Exploring morphological similarity and randomness in Alzheimer's disease using adjacent grey matter voxel-based structural analysis.

BACKGROUND: Alzheimer's disease is characterized by large-scale structural changes in a specific pattern. Recent studies developed morphological similarity networks constructed by brain regions similar in structural features to represent brain structural organization. However, few studies have used local morphological properties to explore inter-regional structural similarity in Alzheimer's disease. METHODS: Here, we sourced T1-weighted MRI images of 342 cognitively normal participants and 276 individuals with Alzheimer's disease from the Alzheimer's Disease Neuroimaging Initiative database. The relationships of grey matter intensity between adjacent voxels were defined and converted to the structural pattern indices. We conducted the information-based similarity method to evaluate the structural similarity of structural pattern organization between brain regions. Besides, we examined the structural randomness on brain regions. Finally, the relationship between the structural randomness and cognitive performance of individuals with Alzheimer's disease was assessed by stepwise regression. RESULTS: Compared to cognitively normal participants, individuals with Alzheimer's disease showed significant structural pattern changes in the bilateral posterior cingulate gyrus, hippocampus, and olfactory cortex. Additionally, individuals with Alzheimer's disease showed that the bilateral insula had decreased inter-regional structural similarity with frontal regions, while the bilateral hippocampus had increased inter-regional structural similarity with temporal and subcortical regions. For the structural randomness, we found significant decreases in the temporal and subcortical areas and significant increases in the occipital and frontal regions. The regression analysis showed that the structural randomness of five brain regions was correlated with the Mini-Mental State Examination scores of individuals with Alzheimer's disease. CONCLUSIONS: Our study suggested that individuals with Alzheimer's disease alter micro-structural patterns and morphological similarity with the insula and hippocampus. Structural randomness of individuals with Alzheimer's disease changed in temporal, frontal, and occipital brain regions. Morphological similarity and randomness provide valuable insight into brain structural organization in Alzheimer's disease.

Optimization of Using Multiple Machine Learning Approaches in Atrial Fibrillation Detection Based on a Large-Scale Data Set of 12-Lead Electrocardiograms: Cross-Sectional Study.

BACKGROUND: Atrial fibrillation (AF) represents a hazardous cardiac arrhythmia that significantly elevates the risk of stroke and heart failure. Despite its severity, its diagnosis largely relies on the proficiency of health care professionals. At present, the real-time identification of paroxysmal AF is hindered by the lack of automated techniques. Consequently, a highly effective machine learning algorithm specifically designed for AF detection could offer substantial clinical benefits. We hypothesized that machine learning algorithms have the potential to identify and extract features of AF with a high degree of accuracy, given the intricate and distinctive patterns present in electrocardiogram (ECG) recordings of AF. OBJECTIVE: This study aims to develop a clinically valuable machine learning algorithm that can accurately detect AF and compare different leads' performances of AF detection. METHODS: We used 12-lead ECG recordings sourced from the 2020 PhysioNet Challenge data sets. The Welch method was used to extract power spectral features of the 12-lead ECGs within a frequency range of 0.083 to 24.92 Hz. Subsequently, various machine learning techniques were evaluated and optimized to classify sinus rhythm (SR) and AF based on these power spectral features. Furthermore, we compared the effects of different frequency subbands and different lead selections on machine learning performances. RESULTS: The light gradient boosting machine (LightGBM) was found to be the most effective in classifying AF and SR, achieving an average F1-score of 0.988 across all ECG leads. Among the frequency subbands, the 0.083 to 4.92 Hz range yielded the highest F1-score of 0.985. In interlead comparisons, aVR had the highest performance (F1=0.993), with minimal differences observed between leads. CONCLUSIONS: In conclusion, this study successfully used machine learning methodologies, particularly the LightGBM model, to differentiate SR and AF based on power spectral features derived from 12-lead ECGs. The performance marked by an average F1-score of 0.988 and minimal interlead variation underscores the potential of machine learning algorithms to bolster real-time AF detection. This advancement could significantly improve patient care in intensive care units as well as facilitate remote monitoring through wearable devices, ultimately enhancing clinical outcomes.

Exploring timescale-specific functional brain networks and their associations with aging and cognitive performance in a healthy cohort without dementia.

INTRODUCTION: Functional brain networks (FBNs) coordinate brain functions and are studied in fMRI using blood-oxygen-level-dependent (BOLD) signal correlations. Previous research links FBN changes to aging and cognitive decline, but various physiological factors influnce BOLD signals. Few studies have investigated the intrinsic components of the BOLD signal in different timescales using signal decomposition. This study aimed to explore differences between intrinsic FBNs and traditional BOLD-FBN, examining their associations with age and cognitive performance in a healthy cohort without dementia. MATERIALS AND METHODS: A total of 396 healthy participants without dementia (men = 157; women = 239; age range = 20-85 years) were enrolled in this study. The BOLD signal was decomposed into several intrinsic signals with different timescales using ensemble empirical mode decomposition, and FBNs were constructed based on both the BOLD and intrinsic signals. Subsequently, network features-global efficiency and local efficiency values-were estimated to determine their relationship with age and cognitive performance. RESULTS: The findings revealed that the global efficiency of traditional BOLD-FBN correlated significantly with age, with specific intrinsic FBNs contributing to these correlations. Moreover, local efficiency analysis demonstrated that intrinsic FBNs were more meaningful than traditional BOLD-FBN in identifying brain regions related to age and cognitive performance. CONCLUSIONS: These results underscore the importance of exploring timescales of BOLD signals when constructing FBN and highlight the relevance of specific intrinsic FBNs to aging and cognitive performance. Consequently, this decomposition-based FBN-building approach may offer valuable insights for future fMRI studies.

Prediction of early postoperative pain using sleep quality and heart rate variability.

PURPOSE: Accurate predictions of postoperative pain intensity are necessary for customizing analgesia plans. Insomnia is a risk factor for severe postoperative pain. Moreover, heart rate variability (HRV) can provide information on the sympathetic-parasympathetic balance in response to noxious stimuli. We developed a prediction model that uses the insomnia severity index (ISI), HRV, and other demographic factors to predict the odds of higher postoperative pain. METHODS: We recruited gynecological surgery patients classified as American Society of Anesthesiologists class 1-3. An ISI questionnaire was completed 1 day before surgery. HRV was calculated offline using intraoperative electrocardiogram data. Pain severity at the postanesthesia care unit (PACU) was assessed with the 0-10 numerical rating scale (NRS). The primary outcome was the model's predictive ability for moderate-to-severe postoperative pain. The secondary outcome was the relationship between individual risk factors and opioid consumption in the PACU. RESULTS: Our study enrolled 169 women. Higher ISI scores (p = 0.001), higher parasympathetic activity (rMSSD, pNN50, HF; p < 0.001, p < 0.001, p < 0.001), loss of fractal dynamics (SD2, alpha 1; p = 0.012, p = 0.039) in HRV analysis before the end of surgery were associated with higher NRS scores, while laparoscopic surgery (p = 0.031) was associated with lower NRS scores. We constructed a multiple logistic model (area under the curve = 0.852) to predict higher NRS scores at PACU arrival. The five selected predictors were age (OR: 0.94; p = 0.020), ISI score (OR: 1.14; p = 0.002), surgery type (laparoscopic or open; OR: 0.12; p < 0.001), total power (OR: 2.02; p < 0.001), and alpha 1 (OR: 0.03; p < 0.001). CONCLUSION: We employed a multiple logistic regression model to determine the likelihood of moderate-to-severe postoperative pain upon arrival at the PACU. Physicians could personalize analgesic regimens based on a deeper comprehension of the factors that contribute to postoperative pain.

Psychiatrist density and risk of suicide: a multilevel case-control study based on a national sample in Taiwan.

AIM: No previous studies, to our knowledge, have investigated the association between psychiatrist density and suicide, accounting for individual- and area-level characteristics. METHODS: We investigated all suicide cases in 2007-2017 identified from the national cause-of-death data files, with each suicide case matched to 10 controls by age and sex and each suicide case/control assigned to one of the 355 townships across Taiwan. Our primary outcome was the odds ratio (OR) of suicide and its 95% confidence interval (CI) estimated via multilevel models, which included both individual- and area-level characteristics. Townships with no psychiatrists were compared with the quartiles of townships with psychiatrists (density per 100,000 population): quartile 1 (Q1) (0.01-3.02); quartile 2 (Q2) (3.02-7.20); quartile 3 (Q3) (7.20-13.82); and quartile 4 (Q4) (>13.82). RESULTS: A total of 40,930 suicide cases and 409,300 age- and sex-matched controls were included. We found that increased psychiatrist density was associated with decreased suicide risk (Q1: adjusted OR [aOR], 0.95 [95% CI, 0.90-1.01]; Q2: aOR, 0.90 [95% CI, 0.85-0.96]; Q3: aOR, 0.89 [95% CI, 0.83-0.94]; Q4: aOR, 0.89 [95% CI, 0.83-0.95]) after adjusting for individual-level characteristics (employment state, monthly income, physical comorbidities, and the diagnosis of psychiatric disorders) and area socioeconomic characteristics. CONCLUSIONS: The psychiatrist density-suicide association suggests an effect of increased availability of psychiatric services on preventing suicide. Suicide prevention strategies could usefully focus on enhancing local access to psychiatric services.

The cysteine-altering p.R544C variant in the NOTCH3 gene is a probable candidate for blood pressure and relevant traits in the Taiwan Biobank.

BACKGROUND: The cysteine-altering variants in NOTCH3 have been suggested to be associated with stroke, dementia, and cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), where aberrant blood pressure levels represent the characteristics of these diseases. We aimed to assess whether the cysteine-altering p.Arg544Cys (p.R544C; rs201118034) variant and common single nucleotide variants (SNVs) in NOTCH3 could contribute to systolic and diastolic blood pressure and related phenotypes in the Taiwan Biobank. METHODS: We employed a discovery sample of 68,925 individuals, an independent replication sample of 45,676 individuals, and a combined/total sample of 114,601 individuals; all from the Taiwan Biobank. Blood pressure, such as systolic and diastolic blood pressure, was measured for all participants. Association was evaluated using a general linear model, where results were considered statistically significant if the P value < 0.05 divided by the number of independent tests per model. RESULTS: From our analysis, we identified and replicated three novel candidates for blood pressure that have not previously been reported: the cysteine-altering p.R544C variant for systolic blood pressure, the common SNV rs11669950 for diastolic blood pressure, and the common SNV rs4808235 for diastolic blood pressure. We also generalized two previously identified SNVs (i.e., rs10418305 and rs7408868) in NOTCH3 for blood pressure in European and non-Taiwanese East Asian populations to the Taiwanese population. Moreover, the participants with NOTCH3 p.R544C had an increased stroke frequency (P < 1.0 × 10-5) and a higher dementia frequency (P = 2.0 × 10-4) compared with the whole Taiwan Biobank population in the combined/total sample. CONCLUSION: NOTCH3 is a strong candidate for a role in stroke, dementia, and CADASIL, which has previously been linked to blood pressure changes. While our preliminary study suggests that NOTCH3 p.R544C may influence blood pressure, stroke, and dementia in the Taiwan Biobank, replication in a well-powered external sample is required. This study also underlines considerable prospects of detecting novel genetic biomarkers in underrepresented worldwide populations.

Reduced slow-wave activity and autonomic dysfunction during sleep precede cognitive deficits in Alzheimer's disease transgenic mice.

Occurrence of amyloid-ß (Aß) aggregation in brain begins before the clinical onset of Alzheimer's disease (AD), as preclinical AD. Studies have reported that sleep problems and autonomic dysfunction associate closely with AD. However, whether they, especially the interaction between sleep and autonomic function, play critical roles in preclinical AD are unclear. Therefore, we investigated how sleep patterns and autonomic regulation at different sleep-wake stages changed and whether they were related to cognitive performance in pathogenesis of AD mice. Polysomnographic recordings in freely-moving APP/PS1 and wild-type (WT) littermates were collected to study sleep patterns and autonomic function at 4 (early disease stage) and 8 months of age (advanced disease stage), cognitive tasks including novel object recognition and Morris water maze were performed, and Aß levels in brain were measured. APP/PS1 mice at early stage of AD pathology with Aß aggregation but without significant differences in cognitive performance had frequent sleep-wake transitions, lower sleep-related delta power percentage, lower overall autonomic activity, and lower parasympathetic activity mainly during sleep compared with WT mice. The same phenomenon was observed in advanced-stage APP/PS1 mice with significant cognitive deficits. In mice at both disease stages, sleep-related delta power percentage correlated positively with memory performance. At early stage, memory performance correlated positively with sympathetic activity during wakefulness; at advanced stage, memory performance correlated positively with parasympathetic activity during both wakefulness and sleep. In conclusion, sleep quality and distinction between wake- and sleep-related autonomic function may be biomarkers for early AD detection.

Rapid detection of nicotine and benzoic acid in e-liquids with surface-enhanced Raman scattering and artificial intelligence-assisted spectrum interpretation.

Electronic cigarettes have rapidly gained acceptance recently. Nicotine-containing electronic cigarette liquids (e-liquids) are prohibited in some countries, but are permitted and simply available online in others. A rapid detection method is therefore required for on-site inspection or screening of a large amount of samples. Our previous study demonstrated a surface-enhanced Raman scattering (SERS)-based approach to identify nicotine-containing e-liquids; without any pre-treatment, e-liquid can be directly tested on our solid-phase SERS substrates, made of silver nanoparticle arrays embedded in anodic aluminium oxide nanochannels (Ag/AAO). However, this approach required manual determination of spectral signatures and negative samples should be validated in the second round detection. Here, after examining 406 commercial e-liquids, we refined this approach by developing artificial intelligence (AI)-assisted spectrum interpretations. We also found that nicotine and benzoic acid can be simultaneously detected in our platform. This increased test sensitivity because benzoic acid is usually used in nicotine salts. Around 64% of nicotine-positive samples in this study showed both signatures. Using either cutoffs of nicotine and benzoic acid peak intensities or a machine learning model based on the CatBoost algorithm, over 90% of tested samples can be correctly discriminated with only one round of SERS measurement. False negative and false positive rates were 2.5-4.4% and 4.4-8.9%, respectively, depending on the interpretation method and thresholds applied. The new approach takes only 1 microliter of sample and can be performed in 1-2 min, suitable for on-site inspection with portable Raman detectors. It could also be a complementary platform to reduce samples that need to be analyzed in the central labs and has the potential to identify other prohibited additives.

Dietary profile of pediatric obstructive sleep apnea patients, effects of routine educational counseling, and predictors for outcomes.

Background: Dietary behavior is a main contributing yet modifiable factor to the body weight status of children and may be involved in the pathophysiology of childhood obstructive sleep apnea (OSA). This study aimed to investigate the dietary profile of pediatric OSA patients, effects of educational counseling after adenotonsillectomy, and predictor for disease resolution. Methods: This observational study included 50 pediatric OSA patients undergoing adenotonsillectomy with routine educational counseling (Group 1), 50 pediatric OSA patients undergoing adenotonsillectomy without formal educational counseling (Group 2), and 303 healthy children without OSA (Control). The three groups were matched by age. The consumption frequency of 25 food items/groups was assessed by the Short Food Frequency Questionnaire. Quality of life was evaluated by the OSA-18 questionnaire. Sleep architecture and OSA severity were measured by standard polysomnography. Between- and within-group comparisons were analyzed by non-parametric approaches and generalized estimating equations. Prediction of disease recovery was performed by multivariable logistic regression models. Results: Group 1 children consumed fruit drinks with sugar, vegetables, sweets, chocolate, rice, and noodles more frequently than Control Group children. At baseline, the distributions of sex, weight status, OSA-18 scores, and polysomnographic variables were comparable between Group 1 and Group 2. After a 12-month follow-up, Group 1 had better improvements in physical suffering, caregiver concerns, sleep architecture, and mean peripheral oxygen saturation compared to Group 2. Furthermore, Group 1 no longer had excessive consumption of fruit drinks with sugar, chocolate, and noodles; however, food consumption frequencies did not change significantly. Notably, younger age and reduced intake of butter/margarine on bread and noodles were independent predictors of cured OSA in Group 1. Conclusion: The present study preliminarily characterized an unhealthy dietary profile among pediatric OSA patients and suggested that routine educational counseling in addition to adenotonsillectomy yielded some clinical benefits. Certain items/groups of food frequencies may be associated with disease recovery and further investigations are warranted.

Right-side frontal-central cortical hyperactivation before the treatment predicts outcomes of antidepressant and electroconvulsive therapy responsivity in major depressive disorder.

Major depressive disorder places a great burden on healthcare resources worldwide. Antidepressants are the first-line treatment for major depressive disorder, but if patients don't respond adequately, brain stimulation therapy may be needed as second-line treatment. Digital phenotyping in patients with major depressive disorder will aid in the timely prediction of treatment effectiveness. This study explored electroencephalographic (EEG) signatures that diversify depression treatment responsivity, including antidepressant administration or brain stimulation therapy. Resting-state, pre-treatment EEG sequences from depressive patients who received fluoxetine treatment (n = 55; 26 remitters and 29 poor responders) or electroconvulsive therapy (ECT, n = 58; 36 remitters and 22 nonremitters) were recorded on 19 channels. Twenty-nine EEG segments were obtained from each patient per recording electrode. Power spectral analysis was conducted for feature extraction and showed the highest predictive accuracy for fluoxetine or ECT outcomes. Both occurred with beta-band oscillations within right-side frontal-central (F1-score = 0.9437) or prefrontal areas of the brain (F1-score = 0.9416), respectively. Significantly higher beta-band power was observed among patients who lacked adequate treatment response than the remitters, specifically at 19.2 Hz or 24.5 Hz for fluoxetine administration or ECT outcome, respectively. Our findings indicated that pre-treatment, right-side cortical hyperactivation is associated with poor outcomes of antidepressant-based or ECT-based treatment in major depression. Whether depression treatment response rates can be improved by reducing the high-frequency EEG power in corresponding areas of the brain to provide a protective effect against depression recurrence warrants further study.

Investigating brain aging trajectory deviations in different brain regions of individuals with schizophrenia using multimodal magnetic resonance imaging and brain-age prediction: a multicenter study.

Although many studies on brain-age prediction in patients with schizophrenia have been reported recently, none has predicted brain age based on different neuroimaging modalities and different brain regions in these patients. Here, we constructed brain-age prediction models with multimodal MRI and examined the deviations of aging trajectories in different brain regions of participants with schizophrenia recruited from multiple centers. The data of 230 healthy controls (HCs) were used for model training. Next, we investigated the differences in brain age gaps between participants with schizophrenia and HCs from two independent cohorts. A Gaussian process regression algorithm with fivefold cross-validation was used to train 90, 90, and 48 models for gray matter (GM), functional connectivity (FC), and fractional anisotropy (FA) maps in the training dataset, respectively. The brain age gaps in different brain regions for all participants were calculated, and the differences in brain age gaps between the two groups were examined. Our results showed that most GM regions in participants with schizophrenia in both cohorts exhibited accelerated aging, particularly in the frontal lobe, temporal lobe, and insula. The parts of the white matter tracts, including the cerebrum and cerebellum, indicated deviations in aging trajectories in participants with schizophrenia. However, no accelerated brain aging was noted in the FC maps. The accelerated aging in 22 GM regions and 10 white matter tracts in schizophrenia potentially exacerbates with disease progression. In individuals with schizophrenia, different brain regions demonstrate dynamic deviations of brain aging trajectories. Our findings provided more insights into schizophrenia neuropathology.

Adenotonsillectomy-related changes in systemic inflammation among children with obstructive sleep apnea.

BACKGROUND: Adenotonsillar hypertrophy is the most common cause of pediatric obstructive sleep apnea (OSA). Although adenotonsillectomy considerably reduces OSA and systemic inflammation, whether and how systemic inflammation influences the effects of adenotonsillectomy on OSA has yet to be determined. METHODS: This study investigated the associations between changes in anatomical variables, % changes in subjective OSA-18 questionnaire scores, % changes in 11 polysomnographic parameters, and % changes in 27 systemic inflammatory biomarkers in 74 children with OSA. RESULTS: Fifty-six (75.6%) boys and 18 (24.4%) girls with the mean age of 7.4 ± 2.2 years and apnea-hypopnea index (AHI) of 14.2 ± 15.9 events/h were included in the statistical analysis. The mean period between before and after adenotonsillectomy was 5.6 ± 2.6 months. After adenotonsillectomy, the OSA-18 score, eight of 11 polysomnographic parameters, and 20 of 27 inflammatory biomarkers significantly improved (all p < 0.005). Notably, there were significant associations between change in tonsil size and % change in AHI ( r = 0.23), change in tonsil size and % changes in interleukin-8 (IL-8) ( r = 0.34), change in tonsil size and % change in and IL-10 ( r = -0.36), % change in IL-8 and % change in C-C chemokine ligand 5 (CCL5) ( r = 0.30), and % change in CCL5 and % change in AHI ( r = 0.38) (all p < 0.005). Interestingly, % change in IL-8 and % change in CCL5 serially mediated the relationship between change in tonsil size and % change in AHI (total effect: ß = 16.672, standard error = 8.274, p = 0.048). CONCLUSION: These preliminary findings suggest that systemic inflammation is not only a complication of OSA but also that it mediates the surgical effects, which may open avenues for potential interventions to reduce tonsil size and OSA severity through the regulation of IL-8 and CCL5.

Using sleep heart rate variability to investigate the sleep quality in children with obstructive sleep apnea.

Background: Obstructive sleep apnea (OSA) is associated with impaired sleep quality and autonomic dysfunction. Adenotonsillectomy significantly improves subjective and objective sleep quality in children with OSA. However, the postoperative changes in heart rate variability (HRV) indices (indicators of cardiac autonomic function) and their importance remain inconclusive in childhood OSA. This retrospective case series aimed to investigate the association of sleep HRV indices, total OSA-18 questionnaire score (a subjective indicator of sleep quality) and polysomnographic parameters (objective indicators of sleep quality), and effects of adenotonsillectomy on HRV indices, total OSA-18 questionnaire score and polysomnographic parameters in children with OSA. Methods: Seventy-six children with OSA were included in baseline analysis, of whom 64 (84%) completed at least 3 months follow-up examinations after adenotonsillectomy and were included in outcome analysis. Associations between baseline variables, and relationships with treatment-related changes were examined. Results: Multivariable linear regression models in the baseline analysis revealed independent relationships between tonsil size and obstructive apnea-hypopnea index (OAHI), adenoidal-nasopharyngeal ratio and very low frequency (VLF) power of HRV (an indicator of sympathetic activity), and normalized low frequency power (an indicator of sympathetic activity) and OAHI. The outcome analysis showed that adenotonsillectomy significantly improved standard deviation of all normal-to-normal intervals, and high frequency power, QoL (in terms of reduced total OSA-18 questionnaire score), OAHI and hypoxemia. Using a conceptual serial multiple mediation model, % change in OSA-18 questionnaire score and % change in VLF power serially mediated the relationships between change in tonsil size and % change in OAHI. Conclusions: The improvement in OAHI after adenotonsillectomy was serially mediated by reductions in total OSA-18 questionnaire score and VLF power. These preliminary findings are novel and provide a direction for future research to investigate the effects of VLF power-guided interventions on childhood OSA.

Investigating genetic variants for treatment response to selective serotonin reuptake inhibitors in syndromal factors and side effects among patients with depression in Taiwanese Han population.

Major depressive disorder (MDD) is associated with high heterogeneity in clinical presentation. In addition, response to treatment with selective serotonin reuptake inhibitors (SSRIs) varies considerably among patients. Therefore, identifying genetic variants that may contribute to SSRI treatment responses in MDD is essential. In this study, we analyzed the syndromal factor structures of the Hamilton Depression Rating Scale in 479 patients with MDD by using exploratory factor analysis. All patients were followed up biweekly for 8 weeks. Treatment response was defined for all syndromal factors and total scores. In addition, a genome-wide association study was performed to investigate the treatment outcomes at week 4 and repeatedly assess all visits during follow-up by using mixed models adjusted for age, gender, and population substructure. Moreover, the role of genetic variants in suicidal and sexual side effects was explored, and five syndromal factors for depression were derived: core, insomnia, somatic anxiety, psychomotor-insight, and anorexia. Subsequently, several known genes were mapped to suggestive signals for treatment outcomes, including single-nucleotide polymorphisms (SNPs) in PRF1, UTP20, MGAM, and ENSG00000286536 for psychomotor-insight and in C4orf51 for anorexia. In total, 33 independent SNPs for treatment responses were tested in a mixed model, 12 of which demonstrated a p value <0.05. The most significant SNP was rs2182717 in the ENSR00000803469 gene located on chromosome 6 for the core syndromal factor (ß = -0.638, p = 1.8 × 10-4) in terms of symptom improvement over time. Patients with a GG or GA genotype with the rs2182717 SNP also exhibited a treatment response (ß = 0.089, p = 2.0 × 10-6) at week 4. Moreover, rs1836075352 was associated with sexual side effects (p = 3.2 × 10-8). Pathway and network analyses using the identified SNPs revealed potential biological functions involved in treatment response, such as neurodevelopment-related functions and immune processes. In conclusion, we identified loci that may affect the clinical response to treatment with antidepressants in the context of empirically defined depressive syndromal factors and side effects among the Taiwanese Han population, thus providing novel biological targets for further investigation.

Predicting aging trajectories of decline in brain volume, cortical thickness and fractional anisotropy in schizophrenia.

Brain-age prediction is a novel approach to assessing deviated brain aging trajectories in different diseases. However, most studies have used an average brain age gap (BAG) of individuals with schizophrenia of different illness durations for comparison with healthy participants. Therefore, this study investigated whether declined brain structures as reflected by BAGs may be present in schizophrenia in terms of brain volume, cortical thickness, and fractional anisotropy across different illness durations. We used brain volume, cortical thickness, and fractional anisotropy as features to train three models from the training dataset. Three models were applied to predict brain ages in the hold-out test and schizophrenia datasets and calculate BAGs. We divided the schizophrenia dataset into multiple groups based on the illness duration using a sliding time window approach for ANCOVA analysis. The brain volume and cortical thickness models revealed that, in comparison with healthy controls, individuals with schizophrenia had larger BAGs across different illness durations, whereas the BAG in terms of fractional anisotropy did not differ from that of healthy controls after disease onset. Moreover, the BAG at the initial stage of schizophrenia was the largest in the cortical thickness model. In contrast, the BAG from approximately two decades after disease onset was the largest in the brain volume model. Our findings suggest that schizophrenia differentially affects the decline of different brain structures during the disease course. Moreover, different trends of decline in thickness and volume-based measures suggest a differential decline in dimensions of brain structure throughout the course of schizophrenia.

Progressive brain abnormalities in schizophrenia across different illness periods: a structural and functional MRI study.

Schizophrenia is a chronic brain disorder, and neuroimaging abnormalities have been reported in different stages of the illness for decades. However, when and how these brain abnormalities occur and evolve remains undetermined. We hypothesized structural and functional brain abnormalities progress throughout the illness course at different rates in schizophrenia. A total of 115 patients with schizophrenia were recruited and stratified into three groups of different illness periods: 5-year group (illness duration: ≤5 years), 15-year group (illness duration: 12-18 years), and 25-year group (illness duration: ≥25 years); 230 healthy controls were matched by age and sex to the three groups, respectively. All participants underwent resting-state MRI scanning. Each group of patients with schizophrenia was compared with the corresponding controls in terms of voxel-based morphometry (VBM), fractional anisotropy (FA), global functional connectivity density (gFCD), and sample entropy (SampEn) abnormalities. In the 5-year group we observed only SampEn abnormalities in the putamen. In the 15-year group, we observed VBM abnormalities in the insula and cingulate gyrus and gFCD abnormalities in the temporal cortex. In the 25-year group, we observed FA abnormalities in nearly all white matter tracts, and additional VBM and gFCD abnormalities in the frontal cortex and cerebellum. By using two structural and two functional MRI analysis methods, we demonstrated that individual functional abnormalities occur in limited brain areas initially, functional connectivity and gray matter density abnormalities ensue later in wider brain areas, and structural connectivity abnormalities involving almost all white matter tracts emerge in the third decade of the course in schizophrenia.

Population-attributable risk of psychiatric disorders for suicide among adolescents and young adults in Taiwan.

BACKGROUND: Youth suicide rates have increased markedly in some countries. This study aimed to estimate the population-attributable risk of psychiatric disorders associated with suicide among Taiwanese youth aged 10-24 years. METHODS: Data were obtained from the National Death Registry and National Health Insurance (NHI) claims database between 2007 and 2019. Youth who died by suicide were included, and comparisons, 1:10 matched by age and sex, were randomly selected from the Registry for NHI beneficiaries. We used multivariable logistic regression to estimate suicide odds ratios for psychiatric disorders. The population-attributable fractions (PAF) were calculated for each psychiatric disorder. RESULTS: A total of 2345 youth suicide and 23 450 comparisons were included. Overall, 44.8% of suicides had a psychiatric disorder, while only 7.9% of the comparisons had a psychiatric disorder. The combined PAF for all psychiatric disorders was 55.9%. The top three psychiatric conditions of the largest PAFs were major depressive disorder, dysthymia, and sleep disorder. In the analysis stratified by sex, the combined PAF was 45.5% for males and 69.2% for females. The PAF among young adults aged 20-24 years (57.0%) was higher than among adolescents aged 10-19 years (48.0%). CONCLUSIONS: Our findings of high PAF from major depressive disorder, dysthymia, and sleep disorder to youth suicides suggest that youth suicide prevention that focuses on detecting and treating mental illness may usefully target these disorders.

Exploring the role of hub and network dysfunction in brain connectomes of schizophrenia using functional magnetic resonance imaging.

Introduction: Pathophysiological etiology of schizophrenia remains unclear due to the heterogeneous nature of its biological and clinical manifestations. Dysfunctional communication among large-scale brain networks and hub nodes have been reported. In this study, an exploratory approach was adopted to evaluate the dysfunctional connectome of brain in schizophrenia. Methods: Two hundred adult individuals with schizophrenia and 200 healthy controls were recruited from Taipei Veterans General Hospital. All subjects received functional magnetic resonance imaging (fMRI) scanning. Functional connectivity (FC) between parcellated brain regions were obtained. Pair-wise brain regions with significantly different functional connectivity among the two groups were identified and further analyzed for their concurrent ratio of connectomic differences with another solitary brain region (single-FC dysfunction) or dynamically interconnected brain network (network-FC dysfunction). Results: The right thalamus had the highest number of significantly different pair-wise functional connectivity between schizophrenia and control groups, followed by the left thalamus and the right middle frontal gyrus. For individual brain regions, dysfunctional single-FCs and network-FCs could be found concurrently. Dysfunctional single-FCs distributed extensively in the whole brain of schizophrenia patients, but overlapped in similar groups of brain nodes. A dysfunctional module could be formed, with thalamus being the key dysfunctional hub. Discussion: The thalamus can be a critical hub in the brain that its dysfunctional connectome with other brain regions is significant in schizophrenia patients. Interconnections between dysfunctional FCs for individual brain regions may provide future guide to identify critical brain pathology associated with schizophrenia.

Functional MRI and ApoE4 genotype for predicting cognitive decline in amyloid-positive individuals.

Background: In light of advancements in machine learning techniques, many studies have implemented machine learning approaches combined with data measures to predict and classify Alzheimer's disease. Studies that predicted cognitive status with longitudinal follow-up of amyloid-positive individuals remain scarce, however. Objective: We developed models based on voxel-wise functional connectivity (FC) density mapping and the presence of the ApoE4 genotype to predict whether amyloid-positive individuals would experience cognitive decline after 1 year. Methods: We divided 122 participants into cognitive decline and stable cognition groups based on the participants' change rates in Mini-Mental State Examination scores. In addition, we included 68 participants from Alzheimer's Disease Neuroimaging Initiative (ADNI) database as an external validation data set. Subsequently, we developed two classification models: the first model included 99 voxels, and the second model included 99 voxels and the ApoE4 genotype as features to train the models by Wide Neural Network algorithm with fivefold cross-validation and to predict the classes in the hold-out test and ADNI data sets. Results: The results revealed that both models demonstrated high accuracy in classifying the two groups in the hold-out test data set. The model for FC demonstrated good performance, with a mean F 1-score of 0.86. The model for FC combined with the ApoE4 genotype achieved superior performance, with a mean F 1-score of 0.90. In the ADNI data set, the two models demonstrated stable performances, with mean F 1-scores of 0.77 in the first and second models. Conclusion: Our findings suggest that the proposed models exhibited promising accuracy for predicting cognitive status after 1 year in amyloid-positive individuals. Notably, the combination of FC and the ApoE4 genotype increased prediction accuracy. These findings can assist clinicians in predicting changes in cognitive status in individuals with a high risk of Alzheimer's disease and can assist future studies in developing precise treatment and prevention strategies.

Endophenotypic effects of the SORL1 variant rs2298813 on regional brain volume in patients with late-onset Alzheimer's disease.

Introduction: Two common variants of sortilin-related receptor 1 gene (SORL1), rs2298813 and rs1784933, have been associated with late-onset Alzheimer's disease (AD) in the Han Chinese population in Taiwan. However, neuroimaging correlates of these two SORL1 variants remain unknown. We aimed to determine whether the two SORL1 polymorphisms were associated with any volumetric differences in brain regions in late-onset AD patients. Methods: We recruited 200 patients with late-onset AD from Taipei Veterans General Hospital. All patients received a structural magnetic resonance (MR) imaging brain scan and completed a battery of neurocognitive tests at enrollment. We followed up to assess changes in Mini-Mental State Examination (MMSE) scores in 155 patients (77.5%) at an interval of 2 years. Volumetric measures and cortical thickness of various brain regions were performed using FreeSurfer. Regression analysis controlled for apolipoprotein E status. Multiple comparisons were corrected for using the false discovery rate. Results: The homozygous major allele of rs2298813 was associated with larger volumes in the right putamen (p = 0.0442) and right pallidum (p = 0.0346). There was no link between the rs1784933 genotypes with any regional volume or thickness of the brain. In the rs2298813 homozygous major allele carriers, the right putaminal volume was associated with verbal fluency (p = 0.008), and both the right putaminal and pallidal volumes were predictive of clinical progression at follow-up (p = 0.020). In the minor allele carriers, neither of the nuclei was related to cognitive test performance or clinical progression. Conclusion: The major and minor alleles of rs2298813 had differential effects on the right lentiform nucleus volume and distinctively modulated the association between the regional volume and cognitive function in patients with AD.