Optogenetic Multiphysical Fields Coupling Model for Implantable Neuroprosthetic Probes

AuthorsOptogenetic-based neuroprosthetic therapies are increasingly being considered for human trials. However, the optoelectronic design of clinical-grade optogenetic-based neuroprosthetic probes still requires some thought. Design constraints include light penetration into the brain, stimulation efficacy, and probe/tissue heating. Optimisation can be achieved through experimental iteration. However, this is costly, time-consuming and ethically problematic. Hence it is highly desirable to have an alternative to excessive animal trials. Thus, a simulation tool for optimising probe design can be an important benefit for the community. The challenge is to understand the interplay between the optical, neural and thermal aspects in the interaction of probe and living neural tissue. In this work, we propose a model which combines these aspects to allow clinically orientated neuroprosthetic teams to design neuroprosthetic probes for optogenetic therapies. Our model provides analyses for optical, thermal and optogenetic electrophysiological processes based on the energy equivalence and exchange among different physical fields. To validate and calibrate the model, optogenetic implantable neuroprosthetic arrayed probes based on miniature LEDs were developed. Then, optical, thermal measurement and neural photocurrent recording experiments were implemented on the probes. We can then provide analysis on exemplar arrayed neural probes

How does the brain extract acoustic patterns? A behavioural and neural study

In complex auditory scenes the brain exploits statistical regularities to group sound elements into streams. Previous studies using tones that transition from being randomly drawn to regularly repeating, have highlighted a network of brain regions involved during this process of regularity detection, including auditory cortex (AC) and hippocampus (HPC; Barascud et al., 2016). In this thesis, I seek to understand how the neurons within AC and HPC detect and maintain a representation of deterministic acoustic regularity. I trained ferrets (n = 6) on a GO/NO-GO task to detect the transition from a random sequence of tones to a repeating pattern of tones, with increasing pattern lengths (3, 5 and 7). All animals performed significantly above chance, with longer reaction times and declining performance as the pattern length increased. During performance of the behavioural task, or passive listening, I recorded from primary and secondary fields of AC with multi-electrode arrays (behaving: n = 3), or AC and HPC using Neuropixels probes (behaving: n = 1; passive: n = 1). In the local field potential, I identified no differences in the evoked response between presentations of random or regular sequences. Instead, I observed significant increases in oscillatory power at the rate of the repeating pattern, and decreases at the tone presentation rate, during regularity. Neurons in AC, across the population, showed higher firing with more repetitions of the pattern and for shorter pattern lengths. Single-units within AC showed higher precision in their firing when responding to their best frequency during regularity. Neurons in AC and HPC both entrained to the pattern rate during presentation of the regular sequence when compared to the random sequence. Lastly, development of an optogenetic approach to inactivate AC in the ferret paves the way for future work to probe the causal involvement of these brain regions