Abstract
The diagnosis of dementia with Lewy bodies (DLB) versus Alzheimer's disease (AD) can be difficult especially early in the disease process. However, one inexpensive and non‐invasive biomarker which could help is electroencephalography (EEG). Previous studies have shown that the brain network architecture assessed by EEG is altered in AD patients compared with age‐matched healthy control people (HC). However, similar studies in Lewy body diseases, that is, DLB and Parkinson's disease dementia (PDD) are still lacking. In this work, we (a) compared brain network connectivity patterns across conditions, AD, DLB and PDD, in order to infer EEG network biomarkers that differentiate between these conditions, and (b) tested whether opting for weighted matrices led to more reliable results by better preserving the topology of the network. Our results indicate that dementia groups present with reduced connectivity in the EEG α band, whereas DLB shows weaker posterior–anterior patterns within the β‐band and greater network segregation within the θ‐band compared with AD. Weighted network measures were more consistent across global thresholding levels, and the network properties reflected reduction in connectivity strength in the dementia groups. In conclusion, β‐ and θ‐band network measures may be suitable as biomarkers for discriminating DLB from AD, whereas the α‐band network is similarly affected in DLB and PDD compared with HC. These variations may reflect the impairment of attentional networks in Parkinsonian diseases such as DLB and PDD.
Highlights
For the first classification (DLB vs. Alzheimer's disease (AD)), we found a mean accuracy of 66% (± 13), mean F1 score of 65% (± 13%), mean positive predictive value (PPV) of 66% (± 22%), mean negative predictive value (NPV) of 71% (± 13.04%), an optimal sensitivity and specificity respectively of 47 and 100%, and area under the curve (AUROC) of 78% (± 15%)
We showed that weighted measures produce consistent results in a graph theory study, where an altered β-band network in dementia with Lewy bodies (DLB) compared with AD emerged as the most significant result
Our analysis showed that the overall connectivity is weakened in the β band for all groups, but significantly reduced in LBDs compared with healthy control people (HC)
Summary
No EEG studies based on proportional thresholding have been performed in order to assess network property changes related to dementia conditions including LBDs. A crucial aspect in functional network studies is how the connectivity threshold is defined in order to obtain a graph from a connectivity matrix, where the non-relevant edges are pruned off and the edges or connections whose weights are above the threshold are preserved. No further quantitative investigation has been done to date in order to assess whether preserving the EEG connection weights in dementia studies might lead to a more pronounced differentiation between groups and improve consistency of the results across network densities. We hypothesised that performing graph analysis based on proportional thresholding while preserving the weights, produces consistent results by preserving additional topological information stored in the weights
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