Jae-Won Choi

Effects of resilience on quality of life in patients with bipolar disorder

BACKGROUNDnFew studies have examined the effects of resilience on quality of life (QOL) in patients with bipolar disorder (BD). Therefore, this study investigated the association between resilience and QOL in patients with BD and compared it to the relationship between resilience and QOL in healthy individuals.nnnMETHODSnParticipants were 68 euthymic patients with BD and 68 age-, sex-, and length of education-matched controls. Sociodemographic characteristics and clinical variables of the two groups were obtained using face-to-face interviews, and all participants completed the Connor-Davidson Resilience Scale, the World Health Organization QOL-Brief Form.nnnRESULTSnThe QOL of the BD group was significantly impaired compared with that of the controls. Degree of resilience, number of depressive episodes, Clinical Global Impression scores, degree of impulsivity, and length of education were significantly correlated with QOL in the BD group. Resilience was significantly associated with overall QOL, physical subdomains of QOL, psychological subdomains of QOL, social subdomains of QOL, and environmental subdomains of QOL in the BD group, even after controlling for confounders. In the control group, resilience was significantly associated with overall QOL, the physical subdomains of QOL, psychological subdomains of QOL, and social subdomains of QOL.nnnLIMITATIONSnThe number of participants in each group was 68, which is a relatively small sample size.nnnCONCLUSIONSnResilience in patients with BD was independently and positively correlated with various areas of QOL. Various strategies to reinforce resilience in patients with BD are needed to improve the low QOL in this population.

Sleep Period Time Estimation Based on Electrodermal Activity

We proposed and tested a method to estimate sleep period time (SPT) using electrodermal activity (EDA) signals. Eight healthy subjects and six obstructive sleep apnea patients participated in the experiments. Each subjects EDA signals were measured at the middle and ring fingers of the dominant hand during polysomnography (PSG). For nine of the 17 participants, wrist actigraphy was also measured for a quantitative comparison of EDA- and actigraphy-based methods. Based on the training data, we observed that sleep onset was accompanied by a gradual reduction of amplitude of the EDA signals, whereas sleep offset was accompanied by a rapid increase in amplitude of EDA signals. We developed a method based on these EDA fluctuations during sleep–wake transitions, and applied it to a test dataset. The performance of the method was assessed by comparing its results with those from a physicians sleep stage scores. The mean absolute errors in the obtained values for sleep onset, offset, and period time between the proposed method, and the results of the PSG were 4.1, 3.0, and 6.1xa0min, respectively. Furthermore, there were no significant differences in the corresponding values between the methods. We compared these results with those obtained by applying actigraphic methods, and found that our algorithm outperformed these in terms of each estimated parameter of interest in SPT estimation. Long awakening periods were also detected based on sympathetic responses reflected in the EDA signals. The proposed method can be applied to a daily sleep monitoring system.

Estimation of sleep posture using a patch-type accelerometer based device

In this study, we developed a sleep posture estimation algorithm using 3-axis accelerometer signals measured from a patch-type sensor. Firstly, we inspected the characteristics of accelerometer signals for different sleep postures. Based on the results, we established decision rules to estimate 5 postures containing supine, left, right lateral, prone postures, and non-sleep postures such as sitting and standing. The algorithm was tested by the data from thirteen subjects during night time PSG. As a result, the algorithm estimated sleep postures with an average agreement of 99.16%, and cohens kappa of 0.98 compared with reference sleep postures determined by position sensor and video recording. The proposed method with the device could be used as supportive purpose in routine PSG study and out-of-hospital environment.

REM sleep estimation based on autonomic dynamics using R–R intervals

OBJECTIVEnWe developed an automatic algorithm to determine rapid eye movement (REM) sleep on the basis of the autonomic activities reflected in heart rate variations.nnnAPPROACHnThe heart rate variability (HRV) parameters were calculated using the R-R intervals from an electrocardiogram (ECG). A major autonomic variation associated with the sleep cycle was extracted from a combination of the obtained parameters. REM sleep was determined with an adaptive threshold applied to the acquired feature. The algorithm was optimized with the data from 26 healthy subjects and obstructive sleep apnea (OSA) patients and was validated with data from a separate group of 25 healthy and OSA subjects.nnnMAIN RESULTSnAccording to an epoch-by-epoch (30u2009s) analysis, the average of Cohens kappa and the accuracy were respectively 0.63 and 87% for the training set and 0.61 and 87% for the validation set. In addition, the REM sleep-related information extracted from the results of the proposed method revealed a significant correlation with those from polysomnography (PSG).nnnSIGNIFICANCEnThe current algorithm only using R-R intervals can be applied to mobile and wearable devices that acquire heart-rate-related signals; therefore, it is appropriate for sleep monitoring in the home and ambulatory environments. Further, long-term sleep monitoring could provide useful information to clinicians and patients for the diagnosis and treatments of sleep-related disorders and individual health care.

Differences in Resting-state Quantitative Electroencephalography Patterns in Attention Deficit/Hyperactivity Disorder with or without Comorbid Symptoms

Objective The aim of the present study was to evaluate the role of comorbid psychiatric symptoms on quantitative electroencephalogram (QEEG) activities in boys with the attention deficit/hyperactivity disorder (ADHD). Methods All participants were male students in the second, third or fourth grade in elementary school. Therefore, there were no significant differences in age or sex. Participants with ADHD were assigned to one of three groups: pure ADHD (n=22), ADHD with depressive symptoms (n=11), or ADHD with problematic internet use (n=19). The Korean version of the Children’s Depression Inventory and the Korean Internet Addiction Self-scale were used to assess depressive symptoms and problematic internet use, respectively. Resting-state EEG during eyes closed was recorded, and the absolute power of five frequency bands was analyzed: delta (1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz), and gamma (30–50 Hz). Results The ADHD with problematic internet use group showed decreased absolute theta power at the central and posterior region compared with the pure ADHD group. However, The ADHD with depressive symptoms group showed no significant differences compared with the other groups. Conclusion These findings will contribute to a better understanding of brain-based electrophysiological changes in children with ADHD in accordance with comorbid psychiatric symptoms.

Depressed REM Sleep Behavior Disorder Patients Are Less Likely to Recall Enacted Dreams than Non-Depressed Ones

Objective REM sleep behavior disorder (RBD) is associated with psychiatric symptoms, such as anxiety and alexithymia. However, only a few studies on the relationship between depression and RBD have been published. In this study, we investigated the occurrence of depression and associated factors in patients with RBD. Methods In total 94 patients (mean age: 61.9±12.7 years, male: 70.2%) diagnosed as RBD were examined using detailed clinical histories, the Beck Depression Inventory (BDI), the Epworth Sleepiness Scale (ESS), and nocturnal polysomnography (PSG). Results The mean BDI score of all patients was 12.4±10.3 and 44.7% of RBD patients showed depressed mood (BDI >11 points). Depressed RBD patients were less able to recall enacted dreams than were non-depressed patients (61.9% vs. 86.5%, p=0.008). Logistic regression analysis showed that failure to recall enacted dreams was significantly associated with depression, after controlling for confounding variables including the respiratory disturbance index and a history of psychiatric disorders (odds ratio=0.323, p=0.041). Conclusion In this study, 44.7% of RBD patients were found to suffer from depressed mood. And, depression was found to be associated with reduced ability to recall enacted dreams. We suggest that routine evaluation of depression be performed in RBD patients, particularly when failure to recall enacted dreams is evident. We speculate that such failure may be associated with emotional dysregulation or neurodegeneration.

Moderating effect of APOE ε4 on the relationship between sleep-wake cycle and brain β-amyloid

Objectives To clarify the relationships between sleep-wake cycle and cerebral β-amyloid (Aβ) deposition in cognitively normal (CN) older adults, focusing primarily on the moderating effects of the APOE ε4 allele. Methods The present study included 133 CN older adults who participated in the Korean Brain Aging Study for Early Diagnosis & Prediction of Alzheimers Disease cohort. All participants underwent [11C] Pittsburgh compound B-PET imaging to quantify Aβ deposition in the brain and blood sampling for APOE genotyping. Sleep and circadian parameters were measured using actigraphy for 8 consecutive days. Results The APOE ε4 allele had moderating effects on the associations of sleep latency (SL), mesor, and acrophase with cerebral Aβ deposition, and the interactions between APOE ε4 status and SL and between APOE ε4 status and acrophase remained significant after adjusting for multiple comparisons. In APOE ε4 noncarriers, shorter SL, higher mesor, and advanced acrophase were associated with Aβ positivity. In contrast, APOE ε4 carriers showed a relationship between delayed acrophase and Aβ accumulation that approached but did not reach significance. After the Bonferroni correction, the associations of shorter SL and higher mesor with Aβ positivity remained significant for APOE ε4 noncarriers. Conclusions Our findings suggest that the APOE ε4 allele may act as a moderator in the relationship between the sleep-wake cycle and Aβ accumulation in CN older adults. Thus, APOE ε4 status needs to be considered as a key factor when designing related research or interventions.

Slow-Wave Sleep Estimation for Healthy Subjects and OSA Patients Using R–R Intervals

We developed an automatic slow-wave sleep (SWS) detection algorithm that can be applied to groups of healthy subjects and patients with obstructive sleep apnea (OSA). This algorithm detected SWS based on autonomic activations derived from the heart rate variations of a single sensor. An autonomic stability, which is an SWS characteristic, was evaluated and quantified using R–R intervals from an electrocardiogram (ECG). The thresholds and the heuristic rule to determine SWS were designed based on the physiological backgrounds for sleep process and distribution across the night. The automatic algorithm was evaluated based on a fivefold cross validation using data from 21 healthy subjects and 24 patients with OSA. An epoch-by-epoch (30xa0s) analysis showed that the overall Cohens kappa, accuracy, sensitivity, and specificity of our method were 0.56, 89.97%, 68.71%, and 93.75%, respectively. SWS-related information, including SWS duration (min) and percentage (%), were also calculated. A significant correlation in these parameters was found between automatic and polysomnography scorings. Compared with similar methods, the proposed algorithm convincingly discriminated SWS from non-SWS. The simple method using only R–R intervals has the potential to be utilized in mobile and wearable devices that can easily measure this information. Moreover, when combined with other sleep staging methods, the proposed method is expected to be applicable to long-term sleep monitoring at home and ambulatory environments.

Comparison of the Effects of Quetiapine XR and Lithium Monotherapy on Actigraphy-Measured Circadian Parameters in Patients With Bipolar II Depression

Purpose/Background The aim of this study was to evaluate the effects of quetiapine XR and lithium on actigraphy-measured circadian parameters in patients with bipolar II depression. Methods/Procedures This was an 8-week, open-label, prospective, randomized comparative study. The assessments included the 17-item Hamilton Depression Rating Scale score and actigraphic measures concerning the previous 7 days, collected at each visit (weeks 0 [baseline], 1, 2, 4, 6, and 8); the actigraphic data were analyzed with a cosinor analysis. Findings/Results Medication, time, and the interaction between medication and time were significantly associated with acrophase for the entire group (Ps = 0.003, 0.020, and 0.042, respectively). More specifically, acrophase was significantly delayed at weeks 1 and 6 (Ps = 0.004 and 0.039, respectively) in the quetiapine XR group. The F statistics significantly increased over time for the entire group (P < 0.001), and there was a significant increase in F statistics on weeks 4 and 6 in the quetiapine XR group (Ps = 0.016 and 0.020, respectively) and on weeks 4 and 8 in the lithium group (Ps = 0.001 and 0.016, respectively). In addition, scores on the 17-item Hamilton Depression Rating Scale were significantly associated with the F statistics during 8 weeks for the entire group (P = 0.008). Implications/Conclusions Both quetiapine XR and lithium affected several circadian parameters, including peak activity time and robustness of circadian rhythm, but exerted different effects on acrophase in patients with bipolar II depression. In particular, clinical depressive symptoms were associated with robustness of circadian rhythm during the course of the 8-week treatment.

Stroop Task-Related Brain Activity in Patients With Insomnia: Changes After Cognitive-Behavioral Therapy for Insomnia

ABSTRACT Objective/Background: In the present study, we compared differences in brain activity during the Stroop task between patients with chronic insomnia disorder (CID) and good sleepers (GS). Furthermore, we evaluated changes in Stroop task-related brain activity after cognitive–behavioral therapy for insomnia (CBT-I). Participants/Methods: The final analysis included 21 patients with CID and 25 GS. All participants underwent functional magnetic resonance imaging (fMRI) while performing the color–word Stroop task. CBT-I, consisting of 5 sessions, was administered to 14 patients with CID in the absence of medication. After CBT-I, fMRI was repeated in the patients with CID while performing the same task. Sleep-related questionnaires and sleep variables from a sleep diary were also obtained before and after CBT-I. Results: No significant differences in behavioral performance in the Stroop task or task-related brain activation were observed between the CID and GS groups. No changes in behavioral performance or brain activity were found after CBT-I. However, clinical improvement in the Insomnia Severity Index (ISI) score was significantly associated with changes in the Stroop task-related regional blood oxygen level-dependent signals in the left supramarginal gyrus. Conclusions: Our findings suggest that cognitive impairment in patients with CID was not detectable by the Stroop task or Stroop task-related brain activation on fMRI. Moreover, there was no altered brain activity during the Stroop task after CBT-I. However, the ISI score reflected changes in the neural correlates of cognitive processes in patients with CID after CBT-I.