Parkinson’s disease (PD) is a progressive neurodegenerative disorder that impairs motor function, including the fine motor control required for handwriting. Traditional diagnostic methods often lack sensitivity and objectivity in the early stages, limiting opportunities for timely intervention. There is a growing need for non-invasive, accessible tools capable of capturing subtle motor changes that precede overt clinical symptoms. Among early PD manifestations, handwriting impairments such as micrographia have shown potential as digital biomarkers. However, conventional handwriting analysis remains subjective and limited in scope. Recent advances in artificial intelligence (AI) and machine learning (ML) enable automated analysis of handwriting dynamics, such as pressure, velocity, and fluency, collected via digital tablets and smartpens. These tools support the detection of early-stage PD, monitoring of disease progression, and assessment of therapeutic response. This paper highlights how AI-enhanced handwriting analysis provides a scalable, non-invasive method to support diagnosis, enable remote symptom tracking, and personalize treatment strategies in PD. This approach integrates clinical neurology with computer science and rehabilitation, offering practical applications in telemedicine, digital health, and personalized medicine. By capturing dynamic features often missed by traditional assessments, AI-based handwriting analysis contributes to a paradigm shift in the early detection and long-term management of PD, with broad relevance across neurology, digital diagnostics, and public health innovation.

Antisocial Personality Disorder (ASPD) is a complex and often debilitating condition that can emerge from early behavioral disturbances in childhood and adolescence. This narrative review provides a comprehensive overview of the current understanding of ASPD in pediatric and adolescent populations, examining key diagnostic challenges, developmental trajectories, and emerging treatment approaches. Recent research underscores the critical role of the early identification of conduct disorder (CD) and oppositional defiant disorder (ODD) as precursors to ASPD. Specific attention is given to biological, environmental, and psychosocial risk factors, including genetic predispositions, family dynamics, and socio-economic adversity. Additionally, neuro-psychological research highlights deficits in executive function, emotion regulation, and social cognition, which may underline the persistent antisocial patterns. Neuroimaging studies suggest atypical neural activity in regions associated with empathy, reward processing, and impulse control. Effective intervention remains a challenge, as treatment options are limited and often complicated by co-occurring conditions, such as attention deficit hyperactivity disorder (ADHD) and mood disorders. Promising evidence supports the efficacy of integrative, multimodal approaches combining behavioral therapy, family interventions, and pharmacotherapy to reduce symptom severity and improve long-term outcomes. The review concludes by advocating for a public health approach that emphasizes prevention and early intervention, aiming to mitigate the progression to full ASPD in adulthood.

Background: The widespread use of social media among children and adolescents has raised increasing concern about its potential impact on mental health. Given the unique neurodevelopmental vulnerabilities during adolescence, understanding how digital platforms influence psychiatric outcomes is critical. Objectives: This narrative review aims to synthesize current evidence on the relationship between social media exposure and key psychiatric symptoms in youth, including depression, anxiety, body image disturbances, suicidality, and emotional dysregulation. Methods: We conducted a comprehensive narrative review of the literature, drawing from longitudinal, cross-sectional, and neuroimaging studies published in peer-reviewed journals. Specific attention was given to moderators (e.g., age, gender, and personality traits) and mediators (e.g., sleep, emotion regulation, and family context) influencing the relationship between social media use and mental health outcomes. Results: Evidence indicates that certain patterns of social media use, especially passive or compulsive engagement, are associated with increased risk of depression, anxiety, body dissatisfaction, and suicidal ideation. Adolescent girls, younger users, and those with low self-esteem or poor emotional regulation are particularly vulnerable. Neuroimaging studies show that social media activates reward-related brain regions, which may reinforce problematic use. Family support and digital literacy appear to mitigate negative effects. Conclusions: Social media use is not uniformly harmful; its psychological impact depends on how, why, and by whom it is used. Multilevel prevention strategies, including media education, parental involvement, and responsible platform design, are essential to support healthy adolescent development in the digital age.

Background/Objectives: Although depression and anxiety are found to be affected by temperaments, little research has studied these relationships in pregnancy. The present study explored the associations among perinatal depression (PD), anxiety dimensions (state, trait, and generalized anxiety disorder (GAD)), and temperaments between women in the three trimesters of pregnancy through a network analysis approach. Moreover, differences in the severity of PD and anxiety between women in the three trimesters were evaluated. Methods: Women in first (N = 31), second (N = 184), and third (N = 54) trimesters of pregnancy were recruited in the present cross-sectional study. The network analysis included PD, anxiety dimensions, and temperaments. Three network models were estimated, and ANOVAs evaluated the differences in the severity of PD and anxiety, including trimesters as a between-subject factor. Results: PD and GAD were the nodes most strongly connected across the three groups. Cyclothymic, depressive, and anxious temperaments were most frequently associated with PD and GAD. Hyperthymic temperament was in the periphery of the three networks. Lastly, women in the first trimester had the highest severity of PD and GAD. Conclusions: PD and GAD showed the strongest associations. Anxiety dimensions had positive associations with PD and GAD, suggesting their role as possible risk factors. Temperaments were differently associated within the network between the three groups. Clinical interventions during pregnancy should target the central variables, considering their direct and indirect relationships.

Background: The gut microbiota plays a crucial role in brain development and function, especially in early life. Disruptions in the pediatric microbiota–gut–brain axis have been linked to neurodevelopmental and psychiatric disorders. We hypothesize that early-life dysbiosis can perturb neurodevelopment via the pediatric microbiota–gut–brain axis, increasing risk and/or severity of neuropsychiatric outcomes, and that microbiota-targeted strategies may mitigate this risk. Methods: We conducted a narrative review by searching PubMed, Scopus, and Web of Science up to January 2025 for studies addressing pediatric microbiota, neuropsychiatric development, and interventions. Human and animal studies were included if they provided mechanistic or clinical insights. Results: Key determinants of microbiota development in childhood include mode of delivery, feeding practices, antibiotic exposure, diet, and environment. Altered microbial composition has been associated with autism spectrum disorder, attention-deficit/hyperactivity disorder, mood disorders, anxiety, and anorexia nervosa. Mechanistic pathways involve immune modulation, neural signaling (including the vagus nerve and enteric nervous system), and microbial metabolites such as short-chain fatty acids. Interventions targeting the microbiota—ranging from dietary strategies and probiotics to psychobiotics and fecal microbiota transplantation—show promise but require further pediatric-focused trials. Conclusions: The pediatric microbiota–gut–brain axis represents a critical window for neuropsychiatric vulnerability and intervention. Early-life strategies to support a healthy microbiota may help reduce the risk or severity of psychiatric disorders. Future research should prioritize longitudinal pediatric cohorts and clinical trials to translate mechanistic insights into precision interventions.

Background: Bipolar disorder (BD) is a chronic and disabling psychiatric condition characterized by recurring episodes of mania, hypomania, and depression. Despite the availability of mood stabilizers, antipsychotics, and antidepressants, long-term management remains challenging due to incomplete symptom control, adverse effects, and high relapse rates. Methods: This paper is a narrative review aimed at synthesizing emerging trends and future directions in the pharmacological treatment of BD. Results: Future pharmacotherapy for BD is likely to shift toward precision medicine, leveraging advances in genetics, biomarkers, and neuroimaging to guide personalized treatment strategies. Novel drug development will also target previously underexplored mechanisms, such as inflammation, mitochondrial dysfunction, circadian rhythm disturbances, and glutamatergic dysregulation. Physiological endophenotypes, such as immune-metabolic profiles, circadian rhythms, and stress reactivity, are emerging as promising translational tools for tailoring treatment and reducing associated somatic comorbidity and mortality. Recognition of the heterogeneous longitudinal trajectories of BD, including chronic mixed states, long depressive episodes, or intermittent manic phases, has underscored the value of clinical staging models to inform both pharmacological strategies and biomarker research. Disrupted circadian rhythms and associated chronotypes further support the development of individualized chronotherapeutic interventions. Emerging chronotherapeutic approaches based on individual biological rhythms, along with innovative monitoring strategies such as saliva-based lithium sensors, are reshaping the future landscape. Anti-inflammatory agents, neurosteroids, and compounds modulating oxidative stress are emerging as promising candidates. Additionally, medications targeting specific biological pathways implicated in bipolar pathophysiology, such as N-methyl-D-aspartate (NMDA) receptor modulators, phosphodiesterase inhibitors, and neuropeptides, are under investigation. Conclusions: Advances in pharmacogenomics will enable clinicians to predict individual responses and tolerability, minimizing trial-and-error prescribing. The future landscape may also incorporate digital therapeutics, combining pharmacotherapy with remote monitoring and data-driven adjustments. Ultimately, integrating innovative drug therapies with personalized approaches has the potential to enhance efficacy, reduce adverse effects, and improve long-term outcomes for individuals with bipolar disorder, ushering in a new era of precision psychiatry.