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Modulating cortical connectivity in stroke patients by rTMS assessed with fMRI and dynamic causal modeling.

ABSTRACT: Data derived from transcranial magnetic stimulation (TMS) studies suggest that transcallosal inhibition mechanisms between the primary motor cortex of both hemispheres may contribute to the reduced motor performance of stroke patients. We here investigated the potential of modulating pathological interactions between cortical motor areas by means of repetitive TMS using functional magnetic resonance imaging (fMRI) and dynamic causal modeling (DCM). Eleven subacute stroke patients were scanned 1-3 months after symptom onset while performing whole hand fist closure movements. After a baseline scan, patients were stimulated with inhibitory 1-Hz rTMS applied over two different locations: (i) vertex (control stimulation) and (ii) primary motor cortex (M1) of the unaffected (contralesional) hemisphere. Changes in the endogenous and task-dependent effective connectivity were assessed by DCM of a bilateral network comprising M1, lateral premotor cortex, and the supplementary motor area (SMA). The results showed that rTMS applied over contralesional M1 significantly improved the motor performance of the paretic hand. The connectivity analysis revealed that the behavioral improvements were significantly correlated with a reduction of the negative influences originating from contralesional M1 during paretic hand movements. Concurrently, endogenous coupling between ipsilesional SMA and M1 was significantly enhanced only after rTMS applied over contralesional M1. Therefore, rTMS applied over contralesional M1 may be used to transiently remodel the disturbed functional network architecture of the motor system. The connectivity analyses suggest that both a reduction of pathological transcallosal influences (originating from contralesional M1) and a restitution of ipsilesional effective connectivity between SMA and M1 underlie improved motor performance.

SUBMITTER: Grefkes C

PROVIDER: S-EPMC8020334 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description: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.

Project description:IntroductionLearning to self-regulate brain activity by neurofeedback has been shown to lead to changes in the brain and behavior, with beneficial clinical and non-clinical outcomes. Neurofeedback uses a brain-computer interface to guide participants to change some feature of their brain activity. However, the neural mechanism of self-regulation learning remains unclear, with only 50% of the participants succeeding in achieving it. To bridge this knowledge gap, our study delves into the neural mechanisms of self-regulation learning via neurofeedback and investigates the brain processes associated with successful brain self-regulation.MethodsWe study the neural underpinnings of self-regulation learning by employing dynamical causal modeling (DCM) in conjunction with real-time functional MRI data. The study involved a cohort of 18 participants undergoing neurofeedback training targeting the supplementary motor area. A critical focus was the comparison between top-down hierarchical connectivity models proposed by Active Inference and alternative bottom-up connectivity models like reinforcement learning.ResultsOur analysis revealed a crucial distinction in brain connectivity patterns between successful and non-successful learners. Particularly, successful learners evinced a significantly stronger top-down effective connectivity towards the target area implicated in self-regulation. This heightened top-down network engagement closely resembles the patterns observed in goal-oriented and cognitive control studies, shedding light on the intricate cognitive processes intertwined with self-regulation learning.DiscussionThe findings from our investigation underscore the significance of cognitive mechanisms in the process of self-regulation learning through neurofeedback. The observed stronger top-down effective connectivity in successful learners indicates the involvement of hierarchical cognitive control, which aligns with the tenets of Active Inference. This study contributes to a deeper understanding of the neural dynamics behind successful self-regulation learning and provides insights into the potential cognitive architecture underpinning this process.

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Modulating cortical connectivity in stroke patients by rTMS assessed with fMRI and dynamic causal modeling.

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