Evoked brain responses are generated by feedback loops

Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, United Kingdom.
Proceedings of the National Academy of Sciences (Impact Factor: 9.67). 01/2008; 104(52):20961-6. DOI: 10.1073/pnas.0706274105
Source: PubMed


Neuronal responses to stimuli, measured electrophysiologically, unfold over several hundred milliseconds. Typically, they show characteristic waveforms with early and late components. It is thought that early or exogenous components reflect a perturbation of neuronal dynamics by sensory input bottom-up processing. Conversely, later, endogenous components have been ascribed to recurrent dynamics among hierarchically disposed cortical processing levels, top-down effects. Here, we show that evoked brain responses are generated by recurrent dynamics in cortical networks, and late components of event-related responses are mediated by backward connections. This evidence is furnished by dynamic causal modeling of mismatch responses, elicited in an oddball paradigm. We used the evidence for models with and without backward connections to assess their likelihood as a function of peristimulus time and show that backward connections are necessary to explain late components. Furthermore, we were able to quantify the contribution of backward connections to evoked responses and to source activity, again as a function of peristimulus time. These results link a generic feature of brain responses to changes in the sensorium and a key architectural component of functional anatomy; namely, backward connections are necessary for recurrent interactions among levels of cortical hierarchies. This is the theoretical cornerstone of most modern theories of perceptual inference and learning.

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Available from: Marta I Garrido
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    • "While repetition suppression was most prominent at relatively early latencies (40-60 ms) of the auditory evoked response, expectation suppression was present at later latencies (100-200 ms), within the mismatch negativity range. The relatively early onset of repetition effects suggests that repetition suppression may reflect low-level expectations based on local transition probabilities (Garrido et al., 2007;Kiebel et al., 2008;Wacongne et al., 2011), and replicates previous findings showing that deviance magnitude (i.e., the absolute frequency of a deviant in the stimulus sequence) affects early M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 19 components of the evoked response up to the N1 component (Horváth et al., 2008) but not the later components of the MMN proper. The effects of stimulus probability, on the other hand, might rely on hierarchically higher expectations about the sequence structure or likelihood of stimuli, induced by learning of the statistical regularities of the sequence. "
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    ABSTRACT: This paper presents a review of theoretical and empirical work on repetition suppression in the context of predictive coding. Predictive coding is a neurobiologically plausible scheme explaining how biological systems might perform perceptual inference and learning. From this perspective, repetition suppression is a manifestation of minimising prediction error through adaptive changes in predictions about the content and precision of sensory inputs. Simulations of artificial neural hierarchies provide a principled way of understanding how repetition suppression – at different time scales – can be explained in terms of inference and learning implemented under predictive coding. This formulation of repetition suppression is supported by results of numerous empirical studies of repetition suppression and its contextual determinants.
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    • "Another family of neuroimaging modalities includes electrophysiological recordings, namely, electroencephalography (EEG), magnetoencephalography (MEG), and local field potential (LFP). These techniques provide direct measures of cortical activity and offer very high temporal resolution but rather low spatial accuracy [4] [5]. "
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    • "In general under active inference, perceptual awareness is argued to depend upon the inversion of a generative model that encodes error-minimizing expectations, which then constrain activity in lower-order regions via top-down cortical feedback (Friston, 2010; Friston et al., 2012; Rao and Ballard, 1999). With respect to oddball tasks, mismatch responses elicited by the comparison of standard stimuli to unexpected deviants are computationally well fit by prediction error minimization schemes (Garrido et al., 2007, 2008; Lieder et al., 2013a,b), and are mediated by asymmetrical changes in intrinsic and extrinsic effective connectivity within a hierarchical network (Dietz et al., 2014; Garrido et al., 2008, 2009). "

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