A team of scientists from New York University, Stanford University and The Palo Alto Medical Foundation have developed a method for identifying clusters of neurons that work in concert to guide the behaviour. The findings address a long-standing mystery about the organization of the prefrontal cortex (PFC), one of the most recently evolved parts of the primate brain that underlies complex cognitive functions. The opensource study is published in the journal Neuron.
The team state that the current study has established a method to find functional groupings of neurons based on co-fluctuation of their responses. In doing so, the study shows that PFC neurons are organized into spatially contiguous maps, much like their counterparts in sensory cortices. The widely accepted notion that orderly spatial maps are restricted to sensory cortices, therefore, needs revision.
The team explain that their methodology is closely related to the techniques that led to the discovery of functional networks in brain imaging studies. There is, however, a crucial difference. The researchers extend the methodology to cellular scale and demonstrate that it can be used for identifying networks at a neuronal level. By suggesting a potential neural substrate for functional networks in macro-scale brain imaging the findings bridge a critical gap in current knowledge.
The research focused on the parcellation of PFC neurons, or how these cells are grouped together to perform specific functions. The scientists showed that the discovered subnetworks in the prefrontal cortex are linked to the decision-making behaviour but seem to have distinct roles, one subnetwork better represents upcoming choices and another one seems to keep track of past choices.
Previous studies that explored spatial organization of neurons in the prefrontal cortex predominantly focused on the average responses of neurons by examining them one at a time. They missed the organization of the network ‘forest’ for the neuron ‘trees’. In the current study the researchers outlined a vastly different method. In it, they focused on the correlated activity of large numbers of simultaneously recorded neurons to spot the larger topography of the network, and how their groupings may be linked to the behaviour. Specifically, they applied clustering algorithms that discover natural divisions in the matrix of response correlations to divide the recorded neural population.
The team summise that this technique provides an innovative, but straightforward, way to delineate cortical networks adding that the subnetworks in the PFC are stable across behavioural tasks and are apparent even in the spontaneous fluctuations of neural responses. They seem to be largely defined by the intrinsic connectivity of neurons in the local network. The team have provided an objective basis for dividing the cortex into constituent subnetworks, offering a common standard across experiments.
Source: New York University