thology. In this study, we examine the lateralization of resting-state networks assessed with a group-independent component analysis using resting-state functional magnetic resonance imaging from a large cohort consisting of 774 children, ages 6-10 years.
  Autism Spectrum Disorder (ASD) is a highly heterogeneous developmental disorder with diverse clinical manifestations. Neuroimaging studies have explored functional connectivity (FC) of ASD through resting-state functional MRI studies, however the findings have remained inconsistent, thus reflecting the possibility of multiple subtypes. Identification of the relationship between clinical symptoms and FC measures may help clarify the inconsistencies in earlier findings and advance our understanding of ASD subtypes.
 
  Canonical correlation analysis was performed on two-hundred and ten ASD subjects from the Autism Brain Imaging Data Exchange to identify significant linear combinations of resting-state connectomic and clinical profiles of ASD. Then, hierarchical clustering defined ASD subtypes based on distinct brain-behavior relationships. Finally, a support vector machine classifier was used to verify that subtypes were comprised of subjects with distinct clinical and connectivity features.
 
  Three ASD subtypes were identified. Subtype 1 exhibited increased intra-network FC, increased IQ scores and restricted and repetitive behaviors. Subtype 2 was characterized by decreased whole-brain FC and more severe ADI-R and SRS symptoms. Subtype 3 demonstrated mixed FC, low IQ scores, as well as social motivation and verbal deficits. To verify subtype assignment, a multi-class support vector machine using connectomic and clinical profiles yielded an average accuracy of 71.3% and 65.2% respectively for subtype classification, which is significantly higher than chance (33.3%).
 
  The present study demonstrates that combining connectomic and behavioral measures is a powerful approach for disease subtyping and suggests that there are ASD subtypes with distinct connectomic and clinical profiles.
 The present study demonstrates that combining connectomic and behavioral measures is a powerful approach for disease subtyping and suggests that there are ASD subtypes with distinct connectomic and clinical profiles.Aim To evaluate the effectiveness of low-intensity focused ultrasound (LIFU) therapy in the management of cancer-related neuropathic pain (CNP). Methods A retrospective review with 22 patients with CNP treated with LIFU therapy (frequency 3 Hz, 3 W/cm2, pulse mode duty cycle 50%) was conducted. Results Out of the 22 patients, 15 had CNP secondary to chemotherapy-induced peripheral neuropathy. Compared with baseline, there was a significant reduction in numeric pain rating scale (p less then 0.001). Additionally, 76.5% of patients (n = 13) were considered to be responders to LIFU therapy. Conclusion LIFU therapy may be a viable treatment modality in the management of CNP, specifically chemotherapy-induced peripheral neuropathy, with a minimal side effect profile. Larger, prospective studies with a structured protocol are necessary.Aim To evaluate the prevalence of self-directed cannabidiol (CBD) use in patients with end-stage degenerative hip and knee arthritis who underwent total hip arthroplasty and total knee arthroplasty. Materials & methods Anonymous surveys for 109 patients were completed at 6 weeks follow-up after either total hip arthroplasty or total knee arthroplasty at a single tertiary care US orthopedic hospital. Results Within the perioperative window encompassing both preoperative and postoperative periods, 22% (95% CI 14-30%) of patients used CBD.  https://www.selleckchem.com/products/bay-1217389.html  Conclusion There was no difference in pain satisfaction between patients who used CBD and patients who did not. Given high rates of self-directed perioperative CBD use and the mixed body of evidence, further research is needed to better understand whether CBD is safe and effective.
  Alzheimer's disease (AD) is the most common age-related dementia that promotes a decline in memory, thinking, and social skills. The initial stages of dementia can be associated with mild symptoms, and symptom progression to a more severe state is heterogeneous across patients. Recent work has demonstrated the potential for functional network mapping to assist in the prediction of symptomatic progression. However, this work has primarily used static functional connectivity (sFC) from rs-fMRI. Recently, dynamic functional connectivity (dFC) has been recognized as a powerful advance in functional connectivity methodology to differentiate brain network dynamics between healthy and diseased populations.
 
  Group independent component analysis was applied to extract 17 components within the cognitive control network (CCN) from 1385 individuals across varying stages of AD symptomology. We estimated dFC among 17 components within the CCN, followed by clustering the dFCs into 3 recurring brain states, and then estim with increases in connectivity within the middle frontal gyrus. Also, the very mild AD showed less connectivity within the inferior parietal lobule (in both sFC and dFC) and between this region with the rest of CCN (in dFC analysis). Also, we found within-middle frontal gyrus connectivity increases with AD progression in both sFC and dFC results. Finally, comparing with very mild AD, we found that the normal brain spends significantly more time in a state with lower within-middle frontal gyrus connectivity and higher connectivity between the hippocampus and the rest of CCN, highlighting the importance of assessing the dynamics of brain connectivity in this disease.Aim Faces pain scales are widely used to measure pain. So far, no faces pain scale has ever been constructed by Rasch modeling. Hence the authors aimed to construct a new scale by this method. Methods Rasch modeling was used to provide an initial calibration and development of the 'Balparda-Herrera Pain Scale' (BHPS) and this scale was compared with the existing Faces Pain Scale - Revised. The scale was later refined. Results Both the existing scale and the initial version of the BHPS required category collapsing. Statistical tests demonstrated an excellent concordance between both scales. The final version of the BHPS was found to behave excellently and to be capable of adequately measuring pain. Conclusion The BHPS provides an excellent instrument for measuring pain in the adult population.