An exploratory data analysis method for identifying brain regions and frequencies of interest from large-scale neural recordings

  • Macauley S. BreaultEmail author
  • Pierre Sacré
  • Jorge González-Martínez
  • John T. Gale
  • Sridevi V. Sarma


High-resolution whole brain recordings have the potential to uncover unknown functionality but also present the challenge of how to find such associations between brain and behavior when presented with a large number of regions and spectral frequencies. In this paper, we propose an exploratory data analysis method that sorts through a massive quantity of multivariate neural recordings to quickly extract a subset of brain regions and frequencies that encode behavior. This approach combines existing tools and exploits low-rank approximation of matrices without a priori selection of regions and frequency bands for analysis. In detail, the spectral content of neural activity across all frequencies of each recording contact is computed and represented as a matrix. Then, the rank-1 approximation of the matrix is computed using singular value decomposition and the associated singular vectors are extracted. The temporal singular vector, which captures the salient features of the spectrogram, is then correlated to the trial-varying behavioral signal. The distribution of correlations for each brain region is efficiently computed and used to find a subset of regions and frequency bands of interest for further examination. As an illustration, we apply this approach to a data set of local field potentials collected using stereoelectroencephalography from a human subject performing a reaching task. Using the proposed procedure, we produced a comprehensive set of brain regions and frequencies related to our specific behavior. We demonstrate how this tool can produce preliminary results that capture neural patterns related to behavior and aid in formulating data-driven hypotheses, hence reducing the time it takes for any scientist to transition from the exploratory to the confirmatory phase.


Exploratory data analysis Multivariate neural data Singular value decomposition Stereoelectroencephalography 



This work was supported by NSF EFRI 1137237 to S.V.S., J.G.M., and J.T.G. as well as Kavli Foundation to P.S. In addition, M.S.B. was partially supported by the ARCS Foundation as a Paul Wright Memorial Scholar.

Compliance with Ethical Standards

Subject enrollment was completely voluntarily and the subject gave informed consent. Experimental protocols were approved by the Cleveland Clinic Institutional Review Board and the methods were carried out in accordance with the approved guidelines.

Conflict of interests

The authors declare that they have no conflict of interest.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringJohns Hopkins UniversityBaltimoreUSA
  2. 2.Center for EpilepsyCleveland ClinicClevelandUSA
  3. 3.Department of NeurosurgeryEmory UniversityAtlantaUSA

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