Published papers

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.

Electroconvulsive therapy (ECT) is the most effective antidepressant treatment. Biological predictors of clinical outcome to ECT are valuable. We aimed to examine multimodal magnetic resonance imaging (MRI) data that correlates to the efficacy of ECT. Structural and resting-state functional MRI data were acquired from 46 individuals (25 depressed individuals who received ECT, and 21 healthy controls). Whole-brain grey matter volume (GMV) and fractional amplitude of low frequency fluctuations (fALFF) were investigated to identify brain regions associated with post-ECT Hamilton Depression Rating Scale (HAM-D) total scores. GMV and fALFF values were compared with those in healthy controls using analysis of covariance (ANCOVA). Remission was defined by HAM-D ≤7. A multiple regression analysis revealed that pretreatment smaller GMV in the left thalamus was associated with worse response to ECT (i.e. higher post-ECT HAM-D). Pretreatment higher fALFF in the right anterior insula, and lower fALFF in the left thalamus and the cerebellum were associated with worse outcomes. The left thalamus was identified in both GMV and fALFF analyses. Nonremitters showed significantly smaller thalamic GMV compared to remitters and controls. We found that pretreatment thalamic volume and resting-state activity were associated with the efficacy of ECT. Our results highlight the importance of the thalamus as a possible biological predictor and its role in the underlying mechanisms of ECT action.

Microstructural white matter (WM) disruption and resulting abnormal structural connectivity form a potential underlying pathology in traumatic brain injury (TBI). Herein, to determine the potential mechanism of cognitive deterioration in TBI, we examined the association of damage to specific WM tracts with cognitive function in TBI patients. We recruited 18 individuals with mild-to-moderate/severe TBI in the chronic phase and 17 age-matched controls. We determined the pattern of WM aberrations in TBI using tract-based spatial statistics (TBSS) and then examined the relationship between cognitive impairment and WM damage using the threshold-free cluster enhancement correction in TBSS. TBSS analysis showed that TBI patients exhibited WM aberrations in a wide range of brain regions. In the majority of these regions, lower fractional anisotropy (FA) largely overlapped with increased radial diffusivity, but not with axial diffusivity. Further, voxel-wise correction in TBSS demonstrated that higher FA values were associated with better performance in the phonemic verbal fluency task (VFT) in widespread WM regions, but not with the semantic VFT. Despite variation in the magnitude and location of brain injury between individual cases, chronic TBI patients exhibited widespread WM aberrations. We confirmed the findings of previous studies that WM integrity is lower across the spectrum of TBI severity in chronic subjects compared to controls. Further, phonemic VFT may be a more sensitive cognitive measure of executive dysfunction associated with WM aberrations in TBI compared with semantic VFT.

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.

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.

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.

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].

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.

61歳女。58歳、大動脈弁閉鎖不全症・三尖弁閉鎖不全症、洞不全症候群のために弁置換術・ペースメーカー植え込み術を施行した。心労が募り、不眠、食思不振、全身倦怠感、意欲低下をきたした。milnacipranによって症状は改善し、症状改善に伴い、治療を自己中断した。治療中断直後から意欲低下、抑うつ気分が再燃し、病状は一進一退を繰り返した。fulvoxamineで焦燥感、希死念慮の改善を認めたが、左心室瘤が発見され、手術目的にて転院となった。心室瘤切除術は無事終了したが、亜昏迷状態となった。経口摂取困難な状態が続くため、術後管理終了後に再転院となった。難治性のうつ病に加え、高齢で身体消耗が激しいため、ペースメーカー装着中であったが修正型電気けいれん療法を施行した。早期より亜昏迷状態は改善した。心収縮能は良好で、心不全徴候も認めなかった。

少量の抗うつ薬を2回投与することによって、セロトニン症候群を惹起した症例を経験した。合併症として腎不全を有することが一因と考えられたが、これ以外に、併用されていた抗精神病薬perospironeの有する5-hydroxytryptamine 1A(5-HT1A)受容体へのアゴニスト作用の関与も推察された。近年、強迫神経症や難治性うつ病の治療場面で抗うつ薬と非定型抗精神病薬が併用される機会も多いことから、注意を要する薬物相互作用と考えここに報告する。(著者抄録)

原因と特定されない脳炎から緊張病症候群を呈し,修正型電気けいれん療法(mECT)が奏効した1例(31歳女性)を報告した.本症例は脳炎から重度の緊張病症状を呈しており,救命上も早急に治療効果の得られる治療法を選択する必要性があった.mECT導入後,筋緊張亢進といった緊張病症状は軽減しており,脳炎より発症した緊張病症候群に対する選択肢としてmECTが有用である可能性が示唆された.緊張病症候群に対するECTの作用機序は不明であるが,パーキンソン病においてECTによるドーパミン系の亢進が示唆されていることから,緊張病症候群においてもドーパミン系の変化が関与しているのではないかと考えられた

我々は、嘔吐により発症した突発性食道破裂および縦隔気腫を合併した摂食障害の症例(神経性無食欲症、むちゃ食い/排出型)を経験したので、その詳細を報告する。症例は30歳女性、以前より過食嘔吐を頻回に繰り返していた。感冒症状と腹痛を主訴に当院受診し入院となった。胸部X線検査にて皮下気腫と縦隔気腫を認め、上部消化管内視鏡検査にて食道に裂傷を認め食道破裂と診断された。低栄養のため創傷治癒が遅延する可能性が高く、中心静脈栄養を考慮した。しかし肥満恐怖から中心静脈栄養施行への同意が得られず、中心静脈栄養への不安から過呼吸発作が頻回となったため絶飲食を主体とした保存的治療を継続した。胸部CT、内視鏡検査で裂傷の治癒を確認しながら食事を徐々に再開し、32病日に退院となった。摂食障害合併症としての食道破裂の報告は多くないが、致死的経過を辿りうる性質上、嘔吐を伴う症例では、本合併症を念頭に置きながら経過を見る必要があると考えられた。(著者抄録)