TY - GEN
T1 - Species-Shared and -Specific Brain Functional Connectomes Revealed by Shared-Unique Variational Autoencoder
AU - Yang, Li
AU - Zhang, Songyao
AU - Zhang, Weihan
AU - Zhou, Jingchao
AU - Zhong, Tianyang
AU - Wei, Yaonai
AU - Jiang, Xi
AU - Liu, Tianming
AU - Han, Junwei
AU - Yuan, Yixuan
AU - Zhang, Tuo
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - A comparative study of large-scale species-shared and -specific functional connectomes, that are respectively inherited or diverged from their common ancestor, is important to the understanding of emergence and evolution of brain functions, cognitions and behaviors. However, recent works largely relied on the scheme that one species is used as the “reference” to which another is aligned and contrasted, whereas a more reasonable “reference” could be related to their common ancestor and unknown. To this end, we proposed a novel method termed shared-unique variational autoencoder (SU-VAE), and applied it to macaque and human MRI datasets to disentangle species-specific variation of functional connectomes from species-shared one. The reconstructed shared and specific connectomes gain supports from reports. The proposed method was further validated by the results that human-specific latent features, in contrast to shared features, better capture the variation of behavior variables unique to human, such as language comprehension. Our studies outperform other methods developed on VAE and linear regression in the aforementioned validation study. Finally, a graph analysis on the identified shared and specific connectomes reveals that, human/macaque -specific connecomtes positively/negatively contribute to the enhancement of network efficiency.
AB - A comparative study of large-scale species-shared and -specific functional connectomes, that are respectively inherited or diverged from their common ancestor, is important to the understanding of emergence and evolution of brain functions, cognitions and behaviors. However, recent works largely relied on the scheme that one species is used as the “reference” to which another is aligned and contrasted, whereas a more reasonable “reference” could be related to their common ancestor and unknown. To this end, we proposed a novel method termed shared-unique variational autoencoder (SU-VAE), and applied it to macaque and human MRI datasets to disentangle species-specific variation of functional connectomes from species-shared one. The reconstructed shared and specific connectomes gain supports from reports. The proposed method was further validated by the results that human-specific latent features, in contrast to shared features, better capture the variation of behavior variables unique to human, such as language comprehension. Our studies outperform other methods developed on VAE and linear regression in the aforementioned validation study. Finally, a graph analysis on the identified shared and specific connectomes reveals that, human/macaque -specific connecomtes positively/negatively contribute to the enhancement of network efficiency.
KW - Brain functional connectivity
KW - Disentangled representation learning
KW - Neural networks
KW - Species comparison
KW - VAE
UR - http://www.scopus.com/inward/record.url?scp=85163954380&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-34048-2_4
DO - 10.1007/978-3-031-34048-2_4
M3 - 会议稿件
AN - SCOPUS:85163954380
SN - 9783031340475
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 41
EP - 52
BT - Information Processing in Medical Imaging - 28th International Conference, IPMI 2023, Proceedings
A2 - Frangi, Alejandro
A2 - de Bruijne, Marleen
A2 - Wassermann, Demian
A2 - Navab, Nassir
PB - Springer Science and Business Media Deutschland GmbH
T2 - 28th International Conference on Information Processing in Medical Imaging, IPMI 2023
Y2 - 18 June 2023 through 23 June 2023
ER -