TY - JOUR
T1 - Symmetric data-driven fusion of diffusion tensor MRI
T2 - Age differences in white matter
AU - Mendez Colmenares, Andrea
AU - Hefner, Michelle B.
AU - Calhoun, Vince D.
AU - Salerno, Elizabeth A.
AU - Fanning, Jason
AU - Gothe, Neha P.
AU - McAuley, Edward
AU - Kramer, Arthur F.
AU - Burzynska, Agnieszka Z.
N1 - Publisher Copyright:
Copyright © 2023 Mendez Colmenares, Hefner, Calhoun, Salerno, Fanning, Gothe, McAuley, Kramer and Burzynska.
PY - 2023
Y1 - 2023
N2 - In the past 20 years, white matter (WM) microstructure has been studied predominantly using diffusion tensor imaging (DTI). Decreases in fractional anisotropy (FA) and increases in mean (MD) and radial diffusivity (RD) have been consistently reported in healthy aging and neurodegenerative diseases. To date, DTI parameters have been studied individually (e.g., only FA) and separately (i.e., without using the joint information across them). This approach gives limited insights into WM pathology, increases the number of multiple comparisons, and yields inconsistent correlations with cognition. To take full advantage of the information in a DTI dataset, we present the first application of symmetric fusion to study healthy aging WM. This data-driven approach allows simultaneous examination of age differences in all four DTI parameters. We used multiset canonical correlation analysis with joint independent component analysis (mCCA + jICA) in cognitively healthy adults (age 20–33, n = 51 and age 60–79, n = 170). Four-way mCCA + jICA yielded one high-stability modality-shared component with co-variant patterns of age differences in RD and AD in the corpus callosum, internal capsule, and prefrontal WM. The mixing coefficients (or loading parameters) showed correlations with processing speed and fluid abilities that were not detected by unimodal analyses. In sum, mCCA + jICA allows data-driven identification of cognitively relevant multimodal components within the WM. The presented method should be further extended to clinical samples and other MR techniques (e.g., myelin water imaging) to test the potential of mCCA+jICA to discriminate between different WM disease etiologies and improve the diagnostic classification of WM diseases.
AB - In the past 20 years, white matter (WM) microstructure has been studied predominantly using diffusion tensor imaging (DTI). Decreases in fractional anisotropy (FA) and increases in mean (MD) and radial diffusivity (RD) have been consistently reported in healthy aging and neurodegenerative diseases. To date, DTI parameters have been studied individually (e.g., only FA) and separately (i.e., without using the joint information across them). This approach gives limited insights into WM pathology, increases the number of multiple comparisons, and yields inconsistent correlations with cognition. To take full advantage of the information in a DTI dataset, we present the first application of symmetric fusion to study healthy aging WM. This data-driven approach allows simultaneous examination of age differences in all four DTI parameters. We used multiset canonical correlation analysis with joint independent component analysis (mCCA + jICA) in cognitively healthy adults (age 20–33, n = 51 and age 60–79, n = 170). Four-way mCCA + jICA yielded one high-stability modality-shared component with co-variant patterns of age differences in RD and AD in the corpus callosum, internal capsule, and prefrontal WM. The mixing coefficients (or loading parameters) showed correlations with processing speed and fluid abilities that were not detected by unimodal analyses. In sum, mCCA + jICA allows data-driven identification of cognitively relevant multimodal components within the WM. The presented method should be further extended to clinical samples and other MR techniques (e.g., myelin water imaging) to test the potential of mCCA+jICA to discriminate between different WM disease etiologies and improve the diagnostic classification of WM diseases.
KW - aging
KW - diffusion MRI
KW - fusion
KW - multimodal
KW - white matter
UR - http://www.scopus.com/inward/record.url?scp=85159002001&partnerID=8YFLogxK
U2 - 10.3389/fneur.2023.1094313
DO - 10.3389/fneur.2023.1094313
M3 - Article
C2 - 37139071
AN - SCOPUS:85159002001
SN - 1664-2295
VL - 14
JO - Frontiers in Neurology
JF - Frontiers in Neurology
M1 - 1094313
ER -