Using Multivariate Machine Learning Methods and Structural MRI to Classify Childhood Onset Schizophrenia and Healthy Controls

Introduction: Multivariate machine learning methods can be used to classify groups of schizophrenia patients and controls using structural magnetic resonance imaging (MRI). However, machine learning methods to date have not been extended beyond classification and contemporaneously applied in a meaningful way to clinical measures. We hypothesized that brain measures would classify groups, and that increased likelihood of being classified as a patient using regional brain measures would be positively related to illness severity, developmental delays, and genetic risk. Methods: Using 74 anatomic brain MRI sub regions and Random Forest (RF), a machine learning method, we classified 98 childhood onset schizophrenia (COS) patients and 99 age, sex, and ethnicity-matched healthy controls. We also used RF to estimate the probability of being classified as a schizophrenia patient based on MRI measures. We then explored relationships between brain-based probability of illness and symptoms, premorbid development, and presence of copy number variation (CNV) associated with schizophrenia. Results: Brain regions jointly classified COS and control groups with 73.7% accuracy. Greater brain-based probability of illness was associated with worse functioning (p = 0.0004) and fewer developmental delays (p = 0.02). Presence of CNV was associated with lower probability of being classified as schizophrenia (p = 0.001). The regions that were most important in classifying groups included left temporal lobes, bilateral dorsolateral prefrontal regions, and left medial parietal lobes. Conclusion: Schizophrenia and control groups can be well classified using RF and anatomic brain measures, and brain-based probability of illness has a positive relationship with illness severity and a negative relationship with developmental delays/problems and CNV-based risk

Multimodal imaging reveals a role for akt1 in fetal cardiac development

\u3cp\u3eEven though congenital heart disease is the most prevalent malformation, little is known about how mutations affect cardiovascular function during development. Akt1 is a crucial intracellular signaling molecule, affecting cell survival, proliferation, and metabolism. The aim of this study was to determine the role of Akt1 on prenatal cardiac development. In utero echocardiography was performed in fetal wild-type, heterozygous, and Akt1-deficient mice. The same fetal hearts were imaged using ex vivo micro-computed tomography (lCT) and histology. Neonatal hearts were imaged by in vivo magnetic resonance imaging. Additional ex vivo neonatal hearts were analyzed using histology and real-time PCR of all three groups. In utero echocardiography revealed abnormal blood flow patterns at the mitral valve and reduced contractile function of Akt1 null fetuses, while ex vivo μCT and histology unraveled structural alterations such as dilated cardiomyopathy and ventricular septum defects in these fetuses. Further histological analysis showed reduced myocardial capillaries and coronary vessels in Akt1 null fetuses. At neonatal age, Akt1-deficient mice exhibited reduced survival with reduced endothelial cell density in the myocardium and attenuated cardiac expression of vascular endothelial growth factor A and collagen Iα1. To conclude, this study revealed a central role of Akt1 in fetal cardiac function and myocardial angiogenesis inducing fetal cardiomyopathy and reduced neonatal survival. This study links a specific physiological phenotype with a defined genotype, namely Akt1 deficiency, in an attempt to pinpoint intrinsic causes of fetal cardiomyopathies.\u3c/p\u3

Multimodal imaging reveals a role for akt1 in fetal cardiac development

Even though congenital heart disease is the most prevalent malformation, little is known about how mutations affect cardiovascular function during development. Akt1 is a crucial intracellular signaling molecule, affecting cell survival, proliferation, and metabolism. The aim of this study was to determine the role of Akt1 on prenatal cardiac development. In utero echocardiography was performed in fetal wild-type, heterozygous, and Akt1-deficient mice. The same fetal hearts were imaged using ex vivo micro-computed tomography (lCT) and histology. Neonatal hearts were imaged by in vivo magnetic resonance imaging. Additional ex vivo neonatal hearts were analyzed using histology and real-time PCR of all three groups. In utero echocardiography revealed abnormal blood flow patterns at the mitral valve and reduced contractile function of Akt1 null fetuses, while ex vivo μCT and histology unraveled structural alterations such as dilated cardiomyopathy and ventricular septum defects in these fetuses. Further histological analysis showed reduced myocardial capillaries and coronary vessels in Akt1 null fetuses. At neonatal age, Akt1-deficient mice exhibited reduced survival with reduced endothelial cell density in the myocardium and attenuated cardiac expression of vascular endothelial growth factor A and collagen Iα1. To conclude, this study revealed a central role of Akt1 in fetal cardiac function and myocardial angiogenesis inducing fetal cardiomyopathy and reduced neonatal survival. This study links a specific physiological phenotype with a defined genotype, namely Akt1 deficiency, in an attempt to pinpoint intrinsic causes of fetal cardiomyopathies