Shared and connection-specific intrinsic interactions in the default mode network

Samogin, Jessica; Liu, Quanying; Marino, Marco; Wenderoth, Nicole; Mantini, Dante

doi:10.1016/j.neuroimage.2019.07.007

Cited by 72 publications

(150 citation statements)

References 55 publications

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“…This study extends, with the use of hdEEG and weighted networks analysis, previous findings [2], [8], [27] about wholebrain FC structure frequency specificity, showing alpha and low beta band increase in nodal strengths and average connectivity. In these carrier frequencies (i.e., from 8 Hz to 16 Hz), a spatially distributed gradient (in posterior-anterior direction) emerged from high to low nodal strength values (see Fig.…”

Section: A Alpha and Low Beta Band Increase Of The Connectivity Stresupporting

confidence: 89%

Small-World Propensity Reveals the Frequency Specificity of Resting State Networks

Iandolo

Chiappalone

Semprini

et al. 2020

IEEE Open J. Eng. Med. Biol.

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Functional connectivity (FC) is an important indicator of the brain's state in different conditions, such as rest/task or health/pathology. Here we used high-density electroencephalography coupled to source reconstruction to assess frequency-specific changes of FC during resting state. Specifically, we computed the Small-World Propensity (SWP) index to characterize network small-world architecture across frequencies. Methods: We collected resting state data from healthy participants and built connectivity matrices maintaining the heterogeneity of connection strengths. For a subsample of participants, we also investigated whether the SWP captured FC changes after the execution of a working memory (WM) task. Results: We found that SWP demonstrated a selective increase in the alpha and low beta bands. Moreover, SWP was modulated by a cognitive task and showed increased values in the bands entrained by the WM task. Conclusions: SWP is a valid metric to characterize the frequency-specific behavior of resting state networks.

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Section: A Alpha and Low Beta Band Increase Of The Connectivity Stresupporting

confidence: 89%

Small-World Propensity Reveals the Frequency Specificity of Resting State Networks

Iandolo

Chiappalone

Semprini

et al. 2020

IEEE Open J. Eng. Med. Biol.

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“…Further elaborating on this concept, we posited that the neural oscillations supporting functional connectivity between network nodes may relate to their spatial distribution over the cortex (Ganzetti & Mantini, 2013). In the present study, we investigated frequencydependent connectivity within several RSNs using hdEEG, thereby extending the work we recently conducted for the DMN (Samogin et al, 2019). We tested the hypothesis that connectivity in the alpha band is the most prominent in the resting state condition not only for the DMN but also for other RSNs.…”

Section: The Role Of Neural Oscillations In Supporting Brain Network mentioning

confidence: 82%

“…They were previously used in one of our previous studies (Samogin et al, 2019). Ethical approval was granted by the Ethics Committee of ETH Zurich.…”

Section: Eeg Data Collectionmentioning

confidence: 99%

Frequency‐dependent functional connectivity in resting state networks

Samogin

Marino

Porcaro

et al. 2020

Human Brain Mapping

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Functional magnetic resonance imaging studies have documented the resting human brain to be functionally organized in multiple large-scale networks, called resting-state networks (RSNs). Other brain imaging techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG), have been used for investigating the electrophysiological basis of RSNs. To date, it is largely unclear how neural oscillations measured with EEG and MEG are related to functional connectivity in the resting state. In addition, it remains to be elucidated whether and how the observed neural oscillations are related to the spatial distribution of the network nodes over the cortex. To address these questions, we examined frequency-dependent functional connectivity between the main nodes of several RSNs, spanning large part of the cortex. We estimated connectivity using bandlimited power correlations from high-density EEG data collected in healthy participants. We observed that functional interactions within RSNs are characterized by a specific combination of neuronal oscillations in the alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-80 Hz) bands, which highly depend on the position of the network nodes. This finding may contribute to a better understanding of the mechanisms through which neural oscillations support functional connectivity in the brain.

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“…For the forward solution, an individual head model was created for each participant based on the same high-resolution structural MR image as used for the MRS analysis and 3D locations of the electrodes, registered with an optical infrared-camera based (NDI, Ontario, Canada) neuronavigation system (xensor™, ANT Neuro, Enschede, Netherlands). For the individual geometrical description of the head (mesh), the anatomical image was segmented into 12 tissue classes (skin, eyes, muscle, fat, spongy bone, compact bone, cortical gray matter, cerebellar gray matter, cortical white matter, cerebellar white matter, cerebrospinal fluid and brain stem), based on the MIMA model [87] using SPM12 (http://www.fil.ion.ucl.ac.uk/spm/software/spm12/) as described previously [88–90]. The EEG electrode positions were rigidly co-registered to the individual head surface (skin contour) by projecting the electrode coordinates in the native space through a rigid-body transformation, based on: (i) the estimation of anatomical landmarks (nasion, left/right peri-auricular points), (ii) the alignment of the electrode positions on the head surface through Iterative-Closest Point registration, and (iii) the projection of the electrodes onto the surface choosing the smallest Euclidean distance [91].…”

Section: Methodsmentioning

confidence: 99%

The interaction between endogenous GABA, functional connectivity and behavioral flexibility is critically altered with advanced age

Heise

Rueda‐Delgado

Chalavi

et al. 2020

Preprint

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Previous links endogenous gamma-aminobutyric acid (GABA) with behavioral efficiency across perceptual and cognitive domains but with potentially strongest impact on those types of behavior that require a high level of dynamic control. Using a Bayesian moderated mediation analysis, we modeled the age-associated interactions between electroencephalography- and electromyography-derived connectivity metrics and behavior and investigated the specificity of the indirect effect of GABA concentration as measured with magnetic resonance spectroscopy. Only the integrated analysis of all three modalities revealed marked differences between the two age groups. Specifically, relatively lower GABA was more beneficial for the connectivity-behavior association in the young, whereas the older showed a beneficial behavioral effect in the presence of higher GABA given their generally elevated task-related connectivity. Age-related alteration of the preferred state of endogenous GABA concentration may reflect a generic compensatory mechanism allowing for higher temporal precision in neural tuning and maintaining a higher behavioral functioning level.

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Shared and connection-specific intrinsic interactions in the default mode network

Cited by 72 publications

References 55 publications

Small-World Propensity Reveals the Frequency Specificity of Resting State Networks

Small-World Propensity Reveals the Frequency Specificity of Resting State Networks

Frequency‐dependent functional connectivity in resting state networks

The interaction between endogenous GABA, functional connectivity and behavioral flexibility is critically altered with advanced age

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