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Network neuroscience (Cambridge, Mass.)

ISSN 2472-1751

7 papers in the library · 10 citations · publishing 2018-2026

Papers

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Metastability of resting-state bold fMRI as a reliable biomarker of individual brain dynamics: An interrogation of within-subject variability as a function of total acquisition time.

Network neuroscience (Cambridge, Mass.) 2026 Hiba Sheheitli, Robert Hermosillo, Gracie Grimsrud et al.

Metastability of BOLD fMRI signals is a commonly used proxy of brain dynamics in behavioral and clinical studies. To date, little has been done to assess the confidence with which we can use estimates of metastability as reliable biomarkers of individual brain state. We analyze whole-brain and network-specific metastability for a highly sampled individual brain (84 sessions taken over 18...

Dynamic brain states underlying advanced concentrative absorption meditation: A 7-T fMRI-intensive case study.

Network neuroscience (Cambridge, Mass.) 2025 Isaac N Treves, Winson F.z. Yang, Terje Sparby et al. 6 citations

Advanced meditation consists of states and stages of practice that unfold with mastery and time. Dynamic functional connectivity (DFC) analysis of fMRI could identify brain states underlying advanced meditation. We conducted an intensive DFC case study of a meditator who completed 27 runs of jhāna advanced absorptive concentration meditation (ACAM-J), concurrently with 7-T fMRI and...

Thalamocortical interactions reflecting the intensity of flicker light-induced visual hallucinatory phenomena.

Network neuroscience (Cambridge, Mass.) 2025 Ioanna Amaya, Till Nierhaus, Timo T Schmidt

Aberrant thalamocortical connectivity occurs together with visual hallucinations in various pathologies and drug-induced states, highlighting the need to better understand how thalamocortical interactions may contribute to hallucinatory phenomena. Flicker light stimulation (FLS) at 10-Hz reliably and selectively induces transient visual hallucinations in healthy participants. Arrhythmic flicker...

Inducing a meditative state by artificial perturbations: A mechanistic understanding of brain dynamics underlying meditation.

Network neuroscience (Cambridge, Mass.) 2024 Paulina Clara Dagnino, Javier A. Galadí, Estela Càmara et al. 4 citations

Contemplative neuroscience has increasingly explored meditation using neuroimaging. However, the brain mechanisms underlying meditation remain elusive. Here, we implemented a mechanistic framework to explore the spatiotemporal dynamics of expert meditators during meditation and rest, and controls during rest. We first applied a model-free approach by defining a probabilistic metastable substate...

Functional network antagonism and consciousness.

Network neuroscience (Cambridge, Mass.) 2022 Athena Demertzi, Aaron Kucyi, Adrián Ponce-Alvarez et al.

Spontaneous brain activity changes across states of consciousness. A particular consciousness-mediated configuration is the anticorrelations between the default mode network and other brain regions. What this antagonistic organization implies about consciousness to date remains inconclusive. In this Perspective Article, we propose that anticorrelations are the physiological expression of the...

Brain network topology predicts participant adherence to mental training programs.

Network neuroscience (Cambridge, Mass.) 2020 Marzie Saghayi, Jonathan Greenberg, Christopher O'Grady et al.

Adherence determines the success and benefits of mental training (e.g., meditation) programs. It is unclear why some participants engage more actively in programs for mental training than others. Understanding neurobiological factors that predict adherence is necessary for understanding elements of learning and to inform better designs for new learning regimens. Clustering patterns in brain...

On human consciousness: A mathematical perspective.

Network neuroscience (Cambridge, Mass.) 2018 Peter Grindrod

We consider the implications of the mathematical modeling and analysis of large modular neuron-to-neuron dynamical networks. We explain how the dynamical behavior of relatively small-scale strongly connected networks leads naturally to nonbinary information processing and thus to multiple hypothesis decision-making, even at the very lowest level of the brain's architecture. In turn we build on...