We investigated the effects of parental support, individual factors (general self-efficacy and social emotional loneliness), and school adjustment (general aspects about school and teachers, peer relationship, and academic and disciplinary difficulties) on adolescents’ well-being and ill-being. The aim is to study how these different sets of predictors affect well-being and ill-being and the interrelationships between different sets of predictors. A total of 3,541 Secondary 1 adolescents (Mean age = 13.31 years, SD = 0.39) from 18 schools in Singapore participated in this survey. We examined our hypothesized model using EQS. A full latent variable model was used. General self-efficacy and general aspects about school and teachers positively predicted well-being and negatively predicted ill-being. Social emotional loneliness and academic and disciplinary difficulties negatively predicted well-being and positively predicted ill-being. Individual child factors (self-efficacy and social emotional loneliness) had indirect effects on well-being and ill-being through school adjustment. Finally, parental support positively predicted general self-efficacy and negatively predicted social emotional loneliness. Individual child factors, parental support, and general aspects about school and teachers are important predictors of well-being and ill-being. Additionally, our findings highlight the essential role of teachers and the school environment in adolescent school adjustment experiences. Our findings supported the dual factor model of mental health, and emphasized the importance of parental support in alleviating adolescents’ social emotional loneliness.
BACKGROUND:Bipolar disorder (BD) is associated with clinical and biological markers of premature aging. In this largest study of brain age in BD to date, with 2919 participants, we compared brain-predicted age difference (brain-PAD) in individuals with BD and healthy comparison (HC) participants. Brain-PAD is a machine learning-estimated metric that quantifies the difference between an individual's predicted brain age and their chronological age, a potential clinical bio-signature of premature brain aging. Within individuals with BD, we also examined how medication and clinical characteristics were related to brain-PAD. METHODS:Age was predicted from 77 MRI measures of regional subcortical and lateral ventricle volumes, cortical thickness, and surface area for 1342 BD and 1577 HC adult participants, aged 18-75 yrs. old (μ = 37.2; SD = 12.3), from the curated ENIGMA Bipolar Disorder working group (ENIGMA-BD) and leveraging an ENIGMA machine learning model previously trained and validated using independent samples. Chronological age was subtracted from predicted age to produce an individual-level estimate known as brain-PAD. Linear mixed models (adjusting for sex and age as fixed effects and site as a random effect) were used to examine group differences and clinical associations. RESULTS:BD was associated with higher brain-PAD, compared to HC, primarily among older patients, as demonstrated by a significant age by diagnosis interaction (+0.05 [SE: 0.02] years). Individuals with BD on antiepileptic (AED) medications only (+3.20 [SE: 0.78] years) or on both AED and second-generation antipsychotics (SGA) (+3.74 [SE: 0.89] years) demonstrated greater brain-PAD compared to individuals who were not on any of the examined medications. Those taking lithium, whether alone or with AED and SGA independently, showed no difference in brain-PAD compared to individuals not taking any of the examined medications. However, individuals who were taking lithium showed lower brain-PAD compared to those on AED (-4.48 [SE: 0.84] years) or AED and SGA (-5.01 [SE:0.92] years). Individuals with a BD I subtype diagnosis had a higher brain-PAD (+1.50 [SE:0.55] years) compared to those with BDII or subtypes that are not otherwise specified (NOS). CONCLUSIONS:Results from this study suggest compounding effects of BD diagnosis and older age on brain-PAD, an ML-derived summary metric of structural alterations. Within BD, brain-PAD was differentially related to medication use, consistent with prior findings from ENIGMA-BD. Notably, AED use was generally related to more advanced brain age. Lithium use, alone or in combination with other medications, was not associated with advanced brain age, suggesting a possible neuroprotective effect of lithium. Brain-PAD as an ML-derived summary metric of structural alterations of the brain may provide clinical utility in assessing long-term holistic brain health to monitor the effectiveness of lifestyle modifications or treatments over time. LIMITATIONS:The cross-sectional nature of the study design and the limited granularity of the clinical data limit interpretation. Longitudinal studies with detailed chronicity data, medications and clinical measures overtime will improve brain-PAD modeling in BD.
Functional connectivity (FC) is often used to identify personalized targets for transcranial magnetic stimulation (TMS). However, existing methods often overlook individual differences in whole-cortex network organization. Furthermore, in some personalized TMS protocols, lower stimulation intensity is used for targets closer to the scalp, which may improve patient tolerance. Here, we develop an algorithm to simultaneously optimize FC and scalp proximity for target localization. We first use the multi-session hierarchical Bayesian model (MS-HBM) to estimate high-quality individual-specific cortical networks. A tree-based algorithm is then used to select the optimal target. With essentially no parameter to tune, our framework may potentially improve generalizability across populations. We compare our approach with existing "cluster" and "cone" algorithms. In two test-retest datasets of healthy individuals from the United States and Singapore, tree-based MS-HBM reliably identifies personalized TMS targets for depression near the scalp. Tree-based MS-HBM targets compare favorably with cluster and cone targets in terms of reliability, scalp proximity, and FC to the subgenual anterior cingulate cortex (sACC) in new out-of-sample MRI sessions. To demonstrate versatility, the same algorithm identifies personalized anxiety targets without tuning any parameter. In patients with treatment-resistant depression, tree-based MS-HBM targets compare favorably with cluster and cone targets in terms of reliability, scalp proximity, and sACC FC, hypothetically reducing stimulation intensity by 15% and 5%, respectively. MS-HBM also exhibits the best (most negative) electric-field hotspot sACC FC and highest reliability in induced electric fields. Overall, tree-based MS-HBM provides a robust, generalizable framework to estimate near-scalp personalized targets across populations.
We analyze speech embeddings from structured clinical interviews of psychotic patients and healthy controls by treating language production as a high-dimensional dynamical process. Lyapunov exponent (LE) spectra are computed from word-level and answer-level embeddings generated by two distinct large language models, allowing us to assess the stability of the conclusions with respect to different embedding presentations. Word-level embeddings exhibit uniformly contracting dynamics with no positive LE, while answer-level embeddings, in spite of the overall contraction, display a number of positive LEs and higher-dimensional attractors. The resulting LE spectra robustly separate psychotic from healthy speech, while differentiation within the psychotic group is not statistically significant overall, despite a tendency of the most severe cases to occupy distinct dynamical regimes. These findings indicate that nonlinear dynamical invariants of speech embeddings provide a physics-inspired probe of disordered cognition whose conclusions remain stable across embedding models.
Background Females are less likely than males to be diagnosed with attention-deficit hyperactivity disorder (ADHD). When diagnosed, females are older than males.Aims In this study, we examined the childhood antecedents of later ADHD diagnosis and its impact on adolescent/emerging adult outcomes, with a focus on females.Method In this cohort study, we used data from a Welsh nation-wide electronic cohort of 13 593 individuals (n = 2680 (19.7%) females) diagnosed with ADHD and 578 793 individuals (n = 286 734 (49.5%) females) without ADHD. We compared females with later diagnoses (ages 12-25) to those with earlier, timely diagnoses (ages 5-11) and no diagnosis, in terms of childhood (ages 5-11) antecedents and adolescent/adult (ages 12-25) outcomes. We also tested for sex differences.Results Although females with earlier ADHD diagnosis showed more health and educational difficulties in childhood than those with later diagnosed ADHD (odds ratios ranged from 0.18 to 0.92), there was clear evidence of these difficulties in females with later diagnosed ADHD, compared with females without ADHD (odds ratios: 1.07-9.02). In adolescence/early adulthood, females with later diagnosed ADHD used more healthcare services and had worse mental health, educational and socioeconomic outcomes than females diagnosed earlier (odds ratios: 1.39-4.96) and those without ADHD (odds ratios: 1.54-23.98). Many of these outcomes were exacerbated in females compared with males.Conclusions The results demonstrate that later ADHD diagnosis is associated with significant negative outcomes by adolescence and disproportionately disadvantages females. Despite later diagnosis, there was clear evidence of childhood mental health and educational difficulties when compared with females without ADHD. Therefore, timely childhood ADHD diagnosis may help to mitigate later risks, especially for females.
