How neurons coordinate their collective activity for behavioural control is an open question in neuroscience. Several studies have progressively proved, at different scales, that the patterns of neural synchronization change accordingly with behavioural events. However, the topological features of neural dynamics that underlie task-based cognitive decisions at the microscale level are yet not deeply understood. With this research we want to give a contribution to this topic investigating the local functional connectivity of dorsal premotor cortex (PMd) of rhesus monkeys during a countermanding reaching task. Our goal is to investigate collective modulation of neuronal activities by going beyond the standard techniques of analysis commonly used in neurophysiology. More specifically we propose to analyse data with analytic techniques derived from graph theory and statistical physics and algorithms from complex network theory, rarely used in animal models data and even more in data recorded at the miscroscopic scale.
The novelty of the project are not the algorithm themselves. The mathematics behind the proposed algorithms has been
one of the most studied topic in last half-century research. Graph theory has been widely applied in many fields like physics, chemistry, economics, computer science, machine learning and temporal pattern recognition, i.e. bioinformatics, speech, linguistics, etc. In the very recent years, there have been sporadic applications also in the area of neurophysiology but the use of these methodologies in this field is far from explored.
Despite the origins of the theories are not recent, there are many technical problems to still face that are case specific when applying these methods to local-scale datasets such ours. This means there is room for contributions on this side. For example, it would be of great scientific interest the characterization of various network parameters and properties at such a small scale, in order to produce a guideline. Beyond this, the main scientific question belongs to the neuroscience and neurophysiology area and it refers to how the decision whether to execute a movement or not is coded at the cellular level in PMd. More in details, that specific patterns of variability of neural activity in motor and pre-motor areas exist is partially already known in the literature but less clear are the steps that lead the motor act from planned to matured and then executed.
Even more obscure are the topology in which these steps take place and the neuronal computations that underlie them.
We firmly believe that the proposed approaches, used on our dataset, is one of the most suitable to answer this considerable question.