Explainable Depression Detection Based on Facial Expression Using LSTM on Attentional Intermediate Feature Fusion with Label Smoothing.

Machine learning is used for a fast pre-diagnosis approach to prevent the effects of Major Depressive Disorder (MDD). The objective of this research is to detect depression using a set of important facial features extracted from interview video, e.g., radians, gaze at angles, action unit intensity, etc. The model is based on LSTM with an attention mechanism. It aims to combine those features using the intermediate fusion approach. The label smoothing was presented to further improve the model's performance. Unlike other black-box models, the integrated gradient was presented as the model explanation to show important features of each patient. The experiment was conducted on 474 video samples collected at Chulalongkorn University. The data set was divided into 134 depressed and 340 non-depressed categories. The results showed that our model is the winner, with a 88.89% F1-score, 87.03% recall, 91.67% accuracy, and 91.40% precision. Moreover, the model can capture important features of depression, including head turning, no specific gaze, slow eye movement, no smiles, frowning, grumbling, and scowling, which express a lack of concentration, social disinterest, and negative feelings that are consistent with the assumptions in the depressive theories.

Construction of a short version of the Montreal Cognitive Assessment (MoCA) rating scale for the Thai population using Partial Least Squares analysis.

BACKGROUND: The Montreal Cognitive Assessment (MoCA) rating scale is frequently used to assess cognitive impairments in amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD). OBJECTIVES: The aims of this study were to a) evaluate the construct validity of the MoCA and its subdomains or whether the MoCA can be improved by feature reduction, and b) develop a short version of the MoCA (MoCA-Brief) for the Thai population. METHODS: We recruited 181 participants, namely 60 healthy controls, 61 aMCI, and 60 AD patients. RESULTS: The construct reliability of the original MoCA was not optimal and could be improved by deleting one subdomain (Naming) and five items, namely Clock Circle, Lion, Digit Forward, Repeat 2nd Sentence, and Place, which showed inadequate loadings on their latent vectors. To construct the MoCA-Brief, the reduced model underwent further reduction and feature selection based on model quality data of the outer models. We produced a MoCA-Brief rating scale comprising five items, namely Clock Time, Subtract 7, Fluency, Month, and Year. The first latent vector extracted from these five indicators showed adequate construct validity with an Average Variance Extracted of 0.599, composite reliability of 0.822, Cronbach's alpha of 0.832 and rho A of 0.833. The MoCA-Brief factor score showed a strong correlation with the total MoCA score (r = 0.98, p < 0.001) and shows adequate concurrent, test-retest, and inter-rater validity. CONCLUSION: The construct validity of the MoCA may be improved by deleting five items. The new MoCA-Brief rating scale deserves validation in independent samples and especially in other countries.

Using Automatic Speech Recognition to Assess Thai Speech Language Fluency in the Montreal Cognitive Assessment (MoCA).

The Montreal cognitive assessment (MoCA), a widely accepted screening tool for identifying patients with mild cognitive impairment (MCI), includes a language fluency test of verbal functioning; its scores are based on the number of unique correct words produced by the test taker. However, it is possible that unique words may be counted differently for various languages. This study focuses on Thai as a language that differs from English in terms of word combinations. We applied various automatic speech recognition (ASR) techniques to develop an assisted scoring system for the MoCA language fluency test with Thai language support. This was a challenge because Thai is a low-resource language for which domain-specific data are not publicly available, especially speech data from patients with MCIs. Furthermore, the great variety of pronunciation, intonation, tone, and accent of the patients, all of which might differ from healthy controls, bring more complexity to the model. We propose a hybrid time delay neural network hidden Markov model (TDNN-HMM) architecture for acoustic model training to create our ASR system that is robust to environmental noise and to the variation of voice quality impacted by MCI. The LOTUS Thai speech corpus was incorporated into the training set to improve the model's generalization. A preprocessing algorithm was implemented to reduce the background noise and improve the overall data quality before feeding data into the TDNN-HMM system for automatic word detection and language fluency score calculation. The results show that the TDNN-HMM model in combination with data augmentation using lattice-free maximum mutual information (LF-MMI) objective function provides a word error rate (WER) of 30.77%. To our knowledge, this is the first study to develop an ASR with Thai language support to automate the scoring system of MoCA's language fluency assessment.

Automatic Speech Discrimination Assessment Methods Based on Event-Related Potentials (ERP).

Speech discrimination is used by audiologists in diagnosing and determining treatment for hearing loss patients. Usually, assessing speech discrimination requires subjective responses. Using electroencephalography (EEG), a method that is based on event-related potentials (ERPs), could provide objective speech discrimination. In this work we proposed a visual-ERP-based method to assess speech discrimination using pictures that represent word meaning. The proposed method was implemented with three strategies, each with different number of pictures and test sequences. Machine learning was adopted to classify between the task conditions based on features that were extracted from EEG signals. The results from the proposed method were compared to that of a similar visual-ERP-based method using letters and a method that is based on the auditory mismatch negativity (MMN) component. The P3 component and the late positive potential (LPP) component were observed in the two visual-ERP-based methods while MMN was observed during the MMN-based method. A total of two out of three strategies of the proposed method, along with the MMN-based method, achieved approximately 80% average classification accuracy by a combination of support vector machine (SVM) and common spatial pattern (CSP). Potentially, these methods could serve as a pre-screening tool to make speech discrimination assessment more accessible, particularly in areas with a shortage of audiologists.

Selecting the Most Important Features for Predicting Mild Cognitive Impairment from Thai Verbal Fluency Assessments.

Mild cognitive impairment (MCI) is an early stage of cognitive decline or memory loss, commonly found among the elderly. A phonemic verbal fluency (PVF) task is a standard cognitive test that participants are asked to produce words starting with given letters, such as "F" in English and "ก" /k/ in Thai. With state-of-the-art machine learning techniques, features extracted from the PVF data have been widely used to detect MCI. The PVF features, including acoustic features, semantic features, and word grouping, have been studied in many languages but not Thai. However, applying the same PVF feature extraction methods used in English to Thai yields unpleasant results due to different language characteristics. This study performs analytical feature extraction on Thai PVF data to classify MCI patients. In particular, we propose novel approaches to extract features based on phonemic clustering (ability to cluster words by phonemes) and switching (ability to shift between clusters) for the Thai PVF data. The comparison results of the three classifiers revealed that the support vector machine performed the best with an area under the receiver operating characteristic curve (AUC) of 0.733 (N = 100). Furthermore, our implemented guidelines extracted efficient features, which support the machine learning models regarding MCI detection on Thai PVF data.

Use of the Montreal Cognitive Assessment Thai Version to Discriminate Amnestic Mild Cognitive Impairment from Alzheimer's Disease and Healthy Controls: Machine Learning Results.

BACKGROUND: The Montreal Cognitive Assessment (MoCA) is an effective and applicable screening instrument to confirm the diagnosis of amnestic mild cognitive impairment (aMCI) from patients with Alzheimer's disease (AD) and healthy controls (HCs). OBJECTIVES: This study aimed to determine the reliability and validity of the following: (a) Thai translation of the MoCA (MoCA-Thai) and (b) delineate the key features of aMCI based on the MoCA subdomains. METHODS: This study included 60 HCs, 61 aMCI patients, and 60 AD patients. The MoCA-Thai shows adequate psychometric properties including internal consistency, concurrent validity, test-retest validity, and inter-rater reliability. RESULTS: The MoCA-Thai may be employed as a diagnostic criterion to make the diagnosis of aMCI, whereby aMCI patients are discriminated from HC with an area under the receiver-operating characteristic (AUC-ROC) curve of 0.813 and from AD patients with an AUC-ROC curve of 0.938. The best cutoff scores of the MoCA-Thai to discriminate aMCI from HC is ≤24 and from AD > 16. Neural network analysis showed that (a) aberrations in recall was the most important feature of aMCI versus HC with impairments in language and orientation being the second and third most important features and (b) aberrations in visuospatial skills and executive functions were the most important features of AD versus aMCI and that impairments in recall, language, and orientation but not attention, concentration, and working memory, further discriminated AD from aMCI. CONCLUSIONS: The MoCA-Thai is an appropriate cognitive assessment tool to be used in the Thai population for the diagnosis of aMCI and AD.

The factors associated with mild cognitive impairment (MCI) in surgical menopause women.

BACKGROUND AND OBJECTIVE: As a sizeable proportion of persons with mild cognitive impairment will progress to frank dementia, early detection is an important strategy to prevent and decelerate the progression of cognitive decline. In Thailand, the prevalence of mild cognitive impairment in surgical menopause women has not been well established. The objectives of the present study were to determine the percentage and factors associated with mild cognitive impairment in women with surgical menopause. MATERIAL AND METHOD: Between October 2013 and July 2014, 200 eligible women at King Chulalongkorn Memorial Hospital were enrolled. The self-reported questionnaires were used to obtain the demographic data and the Thai version of the Montreal Cognitive Assessment (MoCA) was used to detect mild cognitive impairment (MCI). The MCI was diagnosed when the MoCA score was less than 25. The data were statistically analyzed using SPSS version 17 for student t-test, Chi-square test, and multiple regression analysis. RESULTS: The percentage of MCI in the present study was 43.5%. The univariate analysis showed that factors significantly related to MCI were marital status, educational levels, occupation, monthly income, and duration of hormone replacement therapy (HRT). Nevertheless, multiple regression analysis revealed that only older age at enrollment, marital status, low educational level, and low monthly income were significantly related to MCI. CONCLUSION: Almost half of the surgical menopause women in the present study had MCI. Older age at enrollment, marital status, low educational level, and low monthly income were significantly related to MCI. Age at surgical menopause and HRT were not found to be associated with MCI in this study.

Distress Symptoms of Old Age and Mild Cognitive Impairment are Two Distinct Dimensions in Older Adults Without Major Depression.

Background: Studies in old adults showed bidirectional interconnections between amnestic mild cognitive impairment (aMCI) and affective symptoms and that adverse childhood experiences (ACE) may affect both factors. Nevertheless, these associations may be confined to older adults with clinical depression. Aim: To delineate the relationship between clinical symptoms of aMCI and affective symptoms in older adults without major depression (MDD) or dysfunctions in activities of daily living (ADL). Methods: This case-control study recruited 61 participants with aMCI (diagnosed using Petersen's criteria) and 59 older adults without aMCI and excluded subjects with MDD and ADL dysfunctions. Results: We uncovered 2 distinct dimensions, namely distress symptoms of old age (DSOA), comprising affective symptoms, perceived stress and neuroticism, and mild cognitive dysfunctions, comprising episodic memory test scores, the total Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores. A large part of the variance (37.9%) in DSOA scores was explained by ACE, negative life events (health and financial problems), a subjective feeling of cognitive decline, and education (all positively). ACE and NLE have a highly significant impact on the DSOA score and are not associated with aMCI or its severity. Cluster analysis showed that the diagnosis of aMCI is overinclusive because some subjects with DSOA symptoms may be incorrectly classified as aMCI. Conclusion: The clinical impact is that clinicians should carefully screen older adults for DSOA after excluding MDD. DSOA might be misinterpreted as aMCI.

The prefrontal cortex hemodynamic responses to dual-task paradigms in older adults: A systematic review and meta-analysis.

Background: Functional near-infrared spectroscopy (fNIRS) is a method to measure cerebral hemodynamics. Determining the changes in prefrontal cortex (PFC) hemodynamics during dual-task paradigms is essential in explaining alterations in physical activities, especially in older adults. Aims: To systematically review and meta-analyze the effects of dual-task paradigms on PFC hemodynamics in older adults. Methods: The search was conducted in PubMed, Scopus, and Web of Science from inception until March 2023 to identify studies on the effects of dual-task paradigms on PFC hemodynamics. The meta-analysis included variables of cerebral hemodynamics, such as oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin (HbR). The heterogeneity of the included studies was determined using the I2 statistic. Additionally, subgroup analysis was conducted to compare the effects of different types of cognitive tasks. Results: A total of 37 studies were included in the systematic review, 25 studies comprising 2224 older adults were included in the meta-analysis. Our findings showed that inhibitory control and working memory tasks significantly increased HbO2 in the PFC by 0.53 (p < 0.01, 95% CI = 0.37 to 0.70) and 0.13 (p < 0.01, 95% CI = 0.08 to 0.18) µmol/L, respectively. Overall, HbO2 was significantly increased during dual-task paradigms by 0.36 µmol/L (P < 0.01, 95% CI = 0.27 to 0.45). Moreover, dual-task paradigms also decreased HbR in the PFC by 0.04 (P < 0.01, 95% CI = -0.07 to -0.01). Specifically, HbR decreased by 0.08 during inhibitory control tasks (p < 0.01, 95% CI = -0.13 to -0.02), but did not change during working memory tasks. Conclusion: Cognitive tasks related to inhibitory control required greater cognitive demands, indicating higher pfc activation during dual-task paradigms in older adults. for clinical implications, the increase in pfc oxygenated hemoglobin and decrease in pfc deoxygenated hemoglobin may help explain why older adults are more likely to fall during daily activities.

Effectiveness of internet-based CBT-I for the treatment of chronic subthreshold to moderate insomnia.

Study objectives: To study the effectiveness of the first internet-based cognitive behavioral therapy for insomnia (CBT-i) in Thailand, using the Nitra application, for chronic subthreshold to moderate insomnia treatment. Methods: An interventional study without a control group was conducted between January and June 2022. Participants were adults aged 18 years old and older with subthreshold to moderate severity of chronic insomnia (insomnia severity index (ISI) of 8-21) and had mean sleep efficiency <85% from baseline sleep diaries. Baseline sleep characteristics were obtained from questionnaires and sleep diaries from the Nitra application for 2 weeks. Eligible participants continued using the Nitra application for 4 weeks during the intervention period. Interventions including sleep restriction, stimulus control, cognitive restructuring, relaxation techniques, and sleep hygiene education were implemented via the pre-programmed Nitra application. Post-intervention sleep characteristics were also obtained from questionnaires and sleep diaries from the Nitra application for another 1 week. Results: A total of 40 participants completed the study. All participants had a baseline sleep efficiency of less than 85% with the majority of the participants having a sleep-onset insomnia problem (98%). For the primary outcome, sleep efficiency was significantly improved after using the Nitra application (p < 0.001). Self-reported total sleep time, sleep onset latency, wake after sleep onset, early morning awakening, ISI, Pittsburgh Sleep Quality Index (PSQI), and average subjective sleep quality were also significantly improved (p < 0.001 for all parameters except p = 0.017 for total sleep time and p = 0.018 for wake after sleep onset). Participants who had a low baseline ISI and went to bed and woke up within 30 minutes of a designated bedtime and wake-up time recommended by the Nitra application for ≥70% of all nights demonstrated an increased chance of achieving normal sleep efficiency after using the Nitra application. Conclusion: This first internet-based CBT-i in Thailand, using the Nitra application, effectively improved sleep efficiency and other sleep parameters in chronic subthreshold to moderate insomnia.

Changes in Ocular Biomarkers from Normal Cognitive Aging to Alzheimer's Disease: A Pilot Study.

Purpose: To identify ophthalmic findings in Alzheimer's type dementia (ATD) compared to normal subjects. Patients and Methods: This comparative descriptive study included participants from the institution's cognitive fitness center. Complete ophthalmic examinations were performed. Optical coherence tomography (OCT) and OCT angiography (OCTA) were used to analyze retinal thickness and vascular density. The Ocular Surface Disease Index (OSDI) score and tear breakup time (TBUT) were used to assess dry eye. The blink rate was counted by a well-trained observer. Cognitive function was evaluated using the Thai Mental State Examination (TMSE) score. Correlation analysis was performed to compare OCT, OCTA parameters, and TMSE. Results: We included 24 ATD patients and 39 normal participants as a control group by age and sex-matched. The prevalence of dry eye using the Asia Dry Eye Society criteria was 15% and 13% in normal and ATD patients, respectively. The differences in OSDI scores, TBUT, and blink rate between the two groups were not statistically significant. The parafoveal and perifoveal macular thickness of the ATD group were significantly lower than that of the control group (p<0.01). All parameters of the vessel density of the ATD group were significantly lower than in the control group, including the whole macular vessel density (p<0.01), optic disc vessel density at the nerve head level (p<0.01), and optic disc vessel density at the radial peripapillary capillary level (p<0.05). After age adjustment, there were no statistically significant differences in all the OCT and OCTA parameters. There was a positive correlation between retinal thickness and vessel density in the macular and optic disc region and TMSE scores. Conclusion: Perifoveal and parafoveal retinal thickness might be more sensitive than peripapillary RNFL thickness to detect neurodegenerative changes in patients with ATD. Macular thickness and vessel density reduction were also positively correlated with cognitive decline.

Cognitive impairments predict the behavioral and psychological symptoms of dementia.

Introduction: The purpose of this study was to (1) validate the Thai version of the Neuropsychiatric Inventory Questionnaire (NPI-Q) as a screening tool for behavioral and psychological symptoms of dementia (BPSD), and (2) examine the relationship between cognitive performance and BPSD in an elderly population with amnestic mild cognitive impairment (aMCI) and dementia of Alzheimer's type (DAT). Methods: One hundred and twenty participants, comprising 80 aMCI and 40 DAT patients, and their respective caregivers were included in the study. Participants completed the NPI-Q and the Neuropsychiatric Inventory (NPI) within 2 weeks of each other and cognitive performance was primarily assessed using the Montreal Cognitive Assessment (MoCA). Results: The Thai NPI-Q had good validity and reliability. Pure exploratory bifactor analysis revealed that a general factor and a single-group factor (with high loadings on delusions, hallucinations, apathy, and appetite) underpinned the NPI-Q domains. Significant negative correlations between the MoCA total score and the general and single-group NPI-Q scores were found in all subjects (aMCI + DAT combined) and DAT alone, but not in aMCI. Cluster analysis allocated subjects with BPSD (10% of aMCI and 50% of DAT participants) into a distinct "DAT + BPSD" class. Conclusion: The NPI-Q is an appropriate instrument for assessing BPSD and the total score is largely predicted by cognitive deficits. It is plausible that aMCI subjects with severe NPI-Q symptoms (10% of our sample) may have a poorer prognosis and constitute a subgroup of aMCI patients who will likely convert into probable dementia.

An explainable self-attention deep neural network for detecting mild cognitive impairment using multi-input digital drawing tasks.

BACKGROUND: Mild cognitive impairment (MCI) is an early stage of cognitive decline which could develop into dementia. An early detection of MCI is a crucial step for timely prevention and intervention. Recent studies have developed deep learning models to detect MCI and dementia using a bedside task like the classic clock drawing test (CDT). However, it remains a challenge to predict the early stage of the disease using the CDT data alone. Moreover, the state-of-the-art deep learning techniques still face black box challenges, making it questionable to implement them in a clinical setting. METHODS: We recruited 918 subjects from King Chulalongkorn Memorial Hospital (651 healthy subjects and 267 MCI patients). We propose a novel deep learning framework that incorporates data from the CDT, cube-copying, and trail-making tests. Soft label and self-attention were applied to improve the model performance and provide a visual explanation. The interpretability of the visualization of our model and the Grad-CAM approach were rated by experienced medical personnel and quantitatively evaluated using intersection over union (IoU) between the models' heat maps and the regions of interest. RESULTS: Rather than using a single CDT image in the baseline VGG16 model, using multiple drawing tasks as inputs into our proposed model with soft label significantly improves the classification performance between the healthy aging controls and the MCI patients. In particular, the classification accuracy increases from 0.75 (baseline model) to 0.81. The F1-score increases from 0.36 to 0.65, and the area under the receiver operating characteristic curve (AUC) increases from 0.74 to 0.84. Compared to the multi-input model that also offers interpretable visualization, i.e., Grad-CAM, our model receives higher interpretability scores given by experienced medical experts and higher IoUs. CONCLUSIONS: Our model achieves better classification performance at detecting MCI compared to the baseline model. In addition, the model provides visual explanations that are superior to those of the baseline model as quantitatively evaluated by experienced medical personnel. Thus, our work offers an interpretable machine learning model with high classification performance, both of which are crucial aspects of artificial intelligence in medical diagnosis.

Brain Exercising Games With Consumer-Grade Single-Channel Electroencephalogram Neurofeedback: Pre-Post Intervention Study.

BACKGROUND: The aging population is one of the major challenges affecting societies worldwide. As the proportion of older people grows dramatically, so does the number of age-related illnesses such as dementia-related illnesses. Preventive care should be emphasized as an effective tool to combat and manage this situation. OBJECTIVE: The aim of this pilot project was to study the benefits of using neurofeedback-based brain training games for enhancing cognitive performance in the elderly population. In particular, aiming for practicality, the training games were designed to operate with a low-cost consumer-grade single-channel electroencephalogram (EEG) headset that should make the service scalable and more accessible for wider adoption such as for home use. METHODS: Our training system, which consisted of five brain exercise games using neurofeedback, was serviced at 5 hospitals in Thailand. Participants were screened for cognitive levels using the Thai Mental State Examination and Montreal Cognitive Assessment. Those who passed the criteria were further assessed with the Cambridge Neuropsychological Test Automated Battery (CANTAB) computerized cognitive assessment battery. The physiological state of the brain was also assessed using 16-channel EEG. After 20 sessions of training, cognitive performance and EEG were assessed again to compare pretraining and posttraining results. RESULTS: Thirty-five participants completed the training. CANTAB results showed positive and significant effects in the visual memory (delayed matching to sample [percent correct] P=.04), attention (median latency P=.009), and visual recognition (spatial working memory [between errors] P=.03) domains. EEG also showed improvement in upper alpha activity in a resting state (open-eyed) measured from the occipital area (P=.04), which similarly indicated improvement in the cognitive domain (attention). CONCLUSIONS: Outcomes of this study show the potential use of practical neurofeedback-based training games for brain exercise to enhance cognitive performance in the elderly population.

A game-based neurofeedback training system to enhance cognitive performance in healthy elderly subjects and in patients with amnestic mild cognitive impairment.

INTRODUCTION: This study examines the clinical efficacy of a game-based neurofeedback training (NFT) system to enhance cognitive performance in patients with amnestic mild cognitive impairment (aMCI) and healthy elderly subjects. The NFT system includes five games designed to improve attention span and cognitive performance. The system estimates attention levels by investigating the power spectrum of Beta and Alpha bands. METHODS: We recruited 65 women with aMCI and 54 healthy elderly women. All participants were treated with care as usual (CAU); 58 were treated with CAU + NFT (20 sessions of 30 minutes each, 2-3 sessions per week), 36 with CAU + exergame-based training, while 25 patients had only CAU. Cognitive functions were assessed using the Cambridge Neuropsychological Test Automated Battery both before and after treatment. RESULTS: NFT significantly improved rapid visual processing and spatial working memory (SWM), including strategy, when compared with exergame training and no active treatment. aMCI was characterized by impairments in SWM (including strategy), pattern recognition memory, and delayed matching to samples. CONCLUSION: In conclusion, treatment with NFT improves sustained attention and SWM. Nevertheless, NFT had no significant effect on pattern recognition memory and short-term visual memory, which are the other hallmarks of aMCI. The NFT system used here may selectively improve sustained attention, strategy, and executive functions, but not other cognitive impairments, which characterize aMCI in women.

Early prediction of donepezil cognitive response in Alzheimer's disease by brain perfusion single photon emission tomography.

Currently, there is no effective means to evaluate donepezil response. We evaluated brain perfusion change at 4 h after donepezil administration (4 h DNPZ) to predict cognitive responses after 6 months of medication. CERAD neuropsychological assessment battery was used to define cognitive response at 6 months. We compared 4 h DNPZ to baseline single photon emission tomography (SPECT) by statistical parametric mapping to identify perfusion changes in responders (N = 16) and non-responders (N = 7). In responders, there were significant relatively increase in perfusion in left parietal lobe (BA39, 7, 1), right superior frontal gyrus (BA6) and right middle occipital gyrus (BA39). In the non-responders, perfusion was relatively increase in the left parietal lobe (BA39) only. In an explorative analysis, we found a significant correlation between perfusion changes in right BA6 and CERAD score changes at 6 months. Different SPECT perfusion changes at 4 h after donepezil administration were demonstrated in the group of responders and non-responders with potential correlation with CERAD score change. Thus, 4 h DNPZ brain perfusion SPECT can be used to predict donepezil response at 6 months.

Peripheral Blood Biomarkers Coupled with the Apolipoprotein E4 Genotype Are Strongly Associated with Semantic and Episodic Memory Impairments in Elderly Subjects with Amnestic Mild Cognitive Impairment and Alzheimer's Disease.

BACKGROUND: The Apolipoprotein E4 (ApoE4) genotype is strongly associated with Alzheimer's disease (AD), although the presence of the ApoE4 allele alone is not sufficient to explain AD. The pathophysiology of amnestic mild cognitive impairment (aMCI) remains unclear. OBJECTIVE: This study aims to examine associations between peripheral blood biomarkers coupled with ApoE4 and episodic and semantic memory. METHODS: The CERAD battery was completed and various biomarkers were assayed in 60 subjects with aMCI, 60 with AD, and 62 healthy controls. RESULTS: Deficits in semantic and episodic memory were significantly predicted by anion gap and bicarbonate, albumin, and glucose coupled with ApoE4. Furthermore, these peripheral biomarkers interacted with ApoE to predict greater memory impairments. CONCLUSIONS: Peripheral blood biomarkers may interact with pathways related to ApoE4 to predict greater semantic and episodic memory impairments, thus contributing to the pathophysiology of aMCI and AD. Our data suggest that the transition from aMCI to AD could at least in some cases be associated with significant interactions between ApoE4 and those peripheral blood biomarkers.

Changes in Tryptophan Catabolite (TRYCAT) Pathway Patterning Are Associated with Mild Impairments in Declarative Memory in Schizophrenia and Deficits in Semantic and Episodic Memory Coupled with Increased False-Memory Creation in Deficit Schizophrenia.

Evidence indicates that schizophrenia and in particular negative symptoms and deficit schizophrenia are accompanied by neurocognitive impairments and changes in the patterning of the tryptophan catabolite (TRYCAT) pathway. This cross-sectional study was carried out to examine the associations between cognitive functions (as measured with Consortium to Establish a Registry for Alzheimer's disease (CERAD)) and TRYCAT pathway patterning in patients with (n = 40) and without (n = 40) deficit schizophrenia and normal controls (n = 40). Cognitive measures were assessed with the Verbal Fluency Test (VFT), Boston Naming Test (BNT), Mini-Mental State Examination (MMSE), Word List Memory (WLM), Constructional Praxis, Word List Recall (WLRecall), and Word List Recognition (WLRecognition), while TRYCAT measurements assessed the IgA/IgM responses to noxious TRYCATs, namely quinolinic acid (QA), 3-OH-kynurenine (3HK), picolinic acid (PA), and xanthurenic (XA) acid, and more protective (PRO) TRYCATs, including kynurenic acid (KA) and anthranilic acid (AA). IgA NOX/PRO, IgM KA/3HK, and IgA/IgM NOX/PRO ratios were computed. Schizophrenia was accompanied by lower VFT and WLM, while BNT (dysnomia) and MMSE are significantly lower in multiple- than first-episode schizophrenia. Deficit schizophrenia is strongly associated with worse outcomes on VFT, MMSE, WLM, WLRecall, WLRecognition, and delayed recall savings and increased false memories. Around 40-50% of the variance in negative symptoms' scores was explained by VFT, WLM, WLRecall, and MMSE. Increases in IgA NOX/PRO, IgM KA/3HK, and/or IgA/IgM NOX/PRO ratios were associated with impairments in VFT, BNT, MMSE, WLM, WLRecall, WLRecognition, and false-memory creation. In conclusion, nondeficit schizophrenia is accompanied by mild memory impairments, while disease progression is accompanied by broader cognitive impairments. Deficit schizophrenia and negative symptoms are strongly associated with deficits in working memory, delayed recall and recognition, and increased false-memory creation. These cognitive impairments and memory deficits are in part explained by increased production and/or attenuated regulation of TRYCATs with neurotoxic, excitotoxic, immune-inflammatory, oxidative, and nitrosative potential, which may contribute to neuroprogression.

Results of a real-world study on vortioxetine in patients with major depressive disorder in South East Asia (REVIDA).

OBJECTIVE: The REVIDA study aimed to assess the evolution of major depression symptoms in South East Asian (SEA) patients treated with vortioxetine for major depression in real-world clinical practice. METHODS: This non-interventional study was conducted from August 2016 to April 2017. A total of 138 patients (aged 18-65 years) with an active episode of major depression were recruited from Malaysia, Philippines, Singapore and Thailand. Vortioxetine was initiated on the first visit and patients were followed for 3 months. Depression severity was assessed using the PHQ-9 questionnaire (patient assessed) and CGI-S scale (physician assessed); cognitive function was assessed with the PDQ-D questionnaire; work productivity and activity impairment (WPAI) was assessed with the WPAI questionnaire. RESULTS: At baseline, 89.9% of patients were moderately to severely depressed (PHQ-9 score ≥10). During the 3 month treatment period, mean ± SD PHQ-9 score decreased from 18.7 ± 5.7 to 5.0 ± 5.3, mean ± SD CGI-S score decreased from 4.4 ± 0.7 to 2.2 ± 1.1 and mean ± SD PDQ-D score decreased from 42.1 ± 18.8 to 13.4 ± 13.0. By Month 3, response and remission rates reached 80.8% and 59.0%, respectively. Work productivity loss decreased from 73.6% to 30.5%, while activity impairment decreased from 71.5% to 24.6%. Positive correlations were observed between PHQ-9, PDQ-D, and WPAI work productivity loss and activity impairment. By Month 3, 82.0% of patients were either not depressed or only mildly depressed (PHQ-9 score ≤9). CONCLUSION: In real-world clinical settings, vortioxetine was effective in reducing depression severity and improving cognitive function and work productivity in SEA patients with major depression.

Role of Donepezil in the Management of Neuropsychiatric Symptoms in Alzheimer's Disease and Dementia with Lewy Bodies.

Alzheimer's disease (AD) is a progressive condition that affects cognition, function, and behavior. Approximately 60-90% of patients with AD develop neuropsychiatric symptoms (NPS) such as hallucinations, delusions, agitation/aggression, dysphoria/depression, anxiety, irritability, disinhibition, euphoria, apathy, aberrant motor behavior, sleep disturbances, appetite and eating changes, or altered sexual behavior. These noncognitive behavior changes are thought to result from anatomical and biochemical changes within the brain, and have been linked, in part, to cholinergic deficiency. Cholinesterase inhibitors may reduce the emergence of NPS and have a role in their treatment. These agents may delay initiation of, or reduce the need for, other drugs such as antipsychotics. This article summarizes the effects of donepezil, a cholinesterase inhibitor, on the NPS of dementia with emphasis on AD and dementia with Lewy bodies.