Cognitive-Motor coordination (CMC), the ability to coordinate cognitive and motor tasks simultaneously is essential for daily functioning and children's development. While extensive research shows that motor tasks can modulate cognitive performance, the reverse influence-how cognitive tasks modulate motor performance-remains debated, particularly regarding the mechanisms and age-related differences across cognitive processing stages. To address this gap, the present study employed a stage-based CMC task in three age groups: 7-8-year-olds, 9-12-year-olds, and young adults. Participants simultaneously performed a cognitive task (subitizing for small numerosities and estimation for larger ones) while maintaining different levels of grip force (low-, medium-, and high-level motor loads). Motor load modulated the error rate of estimation in a U-shape pattern-with optimal cognitive performance occurring under medium motor loads-and this pattern was evident in both children and young adults. Critically, the stage-based design enabled us to assess motor performance during encoding and post-encoding stages. CMC differed across stages under medium and high motor loads. Specifically, during the encoding stage, grip maintenance was better during estimation than subitizing, and this estimation advantage was observed only in children. In contrast, during the post-encoding stage, grip maintenance was better during subitizing than estimation, and this subitizing advantage was observed across all age groups. These findings demonstrate that cognitive processing affects motor control differently across cognitive processing stages, with each stage exhibiting distinct age-related changes. The "cognitive stage-dependent" modulation mechanism provides new insights into the nature and development of CMC.

The accuracy of a diagnostic test has always been crucial for detecting disease staging. Numerous diagnostic precision tests have been extensively used in binary diagnosis. Some existing measures apply to multi-stage diagnosis. However, implementation has limitations, and performance strongly depends on the distribution of diagnostic results. Considering the rule-in/out information provided by the Kullback-Leibler divergence, we propose a new measure that generalizes the total Kullback-Leibler (GTKL) divergence as an accuracy measure in the multi-stage diagnosis. We further examine the fact that the generalized measure can serve as an optimal cut-point selection criterion when the number of stages increases. Moreover, we conduct a series of simulation studies to compare existing measures' power and optimal cut-point selection and performance, including the generalized Youden index, maximum absolute determinant, closest-to-perfection, and maximum volume. Furthermore, we illustrate the application of our measures and the comparison with other measures using an example of Alzheimer's disease data. The results show GTKL's outstanding performance in some situations. A detailed performance analysis of existing metrics is also presented throughout the document.

BackgroundPoor glycemic control with elevated levels of hemoglobin A1c (HbA1c) is associated with increased risk of cognitive impairment, with potentially varying effects between sexes. However, the causal impact of poor glycemic control on white matter brain aging in men and women is uncertain.MethodsWe used two nonoverlapping data sets from UK Biobank cohort: gene-outcome group (with neuroimaging data, (N = 15,193; males/females: 7,101/8,092)) and gene-exposure group (without neuroimaging data, (N = 279,011; males/females: 122,638/156,373)). HbA1c was considered the exposure and adjusted “brain age gap” (BAG) was calculated on fractional anisotropy (FA) obtained from brain imaging as the outcome, thereby representing the difference between predicted and chronological age. The causal effects of HbA1c on adjusted BAG were studied using the generalized inverse variance weighted (gen-IVW) and other sensitivity analysis methods, including Mendelian randomization (MR)-weighted median, MR-pleiotropy residual sum and outlier, MR-using mixture models, and leave-one-out analysis.ResultsWe found that for every 6.75 mmol/mol increase in HbA1c, there was an increase of 0.49 (95% CI = 0.24, 0.74; p-value = 1.30 × 10−4) years in adjusted BAG. Subgroup analyses by sex and age revealed significant causal effects of HbA1c on adjusted BAG, specifically among men aged 60–73 (p-value = 2.37 × 10−8).ConclusionPoor glycemic control has a significant causal effect on brain aging, and is most pronounced among older men aged 60–73 years, which provides insights between glycemic control and the susceptibility to age-related neurodegenerative diseases.

Apolipoprotein E epsilon 4 ( APOE4 ) is a strong genetic risk factor of Alzheimer's disease and metabolic dysfunction. However, whether APOE4 and markers of metabolic dysfunction synergistically impact the deterioration of white matter (WM) integrity in older adults remains unknown. In the UK Biobank data, we conducted a multivariate analysis to investigate the interactions between APOE4 and 249 plasma metabolites (measured using nuclear magnetic resonance spectroscopy) with whole-brain WM integrity (measured by diffusion-weighted magnetic resonance imaging) in a cohort of 1917 older adults (aged 65.0-81.0 years; 52.4 % female). Although no main association was observed between either APOE4 or metabolites with WM integrity (adjusted P > 0.05), significant interactions between APOE4 and metabolites with WM integrity were identified. Among the examined metabolites, higher concentrations of low-density lipoprotein and very low-density lipoprotein were associated with a lower level of WM integrity (b=- 0.12, CI=[-0.14,-0.10]) among APOE4 carriers. Conversely, among non-carriers, they were associated with a higher level of WM integrity (b=0.05, CI=[0.04, 0.07]), demonstrating a significant moderation role of APOE4 (b =- 0.18, CI=[-0.20,-0.15], P<0.00001).

Although digital health solutions are increasingly popular in clinical psychiatry, one application that has not been fully explored is the utilization of survey technology to monitor patients outside of the clinic. Supplementing routine care with digital information collected in the "clinical whitespace" between visits could improve care for patients with severe mental illness. This study evaluated the feasibility and validity of using online self-report questionnaires to supplement in-person clinical evaluations in persons with and without psychiatric diagnoses. We performed a rigorous in-person clinical diagnostic and assessment battery in 54 participants with schizophrenia (N = 23), depressive disorder (N = 14), and healthy controls (N = 17) using standard assessments for depressive and psychotic symptomatology. Participants were then asked to complete brief online assessments of depressive (Quick Inventory of Depressive Symptomatology) and psychotic (Community Assessment of Psychic Experiences) symptoms outside of the clinic for comparison with the ground-truth in-person assessments. We found that online self-report ratings of severity were significantly correlated with the clinical assessments for depression (two assessments used: R = 0.63, p < 0.001; R = 0.73, p < 0.001) and psychosis (R = 0.62, p < 0.001). Our results demonstrate the feasibility and validity of collecting psychiatric symptom ratings through online surveys. Surveillance of this kind may be especially useful in detecting acute mental health crises between patient visits and can generally contribute to more comprehensive psychiatric treatment.

Sclerosing stromal tumors (SSTs) are rare benign ovarian tumors. They represent 6% of sex cord stromal tumors. Its preoperative diagnosis is often a challenge due to its similarity to malignant tumors on ultrasound imaging. We present two cases of SSTs to emphasize the consideration of this type of tumors in the differential diagnosis of solid adnexal masses in young women. A review of the literature on the typical ultrasound features, clinical presentation, and management of SSTs was performed.Pelvic pain was the main symptom in both cases. In the first case, transvaginal ultrasound revealed an unilocular solid adnexal mass of 59 mm × 44 mm × 45 mm with cystic areas and marked peripheral and central vascularization. MRI (magnetic resonance imaging) revealed a 50 mm × 50 mm heterogeneous adnexal mass with a solid peripheral component and a cystic-necrotic center. In the second case, pelvic ultrasound showed a solid cystic adnexal mass of 103 mm × 77 mm with marked peripheral vascularity.Postoperative anatomopathological diagnosis in both cases was an ovarian SST.Unilateral laparoscopic salpingo-oophorectomy and oophorectomy, respectively, was performed without incidents. There has been no recurrence during follow-up.It is important to consider SSTs in the differential diagnosis of young women with a unilateral solid-cystic adnexal mass with a high degree of peripheral and central vascularization. Laparoscopic approach together with fertility-sparing techniques should be considered the treatment of choice.Los tumores esclerosantes del estroma (SST) son tumores benignos raros del ovario. Representan un 6% de los tumores del estroma de los cordones sexuales. Su diagnóstico preoperatorio suele ser un desafío por su similitud ecográfica con los tumores malignos. Presentamos 2 casos de SST para enfatizar la consideración de este tipo de tumores en el diagnóstico diferencial de masas anexiales sólidas en mujeres jóvenes. Se realizó una revisión de la literatura sobre las características ecográficas típicas, la presentación clínica y el manejo de los SST.El dolor pélvico fue el síntoma principal en ambos casos. En el primer caso, la ecografía transvaginal reveló una masa anexial unilocular sólida de 59 × 44 × 45 mm con áreas quísticas y marcada vascularización periférica y central. La resonancia magnética nuclear reveló una masa anexial heterogénea de 50 × 50 mm con componente sólido periférico y un centro quístico-necrótico. En el segundo caso, la ecografía pélvica mostró una masa anexial sólido quística de 103 × 77 mm con marcada vascularización periférica.El diagnóstico anatomopatológico postoperatorio en ambos casos fue de un SST de ovario.Se realizó ooforectomía y salpingooforectomía unilateral laparoscópica, respectivamente, sin incidencias. No se ha producido recidiva durante el seguimiento.Es importante considerar los SST en el diagnóstico diferencial ante mujeres jóvenes con una masa anexial sólido-quística unilateral con un alto grado de vascularización periférica y central. El abordaje laparoscópico junto con técnicas preservadoras de fertilidad deben ser consideradas el tratamiento de elección.

Tobacco smoking is a risk factor for impaired brain function, but its causal effect on white matter brain aging remains unclear. This study aimed to measure the causal effect of tobacco smoking on white matter brain aging. Mendelian randomization (MR) analysis using two non-overlapping data sets (with and without neuroimaging data) from UK Biobank (UKB). The group exposed to smoking and control group consisted of current smokers and never smokers, respectively. Our main method was generalized weighted linear regression with other methods also included as sensitivity analysis. United Kingdom. The study cohort included 23 624 subjects [10 665 males and 12 959 females with a mean age of 54.18 years, 95% confidence interval (CI) = 54.08, 54.28]. Genetic variants were selected as instrumental variables under the MR analysis assumptions: (1) associated with the exposure; (2) influenced outcome only via exposure; and (3) not associated with confounders. The exposure smoking status (current versus never smokers) was measured by questionnaires at the initial visit (2006–10). The other exposure, cigarettes per day (CPD), measured the average number of cigarettes smoked per day for current tobacco users over the life-time. The outcome was the ‘brain age gap’ (BAG), the difference between predicted brain age and chronological age, computed by training machine learning model on a non-overlapping set of never smokers. The estimated BAG had a mean of 0.10 (95% CI = 0.06, 0.14) years. The MR analysis showed evidence of positive causal effect of smoking behaviors on BAG: the effect of smoking is 0.21 (in years, 95% CI = 6.5 × 10 −3 , 0.41; P -value = 0.04), and the effect of CPD is 0.16 year/cigarette (UKB: 95% CI = 0.06, 0.26; P -value = 1.3 × 10 −3 ; GSCAN: 95% CI = 0.02, 0.31; P -value = 0.03). The sensitivity analyses showed consistent results. There appears to be a significant causal effect of smoking on the brain age gap, which suggests that smoking prevention can be an effective intervention for accelerated brain aging and the age-related decline in cognitive function.

Table S1. A list of 39 regional white matter (WM) integrity measured by fractional antitropy (FA). Table S2. Self-report neurological characteristics of UK Biobank's participants from two independent association sample sets for the two-sample Mendelian randomization (MR) analysis. Table S3. Numbers of candidate instrumental variables (IVs) determined at each step of our statistical method by separately using UK Biobank (UKB) GWAS and existing meta-analyzed GWAS. Table S4. Gene annotations for determined valid instrumental variables (IVs) carried into the two-sample Mendelian Randomization (MR) analysis by separately using UK Biobank (UKB) GWAS and existing meta-analyzed GWAS. Table S5. Results of association analysis and Mendelian Randomization analysis (implemented with generalized version of inverse-variance weighted (gen-IVW) approach) in the study samples. Table S6. Results of alternative Mendelian randomization (MR) approach and sensitivity analyses by using data from UK Biobank (UKB) cohort. Table S7. Results of adaptively weighted (AW) Fisher's method and mendelian randomization analysis implemented with a generalized version of inverse-variance weights method (gen-IVW) Table S8. Results and numbers of candidate instrumental variables (IVs) in each step of our two reverse two-sample Mendelian Randomization (MR) analyses using UK Biobank (UKB) cohort. Supplmentary File. Results of leave-one-out analyses STROBE-MR checklist. STrengthening the Reporting of OBservational studies in Epidemiology - Molecular Epidemiology (STROBE-ME) checklist

INTRODUCTION:APOE4 is a strong genetic risk factor of Alzheimer's disease and is associated with changes in metabolism. However, the interactive relationship between APOE4 and plasma metabolites on the brain remains largely unknown. MEHODS:In the UK Biobank, we investigated the moderation effects of APOE4 on the relationship between 249 plasma metabolites derived from nuclear magnetic resonance spectroscopy on whole-brain white matter integrity, measured by fractional anisotropy using diffusion magnetic resonance imaging. RESULTS:The increase in the concentration of metabolites, mainly LDL and VLDL, is associated with a decrease in white matter integrity (b= -0.12, CI= [-0.14, -0.10]) among older APOE4 carriers, whereas an increase (b= 0.05, CI= [0.04, 0.07]) among non-carriers, implying a significant moderation effect of APOE4 (b= -0.18, CI= [-0.20,-0.15]). DISCUSSION:The results suggest that lipid metabolism functions differently in APOE4 carriers compared to non-carriers, which may inform the development of targeted interventions for APOE4 carriers to mitigate cognitive decline.

Elevated arterial blood pressure (BP) is a common risk factor for cerebrovascular and cardiovascular diseases, but no causal relationship has been established between BP and cerebral white matter (WM) integrity. In this study, we performed a two‐sample Mendelian randomization (MR) analysis with individual‐level data by defining two nonoverlapping sets of European ancestry individuals (genetics–exposure set: N = 203,111; mean age = 56.71 years, genetics–outcome set: N = 16,156; mean age = 54.61 years) from UK Biobank to evaluate the causal effects of BP on regional WM integrity, measured by fractional anisotropy of diffusion tensor imaging. Two BP traits: systolic and diastolic blood pressure were used as exposures. Genetic variant was carefully selected as instrumental variable (IV) under the MR analysis assumptions. We existing large‐scale genome‐wide association study summary data for validation. The main method used was a generalized version of inverse‐variance weight method while other MR methods were also applied for consistent findings. Two additional MR analyses were performed to exclude the possibility of reverse causality. We found significantly negative causal effects (FDR‐adjusted p < .05; every 10 mmHg increase in BP leads to a decrease in FA value by .4% ~ 2%) of BP traits on a union set of 17 WM tracts, including brain regions related to cognitive function and memory. Our study extended the previous findings of association to causation for regional WM integrity, providing insights into the pathological processes of elevated BP that might chronically alter the brain microstructure in different regions.

In the last two decades of Genome-wide association studies (GWAS), nicotine-dependence-related genetic loci (e.g., nicotinic acetylcholine receptor - nAChR subunit genes) are among the most replicable genetic findings. Although GWAS results have reported tens of thousands of SNPs within these loci, further analysis (e.g., fine-mapping) is required to identify the causal variants. However, it is computationally challenging for existing fine-mapping methods to reliably identify causal variants from thousands of candidate SNPs based on the posterior inclusion probability. To address this challenge, we propose a new method to select SNPs by jointly modeling the SNP-wise inference results and the underlying structured network patterns of the linkage disequilibrium (LD) matrix. We use adaptive dense subgraph extraction method to recognize the latent network patterns of the LD matrix and then apply group LASSO to select causal variant candidates. We applied this new method to the UK biobank data to identify the causal variant candidates for nicotine addiction. Eighty-one nicotine addiction-related SNPs (i.e.,-log(p) > 50) of nAChR were selected, which are highly correlated (average r2>0.8) although they are physically distant (e.g., >200 kilobase away) and from various genes. These findings revealed that distant SNPs from different genes can show higher LD r2 than their neighboring SNPs, and jointly contribute to a complex trait like nicotine addiction.

Background: Elevated blood pressure (BP) is a modifiable risk factor associated with cognitive impairment and cerebrovascular diseases. However, the causal effect of BP on white matter brain aging remains unclear. Methods: In this study, we focused on N = 228 473 individuals of European ancestry who had genotype data and clinical BP measurements available (103 929 men and 124 544 women, mean age = 56.49, including 16 901 participants with neuroimaging data available) collected from UK Biobank (UKB). We first established a machine learning model to compute the outcome variable brain age gap (BAG) based on white matter microstructure integrity measured by fractional anisotropy derived from diffusion tensor imaging data. We then performed a two-sample Mendelian randomization analysis to estimate the causal effect of BP on white matter BAG in the whole population and subgroups stratified by sex and age brackets using two nonoverlapping data sets. Results: The hypertension group is on average 0.31 years (95% CI = 0.13–0.49; P < 0.0001) older in white matter brain age than the nonhypertension group. Women are on average 0.81 years (95% CI = 0.68–0.95; P < 0.0001) younger in white matter brain age than men. The Mendelian randomization analyses showed an overall significant positive causal effect of DBP on white matter BAG (0.37 years/10 mmHg, 95% CI 0.034–0.71, P = 0.0311). In stratified analysis, the causal effect was found most prominent among women aged 50–59 and aged 60–69. Conclusion: High BP can accelerate white matter brain aging among late middle-aged women, providing insights on planning effective control of BP for women in this age group.

Cognitive impairments predict poor functional outcomes in people with schizophrenia. These impairments may be causally related to increased levels of kynurenic acid (KYNA), a major metabolic product of tryptophan (TRYP). In the brain, KYNA acts as an antagonist of the of α7-nicotinic acetylcholine and NMDA receptors, both of which are involved in cognitive processes. To examine whether KYNA plays a role in the pathophysiology of schizophrenia, we compared the acute effects of a single oral dose of TRYP (6 g) in 32 healthy controls (HC) and 37 people with either schizophrenia (Sz), schizoaffective or schizophreniform disorder, in a placebo-controlled, randomized crossover study. We examined plasma levels of KYNA and its precursor kynurenine; selected cognitive measures from the MATRICS Consensus Cognitive Battery; and resting cerebral blood flow (CBF) using arterial spin labeling imaging. In both cohorts, the TRYP challenge produced significant, time-dependent elevations in plasma kynurenine and KYNA. The resting CBF signal (averaged across all gray matter) was affected differentially, such that TRYP was associated with higher CBF in HC, but not in participants with a Sz-related disorder. While TRYP did not significantly impair cognitive test performance, there was a trend for TRYP to worsen visuospatial memory task performance in HC. Our results demonstrate that oral TRYP challenge substantially increases plasma levels of kynurenine and KYNA in both groups, but exerts differential group effects on CBF. Future studies are required to investigate the mechanisms underlying these CBF findings, and to evaluate the impact of KYNA fluctuations on brain function and behavior. (Clinicaltrials.gov: NCT02067975).

This paper aims to develop a new diagnosis and treatment platform for autism spectrum disorder based on artificial intelligence in view of the difficulties in the autism diagnosis and treatment industry in the market, such as a mix of institutions, shortage of talents and high treatment costs, as well as the shortcomings of related diagnosis and treatment digital platforms with single functions and low differentiation. The platform is mainly presented in the form of websites, apps and small programs. It integrates the functions of autism diagnosis, autism intervention, prognosis monitoring and other functions. It quantifies and visualizes the diagnosis data of autism through artificial intelligence technology, and provides an intelligent "stethoscope" for autism diagnosis, with a view to further advancing the diagnosis age of children. To achieve early detection, intervention and treatment of autism, so that children with autism have a better prognosis. Families will be connected with professional resources through artificial intelligence, and services between people will be connected through science and technology, allowing professional intervention from organizations to enter families.

The pivotal tryptophan (TRP) metabolite kynurenine is converted to several neuroactive compounds, including kynurenic acid (KYNA), which is elevated in the brain and cerebrospinal fluid of people with schizophrenia (SZ) and may contribute to cognitive abnormalities in patients. A small proportion of TRP is metabolized to serotonin and further to 5-hydroxyindoleacetic acid (5-HIAA). Notably, KP metabolism is readily affected by immune stimulation. Here, we assessed the acute effects of an oral TRP challenge (6 g) on peripheral concentrations of kynurenine, KYNA and 5-HIAA, as well as the cytokines interferon-γ, TNF-α and interleukin-6, in 22 participants with SZ and 16 healthy controls (HCs) using a double-blind, placebo-controlled, crossover design. TRP raised the levels of kynurenine, KYNA and 5-HIAA in a time-dependent manner, causing >20-fold, >130-fold and 1.5-fold increases in kynurenine, KYNA and 5-HIAA concentrations, respectively, after 240 min. According to multivariate analyses, neither baseline levels nor the stimulating effects of TRP differed between participants with SZ and HC. Basal cytokine levels did not vary between groups, and remained unaffected by TRP. Although unlikely to be useful diagnostically, measurements of circulating metabolites following an acute TRP challenge may be informative for assessing the in vivo efficacy of drugs that modulate the neosynthesis of KYNA and other products of TRP degradation.

The advent of simultaneously collected imaging-genetics data in large study cohorts provides an unprecedented opportunity to assess the causal effect of brain imaging traits on externally measured experimental results (e.g., cognitive tests) by treating genetic variants as instrumental variables. However, classic Mendelian Randomization methods are limited when handling high-throughput imaging traits as exposures to identify causal effects. We propose a new Mendelian Randomization framework to jointly select instrumental variables and imaging exposures, and then estimate the causal effect of multivariable imaging data on the outcome. We validate the proposed method with extensive data analyses and compare it with existing methods. We further apply our method to evaluate the causal effect of white matter microstructure integrity on cognitive function. The findings suggest that our method achieved better performance regarding sensitivity, bias, and false discovery rate compared to individually assessing the causal effect of a single exposure and jointly assessing the causal effect of multiple exposures without dimension reduction. Our application results indicated that WM measures across different tracts have a joint causal effect that significantly impacts the cognitive function among the participants from the UK Biobank.

Positive predicted value and negative predicted value are used by clinicians to evaluate how likely a disease stage is present given the test results. In contrast, positive and negative likelihood ratios (LRs) are used in practice to assess the potential utility of a specific diagnostic test and the likelihood of a patient having the condition. This article introduces the concepts and the derivation of generalized predictive values and LRs from binary diseases to ordinal multistages diseases. We evaluate the performance of the proposed methods with numerical examples. We illustrated the proposed methods provided using real data.

Genome-wide association studies (GWAS) have identified and reproduced thousands of diseases associated loci, but many of them are not directly interpretable due to the strong linkage disequilibrium among variants. Transcriptome-wide association studies (TWAS) incorporated expression quantitative trait loci (eQTL) cohorts as a reference panel to detect associations with the phenotype at the gene level and have been gaining popularity in recent years. For nicotine addiction, several important susceptible genetic variants were identified by GWAS, but TWAS that detected genes associated with nicotine addiction and unveiled the underlying molecular mechanism were still lacking. In this study, we used eQTL data from the Genotype-Tissue Expression (GTEx) consortium as a reference panel to conduct tissue-specific TWAS on cigarettes per day (CPD) over thirteen brain tissues in two large cohorts: UK Biobank (UKBB; number of participants (N) = 142,202) and the GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN; N = 143,210), then meta-analyzing the results across tissues while considering the heterogeneity across tissues. We identified three major clusters of genes with different meta-patterns across tissues consistent in both cohorts, including homogenous genes associated with CPD in all brain tissues; partially homogeneous genes associated with CPD in cortex, cerebellum, and hippocampus tissues; and, lastly, the tissue-specific genes associated with CPD in only a few specific brain tissues. Downstream enrichment analyses on each gene cluster identified unique biological pathways associated with CPD and provided important biological insights into the regulatory mechanism of nicotine dependence in the brain.

Short interval intracortical inhibition (SICI) is a biomarker for altered motor inhibition in schizophrenia, but the manner in which distant sites influence the inhibitory cortical-effector response remains elusive. Our study investigated local and long-distance resting state functional connectivity (rsFC) markers of SICI in a sample of N = 23 patients with schizophrenia and N = 29 controls. Local functional connectivity was quantified using regional homogeneity (ReHo) analysis and long-range connectivity was estimated using seed-based rsFC analysis. Direct and indirect effects of connectivity measures on SICI were modeled using mediation analysis. Higher SICI ratios (indicating reduced inhibition) in patients were associated with lower ReHo in the right insula. Follow-up rsFC analyses showed that higher SICI scores (indicating reduced inhibition) were associated with reduced connectivity between right insula and hubs of the corticospinal pathway: sensorimotor cortex and basal ganglia. Mediation analysis supported a model in which the direct effect of local insular connectivity strength on SICI is mediated by the interhemispheric connectivity between insula and left sensorimotor cortex. The broader clinical implications of these findings are discussed with emphasis on how these preliminary findings might inform novel interventions designed to restore or improve SICI in schizophrenia and deepen our understanding of motor inhibitory control and impact of abnormal signaling in motor-inhibitory pathways in schizophrenia.

Social media platforms have become accessible resources for health data analysis. However, the advanced computational techniques involved in big data text mining and analysis are challenging for public health data analysts to apply. This study proposes and explores the feasibility of a novel yet straightforward method by regressing the outcome of interest on the aggregated influence scores for association and/or classification analyses based on generalized linear models. The method reduces the document term matrix by transforming text data into a continuous summary score, thereby reducing the data dimension substantially and easing the data sparsity issue of the term matrix. To illustrate the proposed method in detailed steps, we used three Twitter datasets on various topics: autism spectrum disorder, influenza, and violence against women. We found that our results were generally consistent with the critical factors associated with the specific public health topic in the existing literature. The proposed method could also classify tweets into different topic groups appropriately with consistent performance compared with existing text mining methods for automatic classification based on tweet contents.