The brain consists of neural networks or circuits, both macrocircuits and microcircuits, which are groups of interconnected neurons that work together by firing in a coordinated manner in order to perform specific functions. When we say that neurons "fire", we mean that they generate action potentials, which are brief spikes (peaks) of electrical voltage linked to corresponding electric currents. These signals are propagated along the neuron's axon and are transmitted from one neuron to the next, allowing information to flow through the circuit.
Microcircuits constitute small, local networks of neurons (dozens to hundreds) within a brain region, which perform specific processes, such as generating rhythmic patterns or integrating local inputs. Examples include: (1) canonical cortical microcircuit (layers II–VI and their stereotyped connections), (2) Hippocampal CA1 local inhibitory–excitatory loops and (3) Spinal cord central pattern generator networks.
Macrocircuits constitute large-scale networks connecting different brain regions or large assemblies of microcircuits. They coordinate higher-level behaviors, integrate information, and link specialized regions. Examples include: (1) Visual pathway from retina → thalamus → visual cortex, (2) Cortico–basal ganglia–thalamic loops involved in movement and decision-making, (3) Limbic system circuits underlying emotion.
Please refer to the study entitled "Shaping Intrinsic Neural Oscillations with Periodic Stimulation": a computational modelling study" and specifically to Figure 1, which represents the entrainment of a neural microcircuit. Neural circuits can adjust their frequency to a given stimulation frequency. In this case the microcircuit initially receives a constant input (P(t)=P) and oscillates at its natural frequency (f). It then receives a sinusoidal wave input which entrains it after a certain time (convergence) to the input frequency. The entrainment index is calculated from the power spectrum of the signal E(t) after its convergence.
Please also refer to the study entitled "Divisive gain modulation enables flexible and rapid entrainment in a neocortical microcircuit model" and specifically to Figure 2 and its legend.
Below is featured the publication by Vidaurre et al (2017) https://www.pnas.org/doi/10.1073/pnas.1705120114 and specifically Figure 1.
First of all, how many different networks are there in the resting state? Or more generally, how many components or states can we distinguish? If we acquire brain activity data, we will notice that there is activation of different brain areas in different time points. Is there some kind of pattern? A model is used to find if there is a hidden pattern in the activation. In other words, the model will try to distinguish hidden components or states. The model used is termed a Hidden Markov Model or HMM, named after the mathematician that postulated it.
In the cited study, the model determined that there are 12 different components or states that repeat themselves in time.
A time course of 60 seconds is presented in Figure 1.
It is noted that the y axis is the probability of activation provided by the model.
The scientists then analyzed which networks are associated with the activated areas.
They found for instance that state 6 corresponds to the default-mode network, state 9 to the language network and state 4 to the visual network.
By examining the figure, we may determine at which time point each network is activated. (E.g. approximately 15 seconds for the default-mode network).
"Focus on six circuits that have been implicated in dysfunctions expressed in depression and anxiety: default mode, salience, negative affect, positive affect (reward), attention, and cognitive control (Figure 1)".
Williams, Leanne M. “Precision Psychiatry: A Neural Circuit Taxonomy for Depression and Anxiety.” The Lancet Psychiatry, vol. 3, no. 5, May 2016, pp. 472–480, www.ncbi.nlm.nih.gov/pmc/articles/PMC4922884/, https://doi.org/10.1016/s2215-0366(15)00579-9.
"We lack a circuit-based taxonomy for depression & anxiety that captures transdiagnostic heterogeneity & informs clinical decision making. We developed & tested a novel system for quantifying 6 brain circuits reproducibly & at the individual patient level" (X).
"The language of brain circuits has not been incorporated into training programmes"."A brain-based taxonomy for mental disease is still lacking". "Advances have not been translated into actionable clinical tools" (*)
Video entitled "Reconstruction and Simulation of Neocortical Microcircuitry" available at URL https://www.youtube.com/watch?v=IL08fnRCb0k. An excerpt is provided (from time stamp 357s):
"We found that modulation of calcium played a fundamental role in determining network state.. In particular, we found a sharp transition between syncronous and asynchronous states that was calcium-mediated. And we found that at this very sharp transition, the circuit has very interesting computational properties".