Connectivity network measures predict volumetric atrophy in mild cognitive impairment

Talia M Nir; Neda Jahanshad; Arthur W Toga; Matt A Bernstein; Clifford R Jack Jr; Michael W Weiner; Paul M Thompson; Alzheimer's Disease Neuroimaging Initiative (ADNI)

doi:10.1016/j.neurobiolaging.2014.04.038

Connectivity network measures predict volumetric atrophy in mild cognitive impairment

Neurobiol Aging. 2015 Jan;36 Suppl 1(0 1):S113-20. doi: 10.1016/j.neurobiolaging.2014.04.038. Epub 2014 Aug 30.

Authors

Talia M Nir¹, Neda Jahanshad², Arthur W Toga¹, Matt A Bernstein³, Clifford R Jack Jr³, Michael W Weiner⁴, Paul M Thompson⁵; Alzheimer's Disease Neuroimaging Initiative (ADNI)

Affiliations

¹ Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA.
² Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA; Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA.
³ Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA.
⁴ Department of Radiology and Biomedical Imaging, University of California-San Francisco School of Medicine, San Francisco, CA, USA.
⁵ Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA; Department of Neurology, University of Southern California, Los Angeles, CA, USA; Department of Psychiatry, University of Southern California, Los Angeles, CA, USA; Department of Radiology, University of Southern California, Los Angeles, CA, USA; Department of Engineering, University of Southern California, Los Angeles, CA, USA; Department of Pediatrics, University of Southern California, Los Angeles, CA, USA; Department of Ophthalmology, University of Southern California, Los Angeles, CA, USA. Electronic address: pthomp@usc.edu.

PMID: 25444606
PMCID: PMC4276308
DOI: 10.1016/j.neurobiolaging.2014.04.038

Abstract

Alzheimer's disease (AD) is characterized by cortical atrophy and disrupted anatomic connectivity, and leads to abnormal interactions between neural systems. Diffusion-weighted imaging (DWI) and graph theory can be used to evaluate major brain networks and detect signs of a breakdown in network connectivity. In a longitudinal study using both DWI and standard magnetic resonance imaging (MRI), we assessed baseline white-matter connectivity patterns in 30 subjects with mild cognitive impairment (MCI, mean age 71.8 ± 7.5 years, 18 males and 12 females) from the Alzheimer's Disease Neuroimaging Initiative. 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, including mean clustering coefficient and characteristic path length. We evaluated whether these baseline network measures predicted future volumetric brain atrophy in MCI subjects, who are at risk for developing AD, as determined by 3-dimensional Jacobian "expansion factor maps" between baseline and 6-month follow-up anatomic scans. This study suggests that DWI-based network measures may be a novel predictor of AD progression.

Keywords: ADNI; Brain networks; Connectivity; DTI; Graph theory; Small worldness; TBM; Tractography; White matter.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Aged
Alzheimer Disease
Atrophy
Cognitive Dysfunction / pathology*
Cognitive Dysfunction / physiopathology*
Diffusion Magnetic Resonance Imaging / methods*
Disease Progression
Female
Forecasting
Humans
Magnetic Resonance Imaging / methods*
Male
Middle Aged
Nerve Net / pathology
Nerve Net / physiopathology
Neural Conduction
Neuroimaging / methods*
White Matter / pathology*
White Matter / physiopathology*

Abstract

Publication types

MeSH terms

Grants and funding