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Abstract 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.

Abstract Aim Interview quality is an important factor in the success of clinical trials for major depressive disorder (MDD). There is a substantial need to establish a reliable, remote clinical assessment interview system that can replace traditional in‐person interviews. Methods We conducted a multicenter, randomized, unblinded, prospective, cross‐sectional study to assess the reliability of remote interviews in patients with MDD (UMIN000041839). Eligible patients with MDD underwent remote and in‐person sessions of the Montgomery‐Åsberg Depression Rating Scale (MADRS) assessment performed by different raters within 28 days of providing consent. Patients were randomized to a group first assessed using in‐person interviews and secondarily using remote interviews (in‐person‐first group) or a group first assessed by remote interviews and secondarily using in‐person interviews (remote‐first group). Nineteen trained people (15 clinical psychologists, 3 nurses, and 1 clinical laboratory technologist) performed interviews. Results Of 59 patients (in‐person‐first group, n = 32; remote‐first group, n = 27) who completed both remote and in‐person interviews, 51% (n = 30) were women; the mean age was 41.6 years (range, 21–64 years). There was a strong association between remote and in‐person MADRS scores (r = 0.891, kappa = 0.901). An overall intraclass correlation coefficient (ICC) of 0.886 (95% confidence interval, 0.877–0.952) indicated good consistency between MADRS scores in remote and in‐person interviews. The ICC decreased as the severity of depression increased. Conclusion Our results suggest remote interviews are a feasible alternative option to in‐person interviews in assessing symptom severity in MDD patients and could promote clinical trials in Japan.

Abstract Background The impact of the gut microbiota on neuropsychiatric disorders has gained much attention in recent years; however, comprehensive data on the relationship between the gut microbiome and its metabolites and resistance to treatment for depression and anxiety is lacking. Here, we investigated intestinal metabolites in patients with depression and anxiety disorders, and their possible roles in treatment resistance. Results We analyzed fecal metabolites and microbiomes in 34 participants with depression and anxiety disorders. Fecal samples were obtained three times for each participant during the treatment. Propensity score matching led us to analyze data from nine treatment responders and nine non-responders, and the results were validated in the residual sample sets. Using elastic net regression analysis, we identified several metabolites, including N-ε-acetyllysine; baseline levels of the former were low in responders (AUC = 0.86; 95% confidence interval, 0.69–1). In addition, fecal levels of N-ε-acetyllysine were negatively associated with the abundance of Odoribacter. N-ε-acetyllysine levels increased as symptoms improved with treatment. Conclusion Fecal N-ε-acetyllysine levels before treatment may be a predictive biomarker of treatment-refractory depression and anxiety. Odoribacter may play a role in the homeostasis of intestinal L-lysine levels. More attention should be paid to the importance of L-lysine metabolism in those with depression and anxiety.

BackgroundPoor sleep quality can elevate stress levels and diminish overall well-being. Japanese individuals often experience sleep deprivation, and workers have high levels of stress. Nevertheless, research examining the connection between objective sleep assessments and stress levels, as well as overall well-being, among Japanese workers is lacking. ObjectiveThis study aims to investigate the correlation between physiological data, including sleep duration and heart rate variability (HRV), objectively measured through wearable devices, and 3 states (sleepiness, mood, and energy) assessed through ecological momentary assessment (EMA) and use of rating scales for stress and well-being. MethodsA total of 40 office workers (female, 20/40, 50%; mean age 40.4 years, SD 11.8 years) participated in the study. Participants were asked to wear a wearable wristband device for 8 consecutive weeks. EMA regarding sleepiness, mood, and energy levels was conducted via email messages sent by participants 4 times daily, with each session spaced 3 hours apart. This assessment occurred on 8 designated days within the 8-week timeframe. Participants’ stress levels and perception of well-being were assessed using respective self-rating questionnaires. Subsequently, participants were categorized into quartiles based on their stress and well-being scores, and the sleep patterns and HRV indices recorded by the Fitbit Inspire 2 were compared among these groups. The Mann-Whitney U test was used to assess differences between the quartiles, with adjustments made for multiple comparisons using the Bonferroni correction. Furthermore, EMA results and the sleep and HRV indices were subjected to multilevel analysis for a comprehensive evaluation. ResultsThe EMA achieved a total response rate of 87.3%, while the Fitbit Inspire 2 wear rate reached 88.0%. When participants were grouped based on quartiles of well-being and stress-related scores, significant differences emerged. Specifically, individuals in the lowest stress quartile or highest subjective satisfaction quartile retired to bed earlier (P

Abstract BackgroundAsking questions is common in conversations, and while asking questions, we need to listen carefully to what others say and consider the perspective our questions adopt. However, difficulties persist in verifying the effect of asking questions on older adults’ cognitive function due to the lack of a standardized system for conducting experiments at participants’ homes. ObjectiveThis study examined the intervention effect of cognitive training moderated by robots on healthy older adults. A focus on the feasibility of the intervention at participants’ homes was also maintained. Feasibility was evaluated by considering both the dropout rate during the intervention and the number of questions posed to each participant during the experiment. MethodsWe conducted a randomized controlled trial with 81 adults older than 65 years. Participants were recruited through postal invitations and then randomized into 2 groups. The intervention group (n=40) received sessions where participants listened to photo-integrated stories and posed questions to the robots. The control group (n=41) received sessions where participants listened to photo-integrated stories and only thanked the robots for confirming participation. The participants participated in 12 dialogue sessions for 2-3 weeks. Scores of global cognitive functioning tests, recall tests, and verbal fluency tasks measured before and after the intervention were compared between the 2 groups. ResultsThere was no significant intervention effect on the Telephone Interview for Cognitive Status-Japanese scores, recall tests, and verbal fluency tasks. Additionally, our study successfully concluded with no participant dropouts at follow-up, confirming the feasibility of our approach. ConclusionsThere was no statistically significant evidence indicating intervention benefits for cognitive functioning. Although the feasibility of home-based interventions was demonstrated, we identified areas for improvement in the future, such as setting up more efficient session themes. Further research is required to identify the effectiveness of an improved cognitive intervention involving the act of asking questions.

BackgroundGiven the global shortage of child psychiatrists and barriers to specialized care, remote assessment is a promising alternative for diagnosing and managing attention-deficit/hyperactivity disorder (ADHD). However, only a few studies have validated the accuracy and acceptability of these remote methods. ObjectiveThis study aimed to test the agreement between remote and face-to-face assessments. MethodsPatients aged between 6 and 17 years with confirmed Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition diagnoses of ADHD or autism spectrum disorder (ASD) were recruited from multiple institutions. In a randomized order, participants underwent 2 evaluations, face-to-face and remotely, with distinct evaluators administering the ADHD Rating Scale-IV (ADHD-RS-IV). Intraclass correlation coefficient (ICC) was used to assess the reliability of face-to-face and remote assessments. ResultsThe participants included 74 Japanese children aged between 6 and 16 years who were primarily diagnosed with ADHD (43/74, 58%) or ASD (31/74, 42%). A total of 22 (30%) children were diagnosed with both conditions. The ADHD-RS-IV ICCs between face-to-face and remote assessments showed “substantial” agreement in the total ADHD-RS-IV score (ICC=0.769, 95% CI 0.654-0.849; P

Abstract Aim Continued antipsychotic treatment is the key to preventing relapse. Maintenance antipsychotic monotherapy and optimal dose use are recommended for individuals with stable schizophrenia because of their undesirable effects. Decision aids (DAs) are clinical conversation tools that facilitate shared decision‐making (SDM) between patients and health‐care providers. This study aimed to describe the development process and results of acceptability testing of a DA for individuals with stable schizophrenia, considering (i) whether to continue high‐dose antipsychotics or reduce to the standard dose and (ii) whether to continue two antipsychotics or shift to monotherapy. Methods A DA was developed according to the guidelines for the appropriate use of psychotropic medications and International Patient Decision Aid Standards (IPDAS). First, a DA prototype was developed based on a previous systematic review and meta‐analysis conducted for identifying the effects of continuing or reducing antipsychotic treatment. Second, mixed‐method survey was performed among individuals with schizophrenia and health‐care providers to modify and finalize the DA. Results The DA consisted of an explanation of schizophrenia, options to continue high‐dose antipsychotics or reduce to the standard dose, options to continue two antipsychotics or shift to monotherapy, pros and cons of each option, and a value‐clarification worksheet for each option. The patients (n = 20) reported acceptable language use (75%), adequate information (75%), and well‐balanced presentation (79%). Health‐care providers (n = 20) also provided favorable overall feedback. The final DA covered six IPDAS qualifying criteria. Conclusion A DA was successfully developed for schizophrenia, considering whether to reduce antipsychotics, which can be used in the SDM process.

Abstract Background Polypharmacy of additional psychotropics alongside the main treatment drug (antipsychotics in schizophrenia and antidepressants in major depressive disorder) is common in Japan. Our goal is to align psychotropic prescription in Japan with international standards, while reducing the differences between facilities. To achieve this goal, we aimed to compare prescriptions at the time of hospital admission and discharge. Methods Data on prescriptions at admission and discharge from 2016 to 2020 were collected. We divided the patients into four groups: (1) mono_mono group, monotherapy of the main drug at admission and discharge; (2) mono_poly group, monotherapy at admission and polypharmacy at discharge; (3) poly_poly group, polypharmacy at admission and discharge; and (4) poly_mono group, polypharmacy at admission and monotherapy at discharge. We compared the changes in dosage and number of psychotropics among the four groups. Results For both schizophrenia and major depressive disorder, the patients who received monotherapy with the main drug at admission were likely to receive main drug monotherapy at discharge and vice versa. For schizophrenia, the polypharmacy was prescribed more often in the mono_poly group than that in the mono_mono group. The prescription was not changed at all for more than 10% of the patients. Conclusions It is critical to avoid a polypharmacy regimen to ensure that guideline-compliant treatment is provided. We expect higher rates of monotherapy with the main drug after the EGUIDE lectures. Trial registration The study protocol was registered in the University Hospital Medical Information Network Registry (UMIN000022645).

Background: Since there are limited studies on the associations between glycemic variability (GV) and sleep quality or physical activity in subjects without diabetes, we evaluated the associations between GV, as assessed by continuous glucose monitoring (CGM), and both sleep quality and daily steps using wearable devices in healthy individuals. Methods: Forty participants without diabetes were monitored by both an intermittently scanned CGM and a smartwatch-type activity tracker for 2 weeks. The standard deviation (SD) and coefficient of variation (CV) of glucose were evaluated as indices of GV. The activity tracker was used to calculate each participant's average step count per day. We also calculated sleep duration, sleep efficiency, and sleep latency based on data from the activity tracker. Spearman's correlation coefficient was used to assess the association between GV and sleep indices or daily steps. For each participant, periods were divided into quartiles according to step counts throughout the day. We compared mean parameter differences between the periods of lowest quartile and highest quartile (lower 25% and upper 25%). Results: SD glucose was significantly positively correlated with sleep latency (R = 0.23, P 0.05). SD glucose and CV glucose levels in the upper 25% period of daily steps were lower than those in the lower 25% period in each participant (both, P

Abstract Aim Treatment guidelines are designed to assist patients and health care providers and are used as tools for making treatment decisions in clinical situations. The treatment guidelines of the Japanese Society of Mood Disorders establish treatment recommendations for each severity of depression. The individual fitness score (IFS) was developed as a simple and objective indicator to assess whether individual patients are practicing treatment by the recommendations of the depression treatment guidelines of the Japanese Society of Mood Disorders. Methods The EGUIDE project members determined the IFS through the modified Delphi method. In this article, the IFS was calculated based on the treatment of depressed patients treated and discharged between 2016 and 2020 at facilities participating in the EGUIDE project. In addition, we compared scores at admission and discharge. Results The study included 428 depressed patients (mild n = 22, moderate/severe n = 331, psychotic n = 75) at 57 facilities. The mean IFS scores by severity were statistically significantly higher at discharge than at admission with moderate/severe depression (mild 36.1 ± 34.2 vs. 41.6 ± 36.9, p = 0.49; moderate/severe 50.2 ± 33.6 vs. 55.7 ± 32.6, p = 2.1 × 10–3; psychotic 47.4 ± 32.9 versus 52.9 ± 36.0, p = 0.23). Conclusion We developed the IFS based on the depression treatment guideline, which enables us to objectively determine how close the treatment is to the guideline at the time of evaluation in individual cases. Therefore, the IFS may influence guideline‐oriented treatment behavior and lead to the equalization of depression treatment in Japan, including pharmacotherapy.

Abstract Aims The Guidelines for the Pharmacotherapy of Schizophrenia were established to improve the quality of medical care, and the EGUIDE project was conducted to train clinicians on guideline usage. A quality indicator (QI) was established to measure the prevalence of the guidelines, and a survey was conducted, which revealed a gap between the guidelines and actual clinical practice (evidence‐practice‐gap). The purpose of this study was to develop an individual fitness score (IFS) formula that expresses the degree to which prescribers adhere to the Guidelines for Pharmacological Therapy of Schizophrenia in a simple manner, and to determine the validity of this formula from a survey of the prescriptions of the EGUIDE project participants'. Methods To establish appropriate scores, members discussed the proposed formula and then voted on them. The IFS formula developed was set up so that antipsychotic monotherapy would be given 100 points, with points deducted if concomitant or adjunctive antipsychotic medications were used, and a minimum score of 0. To validate this formula, prescriptions of hospitalized schizophrenic patients at admission and at discharge were scored and compared. Result IFS points vary and ranged from 0 to100. The average pre‐admission score for all subjects was 45.6, and the average score at discharge was 54, those were significantly higher during discharge. Conclusions We developed an IFS formula, a tool to easily visualize the degree to which current prescriptions conform to the guidelines for the pharmacological treatment of schizophrenia.

Abstract Background Several guidelines recommend monotherapy in pharmacotherapy for schizophrenia and major depressive disorder. The content of regular prescriptions has been reported in several studies, but not enough research has been conducted on the content of pharmacotherapy, including pro re nata (PRN) medications. The purpose of this study was to evaluate the content of pharmacotherapy, including PRN medications, and to clarify the relationship with regular prescriptions. Methods We used data from the “Effectiveness of Guidelines for Dissemination And Education in psychiatric treatment” (EGUIDE) project to investigate the presence or absence of PRN psychotropic medications at discharge for each drug category. We compared the PRN psychotropic prescription ratio at discharge by diagnosis for each drug category. The antipsychotic monotherapy ratio and no prescription ratio of other psychotropics for schizophrenia at discharge and the antidepressant monotherapy ratio and no prescription ratio of other psychotropics for major depressive disorder at discharge were calculated for each regular prescription, including PRN psychotropic medications, as quality indicators (QIs). Spearman's rank correlation test was performed for QI values of regular prescriptions and the QI ratio between regular prescriptions and prescriptions including PRN medications for each diagnosis. Results The PRN psychotropic prescription ratio at discharge was 28.7% for schizophrenia and 30.4% for major depressive disorder, with no significant differences by diagnosis. The prescription ratios of PRN antipsychotic medications and PRN antiparkinsonian medications were significantly higher for schizophrenia. The prescription ratios of PRN anxiolytic and hypnotic and PRN antidepressant medications were significantly higher for patients with major depressive disorder. For both schizophrenia and major depressive disorder, the QI was lower for discharge prescriptions, including PRN medications, than for regular prescriptions. QI values for regular prescriptions and the QI ratio were positively correlated. Conclusions Considering PRN psychotropic medications, the monotherapy ratio and no prescription ratio of other psychotropics at discharge decreased in pharmacotherapy for schizophrenia and major depressive disorder. A higher ratio of monotherapy and no prescription of other psychotropics on regular prescriptions may result in less concomitant use of PRN psychotropic medications. Further studies are needed to optimize PRN psychotropic prescriptions.

Background: Electroconvulsive therapy (ECT) is provided for patients with severe and often life-threatening illness, who lack decision making capacity to consent to treatment (DMC-T) in clinical settings. Objective: The aim of this study is to summarize previous studies investigating clinical outcomes of ECT in patients lacking DMC-T. Methods: A systematic review and meta-analysis of studies reporting clinical outcomes of ECT in patients lacking DMC-T with any psychiatric diagnoses was conducted. The primary outcome was clinical improvement. Secondary outcomes were cognitive outcomes and six month readmission rate. Hedges’ g and odds ratios were calculated using a random-effects model. The protocol was registered in Open Science Framework (https://osf.io/rxjkm). Results: Of 3552 identified articles, 41 studies (n = 1299) were included. Approximately 80% of patients lacking DMC-T responded to ECT, and part of the patients regained capacity to consent and consented to further treatment with ECT. A total of seven studies (n = 1081) were included for meta-analysis. Patients without DMC-T showed superior clinical improvement and less cognitive side effects compared with those with DMC-T, whereas the groups did not show any difference in readmission rate. Several clinical characteristics at baseline and ECT techniques were significantly different between the groups. Conclusion: ECT is equally, if not superiorly, effective in patients lacking DMC-T compared to patients with DMC-T. ECT can potentially enhance patients’ autonomy, without increasing the risk of cognitive side effects. These results support the clinical and ethical legitimacy of ECT provision for patients with the most severe illness who lack DMC-T at start of treatment.

Abstract In recent years, studies on the use of natural language processing (NLP) approaches to identify dementia have been reported. Most of these studies used picture description tasks or other similar tasks to encourage spontaneous speech, but the use of free conversation without requiring a task might be easier to perform in a clinical setting. Moreover, free conversation is unlikely to induce a learning effect. Therefore, the purpose of this study was to develop a machine learning model to discriminate subjects with and without dementia by extracting features from unstructured free conversation data using NLP. We recruited patients who visited a specialized outpatient clinic for dementia and healthy volunteers. Participants’ conversation was transcribed and the text data was decomposed from natural sentences into morphemes by performing a morphological analysis using NLP, and then converted into real-valued vectors that were used as features for machine learning. A total of 432 datasets were used, and the resulting machine learning model classified the data for dementia and non-dementia subjects with an accuracy of 0.900, sensitivity of 0.881, and a specificity of 0.916. Using sentence vector information, it was possible to develop a machine-learning algorithm capable of discriminating dementia from non-dementia subjects with a high accuracy based on free conversation.

Exposure therapy is a mainstream of treatment for social anxiety disorder (SAD). However, effort and time are required to recreate interpersonal situations that produce moderate anxiety. On the other hand, virtual reality exposure therapy can easily control anxiety-inducing conditions and allow for graduated exposure. However, artificial intelligence and animations that speak as naturally as actual humans are not yet practical, adding to the limitations of these treatments. The authors propose the use of a virtual reality technology that can transform facial expressions into smiling or sad faces in real time and display them on a monitor, potentially solving the above-mentioned problems associated with virtual reality animations. This feasibility study was conducted to determine whether this system can be safely applied to the treatment of SAD patients. A total of four SAD patients received 16 exposure therapy sessions led by an experienced therapist over a monitor; throughout the sessions, the facial expressions of the therapist were modified using software to display expressions ranging from smiling to sad on the monitor that was being viewed by the patient. Client satisfaction, treatment alliance, and symptom assessments were then conducted. Although one patient dropped out of the study, treatment satisfaction and treatment alliance were scored high in all the cases. In two of the four cases, the improvement in symptoms was sustained over time. Exposure therapy in which the interviewer’s facial expressions are modified to induce appropriate levels of anxiety in the patient can be safely used for the treatment of SAD patients and may be effective for some patients.

Abstract The Effectiveness of Guidelines for Dissemination and Education in psychiatric treatment (EGUIDE) project, which is a nationwide dissemination and implementation program for clinical practice guidelines (CPGs) in the field of psychiatry, is currently ongoing. In the current study, a subjective assessment of the participants in the EGUIDE programs was assessed using a questionnaire. Then, the relationships between the subjective assessment, the characteristics of the participants, and the clinical knowledge of the CPGs were evaluated. More than 90% of the participants gave a high rating for the components of content, recommendation, knowledge, skill, and adherence, but not for the component of confidence. A positive correlation was found between years of professional experience and the score of confidence. These results suggest that it may be necessary to apply the knowledge and skills of CPGs obtained in the education programs into practice to increase confidence in the proper use of psychiatric therapies based on CPGs.

BackgroundThere are limited data about the association between body mass index (BMI), glycemic variability (GV), and life-related factors in healthy nondiabetic adults.MethodsThis cross-sectional study was carried out within our ethics committee-approved study called "Exploring the impact of nutrition advice on blood sugar and psychological status using continuous glucose monitoring (CGM) and wearable devices". Prediabetes was defined by the HbA1c level of 5.7-6.4% and /or fasting glucose level of 100-125 mg/dL. Glucose levels and daily steps were measured for 40 participants using Free Style Libre and Fitbit Inspire 2 under normal conditions for 14 days. Dietary intakes and eating behaviors were assessed using a brief-type self-administered dietary history questionnaire and a modified questionnaire from the Obesity Guidelines.ResultsAll indices of GV were higher in the prediabetes group than in the healthy group, but a significant difference was observed only in mean amplitude of glycemic excursions (MAGE). In the multivariate analysis, only the presence of prediabetes showed a significant association with the risk of higher than median MAGE (Odds, 6.786; 95% CI, 1.596-28.858; P = 0.010). Additionally, the underweight (BMI < 18.5) group had significantly higher value in standard deviation (23.7 ± 3.5 vs 19.8 ± 3.7 mg/dL, P = 0.038) and coefficient variability (22.6 ± 4.6 vs 18.4 ± 3.2%, P = 0.015), compared to the normal group. This GV can be partially attributed to irregularity of eating habits. On the contrary, the overweight (BMI ≥ 25) group had the longest time above the 140 or 180 mg/dL range, which may be due to eating style and taking fewer steps (6394 ± 2337 vs 9749 ± 2408 steps, P = 0.013).ConclusionsConcurrent CGM with diet and activity monitoring could reduce postprandial hyperglycemia through assessment of diet and daily activity, especially in non- normal weight individuals.

Alzheimer’s disease (AD) is a type of dementia that is more likely to occur as people age. It currently has no known cure. As the world’s population is aging quickly, early screening for AD has become increasingly important. Traditional screening methods such as brain scans or psychiatric tests are stressful and costly. The patients are likely to feel reluctant to such screenings and fail to receive timely intervention. While researchers have been exploring the use of language in dementia detection, less attention has been given to face-related features. The paper focuses on investigating how face-related features can aid in detecting dementia by exploring the PROMPT dataset that contains video data collected from patients with dementia during interviews. In this work, we extracted three types of features from the videos, including face mesh, Histogram of Oriented Gradients (HOG) features, and Action Units (AU). We trained traditional machine learning models and deep learning models on the extracted features and investigated their effectiveness in dementia detection. Our experiments show that the use of HOG features achieved the highest accuracy of 79% in dementia detection, followed by AU features with 71% accuracy, and face mesh features with 66% accuracy. Our results show that face-related features have the potential to be a crucial indicator in automated computational dementia detection.

IntroductionFew 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 analysisPatients 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.DiscussionOur 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].

IntroductionPsychiatric disorders are diagnosed through observations of psychiatrists according to diagnostic criteria such as the DSM-5. Such observations, however, are mainly based on each psychiatrist's level of experience and often lack objectivity, potentially leading to disagreements among psychiatrists. In contrast, specific linguistic features can be observed in some psychiatric disorders, such as a loosening of associations in schizophrenia. Some studies explored biomarkers, but biomarkers have yet to be used in clinical practice.AimThe purposes of this study are to create a large dataset of Japanese speech data labeled with detailed information on psychiatric disorders and neurocognitive disorders to quantify the linguistic features of those disorders using natural language processing and, finally, to develop objective and easy-to-use biomarkers for diagnosing and assessing the severity of them.MethodsThis study will have a multi-center prospective design. The DSM-5 or ICD-11 criteria for major depressive disorder, bipolar disorder, schizophrenia, and anxiety disorder and for major and minor neurocognitive disorders will be regarded as the inclusion criteria for the psychiatric disorder samples. For the healthy subjects, the absence of a history of psychiatric disorders will be confirmed using the Mini-International Neuropsychiatric Interview (M.I.N.I.). The absence of current cognitive decline will be confirmed using the Mini-Mental State Examination (MMSE). A psychiatrist or psychologist will conduct 30-to-60-min interviews with each participant; these interviews will include free conversation, picture-description task, and story-telling task, all of which will be recorded using a microphone headset. In addition, the severity of disorders will be assessed using clinical rating scales. Data will be collected from each participant at least twice during the study period and up to a maximum of five times at an interval of at least one month.DiscussionThis study is unique in its large sample size and the novelty of its method, and has potential for applications in many fields. We have some challenges regarding inter-rater reliability and the linguistic peculiarities of Japanese. As of September 2022, we have collected a total of >1000 records from >400 participants. To the best of our knowledge, this data sample is one of the largest in this field.Clinical Trial RegistrationIdentifier: UMIN000032141.

We report a patient with logopenic variant primary progressive aphasia (lv-PPA) who was diagnosed as having non-Alzheimer's disease (AD) tauopathy after multiple biophysical/biological examinations, including amyloid and 18F-florzolotau tau positron emission tomography (PET), had been performed. A woman in her late 60s who had previously been diagnosed as having AD was referred to us for a further, detailed examination. She had been unaware of any symptoms at the time of AD diagnosis, but she subsequently became gradually aware of a speech impairment. She talked nearly completely and fluently, although she occasionally exhibited word-finding difficulty and made phonological errors during naming, word fluency testing, and sentence repetition; these findings met the criteria for the diagnosis of lv-PPA, which is known to be observed more commonly in AD than in other proteinopathies. Magnetic resonance imaging, single photon emission computed tomography, and plasma phosphorylated tau and plasma neurofilament light chain measurements showed an AD-like pattern. However, both 11C-Pittsburgh compound-B and 18F-florbetaben amyloid PET showed negative results, whereas 18F-florzolotau tau PET yielded positive results, with radio signals predominantly in the left superior temporal gyrus, middle temporal gyrus, supramarginal gyrus, and frontal operculum. Whole-genome sequencing revealed no known dominantly inherited mutations in AD or frontotemporal lobar degeneration genes, including the genes encoding amyloid precursor protein, microtubule-associated protein tau, presenilin 1 and 2. To the best of our knowledge, this patient was a rare case of lv-PPA who was diagnosed as having non-AD tauopathy based on the results of multiple examinations, including whole-genome sequencing, plasma measurement, and amyloid and 18F-florzolotau tau PET. This case underscores the clinicopathologically heterogeneous nature of this syndrome.

Abstract Background To implement clinical practice guidelines (CPGs), it is necessary for psychiatrists to deepen their understanding of the CPGs. The Effectiveness of Guidelines for Dissemination and Education in Psychiatric Treatment (EGUIDE) project is a nationwide dissemination and implementation study of two sets of CPGs for schizophrenia and major depressive disorder (MDD). Methods A total of 413 psychiatrists (n = 212 in 2016; n = 201 in 2017) learned the two CPGs in the education program of the EGUIDE project, and clinical knowledge of these CPGs was evaluated at baseline and after the programs. To improve the correct answer rate for clinical knowledge after the programs, we revised the lecture materials associated with items that had a low correct answer rate in 2016 and used the revised lecture materials with the CPGs in 2017. The rates of correct answers after the programs between the 2016 and 2017 groups were compared. Results The correct answer rate of one item on the schizophrenia CPG and one item on the MDD CPG tended to be improved (S‐D5 and D‐C6) and that of one on the MDD CPG was significantly improved (D‐D3, P = 0.0008) in the 2017 group compared to those in the 2016 group. Conclusions We reported improvements in clinical knowledge of CPGs after the EGUIDE program in the 2017 group following revision of the lecture materials based on results from the 2016 group. These attempts to improve the degree of understanding of CPGs may facilitate the successful dissemination and implementation of psychiatric guidelines in everyday practice.

Background Clinical practice guidelines for schizophrenia and major depressive disorder have been published. However, these have not had sufficient penetration in clinical settings. We developed the Effectiveness of Guidelines for Dissemination and Education in Psychiatric Treatment (EGUIDE) project as a dissemination and education programme for psychiatrists. Aims The aim of this study is to assess the effectiveness of the EGUIDE project on the subjective clinical behaviour of psychiatrists in accordance with clinical practice guidelines before and 1 and 2 years after participation in the programmes. Method A total of 607 psychiatrists participated in this study during October 2016 and March 2019. They attended both 1-day educational programmes based on the clinical practice guidelines for schizophrenia and major depressive disorder, and answered web questionnaires about their clinical behaviours before and 1 and 2 years after attending the programmes. We evaluated the changes in clinical behaviours in accordance with the clinical practice guidelines between before and 2 years after the programme. Results All of the scores for clinical behaviours in accordance with clinical practice guidelines were significantly improved after 1 and 2 years compared with before attending the programmes. There were no significant changes in any of the scores between 1 and 2 years after attending. Conclusions All clinical behaviours in accordance with clinical practice guidelines improved after attending the EGUIDE programme, and were maintained for at least 2 years. The EGUIDE project could contribute to improved guideline-based clinical behaviour among psychiatrists.

Background Previous studies have attempted to characterize depression using electroencephalography (EEG), but results have been inconsistent. New noise reduction technology allows EEG acquisition during conversation. Methods We recorded EEG from 40 patients with depression as they engaged in conversation using a single-channel EEG device while conducting real-time noise reduction and compared them to those of 40 healthy subjects. Differences in EEG between patients and controls, as well as differences in patients’ depression severity, were examined using the ratio of the power spectrum at each frequency. In addition, the effects of medications were examined in a similar way. Results In comparing healthy controls and depression patients, significant power spectrum differences were observed at 3 Hz, 4 Hz, and 10 Hz and higher frequencies. In the patient group, differences in the power spectrum were observed between asymptomatic patients and healthy individuals, and between patients of each respective severity level and healthy individuals. In addition, significant differences were observed at multiple frequencies when comparing patients who did and did not take antidepressants, antipsychotics, and/or benzodiazepines. However, the power spectra still remained significantly different between non-medicated patients and healthy individuals. Limitations The small sample size may have caused Type II error. Patients’ demographic characteristics varied. Moreover, most patients were taking various medications, and cannot be compared to the non-medicated control group. Conclusion A study with a larger sample size should be conducted to gauge reproducibility, but the methods used in this study could be useful in clinical practice as a biomarker of depression.

Abstract Background Guideline for Pharmacological Therapy for Schizophrenia was published by the Japanese Society of Neuropsychopharmacology in 2015. “Effectiveness of Guidelines for Dissemination and Education in psychiatric treatment (EGUIDE)” project aimed to standardize medical practice using quality indicators (QIs) as indices to evaluate the quality of medical practice. In this study, we have reported the quality indicator values of prescription before the beginning of the guideline lectures in the EGUIDE project to ascertain the baseline status of treating patients with schizophrenia. Methods A cross‐sectional, retrospective case record survey was conducted, involving 1164 patients with schizophrenia at the time of discharge. We checked all types and dosage of psychotropic drugs. Results Forty‐three percent of patients had antipsychotic polypharmacy, and substantial concomitant medication was observed (antidepressants; 8%, mood stabilizers: 37%, anxiolytics or hypnotics: 68%). Conclusions In the results obtained in this study, we plant to report changes in the effectiveness of education in the EGUIDE project near the future.

Abstract Background The novel coronavirus pandemic calls for a rapid adaptation of conventional medical practices to meet the evolving needs of such vulnerable patients. People with coronavirus disease (COVID-19) may frequently require treatment with psychotropic medications, but are at the same time at higher risk for safety issues because of the complex underlying medical condition and the potential interaction with medical treatments. Methods In order to produce evidence-based practical recommendations on the optimal management of psychotropic medications in people with COVID-19, an international, multi-disciplinary working group was established. The methodology of the WHO Rapid Advice Guidelines in the context of a public health emergency and the principles of the AGREE statement were followed. Available evidence informing on the risk of respiratory, cardiovascular, infective, hemostatic, and consciousness alterations related to the use of psychotropic medications, and drug–drug interactions between psychotropic and medical treatments used in people with COVID-19, was reviewed and discussed by the working group. Results All classes of psychotropic medications showed potentially relevant safety risks for people with COVID-19. A set of practical recommendations was drawn in order to inform frontline clinicians on the assessment of the anticipated risk of psychotropic-related unfavorable events, and the possible actions to take in order to effectively manage this risk, such as when it is appropriate to avoid, withdraw, switch, or adjust the dose of the medication. Conclusions The present evidence-based recommendations will improve the quality of psychiatric care in people with COVID-19, allowing an appropriate management of the medical condition without worsening the psychiatric condition and vice versa.

Abstract Background Besides research on psychiatric diseases related to problematic Internet use (PIU), a growing number of studies focus on the impact of Internet on subjective well-being (SWB). However, in previous studies on the relationship between PIU and SWB, there is little data for Japanese people specifically, and there is a lack of consideration for differences in perception of happiness due to cultural differences. Therefore, we aimed to clarify how happiness is interdependent on PIU measures, with a focus on how the concept of happiness is interpreted among Japanese people, and specifically among Japanese university students. Methods A paper-based survey was conducted with 1258 Japanese university students. Respondents were asked to fill out self-report scales regarding their happiness using the Interdependent Happiness Scale (IHS). The relationship between IHS and Internet use (Japanese version of the Internet addiction test, JIAT), use of social networking services, as well as social function and sleep quality (Pittsburgh Sleep Quality Index, PSQI) were sought using multiple regression analyses. Results Based on multiple regression analyses, the following factors related positively to IHS: female gender and the number of Twitter followers. Conversely, the following factors related negatively to IHS: poor sleep, high- PIU, and the number of times the subject skipped a whole day of school. Conclusions It was shown that there was a significant negative correlation between Japanese youths’ happiness and PIU. Since epidemiological research on happiness that reflects the cultural background is still scarce, we believe future studies shall accumulate similar evidence in this regard.

We aimed to investigate the impact of aging on the relationship among the composition of gut microbiota, gastrointestinal (GI) symptoms, and the course of treatment for major depressive disorder (MDD) by analyzing the datasets from our previous study. Patients with MDD were recruited, and their stools were collected at three time points (baseline, midterm, and endpoint) following the usual antidepressant treatment. Gut microbiota were analyzed using 16S rRNA gene sequencing. Patients were categorized into two groups based on their age: the late-life group over 60 years and the middle-aged group under 60 years. GI symptoms were assessed with scores of item 11 of the Hamilton Anxiety Rating Scale. One hundred and ninety samples were collected from 32 patients with MDD. Several gut microbes had higher relative abundances in the late-life group than in the middle-aged group. In addition, the late-life group showed significantly higher diversity in the Chao1 index at baseline compared with the middle-aged group. We further found possible microbial taxa related to GI symptoms in patients with late-life depression. The abundance of several bacterial taxa may contribute to GI symptoms in the late-life depression, and our findings suggest that the therapeutic targets for the application of gut microbiota may differ depending on the age group of patients with depression.

Social interaction might prevent or delay dementia, but little is known about the specific effects of various social activity interventions on cognition. This study conducted a single-site randomized controlled trial (RCT) of Photo-Integrated Conversation Moderated by Robots (PICMOR), a group conversation intervention program for resilience against cognitive decline and dementia. In the RCT, PICMOR was compared to an unstructured group conversation condition. Sixty-five community-living older adults participated in this study. The intervention was provided once a week for 12 weeks. Primary outcome measures were the cognitive functions; process outcome measures included the linguistic characteristics of speech to estimate interaction quality. Baseline and post-intervention data were collected. PICMOR contains two key features: 1) photos taken by the participants are displayed and discussed sequentially; and 2) a robotic moderator manages turn-taking to make sure that participants are allocated the same amount of time. Among the primary outcome measures, one of the subcategories of cognitive functions, verbal fluency significantly improved in the intervention group. Among the process outcome measures, a part of the subcategories of linguistic characteristics of speech, the amount of speech and richness of words, proportion of providing topics, questions, and answers in total utterances were larger for the intervention group. This study demonstrated for the first time the positive effects of a robotic social activity intervention on cognitive function in healthy older adults via RCT. The group conversation generated by PICMOR may improve participants’ verbal fluency since participants have more opportunity to provide their own topics, asking and answering questions which results in exploring larger vocabularies. PICMOR is available and accessible to community-living older adults.Clinical Trial Registration:UMIN Clinical Trials Registry, identifier UMIN000036667.

Introduction: Mental disorders are a leading cause of disability worldwide. Depression has a significant impact in the field of occupational health because it is particularly prevalent during working age. On the other hand, there are a growing number of studies on the relationship between “well-being” and employee productivity. To promote healthy and productive workplaces, this study aims to develop a technique to quantify stress and well-being in a way that does not disturb the workplace.Methods and analysis: This is a single-arm prospective observational study. The target population is adult (>20 years old) workers at companies that often engage in desk work; specifically, a person who sits in front of a computer for at least half their work hours. The following data will be collected: (a) participants' background characteristics; (b) participants' biological data during the 4-week observation period using sensing devices such as a camera built into the computer (pulse wave data extracted from the facial video images), a microphone built into their work computer (voice data), and a wristband-type wearable device (electrodermal activity data, body motion data, and body temperature); (c) stress, well-being, and depression rating scale assessment data. The analysis workflow is as follows: (1) primary analysis, comprised of using software to digitalize participants' vital information; (2) secondary analysis, comprised of examining the relationship between the quantified vital data from (1), stress, well-being, and depression; (3) tertiary analysis, comprised of generating machine learning algorithms to estimate stress, well-being, and degree of depression in relation to each set of vital data as well as multimodal vital data.Discussion: This study will evaluate digital phenotype regarding stress and well-being of white-collar workers over a 4-week period using persistently obtainable biomarkers such as heart rate, acoustic characteristics, body motion, and electrodermal activity. Eventually, this study will lead to the development of a machine learning algorithm to determine people's optimal levels of stress and well-being.Ethics and dissemination: Collected data and study results will be disseminated widely through conference presentations, journal publications, and/or mass media. The summarized results of our overall analysis will be supplied to participants.Registration: UMIN000036814

The importance of workers' well-being has been recognized in recent years. The assessment of well-being has been subjective, and few studies have sought potential biomarkers of well-being to date. This study examined the relationship between well-being and the LF/HF ratio, an index of heart rate variability that reflects sympathetic and parasympathetic nerve activity. Pulse waves were measured using photoplethysmography through a web camera attached to the computer used by each participant. The participants were asked to measure their pulse waves while working for 4 weeks, and well-being was assessed using self-reported measures such as the Satisfaction With Life Scale (SWLS), the Positive and Negative Affect Schedule (PANAS), and the Flourishing Scale (FS). Each of the well-being scores were split into two groups according to the median value, and the LF/HF ratio during work, as well as the number of times an LF/HF ratio threshold was either exceeded or subceeded, were compared between the high and low SWLS, positive emotion, negative emotion, and FS groups. Furthermore, to examine the effects of the LF/HF ratio and demographic characteristics on well-being, a multiple regression analysis was conducted. Data were obtained from 169 participants. The results showed that the low FS group had a higher mean LF/HF ratio during work than the high FS group. No significant differences were seen between the high and low SWLS groups, the high and low positive emotion groups, or the high and low negative emotion groups. The multiple regression analysis showed that the mean LF/HF ratio during work affected the FS and SWLS scores, and the number of times the mean LF/HF ratio exceeded +3 SD had an effect on the positive emotion. No effect of the LF/HF ratio on negative emotions was shown. The LF/HF ratio might be applicable as an objective measure of well-being.

Introduction: Depressive and neurocognitive disorders are debilitating conditions that account for the leading causes of years lived with disability worldwide. However, there are no biomarkers that are objective or easy-to-obtain in daily clinical practice, which leads to difficulties in assessing treatment response and developing new drugs. New technology allows quantification of features that clinicians perceive as reflective of disorder severity, such as facial expressions, phonic/speech information, body motion, daily activity, and sleep. Methods: Major depressive disorder, bipolar disorder, and major and minor neurocognitive disorders as well as healthy controls are recruited for the study. A psychiatrist/psychologist conducts conversational 10-min interviews with participants ≤10 times within up to five years of follow-up. Interviews are recorded using RGB and infrared cameras, and an array microphone. As an option, participants are asked to wear wrist-band type devices during the observational period. Various software is used to process the raw video, voice, infrared, and wearable device data. A machine learning approach is used to predict the presence of symptoms, severity, and the improvement/deterioration of symptoms. Discussion: The overall goal of this proposed study, the Project for Objective Measures Using Computational Psychiatry Technology (PROMPT), is to develop objective, noninvasive, and easy-to-use biomarkers for assessing the severity of depressive and neurocognitive disorders in the hopes of guiding decision-making in clinical settings as well as reducing the risk of clinical trial failure. Challenges may include the large variability of samples, which makes it difficult to extract the features that commonly reflect disorder severity. Trial Registration: UMIN000021396, University Hospital Medical Information Network (UMIN).

Background: Mood disorders have long been known to affect motor function. While methods to objectively assess such symptoms have been used in experiments, those same methods have not yet been applied in clinical practice because the methods are time-consuming, labor-intensive, or invasive. Methods: We videotaped the upper body of each subject using a Red-Green-Blue-Depth (RGB-D) sensor during a clinical interview setting. We then examined the relationship between depressive symptoms and body motion by comparing the head motion of patients with major depressive disorders (MDD) and bipolar disorders (BD) to the motion of healthy controls (HC). Furthermore, we attempted to predict the severity of depressive symptoms by using machine learning. Results: A total of 47 participants (HC, n = 16; MDD, n = 17; BD, n = 14) participated in the study, contributing to 144 data sets. It was found that patients with depression move significantly slower compared to HC in the 5th percentile and 50th percentile of motion speed. In addition, Hamilton Depression Rating Scale (HAMD)-17 scores correlated with 5th percentile, 50th percentile, and mean speed of motion. Moreover, using machine learning, the presence and/or severity of depressive symptoms based on HAMD-17 scores were distinguished by a kappa coefficient of 0.37 to 0.43. Limitations: Limitations include the small number of subjects, especially the number of severe cases and young people. Conclusions: The RGB-D sensor captured some differences in upper body motion between depressed patients and controls. If much larger samples are accumulated, machine learning may be useful in identifying objective measures for depression in the future. Keywords: Upper body motion, Depression, Psychomotor retardation, Psychomotor agitation, RGB-Depth sensor, Machine learning

Objective: We aimed to develop a machine learning algorithm to screen for depression and assess severity based on data from wearable devices. Methods: We used a wearable device that calculates steps, energy expenditure, body movement, sleep time, heart rate, skin temperature, and ultraviolet light exposure. Depressed patients and healthy volunteers wore the device continuously for the study period. The modalities were compared hourly between patients and healthy volunteers. XGBoost was used to build machine learning models and 10-fold cross-validation was applied for the validation. Results: Forty-five depressed patients and 41 healthy controls participated, creating a combined 5,250 days' worth of data. Heart rate, steps, and sleep were significantly different between patients and healthy volunteers in some comparisons. Similar differences were also observed longitudinally when patients' symptoms improved. Based on seven days' data, the model identified symptomatic patients with 0.76 accuracy and predicted Hamilton Depression Rating Scale-17 scores with a 0.61 correlation coefficient. Skin temperature, sleep time-related features, and the correlation of those modalities were the most significant features in machine learning. Limitations: The small number of subjects who participated in this study may have weakened the statistical significance of the study. There are differences in the demographic data among groups although we performed a correction for multiple comparisons. Validation in independent datasets was not performed, although 10-fold cross validation with the internal data was conducted. Conclusion: The results indicated that utilizing wearable devices and machine learning may be useful in identifying depression as well as assessing severity.

BackgroundThere are no reliable and validated objective biomarkers for the assessment of depression severity. We aimed to investigate the association between depression severity and timing-related speech features using speech recognition technology.MethodPatients with major depressive disorder (MDD), those with bipolar disorder (BP), and healthy controls (HC) were asked to engage in a non-structured interview with research psychologists. Using automated speech recognition technology, we measured three timing-related speech features: speech rate, pause time, and response time. The severity of depression was assessed using the Hamilton Depression Rating Scale 17-item version (HAMD-17). We conducted the current study to answer the following questions: 1) Are there differences in speech features among MDD, BP, and HC? 2) Do speech features correlate with depression severity? 3) Do changes in speech features correlate with within-subject changes in depression severity?ResultsWe collected 1058 data sets from 241 individuals for the study (97 MDD, 68 BP, and 76 HC). There were significant differences in speech features among groups; depressed patients showed slower speech rate, longer pause time, and longer response time than HC. All timing-related speech features showed significant associations with HAMD-17 total scores. Longitudinal changes in speech rate correlated with changes in HAMD-17 total scores.ConclusionsDepressed individuals showed longer response time, longer pause time, and slower speech rate than healthy individuals, all of which were suggestive of psychomotor retardation. Our study suggests that speech features could be used as objective biomarkers for the assessment of depression severity.

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.

An amendment to this paper has been published and can be accessed via the original article.

Loss of cognitive ability is commonly associated with dementia, a broad category of progressive brain diseases. However, major depressive disorder may also cause temporary deterioration of one’s cognition known as pseudodementia. Differentiating a true dementia and pseudodementia is still difficult even for an experienced clinician and extensive and careful examinations must be performed. Although mental disorders such as depression and dementia have been studied, there is still no solution for shorter and undemanding pseudodementia screening. This study inspects the distribution and statistical characteristics from both dementia patient and depression patient, and compared them. It is found that some acoustic features were shared in both dementia and depression, albeit their correlation was reversed. Statistical significance was also found when comparing the features. Additionally, the possibility of utilizing machine learning for automatic pseudodementia screening was explored. The machine learning part includes feature selection using LASSO algorithm and support vector machine (SVM) with linear kernel as the predictive model with age-matched symptomatic depression patient and dementia patient as the database. High accuracy, sensitivity, and specificity was obtained in both training session and testing session. The resulting model was also tested against other datasets that were not included and still performs considerably well. These results imply that dementia and depression might be both detected and differentiated based on acoustic features alone. Automated screening is also possible based on the high accuracy of machine learning results.