This title appears in the Scientific Report :
2016
Graph properties of the functionally connected brain under the influence of Alzheimer’s disease
Graph properties of the functionally connected brain under the influence of Alzheimer’s disease
Diagnosing Alzheimer's Disease (AD), especially in the early stage, is costly and burdensome for the patients, since it comprises a battery of psychological tests and an extraction of disease specific biomarkers from the cerebrospinal fluid. A cheaper more convenient procedure would be a diagno...
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Personal Name(s): | Bachmann, Claudia (Corresponding author) |
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Buttler, Simone / Jacobs, Heidi / Dillen, Kim / Fink, Gereon Rudolf / Kukolja, Juraj / Morrison, Abigail | |
Contributing Institute: |
Computational and Systems Neuroscience; IAS-6 Computational and Systems Neuroscience; INM-6 |
Imprint: |
2016
|
Conference: | IAS symposium, FZJ Juelich (Germany), 2016-12-05 - 2016-12-06 |
Document Type: |
Poster |
Research Program: |
W2/W3 Professorinnen Programm der Helmholtzgemeinschaft Supercomputing and Modelling for the Human Brain Theory, modelling and simulation Neuroimaging |
Publikationsportal JuSER |
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100 | 1 | |a Bachmann, Claudia |0 P:(DE-Juel1)156326 |b 0 |e Corresponding author | |
111 | 2 | |a IAS symposium |c FZJ Juelich |d 2016-12-05 - 2016-12-06 |w Germany | |
245 | |a Graph properties of the functionally connected brain under the influence of Alzheimer’s disease | ||
260 | |c 2016 | ||
520 | |a Diagnosing Alzheimer's Disease (AD), especially in the early stage, is costly and burdensome for the patients, since it comprises a battery of psychological tests and an extraction of disease specific biomarkers from the cerebrospinal fluid. A cheaper more convenient procedure would be a diagnosis based on images obtained through fMRI. Based on previous polymodal studies demonstrating disrupted inter- and intra-cortical connectivity in AD [1], we argue that the functional connectivity of the whole cortex might be a good predictor for the cause of the disease. In resting state fMRI, previous attempts to analyze graph properties of whole brain networks contradict each other [2]. In our opinion there are two general critical points in the methodical part of these studies that are likely to contribute to the variability of the results. First, we criticize that activities of the brain areas (graph nodes) which are used to calculate the functional connectivities (weights of the graphs) are composed of functionally inhomogeneous signals [3]. Individual brains are often mapped onto a standard atlas brain of known functional coherent areas [2]. The second problem consists in converting the resulting weighted graphs into simple graphs setting weights above an arbitrary threshold wmin to 1 , and those below it to 0 [2] . The drawback is that there is no validation for an optimal threshold and information that might be relevant in AD gets lost. In this work we address the first problem by applying an activity-driven, region-growing clustering algorithm derived from image processing [4]. In order to guarantee functionally homogeneous cluster, the threshold for inclusion of a voxel in a region is regulated by a heterogeneity criterion [3]. Applying this algorithm we end up with undirected weighted graphs with varying numbers of nodes for three sets of data: healthy elderly controls, mild cognitive impairment and Alzheimer’s disease. Targeting the second problem, we analyze the dependence of graph theoretic measures (shortest path length, in- and out-degree distribution, clustering coefficient, modularity and minimal spanning tree [5]) on wmin . Finally, we investigate the distribution of these measures for each data set to determine candidates for a predictive measure. | ||
700 | 1 | |a Buttler, Simone |0 P:(DE-Juel1)169505 |b 1 | |
700 | 1 | |a Jacobs, Heidi |0 P:(DE-Juel1)144971 |b 2 | |
700 | 1 | |a Dillen, Kim |0 P:(DE-Juel1)136676 |b 3 | |
700 | 1 | |a Fink, Gereon Rudolf |0 P:(DE-Juel1)131720 |b 4 | |
700 | 1 | |a Kukolja, Juraj |0 P:(DE-Juel1)131730 |b 5 | |
700 | 1 | |a Morrison, Abigail |0 P:(DE-Juel1)151166 |b 6 | |
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