論文

Background: Electroconvulsive therapy (ECT) is the most effective treatment for severe depression. Recent neuroimaging studies have consistently reported that ECT induces volume increases in widely distributed brain regions. However, it still remains unclear about ECT-induced volume changes associated with clinical improvement. Methods: Longitudinal assessments of structural magnetic resonance imaging were conducted in 48 participants. Twenty-seven elderly melancholic depressed individuals (mean 67.5 ± 8.1 years old; 19 female) were scanned before (TP1) and after (TP2) ECT. Twenty-one healthy controls were also scanned twice. Whole-brain gray matter volume (GMV) was analyzed via group (remitters, nonremitters, and controls) by time (TP1 and TP2) analysis of covariance to identify ECT-related GMV changes and GMV changes specific to remitters. Within-subject and between-subjects correlation analyses were conducted to investigate the associations between clinical improvement and GMV changes. Depressive symptoms were evaluated using the 17-item Hamilton Depression Rating Scale (HAM-D), and remission was defined as HAM-D total score ≤ 7. Results: Bilateral ECT increased GMV in multiple brain regions bilaterally regardless of clinical improvement. Remitters showed a larger GMV increase in the right-lateralized frontolimbic brain regions compared to nonremitters and healthy controls. GMV changes in the right hippocampus/amygdala and right middle frontal gyrus showed correlations with clinical improvement in within−/between-subjects correlation analyses. Conclusions: ECT-induced GMV increase in the right frontolimbic regions was associated with clinical remission.

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.

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.

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.

Cognitive behavioral therapy (CBT) and pharmacotherapy are primary treatments for major depressive disorder (MDD). However, their differential effects on the neural networks associated with rumination, or repetitive negative thinking, remain poorly understood. This study included 135 participants, whose rumination severity was measured using the rumination response scale (RRS) and whose resting brain activity was measured using functional magnetic resonance imaging (fMRI) at baseline and after 16 weeks. MDD patients received either standard CBT based on Beck’s manual (n = 28) or pharmacotherapy (n = 32). Using a hidden Markov model, we observed that MDD patients exhibited increased activity in the default mode network (DMN) and decreased occupancies in the sensorimotor and central executive networks (CEN). The DMN occurrence rate correlated positively with rumination severity. CBT, while not specifically designed to target rumination, reduced DMN occurrence rate and facilitated transitions toward a CEN-dominant brain state as part of broader therapeutic effects. Pharmacotherapy shifted DMN activity to the posterior region of the brain. These findings suggest that CBT and pharmacotherapy modulate brain network dynamics related to rumination through distinct therapeutic pathways.

Electroconvulsive therapy (ECT) is one of the most effective treatments for depression. ECT induces volume changes in the amygdala, a key center of anxiety. However, the clinical relevance of ECT-induced changes in amygdala volume remains uncertain. We hypothesized that nuclei-specific amygdala volumes and anxiety symptoms in depression could explain the clinical correlates of ECT-induced volume changes. To test this hypothesis, we enrolled patients with depression who underwent ECT (N = 20) in this multicenter observational study and collected MRI data at three time points: before and after treatment and a 6-month follow-up. Patients who received medication (N = 52), cognitive behavioral therapy (N = 63), or transcranial magnetic stimulation (N = 20), and healthy participants (N = 147) were included for comparison. Amygdala nuclei were identified using FreeSurfer and clustered into three subdivisions to enhance reliability and interpretability. Anxiety symptoms were quantified using the anxiety factor scores derived from the Hamilton Depression Rating Scale. Before treatment, basolateral and basomedial subdivisions of the right amygdala were smaller than those of healthy controls. The volumes of the amygdala subdivisions increased after ECT and decreased during the follow-up period, but the volumes at 6-month follow-up were larger than those observed before treatment. These volume changes were specific to ECT. Long-term volume changes in the right basomedial amygdala correlated with improvements in anxiety symptoms. Baseline volumes in the right basolateral amygdala correlated with long-term improvements in anxiety symptoms. These findings demonstrate that clinical correlates of ECT-induced amygdala volume changes are existent, but in a nucleus and symptom-specific manner.

The glymphatic system, an expansive cerebral waste-disposal network, harbors myriad enigmatic facets necessitating elucidation of their nexus with diverse pathologies. Murine investigations have revealed a relationship between the glymphatic system and affective disorders. This study aimed to illuminate the interplay between bipolar disorder and the glymphatic system. Fifty-eight individuals afflicted with bipolar disorder were identified through meticulous psychiatric assessment. These individuals were juxtaposed with a cohort of 66 comparably aged and sex-matched, mentally stable subjects. Subsequent analysis entailed the application of covariance analysis to evaluate along with the perivascular space (ALPS) index, a novel magnetic resonance imaging method for assessing brain interstitial fluid dynamics via diffusion tensor imaging within the bipolar and control cohorts. We also evaluated the correlation between the ALPS index and clinical parameters, which included the Hamilton Depression scale scores, disease duration, and other clinical assessments. Moreover, partial correlation analyses, incorporating age and sex as covariates, were performed to investigate the relationships between the ALPS index and clinical measures within the two cohorts. A noteworthy adverse correlation was observed between the ALPS index and illness duration. A free-water imaging analysis revealed a substantial elevation in the free-water index within the white-matter tracts, prominently centered on the corpus callosum, within the bipolar cohort relative to that in the control group. In analogous cerebral regions, a conspicuous affirmative correlation was observed between the free-water–corrected radial diffusivity and depression rating scales. Our results showed that the protracted course of bipolar disorder concomitantly exacerbated glymphatic system dysregulation.

Neuroimaging databases for neuro-psychiatric disorders enable researchers to implement data-driven research approaches by providing access to rich data that can be used to study disease, build and validate machine learning models, and even redefine disease spectra. The importance of sharing large, multi-center, multi-disorder databases has gradually been recognized in order to truly translate brain imaging knowledge into real-world clinical practice. Here, we review MRI databases that share data globally to serve multiple psychiatric or neurological disorders. We found 42 datasets consisting of 23,293 samples from patients with psychiatry and neurological disorders and healthy controls; 1245 samples from mood disorders (major depressive disorder and bipolar disorder), 2015 samples from developmental disorders (autism spectrum disorder, attention-deficit hyperactivity disorder), 675 samples from schizophrenia, 1194 samples from Parkinson's disease, 5865 samples from dementia (including Alzheimer's disease), We recognize that large, multi-center databases should include governance processes that allow data to be shared across national boundaries. Addressing technical and regulatory issues of existing databases can lead to better design and implementation and improve data access for the research community. The current trend toward the development of shareable MRI databases will contribute to a better understanding of the pathophysiology, diagnosis and assessment, and development of early interventions for neuropsychiatric disorders.

Background: Major depressive disorder (MDD) is a heterogeneous condition. Identifying the brain responses to antidepressant treatment is of particular interest as these may represent potential neural networks related to treatment response, forming one aspect of the biological markers of MDD. Near-infrared spectroscopy (NIRS) is suitable for repeated measurements with short intervals because of its noninvasiveness, and can provide detailed time courses of functional alterations in prefrontal regions. Methods: We conducted a 12-week longitudinal study to explore prefrontal hemodynamic changes at 4-week intervals following sertraline treatment in 11 medication-naïve participants with MDD using 52-channel NIRS. Results: While all participants achieved remission after treatment, intra-class correlation coefficient of oxygenated hemoglobin [oxy-Hb] values throughout the 12-week observation was moderate at the spatially and temporally contiguous cluster located in the left inferior frontal and temporal gyri. There was a significant negative correlation between mean [oxy-Hb] values in the significant cluster at 4 weeks and changes in Hamilton Rating Scale for Depression total score from 4 to 8 weeks (r = −0.73, P = 0.011) and from 4 to 12 weeks (r = −0.63, P = 0.039). Limitations: Without healthy controls for comparison, we were unable to fully evaluate whether improvement of [oxy-Hb] activations after treatment in MDD reached normal levels or not. Conclusion: Our NIRS findings of detailed prefrontal hemodynamic alterations over short interval observations such as 4 weeks may have revealed potential trait marker for MDD and biological maker for predicting clinical response to sertraline treatment in MDD.

Concomitant use of benzodiazepines and alcohol appears prevalent in a clinical setting. The objectives of this study were as follows: (1) to investigate the prevalence of concomitant use of benzodiazepine hypnotics and alcohol in psychiatric outpatients, (2) to examine the clinical characteristics and factors associated with the concomitant use, and (3) to investigate the awareness of the psychiatrists-in-charge about the concomitant use. Outpatients with schizophrenia, depression, and insomnia who were receiving benzodiazepine hypnotics were asked to fill in a sleeping diary for seven consecutive days in which use of hypnotics and alcohol was also recorded. Clinical characteristics were assessed, and logistic analysis was performed to examine factors associated with the concomitant use. In addition, psychiatrists-in-charge were asked as to whether they thought their patients were concomitantly using them. The prevalence rate of the concomitant use was 39.8% (37/93). The CAGE score showed significant positive association with the concomitant use (odds ratio = 2.40, 95% confidence interval = 1.39-4.16, P = 0.002). Only in 32.4% of the concomitant users were suspected by their psychiatrists. The results suggest that concomitantly used benzodiazepine hypnotics and alcohol appears prevalent, and has been frequently overlooked by treating psychiatrists. The CAGE questionnaire may be helpful to screen such potentially hazardous users.

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.

Introduction: Electroconvulsive therapy (ECT) has antidepressant effects, but it also has possible cognitive side effects. The effects of ECT on neuronal oscillatory pattern and phase synchronization, and the relationship between clinical response or cognitive change and electroencephalogram (EEG) measurements remain elusive. Methods: Individuals with unipolar depressive disorder receiving bilateral ECT were recruited. Five minutes of resting, eyes-closed, 19-lead EEG recordings were obtained before and after a course of ECT. Non-overlapping 60 artifact-free epocs of 2-s duration were used for the analyses. We used exact low resolution electromagnetic tomography (eLORETA) to compute the whole-brain three-dimensional intracortical distribution of current source density (CSD) and phase synchronization among 28 regions-of-interest (ROIs). Paired t-tests were used to identify cortical voxels and connectivities showing changes after ECT. Montgomery Asberg Depression Rating Scale (MADRS) and Mini-Mental State Examination (MMSE) were used to evaluate the severity of depression and the global cognitive function. Correlation analyses were conducted to identify the relationship between changes in the EEG measurements and changes in MADRS or MMSE. Results: Thirteen depressed patients (five females, mean age: 58.4 years old) were included. ECT increased theta CSD in the anterior cingulate cortex (ACC), and decreased beta CSD in the frontal pole (FP), and gamma CSD in the inferior parietal lobule (IPL). ECT increased theta phase synchronization between the posterior cingulate cortex (PCC) and the anterior frontal cortex, and decreased beta phase synchronization between the PCC and temporal regions. A decline in beta synchronization in the left hemisphere was associated with cognitive changes after ECT. Conclusion: ECT modulated resting-state EEG oscillatory patterns and phase synchronization in central nodes of the default mode network (DMN). Changes in beta synchronization in the left hemisphere might explain the ECT-related cognitive side effects.

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.

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.

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.

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.

In developed countries, the number of traffic accidents caused by older drivers is increasing. Approximately half of the older drivers who cause fatal accidents are cognitively normal. Thus, it is important to identify older drivers who are cognitively normal but at high risk of causing fatal traffic accidents. However, no standardized method for assessing the driving ability of older drivers has been established. We aimed to establish an objective assessment of driving ability and to clarify the neural basis of unsafe driving in healthy older people. We enrolled 32 healthy older individuals aged over 65 years and classified unsafe drivers using an on-road driving test. We then utilized a machine learning approach to distinguish unsafe drivers from safe drivers based on clinical features and gray matter volume data. Twenty-one participants were classified as safe drivers and 11 participants as unsafe drivers. A linear support vector machine classifier successfully distinguished unsafe drivers from safe drivers with 87.5% accuracy (sensitivity of 63.6% and specificity of 100%). Five parameters (age and gray matter volume in four cortical regions, including the left superior part of the precentral sulcus, the left sulcus intermedius primus [of Jensen], the right orbital part of the inferior frontal gyrus, and the right superior frontal sulcus), were consistently selected as features for the final classification model. Our findings indicate that the cortical regions implicated in voluntary orienting of attention, decision making, and working memory may constitute the essential neural basis of driving behavior.