Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer's disease.
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.
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.
Liang
L
Zhan
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.
Jiayu
J
Zhou
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe AZ, USA.
Yalin
Y
Wang
Imaging Genetics Center, University of Southern California, Los Angeles CA, USA.
Yan
Y
Jin
Imaging Genetics Center, University of Southern California, Los Angeles CA, USA.
Neda
N
Jahanshad
Imaging Genetics Center, University of Southern California, Los Angeles CA, USA.
Gautam
G
Prasad
Imaging Genetics Center, University of Southern California, Los Angeles CA, USA.
Talia M
TM
Nir
Imaging Genetics Center, University of Southern California, Los Angeles CA, USA.
Cassandra D
CD
Leonardo
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.
Jieping
J
Ye
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.
Paul M
PM
Thompson
Imaging Genetics Center, University of Southern California, Los Angeles CA, USA.
For The Alzheimer's Disease Neuroimaging Initiative
Alzheimer’s disease; GLRAM; PCA; brain network; classification; diffusion MRI; tractography