Tracking Dynamic Interactions Between Structural and Functional Connectivity: A TMS/EEG-dMRI Study

Amico, Enrico; Bodart, Olivier; Rosanova, Mario; Gosseries, Olivia; Heine, Lizette; Mierlo, Pieter van; Martial, Charlotte; Massimini, Marcello; Marinazzo, Daniele; Laureys, Steven

doi:10.1089/brain.2016.0462

Cited by 26 publications

(20 citation statements)

References 60 publications

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“…However, we did not Figure 2D-E). In summary, TMS-evoked EEG potentials, in particular later TEP components, seem to reflect more complex brain responses caused by propagation of the initial response at the site of stimulation to remote brain areas [26,47,48]. As the interaction of CS-and TS-evoked EEG responses seem to be largely uniform irrespective of the interstimulus interval (3 ms vs. 11 ms in [12]; 2 ms vs. 100 ms in the present study and [14]), it can be concluded that ppTMS-EEG protocols are not identical to the findings of ppTMS-EMG protocols that would result in SICI, ICF and LICI of MEP amplitudes at interstimulus intervals of 2-3 ms, 11 ms and 100 ms, respectively [36,44,45].…”

Section: Macroscopic Evaluation Of Paired-pulse Tms Protocolsmentioning

confidence: 99%

Short-interval and long-interval intracortical inhibition of TMS-evoked EEG potentials

et al. 2018

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Section: Macroscopic Evaluation Of Paired-pulse Tms Protocolsmentioning

confidence: 99%

Short-interval and long-interval intracortical inhibition of TMS-evoked EEG potentials

et al. 2018

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“…Because it is known that the effects of stimulation are propagated through the brain via anatomical and functional connections (Amico et al, 2017;Fox et al, 2014), the effect of aiTBS might occur on a network level. Because it is known that the effects of stimulation are propagated through the brain via anatomical and functional connections (Amico et al, 2017;Fox et al, 2014), the effect of aiTBS might occur on a network level.…”

mentioning

confidence: 99%

“…How aiTBS has the potential to improve depression symptoms over such a limited period in medication resistant MDD patients remains to be elucidated. Because it is known that the effects of stimulation are propagated through the brain via anatomical and functional connections (Amico et al, 2017;Fox et al, 2014), the effect of aiTBS might occur on a network level. In this study, the effect of this aiTBS protocol on the brain's network topology is investigated by means of graph analysis derived from resting-state functional MRI (rs-fMRI) data of a group of MDD patients.…”

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confidence: 99%

Focal application of accelerated iTBS results in global changes in graph measures

Klooster

Franklin

Besseling

et al. 2018

Human Brain Mapping

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Graph analysis was used to study the effects of accelerated intermittent theta burst stimulation (aiTBS) on the brain's network topology in medication‐resistant depressed patients. Anatomical and resting‐state functional MRI (rs‐fMRI) was recorded at baseline and after sham and verum stimulation. Depression severity was assessed using the Hamilton Depression Rating Scale (HDRS). Using various graph measures, the different effects of sham and verum aiTBS were calculated. It was also investigated whether changes in graph measures were correlated to clinical responses. Furthermore, by correlating baseline graph measures with the changes in HDRS in terms of percentage, the potential of graph measures as biomarker was studied. Although no differences were observed between the effects of verum and sham stimulation on whole‐brain graph measures and changes in graph measures did not correlate with clinical response, the baseline values of clustering coefficient and global efficiency showed to be predictive of the clinical response to verum aiTBS. Nodal effects were found throughout the whole brain. The distribution of these effects could not be linked to the strength of the functional connectivity between the stimulation site and the node. This study showed that the effects of aiTBS on graph measures distribute beyond the actual stimulation site. However, additional research into the complex interactions between different areas in the brain is necessary to understand the effects of aiTBS in more detail.

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“…Complementing these findings, computational models of neurodynamics have demonstrated that regional differences in structural connectivity may provide a mechanistic account for how local network activity that is induced from a focal TMS pulse can propagate along cortico-cortical fibers to influence global network synchronization (Gollo et al, 2017;Muldoon et al, 2016). This idea is supported by neurostimulation research that shows a structure-function constraint to the local stimulation and subsequent global (de)synchronization (Amico et al, 2017). While both experimental and modeling work have suggested the importance of interacting networks, few studies have employed the rich set of tools of network science to understand the propagation of TMS stimulation throughout the brain (Bortoletto et al, 2015).…”

Section: Introductionmentioning

confidence: 88%

Reconfigurations within resonating communities of brain regions following TMS reveal different scales of processing

Garcia

Ashourvan

Thurman

et al. 2020

Network Neuroscience

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An overarching goal of neuroscience research is to understand how heterogeneous neuronal ensembles cohere into networks of coordinated activity to support cognition. To investigate how local activity harmonizes with global signals, we measured electroencephalography (EEG) while single pulses of transcranial magnetic stimulation (TMS) perturbed occipital and parietal cortices. We estimate the rapid network reconfigurations in dynamic network communities within specific frequency bands of the EEG, and characterize two distinct features of network reconfiguration, flexibility and allegiance, among spatially distributed neural sources following TMS. Using distance from the stimulation site to infer local and global effects, we find that alpha activity (8–12 Hz) reflects concurrent local and global effects on network dynamics. Pairwise allegiance of brain regions to communities on average increased near the stimulation site, whereas TMS-induced changes to flexibility were generally invariant to distance and stimulation site. In contrast, communities within the beta (13–20 Hz) band demonstrated a high level of spatial specificity, particularly within a cluster comprising paracentral areas. Together, these results suggest that focal magnetic neurostimulation to distinct cortical sites can help identify both local and global effects on brain network dynamics, and highlight fundamental differences in the manifestation of network reconfigurations within alpha and beta frequency bands.

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Tracking Dynamic Interactions Between Structural and Functional Connectivity: A TMS/EEG-dMRI Study

Cited by 26 publications

References 60 publications

Short-interval and long-interval intracortical inhibition of TMS-evoked EEG potentials

Short-interval and long-interval intracortical inhibition of TMS-evoked EEG potentials

Focal application of accelerated iTBS results in global changes in graph measures

Reconfigurations within resonating communities of brain regions following TMS reveal different scales of processing

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