Neocortical models of response inhibition

Project Outline

An important final step of human neurocognitive maturation includes the development of response inhibition and cognitive control. Prefrontal cortex is critically involved in inhibitory control and is the last cortical region to develop. Whilst much is known about the neural connections from prefrontal cortex to motor cortex for action planning in adults, neural connectivity and neural mechanisms are less well studied in children.

One important neural hallmark of motor responses is the modulation of oscillations in the beta band (13-30 Hz) with a decrease in power just prior to and during the movement, indicating a release from inhibition, followed by a rebound/overshoot, with a suggested purpose of inhibition and resetting. Recent research has uncovered that these beta band oscillations are underpinned by discrete ‘beta bursts’ (Sherman et al., 2016). Furthermore, Jones and colleagues have developed a neural circuit modelling software (Human Neocortical Neurosolver, https://hnn.brown.edu) that explains the underlying neural basis of the peaks and troughs of these beta bursts. Notably, this software gives insight to the ‘best fit’ model of the balance between inhibitory (proximal or distal) and excitatory (layer 4) inputs to pyramidal cells, whose activity is the basis for what is measured in routine non-invasive electroencephalography (EEG) or magnetoencephalography (MEG) recordings.

The aim of this proposed PhD study is to model MEG recordings using the Human Neocortical Neurosolver to explore how cortical layer-specific models of neural activity mature through development and how their outputs are linked to fronto-motor connectivity, to provide insight into the neural basis of the maturation of inhibitory control.

Specifically, children and adults (aged 8-35) will be recruited to participate in motor / response tasks in the MEG. Tasks such as the Continuous Performance Task (CPT; or Go/No-go) or the stop-signal task are simple to perform yet give insight of inhibitory thresholds, which will individually vary with maturation and with traits such as inattention and/or hyperactivity, even within the ‘neurotypical’ spectrum. For this reason, participants will also complete behaviour trait questionnaires. While only neurotypical individuals will be recruited in this project, with the focus on understanding typical development, the findings here will provide the fundamental basis for future work in investigations of psychiatric, neurological, or neurodevelopmental disorders.

Dr Zumer, primary supervisor, brings her expertise in MEG experimental design, data analysis including of oscillatory activity such as beta, and neural source localisation, and neural connectivity. Dr Zumer’s background in both applied physics and psychology have meant that her research has largely always had a multidisciplinary focus.

Prof. Burgess, secondary supervisor, brings his expertise in innovative non-traditional signal analysis applied to neural signals for extracting novel conclusions from neurophysiological recordings. Prof. Burgess’s background of clinical psychology and mathematically solving ‘unusual problems’ in experimental psychology also provides a unique and multidisciplinary focus. Particularly relevant for this project, the data will also be analysed for the background ‘1/f’ fit.

This project will follow best practice, including use of open source software packages and providing data open source, following conventional BIDS format. The project will be submitted on OSF.io (open source framework) as preregistered reports.

References

Sherman, M. A., Lee, S., Law, R., Haegens, S., Thorn, C. A., Hämäläinen, M. S., Moore, C. I., & Jones, S. R. (2016). Neural mechanisms of transient neocortical beta rhythms: Converging evidence from humans, computational modeling, monkeys, and mice. Proc. of the Nat. Acad. of Sci., 113(33)

Techniques

• Preparation and behavioural testing of a cognitive experimental paradigm
• Non-invasive neural measurements using MEG (magnetoencephalography) and subsequent advanced data analysis
• Structural MRI
• Data modelling of the underlying the non-invasive measurements, including neural circuits of cortical layers (Human Neocortical Neurosolver)
• Data modelling using other metrics, such as 1/f fits and ‘beta burst’ characteristics