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Recent studies have indicated the potential clinical use of near infrared spectroscopy (NIRS) as a tool in assisting the diagnosis of major depressive disorder (MDD); however, it is still unclear whether NIRS signal changes during cognitive task are state- or trait-dependent, and whether NIRS could be a neural predictor of treatment response. Therefore, we conducted a longitudinal study to explore frontal haemodynamic changes following antidepressant treatment in medication-naïve MDD using 52-channel NIRS. This study included 25 medication-naïve individuals with MDD and 62 healthy controls (HC). We performed NIRS scans before and after antidepressant treatment and measured changes of [oxy-Hb] activation during a verbal fluency task (VFT) following treatment. Individuals with MDD showed significantly decreased [oxy-Hb] values during a VFT compared with HC in the bilateral frontal and temporal cortices at baseline. There were no [oxy-Hb] changes between pre- and post-antidepressant treatment time points in the MDD cohort despite significant improvement in depressive symptoms. There was a significant association between mean [oxy-Hb] values during a VFT at baseline and improvement in depressive symptoms following treatment in the bilateral inferior frontal and middle temporal gyri in MDD. These findings suggest that hypofrontality response to a VFT may represent a potential trait marker for depression rather than a state marker. Moreover, the correlation analysis indicates that the NIRS signals before the initiation of treatment may be a biological marker to predict patient's clinical response to antidepressant treatment. The present study provides further evidence to support a potential application of NIRS for the diagnosis and treatment of depression.

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.

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.

: 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. : 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 -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. : 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. : 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.

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.

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 to those of thiopental, on the course of ECT in patients with major depressive disorder. 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. The between-group differences were examined using the independent t-test for continuous variables and χ2 test for categorical variables. Results: Patients that 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. Conclusions: The present findings indicate that sevoflurane should be used with caution in ECT and only when the clinical rationale is clear.

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.

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.

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, R 2 = 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 (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. Resting-state functional MRI was acquired before and after bilateral ECT in 27 depressed individuals. 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 < 0.05. 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. 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.