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@PHDTHESIS{Bachmann:865218,
      author       = {Bachmann, Claudia},
      title        = {{V}ariability and compensation in {A}lzheimer‘s disease
                      across different neuronal network scales},
      volume       = {200},
      school       = {RWTH Aachen},
      type         = {Dr.},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2019-04752},
      isbn         = {978-3-95806-420-1},
      series       = {Schriften des Forschungszentrums Jülich. Reihe
                      Schlüsseltechnologien / Key Technologies},
      pages        = {XVI, 165 S.},
      year         = {2019},
      note         = {RWTH Aachen, Diss., 2019},
      abstract     = {Every human is unique and so is her diseases. This
                      statement seems trivial but its consequences 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. It is 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          = {INM-6 / IAS-6},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828},
      pnm          = {572 - (Dys-)function and Plasticity (POF3-572) / 574 -
                      Theory, modelling and simulation (POF3-574) / W2Morrison -
                      W2/W3 Professorinnen Programm der Helmholtzgemeinschaft
                      (B1175.01.12)},
      pid          = {G:(DE-HGF)POF3-572 / G:(DE-HGF)POF3-574 /
                      G:(DE-HGF)B1175.01.12},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:0001-2019100939},
      url          = {https://juser.fz-juelich.de/record/865218},
}