This paper adapts a method for detecting influential nodes and communities in networked systems to reveal changes in functional networks of subjects with Alzheimer's disease (AD) and amnestic Mild Cognitive Impairment (aMCI). Well-established methods exist for analysing connectivity networks composed of brain regions, including the widespread use of centrality metrics such as eigenvector centrality. However, these metrics provide only limited information on the relationship between regions, with this understanding often sought by comparing the strength of pairwise functional connectivity. Our holistic approach, eigenvector alignment, considers the impact of all the functional connectivity changes on the alignment of any two regions. This is achieved by comparing their placement in a Euclidean space defined by the network's dominant eigenvectors. Eigenvector alignment reveals that key regions in the Default Mode Network lack clear and consistent alignments in healthy control subjects. This results in relatively few significant alignment changes in AD, despite clear reductions in functional connectivity within this network. For AD subjects, compared with healthy controls, the posterior superior temporal gyrus displays the most significant bilateral decrease in alignment where both sides become significantly more aligned with the brainstem. Substantial realignments were also noted bilaterally within regions such as the Heschl's gyrus and the planum polare, which begin in aMCI, and result in these brain regions having significantly increased separation from each other in AD. These findings present a case for the use of eigenvector alignment, alongside established network analytic approaches, to capture how the brain's functional networks develop and adapt when challenged by disease processes such as AD.Functional connectivity is one of the gateways through which a network representation 2 of the brain's interactions can be sought. These connectivities can be weighted based on 3 the strength of the correlations between regions over time. The measures may provide 4 information on the underlying reasons that clinical symptoms occur at different disease 5 stages. Researchers have used functional connectivity values to assess changes in the 6 strength of interaction between groups of key regions [1, 2]. These methods can provide 7 clear results but do not take the holistic view required to capture how variations in 8 functional connectivity can change the functional networks of the brain. Graph theory 9 and network neuroscience methods exist for evaluating structural network changes [3], 10 March 20, 2020 1/18with the assessment of functional connectivity of subjects in resting-state a popular field 11 of study [4]. But these graph structural assessments do not explicitly capture the 12 changing relationship between nodes. It is also important to note that these methods 13 often require binarisation and thresholding of the functional connectivity matrices, 14 which are all-to-all, weighted, adjacency matrices. Binarisatio...