ArticlePloS one2018
Graph theory applied to the analysis of motor activity in patients with schizophrenia and depression.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Capturing sex differences in spontaneous autonomic fluctuations of resting heart rate using a similarity graph theory approach.Biology of sex differences · 2026Article
- Capturing trial-by-trial variability in behaviour: people with Parkinson's disease exhibit a greater rate of short-term fluctuations in response times.Translational psychiatry · 2025Article
- Fractal biomarker of daily activity for women with early onset depression.BMJ mental health · 2025Article
- A systematic review of passive data for remote monitoring in psychosis and schizophrenia.NPJ digital medicine · 2025Article
- Altered topological properties of functional brain networks in patients with first episode, late-life depression before and after antidepressant treatment.Frontiers in aging neuroscience · 2023Article
- Alterations in Plasma Lipidomic Profiles in Adult Patients with Schizophrenia and Major Depressive Disorder.Medicina (Kaunas, Lithuania) · 2022Article
- Complexity and variability analyses of motor activity distinguish mood states in bipolar disorder.PloS one · 2022Article
- Applying machine learning in motor activity time series of depressed bipolar and unipolar patients compared to healthy controls.PloS one · 2020Article
- Diurnal variation of motor activity in adult ADHD patients analyzed with methods from graph theory.PloS one · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Depression and schizophrenia are defined only by their clinical features, and diagnostic separation between them can be difficult. Disturbances in motor activity pattern are central features of both types of disorders. We introduce a new method to analyze time series, called the similarity graph algorithm. Time series of motor activity, obtained from actigraph registrations over 12 days in depressed and schizophrenic patients, were mapped into a graph and we then applied techniques from graph theory to characterize these time series, primarily looking for changes in complexity. The most marked finding was that depressed patients were found to be significantly different from both controls and schizophrenic patients, with evidence of less regularity of the time series, when analyzing the recordings with one hour intervals. These findings support the contention that there are important differences in control systems regulating motor behavior in patients with depression and schizophrenia. The similarity graph algorithm we have described can easily be applied to the study of other types of time series.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.