Horowitz Frost (benchfield2)

By applying our framework to 50 subjects of the Human Connectome Project, we reproduce 6 recently published results, devise two new models and provide a direct comparison between all mappings. Finally, we show that a glass ceiling on the performance of mappings based on eigenmodes seems to be reached and conclude with possible approaches to break this performance limit.Diagnosis of Parkinson's disease (PD) remains a challenge in clinical practice, mostly due to lack of peripheral blood markers. Transcriptomic analysis of blood samples has emerged as a potential means to identify biomarkers and gene signatures of PD. In this context, classification algorithms can assist in detecting data patterns such as phenotypes and transcriptional signatures with potential diagnostic application. In this study, we performed gene expression meta-analysis of blood transcriptome from PD and control patients in order to identify a gene-set capable of predicting PD using classification algorithms. We examined microarray data from public repositories and, after systematic review, 4 independent cohorts (GSE6613, GSE57475, GSE72267 and GSE99039) comprising 711 samples (388 idiopathic PD and 323 healthy individuals) were selected. Initially, analysis of differentially expressed genes resulted in minimal overlap among datasets. To circumvent this, we carried out meta-analysis of 17,712 genes across datasets, and calculated weighted mean Hedges' g effect sizes. From the top-100- positive and negative gene effect sizes, algorithms of collinearity recognition and recursive feature elimination were used to generate a 59-gene signature of idiopathic PD. This signature was evaluated by 9 classification algorithms and 4 sample size-adjusted training groups to create 36 models. Of these, 33 showed accuracy higher than the non-information rate, and 2 models built on Support Vector Machine Regression bestowed best accuracy to predict PD and healthy control samples. In summary, the gene meta-analysis followed by machine learning methodology employed herein identified a gene-set capable of accurately predicting idiopathic PD in blood samples. Sleep problems are a common clinically reported area of concern for children and adolescents with fetal alcohol spectrum disorder (FASD). However, limited empirical research has been undertaken investigating sleep problems for children with FASD. The current study aimed to examine the associations between parent-reported sleep problems in children with FASD and child behaviour, caregiver mental health and health-related quality of life and family functioning. 163 caregivers of children diagnosed with FASD aged 5-17 years were included in the current study. Cross-sectional online survey that collected information pertaining to child sleep problems (difficulty falling asleep, difficulty staying asleep and/or frequent waking during the night and waking early in the morning) and standardised caregiver reported measures of child behaviour, caregiver mental wellbeing, caregiver health-related quality of life, and family functioning. Sleep problems were common, affecting 65.6% (n=107) of participants. Difficulty falling asleep (56.4%) was the most common sleep problem encountered, followed by difficulty staying asleep (44.8%) and waking early (29.4%). see more Sleep problems were associated with increased rates of child behaviour problems and caregiver anxiety and negative impacts on caregiver and family quality of life. Sleep problems in children and adolescents with FASD are common and associated with poorer child, caregiver and family outcomes. Future research needs to determine whether effective identification and management of sleep problems can reduce adverse outcomes. Sleep problems in children and adolescents with FASD are common and associated with poorer child, caregiver and family outcomes. Future research needs to determine whether effective identification