Article

Inferring evoked brain connectivity through adaptive perturbation

Department of Mathematics & Statistics, Boston University, Boston, MA, 02215, USA, .
Journal of Computational Neuroscience (Impact Factor: 2.09). 09/2012; 34. DOI: 10.1007/s10827-012-0422-8
Source: PubMed

ABSTRACT 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.

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    • "Indeed, a weak, but highly novel input may be more easily perceived than an intense, but more familiar, stimulus. The ability to assess the responsiveness of neuronal networks to novelty – at a particular moment in time, relative to past inputs – has immediate implications in the analysis and control of biophysiological neuronal network dynamics in different behavioral and clinical regimes [9]–[11]. Here, as a first step, we seek to characterize the controllability of linear systems (linear networks) possessing high dimensional input-spaces, with respect to input novelty. "
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    • "Alternatively, methods using perturbation to infer the underlying connectivity of a network may be limited by similar constraints as those seen here. A recent study by Lepage et al. (2012) used mean-field approximations of cortical columnar processing to give a proof of concept for such a perturbation-based circuit mapping method. As opposed to neuroanatomical studies, such methods capture the functional connectivity of the network, which is inherently biased by the ongoing activity in a circuit. "
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    • "Such a problem is relevant to applications in social networks [6] [7] [8] and neuroscience [9]. In [10], such edge structure was identified in a neuronal network using an adaptive Bayesian inferential method. There, a central assumption was that only a single node could be excited at a given time to avoid the interference confound (i.e., determining from where an evoked response originated). "
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