Brain Topography

, Volume 32, Issue 2, pp 255–270 | Cite as

Functional Brain Connectivity Revealed by Sparse Coding of Large-Scale Local Field Potential Dynamics

  • Han Wang
  • Kun Xie
  • Li Xie
  • Xiang Li
  • Meng Li
  • Cheng Lyu
  • Hanbo Chen
  • Yaowu Chen
  • Xuesong Liu
  • Joe Tsien
  • Tianming LiuEmail author
Original Paper


Exploration of brain dynamics patterns has attracted increasing attention due to its fundamental significance in understanding the working mechanism of the brain. However, due to the lack of effective modeling methods, how the simultaneously recorded LFP can inform us about the brain dynamics remains a general challenge. In this paper, we propose a novel sparse coding based method to investigate brain dynamics of freely-behaving mice from the perspective of functional connectivity, using super-long local field potential (LFP) recordings from 13 distinct regions of the mouse brain. Compared with surrogate datasets, six and four reproducible common functional connectivities were discovered to represent the space of brain dynamics in the frequency bands of alpha and theta respectively. Modeled by a finite state machine, temporal transition framework of functional connectivities was inferred for each frequency band, and evident preference was discovered. Our results offer a novel perspective for analyzing neural recording data at such high temporal resolution and recording length, as common functional connectivities and their transition framework discovered in this work reveal the nature of the brain dynamics in freely behaving mice.


Local field potential (LFP) Brain dynamics Sparse coding Freely behaving Volume conduction 



H Wang was supported by the Fundamental Research Funds for the Central Universities, the National Natural Science Foundation of China (Grant No. 31627802). T. Liu is supported by NIH R01 DA-033393, NIH R01 AG-042599, NSF CAREER Award IIS-1149260, NSF BME-1302089, NSF BCS-1439051 and NSF DBI-1564736. L Xie is supported by the Zhejiang Province Science and Technology Planning Project (Grant No. 2016C33069).

Supplementary material

10548_2018_682_MOESM1_ESM.pdf (262 kb)
Supplementary material 1 (PDF 262 KB)


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Copyright information

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Authors and Affiliations

  • Han Wang
    • 1
  • Kun Xie
    • 2
  • Li Xie
    • 3
  • Xiang Li
    • 4
  • Meng Li
    • 2
  • Cheng Lyu
    • 4
  • Hanbo Chen
    • 4
  • Yaowu Chen
    • 5
  • Xuesong Liu
    • 6
  • Joe Tsien
    • 2
  • Tianming Liu
    • 4
    Email author
  1. 1.College of Biomedical Engineering and Instrument ScienceZhejiang UniversityHangzhouChina
  2. 2.Brain and Behavior Discovery Institute, Medical College of GeorgiaAugusta UniversityAugustaUSA
  3. 3.The State Key Laboratory of Industrial Control TechnologyZhejiang UniversityHangzhouChina
  4. 4.Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research CenterUniversity of GeorgiaAthensUSA
  5. 5.Zhejiang University Embedded System Engineering Research CenterMinistry of Education of ChinaHangzhouChina
  6. 6.Zhejiang Provincial Key Laboratory for Network Multimedia TechnologiesZhejiang UniversityHangzhouChina

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