International Neuropsychiatric Disease Journal

Fear of Missing Out as a Neuropsychiatric Vulnerability Syndrome in Adults: A Conceptual Review Integrating Anxiety, Predictive Cognition, Emotional Dysregulation and Digital Allostatic Load

Sunil Kumar Gupta — 2026-02-25

Fear of Missing Out (FOMO) has become a defining psychological experience of the digital era. Although widely studied in relation to social media use and problematic smartphone behaviour, FOMO has rarely been examined through a neuropsychiatric lens, particularly in adult populations. Existing research predominantly conceptualizes FOMO as a situational motivator or behavioural correlate, underestimating its role as a chronic cognitive-emotional vulnerability. The present article advances a novel framework positioning FOMO as a neuropsychiatric vulnerability syndrome characterized by persistent anticipatory anxiety, predictive cognitive overload, attentional fragmentation, emotional dysregulation, and cumulative digital allostatic load. This study employed a structured narrative-conceptual review methodology. A targeted literature search was conducted across PsycINFO, PubMed, and Scopus using predefined keywords related to FOMO, anxiety vulnerability, predictive processing, emotional regulation, adult development, and digital stress. Articles were screened for relevance to adult populations and neuropsychiatric mechanisms, with priority given to empirical studies, longitudinal research, and theoretical integrations published in peer-reviewed journals. Findings were critically synthesized through thematic analysis to identify recurring cognitive, emotional, and developmental mechanisms, which were subsequently integrated into a unified vulnerability model. Drawing upon psychological, cognitive, developmental, and neuropsychiatric scholarship, this review demonstrates how FOMO operates as a sustained vulnerability mechanism rather than a transient digital concern. Adult developmental contexts are emphasized, highlighting how identity consolidation, career pressures, and perceived irreversibility of choices intensify FOMO-related distress. By reframing FOMO as a syndrome-level vulnerability embedded within adult cognitive-emotional functioning, this article contributes a theoretically advanced perspective to contemporary neuropsychiatric discourse.

Regulatory, Ethical, and Legal Provisions: Integrating Positron Emission Tomography, Magnetic Resonance Imaging, Electroencephalography Data with Machine Learning for Differential Diagnosis of Neurodegenerative Disorders

Kimberly Morton Cuthrell — 2026-03-05

Differentially diagnosing neurodegenerative disorders is challenging due to similar appearing symptoms and various subtype mechanisms that disrupts behavioral, cognitive, and physical functions. Single modality, unimodal, biomarker types such as positron emission tomography (PET), magnetic resonance imaging (MRI), and electroencephalogram (EEG) may provide incomplete representation of neurological processes in the brain. Current evidence suggests the combined use of trimodal neuroimaging techniques (PET, MRI, EEG) and electrophysiological approaches with machine learning (ML) may generate a complete representation in the diagnosis of Alzheimer's disease, frontotemporal dementia, Dementia with Lewy bodies, Parkinson's disease, and Primary Progressive Aphasia. Classical and deep learning techniques with particular focus on data fusion methods, diagnostic accuracies, clinical applicability, and translational challenges, including the integration of tri-modal neuroimaging may expands explainability artificial intelligence (XAI) and clinical validation. Advancements in machine learning-based multimodal data with integration of tri-modal neuroimaging may leverage biologically relevance and improve early diagnosis and prognosis to differentiate subtypes of neurodegenerative disorders. Though tri-modal usage withML may yield promising results with detecting molecular specificity and visualization of neuroanatomical substrates of the brain, challenges may with limitations in dataset sizes, variations across sites, insufficient standardization, machine-learning malfunctions, interpretability issues, including obstacles with regulatory compliance, ethical, and legal provisions.

Antidepressant-like Effects of Cannabidiol and Tetrahydrocannabinol in a Reserpine-induced Mouse Model of Parkinson’s Disease

H. O. Tanko — 2026-03-27

Parkinson's disease (PD) is the second most common neurodegenerative disorder after Alzheimer's disease, characterised by the progressive degeneration of dopaminergic neurons in the substantia nigra. Beyond its well-recognised motor manifestations, PD is associated with a broad spectrum of non-motor symptoms including constipation, pain, cognitive impairment, and depressive symptoms. The aim of this study was to investigate the antidepressant-like effects of cannabidiol (CBD) and tetrahydrocannabinol (THC) in a reserpine-induced Parkinson's disease mouse model. Forty-two adult mice were randomly allocated into seven groups of six animals each. Group I received distilled water (10 ml/kg) and served as the normal control; Group II received reserpine (0.5 mg/kg) only; Groups III and IV received reserpine plus CBD at 30 mg/kg and 60 mg/kg respectively; Groups V and VI received reserpine plus THC at 4 mg/kg and 6 mg/kg respectively; and Group VII received reserpine combined with CBD (60 mg/kg) and THC (6 mg/kg). The primary outcome measures were immobility time assessed using the forced swim test (FST) as a behavioural index of depression, and brain dopamine concentration measured by enzyme-linked immunosorbent assay (ELISA). Treatment with CBD and THC, both individually and in combination, significantly reduced immobility time (P < 0.05) compared to the reserpine-only group, with the combination treatment producing the most pronounced effect. Mice treated with THC (4 mg/kg) and the combination of CBD (60 mg/kg) and THC (6 mg/kg) demonstrated significantly elevated brain dopamine concentrations relative to the reserpine-only group. These findings suggest that CBD and THC may exhibit antidepressant-like properties in a preclinical model of Parkinson's disease, with effects potentially mediated through modulation of dopaminergic neurotransmission. Further studies are warranted to evaluate the long-term safety, dose optimisation, and translational relevance of these findings before clinical applications can be considered.

Challenges and Support Strategies During the Transition to Adulthood in Autism Spectrum Disorder

Godwin Onu — 2026-02-18

The transition from adolescence to adulthood stands as one of the most difficult and challenging life phases for individuals with Autism Spectrum Disorder (ASD). This systematic review synthesizes findings exclusively from peer-reviewed literature published between 2010 and 2024 to provide a comprehensive analysis of this critical period. Following a structured search, PRISMA guidelines and selection protocol, 67 articles satisfied the inclusion criteria. The review described the challenges encountered by individuals with Autism Spectrum Disorder during this transition stage namely; independence, social relationships and general well-being. Analyzes the major systemic, familial and individual-level challenges inherent to the transition phase itself and evaluates evidence-based support strategies. Studies show that competitive Integrated Employment (CIE) remains low, with a pooled prevalence of 21.7% among young individuals within five years of high school exit. This is in stark contrast to the 58% employment rate for the broader impaired population of the same age group.

Finally, the review delineates concrete implications for practice advocating for early, strengths-based, person-centered planning and policy calling for coordinated, funded, and inclusive systemic reforms. The synthesis comes to the conclusion that, despite the significant and varied difficulties, there is a robust evidence supporting successful interventions. Therefore, a widespread, long-term, coordinated support that respects neurodiversity and encourages self-determination is critically needed. The goal should be not just adequacy but also a high quality of life and meaningful engagement in adult society.

Salivary MicroRNAs as Non-invasive Epigenetic Biomarkers for Early Detection of Autism Spectrum Disorder: A Systematic Review

Juan Ramón Escalante González — 2026-03-05

Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with a multifactorial etiology, with a male-to-female predominance of 4:1. Currently, clinical diagnosis is late, missing the window of therapeutic neuroplasticity for timely intervention. Salivary microRNAs (miRNAs) are stable epigenetic regulators that offer a non-invasive collection method. This study aimed to evaluate the evidence on the diagnostic accuracy of salivary miRNAs in ASD and polyomics integration.

Methods: A systematic review was conducted under PRISMA 2020 guidelines using PubMed and Google Scholar between 2016 and 2025. Observational case-control studies in the pediatric population with a validated ASD diagnosis versus controls were selected. Quality and risk of bias were assessed using the Newcastle-Ottawa Scale (NOS). A total of 657 records were identified, and after screening and eligibility assessment, six studies were included in the qualitative synthesis.

Results: We included six studies with 1,149 participants, 73.2% male and 26.8% female. The predictive models showed high diagnostic performance, a panel of 5 miRNAs achieved an area under the ROC curve (AUC) of 0.952, with sensitivity of 90.32% and specificity of 90%, while the polyomic approach integrating human and microbial RNA reached an AUC value of 0.88 (95% CI: 0.86-0.88), with sensitivity of 82% and specificity of 88%. Individually, miR-451a exhibited significant downregulation in patients with ASD (FC = -3.58; P < 0.0001). Alterations in axon guidance, circadian rhythms, and synaptic signaling pathways were confirmed. A negative correlation (r = -0.30) was found between miR-141-3p and the genus Tannerella.

Conclusion: Salivary miRNA panels demonstrate promising diagnostic discrimination in case-control settings, overcoming subjective behavioral tool limitations, but prospective screening validation remains needed. Non-invasive collection facilitates timely detection within the neuroplasticity window, where early intervention is most effective. Furthermore, integrating host and microbial transcriptomic data supports a multifactorial ASD pathophysiology.