MultiClass Brain Age Discrimination Using Machine Learning Algorithm Restingstate functional connectivity analyses have revealed a significant effect on the interregional interactions in brain. The brain age prediction based on restingstate functional magnetic resonance imaging has been proved as biomarkers to characterize the typical brain development and neuropsychiatric disorders. The brain age prediction model based on functional connectivity measurements derived from restingstate functional magnetic resonance imaging has received a lots of interest in recent years due to its great success in age prediction. However, some of the recent studies rely on experienced neuroscientist experts to select appropriate connectivity features in order to build a robust model for prediction while the others just selected the features based on trialanderror test. Besides, the subjects used in this studies omitted some subjects that can be divided into two groups with less similarity which may confused the prediction model. In this study, we proposed a multiclass age categories discrimination method with the connectivity features selected via Kmeans clustering with no prior knowledge provided. The experimental results show that with Kmeans selected features the proposed model better discriminate multiclass age categories.

A Joint Model for Predicting Structural and Functional Brain Health in Elderly Individuals This paper presents a machinelearningbased joint model of brain age and cognitive performance, and demonstrates its superior performance relative to isolated models. Previous studies have chosen to study those two measures of brain health separately for two reasons: 1) although cognition can be measured regardless of an individualu0027s health, brainage groundtruth can be defined only for healthy individuals; and 2) while brainage models are developed using neuroimaging data alone, modeling of cognitive performance additionally requires measures of cognitive reserve and biomarkers of cognitive disorders. However, those two measures are biologically related to each other, because they both depend on brain structure. Hence, we developed a joint model by 1) explicitly defining the commonalities and differences between them in a graph, and 2) converting that graph into a multitasklearning model to facilitate learning from populationlevel data. Our model took as inputs structural neuroimaging data and information related to cognitive reserve and disorders, and predicted brain age and cognitive performance in terms of a MiniMental State Examination (MMSE) score. We implemented linear and nonlinear joint models and compared them against isolated models. Our results indicate that joint modeling substantially improves the accuracy of the modeling of individual measures, relative to isolated models.