Combining DTI and MRI for the Automated Detection of Alzheimer’s Disease Using a Large European Multicenter Dataset

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• 作者：Martin Dyrba (20) (34)
  Michael Ewers (21) (22)
  Martin Wegrzyn (20)
  Ingo Kilimann (20)
  Claudia Plant (23)
  Annahita Oswald (24)
  Thomas Meindl (25)
  Michela Pievani (26)
  Arun L. W. Bokde (27) (28)
  Andreas Fellgiebel (29)
  Massimo Filippi (30)
  Harald Hampel (31)
  Stefan Kl?ppel (32)
  Karlheinz Hauenstein (33)
  Thomas Kirste (34)
  Stefan J. Teipel (20) (35)
  
• 关键词：Alzheimer’s disease ; Magnetic Resonance Imaging ; Diffusion Tensor Imaging ; Support Vector Machine ; multimodal analysis ; combining classifiers ; multicenter study
• 刊名：Lecture Notes in Computer Science
• 出版年：2012
• 出版时间：2012
• 年：2012
• 卷：7509
• 期：1
• 页码：29-40
• 全文大小：195KB
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• 作者单位：Martin Dyrba (20) (34)
  Michael Ewers (21) (22)
  Martin Wegrzyn (20)
  Ingo Kilimann (20)
  Claudia Plant (23)
  Annahita Oswald (24)
  Thomas Meindl (25)
  Michela Pievani (26)
  Arun L. W. Bokde (27) (28)
  Andreas Fellgiebel (29)
  Massimo Filippi (30)
  Harald Hampel (31)
  Stefan Kl?ppel (32)
  Karlheinz Hauenstein (33)
  Thomas Kirste (34)
  Stefan J. Teipel (20) (35)
  
  20. German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
  34. Mobile Multimedia Information Systems Group (MMIS), University of Rostock, Rostock, Germany
  21. Department of Radiology, University of California, San Francisco, USA
  22. Center for Imaging of Neurodegenerative Diseases, VA Medical Center, San Francisco, USA
  23. Department of Scientific Computing, Florida State University, Tallahassee, USA
  24. Institute for Informatics, Ludwig-Maximilians-Universit?t München, Munich, Germany
  25. Institute for Clinical Radiology, Department of MRI, Ludwig-Maximilians-Universit?t München, Munich, Germany
  26. LENITEM Laboratory of Epidemiology, Neuroimaging and Telemedicine, IRCCS, Centro San Giovanni di Dio, FBF, Brescia, Italy
  27. Cognitive Systems Group, Discipline of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland
  28. Trinity College Institute of Neuroscience (TCIN), Trinity College Dublin, Dublin, Ireland
  29. Department of Psychiatry, University Medical Center of Mainz, Mainz, Germany
  30. Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, Scientific Institute and University Vita-Salute San Raffaele, Milan, Italy
  31. Department of Psychiatry, Goethe University, Frankfurt, Germany
  32. Department of Psychiatry and Psychotherapy, Department of Neurology, Freiburg Brain Imaging, University Medical Center Freiburg, Freiburg, Germany
  33. Department of Radiology, University of Rostock, Rostock, Germany
  35. Department of Psychiatry, University of Rostock, Rostock, Germany
  

文摘

Diffusion tensor imaging (DTI) allows assessing neuronal fiber tract integrity in vivo to support the diagnosis of Alzheimer’s disease (AD). It is an open research question to which extent combinations of different neuroimaging techniques increase the detection of AD. In this study we examined different methods to combine DTI data and structural T 1-weighted magnetic resonance imaging (MRI) data. Further, we applied machine learning techniques for automated detection of AD. We used a sample of 137 patients with clinically probable AD (MMSE 20.6 ±5.3) and 143 healthy elderly controls, scanned in nine different scanners, obtained from the recently created framework of the European DTI study on Dementia (EDSD). For diagnostic classification we used the DTI derived indices fractional anisotropy (FA) and mean diffusivity (MD) as well as grey matter density (GMD) and white matter density (WMD) maps from anatomical MRI. We performed voxel-based classification using a Support Vector Machine (SVM) classifier with tenfold cross validation. We compared the results from each single modality with those from different approaches to combine the modalities. For our sample, combining modalities did not increase the detection rates of AD. An accuracy of approximately 89% was reached for GMD data alone and for multimodal classification when GMD was included. This high accuracy remained stable across each of the approaches. As our sample consisted of mildly to moderately affected patients, cortical atrophy may be far progressed so that the decline in structural network connectivity derived from DTI may not add additional information relevant for the SVM classification. This may be different for predementia stages of AD. Further research will focus on multimodal detection of AD in predementia stages of AD, e.g. in amnestic mild cognitive impairment (aMCI), and on evaluating the classification performance when adding other modalities, e.g. functional MRI or FDG-PET.