OBJECTIVES

This study described the demographic and clinical profile, mental health problems, prevalence of psychiatric conditions, psychosocial interventions used, and outcomes of the management of mental health problems among in-patients admitted to non-psychiatry units of tertiary hospitals referred to mental health care providers; and described gender-disaggregated data related to mental health care providers and patients receiving psychosocial interventions in tertiary hospitals.

This study employed a mixed-method design, using both qualitative and quantitative methodologies following the convergence model of triangulation. The following were the data sources: (1) cross-sectional review of charts of patients referred for psychosocial problems using the ICD-10 classification; (2) a survey of mental health service providers; (3) key informant interviews of mental health service providers; and (4) focus group discussions of mental health providers. All data were collated, compared, and contrasted, then analyzed using the convergence model of triangulation design.

Among the 3,502 patients in the chart review, 1,870 (53.40%) were males. The median age was 46.08 years and 92.06% were adults. The most common diagnosis among the patients were mood disorder (744, 21.25%) and organic mental disorder (710, 20.27%). Combination treatment of psychosocial intervention and pharmacology was the most common strategy received by patients. There was a higher proportion of patients admitted to public hospitals (996, 45.27%) who received psychosocial interventions only compared to those admitted to private hospitals (235, 18.05%). There were 3,453 out of 3,502 in-patients referred for psychiatric intervention. Of these 2,420 (70%) received psychoeducation, 2,365 (68.5%), received supportive psychotherapy/counseling, 535 (15.5%) family therapy, and 286 (8.3%) behavior modification. There were more patients given psychosocial interventions 2,541 (72.56%) who were discharged with instruction to follow-up, while around one in 10 (456, 13.02%) was not instructed to do a follow-up consultation. The types of interventions across all data sources were similar.

The most common type of management for psychosocial problems of in-patients in tertiary hospitals was a combination of psychosocial intervention and pharmacotherapy. Psychoeducation, supportive psychotherapy/ counseling, and family therapy were the most often given psychosocial interventions. The patient-related reasons for the choice of interventions were patient’s medical status (diagnosis and severity of symptoms) and psychological status (psychological mindedness), while the provider-related factors influencing the choice of intervention were provider’s skills and personal preference. Moreover, resources (human and material) and service provision policies (treatment guidelines and aftercare interventions) were the most common hospital-related factors. Further prospective research to determine the associated patients, providers, and hospital factors in larger geographic and cultural settings will provide evidence for the effectiveness and outcomes of psychosocial interventions.

Cerebral stroke is a common cerebrovascular disease that gives rise to the increases of the morbidity，disability and fatality rates，and imposes burdens to the patients family and the whole society. Post-stroke depression is a kind of clinical complications for cerebral stroke patients，as a complication， post-stroke depression affects the treatment effect and prognosis of stroke survivors， and has become a widely concerned public health issue. At present， oral psychotropic drugs commonly used in clinical practice. Patients often have poor compliance and clinical efficacy is not ideal. Psychological rehabilitation ， as a safe and effective clinical treatment method， has been accepted by patients and clinicians. The current research progress is reviewed， in order to provide a basis and reference for clinical treatment of post stroke depression and future research development.
Psychotherapy

This paper summarizes a lecture on psychiatry and spirituality, which examined research onthe relationship between religion, spirituality and mental health, and discussed theimportance of addressing spiritual issues in psychotherapy. In this article, religion andspirituality are first differentiated from one another. Next, research on the relationshipbetween religion and mental health is examined. Third, a theoretical model is presentedexplaining how religious involvement may affect mental and social health. Fourth, a review of religious/spiritually-integrated psychotherapy is presented with a focus ondepression/anxiety, moral injury, and PTSD. Finally, further resources for more informationabout the topic is provided. Because many people in the Philippines are religious, and religionaffects mental health one way or the other, it cannot be ignored by psychiatrists who practicein this country.
Religion ; Spirituality ; Depression ; Anxiety ; Suicide ; Substance-Related Disorders ; Stress Disorders, Post-Traumatic ; Psychotherapy

INTRODUCTION: Behavioural and psychological symptoms of dementia (BPSD) are considered integral parts of dementia. While pharmacotherapy is reserved for severe symptoms of BPSD, the associated adverse effects can be detrimental. Therefore, non-pharmacological intervention is recommended as the first line of treatment in the management of BPSD. This study aimed to explore the non-pharmacological approaches for the management of BPSD and the strategies and barriers to implementing them in secondary care facilities in Malaysia. METHODS: A qualitative study design was employed. Data were collected through observations and semi-structured interviews of 12 caregivers and 11 people with dementia (PWD) at seven secondary care facilities. Observations were written in the field notes, and interviews were audio-recorded and transcribed. All data were subjected to thematic analysis. RESULTS: Some personalised non-pharmacological interventions, such as physical exercise, music therapy, reminiscence therapy and pet therapy, were conducted in several nursing care centres. Collaborative care from the care providers and family members was found to be an important facilitating factor. The lack of family support led to care providers carrying additional workload beyond their job scope. Other barriers to non-pharmacological interventions were cultural and language differences between the care providers and PWD, inadequate staff numbers and training, and time constraints. CONCLUSION Although non-pharmacological approaches have been used to some extent in Malaysia, continuous education and training of healthcare providers and the family members of PWD is needed to overcome the challenges to their successful implementation.
Humans ; Dementia/diagnosis* ; Psychotherapy ; Qualitative Research ; Health Personnel ; Caregivers

To introduce the clinical experience of professor SUN Shen-tian in treatment of Tourette's syndrome (TS) with acupuncture. TS is a psychosomatic disease and the core pathogenesis refers to blood deficiency producing internal wind. The disease is located in the heart and liver. Acupoints are selected according to the functional orientation of the cerebral cortex. The extrapyramidal system area is preferred for tic disorder, and the emotional area is for behavioral disorder. The treatment focuses on regulating the mind by multiple methods, including manual needling technique used the transcranial repeated acupuncture, and regulating the mind by taking multiple acupoints, Baihui (GV 20), Neiguan (PC 6), Shenmen (HT 7) and Dazhong (KI 4) are stimulated. For abdominal twitching and mental symptoms of TS children, the first and third abdominal areas are selected. The target symptoms (biao) are treated specially by local acupoints, the combination of the starting and ending acupoints of the affected meridian, or the acupoints of the meridians with same name. The modified chaihu longgu muli decoction and siwu decoction are prescribed to sooth liver, nourish blood and soothe wind. In association with the characteristics and target symptoms of TS, the sequential therapy is used with filiform needling, intradermal needling, Chinese herbal medication and psychotherapy.
Child ; Humans ; Tourette Syndrome ; Acupuncture Therapy ; Meridians ; Liver ; Psychotherapy

The brain-computer interface (BCI) based on motor imagery electroencephalography (MI-EEG) enables direct information interaction between the human brain and external devices. In this paper, a multi-scale EEG feature extraction convolutional neural network model based on time series data enhancement is proposed for decoding MI-EEG signals. First, an EEG signals augmentation method was proposed that could increase the information content of training samples without changing the length of the time series, while retaining its original features completely. Then, multiple holistic and detailed features of the EEG data were adaptively extracted by multi-scale convolution module, and the features were fused and filtered by parallel residual module and channel attention. Finally, classification results were output by a fully connected network. The application experimental results on the BCI Competition IV 2a and 2b datasets showed that the proposed model achieved an average classification accuracy of 91.87% and 87.85% for the motor imagery task, respectively, which had high accuracy and strong robustness compared with existing baseline models. The proposed model does not require complex signals pre-processing operations and has the advantage of multi-scale feature extraction, which has high practical application value.
Humans ; Time Factors ; Brain ; Electroencephalography ; Imagery, Psychotherapy ; Neural Networks, Computer

Brain-computer interaction (BCI) is a transformative human-computer interaction, which aims to bypass the peripheral nerve and muscle system and directly convert the perception, imagery or thinking activities of cranial nerves into actions for further improving the quality of human life. Magnetoencephalogram (MEG) measures the magnetic field generated by the electrical activity of neurons. It has the unique advantages of non-contact measurement, high temporal and spatial resolution, and convenient preparation. It is a new BCI driving signal. MEG-BCI research has important brain science significance and potential application value. So far, few documents have elaborated the key technical issues involved in MEG-BCI. Therefore, this paper focuses on the key technologies of MEG-BCI, and details the signal acquisition technology involved in the practical MEG-BCI system, the design of the MEG-BCI experimental paradigm, the MEG signal analysis and decoding key technology, MEG-BCI neurofeedback technology and its intelligent method. Finally, this paper also discusses the existing problems and future development trends of MEG-BCI. It is hoped that this paper will provide more useful ideas for MEG-BCI innovation research.
Brain/physiology* ; Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Magnetoencephalography ; Technology

Speech expression is an important high-level cognitive behavior of human beings. The realization of this behavior is closely related to human brain activity. Both true speech expression and speech imagination can activate part of the same brain area. Therefore, speech imagery becomes a new paradigm of brain-computer interaction. Brain-computer interface (BCI) based on speech imagery has the advantages of spontaneous generation, no training, and friendliness to subjects, so it has attracted the attention of many scholars. However, this interactive technology is not mature in the design of experimental paradigms and the choice of imagination materials, and there are many issues that need to be discussed urgently. Therefore, in response to these problems, this article first expounds the neural mechanism of speech imagery. Then, by reviewing the previous BCI research of speech imagery, the mainstream methods and core technologies of experimental paradigm, imagination materials, data processing and so on are systematically analyzed. Finally, the key problems and main challenges that restrict the development of this type of BCI are discussed. And the future development and application perspective of the speech imaginary BCI system are prospected.
Brain ; Computers ; Humans ; Imagery, Psychotherapy ; Speech ; Technology

Motor imagery electroencephalogram (EEG) signals are non-stationary time series with a low signal-to-noise ratio. Therefore, the single-channel EEG analysis method is difficult to effectively describe the interaction characteristics between multi-channel signals. This paper proposed a deep learning network model based on the multi-channel attention mechanism. First, we performed time-frequency sparse decomposition on the pre-processed data, which enhanced the difference of time-frequency characteristics of EEG signals. Then we used the attention module to map the data in time and space so that the model could make full use of the data characteristics of different channels of EEG signals. Finally, the improved time-convolution network (TCN) was used for feature fusion and classification. The BCI competition IV-2a data set was used to verify the proposed algorithm. The experimental results showed that the proposed algorithm could effectively improve the classification accuracy of motor imagination EEG signals, which achieved an average accuracy of 83.03% for 9 subjects. Compared with the existing methods, the classification accuracy of EEG signals was improved. With the enhanced difference features between different motor imagery EEG data, the proposed method is important for the study of improving classifier performance.
Algorithms ; Brain-Computer Interfaces ; Electroencephalography/methods* ; Humans ; Imagery, Psychotherapy ; Imagination

The effective classification of multi-task motor imagery electroencephalogram (EEG) is helpful to achieve accurate multi-dimensional human-computer interaction, and the high frequency domain specificity between subjects can improve the classification accuracy and robustness. Therefore, this paper proposed a multi-task EEG signal classification method based on adaptive time-frequency common spatial pattern (CSP) combined with convolutional neural network (CNN). The characteristics of subjects' personalized rhythm were extracted by adaptive spectrum awareness, and the spatial characteristics were calculated by using the one-versus-rest CSP, and then the composite time-domain characteristics were characterized to construct the spatial-temporal frequency multi-level fusion features. Finally, the CNN was used to perform high-precision and high-robust four-task classification. The algorithm in this paper was verified by the self-test dataset containing 10 subjects (33 ± 3 years old, inexperienced) and the dataset of the 4th 2018 Brain-Computer Interface Competition (BCI competition Ⅳ-2a). The average accuracy of the proposed algorithm for the four-task classification reached 93.96% and 84.04%, respectively. Compared with other advanced algorithms, the average classification accuracy of the proposed algorithm was significantly improved, and the accuracy range error between subjects was significantly reduced in the public dataset. The results show that the proposed algorithm has good performance in multi-task classification, and can effectively improve the classification accuracy and robustness.
Humans ; Adult ; Imagination ; Neural Networks, Computer ; Imagery, Psychotherapy/methods* ; Electroencephalography/methods* ; Algorithms ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted