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Neurodevelopmental abnormalities together with neurodegenerative processes contribute to schizophrenia, an etiologically heterogeneous, complex disease phenotype that has been difficult to model in animals. The neurodegenerative component of schizophrenia is best documented by magnetic resonance imaging (MRI), demonstrating progressive cortical gray matter loss over time. No treatment exists to counteract this slowly proceeding atrophy. The hematopoietic growth factor erythropoietin (EPO) is neuroprotective in animals. Here, we show by voxel-based morphometry in 32 human subjects in a placebo-controlled study that weekly high-dose EPO for as little as 3 months halts the progressive atrophy in brain areas typically affected in schizophrenia, including hippocampus, amygdala, nucleus accumbens, and several neocortical areas. Specifically, gray matter protection is highly associated with improvement in attention and memory functions. These findings suggest that a neuroprotective strategy is effective against common pathophysiological features of schizophrenic patients, and strongly encourage follow-up studies to optimize EPO treatment dose and duration. [ABSTRACT FROM AUTHOR]

Neuroimaging markers based on Magnetic Resonance Imaging (MRI) combined with various other measures (such as genetic covariates, biomarkers, vascular risk factors, neuropsychological tests etc.) might provide useful predictions of clinical outcomes during the progression towards Alzheimer's disease (AD). The use of multiple features in predictive frameworks for clinical outcomes has become increasingly prevalent in AD research. However, many studies do not focus on systematically and accurately evaluating combinations of multiple input features. Hence, the aim of the present work is to explore and assess optimal combinations of various features for MR-based prediction of (1) cognitive status and (2) biomarker positivity with a multi-kernel learning Gaussian process framework. The explored features and parameters included (A) combinations of brain tissues, modulation, smoothing, and image resolution; (B) incorporating demographics & clinical covariates; (C) the impact of the size of the training data set; (D) the influence of dimensionality reduction and the choice of kernel types. The approach was tested in a large German cohort including 959 subjects from the multicentric longitudinal study of cognitive impairment and dementia (DELCODE). Our evaluation suggests the best prediction of memory performance was obtained for a combination of neuroimaging markers, demographics, genetic information (ApoE4) and CSF biomarkers explaining 57% of outcome variance in out-of-sample predictions. The highest performance for A β 42/40 status classification was achieved for a combination of demographics, ApoE4, and a memory score while usage of structural MRI further improved the classification of individual patient's pTau status. • MRI+CSF features revealed an accuracy of 57% (R 2) in predicting cognitive scores. • MR-based patterns were more predictive of cognitive performance than CSF biomarkers. • Optimal A β 42/40 prediction was achieved using demographics+ApoE4+cognitive scores. • MRI features improved pTau prediction with high specificity & lower sensitivity. • We studied the influence of various imaging parameters and training sample size. [ABSTRACT FROM AUTHOR]