Connectivity network breakdown predicts imminent volumetric atrophy in early mild cognitive impairment

  • Authors:

  • Talia M. Nir;Neda Jahanshad;Arthur W. Toga;Clifford R. Jack;Michael W. Weiner;Paul M. Thompson

  • Affiliations:

  • Center for Imaging Genetics, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA;Center for Imaging Genetics, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA;Center for Imaging Genetics, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA;Department of Radiology, Mayo Clinic and Foundation, Rochester, MN;Department of Radiology and Biomedical Imaging, UCSF School of Medicine, San Francisco, CA;Center for Imaging Genetics, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA

  • Venue:
  • MBIA'12 Proceedings of the Second international conference on Multimodal Brain Image Analysis
  • Year:
  • 2012

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Abstract

Alzheimer's disease (AD) is characterized both by cortical atrophy and disrupted connectivity, resulting in abnormal interactions between neural systems. Diffusion weighted imaging (DWI) and graph theory can be used to evaluate major brain networks, and detect signs of abnormal breakdown in network connectivity. In a longitudinal study using both DWI and standard MRI, we assessed baseline white matter connectivity patterns in 24 early mild cognitive impairment (eMCI) subjects (mean age: 74.5 +/- 8.3 yrs). Using both standard MRI-based cortical parcellations and whole-brain tractography, we computed baseline connectivity maps from which we calculated global "small-world" architecture measures. We evaluated whether these network measures predicted future volumetric brain atrophy in eMCI subjects, who are at risk for developing AD, as determined by 3D Jacobian "expansion factor maps" between baseline and 6-month follow-up scans. This study suggests that DWI-based network measures may be a novel predictor of AD progression.