TMS-induced theta phase synchrony reveals a bottom-up network in working memory

Miyauchi, Eri; Kitajo, Keiichi; Kawasaki, Masahiro

doi:10.1016/j.neulet.2016.04.008

Cited by 14 publications

(15 citation statements)

References 23 publications

(33 reference statements)

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“…However, differently from previous WM studies, we did not find a material‐dependent activation of DLPFC. Opposite to what we expected, a strong involvement of parietal cortices in verbal n‐back task was instead noticed, which has been previously described during short term storage of verbal material (Jonides et al, ; Miyauchi, Kitajo, & Kawasaki, ). Nevertheless, the IPL is known to underlie many higher‐order functions, including numerical judgments and arithmetic (Göbel & Rushworth, ; Hubbard, Piazza, Pinel, & Dehaene, ), reading (Turkeltaub, Eden, Jones, & Zeffiro, ), and semantic processing (Chou et al, ; Raposo, Moss, Stamatakis, & Tyler, ).…”

Section: Discussioncontrasting

confidence: 61%

“…In the following paragraphs we will discuss the functional role of the retrieved core regions for each n-back task as well as of the observed overlap between n-back related brain regions and RSNs related to executive control, salience, and attention. We will then discuss possi- expected, a strong involvement of parietal cortices in verbal n-back task was instead noticed, which has been previously described during short term storage of verbal material (Jonides et al, 1998;Miyauchi, Kitajo, & Kawasaki, 2016). Nevertheless, the IPL is known to underlie many higher-order functions, including numerical judgments and arithmetic (Göbel & Rushworth, 2004;Hubbard, Piazza, Pinel, & Dehaene, 2005), reading (Turkeltaub, Eden, Jones, & Zeffiro, 2002), and semantic processing (Chou et al, 2006;Raposo, Moss, Stamatakis, & Tyler, 2006).…”

Section: Discussionmentioning

confidence: 72%

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Stimuli, presentation modality, and load‐specific brain activity patterns during n‐back task

Mencarelli

Neri

Momi

et al. 2019

Human Brain Mapping

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Working memory (WM) refers to a set of cognitive processes that allows for the temporary storage and manipulation of information, crucial for everyday life skills. WM deficits are present in several neurological, psychiatric, and neurodevelopmental disorders, thus making the full understanding of its neural correlates a key aspect for the implementation of cognitive training interventions. Here, we present a quantitative meta‐analysis focusing on the underlying neural substrates upon which the n‐back, one of the most commonly used tasks for WM assessment, is believed to rely on, as highlighted by functional magnetic resonance imaging and positron emission tomography findings. Relevant published work was scrutinized through the activation likelihood estimate (ALE) statistical framework in order to generate a set of task‐specific activation maps, according to n‐back difficulty. Our results confirm the known involvement of frontoparietal areas across different types of n‐back tasks, as well as the recruitment of subcortical structures, cerebellum and precuneus. Specific activations maps for four stimuli types, six presentation modalities, three WM loads and their combination are provided and discussed. Moreover, functional overlap with resting‐state networks highlighted a strong similarity between n‐back nodes and the Dorsal Attention Network, with less overlap with other networks like Salience, Language, and Sensorimotor ones. Additionally, neural deactivations during n‐back tasks and their functional connectivity profile were examined. Clinical and functional implications are discussed in the context of potential noninvasive brain stimulation and cognitive enhancement/rehabilitation programs.

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Section: Discussioncontrasting

confidence: 61%

Section: Discussionmentioning

confidence: 72%

Stimuli, presentation modality, and load‐specific brain activity patterns during n‐back task

Mencarelli

Neri

Momi

et al. 2019

Human Brain Mapping

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“…In our study, TMS of the motor cortex produced a robust and highly significant increase in phase locking in children and adolescents compared with adults, while the ERSP measures showed no statistically significant developmental differences. Thus, various factors, including differences in (GABAergic) inhibitory modulation of the evoked oscillations [Cho et al, 2015;Ferrarelli et al, 2008;Fukui et al, 2010;Julkunen et al, 2013], in signal variability, or in neural network connectivity [Haider et al, 2010;Lippe et al, 2009;Miyauchi et al, 2016;Moldakarimov et al, 2015;Vakorin et al, 2011], may explain the agerelated differences in ITC in our study. The ITC increment of children was not frequency specific and covered a large time range.…”

Section: Motor Network Developmental Changes In the Frequency Domainmentioning

confidence: 79%

Development of cortical motor circuits between childhood and adulthood: A navigated TMS‐HdEEG study

Määttä

Könönen

Kallioniemi

et al. 2017

Human Brain Mapping

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Motor functions improve during childhood and adolescence, but little is still known about the development of cortical motor circuits during early life. To elucidate the neurophysiological hallmarks of motor cortex development, we investigated the differences in motor cortical excitability and connectivity between healthy children, adolescents, and adults by means of navigated suprathreshold motor cortex transcranial magnetic stimulation (TMS) combined with high-density electroencephalography (EEG). We demonstrated that with development, the excitability of the motor system increases, the TMS-evoked EEG waveform increases in complexity, the magnitude of induced activation decreases, and signal spreading increases. Furthermore, the phase of the oscillatory response to TMS becomes less consistent with age. These changes parallel an improvement in manual dexterity and may reflect developmental changes in functional connectivity. Hum Brain Mapp 38:2599-2615, 2017. © 2017 Wiley Periodicals, Inc.

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“…Next, we investigated the spatial extent of this difference in the beta (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma bands (31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45). Figure 3b shows a topographical map of the mean significant t-values identified by the cluster-based permutation test.…”

Section: Alpha Lateralization-dependent Effective Connectivitymentioning

confidence: 99%

“…The most straightforward way to measure effective connectivity is to perturb a part of the brain network and observe how its impact is transmitted to other sites. In human studies, this has been achieved by combining transcranial magnetic stimulation (TMS) with EEG [13][14][15][16][17][18][19]. TMS-EEG has allowed demonstration of the dynamical properties of effective connectivity by showing how the propagation patterns of TMS evoked potentials could be used to differentiate between sleep and wakeful states [13], as well as the propagation of TMS-induced transient phase resetting of ongoing oscillations from visual to motor areas [17].…”

Section: Introductionmentioning

confidence: 99%

Probing dynamical cortical gating of attention with concurrent TMS-EEG

Okazaki

Mizuno

Kitajo

2019

Preprint

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HighlightsAttention-regulated cortical excitability and effective connectivity were probed by TMS-EEG.Stronger TMS evoked potentials were observed in the task-relevant visual hemisphere. Strong effective connectivity between task-relevant visual cortex and other areas were found. AbstractBackground: Attention facilitates gating of information from the sending brain area to receiving areas, with this being achieved by dynamical change in effective connectivity between cortices.However, it is difficult to assess effective connectivity, which refers to the causal influence of one cortical area on another.Objective: The aim of this study is to directly probe the effective connectivity between cortical areas, which is modulated by covertly shifted attention, excluding the thalamic influence.Methods: Transcranial magnetic stimulation (TMS) was used to directly perturb the right retinotopic visual cortex in task-relevant (TR) and task-irrelevant (TIR) networks, and the impact of this was tracked to other areas by concurrent use of electroencephalography (EEG).Results: Key finding was that TMS to the TR hemisphere led to a stronger evoked potential than did stimulation to the TIR hemisphere. Moreover, stronger beta-and gamma-band effective connectivities assessed as lagged phase synchronizations between stimulated areas and other areas were observed when TMS was delivered to the TR area. These effects were more enhanced when they preceded more prominent alpha lateralization, which is known to be associated with gating information. Conclusion:Our results indicate that attention-regulated cortical excitability and feedforward dynamical effective connectivity can be probed by direct cortical stimulation to the TR or TIR area, thereby bypassing thalamic gating. These results bear out the idea that TMS-EEG could help to characterize changes in the functional architecture of brain networks that are required for rapid adaptation to the environment or due to brain diseases.

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TMS-induced theta phase synchrony reveals a bottom-up network in working memory

Cited by 14 publications

References 23 publications

Stimuli, presentation modality, and load‐specific brain activity patterns during n‐back task

Stimuli, presentation modality, and load‐specific brain activity patterns during n‐back task

Development of cortical motor circuits between childhood and adulthood: A navigated TMS‐HdEEG study

Probing dynamical cortical gating of attention with concurrent TMS-EEG

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