TY - JOUR
T1 - Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer's disease
AU - for the Alzheimer's Disease Neuroimaging Initiative
AU - Zhan, Liang
AU - Zhou, Jiayu
AU - Wang, Yalin
AU - Jin, Yan
AU - Jahanshad, Neda
AU - Prasad, Gautam
AU - Nir, Talia M.
AU - Leonardo, Cassandra D.
AU - Ye, Jieping
AU - Thompson, Paul M.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Alzheimer's disease
KW - Brain network
KW - Classification
KW - Diffusion MRI
KW - GLRAM
KW - PCA
KW - Tractography
UR - http://www.scopus.com/inward/record.url?scp=84992236271&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84992236271&partnerID=8YFLogxK
U2 - 10.3389/fnagi.2015.00048
DO - 10.3389/fnagi.2015.00048
M3 - Article
AN - SCOPUS:84992236271
SN - 1663-4365
VL - 7
JO - Frontiers in Aging Neuroscience
JF - Frontiers in Aging Neuroscience
IS - APR
M1 - 48
ER -