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Dynamic causal modeling of cortical activity from the acute to the chronic stage after stroke.

ABSTRACT: Functional neuroimaging studies frequently demonstrated that stroke patients show bilateral activity in motor and premotor areas during movements of the paretic hand in contrast to a more lateralized activation observed in healthy subjects. Moreover, a few studies modeling functional or effective connectivity reported performance-related changes in the motor network after stroke. Here, we investigated the temporal evolution of intra- and interhemispheric (dys-) connectivity during motor recovery from the acute to the early chronic phase post-stroke. Twelve patients performed hand movements in an fMRI task in the acute (≤72 hours) and subacute stage (2 weeks) post-stroke. A subgroup of 10 patients participated in a third assessment in the early chronic stage (3-6 months). Twelve healthy subjects served as reference for brain connectivity. Changes in effective connectivity within a bilateral network comprising M1, premotor cortex (PMC), and supplementary motor area (SMA) were estimated by dynamic causal modeling. Motor performance was assessed by the Action Research Arm Test and maximum grip force. Results showed reduced positive coupling of ipsilesional SMA and PMC with ipsilesional M1 in the acute stage. Coupling parameters among these areas increased with recovery and predicted a better outcome. Likewise, negative influences from ipsilesional areas to contralesional M1 were attenuated in the acute stage. In the subacute stage, contralesional M1 exerted a positive influence on ipsilesional M1. Negative influences from ipsilesional areas on contralesional M1 subsequently normalized, but patients with poorer outcome in the chronic stage now showed enhanced negative coupling from contralesional upon ipsilesional M1. These findings show that the reinstatement of effective connectivity in the ipsilesional hemisphere is an important feature of motor recovery after stroke. The shift of an early, supportive role of contralesional M1 into enhanced inhibitory coupling might indicate maladaptive processes which could be a target of non-invasive brain stimulation techniques.

SUBMITTER: Rehme AK

PROVIDER: S-EPMC8053821 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Dynamic causal modeling (DCM) is a methodological approach to study effective connectivity among brain regions. Based on a set of observations and a biophysical model of brain interactions, DCM uses a Bayesian framework to estimate the posterior distribution of the free parameters of the model (e.g. modulation of connectivity) and infer architectural properties of the most plausible model (i.e. model selection). When modeling electrophysiological event-related responses, the estimation of the model relies on the integration of the system of delay differential equations (DDEs) that describe the dynamics of the system. In this technical note, we compared two numerical schemes for the integration of DDEs. The first, and standard, scheme approximates the DDEs (more precisely, the state of the system, with respect to conduction delays among brain regions) using ordinary differential equations (ODEs) and solves it with a fixed step size. The second scheme uses a dedicated DDEs solver with adaptive step sizes to control error, making it theoretically more accurate. To highlight the effects of the approximation used by the first integration scheme in regard to parameter estimation and Bayesian model selection, we performed simulations of local field potentials using first, a simple model comprising 2 regions and second, a more complex model comprising 6 regions. In these simulations, the second integration scheme served as the standard to which the first one was compared. Then, the performances of the two integration schemes were directly compared by fitting a public mismatch negativity EEG dataset with different models. The simulations revealed that the use of the standard DCM integration scheme was acceptable for Bayesian model selection but underestimated the connectivity parameters and did not allow an accurate estimation of conduction delays. Fitting to empirical data showed that the models systematically obtained an increased accuracy when using the second integration scheme. We conclude that inference on connectivity strength and delay based on DCM for EEG/MEG requires an accurate integration scheme.

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Dynamic causal modeling of cortical activity from the acute to the chronic stage after stroke.

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