In the last decades, complex networks have been used extensively in neuroscience and other fields by employing networks to model interactions among system’s components. Moreover, there is cross-breeding between mathematical neuroscience and clinical neuroimaging techniques. This interaction helps improve existing dynamical models or derive new ones which can then reproduce more accurately healthy and pathological brain activity. Consequently, they can help better detect underlying causes, and lead to testing treatment strategies in simulated environments.
The human brain is a prime example of a complex system that can self-organize into different emergent states, crucial for healthy, abnormal functioning, and cognition. These originate from certain types of synchronous neural activity. Brain disorders such as Parkinson's disease, tinnitus, and epilepsy are associated with abnormal neural synchrony. At a microscopic level, neural ensembles are responsible for performing specific tasks, while the activity patterns of their connecting networks have been associated with certain healthy or pathological states. At a mesoscopic level, a connectivity network consists of nodes and edges. Edges can represent white matter tracts in structural network connectomes (structural connectivity) or functional connectivity, where time-series data might come from EEG, fMRI, or other brain imaging techniques.
There is a close link between connectivity and function in the brain: they are actually strongly related and the brain may undergo plastic changes in time. The exact distribution of connections and their strength may be time-independent (fixed during system evolution) or time-dependent (plastic). Spike-timing-dependent or synaptic plasticity is a pivotal mechanism by which neurons adapt the strength of their synapses to the relative timing (typically within ms or sec) of their action potentials and can influence the collective neural dynamics, giving rise to qualitatively different emerging stable dynamical regimes (multistability). During relatively longer time periods, another type of plastic change might also take place, the so-called structural plasticity: a mechanism that allows the pruning of pre-existing or generation of new synapses.
A common research theme in the study of complex networks is that of the determination of the role and impact of network topology on the emerging properties due to interactions and dynamics. How structural properties in brain regions affect neural dynamics or how do they differ in healthy and diseased subjects? How structural plasticity affects the dynamics of neural activity in a network? How can we study neural systems that incorporate synaptic and structural plasticity as they require models that can operate at different time scales?
This Research Topic is interdisciplinary in nature and, open to analytical and computational work or tools. It welcomes contributions from different related fields, such as from complex systems, biophysics, systems biology, and computational neuroscience. We expect the contributions will advance our current understanding of the underlying mechanisms in complex networks at large and of the brain, in particular. Finally, the Research Topic is at the forefront of studies of the role of synaptic and structural plasticity in oscillatory brain activity, which will ultimately help develop new tools and methods to understand deeper the inner workings of the brain.
In the last decades, complex networks have been used extensively in neuroscience and other fields by employing networks to model interactions among system’s components. Moreover, there is cross-breeding between mathematical neuroscience and clinical neuroimaging techniques. This interaction helps improve existing dynamical models or derive new ones which can then reproduce more accurately healthy and pathological brain activity. Consequently, they can help better detect underlying causes, and lead to testing treatment strategies in simulated environments.
The human brain is a prime example of a complex system that can self-organize into different emergent states, crucial for healthy, abnormal functioning, and cognition. These originate from certain types of synchronous neural activity. Brain disorders such as Parkinson's disease, tinnitus, and epilepsy are associated with abnormal neural synchrony. At a microscopic level, neural ensembles are responsible for performing specific tasks, while the activity patterns of their connecting networks have been associated with certain healthy or pathological states. At a mesoscopic level, a connectivity network consists of nodes and edges. Edges can represent white matter tracts in structural network connectomes (structural connectivity) or functional connectivity, where time-series data might come from EEG, fMRI, or other brain imaging techniques.
There is a close link between connectivity and function in the brain: they are actually strongly related and the brain may undergo plastic changes in time. The exact distribution of connections and their strength may be time-independent (fixed during system evolution) or time-dependent (plastic). Spike-timing-dependent or synaptic plasticity is a pivotal mechanism by which neurons adapt the strength of their synapses to the relative timing (typically within ms or sec) of their action potentials and can influence the collective neural dynamics, giving rise to qualitatively different emerging stable dynamical regimes (multistability). During relatively longer time periods, another type of plastic change might also take place, the so-called structural plasticity: a mechanism that allows the pruning of pre-existing or generation of new synapses.
A common research theme in the study of complex networks is that of the determination of the role and impact of network topology on the emerging properties due to interactions and dynamics. How structural properties in brain regions affect neural dynamics or how do they differ in healthy and diseased subjects? How structural plasticity affects the dynamics of neural activity in a network? How can we study neural systems that incorporate synaptic and structural plasticity as they require models that can operate at different time scales?
This Research Topic is interdisciplinary in nature and, open to analytical and computational work or tools. It welcomes contributions from different related fields, such as from complex systems, biophysics, systems biology, and computational neuroscience. We expect the contributions will advance our current understanding of the underlying mechanisms in complex networks at large and of the brain, in particular. Finally, the Research Topic is at the forefront of studies of the role of synaptic and structural plasticity in oscillatory brain activity, which will ultimately help develop new tools and methods to understand deeper the inner workings of the brain.
![The original signal [continuous line in (A1)] has a smooth upper envelope [dashed line in (A1)] and a lower envelope [dashed-dotted line in (A1)]. The two envelopes from (A1) determine the mean envelope in (B1) (dashed line). By subtracting the mean envelope of B1 from the signal, one obtains the first IMF in (C1) (dashed line). The difference between the signal [continuous line in (C1)] and the first IMF [dashed line in (C1)] gives the first residue in (A2) (continuous line). The process depicted in (A1–C1) is repeated with the signal shown in (A2) until one obtains the second IMF in (C2). This one-dimensional signal was generated from an analytic function to demonstrate the use of the EMD technique.](https://www.frontiersin.org/_rtmag/_next/image?url=https%3A%2F%2Fwww.frontiersin.org%2Ffiles%2FArticles%2F1223879%2Ffncom-17-1223879-HTML%2Fimage_m%2Ffncom-17-1223879-g001.jpg&w=3840&q=75)
