Model selection and gobbledygook: Response to Lohmann et al.

Friston, Karl J.; Daunizeau, Jean; Stephan, Klaas Ε.

doi:10.1016/j.neuroimage.2011.11.064

Cited by 28 publications

(25 citation statements)

References 13 publications

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“…Depending on the number of nodes, the number of possible models can be impossible to explore exhaustively (Stephan et al, 2010, Friston et al, 2011a). Thus, the number of plausible models was reduced based on prior knowledge of structural and functional connectivity between these regions.…”

Section: Methodsmentioning

confidence: 99%

Network interactions underlying mirror feedback in stroke: A dynamic causal modeling study

Saleh

Yarossi

Manuweera

et al. 2017

NeuroImage: Clinical

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Mirror visual feedback (MVF) is potentially a powerful tool to facilitate recovery of disordered movement and stimulate activation of under-active brain areas due to stroke. The neural mechanisms underlying MVF have therefore been a focus of recent inquiry. Although it is known that sensorimotor areas can be activated via mirror feedback, the network interactions driving this effect remain unknown. The aim of the current study was to fill this gap by using dynamic causal modeling to test the interactions between regions in the frontal and parietal lobes that may be important for modulating the activation of the ipsilesional motor cortex during mirror visual feedback of unaffected hand movement in stroke patients. Our intent was to distinguish between two theoretical neural mechanisms that might mediate ipsilateral activation in response to mirror-feedback: transfer of information between bilateral motor cortices versus recruitment of regions comprising an action observation network which in turn modulate the motor cortex. In an event-related fMRI design, fourteen chronic stroke subjects performed goal-directed finger flexion movements with their unaffected hand while observing real-time visual feedback of the corresponding (veridical) or opposite (mirror) hand in virtual reality. Among 30 plausible network models that were tested, the winning model revealed significant mirror feedback-based modulation of the ipsilesional motor cortex arising from the contralesional parietal cortex, in a region along the rostral extent of the intraparietal sulcus. No winning model was identified for the veridical feedback condition. We discuss our findings in the context of supporting the latter hypothesis, that mirror feedback-based activation of motor cortex may be attributed to engagement of a contralateral (contralesional) action observation network. These findings may have important implications for identifying putative cortical areas, which may be targeted with non-invasive brain stimulation as a means of potentiating the effects of mirror training.

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Section: Methodsmentioning

confidence: 99%

Network interactions underlying mirror feedback in stroke: A dynamic causal modeling study

Saleh

Yarossi

Manuweera

et al. 2017

NeuroImage: Clinical

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“…The models, estimation methods, and notions of causality involved in DCM are different from the time seriesbased causality approaches discussed here. The interpretation and statistical properties of DCM are the subject of ongoing analysis (50)(51)(52)(53).…”

Section: Resultsmentioning

confidence: 99%

A study of problems encountered in Granger causality analysis from a neuroscience perspective

Stokes

Purdon

2017

Proc. Natl. Acad. Sci. U.S.A.

255

189

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, we misunderstood the method described in Barnett and Seth (25) to compute the conditional Granger causality. We realize now that the spectral factorization method they describe can be used to obtain the conditional Granger causality with a single model fit, which would avoid the computational problems associated with separate model fits. We apologize for this error." Granger causality methods were developed to analyze the flow of information between time series. These methods have become more widely applied in neuroscience. Frequency-domain causality measures, such as those of Geweke, as well as multivariate methods, have particular appeal in neuroscience due to the prevalence of oscillatory phenomena and highly multivariate experimental recordings. Despite its widespread application in many fields, there are ongoing concerns regarding the applicability of Granger causality methods in neuroscience. When are these methods appropriate? How reliably do they recover the system structure underlying the observed data? What do frequency-domain causality measures tell us about the functional properties of oscillatory neural systems? In this paper, we analyze fundamental properties of Granger-Geweke (GG) causality, both computational and conceptual. Specifically, we show that (i) GG causality estimates can be either severely biased or of high variance, both leading to spurious results; (ii) even if estimated correctly, GG causality estimates alone are not interpretable without examining the component behaviors of the system model; and (iii) GG causality ignores critical components of a system's dynamics. Based on this analysis, we find that the notion of causality quantified is incompatible with the objectives of many neuroscience investigations, leading to highly counterintuitive and potentially misleading results. Through the analysis of these problems, we provide important conceptual clarification of GG causality, with implications for other related causality approaches and for the role of causality analyses in neuroscience as a whole.Granger causality | time series analysis | neural oscillations | connectivity | system identification G ranger causality is a statistical tool developed to analyze the flow of information between time series. Neuroscientists have applied Granger causality methods to diverse sources of data, including electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), and local field potentials (LFP). These studies have investigated functional neural systems at scales of organization from the cellular level (1-3) to whole-brain network activity (4), under a range of conditions, including sensory stimuli (5-7), varying levels of consciousness (8-10), cognitive tasks (11), and pathological states (12, 13). In such analyses, the time series data are interpreted to reflect neural activity from a particular source, and Granger causality is used to characterize the directionality, directness, and dynamics of influence between sources.Oscillations are a ...

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“…In DCM, these a priori defined models are compared based on their estimated likelihood given the data while taking into account model complexity, referred to as free energy bound on the log model evidence. Note that model comparison in the DCM framework is conducted to compare models, not to validate them (see Friston et al, 2013). As a result, three types of parameters are calculated for the winning model: the direct influences of the external input or stimuli on regional activity (i.e., the driving input); the strength of the intrinsic connections between two regions in the absence of modulating experimental effects; and the changes in the intrinsic connectivity between regions induced by the experimental design (Mechelli et al, 2005).…”

Section: Dynamic Causal Modelingmentioning

confidence: 99%

Modeling the effects of noninvasive transcranial brain stimulation at the biophysical, network, and cognitive Level

Hartwigsen

Bergmann

Herz

et al. 2015

Progress in Brain Research

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Model selection and gobbledygook: Response to Lohmann et al.

Cited by 28 publications

References 13 publications

Network interactions underlying mirror feedback in stroke: A dynamic causal modeling study

Network interactions underlying mirror feedback in stroke: A dynamic causal modeling study

A study of problems encountered in Granger causality analysis from a neuroscience perspective

Modeling the effects of noninvasive transcranial brain stimulation at the biophysical, network, and cognitive Level

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