Deep learning models reveal the link between dynamic brain connectivity patterns and states of consciousness

被引:0
作者
Gomez, Chloe [1 ]
Uhrig, Lynn [1 ,2 ]
Frouin, Vincent [3 ]
Duchesnay, Edouard [3 ]
Jarraya, Bechir [1 ,4 ]
Grigis, Antoine [3 ]
机构
[1] Univ Paris Saclay, NeuroSpin Ctr, Cognit Neuroimaging Unit, CEA,INSERM,U992, Gif Sur Yvette, France
[2] Univ Paris Cite, Necker Hosp, AP HP, Dept Anesthesiol & Crit Care, Paris, France
[3] Univ Paris Saclay, NeuroSpin Ctr, BAOBAB Unit, CEA, Gif Sur Yvette, France
[4] Univ Paris Saclay, Foch Hosp, Neurosci Pole, UVSQ, Suresnes, France
来源
SCIENTIFIC REPORTS | 2024年 / 14卷 / 01期
基金
欧盟地平线“2020”;
关键词
FUNCTIONAL CONNECTIVITY; RESPONSES;
D O I
10.1038/s41598-024-76695-1
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Decoding states of consciousness from brain activity is a central challenge in neuroscience. Dynamic functional connectivity (dFC) allows the study of short-term temporal changes in functional connectivity (FC) between distributed brain areas. By clustering dFC matrices from resting-state fMRI, we previously described "brain patterns" that underlie different functional configurations of the brain at rest. The networks associated with these patterns have been extensively analyzed. However, the overall dynamic organization and how it relates to consciousness remains unclear. We hypothesized that deep learning networks would help to model this relationship. Recent studies have used low-dimensional variational autoencoders (VAE) to learn meaningful representations that can help explaining consciousness. Here, we investigated the complexity of selecting such a generative model to study brain dynamics, and extended the available methods for latent space characterization and modeling. Therefore, our contributions are threefold. First, compared with probabilistic principal component analysis and sparse VAE, we showed that the selected low-dimensional VAE exhibits balanced performance in reconstructing dFCs and classifying brain patterns. We then explored the organization of the obtained low-dimensional dFC latent representations. We showed how these representations stratify the dynamic organization of the brain patterns as well as the experimental conditions. Finally, we proposed to delve into the proposed brain computational model. We first applied a receptive field analysis to identify preferred directions in the latent space to move from one brain pattern to another. Then, an ablation study was achieved where we virtually inactivated specific brain areas. We demonstrated the model's efficiency in summarizing consciousness-specific information encoded in key inter-areal connections, as described in the global neuronal workspace theory of consciousness. The proposed framework advocates the possibility of developing an interpretable computational brain model of interest for disorders of consciousness, paving the way for a dynamic diagnostic support tool.
引用
收藏
页数:17
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