A Principled Framework to Assess the Information-Theoretic Fitness of Brain Functional Sub-Circuits
Duy Duong-Tran,
Nghi Nguyen,
Shizhuo Mu,
Jiong Chen,
Jingxuan Bao,
Frederick H. Xu,
Sumita Garai,
Jose Cadena-Pico,
Alan David Kaplan,
Tianlong Chen,
Yize Zhao,
Li Shen,
Joaquín Goñi
Affiliations
Duy Duong-Tran
Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
Nghi Nguyen
Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel
Shizhuo Mu
Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
Jiong Chen
Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
Jingxuan Bao
Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
Frederick H. Xu
Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
Sumita Garai
Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
Jose Cadena-Pico
Machine Learning Group, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
Alan David Kaplan
Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
Tianlong Chen
Department of Computer Science, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
Yize Zhao
School of Public Health, Yale University, New Heaven, CT 06520-8034, USA
Li Shen
Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
Joaquín Goñi
School of Industrial Engineering, Purdue University, West Lafayette, IN 47907, USA
In systems and network neuroscience, many common practices in brain connectomic analysis are often not properly scrutinized. One such practice is mapping a predetermined set of sub-circuits, like functional networks (FNs), onto subjects’ functional connectomes (FCs) without adequately assessing the information-theoretic appropriateness of the partition. Another practice that goes unchallenged is thresholding weighted FCs to remove spurious connections without justifying the chosen threshold. This paper leverages recent theoretical advances in Stochastic Block Models (SBMs) to formally define and quantify the information-theoretic fitness (e.g., prominence) of a predetermined set of FNs when mapped to individual FCs under different fMRI task conditions. Our framework allows for evaluating any combination of FC granularity, FN partition, and thresholding strategy, thereby optimizing these choices to preserve the important topological features of the human brain connectomes. By applying to the Human Connectome Project with Schaefer parcellations at multiple levels of granularity, the framework showed that the common thresholding value of 0.25 was indeed information-theoretically valid for group-average FCs, despite its previous lack of justification. Our results pave the way for the proper use of FNs and thresholding methods, and provide insights for future research in individualized parcellations.
