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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.

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