TY - GEN
T1 - A Matrix Autoencoder Framework to Align the Functional and Structural Connectivity Manifolds as Guided by Behavioral Phenotypes
AU - D’Souza, Niharika Shimona
AU - Nebel, Mary Beth
AU - Crocetti, Deana
AU - Robinson, Joshua
AU - Mostofsky, Stewart
AU - Venkataraman, Archana
N1 - Funding Information:
This work was supported by the National Science Foundation CRCNS award 1822575, National Science Foundation CAREER award 1845430, National Institute of Mental Health (R01 MH085328-09, R01 MH078160-07, K01 MH109766 and R01 MH106564), National Institute of Neurological Disorders and Stroke (R01NS048527-08), and the Autism Speaks foundation.
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - We propose a novel matrix autoencoder to map functional connectomes from resting state fMRI (rs-fMRI) to structural connectomes from Diffusion Tensor Imaging (DTI), as guided by subject-level phenotypic measures. Our specialized autoencoder infers a low dimensional manifold embedding for the rs-fMRI correlation matrices that mimics a canonical outer-product decomposition. The embedding is simultaneously used to reconstruct DTI tractography matrices via a second manifold alignment decoder and to predict inter-subject phenotypic variability via an artificial neural network. We validate our framework on a dataset of 275 healthy individuals from the Human Connectome Project database and on a second clinical dataset consisting of 57 subjects with Autism Spectrum Disorder. We demonstrate that the model reliably recovers structural connectivity patterns across individuals, while robustly extracting predictive and interpretable brain biomarkers in a cross-validated setting. Finally, our framework outperforms several baselines at predicting behavioral phenotypes in both real-world datasets.
AB - We propose a novel matrix autoencoder to map functional connectomes from resting state fMRI (rs-fMRI) to structural connectomes from Diffusion Tensor Imaging (DTI), as guided by subject-level phenotypic measures. Our specialized autoencoder infers a low dimensional manifold embedding for the rs-fMRI correlation matrices that mimics a canonical outer-product decomposition. The embedding is simultaneously used to reconstruct DTI tractography matrices via a second manifold alignment decoder and to predict inter-subject phenotypic variability via an artificial neural network. We validate our framework on a dataset of 275 healthy individuals from the Human Connectome Project database and on a second clinical dataset consisting of 57 subjects with Autism Spectrum Disorder. We demonstrate that the model reliably recovers structural connectivity patterns across individuals, while robustly extracting predictive and interpretable brain biomarkers in a cross-validated setting. Finally, our framework outperforms several baselines at predicting behavioral phenotypes in both real-world datasets.
KW - Functional connectivity
KW - Manifold alignment
KW - Matrix autoencoder
KW - Phenotypic prediction
KW - Structural connectivity
UR - http://www.scopus.com/inward/record.url?scp=85116433282&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85116433282&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-87234-2_59
DO - 10.1007/978-3-030-87234-2_59
M3 - Conference contribution
AN - SCOPUS:85116433282
SN - 9783030872335
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 625
EP - 636
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings
A2 - de Bruijne, Marleen
A2 - Cattin, Philippe C.
A2 - Cotin, Stéphane
A2 - Padoy, Nicolas
A2 - Speidel, Stefanie
A2 - Zheng, Yefeng
A2 - Essert, Caroline
PB - Springer Science and Business Media Deutschland GmbH
T2 - 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
Y2 - 27 September 2021 through 1 October 2021
ER -