Interactions of the parietal cortex during cognition and sleep

Lead Research Organisation: University of Bristol
Department Name: Physiology and Pharmacology

Abstract

Next time you find yourself staring at the back of a taxi driver's head, remember that as well as learning and remembering all those routes, he needs to be processing all the visual information rushing through the windscreen, following the commands you bark from the rear seat, obeying road traffic regulations, controlling the sharp jabs of his feet on accelerator and brake, and chatting excitedly about the football results. All that takes a lot of brain, and the simultaneously active specialised brain structures that deal with vision, hearing, rule learning, decision-making, movement, language and emotion must somehow coordinate their activities and interactions with one another. Decoding how these networks of neurons are coordinated across multiple brain regions during complex behaviour presents a considerable challenge requiring the latest technology. But rising to meet this challenge is essential: breakdowns of this coordination give rise to devastating diseases like schizophrenia and depression. Our approach to addressing this challenge is quite direct: we use bundles of extremely fine electrodes to record the electrical activity produced by hundreds of brain cells ('neurons') in the brains of rats and mice as they navigate their way around mazes in search of chocolate, like miniature taxi drivers. The electrodes used are approximately one tenth as wide as the average human hair, and up to 128 of them can be monitored simultaneously, 32,000 times per second. Since these electrodes can record simultaneously from multiple neurons in multiple brain structures, this technology allows us to monitor the activity of hundreds of neurons, and hence their interactions underlying behaviour. A striking feature of brain activity in rodents and taxi drivers alike is its rhythmicity: the electrical signals that neurons use to communicate with one another wax and wane at a range of different frequencies, a bit like the different wavelengths on your radio. We have recently shown that when certain brain structures need to share information (for example, 'Where have I just been and what are the rules at the next junction?'), they 'tune in' to one another by aligning their activities at a specific frequency band. However, we have only shown this for two brain structures - and it takes more than two brain structures to make a decision and guide your behaviour accordingly. This project aims to extend this work into a third brain structure called the 'parietal cortex'. People with damage to their parietal cortex are impaired at navigating around their environment, and tend to have short attention spans and unreliable memory. Records from the parietal cortex of monkeys have found that its neurons seem to encode complex signals like plans; a given neuron fires, for example, every time the monkey plans to reach its arm to the left. Clearly the parietal cortex is dealing with some useful information, but how does it share this information with the rest of the brain? Using our multi-site recordings in rats, we are going to see how parietal activity 'tunes in' with activity elsewhere in the brain as rats try to figure out where their next chocolate treat is coming from. We are also going to see if this 'tuning in' carries on during sleep, when it might relate to strengthening memories acquired during wakefulness (e.g. a recent study in Germany showed that rhythmical stimulation of volunteers' brains during sleep improved their memories of previously learned facts). Understanding the parietal cortex's interactions and functions will add another essential piece to the enormously complex jigsaw puzzle that is our brains. Modern technology and analysis is making this puzzle solvable; projects like this are therefore essential to both our understanding of normal brain function, and to understanding how brains go wrong during complex psychiatric diseases like schizophrenia.

Technical Summary

Complex behaviour requires interactions between networks of neurons across numerous brain regions. Beyond studying single brain structures in isolation, it is therefore critical to understand how coordinated neuronal activity reflects and mediates functional connectivity, allowing specialized structures to both encode information independently and to interact selectively. We use state-of-the-art electrophysiological techniques in behaving rodents to directly address the nature of such encoding and interactions at cellular, synaptic and neuronal network levels. This project will centre on simultaneous recordings of multiple single neuron and local field potential activity from the hippocampus (HPC), prefrontal cortex (PFC) and parietal cortex (PC) in rats performing spatial learning and decision-making tasks. Lesion, single neuron recording and functional imaging studies in rodents, primates and humans implicate the PC in a broad range of cognitive processes, yet the precise nature of parietal activity and interactions at network and systems levels is unresolved. How does activity in these three structures differ during a given task? How do PC and PFC interact with one another and with other brain regions like the hippocampus involved in cognitive processing? We will be the first to address these issues directly by recording neuronal network activity simultaneously from PC, PFC and HPC as rats perform cognitive tasks involving rule learning, spatial navigation and decision-making. We expect to show that HPC-PC-PFC activities become more tightly correlated during behaviours requiring integration of spatial and mnemonic information to guide motor responses. We also hope to reveal differences between PFC and PC activities, which we expect to reflect working memory and motor intention respectively. These data will therefore shed light on the fundamental nature of information coding, sharing and integration in the brain.

Publications

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Box M (2016) A hidden Markov model for decoding and the analysis of replay in spike trains. in Journal of computational neuroscience

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Gardner RJ (2013) Differential spike timing and phase dynamics of reticular thalamic and prefrontal cortical neuronal populations during sleep spindles. in The Journal of neuroscience : the official journal of the Society for Neuroscience

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Watson TC (2014) Back to front: cerebellar connections and interactions with the prefrontal cortex. in Frontiers in systems neuroscience

 
Description Different parts of the brain have to communicate with one another to share information and drive behaviour. We found that some parts of the brain which do not communicate during wakefulness, do communicate during subsequent sleep. This sleep-dependent communication allows is to integrate new information into memory.
Exploitation Route Data recorded during this project have been shared with collaborators in the UK and Germany.
Sectors Pharmaceuticals and Medical Biotechnology

 
Title CA1-PPC-PFC dataset 
Description Neural data recorded simultaneously from 3 brain regions of behaving rats. 
Type Of Material Database/Collection of data 
Year Produced 2016 
Provided To Others? Yes  
Impact Used during development of a new neural data analysis algorithm 
 
Description Bazhenov Lab UCSD 
Organisation University of California, San Diego (UCSD)
Country United States 
Sector Academic/University 
PI Contribution The Bazhenov lab is a computational neuroscience group who have developed biologically realistic but computationally tractable models of thalamocortical activity during sleep.
Collaborator Contribution We have been testing predictions made by their models using our neurophysiological data, and have one manuscript in revision.
Impact Manuscript in revision at Journal of Computational Neuroscience
Start Year 2016
 
Description Bernstein Center for Computational Neuroscience 
Organisation Heidelberg University
Country Germany 
Sector Academic/University 
PI Contribution Neurophysiological data shared for collaborative development of new analysis methods
Collaborator Contribution Computational expertise
Impact Funding application to The Wellcome Trust
Start Year 2015