Format

Send to

Choose Destination
Front Aging Neurosci. 2015 Apr 14;7:48. doi: 10.3389/fnagi.2015.00048. eCollection 2015.

Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer's disease.

Author information

1
Imaging Genetics Center, University of Southern California, Los Angeles CA, USA ; Department of Neurology, Psychiatry, Pediatrics, Engineering, Radiology, and Ophthalmology, Keck School of Medicine, University of Southern California, Los Angeles CA, USA.
2
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe AZ, USA ; Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, Tempe AZ, USA.
3
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe AZ, USA.
4
Imaging Genetics Center, University of Southern California, Los Angeles CA, USA.

Abstract

Alzheimer's disease (AD) involves a gradual breakdown of brain connectivity, and network analyses offer a promising new approach to track and understand disease progression. Even so, our ability to detect degenerative changes in brain networks depends on the methods used. Here we compared several tractography and feature extraction methods to see which ones gave best diagnostic classification for 202 people with AD, mild cognitive impairment or normal cognition, scanned with 41-gradient diffusion-weighted magnetic resonance imaging as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. We computed brain networks based on whole brain tractography with nine different methods - four of them tensor-based deterministic (FACT, RK2, SL, and TL), two orientation distribution function (ODF)-based deterministic (FACT, RK2), two ODF-based probabilistic approaches (Hough and PICo), and one "ball-and-stick" approach (Probtrackx). Brain networks derived from different tractography algorithms did not differ in terms of classification performance on ADNI, but performing principal components analysis on networks helped classification in some cases. Small differences may still be detectable in a truly vast cohort, but these experiments help assess the relative advantages of different tractography algorithms, and different post-processing choices, when used for classification.

KEYWORDS:

Alzheimer’s disease; GLRAM; PCA; brain network; classification; diffusion MRI; tractography

Supplemental Content

Full text links

Icon for Frontiers Media SA Icon for PubMed Central
Loading ...
Support Center