Frequency‐dependent functional connectivity in resting state networks

Samogin, Jessica; Marino, Marco; Porcaro, Camillo; Wenderoth, Nicole; Dupont, Patrick; Swinnen, Stephan; Mantini, Dante

doi:10.1002/hbm.25184

Cited by 53 publications

(89 citation statements)

References 69 publications

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“…A similar agreement between EEG (Miltner et al, 2003;Debener et al, 2005;Wang et al, 2005;Cohen et al, 2008;Vocat et al, 2008) and fMRI (Van Veen and Carter, 2002;Mathalon et al, 2003) is visible when investigating presupplementary motor area or anterior cingulate cortex as the source of medial frontal theta activity. And, this extends to studies on the default mode network (Cannon et al, 2011) and resting state network (Samogin et al, 2020), where EEG coherence turns out to be consistent with (the spectral power of) BOLD (Ko et al, 2011). Both modalities have shown similar, age-related increases in functional connectivity (Solesio-Jofre et al, 2014;Babaeeghazvini et al, 2018).…”

Section: Comparison Between Electro-encephalography and Functional Magnetic Resonance Imagingsupporting

confidence: 69%

Brain Structural and Functional Connectivity: A Review of Combined Works of Diffusion Magnetic Resonance Imaging and Electro-Encephalography

Babaeeghazvini

Rueda‐Delgado

Gooijers

et al. 2021

Front. Hum. Neurosci.

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Implications of structural connections within and between brain regions for their functional counterpart are timely points of discussion. White matter microstructural organization and functional activity can be assessed in unison. At first glance, however, the corresponding findings appear variable, both in the healthy brain and in numerous neuro-pathologies. To identify consistent associations between structural and functional connectivity and possible impacts for the clinic, we reviewed the literature of combined recordings of electro-encephalography (EEG) and diffusion-based magnetic resonance imaging (MRI). It appears that the strength of event-related EEG activity increases with increased integrity of structural connectivity, while latency drops. This agrees with a simple mechanistic perspective: the nature of microstructural white matter influences the transfer of activity. The EEG, however, is often assessed for its spectral content. Spectral power shows associations with structural connectivity that can be negative or positive often dependent on the frequencies under study. Functional connectivity shows even more variations, which are difficult to rank. This might be caused by the diversity of paradigms being investigated, from sleep and resting state to cognitive and motor tasks, from healthy participants to patients. More challenging, though, is the potential dependency of findings on the kind of analysis applied. While this does not diminish the principal capacity of EEG and diffusion-based MRI co-registration, it highlights the urgency to standardize especially EEG analysis.

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Section: Comparison Between Electro-encephalography and Functional Magnetic Resonance Imagingsupporting

confidence: 69%

Brain Structural and Functional Connectivity: A Review of Combined Works of Diffusion Magnetic Resonance Imaging and Electro-Encephalography

Babaeeghazvini

Rueda‐Delgado

Gooijers

et al. 2021

Front. Hum. Neurosci.

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“…4 are in agreement with the strongest empirically observed phenomena. It is the frequency dependent normalized links that support the visual network areas, which are mainly located in the occipital lobe, to be the most active at alpha frequencies, as it is observed during rest with closed eyes and absence of visual inputs ( 43, 44 ). Similarly, while activity is dominant in the delta/theta frequencies for the anterior-cognitive state, the posterior-cognitive state is dominated by alpha ( 39 ).…”

Section: Resultsmentioning

confidence: 98%

Normalizing the brain connectome for communication through synchronization

Petkoski

Jirsa

2020

Preprint

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Networks in neuroscience determine how brain function unfolds. Perturbations of the network lead to psychiatric disorders and brain disease. Brain networks are characterized by their connectomes, which comprise the totality of all connections, and are commonly described by graph theory. This approach is deeply rooted in a particle view of information processing, based on the quantification of informational bits such as firing rates. Oscillations and brain rhythms demand, however, a wave perspective of information processing based on synchronization. We extend traditional graph theory to a dual particle-wave-perspective, integrate time delays due to finite transmission speeds and derive a renormalization of the connectome. When applied to the data base of the Human Connectome project, we explain the emergence of frequency-specific network cores including the visual and default mode networks. These findings are robust across human subjects (N=100) and are a fundamental network property within the wave picture. The renormalized connectome comprises the particle view in the limit of infinite transmission speeds and opens the applicability of graph theory to a wide range of novel network phenomena, including physiological and pathological brain rhythms.One Sentence SummarySpatiotemporal and topological network properties are unified within a novel common framework, the renormalized connectome, that explains the organization of fundamental frequency-specific network cores.

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“…In the same line, changes in alpha power in the absence of changes in individual alpha frequency (mind wandering in non-experienced practitioners; see Figure 4H) could be interpreted as a change in activity or connectivity of a specific network (e.g. the default mode network) (Chapeton et al, 2019;Lobier et al, 2018;Samogin et al, 2019Samogin et al, , 2020. In order to empirically assess these speculations, further research is needed to test whether i) different brain networks oscillate at different alpha frequencies and ii) whether the level of activity or connectivity within a network modulates alpha power at EEG sensor level.…”

Section: Discussionmentioning

confidence: 97%

“…In order to empirically assess these speculations, further research is needed to test whether i) different brain networks oscillate at different alpha frequencies and ii) whether the level of activity or connectivity within a network modulates alpha power at EEG sensor level. For this purpose, it is imperative to identify the spectral profile of different networks in the future by either combining EEG with intracranial electrodes (Fahimi Hnazaee et al, 2020) or through high density EEG and source localization (Samogin et al, 2019(Samogin et al, , 2020.…”

Section: Discussionmentioning

confidence: 99%

The EEG spectral fingerprints of meditation and mind wandering differ between experienced meditators and novices

Rodriguez‐Larios

Oca

Alaerts

2021

Preprint

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Previous literature suggests that meditation training is associated with changes in participants' experience during meditation practice. In this study, we assess whether putative differences in the experience of meditation between meditators and non-meditators are reflected in EEG spectral modulations. For this purpose, we recorded electroencephalography (EEG) during rest and two breath focus meditations (with and without experience sampling) in a group of 29 adult participants with more than 3 years of meditation experience and a control group of 29 participants without any meditation experience. Experience sampling in one of the meditation conditions allowed us to disentangle periods of breath focus from mind wandering (i.e. moments of distraction driven by task-irrelevant thoughts) during meditation practice. Overall, meditators reported a greater level of focus and reduced mind wandering during meditation practice than controls. In line with these reports, EEG spectral modulations associated to meditation and mind wandering also differed significantly between meditators and controls. While meditators (but not controls) showed a significant decrease in individual alpha frequency and amplitude and a steeper 1/f slope during meditation relative to rest, controls (but not meditators) showed a relative increase in individual alpha amplitude during mind wandering relative to breath focus periods. Together, our results show that the experience of meditation changes with training and that this is reflected in oscillatory and non-oscillatory components of brain activity.

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Frequency‐dependent functional connectivity in resting state networks

Cited by 53 publications

References 69 publications

Brain Structural and Functional Connectivity: A Review of Combined Works of Diffusion Magnetic Resonance Imaging and Electro-Encephalography

Brain Structural and Functional Connectivity: A Review of Combined Works of Diffusion Magnetic Resonance Imaging and Electro-Encephalography

Normalizing the brain connectome for communication through synchronization

The EEG spectral fingerprints of meditation and mind wandering differ between experienced meditators and novices

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