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@PHDTHESIS{Bachmann:766541,
author = {Bachmann, Claudia},
othercontributors = {Kampa, Björn Michael and Morrison, Abigail},
title = {{V}ariability and compensation in {A}lzheimer's disease
across different neuronal network scales},
volume = {200},
school = {RWTH Aachen University},
type = {Dissertation},
address = {Jülich},
publisher = {Forschungszentrum Jülich GmbH, Zentralbibliothek, Verlag},
reportid = {RWTH-2019-08145},
isbn = {978-3-95806-420-1},
series = {Schriften des Forschungszentrums Jülich. Reihe
Schlüsseltechnologien = Key technologies},
pages = {1 Online-Ressource (xvi, 165 Seiten) : Illustrationen,
Diagramme},
year = {2019},
note = {Druckausgabe: 2019. - Onlineausgabe: 2019. - Auch
veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, RWTH Aachen University, 2019},
abstract = {Every human is unique and so is her diseases. This
statement seems trivial but its con-sequences are
far-reaching, especially for researchers and medical doctors
trying to investigate and diagnose diseases. Some diseases
progress in a stereotyped way, but many others show a
variable phenotype. Especially diseases that interact with
the intrinsic compensatory system are likely to feature
manifold pathological changes. By observing individual,
specific disease variables, in isolation, healthy and
degenerated systems may be indistinguishable. Itis mostly a
combination of multiple variables that form the basis for
disease understanding and diagnosis. The pathology of
Alzheimer’s disease (AD) is associated with an
inappropriate homeostatic compensation. The resulting
complexity of this disease may be the reason for the two
fundamental, unsolved challenges in AD. There is a lack of
disease markers that can detect the disease onset in the
preclinical phase itself. Moreover, there is no treatment
that can effectively slow down the disease progression. The
later might be a consequence of the poorly understood
disease causes, which is aggravated by homeostatic
interference. In this thesis the above stated difficulties
in AD research are addressed in two different ways: The
first part deals with the systematic investigation of a
potential disease diagnosis tool. It is based on the
structure of networks derived from functional magnetic
resonance imaging (fMRI).The second part investigates the
implication of AD and a particular type of homeostatic on
the characteristics of small neuronal networks. With respect
to AD diagnosis, we construct brain graphs in which nodes
represent brain areas and edges represent the functional
connectivities. We then evaluate the resulting graph
properties with respect to their diagnostic power, for three
different health conditions: healthy, mild cognitive
impaired and AD. We systematically examine which
combinations of methods yield significant differences in the
marginal distributions of the graph properties. The results
are then evaluated with respect to consistency across
different methods and predictability of diagnostic power.
Crucial in these approaches is the definition of the
diagnostic power, which is either based on a classification
or on a probability measure. The latter can be directly
combined with the results of other diagnostic tests, but
requires the choice of an appropriate statistical model.
Starting from first principles and approximations, we
explain step-by-step how to construct such statistical
models. In particular, we detail which models imply what
assumptions on the data. In addition, we show how these
statistical models can be evaluated and compared. In the
second part of this thesis, we use simulation to examine how
the prominent synapse loss in AD (a network feature that
best correlates with cognitive decline) affects
computational performance of a simple recurrent network. We
observe that deleting excitatory-excitatory synapses reduces
the network’s sensitivity to perturbations. It also
increases generalization and reduces discrimination
capability. Surprisingly, firing rate homeostasis based on
an increase of the remaining excitatory-excitatory synapses,
recovers performance for a wide range of lost connections.
This phenomenon is examined further in an analytical model,
substantiating the robustness of the results and providing
more insight into underlying mechanisms.},
cin = {162320 / 160000},
ddc = {570},
cid = {$I:(DE-82)162320_20140620$ / $I:(DE-82)160000_20140620$},
typ = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
doi = {10.18154/RWTH-2019-08145},
url = {https://publications.rwth-aachen.de/record/766541},
}
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