Although autism spectrum disorder (ASD) is a serious lifelong condition, its underlying neural mechanism remains unclear. Recently, neuroimaging-based classifiers for ASD and typically developed (TD) individuals were developed to identify the abnormality of functional connections (FCs). Due to over-fitting and interferential effects of varying measurement conditions and demographic distributions, no classifiers have been strictly validated for independent cohorts. Here we overcome these difficulties by developing a novel machine-learning algorithm that identifies a small number of FCs that separates ASD versus TD. The classifier achieves high accuracy for a Japanese discovery cohort and demonstrates a remarkable degree of generalization for two independent validation cohorts in the USA and Japan. The developed ASD classifier does not distinguish individuals with major depressive disorder and attention-deficit hyperactivity disorder from their controls but moderately distinguishes patients with schizophrenia from their controls. The results leave open the viable possibility of exploring neuroimaging-based dimensions quantifying the multiple-disorder spectrum.

When collecting large amounts of neuroimaging data associated with psychiatric disorders, images must be acquired from multiple sites because of the limited capacity of a single site. However, site differences represent a barrier when acquiring multisite neuroimaging data. We utilized a traveling-subject dataset in conjunction with a multisite, multidisorder dataset to demonstrate that site differences are composed of biological sampling bias and engineering measurement bias. The effects on resting-state functional MRI connectivity based on pairwise correlations because of both bias types were greater than or equal to psychiatric disorder differences. Furthermore, our findings indicated that each site can sample only from a subpopulation of participants. This result suggests that it is essential to collect large amounts of neuroimaging data from as many sites as possible to appropriately estimate the distribution of the grand population. Finally, we developed a novel harmonization method that removed only the measurement bias by using a traveling-subject dataset and achieved the reduction of the measurement bias by 29% and improvement of the signal-to-noise ratios by 40%. Our results provide fundamental knowledge regarding site effects, which is important for future research using multisite, multidisorder resting-state functional MRI data.

Eosinophil differentiation is thought to occur by the action of interleukin (IL)-5 on CD34(+) progenitor cells. The allergen-induced increase in eosinophil numbers in isolated airway preparations in vitro, and detection of increased numbers of circulating CD34(+) cells in atopic subjects, led us to the hypothesis that the eosinophil infiltration of the airway in asthma may result from local mucosal differentiation, in addition to recruitment from the bone marrow. We examined CD34(+) cell numbers by immunohistochemistry and IL-5 receptor alpha (IL-5Ralpha) messenger RNA (mRNA) expression by in situ hybridization in bronchial biopsies from atopic asthmatic patients, and from atopic and nonatopic control subjects. CD34(+) cell numbers were increased in the airway in atopic asthmatic and atopic nonasthmatic subjects. In contrast, CD34(+)/ IL-5Ralpha mRNA+ cells were increased in asthmatic subjects when compared with both atopic and nonatopic control subjects. Airway numbers of CD34(+)/IL-5Ralpha mRNA+ cells were correlated to airway caliber in asthmatic subjects and to eosinophil numbers. These findings support the concept that eosinophils may differentiate locally in the airway in asthma.