Objective The aim of this study was to explore the psychometric properties of the Insomnia Severity Index (ISI) based on modern test theory, such as item response theory (IRT) and Rasch analysis, with shortened versions of the ISI among the general population.Methods We conducted two studies to evaluate the reliability and validity of the shortened versions of the ISI in a Korean population. In Study I, conducted via online survey, we performed an exploratory factor analysis (n=400). In Study II, confirmatory factor analysis (CFA) was conducted (n=400). IRT and Rasch analysis were performed on all samples. Participants symptoms were rated using the ISI, Dysfunctional Beliefs and Attitudes about Sleep–16 items, Dysfunctional Beliefs about Sleep–2 items, Patient Health Questionnaire–9 items, and discrepancy between desired time in bed and desired total sleep time.Results CFA showed a good fit for the 2-factor model of the ISI (comparative fit index=0.994, Tucker–Lewis index=0.990, root-meansquare-error of approximation=0.039, and standardized root-mean-square residual=0.046). The 3-item versions also showed a good fit for the model. All scales showed good internal consistency reliability. The scale information curve of the 2-item scale was similar to that of the full-scale ISI. The Rasch analysis outputs suggested a good model fit.Conclusion The shortened 2-factor ISI is a reliable and valid model for assessing the severity of insomnia in the Korean population. The results are needed to be explored further among the clinical sample of insomnia.

Objective Alzheimer’s disease (AD) is the most common type of dementia and the prevalence rapidly increased as the elderly population increased worldwide. In the contemporary model of AD, it is regarded as a disease continuum involving preclinical stage to severe dementia. For accurate diagnosis and disease monitoring, objective index reflecting structural change of brain is needed to correctly assess a patient’s severity of neurodegeneration independent from the patient’s clinical symptoms. The main aim of this paper is to develop a random forest (RF) algorithm-based prediction model of AD using structural magnetic resonance imaging (MRI).Methods We evaluated diagnostic accuracy and performance of our RF based prediction model using newly developed brain segmentation method compared with the Freesurfer’s which is a commonly used segmentation software.Results Our RF model showed high diagnostic accuracy for differentiating healthy controls from AD and mild cognitive impairment (MCI) using structural MRI, patient characteristics, and cognitive function (HC vs. AD 93.5%, AUC 0.99; HC vs. MCI 80.8%, AUC 0.88). Moreover, segmentation processing time of our algorithm (<5 minutes) was much shorter than of Freesurfer’s (6–8 hours).Conclusion Our RF model might be an effective automatic brain segmentation tool which can be easily applied in real clinical practice.

Objective We aim to present a clinical guideline for the diagnosis and treatment of insomnia in adults by reviewing and integrating existing clinical guidelines. The purpose of this guideline is to assist clinicians who perform evidence-based insomnia treatment.Methods We selected literature that may be appropriate for use in guideline development from evidence-based practice guidelines that have been issued by an academic or governmental institution within the last five years. The core question of this guideline was made in sentence form including Patient/Problem, Intervention, Comparison, Outcome (PICO) elements. After searching PubMed, EMBASE, and medical guideline issuing agencies, three guidelines were judged to be the most appropriately reviewed, up-to-date, and from trusted sources.Results The Appraisal of Guidelines for Research and Evaluation (AGREE) II tool was used to evaluate the quality of the three clinical guidelines. The final outcome of the guideline development process is a total of 15 recommendations that report the strength of the recommendation, the quality of evidence, a summary of content, and considerations in applying the recommendation.Conclusion It is vital for clinical guidelines for insomnia to be developed and continually updated in order to provide more accurate evidence-based treatments to patients.

Background and Objective This study aimed to investigate the changes in sleep quality with increasing age and the effect of age on the components of the Pittsburgh Sleep Quality Index (PSQI).Methods We used data from the Community Health Survey conducted by the Korea Center for Disease Control and Prevention in 2018. A total of 228340 participants in this nationwide survey. Sleep quality was assessed using the PSQI. Adults aged ≥ 19 years were divided into six age groups and one-way analysis of variance (one-way ANOVA) was used to compare the mean values of PSQI of each group. By comparing the scores for each PSQI component in those aged ≥ 65 years and < 65 years, we aimed to reveal the differences in special components according to age group.Results In total, 223334 respondents were included in the study. Based on a one-way ANOVA, the PSQI score generally increased with age. Although the average PSQI score of patients in their 40s was lower than that of patients in their 30s, there was no significant difference between the two groups (p = 0.11). When the PSQI component was compared between the population aged over and under 65 years, the population aged ≥ 65 years scored higher in most components. In contrast, daytime dysfunction scored higher in the population aged < 65 years.Conclusions Sleep quality tends to decrease with increasing age. Several factors, including physiological changes, underlying physical conditions, and psychosocial factors, may contribute to a decrease in sleep quality with age.

Orexin’s role in human cognition has recently been emphasized and emerging evidences indicate its close relationship with Alzheimer’s disease (AD). This review aimed to demonstrate recent research on the relationship between orexin and AD. Orexin’s role in stress regulation and memory is discussed, with significant findings related to sexual disparities in stress response, with potential clinical implications pertaining to AD pathology. There are controversies regarding the orexin levels in AD patients, but the role of orexin in the trajectory of AD is still emphasized in recent literatures. Orexin is also accentuated in the context of tau pathology, and orexin as a potential therapeutic target for AD is frequently discussed. Future directions with regard to the relationship between orexin and AD are suggested: 1) consideration for AD trajectory in the measurement of orexin levels, 2) the need for objective measure such as polysomnography and actigraphy, 3) the need for close observation of cognitive profiles of orexin-deficient narcolepsy patients, 4) the need for validation studies by neuroimaging 5) the need for taking account sexual disparities in orexinergic activiation, and 6) consideration for orexin’s role as a stress regulator. The aforementioned new perspectives could help unravel the relationship between orexin and AD.

Dementia subtypes, including Alzheimer’s dementia (AD), dementia with Lewy bodies (DLB), and frontotemporal dementia (FTD), pose diagnostic challenges. This review examines the effectiveness of ¹⁸F-Fluorodeoxyglucose Positron Emission Tomography (¹⁸F-FDG PET) in differentiating these subtypes for precise treatment and management. A systematic review following Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines was conducted using databases like PubMed and Embase to identify studies on the diagnostic utility of ¹⁸F-FDG PET in dementia. The search included studies up to November 16, 2022, focusing on peer-reviewed journals and applying the gold-standard clinical diagnosis for dementia subtypes. From 12,815 articles, 14 were selected for final analysis. For AD versus FTD, the sensitivity was 0.96 (95% confidence interval [CI], 0.88–0.98) and specificity was 0.84 (95% CI, 0.70–0.92). In the case of AD versus DLB, 18F-FDG PET showed a sensitivity of 0.93 (95% CI 0.88-0.98) and specificity of 0.92 (95% CI, 0.70–0.92). Lastly, when differentiating AD from non-AD dementias, the sensitivity was 0.86 (95% CI, 0.80–0.91) and the specificity was 0.88 (95% CI, 0.80–0.91). The studies mostly used case-control designs with visual and quantitative assessments. ¹⁸F-FDG PET exhibits high sensitivity and specificity in differentiating dementia subtypes, particularly AD, FTD, and DLB. This method, while not a standalone diagnostic tool, significantly enhances diagnostic accuracy in uncertain cases, complementing clinical assessments and structural imaging.