Dear Guest Editors, On behalf of my co-authors, I am pleased to submit our manuscript entitled “IoT-Based Preventive Mental Health Using Knowledge Graphs and Standards for Better Well-Being” for consideration in the Special Issue on “Trustworthy Neurosymbolic AI in Regulated Domains: Advances and Challenges” (Neurosymbolic Artificial Intelligence, IOS Press), guest edited by Prof. Dhaval Thakker (University of Hull, UK), Prof. Amit Sheth (University of South Carolina, USA), Dr. Bhupesh Mishra (University of Hull, UK), Dr. Koorosh Aslansefat (University of Hull, UK), and Dr. Tejal Shah (Newcastle University, UK). This work contributes to the core mission of the special issue by demonstrating how neurosymbolic AI methods, ontologies, and standards can be harnessed for a highly regulated and socially impactful domain: mental health. Specifically, our paper: Proposes a neurosymbolic AI-enabled framework for preventive mental health, integrating statistical learning with symbolic reasoning over knowledge graphs. This enables explainable, trustworthy, and standards-compliant decision support. Introduces a curated ontology catalog (LOV4IoT-Mental Health) that systematically classifies mental health and depression ontologies, addressing gaps in existing catalogs such as BioPortal and LOV by emphasizing IoT and sensor-based data. Maps ontology-based mental health knowledge to international regulatory standards (e.g., ETSI SAREF4EHAW, ISO/IEEE 11073, W3C Semantic Web initiatives), ensuring regulatory semantic interoperability. Demonstrates preventive healthcare use cases—including digital twins, social robotics for aging, and AI-based recommendation systems—showcasing how neurosymbolic AI applications extend beyond traditional reasoning tasks into domains with direct societal impact. Bridges research and practice by showing how mental health knowledge graphs and standards-based reasoning can improve explainability, compliance, and trustworthiness of AI systems in sensitive clinical contexts. We believe this contribution will resonate strongly with the readership of the special issue, as it demonstrates both methodological advances (neurosymbolic reasoning, ontology-driven KG design) and applied insights in a pressing real-world domain (preventive mental health). The manuscript is approximately 1007 words, comfortably within the journal’s limits and leaving room for revisions if requested. We confirm that this manuscript is original, has not been published previously, and is not under consideration elsewhere. Thank you very much for considering our submission. We look forward to your feedback. Sincerely, Manas Gaur (on behalf of all authors) University of Maryland, Baltimore County Email: manas@umbc.edu