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The Alzheimer's Disease Challenge

Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Neurosci. | doi: 10.3389/fnins.2018.00528

On the extraction and analysis of graphs from resting-state fMRI to support a correct and robust diagnostic tool for Alzheimer's disease

 Claudia Bachmann1, 2*,  Heidi I. Jacobs3, 4, 5,  PierGianLuca Porta Mana6,  Kim Dillen2, Nils Richter7, Boris von Reutern7, 8, Julian Dronse7,  Oezguer A. Onur9,  Karl-Josef Langen10,  Gereon R. Fink2, 11,  Juraj Kukolja2, 11 and  Abigail Morrison1, 2, 12, 13
  • 1Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I,, Forschungszentrum Jülich, Helmholtz-Gemeinschaft Deutscher Forschungszentren (HZ), Germany
  • 2Cognitive Neuroscience, Inst. of Neuroscience and Medicine (INM-3), Forschungszentrum Jülich, Helmholtz-Gemeinschaft Deutscher Forschungszentren (HZ), Germany
  • 3Faculty of Health, Medicine and Life Science, School for Mental Health and Neuroscience (MHeNS), Alzheimer Centre Limburg, Maastricht University Medical Centre, Netherlands
  • 4Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, United States
  • 5Faculty of Psychology and Neuroscience, Department of Cognitive Neuroscience, Maastricht University, Netherlands
  • 6independent researcher, Italy
  • 7Department of Neurology, Uniklinik Köln, Germany
  • 8JülichResearch Centre, Institut für Neurowissenschaften und Medizin, Forschungszentrum Jülich, Germany
  • 9Department of Neurology, Universität zu Köln, Germany
  • 10INM-4, Forschungszentrum Jülich, Helmholtz-Gemeinschaft Deutscher Forschungszentren (HZ), Germany
  • 11Department of Neurology, Uniklinik Köln, Germany
  • 12Simulation Laboratory Neuroscience, Inst. for Advanced Simulation, Institut für Gehirn, Jülich Aachen Forschungsverbund, Germany
  • 13Faculty of Psychology, Ruhr-Universität Bochum, Germany

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 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, 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 mild cognitive impairment and 14 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, graph theory, Resting-state fMRI, diagnosis, model by sufficiency, Negative surprise

Received: 31 Jan 2018; Accepted: 13 Jul 2018.

Edited by:

Athanasios Alexiou, Novel Global Community Educational Foundation (NGCEF), Hebersham, Australia

Reviewed by:

Alessandro Giuliani, Istituto Superiore di Sanità (ISS), Italy
Rui Li, Institute of Psychology (CAS), China  

Copyright: © 2018 Bachmann, Jacobs, Porta Mana, Dillen, Richter, von Reutern, Dronse, Onur, Langen, Fink, Kukolja and Morrison. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mrs. Claudia Bachmann, Forschungszentrum Jülich, Helmholtz-Gemeinschaft Deutscher Forschungszentren (HZ), Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I,, Wilhelm-Johnen-Straße, Jülich, 52428, Germany,