Information-selectivity of Alzheimer's disease progression

221 Pages·2013·12.37 MB·English

by  Mark Stephen Rowan

INFORMATION-SELECTIVITY OF ALZHEIMER’S
DISEASE PROGRESSION
by
MARK STEPHEN ROWAN
A thesis submitted to
The University of Birmingham
for the degree of
DOCTOR OF PHILOSOPHY
School of Computer Science
College of Engineering and Physical Sciences
The University of Birmingham
April 2013
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For Frances,
who inspired
this work.

ACKNOWLEDGEMENTS
Numerous people helped me throughout the journey which constituted the writing of this
thesis – and this is the part where I get to thank them.
Firstly, my supervisor, John Bullinaria, whose gentle guidance (and occasional con-
tributory head-scratching) gave me the freedom to learn and explore what, to both of us,
was a new field. Even when things seemed to be going down a dead-end, John could find
something for us to laugh about, then calmly suggest a new direction. (As my MSc and
BSc supervisor as well, it’s a wonder he didn’t get fed up of me – but working together for
eight years has been productive and, above all, fun – so thank you!) Also Jon Rowe and
Xin Yao, for their comments and contributions during thesis group meetings, and John
Jefferys from the School of Neuroscience for offering additional advice.
Sam Neymotin and Bill Lytton from SUNY Downstate, Brooklyn, with whom I col-
laborated on the spiking neural network experiments, for their help and patience when
debugging the model and trying to understand the code; and Marshall Crumiller from
Mount Sinai School of Medicine, New York, for his help with getting the Fourier infor-
mation calculation code running. Thanks also to the anonymous reviewers of my papers.
A PhD may ultimately be a solo journey, but it never has to be lonely. So thank you
to my friends and colleagues in Computer Science – in particular, the past and present
occupants of Room 117, and Vivek Nallur, for the various discussions and activities we
enjoyed, which took my mind off the PhD for long enough to remember to have fun.
Finally, biggest thanks of all go to Hannah who, during the course of my PhD, on
top of a mountain in the rain in Zu¨rich, made me incredibly happy by agreeing to be my
fianc´ee. You have helped me in more ways than you can know :)

Abstract
Alzheimer’s disease (AD) is a growing global healthcare problem, as life expectancy
increases and populations age. Current treatments focus on reducing symptoms, rather
than treating the underlying causes of the disease, and as such are disappointing in their
efficacy. One reason for this is the current poor understanding of the mechanisms of
disease pathology.
An existing hypothesis of AD progression predicts that homeostatic synaptic scaling
mechanisms, which normally act to balance potentiation during learning, may also direct
the progression of the disease throughout the brain as cells scale up their sensitivity to
compensate for lost activation. This thesis makes the additional prediction that such a
mechanismwouldbelikelytotargetthosecellswiththelowestcontributionofinformation
to the network in early stages of the disease, resulting in the delayed onset of cognitive
symptoms and making timely intervention and treatment of the disease more difficult.
A computational modelling approach is used to investigate these hypotheses, firstly
using an existing abstract Hopfield-type neural network. The model was extended to
incorporate homeostatic synaptic scaling, and information theoretic measures were used
to characterise the information contribution of individual cells within the network. The
model was then lesioned according to the scaling-driven progression hypothesis of AD,
showing that the pathology is capable of targeting neurons with the lowest information
contribution to the network at early stages of the disease, and therefore resulting in a
delayedonsetofcognitivesymptoms. Additionalexperimentsrevealedapositive-feedback
loop by which noisy compensatory synaptic scaling mechanisms caused the accelerated
degradation of recent memories, which were themselves preferentially used as drivers of
the compensatory mechanism.
The hypothesis was then tested in a biologically-realistic spiking model of neocortex,
which was also extended to include the effects of synaptic scaling and to operate on very
long simulated timescales. A study was undertaken to demonstrate the effects of the in-
teraction between synaptic scaling and potentiation during learning. A recent method for
obtaining mutual information between cells in large networks of spiking neurons, based
on Fourier analysis of spike times, was applied to the model, and a regime of stimulation
was developed to elicit reliable information measures. Cell death, modelled as an abstract
excitotoxicity mechanism based on scaling factor values, confirmed the earlier results and
showed that low-information neurons (and neurons from cortical layers with the lowest
information contribution) were the first to die in scaling-driven AD pathology. Another
(not validated) biophysical mechanism for excitotoxicity revealed that, although the ef-
fect of information-selectivity was maintained, the order of deletion was reversed, with
high-information cells dying first. However, other features of this mechanism produced
biologically implausible results, suggesting that the true identity of biological toxicity
mechanisms may be different.
CONTENTS
Acknowledgements v
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Research questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Contributions to knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.5 Thesis outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.6 Resulting publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2 Background 9
2.1 Alzheimer’s disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1.1 Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1.2 Cholinergic hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.1.3 Tau hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.1.4 Amyloid hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.1.5 Summary of pathologies and possible lesions . . . . . . . . . . . . . 19
2.2 Artificial neural networks for modelling . . . . . . . . . . . . . . . . . . . . 21
2.2.1 Basic associative networks . . . . . . . . . . . . . . . . . . . . . . . 21
2.2.2 Biologically-inspired learning rules . . . . . . . . . . . . . . . . . . 24
2.2.3 Sparse connectivity strategies . . . . . . . . . . . . . . . . . . . . . 27
2.2.4 Networks of spiking neurons . . . . . . . . . . . . . . . . . . . . . . 28