Publications

2024

1. Sci. Data

   Exploring de-anonymization risks in PET imaging: Insights from a comprehensive analysis of 853 patient scans

   Bou Hanna, Emma, Partarrieu, Sebastian, Berenbaum, Arnaud, Allassonnière, Stéphanie, and Besson, L. Florent

   Scientific Data 2024

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   Due to their high resolution, anonymized CT scans can be reidentified using face recognition tools. However, little is known regarding PET deanonymization because of its lower resolution. In this study, we analysed PET/CT scans of 853 patients from a TCIA-restricted dataset (AutoPET). First, we built denoised 2D morphological reconstructions of both PET and CT scans, and then we determined how frequently a PET reconstruction could be matched to the correct CT reconstruction with no other metadata. Using the CT morphological reconstructions as ground truth allows us to frame the problem as a face recognition problem and to quantify our performance using traditional metrics (top k accuracies) without any use of patient pictures. Using our denoised PET 2D reconstructions, we achieved 72% top 10 accuracy after the realignment of all CTs in the same reference frame, and 71% top 10 accuracy after realignment and mixing within a larger face dataset of 10, 168 pictures. This highlights the need to consider face identification issues when dealing with PET imaging data.

2023

1. Nat. Nanotechnol

   3D spatiotemporally scalable in vivo neural probes based on fluorinated elastomers

   Le Floch, Paul, Zhao, Siyuan, Liu, Ren, Molinari, Nicola, Medina, Eder, Shen, Hao, Wang, Zheliang, Kim, Junsoo, Sheng, Hao, Partarrieu, Sebastian, Wang, Wenbo, Sessler, Chanan, Zhang, Guogao, Park, Hyunsu, Gong, Xian, Spencer, Andrew, Lee, Jongha, Ye, Tianyang, Tang, Xin, Wang, Xiao, Bertoldi, Katia, Lu, Nanshu, Kozinsky, Boris, Suo, Zhigang, and Liu, Jia

   Nature Nanotechnology 2023

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   Electronic devices for recording neural activity in the nervous system need to be scalable across large spatial and temporal scales while also providing millisecond and single-cell spatiotemporal resolution. However, existing high-resolution neural recording devices cannot achieve simultaneous scalability on both spatial and temporal levels due to a trade-off between sensor density and mechanical flexibility. Here we introduce a three-dimensional (3D) stacking implantable electronic platform, based on perfluorinated dielectric elastomers and tissue-level soft multilayer electrodes, that enables spatiotemporally scalable single-cell neural electrophysiology in the nervous system. Our elastomers exhibit stable dielectric performance for over a year in physiological solutions and are 10,000 times softer than conventional plastic dielectrics. By leveraging these unique characteristics we develop the packaging of lithographed nanometre-thick electrode arrays in a 3D configuration with a cross-sectional density of 7.6 electrodes per 100 µm2. The resulting 3D integrated multilayer soft electrode array retains tissue-level flexibility, reducing chronic immune responses in mouse neural tissues, and demonstrates the ability to reliably track electrical activity in the mouse brain or spinal cord over months without disrupting animal behaviour.

2. Nat. Commun

   Explainable multi-task learning for multi-modality biological data analysis

   Tang, Xin, Zhang, Jiawei, He, Yichun, Zhang, Xinhe, Lin, Zuwan, Partarrieu, Sebastian, Bou Hanna, Emma, Ren, Zhaolin, Shen, Hao, Yang, Yuhong, Wang, Xiao, Li, Na, Ding, Jie, and Liu, Jia

   Nature Communications 2023

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   Current biotechnologies can simultaneously measure multiple high-dimensional modalities (e.g., RNA, DNA accessibility, and protein) from the same cells. A combination of different analytical tasks (e.g., multi-modal integration and cross-modal analysis) is required to comprehensively understand such data, inferring how gene regulation drives biological diversity and functions. However, current analytical methods are designed to perform a single task, only providing a partial picture of the multi-modal data. Here, we present UnitedNet, an explainable multi-task deep neural network capable of integrating different tasks to analyze single-cell multi-modality data. Applied to various multi-modality datasets (e.g., Patch-seq, multiome ATAC + gene expression, and spatial transcriptomics), UnitedNet demonstrates similar or better accuracy in multi-modal integration and cross-modal prediction compared with state-of-the-art methods. Moreover, by dissecting the trained UnitedNet with the explainable machine learning algorithm, we can directly quantify the relationship between gene expression and other modalities with cell-type specificity. UnitedNet is a comprehensive end-to-end framework that could be broadly applicable to single-cell multi-modality biology. This framework has the potential to facilitate the discovery of cell-type-specific regulation kinetics across transcriptomics and other modalities.

3. Nat. Neurosci

   Tracking neural activity from the same cells during the entire adult life of mice

   Zhao, Siyuan, Tang, Xin, Tian, Weiwen, Partarrieu, Sebastian, Liu, Ren, Shen, Hao, Lee, Jaeyong, Guo, Shiqi, Lin, Zuwan, and Liu, Jia

   Nature Neuroscience 2023

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   Stably recording the electrical activity of the same neurons over the adult life of an animal is important to neuroscience research and biomedical applications. Current implantable devices cannot provide stable recording on this timescale. Here, we introduce a method to precisely implant electronics with an open, unfolded mesh structure across multiple brain regions in the mouse. The open mesh structure forms a stable interwoven structure with the neural network, preventing probe drifting and showing no immune response and neuron loss during the year-long implantation. Rigorous statistical analysis, visual stimulus-dependent measurement and unbiased, machine-learning-based analysis demonstrated that single-unit action potentials have been recorded from the same neurons of behaving mice in a very long-term stable manner. Leveraging this stable structure, we demonstrated that the same neurons can be recorded over the entire adult life of the mouse, revealing the aging-associated evolution of single-neuron activities.