The thalamocortical structure represents the majority of the brain, consisting of the thalamus and the cortex, recurrently connected to each other with neural pathways. The collective firing activity of the reciprocally connected neuronal populations in the thalamocortical system, referred to as thalamocortical oscillations, plays a significant role in controlling our functional and cognitive behaviours. It is measured via electroencephalography (EEG) tools and utilized in many clinical applications such as the diagnosis of critical brain disorders, of particular importance to this project is Alzheimer’s disease (AD).

There is a strong correlation between cognitive deficit and the degree of the EEG abnormality in AD. At the lower end of the scale, it is confirmed that the structure and function of thalamocortical circuits is disrupted by neuropathological factors. These circuits are considered as the sources of the recorded EEG.  Various lab based studies have indicated several hypotheses to describe the neuropathological mechanisms of AD.  The EEG abnormalities in AD indicate functional and anatomical impairment of the thalamocortical system affected by the disease. More investigations are needed to provide insights to the underlying neurological basis of those abnormalities as well as to couple those findings with the severity of the disease.

The PhD student will investigate which hypotheses (neuropathological factors) are most directly related to the observed EEG dynamics (cognitive decline) in AD. A large-scale model of mammalian thalamocortical system which can reproduce the EEG dynamics observed in the human brain will be produced. The proposed model is biologically-plausible, involving three anatomical organizations: the connectivity and geometry of the model will be informed by diffusion tensor imaging (DTI); a combination of different types of thalamic neurons and six-layered gray matter microcircuitry derived from research on cat visual cortex as well as various types of neurons, each with at least one dendritic compartment. The simulated EEG output of the model will be verified against real EEG datasets. EEG tools (e.g., power spectrum analysis) and statistical analyses tools will be utilized. Research on computational modelling in neuroscience using various modelling approaches is taking place at the ISRC.  By using DTI of AD patients to inform the model, the PhD student will simulate how the connectivity alone can alter brain dynamics. The project will involve utilizing DTI data that represent different stages of the disease to model AD progression and evaluating the effect of synaptic loss in thalamic areas, cortical areas, thalamocortical pathways, corticocortical pathways and corticothalamic pathways. The student will extend these investigations by incorporating compensatory mechanisms, a homeostatic mechanism which maintains the excitatory response of individual neurons and prevents the catastrophic amnesia associated with synapse loss. To simulate the progression of the disease, the student will model the neural loss which results by the spreading of certain neuropathological factors over cortical and thalamic areas. Massive neural loss is a hallmark of AD. Brain imaging studies on AD found reduced volumes of thalamus and cortex in AD patients. The findings of these studies will be investigated using the developed model to elucidate the relationship between abnormal EEG dynamics and atrophy.  The framework for this research has been established in ISRC. The PhD student will have access to software tools, various types of computational models and several publications detailing a systematic approach to conduct this research. Most importantly, a software framework has been developed using C with message passing interface (MPI) at the ISRC to simulate large-scale networks on more than a hundred cores. The framework includes different types of single-compartmental neurons, distribution of axonal conduction delays, long-term spike-timing-dependent synaptic plasticity (STDP), receptor kinetics (AMPA, NMDA, GABAA and GABAB), and short-term plasticity. The PhD student will extend the framework by first, implementing multi-compartmental neurons, second, using DTI data, he/she will determine the shape and coordinates of the Thalamus (and the cortex) to locate the positions of neurons (visual, auditory, motor cortical areas are connected to its corresponding thalamic nucleus - LGN, MGN, and VPN, respectively). Thirdly, using information acquired from DTI and fMRI data provided by other researchers, the student will be able to inform the fiber direction, e.g. thalamus-to-cortex (thalamocortical), cortex-to-thalamus (corticothalamic) and the direction of corticocortical fibers (e.g. motor-visual or visual-motor). 

This project will provide a better understanding of neuropathological biomarkers of abnormal brain oscillations during AD. The results of this project will aid in the development of effective and predictive diagnostic tools and preventative measures.

The student will have access to state-of the-art hardware and software for brainwave recording, data analysis and computational modelling as well as extensive computational resources. A number of researchers within the team are currently working on thalamocortical system models, hippocampal models, neuron-astrocyte models and large scale neuronal network models and the student will have access to these models and useful interaction with experienced modellers.