Non-linear analysis of the heart rate variability in characterization of manic and euthymic phases of bipolar disorder

Efficacy of automatic pupillometry as a screening technique to detect autonomic dysfunction in bipolar disorder

CLINICAL RELEVANCE

Autonomic nervous system abnormalities in the pathophysiology of bipolar disorder are controversial. Pupillary features may be affected as a result of autonomic nervous system abnormalities in bipolar disorder. Small changes in pupillary responses may not be noticeable on clinical examination. Automated pupillemetries can be helpful in demonstrating these changes reliably and quantitatively.

BACKGROUND

The aim of this study was to compare the static and dynamic pupillary responses of bipolar patients with healthy controls. In addition, pupillary response differences between mania, depression and remission stages were investigated.

METHODS

The bipolar patient group consisted of 39 eyes of 39 patients with 13 patients in each of the stages: mania, depression and remission. The control group consisted of 39 eyes of 39 healthy volunteers. After the ophthalmic examination, static and dynamic pupillometry measurements were made. The mean pupil dilatation speed was calculated according to dynamic measurements. Static pupillometry measurements including scotopic, mesopic and photopic pupil diameters; the first dynamic measurements at 0th second and pupillary dilatation speed were used for statistical analysis.

RESULTS

There was no difference static and the first dynamic pupillometry measurements between the bipolar and control groups (p > 0.05 for all parameters), but there was a significant difference in mean pupil dilatation speed (p = 0.041). No significant differences were found between the 3 groups for all static and the first dynamic pupillometry measurements and the mean pupil dilatation speed (p > 0.05).

CONCLUSION

Static and the first dynamic measurements of bipolar patients were not different from healthy controls. The mean pupil dilatation speed of bipolar patients was significantly lower, but this difference had a low effect size.

An exploration of actigraphy in the context of ketamine and treatment-resistant depression

OBJECTIVES

This study explored the potential of non-parametric and complexity analysis metrics to detect changes in activity post-ketamine and their association with depressive symptomatology.

METHODS

Individuals with treatment-resistant depression (TRD: n = 27, 16F, 35.9 ± 10.8 years) and healthy volunteers (HVs: n = 9, 4F, 36.4 ± 9.59 years) had their activity monitored during an inpatient, double-blind, crossover study where they received an infusion of ketamine or saline placebo. All participants were 18-65 years old, medication-free, and had a MADRS score ≥20. Non-parametric metrics averaged over each study day, metrics derived from complexity analysis, and traditionally calculated non-parametric metrics averaged over two weeks were calculated from the actigraphy time series. A separate analysis was conducted for a subsample (n = 17) to assess the utility of these metrics in a hospital setting.

RESULTS

In HVs, lower intradaily variability was observed within daily rest/activity patterns post-ketamine versus post-placebo (F = 5.16(1,15), p = 0.04). No other significant effects of drug or drug-by-time or correlations between depressive symptomatology and activity were detected.

CONCLUSIONS

Weak associations between non-parametric variables and ketamine were found but were not consistent across actigraphy measures.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov, NCT00088699.

Digital phenotype of mood disorders: A conceptual and critical review

BACKGROUND

Mood disorders are commonly diagnosed and staged using clinical features that rely merely on subjective data. The concept of digital phenotyping is based on the idea that collecting real-time markers of human behavior allows us to determine the digital signature of a pathology. This strategy assumes that behaviors are quantifiable from data extracted and analyzed through digital sensors, wearable devices, or smartphones. That concept could bring a shift in the diagnosis of mood disorders, introducing for the first time additional examinations on psychiatric routine care.

OBJECTIVE

The main objective of this review was to propose a conceptual and critical review of the literature regarding the theoretical and technical principles of the digital phenotypes applied to mood disorders.

METHODS

We conducted a review of the literature by updating a previous article and querying the PubMed database between February 2017 and November 2021 on titles with relevant keywords regarding digital phenotyping, mood disorders and artificial intelligence.

RESULTS

Out of 884 articles included for evaluation, 45 articles were taken into account and classified by data source (multimodal, actigraphy, ECG, smartphone use, voice analysis, or body temperature). For depressive episodes, the main finding is a decrease in terms of functional and biological parameters [decrease in activities and walking, decrease in the number of calls and SMS messages, decrease in temperature and heart rate variability (HRV)], while the manic phase produces the reverse phenomenon (increase in activities, number of calls and HRV).

CONCLUSION

The various studies presented support the potential interest in digital phenotyping to computerize the clinical characteristics of mood disorders.

Autonomic responses to facial expression tasks in children with autism spectrum disorders: Cross-section study

BACKGROUND

The autonomic nervous system has an influence on emotions and behavior modulation, however, the relationship between autonomic modulation impairment and the autism spectrum disorder (ASD) is yet to be fully described.

AIMS

To evaluate the autonomic responses of children with and without ASD through the non-linear, and linear heart rate variability (HRV) measures, and assess the correlation between these responses, the severity and behavioral symptoms of autism.

METHODS AND PROCEDURES

27 children diagnosed with ASD (EG = experimental group) and 28 matching controls (CG = control group) were evaluated. The HRV was evaluated in 15 min sections at the following moments: I) Resting condition; II) During facial expression tasks; and III) Recovery. The severity and behavioral symptoms of autism were evaluated by the Childhood Autism Rating Scale (CARS) and Autistic Behaviors Checklist (ABC) scales.

OUTCOMES AND RESULTS

The facial expression tasks influenced the activity of the autonomic nervous system in both groups, however the EG experienced more autonomic changes. These changes were mostly evidenced by the non-linear indices. Also, the CARS and ABC scales showed significant correlations with HRV indices.

CONCLUSIONS AND IMPLICATIONS

Children with ASD presented an autonomic modulation impairment, mostly identified by the non-linear indices of HRV. Also, this autonomic impairment is associated with the severity and behavioral symptoms of autism.