Resting-state quantitative EEG characteristics of insomniac patients with depression

Cortical hyperarousal in individuals with frequent nightmares

Nightmares are common among the general population and psychiatric patients and have been associated with signs of nocturnal arousal such as increased heart rate or increased high-frequency electroencephalographic (EEG) activity. However, it is still unclear, whether these characteristics are more of a trait occurring in people with frequent nightmares or rather indicators of the nightmare state. We compared participants with frequent nightmares (NM group; n = 30) and healthy controls (controls; n = 27) who spent 4 nights in the sleep laboratory over the course of 8 weeks. The NM group received six sessions of imagery rehearsal therapy (IRT), the 'gold standard' of cognitive-behavioural therapy for nightmares, between the second and the third night. Sleep architecture and spectral power were compared between groups, and between nights of nightmare occurrence and nights without nightmare occurrence in the NM group. Additionally, changes before and after therapy were recorded. The NM group showed increased beta (16.25-31 Hz) and low gamma (31.25-35 Hz) power during the entire night compared to the controls, but not when comparing nights of nightmare occurrence to those without. Moreover, low gamma activity in rapid eye movement sleep was reduced after therapy in the NM group. Our findings indicate, cortical hyperarousal is more of a trait in people with frequent nightmares within a network of other symptoms, but also malleable by therapy. This is not only a new finding for IRT but could also lead to improved treatment options in the future that directly target high-frequency EEG activity.

Hyperarousal-state of Insomnia Disorder in Wake-resting State Quantitative Electroencephalography

Objective

: Insomnia is associated with elevated high-frequency electroencephalogram power in the waking state. Although affective symptoms (e.g., depression and anxiety) are commonly comorbid with insomnia, few reports distinguished objective sleep disturbance from affective symptoms. In this study, we investigated whether daytime electroencephalographic activity explains insomnia, even after controlling for the effects of affective symptoms.

Methods

: A total of 107 participants were divided into the insomnia disorder (n = 58) and healthy control (n = 49) groups using the Mini-International Neuropsychiatric Interview and diagnostic criteria for insomnia disorder. The participants underwent daytime resting-state electroencephalography sessions (64 channels, eye-closed).

Results

: The insomnia group showed higher levels of anxiety, depression, and insomnia than the healthy group, as well as increased beta [t(105) = -2.56, p = 0.012] and gamma [t(105) = -2.44, p = 0.016] spectra. Among all participants, insomnia symptoms positively correlated with the intensity of beta (r = 0.28, p ＜ 0.01) and gamma (r = 0.25, p ＜ 0.05) spectra. Through hierarchical multiple regression, the beta power showed the additional ability to predict insomnia symptoms beyond the effect of anxiety (ΔR2 = 0.041, p = 0.018).

Conclusion

: Our results showed a significant relationship between beta electroencephalographic activity and insomnia symptoms, after adjusting for other clinical correlates, and serve as further evidence for the hyperarousal theory of insomnia. Moreover, resting-state quantitative electroencephalography may be a supplementary tool to assess insomnia.

The Three-Lead EEG Sensor: Introducing an EEG-Assisted Depression Diagnosis System Based on Ant Lion Optimization

For depression diagnosis, traditional methods such as interviews and clinical scales have been widely leveraged in the past few decades, but they are subjective, time-consuming, and labor-consuming. With the development of affective computing and Artificial Intelligence (AI) technologies, Electroencephalogram (EEG)-based depression detection methods have emerged. However, previous research has virtually neglected practical application scenarios, as most studies have focused on analyzing and modeling EEG data. Furthermore, EEG data is typically obtained from specialized devices that are large, complex to operate, and poorly ubiquitous. To address these challenges, a wearable three-lead EEG sensor with flexible electrodes was developed to obtain prefrontal-lobe EEG data. Experimental measurements show that the EEG sensor achieves promising performance (background noise of no more than 0.91 μVpp, Signal-to-Noise Ratio (SNR) of 26--48 dB, and electrode-skin contact impedance of less than 1 K Ω). In addition, EEG data from 70 depressed patients and 108 healthy controls were collected using the EEG sensor, and the linear and nonlinear features were extracted. The features were then weighted and selected using the Ant Lion Optimization (ALO) algorithm to improve classification performance. The experimental results show that the k-NN classifier achieves a classification accuracy of 90.70%, specificity of 96.53%, and sensitivity of 81.79%, indicating the promising potential of the three-lead EEG sensor combined with the ALO algorithm and the k-NN classifier for EEG-assisted depression diagnosis.

Quantitative electroencephalography as a potential biomarker in migraine

OBJECTIVE

The aim of this study was to investigate the utility of quantitative electroencephalography (QEEG) as a diagnostic tool for migraine and as an indicator of treatment response by comparing QEEG characteristics between migraine patients and controls, and monitoring changes in these characteristics alongside clinical symptoms in response to treatment BACKGROUND: We hypothesized that patients with migraine exhibit distinctive characteristics in QEEG measurements, which could be used as potential diagnostic biomarkers and as a tool for monitoring treatment response.

METHODS

A total of 720 patients were included in the study, comprising 619 patients with migraine and 101 subjects as a control group. QEEG measurements were analyzed for absolute power across specific frequency bands: delta wave (0.5-4 Hz), theta wave (4-8 Hz), alpha wave (8-12 Hz), beta wave (12-25 Hz), and high beta wave (25-30 Hz). The absolute power was normalized against a normative dataset from NeuroGuide, with electrodes being highlighted for significance if they exceeded 1.96. Clinical symptoms were also monitored for correlation with QEEG changes.

RESULTS

Our analysis showed that patients with migraine exhibited significantly higher absolute power across all frequencies, most markedly within the high beta frequency range. When considering electrodes with z-scores exceeding the threshold of 1.96 in the high beta range, a significant association with migraine diagnosis was observed (per 1 electrode increase, OR 1.06; 95% CI 1.01-1.11; p = .012). Moreover, pre- and posttreatment changes in QEEG measurements corresponded with changes in clinical symptoms.

CONCLUSION

Patients with migraine have distinctive QEEG measurements, particularly regarding absolute power and the number of electrodes that surpassed the z-score threshold in high beta wave activity. These findings suggest the potential of QEEG as a diagnostic biomarker and as a tool for monitoring treatment response in migraine patients, warranting further large-scale studies for confirmation and expansion.

EEG Biofeedback Decreases Theta and Beta Power While Increasing Alpha Power in Insomniacs: An Open-Label Study

Insomnia, often associated with anxiety and depression, is a prevalent sleep disorder. Biofeedback (BFB) treatment can help patients gain voluntary control over physiological events such as by utilizing electroencephalography (EEG) and electromyography (EMG) power. Previous studies have rarely predicted biofeedback efficacy by measuring the changes in relative EEG power; therefore, we investigated the clinical efficacy of biofeedback for insomnia and its potential neural mechanisms. We administered biofeedback to 82 patients with insomnia, of whom 68 completed 10 sessions and 14 completed 20 sessions. The average age of the participants was 49.38 ± 12.78 years, with 26 men and 56 women. Each biofeedback session consisted of 5 min of EMG and 30 min of EEG feedback, with 2 min of data recorded before and after the session. Sessions were conducted every other day, and four scale measures were taken before the first, fifth, and tenth sessions and after the twentieth session. After 20 sessions of biofeedback treatment, scores on the Pittsburgh Sleep Quality Index (PSQI) were significantly reduced compared with those before treatment (-5.5 ± 1.43,t = -3.85, p = 0.006), and scores on the Beck Depression Inventory (BDI-II) (-7.15 ± 2.43, t = -2.94, p = 0.012) and the State-Trait Anxiety Inventory (STAI) (STAI-S: -12.36 ± 3.40, t = -3.63, p = 0.003; and STAI-T: -9.86 ± 2.38, t = -4.41, p = 0.001) were significantly lower after treatment than before treatment. Beta and theta power were significantly reduced after treatment, compared with before treatment (F = 6.25, p = 0.014; and F = 11.91, p = 0.001). Alpha power was increased after treatment, compared with before treatment, but the difference was not prominently significant (p > 0.05). EMG activity was significantly decreased after treatment, compared with before treatment (F = 2.11, p = 0.015). Our findings suggest that BFB treatment based on alpha power and prefrontal EMG relieves insomnia as well as anxiety and depression and may be associated with increased alpha power, decreased beta and theta power, and decreased EMG power.

Mood Disorders are Correlated with Autonomic Nervous Function in Chronic Insomnia Patients with OSA

Purpose

To evaluate the correlation between sleep microstructure, autonomic nervous system activity, and neuropsychological characteristics in chronic insomnia (CI) patients with obstructive sleep apnea (OSA).

Patients and Methods

Forty-five CI-OSA patients, forty-six CI patients and twenty-two matched healthy control subjects (HCs) were enrolled. CI-OSA patients were then divided into two groups: mild OSA and moderate-to-severe OSA. All participants completed neuropsychological tests, which included the Hamilton Depression and Anxiety Scales (HAMD and HAMA), the Pittsburgh Sleep Quality Index (PSQI), the Insomnia Severity Index (ISI), the Epworth Sleepiness Scale (ESS), and the Mini-mental State Examination (MMSE). The autonomic nervous system activity and sleep microstructure were examined by the PSM-100A.

Results

The CI-OSA patients exhibited higher scores on the PSQI, ESS, ISI, HAMA, and HAMD than HCs and CI patients (all p < 0.01). The CI-OSA patients had a lower proportion of stable sleep, REM sleep and a higher proportion of unstable sleep ratio (all p < 0.01) than HCs and CI patients (all p < 0.01). The CI-OSA patients had higher ratios of LF and LF/HF, and lower ratios of HF and Pnn50% (all p < 0.01) than HCs and CI patients (all p < 0.01). Compared to CI-mild OSA patients, the CI-moderate-to-severe OSA patients presented with a higher ESS scores, higher ratios of LF and LF/HF, and lower ratios of HF (all p < 0.05). In CI-OSA patients, higher HAMD scores were correlated with decreased MMSE scores (r=-0.678, p < 0.01). A higher LF ratio was correlated with higher HAMD and HAMA scores (r=0.321, p=0.031, r =0.449, p =0.002), and a higher HF ratio was correlated with lower HAMD and HAMA scores (r=-0.321, P =0.031, r =-0.449, p =0.002).

Conclusion

OSA exacerbates the abnormalities of sleep microstructure and the autonomic nervous dysfunction in CI patients. Dysfunction of the autonomic nervous system could contribute to mood deterioration in CI with OSA patients.

Depression signal correlation identification from different EEG channels based on CNN feature extraction

Depression is a mental illness and can even lead to suicide if not be diagnosed and treated. Electroencephalograph (EEG) is used to diagnose depression and it is more complexity to extract the features from all the multimodal channel information . In order to simplify the diagnose process and detect clinical depression, the EEG channels with strong depression information should be identified firstly. Therefore, a depression signal correlation identification method based on convolutional neural network (CNN) is proposed. In the method, the labeled multi-channel EEG is used as data. The EEG signals of each channel are divided into neural network training data set and these data is trained by AlexNet network. Then the correlation classification of each channel for depression is identified based on the trained sample. Accuracy and loss functions are used to evaluate classification index.Conversely, the correlation is lower. An experiments is conducted and the results show that the correlation is not consistent. A few of channels are strongly correlated with depression, such as 13, 17, 28, 40, 46, 66 and 69. These EEG channels are selected to diagnose depression.

Correlation Between Sleep Electroencephalogram, Brain-Derived Neurotrophic Factor, AVPR1B Gene Polymorphism, and Suicidal Behavior in Patients with Depression

The purpose of this article was to investigate the level of serum brain-derived neurotrophic factor and its relationship with suicidal symptoms and severity in mentally ill suicide attempt patients, and to explore the possible role of serum brain-derived neurotrophic factor (BDNF) in the occurrence of psychotic suicidal behavior. A retrospective analysis was performed on patients with depression in the neurology department of a hospital. General physical examination, neurological specialist examination, and cranial magnetic resonance imaging (MRI) examination were performed on any selected group. We applied the 24-item Hamilton Depression Scale, Hamilton Anxiety Scale, and Mini-Intelligence Mental State Scale, and performed polysomnography and electroencephalography (EEG) monitoring to conduct statistical analysis on sleep indicators. The amplitude of low-frequency fluctuation (ALFF) values of the right frontal gyrus, left posterior cerebellar lobe, right anterior cerebellar lobe, and right occipital lingual gyrus of the patient group were significantly lower than those of the control group. The ReHo values of the right superior parietal lobule, the left precuneus, and the right occipital lingual gyrus were significantly higher than those of the control group. The genotype and allele frequency distribution of FKBP5, AVPR1B, and CRHR2 gene SNPs had no significant difference between the case group and the control group (P > 0.05). The ReHo value of the precuneus is significantly correlated with the proportion of N3 sleep, and the dysfunction of the precuneus or default network may be related to the altered sleep structure in patients with depression. The GT and TT genotypes at rs9324924 of the NR3C1 gene are associated with suicide attempts.

Alterations in patients with first-episode depression in the eyes-open and eyes-closed conditions: A resting-state EEG study

Altered resting-state EEG activity has been repeatedly reported in major depressive disorder (MDD), but no robust biomarkers have been identified until now. The poor consistency of EEG alterations may be due to inconsistent resting conditions; that is, the eyes-open (EO) and eyes-closed (EC) conditions. Here, we explored the effect of the EO and EC conditions on EEG biomarkers for discriminating MDD subjects and healthy control (HC) subjects. EEG data were recorded from 30 first-episode MDD and 26 HC subjects during an 8-min resting-state session. The features were extracted using spectral power, Lempel-Ziv complexity, and detrended fluctuation analysis. Significant features were further selected via the sequential backward feature selection algorithm. Support vector machine (SVM), logistic regression, and linear discriminate analysis were used to determine a better resting condition to provide more reliable estimates for identifying MDD. Compared with the HC group, we found that the MDD group exhibited widespread increased β and γ powers (p < 0.01) in both conditions. In the EO condition, the MDD group showed increased complexity and scaling exponents in the α band relative to HC subjects (p < 0.05). The best classification performance of the combined feature sets was found in the EO condition, with the leave-one-out classification accuracy of 89.29%, sensitivity of 90.00%, and specificity of 88.46% using SVM with the linear kernel classifier when the threshold was set to 0.7, followed by the β and γ spectral features with an average accuracy of 83.93%. Overall, EO and EC conditions indeed affected the between-group variance, and the EO condition is suggested as the more separable resting condition to identify depression. Specially, the β and γ powers are suggested as potential biomarkers for first-episode MDD.

A Randomized Controlled Trial Comparing Neurofeedback and Cognitive-Behavioral Therapy for Insomnia Patients: Pilot Study

Insomnia is a common disease that negatively affects patients both mentally and physically. While insomnia disorder is mainly characterized by hyperarousal, a few studies that have directly intervened with cortical arousal. This study was conducted to investigate the effect of a neurofeedback protocol for reducing cortical arousal on insomnia compared to cognitive-behavioral treatment for insomnia (CBT-I). Seventeen adults with insomnia, free of other psychiatric illnesses, were randomly assigned to neurofeedback or CBT-I. All participants completed questionnaires on insomnia [Insomnia Severity Index (ISI)], sleep quality [Pittsburgh Sleep Quality Index (PSQI)], and dysfunctional cognition [Dysfunctional Beliefs and Attitudes about Sleep Scale (DBAS-16)]. The neurofeedback group showed decreases in beta waves and increases in theta and alpha waves in various areas of the electroencephalogram (EEG), indicating lowered cortical arousal. The ISI and PSQI scores were significantly decreased, and sleep efficiency and sleep satisfaction were increased compared to the pre-treatment scores in both groups. DBAS scores decreased only in the CBT-I group (NF p = 0.173; CBT-I p = 0.012). This study confirmed that neurofeedback training could alleviate the symptoms of insomnia by reducing cortical hyperarousal in patients, despite the limited effect in reducing cognitive dysfunction compared to CBT-I.

Sleep EEG characteristics associated with total sleep time misperception in young adults: an exploratory study

Background

Power spectral analysis (PSA) is one of the most commonly-used EEG markers of cortical hyperarousal, and can help to understand subjective–objective sleep discrepancy (SOD). Age is associated with decreased sleep EEG activity; however, the PSA of young adults is currently limited. Thus, this study aimed to examine the correlation of spectral EEG power with total sleep time (TST) misperception in young patients.

Methods

Forty-seven young adults were recruited and underwent a polysomnography recording in a sleep laboratory. Clinical records and self-report questionnaires of all patients were collected, and were used to categorize patients into a good sleeper (GS) group (n = 10), insomnia with a low mismatch group (IWLM, n = 19) or participant with a high mismatch group (IWHM, n = 18). PSA was applied to the first 6 h of sleep.

Results

IWHM patients exhibited a higher absolute power and relative beta/delta ratio in the frontal region compared to the GS group. No significant difference was observed between the IWLM and GS groups. No significant difference in the above parameters was observed between the IWHM and IWLM groups. Moreover, The SOD of TST was positively correlated with frontal absolute power and the relative beta/delta ratio (r = 0.363, P = 0.012; r = 0.363, P = 0.012), and absolute beta EEG spectral power (r = 0.313, P = 0.032) as well as the number of arousals.

Conclusions

Increased frontal beta/delta ratio EEG power was found in young patients with a high mismatch but not in those with a low mismatch, compared with good sleepers. This suggests that there exists increased cortical activity in IWHM patients. In addition, the frontal beta/delta ratio and the number of arousals was positively correlated with the SOD of TST.

Changes in EEG Alpha Activity during Attention Control in Patients: Association with Sleep Disorders

We aimed to assess which quantitative EEG changes during daytime testing in patients with sleep disorder (primary insomnia and excessive daytime sleepiness groups). All experimental study participants were subjected to a long-term test for maintaining attention to sound stimuli, and their EEGs were recorded and then processed, using wavelet analysis, in order to estimate the power and frequency structure of alpha activity. In healthy subjects, the maximum increase in the alpha rhythm occurred near 9 Hz. Patients with primary insomnia were characterized by an increase in the amplitude of the alpha rhythm near 11 Hz. For subjects with sleep disorders, an increase in the amplitude of the alpha rhythm was observed in the entire frequency range (7.5–12.5 Hz), with a maximum increase at 9–10 Hz. Significant differences (p≤0.001) for changes in the alpha rhythm dynamics in the course of performing the attention test were observed in the frequency range of 7.5–10.5 Hz between the control group and patients with sleep disorders. The ratios of the alpha rhythm power values for passive stages with closed eyes before and after active stage were significantly different among the groups of healthy sleep volunteers, patients with primary insomnia, and patients with impaired sleep hygiene within the range of 9.5 to 12.5 Hz. The results of the current study supported the notion of a 24-h hyperarousal in primary insomnia.

Differences in sleep measures and waking electroencephalography of patients with insomnia according to age and sex

STUDY OBJECTIVES

Sleep characteristics are known to be different according to age and sex. The objective of this study was to investigate differences in sleep parameters and quantitative electroencephalography of patients with insomnia according to age and sex.

METHODS

Patients with insomnia disorder ages 40-79 years were recruited. Each participant was assessed with the Pittsburgh Sleep Quality Index, 4-day wrist actigraphy, and quantitative electroencephalography derived from a 64-channel electroencephalogram system. These variables were compared between age groups (40-64 years vs 65-79 years) and sexes.

RESULTS

Among 173 participants, 61 (35%) were ages 65-79 years and 64 (35%) were males. The older group reported shorter (P = .009) total sleep time than the middle-aged group based on the Pittsburgh Sleep Quality Index, while women slept longer than men based on actigraphy (P = .040). Regarding electroencephalography, women had higher relative beta power than men (P = .006). Older patients showed slower dominant occipital frequency than younger patients (P = .008). The age effect was more noticeable on both clinical variables and quantitative electroencephalography for women. Compared with younger women, older women reported shorter total sleep time in the Pittsburgh Sleep Quality Index (P = .025), underestimated their sleep time (Pittsburgh Sleep Quality Index total sleep time/actigraphic total sleep time, P = .034), and showed reduced alpha power in the frontal area (P = .009).

CONCLUSIONS

Clinicians should be aware of the age and sex difference on manifestation of insomnia, which may further impact an individual's behaviors, such as staying in bed for a longer time or seeking sleep aids.

Standardization and Personalized Medicine Using Quantitative EEG in Clinical Settings

Two major trends have been dominant in health care in recent years. First, there is a growing consensus that standardization of health care procedures and methods can result in improved effectiveness and safety of treatments. Second, there is increased interest in "personalized medicine," which refers to the tailoring of treatments to individual patients. Here I discuss how these trends apply to the field of quantitative EEG (qEEG), where de-artifacted resting state EEGs of individuals are compared with a normative database in order to assess clinically meaningful deviations, which can be used for diagnostic procedures, to guide personalized treatment protocols, and to assess treatment effectiveness. Standardized and automated de-artifacting procedures are increasingly being used in scientific research and in clinical practice. The advantages of these procedures over manual de-artifacting will be discussed. The results of a systematic comparison between 2 commonly used qEEG databases show that these databases produce very comparable results, illustrating not only the validity and reliability of both databases but also the opportunity to move forward to a standardized use of qEEG in clinical practice. Finally, the standardization of qEEG interpretation as both a diagnostic and treatment selection tool provides an example of how qEEG can merge both personalized medicine and standardization in the treatment of psychological disorders.

EEG spectral analysis in insomnia disorder: A systematic review and meta-analysis

Insomnia disorder (ID) has become the second-most common mental disorder. Despite burgeoning evidence for increased high-frequency electroencephalography (EEG) activity and cortical hyperarousal in ID, the detailed spectral features of this disorder during wakefulness and different sleep stages remain unclear. Therefore, we adopted a meta-analytic approach to systematically assess existing evidence on EEG spectral features in ID. Hedges's g was calculated by 148 effect sizes from 24 studies involving 977 participants. Our results demonstrate that, throughout wakefulness and sleep, patients with ID exhibited increased beta band power, although such increases sometimes extended into neighboring frequency bands. Patients with ID also exhibited increased theta and gamma power during wakefulness, as well as increased alpha and sigma power during rapid eye movement (REM) sleep. In addition, ID was associated with decreased delta power and increased theta, alpha, and sigma power during NREM sleep. The EEG measures of absolute and relative power have similar sensitivity in detecting spectral features of ID during wakefulness and REM sleep; however, relative power appeared to be a more sensitive biomarker during NREM sleep. Our study is the first statistics-based review to quantify EEG power spectra across stages of sleep and wakefulness in patients with ID.

The effect of repetitive transcranial magnetic stimulation for insomnia: a systematic review and meta-analysis

BACKGROUND

Repetitive transcranial magnetic stimulation (rTMS) might be a promising technique in treating insomnia. A comprehensive meta-analysis of the available literature is conducted to offer evidence.

OBJECTIVE

To evaluate the efficacy and safety of rTMS for insomnia, either as monotherapy or as a complementary strategy.

METHODS

CENTRAL, PubMed, EMBASE, PsycINFO, CINAHL, PEDro, CBM, CNKI, WANFANG, and VIP were searched from earliest record to August 2019. Randomized control trials (RCTs) published in English and Chinese examining effects of rTMS on patients with insomnia were included. Two authors independently completed the article selection, data extraction and rating. Physiotherapy Evidence Database (PEDro) scale was used to assess the methodological quality of the included studies. The RevMan software was used for meta-analysis. The quality of the evidence was assessed by Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach.

RESULTS

A total of 36 trials from 28 eligible studies were included, involving a total of 2357 adult participants (mean age, 48.80 years; 45.33% males). Compared with sham rTMS, rTMS was associated with improved PSQI total score (SMD -2.31, 95% CI -2.95 to -1.66; Z = 7.01, P < 0.00001) and scores of seven subscales. Compared to other treatment, rTMS as an adjunct to other treatment was associated with improved PSQI total score (SMD -1.44, 95% CI -2.00 to -0.88; Z = 5.01, P < 0.00001), and may have effects on scores of seven subscales. Compared with other treatment, rTMS was associated with improved Pittsburgh sleep quality index (PSQI) total score (SMD -0.63, 95% CI -1.22 to -0.04; Z = 2.08, P = 0.04), and may have a better score in sleep latency, sleep disturbance and hypnotic using of seven subscales. In the three pair of comparisons, the results for polysomnography (PSG) outcomes were varied. In general, rTMS may improve sleep quality through increasing slow wave and rapid eye movement (REM) sleep. The rTMS group was more prone to headache than the sham or blank control group (RR 1.71, 95% CI 1.03 to 2.85; Z = 2.07, P = 0.04). No severe adverse events were reported. Reporting biases and low and very low grade of some evidences should be considered when interpreting the results of this meta-analysis.

CONCLUSIONS

Our findings indicate that rTMS may be a safe and effective option for insomnia. Further international, multicenter, high-quality RCTs with more objective, quality of life related and follow-up assessments are needed.