Abstract
We develop an integrative Bayesian predictive modeling framework that identifies individual pathological brain states based on the selection of fluoro-deoxyglucose positron emission tomography (PET) imaging biomarkers and evaluates the association of those states with a clinical outcome. We consider data from a study on temporal lobe epilepsy (TLE) patients who subsequently underwent anterior temporal lobe resection. Our modeling framework looks at the observed profiles of regional glucose metabolism in PET as the phenotypic manifestation of a latent individual pathologic state, which is assumed to vary across the population. The modeling strategy we adopt allows the identification of patient subgroups characterized by latent pathologies differentially associated to the clinical outcome of interest. It also identifies imaging biomarkers characterizing the pathological states of the subjects. In the data application, we identify a subgroup of TLE patients at high risk for post-surgical seizure recurrence after anterior temporal lobe resection, together with a set of discriminatory brain regions that can be used to distinguish the latent subgroups. We show that the proposed method achieves high cross-validated accuracy in predicting post-surgical seizure recurrence.
Highlights
In the era of precision medicine, in order to deliver targeted therapies for neurological disorders, the development of methods to identify reliable and quantifiable biomarkers that are associated to individual clinical outcomes has become of paramount importance (Insel and Cuthbert, 2015)
We develop a statistical model to identify whole-brain biomarkers from positron emission tomography (PET) imaging which are associated to the prediction of post-surgical seizure recurrence following anterior temporal lobe resection
We apply the proposed model to the data we have available from the University of California, Los Angeles Seizure Disorder Center, where we illustrate the utility of our proposed model for predicting a post-surgical outcome among MTLE-HS patients from pre-surgical fluorodeoxyglucose positron emission tomography (FDG-PET) imaging
Summary
In the era of precision medicine, in order to deliver targeted therapies for neurological disorders, the development of methods to identify reliable and quantifiable biomarkers that are associated to individual clinical outcomes has become of paramount importance (Insel and Cuthbert, 2015). Increasing evidence, points at TLE as a network disorder that includes abnormality distributed beyond the temporal lobe, rather than a focal disorder (Bonilha et al, 2005; McDonald et al, 2008; Mueller et al, 2010; Chiang and Haneef, 2014) This suggests that whole-brain statistical approaches may allow for improved identification of quantifiable features from neuroimaging data that can be reliably associated with individual clinical outcomes and improve clinical decision-making. O’Sullivan (2006) and Jiang and Ogden (2008), for example, utilize mixture modeling and conditional autoregressive models to incorporate spatial information into PET analysis, while other work has used functional principal components (Jiang et al, 2009) or wavelets (Millet et al, 2000; Alpert et al, 2006) to analyze dynamic PET signal Each of these approaches represents an important advance in neuroimaging methods development, these methods do not quantify the relative importance of selected regions, which may impact the effectiveness of related clinical decisions. Further assessment of the performance of our method is performed in the Supplementary Material by conducting a comparison study on synthetic data against multi-step approaches and/or approaches that do not condition on latent states
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
