Background: Quantitative EEG (qEEG) power could potentially be used as a diagnostic tool for Alzheimer’s disease (AD) and may further our understanding of the pathophysiology. However, the early qEEG power changes of AD are not well understood. Objective: To investigate the early changes in qEEG power and the possible correlation with memory function and cerebrospinal fluid biomarkers. In addition, whether qEEG power could discriminate between AD, mild cognitive impairment (MCI), and older healthy controls (HC) at the individual level. Methods: Standard EEGs from 138 HC, 117 MCI, and 117 AD patients were included from six Nordic memory clinics. All EEGs were recorded consecutively before the diagnosis and were not used for the consensus diagnosis. Absolute and relative power was calculated for both eyes closed and open condition. Results: At group level using relative power, we found significant increases globally in the theta band and decreases in high frequency power in the temporal regions for eyes closed for AD and, to a lesser extent, for MCI compared to HC. Relative theta power was significantly correlated with multiple neuropsychological measures and had the largest correlation coefficient with total tau. At the individual level, the classification rate for AD and HC was 72.9% for relative power with eyes closed. Conclusion: Our findings suggest that the increase in relative theta power may be the first change in patients with dementia due to AD. At the individual level, we found a moderate classification rate for AD and HC when using EEGs alone.

There is a increasing interest in analysis of large-scale multiway data. The concept of multiway data refers to arrays of data with more than two dimensions, that is, taking the form of tensors. To analyze such data, decomposition techniques are widely used. The two most common decompositions for tensors are the Tucker model and the more restricted PARAFAC model. Both models can be viewed as generalizations of the regular factor analysis to data of more than two modalities. Nonnegative matrix factorization (NMF), in conjunction with sparse coding, has recently been given much attention due to its part-based and easy interpretable representation. While NMF has been extended to the PARAFAC model, no such attempt has been done to extend NMF to the Tucker model. However, if the tensor data analyzed are nonnegative, it may well be relevant to consider purely additive (i.e., nonnegative) Tucker decompositions). To reduce ambiguities of this type of decomposition, we develop updates that can impose sparseness in any combination of modalities, hence, proposed algorithms for sparse nonnegative Tucker decompositions (SN-TUCKER). We demonstrate how the proposed algorithms are superior to existing algorithms for Tucker decompositions when the data and interactions can be considered nonnegative. We further illustrate how sparse coding can help identify what model (PARAFAC or Tucker) is more appropriate for the data as well as to select the number of components by turning off excess components. The algorithms for SN-TUCKER can be downloaded from Mørup (2007).