KEYWORDS: Data modeling, Computer programming, Performance modeling, Neural networks, Magnetic resonance imaging, Genetics, Functional magnetic resonance imaging, Control systems
We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. Our model consists of an encoder, a decoder and a classifier. The encoder learns a non-linear subspace shared between the input data modalities. The classifier and the decoder act as regularizers to ensure that the low-dimensional encoding captures predictive differences between patients and controls. We use a learnable dropout layer to extract interpretable biomarkers from the data, and our unique training strategy can easily accommodate missing data modalities across subjects. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data. Using 10-fold cross validation, we demonstrate that our model achieves better classification accuracy than baseline methods, and that this performance generalizes to a second dataset collected at a different site. In an exploratory analysis we further show that the biomarkers identified by our model are closely associated with the well-documented deficits in schizophrenia.
We present a generative-predictive framework that captures the differences in regional brain activity between a neurotypical cohort and a clinical population, as guided by patient-specific genetic risk. Our model assumes that the functional activations in the neurotypical subjects are distributed around a population mean, and that the altered brain activity in neuropsychiatric patients is defined via deviations from this neurotypical mean. We employ group sparsity to identify a set of brain regions that simultaneously explain the salient functional differences and specify a set of basis vector, that span the low dimensional data subspace. The patient-specific projections onto this subspace are used as feature vectors to identify multivariate associations with genetic risk. We have evaluated our model on a task-based fMRI dataset from a population study of schizophrenia. We compare our model with two baseline methods, regression using Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) regression, which establishes direct association between the brain activity during a working memory task and schizophrenia polygenic risk. Our model demonstrates greater consistency and robustness across bootstrapping experiments than the machine learning baselines. Moreover, the set of brain regions implicated by our model underlie the well documented executive cognitive deficits in schizophrenia.
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