What kind of multi-region interactions and emergence in the brain?

All brain properties are network (or circuit) properties. After all, no region is an island and although most of brain connectivity is relatively local, there’s a tremendous amount of non-local connectivity, too.

Let’s consider two kinds of networks or network interactions to further clarify what is meant by “network properties”. In a Type I network, the nodes – brain regions in this case – carry out (“compute”) fairly specific functions (Figure 1A). For example, in the context of fear extinction, the hippocampus determines contextual information (where is the learning taking place?) and the VTA computes omission prediction errors (the animal was expecting an aversive even but it actually didn’t happen). (Another example would be object processing in the visual system.) In this scenario, a process of interest (say, fear extinction) is viewed as a network property that depends on the interactions of the brain regions involved. That is to say, it is necessary to investigate the orchestration of multiple regions to understand how the regions, collectively, carry out the processes of interest. Importantly, however, the collective properties of the system are not accessible, or predictable, from the behavior of the individual regions alone. That is to say, the multi-region function, F(R1, R2., …, Rn) is poorly understood from considering f(R1), f(R2), and so on.

Figure 1: What kind of multi-region circuits? A) Type I network with nodes with well-defined computational properties. B) Type II network where the “primitive function” involves the interactions of two (or more) regions.

Poorly understood in what sense? In a near-decomposable system, lesion of R1, for example, will cause a deficit to the network that is directly related to the putative function on R1. However, this is not the outcome in an interactionally complex system. For example, consider multi-species ecological systems in which the introduction of a new species, or the removal of an existing one, cause completely unexpected knock-on effects. The claim being made here is that, in many cases, we need to consider brain networks in much the same way: as a complex system that is not well approximated by simple decompositions.

Now let’s turn to Type II networks, where nodes do not instantiate specific functions (Figure 1B). Instead, two or more regions working together instantiate the basic function of interest, such that its implementation is distributed across regions. It is easy to provide an example of this Type II networks if we consider computational models where undifferentiated units are trained together to perform a function of interest (such as typical neural network models). But are there examples of this type of situation in the brain? Multi-area functions are exemplified by reciprocal dynamics between the frontal eye fields and the lateral intraparietal area in macaques supporting persistent activity during a delayed oculomotor task (Hart and Huk, 2020). Based, among others, on the tight link between these areas at the trial level, the authors suggest that the two areas should be viewed as a “single functional unit” (see Murray et al., 2017 for a computational model).

In rodents, motor preparation requires reciprocal excitation across multiple brain areas (Guo et al., 2017). Persistent preparatory activity cannot be sustained within cortical circuits alone, but in addition requires recurrent excitation through a thalamocortical loop. Inactivation of the parts of the thalamus reciprocally connected to the frontal cortex results in strong inhibition frontal cortex neurons. Conversely, the frontal cortex contributes major driving excitation to the higher-order thalamus in question. What is more, persistent activity in frontal cortex also requires activity in the cerebellum and vice-versa (Gao et al., 2018), revealing that persistent activity during motor planning is maintained by circuits that span multiple regions. The claim, thus, is that persistent motor activity is a circuit property that requires multiple brain regions. In such case, one cannot point to a specific brain part and label “working memory” as residing there.

It could be argued that, in the brain, the two types of networks discussed here – with and without well-defined node functions – are not really distinct and that what differs is the granularity of the node function. After all, if above one could decompose the function “persistent motor activity” into basic primitives, it is conceivable that they could be carried out in separate regions. In such case, we would revert back to the situation of networks with nodes that compute well-defined functions. Put another way, a skeptic could quibble that, in the brain, a putative Type II network is a reflection of our temporary state of ignorance. The conjecture advanced here is that, in the brain, such reductive reasoning will fare poorly in the long run: It is not the case that one can develop a system of primitive properties that, together, span the functions/processes of interest. In many cases, network properties are not reducible to component interactions of well-defined sub-functions.

References:

Gao, Z., Davis, C., Thomas, A. M., Economo, M. N., Abrego, A. M., Svoboda, K., … & Li, N. (2018). A cortico-cerebellar loop for motor planning. Nature, 563(7729), 113-116.

Guo ZV, Inagaki HK, Daie K, Druckmann S, Gerfen CR, Svoboda K. 2017. Maintenance of persistent activity in a frontal thalamocortical loop. Nature 545:181–186. DOI: https://doi.org/10.1038/nature22324,

Hart, E., & Huk, A. C. (2020). Recurrent circuit dynamics underlie persistent activity in the macaque frontoparietal network. Elife, 9, e52460.

Murray JD, Jaramillo J, Wang XJ. 2017. Working memory and Decision-Making in a frontoparietal circuit
model. The Journal of Neuroscience 37:12167–12186. DOI: https://doi.org/10.1523/JNEUROSCI.0343-17.2017

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