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Investigating the effect of electrical brain stimulation using a connectome-based brain network model

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We study here the ability of tDCS as a tool to bias functional networks by affecting the connections given the brain structure... We used structural data, that is, a human connectome to construct a large-scale brain network model of 74 cerebral areas, each described by a Jansen and Rit model... The model was designed on the basis of the neuroinformatics platform The Virtual Brain to account for reproducibility of the simulations... The tDCS-induced currents on the cerebral areas were calculated using a finite element method model... We identified the network states during rest and catalogued all states for further modeling studies... Furthermore, tDCS led to sharpened frequency spectra and increased (anode) or decreased (cathode) power in the respective areas... This study supports the notion that noninvasive brain stimulation is able to bias brain dynamics by affecting the competitive interplay of functional subnetworks... Our work constitutes a basis for further modeling studies to test target-oriented manipulation of functional networks (e.g. through adapted electrode montages) to improve pertinent treatment conditions... Furthermore, our approach emphasizes the role of structural data such as the network topology in emerging dynamics... Dynamics cannot necessarily be predicted from the structure but we found the structure especially important at transitions of network states.

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New functional connections are established during tDCS: among cortical areas, Panel A; and among scalp EEG electrodes, Panel B.
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Figure 1: New functional connections are established during tDCS: among cortical areas, Panel A; and among scalp EEG electrodes, Panel B.

Mentions: We identified the network states during rest and catalogued all states for further modeling studies. During tDCS, increased functional connectivity was found among a set of scalp EEG sensors, as reported in measurements [2], as well as among cerebral cortical areas (see Figure 1). Furthermore, tDCS led to sharpened frequency spectra and increased (anode) or decreased (cathode) power in the respective areas.


Investigating the effect of electrical brain stimulation using a connectome-based brain network model
New functional connections are established during tDCS: among cortical areas, Panel A; and among scalp EEG electrodes, Panel B.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4697511&req=5

Figure 1: New functional connections are established during tDCS: among cortical areas, Panel A; and among scalp EEG electrodes, Panel B.
Mentions: We identified the network states during rest and catalogued all states for further modeling studies. During tDCS, increased functional connectivity was found among a set of scalp EEG sensors, as reported in measurements [2], as well as among cerebral cortical areas (see Figure 1). Furthermore, tDCS led to sharpened frequency spectra and increased (anode) or decreased (cathode) power in the respective areas.

View Article: PubMed Central - HTML

AUTOMATICALLY GENERATED EXCERPT
Please rate it.

We study here the ability of tDCS as a tool to bias functional networks by affecting the connections given the brain structure... We used structural data, that is, a human connectome to construct a large-scale brain network model of 74 cerebral areas, each described by a Jansen and Rit model... The model was designed on the basis of the neuroinformatics platform The Virtual Brain to account for reproducibility of the simulations... The tDCS-induced currents on the cerebral areas were calculated using a finite element method model... We identified the network states during rest and catalogued all states for further modeling studies... Furthermore, tDCS led to sharpened frequency spectra and increased (anode) or decreased (cathode) power in the respective areas... This study supports the notion that noninvasive brain stimulation is able to bias brain dynamics by affecting the competitive interplay of functional subnetworks... Our work constitutes a basis for further modeling studies to test target-oriented manipulation of functional networks (e.g. through adapted electrode montages) to improve pertinent treatment conditions... Furthermore, our approach emphasizes the role of structural data such as the network topology in emerging dynamics... Dynamics cannot necessarily be predicted from the structure but we found the structure especially important at transitions of network states.

No MeSH data available.