 53 controls. Main results: Network features enabled perfect discrimination of schizophrenia patients from controls with an areas under the receiver operating characteristic curve (AUC) of 1.00 for training and test data. Other bivariate measures of interaction achieved lower AUCs (train 0.98, test 0.96), and univariate measures of complexity achieved the lowest performance. Conversely, interaction features did not improve discrimination of AFib patients from controls beyond univariate approaches. Significance: Interactions between HR and locomotor activity enabled perfect discrimination of subjects with schizophrenia from controls, but these features were less performant in a non-psychiatric illness. This is the first quantitative evaluation of interactions between physiology and behavior in patients with psychiatric illness.