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Bachmann C, Jacobs HIL, Porta Mana P, et al. On the Extraction and Analysis of Graphs From Resting-State fMRI to Support a Correct and Robust Diagnostic Tool for Alzheimer's Disease. Front Neurosci. 2018;12:528doi: 10.3389/fnins.2018.00528.
Bachmann, C., Jacobs, H. I. L., Porta Mana, P., Dillen, K., Richter, N., von Reutern, B., Dronse, J., Onur, O. A., Langen, K. J., Fink, G. R., Kukolja, J., & Morrison, A. (2018). On the Extraction and Analysis of Graphs From Resting-State fMRI to Support a Correct and Robust Diagnostic Tool for Alzheimer's Disease. Frontiers in neuroscience, 12528. https://doi.org/10.3389/fnins.2018.00528
Bachmann, Claudia, et al. "On the Extraction and Analysis of Graphs From Resting-State fMRI to Support a Correct and Robust Diagnostic Tool for Alzheimer's Disease." Frontiers in neuroscience vol. 12 (2018): 528. doi: https://doi.org/10.3389/fnins.2018.00528
Bachmann C, Jacobs HIL, Porta Mana P, Dillen K, Richter N, von Reutern B, Dronse J, Onur OA, Langen KJ, Fink GR, Kukolja J, Morrison A. On the Extraction and Analysis of Graphs From Resting-State fMRI to Support a Correct and Robust Diagnostic Tool for Alzheimer's Disease. Front Neurosci. 2018 Sep 28;12:528. doi: 10.3389/fnins.2018.00528. eCollection 2018. PMID: 30323734; PMCID: PMC6172342.
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Front Neurosci. 2018 Sep 28;12:528. doi: 10.3389/fnins.2018.00528. eCollection 2018.

On the Extraction and Analysis of Graphs From Resting-State fMRI to Support a Correct and Robust Diagnostic Tool for Alzheimer's Disease.

Frontiers in neuroscience

Claudia Bachmann, Heidi I L Jacobs, PierGianLuca Porta Mana, Kim Dillen, Nils Richter, Boris von Reutern, Julian Dronse, Oezguer A Onur, Karl-Josef Langen, Gereon R Fink, Juraj Kukolja, Abigail Morrison

Affiliations

  1. Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany.
  2. Faculty of Health, Medicine and Life Science, School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, Maastricht, Netherlands.
  3. Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Boston, MA, United States.
  4. Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands.
  5. Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
  6. Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany.
  7. Department of Neurology, University Hospital of Cologne, Cologne, Germany.
  8. Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-4), Jülich Research Centre, Jülich, Germany.
  9. Department of Neurology, Helios University Hospital Wuppertal, Wuppertal, Germany.
  10. Faculty of Psychology, Institute of Cognitive Neuroscience, Ruhr-University Bochum, Bochum, Germany.

PMID: 30323734 PMCID: PMC6172342 DOI: 10.3389/fnins.2018.00528

Abstract

The diagnosis of Alzheimer's disease (AD), especially in the early stage, is still not very reliable and the development of new diagnosis tools is desirable. A diagnosis based on functional magnetic resonance imaging (fMRI) is a suitable candidate, since fMRI is non-invasive, readily available, and indirectly measures synaptic dysfunction, which can be observed even at the earliest stages of AD. However, the results of previous attempts to analyze graph properties of resting state fMRI data are contradictory, presumably caused by methodological differences in graph construction. This comprises two steps: clustering the voxels of the functional image to define the nodes of the graph, and calculating the graph's edge weights based on a functional connectivity measure of the average cluster activities. A variety of methods are available for each step, but the robustness of results to method choice, and the suitability of the methods to support a diagnostic tool, are largely unknown. To address this issue, we employ a range of commonly and rarely used clustering and edge definition methods and analyze their graph theoretic measures (graph weight, shortest path length, clustering coefficient, and weighted degree distribution and modularity) on a small data set of 26 healthy controls, 16 subjects with mild cognitive impairment (MCI) and 14 with Alzheimer's disease. We examine the results with respect to statistical significance of the mean difference in graph properties, the sensitivity of the results to model and parameter choices, and relative diagnostic power based on both a statistical model and support vector machines. We find that different combinations of graph construction techniques yield contradicting, but statistically significant, relations of graph properties between health conditions, explaining the discrepancy across previous studies, but casting doubt on such analyses as a method to gain insight into disease effects. The production of significant differences in mean graph properties turns out not to be a good predictor of future diagnostic capacity. Highest predictive power, expressed by largest negative surprise values, are achieved for both atlas-driven and data-driven clustering (Ward clustering), as long as graphs are small and clusters large, in combination with edge definitions based on correlations and mutual information transfer.

Keywords: Alzheimer's disease; MCI; diagnosis; graph theory; model by sufficiency; negative surprise; resting-state fMRI

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