Classifying schizophrenia patients and healthy individuals: Whole brain SPECT functional connectivity using support...

Functional magnetic resonance imaging (fMRI) has been used to characterize functional brain networks in disorders ranging from depression to schizophrenia. Like fMRI, single photon emission computed tomography (SPECT) is a technique which captures information about neurally linked blood flow activity through radioactive tracers. While a few SPECT studies in schizophrenia populations have been conducted along with fMRI based studies, research on SPECT data for individual subject classification is limited. We used an independent component analysis (ICA) approach to estimate covarying SPECT networks from our prior study. Results were then fed as input to a classifier model to evaluate accuracy of individual diagnostic prediction.

213 subjects (137 schizophrenia patients and 76 healthy controls) were used for the analysis. Classification input was based on loading parameters generated from spatially constrained ICA using a set of network priors derived from fMRI. Fifty-three SPECT components were estimated guided by the NeuroMark fMRI 1.0 template. We initially focused on a support vector machine (SVM) classifier given previous favorable fMRI-SVM results. We also evaluated performance of multiple classifiers post hoc.

Surprisingly, linear SVM performed worse compared to random forest, logistic regression, voting, and multilayer perceptron. Linear SVM had a greater number of false negatives compared to the other classifier approaches. By contrast, random forest and logistic regression performed the highest, with an 88% sensitivity and 61% specificity (random forest), and 87% sensitivity and 68% specificity (logistic regression). These results demonstrate that other approaches such as random forest and logistic regressions should be further investigated for future SPECT studies, and coupled with sc-ICA, provide an improved understanding of aberrant functional networks in schizophrenia.