Strengths

This thesis used clinical, behavioural, imaging, and electrophysiological data from a large group of well-characterised dementia patients and healthy controls by integrating data from several previous studies. Therefore, a particular strength of this work is its multimodal approach: Combining different data modalities allows to make use of specific advantages of each modality. While fMRI has high spatial resolution at the expense of relatively poor temporal resolution, the opposite is true for EEG. This makes the combination of both neuroimaging methods especially suitable for studying spatial as well as temporal aspects of brain function across different timescales. The confirmation of findings across different modalities and analyses strategies strengthens the robustness and reliability of the results and

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makes it more likely that these are actual disease-related effects rather than artefacts specific to the type of data or analysis technique that was used.

A strength of the fMRI analyses presented in this thesis is the application of rigorous motion correction techniques prior to the estimation of static and dynamic functional connectivity measures. It has recently been shown that motion can have a great influence on fMRI measures, in particular with respect to (dynamic) connectivity measures (Power et al., 2012) and new methods providing much more stringent control of motion artefacts have been developed (Ciric et al., 2017). While previous studies in DLB have relied on rather weak motion correction techniques, it has been shown that motion artefacts can mimic group differences in fMRI studies (Parkes et al., 2018; Power et al., 2015). Control of these confounds is therefore especially important when analysing differences between clinical groups (van Dijk et al., 2012) and insufficient control of motion artefacts in previous studies might partly explain why functional connectivity findings in DLB are inconsistent. The present study is therefore a first step towards more robust estimation of functional connectivity in LBD. Furthermore, the use of an independent healthy control group in

conjunction with meta ICA for RSN estimation allowed to study the effect of DLB and AD on robustly estimated healthy networks instead of studying RSNs estimated from an average of all participants as commonly done in previous studies (Lowther et al., 2014; Peraza et al., 2014). The current approach has also been shown to be more sensitive for finding functional connectivity differences between clinical groups (Griffanti et al., 2016).

The EEG microstate analysis presented here provides a novel approach for studying EEG data that has not been applied to LBD before despite a large body of literature on EEG

abnormalities in LBD (Cromarty et al., 2015). Microstates offer a conceptually simple, yet powerful tool to study brain dynamics on a sub-second timescale, they show high test-retest reliability, and can be reliably estimated from a short resting-state EEG recording with as few as eight electrodes (Khanna et al., 2014), which supports their potential use as biomarkers in clinical settings.

Furthermore, combining EEG and fMRI data and using LBD as a probe pathology meant that it was possible to identify a potential link between the dynamic interaction of subcortical and cortical networks and the modulation of the cortical EEG signal. These results do not only provide a better understanding of LBD-related changes in dynamic brain processes, but might have wider implications for other diseases that are characterised by microstate abnormalities such as schizophrenia and depression (Koenig et al., 1999; Lehmann et al., 2005; Strik et al., 1995).

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7.3 Limitations

A limitation that this study shares with all ante-mortem dementia studies is that diagnoses were based on clinical assessment rather than pathological confirmation. However, while a definite LBD/AD diagnosis cannot be obtained without post-mortem examination, it has been shown that the standardised clinical criteria used here show high specificity when validated against autopsy findings (McKeith et al., 2000a). In the future, as pathological information becomes available for more participants, it will be important to see how the results hold up and to investigate specific characteristics of mixed AD/DLB cases if possible.

As mentioned in the individual chapters, a further potential limitation is that some of the DLB and PDD patients were on dopaminergic medication and scanned in the ON motor state which might have influenced their functional connectivity and EEG measures (Szewczyk-

Krolikowski et al., 2014). However, it has been shown that dopaminergic medication tends to normalise the signals towards more healthy levels (Szewczyk-Krolikowski et al., 2014; Tahmasian et al., 2015), which implies that the group differences that are reported here were not due to medication. Additionally, an analysis of the effect of dopaminergic medication was conducted in each chapter by comparing those patients taking dopaminergic medication to those patients who were not on these medications and by evaluating correlations with LEDD. Overall, these analyses showed that the results presented in this thesis did not seem to be influenced by the use of dopaminergic medication. However, in future studies it would be interesting to compare the same LBD patients on and off medication to better understand the effects of dopaminergic medication on fMRI and EEG dynamics.

The majority of dementia patients were also taking acetylcholinesterase inhibitors which have been shown to influence RT measures (Onofrj et al., 2003), and modulate the EEG (Babiloni et al., 2013; Onofrj et al., 2003) and fMRI signal (Solé-Padullés et al., 2013). In contrast to dopaminergic medication, the influence of acetylcholinesterase inhibitors on the results could not be examined further due to the very small number of patients not on these medications and therefore remains as a potential limitation of this work. This is especially salient given that cognitive fluctuations are thought to be related to alterations within the cholinergic

system (Ballard et al., 2002b; Colloby et al., 2017; Pimlott et al., 2006). From a research point of view, further work is therefore required in order to learn more about the influence of

cholinergic medication on the results presented in this thesis. However, in clinical practice most dementia patients will be on acetylcholinesterase inhibitors and it is therefore also important to study medicated patients if a potential clinical application should be considered. Due to the fact that the ARThippo study did not recruit PDD patients, this condition was not

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included in the fMRI analyses where data from the CATFieLD and ARThippo studies were combined. While a previous comparison of static fMRI functional connectivity between DLB and PDD patients found only subtle differences (Peraza et al., 2015a), it remains unknown how connectivity dynamics might differ between the two conditions and this will therefore form an important part of future work.

Additionally, there were some limitations with respect to the quality of the fMRI data. In particular, the relatively long TR posed difficulties when trying to assess dynamic

connectivity fluctuations. Furthermore, the relatively small number of volumes of the resting- state scans might have resulted in difficulties regarding the robustness of the dynamic

connectivity estimates as it has recently been recommended that at least 10 minutes of fMRI resting-state data should be used to obtain reliable dynamic connectivity estimates (Hindriks et al., 2016). The dynamic fMRI results presented in this thesis will therefore need to be replicated in a dataset with longer scan duration and lower TR.