Abnormal dynamic functional connectivity is linked to recovery after acute ischemic stroke.

The aim of the current study was to explore the whole-brain dynamic functional connectivity patterns in acute ischemic stroke (AIS) patients and their relation to short and long-term stroke severity. We investigated resting-state functional MRI-based dynamic functional connectivity of 41 AIS patients two to five days after symptom onset. Re-occurring dynamic connectivity configurations were obtained using a sliding window approach and k-means clustering. We evaluated differences in dynamic patterns between three NIHSS-stroke severity defined groups (mildly, moderately, and severely affected patients). Furthermore, we built Bayesian hierarchical models to evaluate the predictive capacity of dynamic connectivity and examine the interrelation with clinical measures, such as white matter hyperintensity lesions. Finally, we established correlation analyses between dynamic connectivity and AIS severity as well as 90-day neurological recovery (ΔNIHSS). We identified three distinct dynamic connectivity configurations acutely post-stroke. More severely affected patients spent significantly more time in a configuration that was characterized by particularly strong connectivity and isolated processing of functional brain domains (three-level ANOVA: p < .05, post hoc t tests: p < .05, FDR-corrected). Configuration-specific time estimates possessed predictive capacity of stroke severity in addition to the one of clinical measures. Recovery, as indexed by the realized change of the NIHSS over time, was significantly linked to the dynamic connectivity between bilateral intraparietal lobule and left angular gyrus (Pearson's r = -.68, p = .003, FDR-corrected). Our findings demonstrate transiently increased isolated information processing in multiple functional domains in case of severe AIS. Dynamic connectivity involving default mode network components significantly correlated with recovery in the first 3 months poststroke.

Discriminating ADHD From Healthy Controls Using a Novel Feature Selection Method Based on Relative Importance and Ensemble Learning.

Attention-deficit/hyperactivity disorder (ADHD) is a childhood-onset neurodevelopmental disorder that often persists into adulthood, resulting in adverse effects on work performance and social function. The current diagnosis of ADHD primarily depends on the judgment of clinical symptoms, which highlights the need for objective imaging biomarkers. In this study, we aim to classify ADHD (both children and adults [34/112]) from age-matched healthy controls (HCs [28/77]) with functional connectivity (FCs) pattern derived from resting-state functional magnetic resonance imaging (rs-fMRI) data. However, the neuroimaging classification of brain disorders often meets a situation of high dimensional features were presented with limited sample size. Thus an efficient method that is able to reduce original feature dimension into a much more refined subspace is highly desired. Here we proposed a novel Feature Selection method based on Relative Importance and Ensemble Learning (FS_RIEL). Compared with traditional feature selection methods, FS_RIEL algorithm improved the ADHD classification by about 15% in both child and adult ADHD classification, achieving 80-86% accuracy. Moreover, we found the most frequently selected FCs were mainly involved in frontoparietal network, default network, salience network, basal ganglia network and cerebellum network in both child and adult ADHD cohorts, which indicates that ADHD is characterized by a widely-impaired brain connectivity profile that may serve as potential biomarkers for its early diagnosis.