Physiological health has been linked to increased complexity in the output of physiological systems. For example, as the severity of cardiac disease increases, EKG time series show reduced complexity. The present study investigated the relation between mental health and complexity in motor output. In particular, we tested the hypothesis that depression severity—as measured by the Symptom Checklist-90-R (SCL-90-R)—should be negatively correlated with motor output complexity. Measurements of motor output were obtained when participants generated long sequences of movements in a cyclical aiming task. The resultant movement amplitude time series were submitted to spectral analysis, from which an index of motor output complexity was derived. Acc...
The purpose of this study has been to describe motor activity data obtained by using wrist-worn acti...
Major Depressive Disorder (MDD) is exceedingly prevalent and considered to be one of the leading cau...
Background: Growing evidence documents the potential of machine learning for developing brain based ...
Physiological health has been linked to increased complexity in the output of physiological systems....
The diagnosis of depression is often reliant upon subjective measures and self-report. More recently...
Depression is a leading cause of disability worldwide, and objective biomarkers are required for fut...
While the negative association between physical activity and depression has been well established, i...
Major depression affects multiple physiologic systems. Therefore, analysis of signals that reflect i...
1 A hypothesis in characterizing human depression is that change in the brain‟s basal ganglia result...
AbstractBackgroundPoststroke depression (PSD) is one of the most common emotional disorders affectin...
Major depression affects multiple physiologic systems. Therefore, analysis of signals that reflect i...
Background: Depression and anxiety have been linked to serious cardiovascular events in patients wit...
Contains fulltext : 150259.PDF (publisher's version ) (Open Access)It has been sug...
OBJECTIVE To explore the correlations between observer ratings and instrumental parameters across...
Background: Disturbances in motor activity pattern are seen in both schizophrenia and depression. Ho...
The purpose of this study has been to describe motor activity data obtained by using wrist-worn acti...
Major Depressive Disorder (MDD) is exceedingly prevalent and considered to be one of the leading cau...
Background: Growing evidence documents the potential of machine learning for developing brain based ...
Physiological health has been linked to increased complexity in the output of physiological systems....
The diagnosis of depression is often reliant upon subjective measures and self-report. More recently...
Depression is a leading cause of disability worldwide, and objective biomarkers are required for fut...
While the negative association between physical activity and depression has been well established, i...
Major depression affects multiple physiologic systems. Therefore, analysis of signals that reflect i...
1 A hypothesis in characterizing human depression is that change in the brain‟s basal ganglia result...
AbstractBackgroundPoststroke depression (PSD) is one of the most common emotional disorders affectin...
Major depression affects multiple physiologic systems. Therefore, analysis of signals that reflect i...
Background: Depression and anxiety have been linked to serious cardiovascular events in patients wit...
Contains fulltext : 150259.PDF (publisher's version ) (Open Access)It has been sug...
OBJECTIVE To explore the correlations between observer ratings and instrumental parameters across...
Background: Disturbances in motor activity pattern are seen in both schizophrenia and depression. Ho...
The purpose of this study has been to describe motor activity data obtained by using wrist-worn acti...
Major Depressive Disorder (MDD) is exceedingly prevalent and considered to be one of the leading cau...
Background: Growing evidence documents the potential of machine learning for developing brain based ...