研究者詳細 - 平野　仁一

論文 - 平野　仁一

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Dynamic neural network modulation associated with rumination in major depressive disorder: a prospective observational comparative analysis of cognitive behavioral therapy and pharmacotherapy

Katayama N., Shinagawa K., Hirano J., Kobayashi Y., Nakagawa A., Umeda S., Kamiya K., Tajima M., Amano M., Nogami W., Ihara S., Noda S., Terasawa Y., Kikuchi T., Mimura M., Uchida H.

Translational Psychiatry 15 （ 1 ） 267 2025年12月

ISSN 2158-3188

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.

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Electroconvulsive therapy-specific volume changes in nuclei of the amygdala and their relationship to long-term anxiety improvement in depression

Ishikawa Y., Oishi N., Kyuragi Y., Hatakoshi M., Hirano J., Noda T., Yoshihara Y., Ito Y., Miyata J., Nemoto K., Fujita Y., Igarashi H., Takahashi K., Murakami S., Kanno H., Izumi Y., Takamiya A., Matsumoto J., Kodaka F., Nakagome K., Mimura M., Murai T., Suwa T.

Molecular Psychiatry 30 （ 6 ） 2653 - 2664 2025年06月

ISSN 13594184

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.

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

Momota Y., Bun S., Hirano J., Kamiya K., Ueda R., Iwabuchi Y., Takahata K., Yamamoto Y., Tezuka T., Kubota M., Seki M., Shikimoto R., Mimura Y., Kishimoto T., Tabuchi H., Jinzaki M., Ito D., Mimura M.

Scientific Reports （Scientific Reports） 14 （ 1 ） 7633 2024年12月

ISSN 2045-2322

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 (Aβ) 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. Aβ-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. Aβ-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.

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The status of MRI databases across the world focused on psychiatric and neurological disorders

Tanaka S.C., Kasai K., Okamoto Y., Koike S., Hayashi T., Yamashita A., Yamashita O., Johnstone T., Pestilli F., Doya K., Okada G., Shinzato H., Itai E., Takahara Y., Takamiya A., Nakamura M., Itahashi T., Aoki R., Koizumi Y., Shimizu M., Miyata J., Son S., Aki M., Okada N., Morita S., Sawamoto N., Abe M., Oi Y., Sajima K., Kamagata K., Hirose M., Aoshima Y., Hamatani S., Nohara N., Funaba M., Noda T., Inoue K., Hirano J., Mimura M., Takahashi H., Hattori N., Sekiguchi A., Kawato M., Hanakawa T.

Psychiatry and Clinical Neurosciences 78 （ 10 ） 563 - 579 2024年10月

ISSN 13231316

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.

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Glymphatic system dysfunction in mood disorders: Evaluation by diffusion magnetic resonance imaging

Ueda R., Yamagata B., Niida R., Hirano J., Niida A., Yamamoto Y., Mimura M.

Neuroscience 555 69 - 75 2024年09月

ISSN 03064522

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.

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

Kyuragi Y., Oishi N., Hatakoshi M., Hirano J., Noda T., Yoshihara Y., Ito Y., Igarashi H., Miyata J., Takahashi K., Kamiya K., Matsumoto J., Okada T., Fushimi Y., Nakagome K., Mimura M., Murai T., Suwa T.

Biological Psychiatry Global Open Science （Biological Psychiatry Global Open Science） 4 （ 4 ） 100314 2024年07月

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.

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Pregnancy enhances facial recognition of anger: Transition from early to late pregnancy

Minami, F; Iwata, R; Nishida, H; Kudo, S; Yamagishi, M; Kamiya, K; Mimura, M; Hirano, J; Yamagata, B

EUROPEAN PSYCHIATRY 67 S809 - S809 2024年04月

ISSN 0924-9338

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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

Kudo S., Uchida T., Nishida H., Takamiya A., Kikuchi T., Yamagata B., Mimura M., Hirano J.

BMC Psychiatry （BMC Psychiatry） 23 （ 1 ） 243 2023年12月

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.

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Characteristics of refractory late-life depression in the prodromal phase of neurodegenerative diseases

Nishida, H; Takamiya, A; Kudo, S; Miyasaka, Y; Konishi, M; Mimura, M; Hirano, J

INTERNATIONAL PSYCHOGERIATRICS 35 159 - 160 2023年12月

ISSN 1041-6102

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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

Kishimoto T., Kinoshita S., Kikuchi T., Bun S., Kitazawa M., Horigome T., Tazawa Y., Takamiya A., Hirano J., Mimura M., Liang K.C., Koga N., Ochiai Y., Ito H., Miyamae Y., Tsujimoto Y., Sakuma K., Kida H., Miura G., Kawade Y., Goto A., Yoshino F.

Frontiers in Psychiatry （Frontiers in Psychiatry） 13 1025517 2022年12月

ISSN 1664-0640

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].

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

Minami F., Hirano J., Ueda R., Takamiya A., Yamagishi M., Kamiya K., Mimura M., Yamagata B.

Psychiatry and Clinical Neurosciences （Psychiatry and Clinical Neurosciences） 76 （ 11 ） 579 - 586 2022年11月

ISSN 13231316

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.

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

Yamamoto Y., Hirano J., Ueda R., Yoshitake H., Yamagishi M., Kimura M., Kamiya K., Shino M., Mimura M., Yamagata B.

Psychiatry and Clinical Neurosciences Reports （Psychiatry and Clinical Neurosciences Reports） 1 （ 3 ） 2022年09月

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.

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

Nakajima K, Takamiya A, Uchida T, Kudo S, Nishida H, Minami F, Yamamoto Y, Yamagata B, Mimura M, Hirano J

The Journal of clinical psychiatry （Journal of Clinical Psychiatry） 83 （ 5 ） 2022年08月

ISSN 0160-6689

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.

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

Yatomi T., Uchida T., Takamiya A., Wada M., Kudo S., Nakajima K., Nishida H., Yamagata B., Mimura M., Hirano J.

Frontiers in Human Neuroscience （Frontiers in Human Neuroscience） 16 933622 2022年07月

ISSN 1662-5161

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.

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Hirano J., Takamiya A., Yamamoto Y., Minami F., Mimura M., Yamagata B.

Frontiers in Psychiatry （Frontiers in Psychiatry） 12 772339 2021年12月

ISSN 1664-0640

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.

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Neuronal network mechanisms associated with depressive symptom improvement following electroconvulsive therapy

Takamiya A., Kishimoto T., Hirano J., Nishikata S., Sawada K., Kurokawa S., Yamagata B., Kikuchi T., Mimura M.

Psychological Medicine （Psychological Medicine） 51 （ 16 ） 2856 - 2863 2021年12月

ISSN 00332917

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.

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

Takamiya A., Bouckaert F., Sienaert P., Uchida T., Kudo S., Yamagata B., Kishimoto T., Mimura M., Hirano J.

Journal of ECT （Journal of ECT） 37 （ 3 ） 171 - 175 2021年09月

ISSN 10950680

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.

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Association of electroconvulsive therapy-induced structural plasticity with clinical remission

Takamiya A., Kishimoto T., Hirano J., Kikuchi T., Yamagata B., Mimura M.

Progress in Neuro-Psychopharmacology and Biological Psychiatry （Progress in Neuro-Psychopharmacology and Biological Psychiatry） 110 110286 2021年08月

ISSN 02785846

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.

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Regional Gray Matter Volume Identifies High Risk of Unsafe Driving in Healthy Older People

Yamamoto Y., Yamagata B., Hirano J., Ueda R., Yoshitake H., Negishi K., Yamagishi M., Kimura M., Kamiya K., Shino M., Mimura M.

Frontiers in Aging Neuroscience （Frontiers in Aging Neuroscience） 12 592979 2020年12月

ISSN 1663-4365

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.

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AIによる脳波を用いた電気けいれん療法の効果予測

平野仁一

上原記念生命科学財団研究報告集（(公財)上原記念生命科学財団） 34 1 - 4 2020年12月

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平野 仁一

論文 - 平野 仁一

平野　仁一

論文 - 平野　仁一