TY - GEN
T1 - A unified Bayesian approach to extract network-based functional differences from a heterogeneous patient cohort
AU - Venkataraman, Archana
AU - Wymbs, Nicholas
AU - Nebel, Mary Beth
AU - Mostofsky, Stewart
N1 - Funding Information:
Acknowledgments. This work was supported in part by the National Institute of Mental Health (R01 MH085328-09, R01 MH078160-07, and K01 MH109766), the National Institute of Neurological Disorders and Stroke (R01 NS048527-08), and the Autism Speaks foundation.
Publisher Copyright:
© 2017, Springer International Publishing AG.
PY - 2017
Y1 - 2017
N2 - We present a generative Bayesian framework that automatically extracts the hubs of altered functional connectivity between a neurotypical and a patient group, while simultaneously incorporating an observed clinical severity measure for each patient. The key to our framework is the latent or hidden organization in the brain that we cannot directly access. Instead, we observe noisy measurements of the latent structure through functional connectivity data. We derive a variational EM algorithm to infer both the latent network topology and the unknown model parameters. We demonstrate the robustness and clinical relevance of our model on a population study of autism acquired at the Kennedy Krieger Institute in Baltimore, MD. Our model results implicate a more diverse pattern of functional differences than two baseline techniques, which do not incorporate patient heterogeneity.
AB - We present a generative Bayesian framework that automatically extracts the hubs of altered functional connectivity between a neurotypical and a patient group, while simultaneously incorporating an observed clinical severity measure for each patient. The key to our framework is the latent or hidden organization in the brain that we cannot directly access. Instead, we observe noisy measurements of the latent structure through functional connectivity data. We derive a variational EM algorithm to infer both the latent network topology and the unknown model parameters. We demonstrate the robustness and clinical relevance of our model on a population study of autism acquired at the Kennedy Krieger Institute in Baltimore, MD. Our model results implicate a more diverse pattern of functional differences than two baseline techniques, which do not incorporate patient heterogeneity.
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U2 - 10.1007/978-3-319-67159-8_8
DO - 10.1007/978-3-319-67159-8_8
M3 - Conference contribution
AN - SCOPUS:85029457348
SN - 9783319671581
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 60
EP - 69
BT - Connectomics in NeuroImaging - 1st International Workshop, CNI 2017 Held in Conjunction with MICCAI 2017, Proceedings
A2 - Bonilha, Leonardo
A2 - Wu, Guorong
A2 - Laurienti, Paul
A2 - Munsell, Brent C.
PB - Springer Verlag
T2 - 1st International Workshop on Connectomics in NeuroImaging, CNI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
Y2 - 14 September 2017 through 14 September 2017
ER -