Is transcranial direct current stimulation, alone or in combination with antidepressant medications or psychotherapies, effective in treating major depressive disorder? A systematic review and meta-analysis.

BACKGROUND: Transcranial direct current stimulation (tDCS) has shown mixed results for depression treatment. The efficacies of tDCS combination therapies have not been investigated deliberately. This review aims to evaluate the clinical efficacy of tDCS as a monotherapy and in combination with medication, psychotherapy, and ECT for treating adult patients with major depressive disorder (MDD) and identified the factors influencing treatment outcome measures (i.e. depression score, dropout, response, and remission rates). METHODS: The systematic review was performed in PubMed/Medline, EMBASE, PsycINFO, Web of Sciences, and OpenGrey. Two authors performed independent literature screening and data extraction. The primary outcomes were the standardized mean difference (SMD) for continuous depression scores after treatment and odds ratio (OR) dropout rate; secondary outcomes included ORs for response and remission rates. Random effects models with 95% confidence intervals were employed in all outcomes. The overall effect of tDCS was investigated by meta-analysis. Sources of heterogeneity were explored via subgroup analyses, meta-regression, sensitivity analyses, and assessment of publication bias. RESULTS: Twelve randomised, sham-controlled trials (active group: N = 251, sham group: N = 204) were included. Overall, the integrated depression score of the active group after treatment was significantly lower than that of the sham group (g = - 0.442, p = 0.017), and further analysis showed that only tDCS + medication achieved a significant lower score (g = - 0.855, p < 0.001). Moreover, this combination achieved a significantly higher response rate than sham intervention (OR = 2.7, p = 0.006), while the response rate remained unchanged for the other three therapies. Dropout and remission rates were similar in the active and sham groups for each therapy and also for the overall intervention. The meta-regression results showed that current intensity is the only predictor for the response rate. None of publication bias was identified. CONCLUSION: The effect size of tDCS treatment was obviously larger in depression score compared with sham stimulation. The tDCS combined selective serotonin re-uptake inhibitors is the optimized therapy that is effective on depression score and response rate. tDCS monotherapy and combined psychotherapy have no significant effects. The most important parameter for optimization in future trials is treatment strategy.

Functional dynamics of thalamic local field potentials correlate with modulation of neuropathic pain.

Understanding the functional dynamics of neural oscillations in the sensory thalamus is essential for elucidating the perception and modulation of neuropathic pain. Local field potentials were recorded from the sensory thalamus of twelve neuropathic pain patients. Single and combinational neural states were defined by the activity state of a single or paired oscillations. Relationships between the duration or occurrence rate of neural state and pre-operative pain level or pain relief induced by deep brain stimulation were evaluated. Results showed that the occurrence rate of the single neural state of low-beta oscillation was significantly correlated with pain relief. The duration and occurrence rate of combinational neural states of the paired low-beta with delta, theta, alpha, high-beta or low-gamma oscillations were more significantly correlated with pain relief than the single neural states. Moreover, these significant combinational neural states formed a local oscillatory network with low-beta oscillation as a key node. The results also showed correlations between measures of combinational neural states and subjective pain level as well. The duration of combinational neural states of paired alpha with delta or theta oscillations and the occurrence rate of neural states of the paired delta with low-beta or low-gamma oscillations were significantly correlated with pre-operative pain level. In conclusion, this study revealed that the integration of oscillations and the functional dynamics of neural states were differentially involved in modulation and perception of neuropathic pain. The functional dynamics could be biomarkers for developing neural state-dependent deep brain stimulation for neuropathic pain.

An Electroencephalography Profile of Paroxysmal Kinesigenic Dyskinesia.

Paroxysmal kinesigenic dyskinesia (PKD) is associated with a disturbance of neural circuit and network activities, while its neurophysiological characteristics have not been fully elucidated. This study utilized the high-density electroencephalogram (hd-EEG) signals to detect abnormal brain activity of PKD and provide a neural biomarker for its clinical diagnosis and PKD progression monitoring. The resting hd-EEGs are recorded from two independent datasets and then source-localized for measuring the oscillatory activities and function connectivity (FC) patterns of cortical and subcortical regions. The abnormal elevation of theta oscillation in wildly brain regions represents the most remarkable physiological feature for PKD and these changes returned to healthy control level in remission patients. Another remarkable feature of PKD is the decreased high-gamma FCs in non-remission patients. Subtype analyses report that increased theta oscillations may be related to the emotional factors of PKD, while the decreased high-gamma FCs are related to the motor symptoms. Finally, the authors established connectome-based predictive modelling and successfully identified the remission state in PKD patients in dataset 1 and dataset 2. The findings establish a clinically relevant electroencephalography profile of PKD and indicate that hd-EEG can provide robust neural biomarkers to evaluate the prognosis of PKD.

Bilateral Habenula deep brain stimulation for treatment-resistant depression: clinical findings and electrophysiological features.

Deep brain stimulation (DBS) of structures in the brain's reward system is a promising therapeutic option for patients with treatment-resistant depression (TRD). Recently, DBS of the habenula (HB) in the brain's anti-reward system has also been reported to alleviate depressive symptoms in patients with TRD or bipolar disorder (BD). In this pilot open-label prospective study, we explored the safety and clinical effectiveness of HB-DBS treatment in seven patients with TRD or BD. Also, local field potentials (LFPs) were recorded from the patients' left and right HB to explore the power and asymmetry of oscillatory activities as putative biomarkers of the underlying disease state. At 1-month follow-up (FU), depression and anxiety symptoms were both reduced by 49% (n = 7) along with substantial improvements in patients' health status, functional impairment, and quality of life. Although the dropout rate was high and large variability in clinical response existed, clinical improvements were generally maintained throughout the study [56%, 46%, and 64% reduction for depression and 61%, 48%, and 70% reduction for anxiety at 3-month FU (n = 5), 6-month FU (n = 5), and 12-month FU (n = 3), respectively]. After HB-DBS surgery, sustained improvements in mania symptoms were found in two patients who presented with mild hypomania at baseline. Another patient, however, experienced an acute manic episode 2 months after surgery that required hospitalization. Additionally, weaker and more symmetrical HB LFP oscillatory activities were associated with more severe depression and anxiety symptoms at baseline, in keeping with the hypothesis that HB dysfunction contributes to MDD pathophysiology. These preliminary findings indicate that HB-DBS may offer a valuable treatment option for depressive symptoms in patients who suffer from TRD or BD. Larger and well-controlled studies are warranted to examine the safety and efficacy of HB-DBS for treatment-refractory mood disorders in a more rigorous fashion.

Incidence of cervical cancer and age-specific survival of small cell cervical carcinoma in Taiwan.

OBJECTIVE: To determine the incidence of cervical cancer and the age-specific survival from small cell cervical carcinoma in Taiwan. DESIGN: Retrospective study. Setting. Taiwan. POPULATION: Women diagnosed with cervical cancer from 1991 to 2005. METHODS: Analysis of data from the National Cancer Registration System and National Death Certification System. MAIN OUTCOME MEASURES: Incidence and age at diagnosis of cervical carcinoma and age-specific and overall survival from small cell cervical carcinoma. RESULTS: During the study period, 36 122 women were diagnosed with cervical cancer, and 81.8% had squamous cell carcinoma (SCC). For the periods 1991-1995, 1996-2000 and 2001-2005, the mean age at diagnosis increased from 53.9 ± 13.3 to 55.0 ± 14.9 and then to 56.7 ± 14.7 years, respectively. The incidence of SCC decreased from 1991 to 2005. During the same period, non-significant increases of adenocarcinoma and small cell carcinoma were noted. For SCC, occurrence peaked in 1991-1995 in patients 50-59 years of age. From 1996 to 2005, it peaked in patients 40-49 years of age. For cervical adenocarcinoma, occurrence peaked in patients 40-49 years of age, with a steady increase in this age group from 1991 to 2005. Occurrence of small cell cervical carcinoma peaked in the period 1991-1995 in patients 30-39 years of age. During the 15 years of the study, the overall mortality rate of the 198 patients with small cell cervical carcinoma was 65.7%. CONCLUSIONS: In Taiwan, the incidence of small cell cervical carcinoma and adenocarcinoma tended to increase, but the incidence of squamous cell cervical carcinoma significantly decreased during the period 1991-2005.

Neurophysiological characteristics in the periventricular/periaqueductal gray correlate with pain perception, sensation, and affect in neuropathic pain patients.

The periventricular/periaqueductal gray (PAG/PVG) is critical for pain perception and is associated with the emotional feelings caused by pain. However, the electrophysiological characteristics of the PAG/PVG have been little investigated in humans with chronic pain. The present study analyzed the oscillatory characteristics of local field potentials (LFPs) in the PAG/PVG of eighteen neuropathic pain patients. Power spectrum analysis and neural state analysis were applied to the PAG/PVG LFPs. Neural state analysis is based on a dynamic neural state identification approach and discriminates the LFPs into different neural states, including a single neural state based on one oscillation and a combinational neural state based on two paired oscillations. The durations and occurrence rates were used to quantify the dynamic features of the neural state. The results show that the combined neural state forms three local networks based on neural oscillations that are responsible for the perceptive, sensory, and affective components of pain. The first network is formed by the interaction of the delta oscillation with other oscillations and is responsible for the coding of pain perception. The second network is responsible for the coding of sensory pain information, uses high gamma as the main node, and is widely connected with other neural oscillations. The third network is responsible for the coding of affective pain information, and beta oscillations play an important role in it. This study suggested that the combination of two neural oscillations in the PAG/PVG is essential for encoding perceptive, sensory, and affective measures of pain.

Subthalamic dynamic neural states correlate with motor symptoms in Parkinson's Disease.

OBJECTIVE: This study aims to discriminate the dynamic synchronization states from the subthalamic local field potentials and investigate their correlations with the motor symptoms in Parkinson's Disease (PD). METHODS: The resting-state local field potentials of 10 patients with PD were recorded from the subthalamic nucleus. The dynamic neural states of multiple oscillations were discriminated and analyzed. The Spearman correlation was used to investigate the correlations between occurrence rate or duration of dynamic neural states and the severity of motor symptoms. RESULTS: The proportion of long low-beta and theta synchronized state was significantly correlated with the general motor symptom and tremor, respectively. The duration of combined low/high-beta state was significantly correlated with rigidity, and the duration of combined alpha/high-beta state was significantly correlated with bradykinesia. CONCLUSIONS: This study provides evidence that motor symptoms are associated with the neural states coded with multiple oscillations in PD. SIGNIFICANCE: This study may advance the understanding of the neurophysiological mechanisms of the motor symptoms and provide potential biomarkers for closed-loop deep brain stimulation in PD.

Measurement of Step Angle for Quantifying the Gait Impairment of Parkinson's Disease by Wearable Sensors: Controlled Study.

BACKGROUND: Gait impairments including shuffling gait and hesitation are common in people with Parkinson's disease (PD), and have been linked to increased fall risk and freezing of gait. Nowadays the gait metrics mostly focus on the spatiotemporal characteristics of gait, but less is known of the angular characteristics of the gait, which may provide helpful information pertaining to the functional status and effects of the treatment in PD. OBJECTIVE: This study aimed to quantify the angles of steps during walking, and explore if this novel step angle metric is associated with the severity of PD and the effects of the treatment including the acute levodopa challenge test (ALCT) and deep brain stimulation (DBS). METHODS: A total of 18 participants with PD completed the walking test before and after the ALCT, and 25 participants with PD completed the test with the DBS on and off. The walking test was implemented under two conditions: walking normally at a preferred speed (single task) and walking while performing a cognitive serial subtraction task (dual task). A total of 17 age-matched participants without PD also completed this walking test. The angular velocity was measured using wearable sensors on each ankle, and three gait angular metrics were obtained, that is mean step angle, initial step angle, and last step angle. The conventional gait metrics (ie, step time and step number) were also calculated. RESULTS: The results showed that compared to the control, the following three step angle metrics were significantly smaller in those with PD: mean step angle (F1,48=69.75, P<.001, partial eta-square=0.59), initial step angle (F1,48=15.56, P<.001, partial eta-square=0.25), and last step angle (F1,48=61.99, P<.001, partial eta-square=0.56). Within the PD cohort, both the ALCT and DBS induced greater mean step angles (ACLT: F1,38=5.77, P=.02, partial eta-square=0.13; DBS: F1,52=8.53, P=.005, partial eta-square=0.14) and last step angles (ACLT: F1,38=10, P=.003, partial eta-square=0.21; DBS: F1,52=4.96, P=.003, partial eta-square=0.09), but no significant changes were observed in step time and number after the treatments. Additionally, these step angles were correlated with the Unified Parkinson's Disease Rating Scale, Part III score: mean step angle (single task: r=-0.60, P<.001; dual task: r=-0.52, P<.001), initial step angle (single task: r=-0.35, P=.006; dual task: r=-0.35, P=.01), and last step angle (single task: r=-0.43, P=.001; dual task: r=-0.41, P=.002). CONCLUSIONS: This pilot study demonstrated that the gait angular characteristics, as quantified by the step angles, were sensitive to the disease severity of PD and, more importantly, can capture the effects of treatments on the gait, while the traditional metrics cannot. This indicates that these metrics may serve as novel markers to help the assessment of gait in those with PD as well as the rehabilitation of this vulnerable cohort.

Epileptic States Recognition Using Transfer Learning.

Automatic recognition of electroencephalogram (EEG) signals plays a major role in epilepsy diagnosis and assessment. However, the recognition accuracy of conventional methods is usually not satisfactory because of the inconsistent distribution of training and testing data in practical applications. To overcome this problem, we used cross-domain mean joint approximation embedding (CMJAE) transductive transfer learning method to realize the knowledge transfer from the training data to the testing data by measuring the distribution difference between them. We combined the subspace learning and joint distribution to adapt the marginal and conditional distribution discrepancy. Our method was able to effectively learn a model for the testing data from training data with different distribution at a low computational complexity cost. On a public dataset, an ad-hoc cross-validation scheme of the proposed method exhibited that the average recognition accuracy, sensitivity, specificity of different states was 97.5%, 94.3%, 92.7% respectively, much better than conventional machine learning or deep learning methods, which may serve as a promising strategy for epileptic states recognition algorithms.

Dynamic Neural State Identification in Deep Brain Local Field Potentials of Neuropathic Pain.

In neuropathic pain, the neurophysiological and neuropathological function of the ventro-posterolateral nucleus of the thalamus (VPL) and the periventricular gray/periaqueductal gray area (PVAG) involves multiple frequency oscillations. Moreover, oscillations related to pain perception and modulation change dynamically over time. Fluctuations in these neural oscillations reflect the dynamic neural states of the nucleus. In this study, an approach to classifying the synchronization level was developed to dynamically identify the neural states. An oscillation extraction model based on windowed wavelet packet transform was designed to characterize the activity level of oscillations. The wavelet packet coefficients sparsely represented the activity level of theta and alpha oscillations in local field potentials (LFPs). Then, a state discrimination model was designed to calculate an adaptive threshold to determine the activity level of oscillations. Finally, the neural state was represented by the activity levels of both theta and alpha oscillations. The relationship between neural states and pain relief was further evaluated. The performance of the state identification approach achieved sensitivity and specificity beyond 80% in simulation signals. Neural states of the PVAG and VPL were dynamically identified from LFPs of neuropathic pain patients. The occurrence of neural states based on theta and alpha oscillations were correlated to the degree of pain relief by deep brain stimulation. In the PVAG LFPs, the occurrence of the state with high activity levels of theta oscillations independent of alpha and the state with low-level alpha and high-level theta oscillations were significantly correlated with pain relief by deep brain stimulation. This study provides a reliable approach to identifying the dynamic neural states in LFPs with a low signal-to-noise ratio by using sparse representation based on wavelet packet transform. Furthermore, it may advance closed-loop deep brain stimulation based on neural states integrating multiple neural oscillations.

Characteristics of local field potentials correlate with pain relief by deep brain stimulation.

OBJECTIVE: To investigate the link between neuronal activity recorded from the sensory thalamus and periventricular gray/periaqueductal gray (PVAG) and pain relief by deep brain stimulation (DBS). METHODS: Local field potentials (LFPs) were recorded from the sensory thalamus and PVAG post-operatively from ten patients with neuropathic pain. The LFPs were quantified using spectral and time-frequency analysis, the relationship between the LFPs and pain relief was quantified with nonlinear correlation analysis. RESULTS: The theta oscillations of both sensory thalamus and PVAG correlated inversely with pain relief. The high beta oscillations in the sensory thalamus and the alpha oscillations in the PVAG correlated positively with pain relief. Moreover, the ratio of high-power duration to low-power duration of theta band activity in the sensory thalamus and PVAG correlated inversely with pain relief. The duration ratio at the high beta band in the sensory thalamus correlated positively with pain relief. CONCLUSIONS: Our results reveal distinct neuronal oscillations at the theta, alpha, and beta frequencies correlating with pain relief by DBS. SIGNIFICANCE: The study provides quantitative measures for predicting the outcomes of neuropathic pain relief by DBS as well as potential biomarkers for developing adaptive stimulation strategies.