ArticlePhysiological measurement2018
Multiscale network dynamics between heart rate and locomotor activity are altered in schizophrenia.
Article in Physiological measurement, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 19 citations in OpenAlex.
- Locomotor and endocrine alterations link to metabolic dysfunction induced by pathopharmacological interaction between neurodevelopmental disorders and antipsychotics: evidence from clinical and animal study.Frontiers in psychiatry · 2026Article
- Predicting seizure episodes and high-risk events in autism through adverse behavioral patterns.Physiological measurement · 2025Article
- A systematic review of passive data for remote monitoring in psychosis and schizophrenia.NPJ digital medicine · 2025Article
- Accessibility and availability of smartphone apps for schizophrenia.Schizophrenia (Heidelberg, Germany) · 2022Article
- Multimodal Assessment of Schizophrenia and Depression Utilizing Video, Acoustic, Locomotor, Electroencephalographic, and Heart Rate Technology: Protocol for an Observational Study.JMIR research protocols · 2022Article
- Wearables in Schizophrenia: Update on Current and Future Clinical Applications.JMIR mHealth and uHealth · 2022Review
- Allostatic Load Effects on Cortical and Cognitive Deficits in Essentially Normotensive, Normoweight Patients with Schizophrenia.Schizophrenia bulletin · 2021Article
- Choroid Plexus Enlargement and Allostatic Load in Schizophrenia.Schizophrenia bulletin · 2020Article
- Dynamical systems approaches to personalized medicine.Current opinion in biotechnology · 2019Review
- Windows Into Human Health Through Wearables Data Analytics.Current opinion in biomedical engineering · 2019Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveChanges 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
resultsNetwork 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.
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Registered trials
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