The MoCA-B appears to have excellent validity and addresses an unmet need by accurately screening for MCI in poorly educated older adults regardless of literacy.

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.

SUMMARY 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.

<b><i>Background:</i></b> 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). <b><i>Objectives:</i></b> 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. <b><i>Methods:</i></b> 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. <b><i>Results:</i></b> 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 &#x3e; 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. <b><i>Conclusions:</i></b> The MoCA-Thai is an appropriate cognitive assessment tool to be used in the Thai population for the diagnosis of aMCI and AD.

No abstract

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.

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.

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.