TY - JOUR
T1 - EEG functional connectivity as a Riemannian mediator
T2 - An application to malnutrition and cognition
AU - Lopez Naranjo, Carlos
AU - Razzaq, Fuleah Abdul
AU - Li, Min
AU - Wang, Ying
AU - Bosch-Bayard, Jorge F.
AU - Lindquist, Martin A.
AU - Gonzalez Mitjans, Anisleidy
AU - Garcia, Ronaldo
AU - Rabinowitz, Arielle G.
AU - Anderson, Simon G.
AU - Chiarenza, Giuseppe A.
AU - Calzada-Reyes, Ana
AU - Virues-Alba, Trinidad
AU - Galler, Janina R.
AU - Minati, Ludovico
AU - Bringas Vega, Maria L.
AU - Valdes-Sosa, Pedro A.
N1 - Publisher Copyright:
© 2024 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.
PY - 2024/5
Y1 - 2024/5
N2 - Mediation analysis assesses whether an exposure directly produces changes in cognitive behavior or is influenced by intermediate “mediators”. Electroencephalographic (EEG) spectral measurements have been previously used as effective mediators representing diverse aspects of brain function. However, it has been necessary to collapse EEG measures onto a single scalar using standard mediation methods. In this article, we overcome this limitation and examine EEG frequency-resolved functional connectivity measures as a mediator using the full EEG cross-spectral tensor (CST). Since CST samples do not exist in Euclidean space but in the Riemannian manifold of positive-definite tensors, we transform the problem, allowing for the use of classic multivariate statistics. Toward this end, we map the data from the original manifold space to the Euclidean tangent space, eliminating redundant information to conform to a “compressed CST.” The resulting object is a matrix with rows corresponding to frequencies and columns to cross spectra between channels. We have developed a novel matrix mediation approach that leverages a nuclear norm regularization to determine the matrix-valued regression parameters. Furthermore, we introduced a global test for the overall CST mediation and a test to determine specific channels and frequencies driving the mediation. We validated the method through simulations and applied it to our well-studied 50+-year Barbados Nutrition Study dataset by comparing EEGs collected in school-age children (5–11 years) who were malnourished in the first year of life with those of healthy classmate controls. We hypothesized that the CST mediates the effect of malnutrition on cognitive performance. We can now explicitly pinpoint the frequencies (delta, theta, alpha, and beta bands) and regions (frontal, central, and occipital) in which functional connectivity was altered in previously malnourished children, an improvement to prior studies. Understanding the specific networks impacted by a history of postnatal malnutrition could pave the way for developing more targeted and personalized therapeutic interventions. Our methods offer a versatile framework applicable to mediation studies encompassing matrix and Hermitian 3D tensor mediators alongside scalar exposures and outcomes, facilitating comprehensive analyses across diverse research domains.
AB - Mediation analysis assesses whether an exposure directly produces changes in cognitive behavior or is influenced by intermediate “mediators”. Electroencephalographic (EEG) spectral measurements have been previously used as effective mediators representing diverse aspects of brain function. However, it has been necessary to collapse EEG measures onto a single scalar using standard mediation methods. In this article, we overcome this limitation and examine EEG frequency-resolved functional connectivity measures as a mediator using the full EEG cross-spectral tensor (CST). Since CST samples do not exist in Euclidean space but in the Riemannian manifold of positive-definite tensors, we transform the problem, allowing for the use of classic multivariate statistics. Toward this end, we map the data from the original manifold space to the Euclidean tangent space, eliminating redundant information to conform to a “compressed CST.” The resulting object is a matrix with rows corresponding to frequencies and columns to cross spectra between channels. We have developed a novel matrix mediation approach that leverages a nuclear norm regularization to determine the matrix-valued regression parameters. Furthermore, we introduced a global test for the overall CST mediation and a test to determine specific channels and frequencies driving the mediation. We validated the method through simulations and applied it to our well-studied 50+-year Barbados Nutrition Study dataset by comparing EEGs collected in school-age children (5–11 years) who were malnourished in the first year of life with those of healthy classmate controls. We hypothesized that the CST mediates the effect of malnutrition on cognitive performance. We can now explicitly pinpoint the frequencies (delta, theta, alpha, and beta bands) and regions (frontal, central, and occipital) in which functional connectivity was altered in previously malnourished children, an improvement to prior studies. Understanding the specific networks impacted by a history of postnatal malnutrition could pave the way for developing more targeted and personalized therapeutic interventions. Our methods offer a versatile framework applicable to mediation studies encompassing matrix and Hermitian 3D tensor mediators alongside scalar exposures and outcomes, facilitating comprehensive analyses across diverse research domains.
KW - EEG cross-spectrum
KW - Riemannian manifold
KW - causality
KW - matrix regression
KW - mediation analysis
UR - http://www.scopus.com/inward/record.url?scp=85192791618&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85192791618&partnerID=8YFLogxK
U2 - 10.1002/hbm.26698
DO - 10.1002/hbm.26698
M3 - Article
C2 - 38726908
AN - SCOPUS:85192791618
SN - 1065-9471
VL - 45
JO - Human Brain Mapping
JF - Human Brain Mapping
IS - 7
M1 - e26698
ER -