Ph.D. thesis summary
Information-selectivity of Alzheimer's disease progression
Current treatments for Alzheimer's disease only attempt to mask symptoms and delay death, rather than targeting the underlying causes of the disease. The work presented here revealed a mechanism by which Alzheimer's disease, through hijacking the brain's normal synaptic regulatory mechanisms, may selectively target the neurons with least importance to the network after disease onset, thereby sparing the most important neurons until the later stages. Whilst this means that the cognitive symptoms (such as reduced memory) do not appear until long after disease onset, this is actually a major problem as it means that the presence of the disease is hidden from view, and clinical treatment cannot begin until it is already too late to make any difference.
Through use of large-scale two-day simulations on the University of Birmingham's BlueBEAR high-performance computer cluster, individual neurons in a simulated neocortical neural network were profiled for their information contribution to the rest of the network, and then observed as they succumbed to the spread of the disease. These simulations confirmed the existence of a relationship between information contribution of a neuron, and its time of death due to Alzheimer's disease pathology. The results additionally hinted at a novel method of treatment for the underlying mechanisms driving the
disease, using long-term low-level electrostimulation to act on the brain's normal regulatory processes and reduce their susceptibility to Alzheimer's disease.