Clinical characteristics and potential association to Parkinson's disease and dementia with Lewy bodies in patients with major depressive disorder who received maintenance electroconvulsive therapy: a retrospective chart review study.

BACKGROUND: Maintaining remission after electroconvulsive therapy (ECT) is clinically relevant in patients with depression, and maintenance ECT has been introduced in patients who fail to maintain remission after ECT. However, the clinical characteristics and biological background of patients who receive maintenance ECT are poorly understood. Thus, this study aimed to examine the clinical background of patients who underwent maintenance ECT. METHODS: Patients with major depressive disorder who underwent ECT followed by maintenance ECT (mECT group) and those who did not (acute ECT [aECT] group) were included. Clinical characteristics, including the results of neuroimaging examinations for Parkinson's disease (PD) and dementia with Levy body (DLB) such as myocardial 123I-metaiodobenzylguanidine (MIBG) scintigraphy and dopamine transporter imaging single-photon emission computerized tomography (DaT-SPECT), were compared between the groups. RESULTS: In total, 13 and 146 patients were included in the mECT and aECT groups, respectively. Compared to the aECT group, the mECT group showed a significantly higher prevalence of melancholic features (92.3% vs. 27.4%, p < 0.001) and catatonic features (46.2% vs. 9.6%, p = 0.002). Overall, 8 of the 13 patients in the mECT group and 22 of the 146 patients in the aECT group underwent neuroimaging examinations for PD/DLB. The rate of patients examined is significantly higher in the mECT group than in the aECT group (61.5% vs. 11.2%, p < 0.001). Among the groups examined, 7/8 patients in the mECT group and 16/22 patients in the aECT group showed relevant neuroimaging findings for PD/DLB; the positive rate was not significantly different between the two groups (87.5% vs. 72.7%, p = 0.638). CONCLUSIONS: Patients who receive acute and maintenance ECT may have underlying neurodegenerative diseases, including PD/DLB. Investigating the neurobiology of patients who receive maintenance ECT is important for developing appropriate treatments for depression.

Intergenerational concordance of brain structure between depressed mothers and their never-depressed daughters.

AIM: Parents have significant genetic and environmental influences, which are known as intergenerational effects, on the cognition, behavior, and brain of their offspring. These intergenerational effects are observed in patients with mood disorders, with a particularly strong association of depression between mothers and daughters. The main purpose of our study was to investigate female-specific intergenerational transmission patterns in the human brain among patients with depression and their never-depressed offspring. METHODS: We recruited 78 participants from 34 families, which included remitted parents with a history of depression and their never-depressed biological offspring. We used source-based and surface-based morphometry analyses of magnetic resonance imaging data to examine the degree of associations in brain structure between four types of parent-offspring dyads (i.e. mother-daughter, mother-son, father-daughter, and father-son). RESULTS: Using independent component analysis, we found a significant positive correlation of gray matter structure between exclusively the mother-daughter dyads within brain regions located in the default mode and central executive networks, such as the bilateral anterior cingulate cortex, posterior cingulate cortex, precuneus, middle frontal gyrus, middle temporal gyrus, superior parietal lobule, and left angular gyrus. These similar observations were not identified in other three parent-offspring dyads. CONCLUSIONS: The current study provides biological evidence for greater vulnerability of daughters, but not sons, in developing depression whose mothers have a history of depression. Our findings extend our knowledge on the pathophysiology of major psychiatric conditions that show sex biases and may contribute to the development of novel interventions targeting high-risk individuals.

Neuronal network mechanisms associated with depressive symptom improvement following electroconvulsive therapy.

BACKGROUND: Electroconvulsive therapy (ECT) is the most effective antidepressant treatment for severe depression. Although recent structural magnetic resonance imaging (MRI) studies have consistently reported ECT-induced hippocampal volume increases, most studies did not find the association of the hippocampal volume changes with clinical improvement. To understand the underlying mechanisms of ECT action, we aimed to identify the longitudinal effects of ECT on hippocampal functional connectivity (FC) and their associations with clinical improvement. METHODS: Resting-state functional MRI was acquired before and after bilateral ECT in 27 depressed individuals. A priori hippocampal seed-based FC analysis and a data-driven multivoxel pattern analysis (MVPA) were conducted to investigate FC changes associated with clinical improvement. The statistical threshold was set at cluster-level false discovery rate-corrected p < 0.05. RESULTS: Depressive symptom improvement after ECT was positively associated with the change in the right hippocampus-ventromedial prefrontal cortex FC, and negatively associated with the right hippocampus-superior frontal gyrus FC. MVPA confirmed the results of hippocampal seed-based analyses and identified the following additional clusters associated with clinical improvement following ECT: the thalamus, the sensorimotor cortex, and the precuneus. CONCLUSIONS: ECT-induced change in the right frontotemporal connectivity and thalamocortical connectivity, and changes in the nodes of the default mode network were associated with clinical improvement. Modulation of these networks may explain the underlying mechanisms by which ECT exert its potent and rapid antidepressant effect.

Electroconvulsive Therapy for Patients With Depression Who Lack Capacity for Consent: Doing Good and Doing No Harm.

OBJECTIVE: Electroconvulsive therapy (ECT) is provided in real-world clinical settings for patients lacking capacity for consent. The aim of this study was to investigate the clinical characteristics and clinical effectiveness of ECT in this population. METHODS: A retrospective chart review was conducted to collect data from patients who received ECT to treat their depressive episodes between April 2012 and March 2019. Differences in clinical characteristics and short-/long-term clinical outcomes between patients who received ECT with their relatives' consent and patients who received ECT by their own consent were examined. The short-/long-term clinical outcomes were determined by clinical global impression scores and readmission rate, respectively. RESULTS: Of 168 patients with depressive episodes, 34 (20.2%) received ECT with their relatives' consent. Those patients were older, had lower body mass index, and had shorter episode duration. They also exhibited more frequent psychotic, melancholic, and catatonic features. The main indication for ECT in this population was the need for rapid recovery. Patients lacking capacity for consent showed similar remission (61.8%) and response (82.4%) rates to those with capacity for consent. Readmission rate was not significantly different between groups. CONCLUSIONS: There were no significant differences in short-/long-term ECT effectiveness between patients with/without capacity for consent. Electroconvulsive therapy is the only established and effective treatment in clinical settings for the most severe cases, wherein patients are incapable of giving consent but need rapid recovery. A general rejection of this practice due to concerns surrounding consent may be unethical under the ethical principles of medical care.

Machine-learning approach to predict on-road driving ability in healthy older people.

AIM: In Japan, fatal traffic accidents due to older drivers are on the rise. Considering that approximately half the older drivers who have caused fatal accidents are cognitively normal healthy people, it has been required to detect older drivers who are cognitively normal but at high risk of having fatal traffic accidents. However, a standardized method for assessing the driving ability of older drivers has not yet been established. We thus aimed to identify a new sensing method for the evaluation of the on-road driving ability of healthy older people on the basis of vehicle behaviors. METHODS: We enrolled 33 healthy older individuals aged over 65 years and utilized a machine-learning approach to dissociate unsafe drivers from safe drivers based on cognitive assessments and a functional visual acuity test. RESULTS: The linear support vector machine classifier successfully dissociated unsafe drivers from safe drivers with accuracy of 84.8% (sensitivity of 66.7% and specificity of 95.2%). Five clinical parameters, namely age, the first trial of the Rey Auditory Verbal Learning Test immediate recall, the delayed recall of the Rey-Osterrieth Complex Figure Test, the result of the free-drawn Clock Drawing Test, and maximal visual acuity, were consistently selected as essential features for the best classification model. CONCLUSION: Our findings improve our understanding of clinical risk factors leading to unsafe driving and may provide insight into a new intervention that prevents fatal traffic accidents caused by healthy older people.

Predicting Individual Remission After Electroconvulsive Therapy Based on Structural Magnetic Resonance Imaging: A Machine Learning Approach.

OBJECTIVE: To identify important clinical or imaging features predictive of an individual's response to electroconvulsive therapy (ECT) by utilizing a machine learning approach. METHODS: Twenty-seven depressed patients who received ECT were recruited. Clinical demographics and pretreatment structural magnetic resonance imaging (MRI) data were used as candidate features to build models to predict remission and post-ECT Hamilton Depression Rating Scale scores. Support vector machine and support vector regression with elastic-net regularization were used to build models using (i) only clinical features, (ii) only MRI features, and (iii) both clinical and MRI features. Consistently selected features across all individuals were identified through leave-one-out cross-validation. RESULTS: Compared with models that include only clinical variables, the models including MRI data improved the prediction of ECT remission: the prediction accuracy improved from 70% to 93%. Features selected consistently across all individuals included volumes in the gyrus rectus, the right anterior lateral temporal lobe, the cuneus, and the third ventricle, as well as 2 clinical features: psychotic features and family history of mood disorder. CONCLUSIONS: Pretreatment structural MRI data improved the individual predictive accuracy of ECT remission, and only a small subset of features was important for prediction.

Factors Associated With Delirium Following Electroconvulsive Therapy: A Systematic Review.

OBJECTIVES: Delirium following electroconvulsive therapy (ECT) has been a clinical challenge, which, however, has not been investigated through a systematic literature review. The objective of this study was to systematically synthesize available evidence regarding factors associated with post-ECT delirium. METHODS: We conducted a systematic literature search for any type of original investigations that reported risk factors of post-ECT delirium, using PubMed. RESULTS: The literature search identified 43 relevant articles. One study found an association between catatonic feature and increased risk of postictal delirium. Five studies reported that the presence of cerebrovascular disease, Parkinson disease, or dementia was related to higher incidence of post-ECT delirium. Incidence of post-ECT course delirium was increased with bitemporal stimulation (3 studies). One study showed that ultrabrief pulse ECT reduced reorientation time following seizure compared with brief pulse ECT. High stimulus intensity resulted in more prolonged reorientation time after ECT than lower stimulus intensity (2 studies). Longer seizure length was significantly associated with post-ECT delirium in 1 study. Eight studies that examined postictal delirium in association with medications used, including lithium, did not show any consistent finding in their relationships. Four studies showed decreased incidence of postictal delirium in those receiving dexmedetomidine. CONCLUSIONS: Limited evidence suggests that catatonic feature, cerebrovascular disease, Parkinson disease, dementia, bitemporal electrode placement, high stimulus intensity, or longer seizure length are associated with an increased risk of post-ECT delirium. Moreover, dexmedetomidine and ultrabrief pulse ECT seem to have preventive effects of post-ECT delirium.

Resilience and spirituality in patients with depression and their family members: A cross-sectional study.

OBJECTIVE: The degree and quality of resilience in patients with depression have never been investigated in the context of remission status, spirituality/religiosity, and family members' resilience levels, which was addressed in this study. METHODS: This cross-sectional study recruited Japanese outpatients with depressive disorder according to ICD-10 and cohabitant family members who were free from psychiatric diagnoses. Resilience was assessed using the 25-item Resilience Scale (RS). Other assessments included the Montgomery-Asberg Depression Rating Scale (MADRS); the Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being Scale (FACIT) and Kasen et al.'s (2012) scale for spirituality/religiosity; and the Rosenberg Self-Esteem Scale (RSES). RESULTS: One hundred outpatients with depression (mean±SD age, 50.8±14.5years; 44 men; MADRS total score 9.8±9.0) and 36 healthy family members (mean±SD age, 56.5±15.0years; 18 men) were included. Symptom severity, attendance at religious/spiritual services, and self-esteem were significantly associated with RS scores in the patient group. RS total scores were significantly higher in remitted patients compared to non-remitted patients (mean±SD, 112.3±17.1 vs. 84.8±27.7, p<0.001). No correlation was found in RS total scores between patients and their family members (p=0.265), regardless of patients' remission status. CONCLUSIONS: Resilience may be influenced by individual characteristics rather than familial environment; furthermore, self-esteem or spirituality/religiosity may represent reinforcing elements. While caution is necessary in extrapolating these findings to other patient populations, our results suggest that resilience may be considered a state marker in depression.

A review on schizophrenia and relapse--a quest for user-friendly psychopharmacotherapy.

OBJECTIVE: Schizophrenia in general is notoriously associated with relapses rendering the illness progressive to worse outcomes, a concept of which is compatible with neurotoxicity. Therefore, relapse prevention is of utmost clinical relevance. METHODS: In this review, we aim to put relapse into clinical context in the realm of natural history of, or heterogeneity in, schizophrenia and summarize risk factors of relapse. We discuss how to effectively 'define' relapse in schizophrenia and recent meta-analytic studies on this topic to highlight the importance of continuous antipsychotic treatment. RESULTS: The following issues emerged: 'How low maintenance antipsychotic dosage could be?', 'How extended dosing could be?', 'Who could be successfully withdrawn from antipsychotics?' and 'How relapse could be defined in the first place?' The question in particular is how better to deliver antipsychotics at the lowest possible, whereby dose and dosing interval are relevant. While ongoing antipsychotic treatment is the rule, recent works are pointing to a possibility of lower dosage in the maintenance phase of the illness. CONCLUSIONS: Bearing in mind that suboptimal adherence and withdrawal from antipsychotics are an established and unequivocal risk factor for relapse, further investigations are certainly needed to explore user-friendly manner of psychopharmacotherapy to prevent relapse in schizophrenia.

Segmentation and Volume Estimation of the Habenula Using Deep Learning in Patients With Depression.

Background: The habenula is involved in the pathophysiology of depression. However, its small structure limits the accuracy of segmentation methods, and the findings regarding its volume have been inconsistent. This study aimed to create a highly accurate habenula segmentation model using deep learning, test its generalizability to clinical magnetic resonance imaging, and examine differences between healthy participants and patients with depression. Methods: This multicenter study included 382 participants (patients with depression: N = 234, women 47.0%; healthy participants: N = 148, women 37.8%). A 3-dimensional residual U-Net was used to create a habenula segmentation model on 3T magnetic resonance images. The reproducibility and generalizability of the predictive model were tested on various validation cohorts. Thereafter, differences between the habenula volume of healthy participants and that of patients with depression were examined. Results: A Dice coefficient of 86.6% was achieved in the derivation cohort. The test-retest dataset showed a mean absolute percentage error of 6.66, indicating sufficiently high reproducibility. A Dice coefficient of >80% was achieved for datasets with different imaging conditions, such as magnetic field strengths, spatial resolutions, and imaging sequences, by adjusting the threshold. A significant negative correlation with age was observed in the general population, and this correlation was more pronounced in patients with depression (p < 10-7, r = -0.59). Habenula volume decreased with depression severity in women even when the effects of age and scanner were excluded (p = .019, η2 = 0.099). Conclusions: Habenula volume could be a pathophysiologically relevant factor and diagnostic and therapeutic marker for depression, particularly in women.

Accurate segmentation of the habenula, a brain region implicated in depression, is challenging. In this study, we developed an automated human habenula segmentation model using deep learning techniques. The model was confirmed to be reproducible and generalizable at various spatial resolutions. Application of this model to a multicenter dataset confirmed that habenula volume decreased with age in healthy volunteers, an association that was more pronounced in individuals with depression. In addition, habenula volume decreased with the severity of depression in women. This novel model for habenula segmentation enables further study of the role of the habenula in depression.

Amyloid-ß prediction machine learning model using source-based morphometry across neurocognitive disorders.

Previous studies have developed and explored magnetic resonance imaging (MRI)-based machine learning models for predicting Alzheimer's disease (AD). However, limited research has focused on models incorporating diverse patient populations. This study aimed to build a clinically useful prediction model for amyloid-beta (Aß) deposition using source-based morphometry, using a data-driven algorithm based on independent component analyses. Additionally, we assessed how the predictive accuracies varied with the feature combinations. Data from 118 participants clinically diagnosed with various conditions such as AD, mild cognitive impairment, frontotemporal lobar degeneration, corticobasal syndrome, progressive supranuclear palsy, and psychiatric disorders, as well as healthy controls were used for the development of the model. We used structural MR images, cognitive test results, and apolipoprotein E status for feature selection. Three-dimensional T1-weighted images were preprocessed into voxel-based gray matter images and then subjected to source-based morphometry. We used a support vector machine as a classifier. We applied SHapley Additive exPlanations, a game-theoretical approach, to ensure model accountability. The final model that was based on MR-images, cognitive test results, and apolipoprotein E status yielded 89.8% accuracy and a receiver operating characteristic curve of 0.888. The model based on MR-images alone showed 84.7% accuracy. Aß-positivity was correctly detected in non-AD patients. One of the seven independent components derived from source-based morphometry was considered to represent an AD-related gray matter volume pattern and showed the strongest impact on the model output. Aß-positivity across neurological and psychiatric disorders was predicted with moderate-to-high accuracy and was associated with a probable AD-related gray matter volume pattern. An MRI-based data-driven machine learning approach can be beneficial as a diagnostic aid.

Dental conditions in inpatients with schizophrenia: a large-scale multi-site survey.

BACKGROUND: Clinical relevance of dental caries is often underestimated in patients with schizophrenia. The objective of this study was to examine dental caries and to identify clinical and demographic variables associated with poor dental condition in patients with schizophrenia. METHODS: Inpatients with schizophrenia received a visual oral examination of their dental caries, using the decayed-missing-filled teeth (DMFT) index. This study was conducted in multiple sites in Japan, between October and December, 2010. A univariate general linear model was used to examine the effects of the following variables on the DMFT score: age, sex, smoking status, daily intake of sweets, dry mouth, frequency of daily tooth brushing, tremor, the Clinical Global Impression-Schizophrenia Overall severity score, and the Cumulative Illness Rating Scale for Geriatrics score. RESULTS: 523 patients were included in this study (mean ± SD age = 55.6 ± 13.4 years; 297 men). A univariate general linear model showed significant effects of age group, smoking, frequency of daily tooth brushing, and tremor (all p's < 0.001) on the DMFT score (Corrected Model: F(23, 483) = 3.55, p < 0.001, R2 = 0.42) . In other words, older age, smoking, tremor burden, and less frequent tooth brushing were associated with a greater DMFT score. CONCLUSIONS: Given that poor dental condition has been related with an increased risk of physical co-morbidities, physicians should be aware of patients' dental status, especially for aged smoking patients with schizophrenia. Furthermore, for schizophrenia patients who do not regularly brush their teeth or who exhibit tremor, it may be advisable for caregivers to encourage and help them to perform tooth brushing more frequently.

Electroconvulsive therapy and meningioma: a brief review.

Accumulating clinical knowledge indicates that electroconvulsive therapy (ECT) can be used in patients with stable and small brain tumors without any sign of increasing intracranial pressure as long as the risks have been appropriately evaluated. However, there are no clear and detailed clinical guidelines for the application of ECT in patients with brain tumors. Severe complications are described in cases reported before 1980 when the definitive diagnosis of brain tumor before ECT was difficult. We reviewed 13 cases from the literature from the 1980s or later in which ECT was administered to psychiatric patients with a prior diagnosis of meningiomas, a very common type of tumor, before ECT. All cases responded to ECT. Among the cases reviewed, the largest meningioma was 3 x 3 cm. Minor complications such as headache and transient confusion were reported in 5 of 13 cases; however, no severe complications were observed. Accurate identification and careful evaluation of meningioma by routine neuroimaging and other advanced medical techniques surrounding the use of ECT have contributed to the decrease in severe complications.

Impact of Sevoflurane and Thiopental Used Over the Course of Electroconvulsive Therapy: Propensity Score Matching Analysis.

Objective: Although anesthetics play an important role in electroconvulsive therapy (ECT), the clinical efficacy and seizure adequacy of sevoflurane in the course of ECT remain unclear. The purpose of this study was to examine the clinical efficacy and seizure adequacy of sevoflurane, compared with those of thiopental, in the course of ECT in patients with mood disorders. Methods: We conducted a retrospective chart review. Patients who underwent a course of ECT and received sevoflurane (n = 26) or thiopental (n = 26) were included. Factors associated with ECT and treatment outcomes were compared between the two groups using propensity score (PS) matching. Between-group differences were examined using an independent t-test for continuous variables and a χ2-test for categorical variables. Results: Patients who received sevoflurane needed more stimulations (sevoflurane: 13.2 ± 4 times, thiopental: 10.0 ± 2.5 times, df = 51, p = 0.001) and sessions (sevoflurane: 10.0 ± 2.1 times, thiopental: 8.4 ± 2.1 times, df = 51, p = 0.01) and had more inadequate seizures (sevoflurane: 5 ± 3.9 times, thiopental: 2.7 ± 2.7 times, df = 51, p = 0.015). Remission and response rates were similar in both groups. Conclusion: The present findings indicate that sevoflurane should be used with caution in ECT and only when the clinical rationale is clear.

Individual Prediction of Remission Based on Clinical Features Following Electroconvulsive Therapy: A Machine Learning Approach.

Objective: Previous prediction models for electroconvulsive therapy (ECT) responses have predominantly been based on neuroimaging data, which has precluded widespread application for severe cases in real-world clinical settings. The aims of this study were (1) to build a clinically useful prediction model for ECT remission based solely on clinical information and (2) to identify influential features in the prediction model.Methods: We conducted a retrospective chart review to collect data (registered between April 2012 and March 2019) from individuals with depression (unipolar major depressive disorder or bipolar disorder) diagnosed via DSM-IV-TR criteria who received ECT at Keio University Hospital. Clinical characteristics were used as candidate features. A light gradient boosting machine was used for prediction, and 5-fold cross-validation was performed to validate our prediction model.Results: In total, 177 patients with depression underwent ECT during the study period. The remission rate was 63%. Our model predicted individual patient outcomes with 71% accuracy (sensitivity, 86%; specificity, 46%). A shorter duration of the current episodes, lower baseline severity, higher dose of antidepressant medications before ECT, and lower body mass index were identified as important features for predicting remission following ECT.Conclusions: We developed a prediction model for ECT remission based solely on clinical information. Our prediction model demonstrated accuracy comparable to that in previous reports. Our model suggests that introducing ECT earlier in the treatment course may contribute to improvements in clinical outcomes.

White matter alterations in the dorsal attention network contribute to a high risk of unsafe driving in healthy older people.

Aim: Healthy older drivers may be at high risk of fatal traffic accidents. Our recent study showed that volumetric alterations in gray matter in the brain regions within the dorsal attention network (DAN) were strongly related to the risk of unsafe driving in healthy older people. However, the relationship between white matter (WM) structural connectivity and driving ability in healthy older people is still unclear. Methods: We used diffusion tensor imaging to examine the association between microstructural alterations in the DAN and the risk of unsafe driving among healthy older people. We enrolled 32 healthy older individuals aged over 65 years and screened unsafe drivers using an on-road driving test. We then determined the pattern of WM aberrations in unsafe drivers using tract-based spatial statistics. Results: The analysis demonstrated that unsafe drivers had significantly higher axial diffusivity values in nine WM clusters compared with safe drivers. These results were primarily observed bilaterally in the dorsal superior longitudinal fasciculus, which is involved in the DAN. Furthermore, correlation analyses showed that higher axial diffusivity values in the superior longitudinal fasciculus were associated with lower Trail Making Test A scores within unsafe drivers. This result suggests that functionally, WM microstructural alterations in the DAN are associated with attention problems, which may contribute to the risk of unsafe driving among healthy older people. Conclusion: Our findings may elucidate the neurobiological mechanisms underlying the increased risk of unsafe driving in healthy older people, potentially facilitating the development of new interventions to prevent fatal accidents.

Development of medical device software for the screening and assessment of depression severity using data collected from a wristband-type wearable device: SWIFT study protocol.

Introduction: Few biomarkers can be used clinically to diagnose and assess the severity of depression. However, a decrease in activity and sleep efficiency can be observed in depressed patients, and recent technological developments have made it possible to measure these changes. In addition, physiological changes, such as heart rate variability, can be used to distinguish depressed patients from normal persons; these parameters can be used to improve diagnostic accuracy. The proposed research will explore and construct machine learning models capable of detecting depressive episodes and assessing their severity using data collected from wristband-type wearable devices. Methods and analysis: Patients with depressive symptoms and healthy subjects will wear a wristband-type wearable device for 7 days; data on triaxial acceleration, pulse rate, skin temperature, and ultraviolet light will be collected. On the seventh day of wearing, the severity of depressive episodes will be assessed using Structured Clinical Interview for DSM-5 (SCID-5), Hamilton Depression Rating Scale (HAMD), and other scales. Data for up to five 7-day periods of device wearing will be collected from each subject. Using wearable device data associated with clinical symptoms as supervisory data, we will explore and build a machine learning model capable of identifying the presence or absence of depressive episodes and predicting the HAMD scores for an unknown data set. Discussion: Our machine learning model could improve the clinical diagnosis and management of depression through the use of a wearable medical device. Clinical trial registration: [https://jrct.niph.go.jp/latest-detail/jRCT1031210478], identifier [jRCT1031210478].

Clinical and demographic characteristics associated with postural instability in patients with schizophrenia.

As people with schizophrenia grow older, prevention of falls in this older population has become a public health priority. It is therefore critically important to identify risk factors to effectively prevent falls. For this purpose, the degree of postural sway can serve as a convenient index of risk assessment. The objective of this study was to find clinical and demographic characteristics associated with postural instability. Inpatients and outpatients with schizophrenia or related psychosis were recruited at 2 hospitals in Japan. The clinical stabilometric platform, which measured a range of the trunk motion, and extrapyramidal side effects were evaluated between 9 and 11 A.M. Four hundred two subjects were enrolled (age: mean, 55.5 [SD, 14.4] years). A univariate general linear model showed that the use of antipsychotic drugs with a chlorpromazine equivalent of 10 or greater, being overweight, and inpatient treatment setting were associated with a greater degree of the range of postural sway. Another general linear model, including a subgroup of 300 subjects who did not present any extrapyramidal side effects, not only consolidated these findings, but also revealed a great degree of postural sway in older subjects. In addition, quetiapine was found to be associated with a greater range of postural sway among atypical antipsychotics. Schizophrenia patients generally showed a greater degree of postural instability, compared with the reference data of healthy people. These findings highlight truncal instability as a risk factor of falls in patients with schizophrenia, especially when they are overweight, old, and/or receiving antipsychotics with a chlorpromazine equivalent of 10 or greater, including quetiapine.

Similar Hemodynamic Signal Patterns Between Compact NIRS and 52-Channel NIRS During a Verbal Fluency Task.

Multichannel near-infrared spectroscopy (NIRS), including 52-channel NIRS (52ch-NIRS), has been used increasingly to capture hemodynamic changes in the brain because of its safety, low cost, portability, and high temporal resolution. However, optode caps might cause pain and motion artifacts if worn for extended periods of time because of the weight of the cables and the pressure of the optodes on the scalp. Recently, a small NIRS apparatus called compact NIRS (cNIRS) has been developed, and uses only a few flexible sensors. Because this device is expected to be more suitable than 52ch-NIRS in the clinical practice for patients with children or psychiatric conditions, we tested whether the two systems were clinically comparable. Specifically, we evaluated the correlation between patterns of hemodynamic changes generated by 52ch-NIRS and cNIRS in the frontopolar region. We scanned 14 healthy adults with 52ch-NIRS and cNIRS, and measured activation patterns of oxygenated-hemoglobin [oxy-Hb] and deoxygenated-hemoglobin [deoxy-Hb] in the frontal pole while they performed a verbal fluency task. We performed detailed temporal domain comparisons of time-course patterns between the two NIRS-based signals. We found that 52ch-NIRS and cNIRS showed significant correlations in [oxy-Hb] and [deoxy-Hb] time-course changes in numerous channels. Our findings indicate that cNIRS and 52ch-NIRS capture similar task-dependent hemodynamic changes due to metabolic demand, which supports the validity of cNIRS measurement techniques. Therefore, this small device has a strong potential for clinical application with infants and children, as well as for use in the rehabilitation or treatment of patients with psychiatric disorders using biofeedback.