Browsing by Author "Ye, Fengdan"

Now showing 1 - 5 of 5
  • Thumbnail Image
    Item
    Brain Modularity Mediates the Relation between Task Complexity and Performance
    (The MIT Press, 2017) Yue, Qiuhai; Martin, Randi; Fischer-Baum, Simon; Ramos-Nuñez, Aurora I.; Ye, Fengdan; Deem, Michael W.; Center for Theoretical Biological Physics
    Recent work in cognitive neuroscience has focused on analyzing the brain as a network, rather than as a collection of independent regions. Prior studies taking this approach have found that individual differences in the degree of modularity of the brain network relate to performance on cognitive tasks. However, inconsistent results concerning the direction of this relationship have been obtained, with some tasks showing better performance as modularity increases and other tasks showing worse performance. A recent theoretical model [Chen, M., & Deem, M. W. 2015. Development of modularity in the neural activity of children's brains. Physical Biology, 12, 016009] suggests that these inconsistencies may be explained on the grounds that high-modularity networks favor performance on simple tasks whereas low-modularity networks favor performance on more complex tasks. The current study tests these predictions by relating modularity from resting-state fMRI to performance on a set of simple and complex behavioral tasks. Complex and simple tasks were defined on the basis of whether they did or did not draw on executive attention. Consistent with predictions, we found a negative correlation between individuals' modularity and their performance on a composite measure combining scores from the complex tasks but a positive correlation with performance on a composite measure combining scores from the simple tasks. These results and theory presented here provide a framework for linking measures of whole-brain organization from network neuroscience to cognitive processing.
  • Thumbnail Image
    Item
    Machine-learning approach to the design of OSDAs for zeolite beta
    (National Academy of Sciences, 2019) Daeyaert, Frits; Ye, Fengdan; Deem, Michael W.; Bioengineering; Physics and Astronomy
    We report a machine-learning strategy for design of organic structure directing agents (OSDAs) for zeolite beta. We use machine learning to replace a computationally expensive molecular dynamics evaluation of the stabilization energy of the OSDA inside zeolite beta with a neural network prediction. We train the neural network on 4,781 candidate OSDAs, spanning a range of stabilization energies. We find that the stabilization energies predicted by the neural network are highly correlated with the molecular dynamics computations. We further find that the evolutionary design algorithm samples the space of chemically feasible OSDAs thoroughly. In total, we find 469 OSDAs with verified stabilization energies below −17 kJ/(mol Si), comparable to or better than known OSDAs for zeolite beta, and greatly expanding our previous list of 152 such predicted OSDAs. We expect that these OSDAs will lead to syntheses of zeolite beta.
  • Thumbnail Image
    Item
    The Modular Network Structure of Complex Biological Systems: Cancer, Cognition and Genes
    (2020-11-23) Ye, Fengdan; Pascual, Maria; Onuchic, Jose
    Recent years have witnessed a surge in the application of graph theory to complex biological systems. The ability of graph theory to extract essential knowledge from the plethora of information embedded in a complex system has proven rewarding in many disciplines ranging from evolutionary biology to cancer prediction. The modular structure of complex networks, a branch of graph theory, is the focus of this text. Its guiding hypothesis, derived from statistical physics, states that modularity correlates with performances of complex biological systems and that the direction of correlation is mediated by environmental stress. This text tests and expands the theory of modularity in three main contexts - gene co-expression networks, human brain networks, and genome-scale metabolic networks. It is demonstrated that modularity of cancer-associated gene co-expression network is predictive of cancer aggressiveness, that modularity of resting-state functional connectivity in healthy young adults correlates with cognitive performance and the correlation is mediated by task complexity, and that modularity of human brain metabolic network not only predicts risk for Alzheimer’s disease but also defines the brain regions where metabolism correlates with dementia-risk gene expression. In addition, definition of modularity and maximization algorithm for bipartite, directed, and weighted networks are proposed and subsequently tested on a genome-scale bacterial metabolic network under different levels of survival stress. Overall, results presented here support the hypothesis of modularity’s role as a performance predictor for complex systems. The existing theory of modularity has been validated in numerous scenarios and expanded with the concept of ”network fragmentation”. Modularity can be applied to clinical settings for risk evaluation, and even contribute to individualized therapy. It can also help understand the mechanism of biological processes that are currently poorly understood. Of course, future research is needed to further the understanding of the emergence of modularity in complex systems and its application. Better definition of modularity, faster and more functionally appropriate clustering algorithm, and the collection of larger amount of higher quality data are crucial for the advancement of the field.
  • Thumbnail Image
    Item
    Modularity of the metabolic gene network as a prognostic biomarker for hepatocellular carcinoma
    (Impact Journals, 2018) Ye, Fengdan; Jia, Dongya; Lu, Mingyang; Levine, Herbert; Deem, Michael W.; Bioengineering; Biosciences; Physics and Astronomy
    Abnormal metabolism is an emerging hallmark of cancer. Cancer cells utilize both aerobic glycolysis and oxidative phosphorylation (OXPHOS) for energy production and biomass synthesis. Understanding the metabolic reprogramming in cancer can help design therapies to target metabolism and thereby to improve prognosis. We have previously argued that more malignant tumors are usually characterized by a more modular expression pattern of cancer-associated genes. In this work, we analyzed the expression patterns of metabolism genes in terms of modularity for 371 hepatocellular carcinoma (HCC) samples from the Cancer Genome Atlas (TCGA). We found that higher modularity significantly correlated with glycolytic phenotype, later tumor stages, higher metastatic potential, and cancer recurrence, all of which contributed to poorer prognosis. Among patients with recurred tumors, we found the correlation of higher modularity with worse prognosis during early to mid-progression. Furthermore, we developed metrics to calculate individual modularity, which was shown to be predictive of cancer recurrence and patientsﾒ survival and therefore may serve as a prognostic biomarker. Our overall conclusion is that more aggressive HCC tumors, as judged by decreased host survival probability, had more modular expression patterns of metabolic genes. These results may be used to identify cancer driver genes and for drug design.
  • Thumbnail Image
    Item
    Static and Dynamic Measures of Human Brain Connectivity Predict Complementary Aspects of Human Cognitive Performance
    (Frontiers Media S.A., 2017) Ramos-Nuñez, Aurora I.; Fischer-Baum, Simon; Martin, Randi C.; Yue, Qiuhai; Ye, Fengdan; Deem, Michael W.
    In cognitive network neuroscience, the connectivity and community structure of the brain network is related to measures of cognitive performance, like attention and memory. Research in this emerging discipline has largely focused on two measures of connectivity—modularity and flexibility—which, for the most part, have been examined in isolation. The current project investigates the relationship between these two measures of connectivity and how they make separable contribution to predicting individual differences in performance on cognitive tasks. Using resting state fMRI data from 52 young adults, we show that flexibility and modularity are highly negatively correlated. We use a Brodmann parcellation of the fMRI data and a sliding window approach for calculation of the flexibility. We also demonstrate that flexibility and modularity make unique contributions to explain task performance, with a clear result showing that modularity, not flexibility, predicts performance for simple tasks and that flexibility plays a greater role in predicting performance on complex tasks that require cognitive control and executive functioning. The theory and results presented here allow for stronger links between measures of brain network connectivity and cognitive processes.