Ata Pourabbasi

Background: Subclinical metabolic disturbances have been associated with functional brain changes; however, the neural pathways mediating their cognitive effects remain unclear. Objectives: This study investigated whether large-scale brain network connectivity mediates associations between metabolic risk factors and cognition in healthy young adults. Methods: Data were obtained from 676 participants (22 - 37 years) in the Human Connectome Project. Metabolic indices (body mass index (BMI), glycated hemoglobin, thyroid-stimulating hormone (TSH), systolic/diastolic blood pressure, and hematocrit) were assessed alongside the National Institutes of Health (NIH) Toolbox Cognition Battery scores, the Mini-Mental State Examination, and fluid intelligence measures. Resting-state fMRI data underwent group independent component analysis to identify 14 intrinsic connectivity networks. Partial correlations were computed between network time series, and multiple regression models were adjusted for age, sex, race, and education. Mediation analyses with bootstrapped confidence intervals were conducted to test whether network connectivity explained the metabolic–cognition relationships. Results: We found that large-scale resting-state networks significantly mediated the associations between specific metabolic risk factors and cognitive performance. Specifically, BMI, hematocrit, and TSH showed significant associations with both cognition and network connectivity (all P < 0.05). BMI-related reductions in vocabulary performance were fully mediated by altered connectivity in the frontoparietal–language, sensorimotor–frontoparietal, primary visual–auditory, and default mode–executive attention networks and partially mediated (compensatory effect) by salience–sensorimotor connectivity (indirect effects: –0.0238, -0.0115, -0.0110, -0.0096, and 0.0068, respectively; 95% CIs: [–0.0421, –0.0085], [-0.0239, -0.0030], [-0.0237, -0.00016], [-0.0206, -0.0014], and [0.0001, 0.0165], respectively). Hematocrit’s positive association with vocabulary performance was partially mediated by the primary visual–auditory, salience–auditory, and language–dorsal attention networks, with primary visual–ventral attention connectivity exerting a suppressive effect (indirect effects: 0.0094, 0.0149, 0.0074, and -0.0113, respectively; 95% CIs: [0.0007, 0.0215], [0.0032, 0.0319], [0.0001, 0.0197], and [-0.0249, -0.0012], respectively). TSH was positively associated with fluid intelligence via a direct pathway (P = 0.010), without mediation by resting-state connectivity. Conclusions: These findings highlight large-scale brain networks as potential intermediate phenotypes that link metabolic health to cognition, suggesting that targeted neuromodulation of vulnerable circuits, combined with metabolic interventions, may offer novel strategies for preserving cognition in at-risk populations.

Background: Self-care plays an important role in improving health symptoms in patients diagnosed with chronic illnesses such as diabetes. Gamification is among the most effective methods for enhancing health monitoring by applying principles of behavioral economics and motivation. Objective: This study aimed to develop an innovative self-care system using a virtual reality (VR)–gamified intervention and to evaluate its effects on patients diagnosed with diabetes metabolic symptoms and health-related behaviors compared to other standard interventions. Methods: This study was a randomized controlled trial with a parallel-group design, conducted between December 2024 and February 2025. A total number of 78 patients diagnosed with diabetes were recruited from The Children’s Medical Center clinic in a closed, offline setting. Briefing, tutorials, and interventions were partly face-to-face. Eligible participants (n=68) were randomly assigned to the 4 groups using stratified block randomization based on age and sex, with allocation concealment ensured through a sequentially numbered process. Each group received a different type of self-care intervention for 6 weeks, which was health care provider-assisted. One group received the VR gamified intervention, while the other 3 groups received comparator interventions, including the MySugr (mySugr GmbH) monitoring app, traditional counseling, and medication only, respectively. The primary outcomes were fasting blood sugar and health-related behaviors, including physical activity and food intake, while secondary outcomes were long-term metabolic indicators, including hemoglobin A _1c (HbA _1c ) and BMI. All outcomes were measured by self-assessed questionnaires. No blinding was implemented for participants or care providers. However, data analysis was conducted using anonymized group labels. Eight participants were lost to follow-up. Minor missing daily entries were handled by weekly data aggregation. Results: Data from 60 participants were included in the final analysis (VR-gamified group n=16, application group n=14, counseling group n=15, and control group n=15). Linear mixed model showed significant time×group interaction effects for fasting blood sugar ( F _15,280 =2.046; P =.01; partial η²=.099), physical activity ( F _15,280.61 =2.544; P =.001; partial η²=.120), and food intake ( F _15,336 =2 .794; P <.001; partial η ² =.111), indicating greater improvement over time in the VR gamification group compared with the other parallel groups. No statistically significant differences were observed for BMI or glycated hemoglobin. No serious adverse events related to the VR intervention were reported. Conclusions: VR gamified self-care interventions may potentially contribute to better management of diabetes-related symptoms and behaviors compared with conventional self-care approaches. Such gamified VR systems show promise as alternative or complementary tools for enhancing health outcomes among children and adolescents diagnosed with diabetes.

Background Understanding whether commonly available metabolic, demographic, and behavioral factors can explain variability in brain structure may support the development of accessible predictive approaches. This study aims to evaluate the ability of machine learning regression models to predict brain structural measures in young, neurologically healthy adults using clinically accessible risk factors. Methods Data were drawn from the Human Connectome Project Young Adults dataset, including 1082 participants aged 22–37. Predictors comprised metabolic measures (systolic and diastolic blood pressure, hematocrit, body mass index, hemoglobin A1C, thyroid stimulating hormone) and demographic/behavioral variables (age, gender, race, education level, smoking history, alcohol use). Brain structural parameters from MRI included gray matter (GM) and white matter (WM) volumes, surface areas, and cortical thickness. Distance correlation (dcor) guided feature selection, followed by machine learning modeling. Model performance was evaluated using normalized mean absolute error (NMAE), normalized root-mean-squared error (NRMSE), and coefficient of determination (R²). SHAP values were used for feature interpretation. Results XGBoost consistently outperformed other algorithms, showing strong predictive accuracy for total brain segmentation, supratentorial, and total GM volumes (NMAE < 0.35, R² > 0.80). In contrast, cortical surface predictions (left superior frontal) showed moderate performance (NMAE > 0.50, R² < 0.60). SHAP analysis highlighted age, gender, education level, and diastolic and systolic blood pressure as the most influential predictors of brain structure. Conclusions The findings highlight the methodological value of regression-based approaches for estimating brain structural variability and support their potential role as complementary tools for brain health research.

Purpose This systematic review and meta-analysis aimed to synthesize the current evidence on the associations of metabolic syndrome (MetS) and its components, central obesity, hypertension, dyslipidemia, and elevated fasting plasma glucose (FPG) with brain volumes and structural connectivities. Methods We conducted a comprehensive literature search in major databases (PubMed, Web of Science, Scopus, and ScienceDirect) up to December 2024. Studies were included if they reported quantitative data on the association between MetS or its components and brain volumetric or white matter (WM) integrity outcomes in adults. Two independent reviewers screened titles, abstracts, and full texts, extracted data, and assessed study quality using established tools. Random-effects meta-analyses were performed to pool effect sizes (ESs) for key outcomes. Heterogeneity was assessed using I² statistics, and sensitivity analyses were conducted to evaluate the robustness of findings. Results A total of 38 studies (n = 27 to 37395 participants) met the inclusion criteria. Central obesity and hypertension were robustly associated with reduced total brain volume and increased WM hyperintensity volume, respectively (n = 4677, ES = -0.15, 95% CI: -0.24, -0.05 and n = 36135, ES = 0.22, 95% CI: 0.13, 0.31, respectively). Hypertension also demonstrated a significant negative association with WM fractional anisotropy (FA) (n = 31167, ES = -0.20, 95% CI: -0.35, -0.05). Elevated FPG and low HDL cholesterol were linked to adverse brain changes, though the evidence was less consistent. High triglyceride levels did not consistently predict brain structural alterations. Heterogeneity was high for the analyses (I² >80%), and results remained significant in sensitivity analyses. Conclusions MetS and its key components are associated with detrimental effects on brain structure and connectivity. These findings underscore the importance of early detection and management of metabolic risk factors for preserving brain health and reducing the risk of cognitive decline and dementia.

Background and Aims: Eating habits and nutritional behavior impact brain structures and lifestyle modification, yet their bidirectional relationship with neural structures remains underexplored. This review aimed to shed light on this complex relationship in the brain–behavior axis by reviewing publications that have examined the relationship between eating habits, nutritional behaviors, and their neural correlates. Methods: For this review, three databases of Scopus, PubMed, and Web of Science were primarily searched. Twenty‐one English‐language original papers published up to January 2025 were ultimately included focusing on the general population (healthy individuals covering all age groups) and magnetic resonance imaging (MRI) as the neuroimaging modality. Results: Healthier eating habits, such as the Mediterranean diet and high fish intake, were positively associated with brain volumes, microstructures, and cortical surface areas. Conversely, less healthy eating habits, like the Western diet, were linked to reductions in brain structures, particularly in the hippocampus and gray matter. Specific eating behaviors, such as restrained eating and enjoyment of food, showed significant associations with changes in brain structures. The impact of dietary patterns on brain structures varied by age and gender, with some diets showing protective effects against brain atrophy and white matter hyperintensities. Conclusion: This systematic review emphasizes the significant associations between eating habits, nutritional behaviors, and brain structures. Future research should focus on identifying underlying mechanisms and developing personalized dietary interventions coupled with brain stimulation to promote brain health and prevent cognitive decline.

Background: Health systems are addressing adolescents' excessive use of social network sites (SNS) due to growing evidence linking SNS addiction to mental health problems. Objectives: This study aimed to develop, validate, and preliminarily evaluate the effectiveness of an intervention package designed to help adolescents manage their SNS use, focusing on use, attitudes, media literacy, and addiction. Methods: The intervention package was developed under the supervision of eight specialists in psychiatry and psychology. Its validity was assessed using the Content Validity Index (CVI). Experts confirmed the validity of the SNS management package, which was implemented over eight 90-minute sessions. To assess the preliminary effectiveness of the developed package, a quasi-experimental design featuring pre-test/post-test comparisons with control groups was utilized. The target population consisted of middle school students in Tehran, and the study was conducted in 2024. Fifty adolescents with SNS addiction were purposively sampled and randomly assigned to intervention (n = 25) and control groups (n = 25), with 39 completing the study. Data were collected using the Social Media Addiction Scale, the attitude toward social networking sites questionnaire, and the Media Literacy Questionnaire. Data analysis utilized independent sample t-tests and one-way ANCOVA, with a significance level set at 0.05. Results: Experts found the package valid regarding relevance, effectiveness, and appropriateness, with scores ranging from 0.750 to 1.000. The results indicated that the training package for managing SNS use effectively impacts daily SNS use in the past week (F = 7.25; P = 0.011), attitudes toward SNS (F = 8.48; P = 0.006), and media literacy (F = 56.09; P = 0.001). However, it did not reduce SNS addiction (F = 0.55; P = 0.464). Conclusions: The study found that while the educational package did not reduce SNS addiction, it successfully decreased daily SNS use, improved attitudes toward SNS, and enhanced media literacy. These preliminary findings suggest the intervention's potential utility in the primary prevention of problematic social media use.

Optimal glucose control is crucial for maintaining brain health and preventing metabolic and cognitive disorders in the general population. Glycosylated hemoglobin (HbA1c) serves as a key marker for assessing glucose intolerance and its impact on brain structure and function in healthy individuals. However, existing literature presents conflicting findings, necessitating a systematic review to consolidate current knowledge in this domain. This systematic review examines 26 English‐language studies involving participants aged 15 years and above, investigating the relationship between HbA1c levels and brain health. Studies focusing on normal/general populations and utilizing magnetic resonance imaging (MRI) as the imaging modality were included. Exclusion criteria encompassed review articles, abstracts, letters, animal studies, and research involving neuropsychiatric or metabolic diseases. Data were gathered from PubMed, Scopus, and Web of Science databases up to November 2023. Analysis reveals significant associations between HbA1c levels and various brain metrics, including volume, cortical thickness, fractional anisotropy, mean diffusivity, activity, and connectivity. However, findings exhibit inconsistency, likely attributed to disparities in sample characteristics and study sizes. Notably, hippocampal volume, white matter hyperintensity, and ventral attention network connectivity emerge as frequently affected structures and functions, mirroring trends observed in diabetic populations. Despite inconclusive evidence, glucose intolerance appears to exert considerable influence on select brain structures and functions in individuals without diagnosed metabolic disorders. Understanding these associations is critical for mitigating the risk of cognitive decline and dementia in healthy populations. Future investigations should aim to elucidate the intricate relationship between HbA1c concentrations and brain health parameters in normoglycemic individuals.

Purpose The COVID-19 pandemic has sparked concerns about weight management because of the changes in population lifestyle. As previous studies suggest, these changes can affect adolescents to a greater degree, especially as schools shut down. Designing a method for weight management that accounts for the specific conditions of the pandemic can help students attend to their physical conditions during the pandemic. Method An online gamified peer pressure group on WhatsApp was employed to help 10th-grade boys in a high school in Tehran, Iran. The available sample consisted of 15 main group students and 13 control students. A paired t-test examined the difference between each group’s initial and final state as well as the group’s final state. Additionally, an ANCOVA test was done to ensure that the observed effect was because of the intervention. Results The initial BMI for the intervention group (µ = 30.68, δ = 3.23) and the control group (µ = 29.73, δ = 3.23) were the same (p = 0.468). Two dropouts in the intervention group were found after forty days. A significant difference between the intervention group’s steady and final states (p = 0.027) was detected; however, this difference was not significant in the control group (p = 0.634). Conclusion Online gamified social groups can play a key role in adolescents’ weight management during the COVID-19 pandemic.