Forward and backward connections in the brain: A DCM study of functional asymmetries

Chen, Chun-Chuan; Henson, Richard N.; Stephan, Klaas Ε.; Kilner, James M.; Friston, Karl J.

doi:10.1016/j.neuroimage.2008.12.041

Cited by 96 publications

(94 citation statements)

References 86 publications

(97 reference statements)

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“…Finally, Chen et al (2009) addressed functional asymmetries in forward and backward connections during face perception, using DCM for induced responses. These asymmetries were expressed in terms of nonlinear or crossfrequency coupling, where high frequencies in a lower area excited low frequencies in a higher area and the reciprocal influences where inhibitory (see Fig.…”

Section: Forward and Backward Connections In The Brainmentioning

confidence: 99%

Functional and Effective Connectivity: A Review

2011

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Over the past 20 years, neuroimaging has become a predominant technique in systems neuroscience. One might envisage that over the next 20 years the neuroimaging of distributed processing and connectivity will play a major role in disclosing the brain's functional architecture and operational principles. The inception of this journal has been foreshadowed by an ever-increasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses. I accepted the invitation to write this review with great pleasure and hope to celebrate and critique the achievements to date, while addressing the challenges ahead.

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Section: Forward and Backward Connections In The Brainmentioning

confidence: 99%

Functional and Effective Connectivity: A Review

2011

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“…In addition, beta and gamma oscillations could underlie the flow of information in opposite directions, that is, forward vs. backward (Box 3; see [12], for a review). Along the lines of predictive coding, this suggests that prediction errors could be propagated in a feed-forward manner, mainly using the gamma frequency channel, whereas predictions (and their revisions) could be transmitted 'backward' using mainly the beta channel [12,65,79].…”

Section: Reviewmentioning

confidence: 99%

Cortical oscillations and sensory predictions

Arnal

Giraud

2012

Trends in Cognitive Sciences

903

856

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Predictive coding: the idea that the brain generates hypotheses about the possible causes of forthcoming sensory events and that these hypotheses are compared with incoming sensory information. The difference between topdown expectation and incoming sensory inputs, that is, prediction error, is propagated forward throughout the cortical hierarchy. Predictive timing (temporal expectations): an extension of the notion of predictive coding to the exploitation of temporal regularities (such as a beat) or associative contingencies (for instance, temporal relation between two inputs) to infer the occurrence of future sensory events. Top-down processing: efferent neural operations that convey the internal goals or states of the observer. This notion generally includes different cognitive processes, such as attention and expectations (Box 1). Neural oscillations: neurophysiological electromagnetic signals [from LocalField Potentials (LFP), electroencephalographic (EEG) and magnetoencephalographic recordings (MEG)] that reflect coherent neuronal population behavior at different spatial scales. These signals have been labeled as a function of their frequency from human surface EEG: delta (2-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12), and gamma bands (30-100 Hz). The mechanistic properties of oscillations are computationally interesting as a means of explaining various aspects of perception and cognition, for example, longdistance communication across brain regions, unification of various attributes of the same object, segmentation of the sensory input, memory etc.

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“…Clearly, more than one theory of schizophrenia exists and could provide guidance here. For example, long-standing theories have focused on dopamine The figure contains graphics that has been adapted, with permission, from Deco et al (2013a) and Chen et al (2009) …”

Section: Why Computational Modeling-and What To Focus On?mentioning

confidence: 99%

Translational Perspectives for Computational Neuroimaging

Stephan

Iglesias

Heinzle

et al. 2015

Neuron

160

148

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Functional neuroimaging has made fundamental contributions to our understanding of brain function. It remains challenging, however, to translate these advances into diagnostic tools for psychiatry. Promising new avenues for translation are provided by computational modeling of neuroimaging data. This article reviews contemporary frameworks for computational neuroimaging, with a focus on forward models linking unobservable brain states to measurements. These approaches-biophysical network models, generative models, and model-based fMRI analyses of neuromodulation-strive to move beyond statistical characterizations and toward mechanistic explanations of neuroimaging data. Focusing on schizophrenia as a paradigmatic spectrum disease, we review applications of these models to psychiatric questions, identify methodological challenges, and highlight trends of convergence among computational neuroimaging approaches. We conclude by outlining a translational neuromodeling strategy, highlighting the importance of openly available datasets from prospective patient studies for evaluating the clinical utility of computational models.

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Forward and backward connections in the brain: A DCM study of functional asymmetries

Cited by 96 publications

References 86 publications

Functional and Effective Connectivity: A Review

Functional and Effective Connectivity: A Review

Cortical oscillations and sensory predictions

Translational Perspectives for Computational Neuroimaging

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