Journal of Computational Neuroscience

, Volume 34, Issue 2, pp 303–318 | Cite as

Inferring evoked brain connectivity through adaptive perturbation



Inference of functional networks—representing the statistical associations between time series recorded from multiple sensors—has found important applications in neuroscience. However, networksexhibiting time-locked activity between physically independent elements can bias functional connectivity estimates employing passive measurements. Here, a perturbative and adaptive method of inferring network connectivity based on measurement and stimulation—so called “evoked network connectivity” is introduced. This procedure, employing a recursive Bayesian update scheme, allows principled network stimulation given a current network estimate inferred from all previous stimulations and recordings. The method decouples stimulus and detector design from network inference and can be suitably applied to a wide range of clinical and basic neuroscience related problems. The proposed method demonstrates improved accuracy compared to network inference based on passive observation of node dynamics and an increased rate of convergence relative to network estimation employing a naïve stimulation strategy.


Perturbative Network Estimation Functional connectivity Adaptive 



K.Q.L. acknowledges support for this research from the Cognitive Rhythms Collaborative, NSF grant DMS-1042134 S.C. acknowledges support from NIH DP1-OD003646. S.C. holds a Career Award at the Scientific Interface from the Burroughs Wellcome Fund. M.A.K. holds a Career Award at the Scientific Interface from the Burroughs Wellcome Fund.


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Kyle Q. Lepage
    • 1
  • ShiNung Ching
    • 2
  • Mark A. Kramer
    • 1
  1. 1.Department of Mathematics & StatisticsBoston UniversityBostonUSA
  2. 2.Department of Anesthesia, Critical Care & Pain MedicineMassachusetts General HospitalBostonUSA

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