Using the classifier to predict future progression to AD

with MCI was tested on the trained model, and classified as HOA or AD by the classifier. This classification was compared to the dementia status of the patients four years following data acquisition (Figure 6.1C). The location of each subject in the (θRP,C) plane is shown inFigure 6.2C. Four of the four subjects who con- verted to AD (MCIc) were classified as AD by the model, whilst six of the seven subjects who did not receive a dementia diagnosis in the four years following EEG acquisition (MCIs) were classified as HOA. One subject from the MCIs cohort was incorrectly predicted to have an AD diagnosis. Therefore the model correctly pre- dicted the four year diagnosis of 10/11 people with MCI in this preliminary cohort (90.9% classification rate). These results are summarised inTable 6.6. A crucial point of note is that there was almost no differences in MMSE score between the MCIs and MCIc cohorts (Tables 6.1and6.4), suggesting that these patients were cognitively heterogeneous at the time of data acquisition.

6.4

Discussion

One of the key motivations for use of EEG microstates to study AD was due to a much finer temporal resolution (Koenig et al., 1999) than spectral and func- tional network analyses (Gudmundsson et al., 2007) often performed on EEG. We hypothesised that by combining temporal scales in a classifier, additional in- formation could be gained for improved classification. Indeed, we found that by combining results from chapter 4 (theta band relative power; θRP) with results fromchapter 5(microstate LZC; C), classification rate, sensitivity, and specificity in the training set were greatly increased compared to using these measures alone.

A crucial methodological step was choice of the features θRP and C. Altered spectral features have been consistently reported in the EEG of people with AD (Babiloni et al.,2016) and has been identified as a powerful tool for classification of AD from the EEG (Adler et al.,2003;Lindau et al.,2003;Poil et al.,2013;Hatz et al., 2015a; Wang et al., 2015; Simpraga et al., 2017). In chapter 4, θRP was identified as a spectral measure which is able to separate AD patients from HOA patients with large effect size. Inchapter 5, both C and mean microstate duration were identified as biomarkers of AD. C was chosen over mean microstate dura- tion as a complementary biomarker for two reasons. Firstly, inchapter 5, C was able to separate AD and HOA with a much larger effect size than microstate du- ration. Secondly, microstate duration is potentially dependent on the frequency of neuronal oscillations (von Wegner et al.,2017), suggesting that mean microstate

duration may not be orthogonal to θRP. Whilst theta-band functional networks were also identified as a biomarker of AD inchapter 4, functional network mea- sures are dependent on the size of the network (Joudaki et al.,2012), suggesting that clinical EEG montages may give different results to the higher density re- search grade montage presented inchapter 4.

A key advantage to this study was testing the classifier on an independent test cohort to ensure generalizability and robustness. Whilst cross validation of the classifier on the training set theoretically tests these qualities, supervised fea- ture selection was based on group statistics of the full training cohort, meaning cross validation is likely to overestimate the generalizability and robustness of the model (Smialowski et al., 2010). Therefore it is crucial to assess the perfor- mance of the classifier against independent data not used in feature selection (Smialowski et al.,2010). In this data set, the model was validated against a set of clinical EEG recorded by independent neurologists from an independent and geographically distant cohort of patients. Not only did this serve as validation of the model against an independent test set, it demonstrated that whilst features were chosen based on research grade EEG with high spatial and temporal re- sultion (64 channels, 1 kHz sampling rate), the model was generalizable to lower resolution clinical EEG (19 channels, 512 Hz sampling rate). This suggests that the model is potentially useful in a clinical setting.

A key clinical challenge in AD is early diagnosis at the prodromal stages (Nakamura et al., 2018). All subjects in the training data set were free from dementia related medications and EEG was recorded within days of diagnosis, suggesting this data is useful for diagnosis of early stage AD. To test whether this data could be used to aid with prodromal diagnosis (i.e. whether patients with mild cognitive impairment due to an AD aetiology had EEG similar to those with early stage AD), EEG from a test set of four MCI patients who converted to AD within four years of data collection was run through the classification model. All four subjects were classified as AD patients by the classifier, giving 100% specificity. Additionally, we found that patients with MCI who did not receive an AD (or other dementia) diagnosis within four years of data acquisition were mostly (for six out of seven patients) classified as healthy in the model. It is important to note that there were no differences in cognitive test scores between these patients, and all patients within this group were classified as MCI following a battery of cognitive, neuroimaging, and biochemical tests, suggesting that EEG could be a powerful tool for identification of AD aetiology in MCI. However, it should be stated that this cohort was small, so whilst these results are promising, future work is required to validate these results on a larger cohort.

Chapter 7

General discussion

7.1

Summary of key findings

In this thesis, we have studied how the dynamics of the brain are altered in clini- cal Alzheimer’s disease and experimental models of related pathologies to under- stand mechanisms underpinning AD and develop electrophysiological biomarkers for aiding early diagnosis. The key findings of this thesis are summarised below.

In chapter 2, a biophysical model of layer II medial entorhinal cortex stellate cells (mEC-SCs) was used to uncover the ionic mechanisms underpinning clus- tered AP firing patterns observed electrophysiological data. mEC-SCs are crucial for spatial navigation and memory (Tennant et al., 2018), which are known to be impaired in clinical AD (Lithfous et al., 2013; Allison et al., 2016) and animal models featuring AD pathologies (Ramsden et al., 2005; Yue et al., 2011;Black- more et al., 2017; Fu et al., 2017). Booth et al. (2016a) identified alterations to the clustering dynamics of mEC-SCs in the rTg4510 animal model of tauopathy, motivating our use of a biophysical neuron model to understand the underpinning ionic mechanisms.

By performing a bifurcation analysis on the deterministic formulation of the model, we found that the clustering dynamics in the stochastic model were due to noisy perturbations on a deterministic burster. Bursting was of the fast-slow subHopf/homoclinic type (Izhikevich, 2000) with the persistent sodium and slow A-type potassium currents driving the slow dynamics. Alterations to the AHP and h-currents were sufficient to alter the clustering dynamics by changing the num- ber of spikes per cluster via flip bifurcations, or into tonic firing or resting regimes. Experimental data suggested that there were no changes to the h-current but an increase in AHP amplitude in the rTg4510 animals (Booth et al., 2016a), so we concluded it was likely (based on inspection of realistic dynamic regimes) that reductions in the proportion of clustered APs in rTg4510 mEC-SCs (Booth et al.,

2016a) arose due to increased conductance of the AHP current driving the un- derlying dynamics through a flip (spike-subtracting) bifurcation. An independent

model validation was the finding that the model additionally displayed theta (4-12 Hz) range subthreshold resonance in realistic AP firing regimes, which is in line with experimental data (Alonso and Klink,1993).

In chapter 3, functional connectivity was analysed from multi-scaled electro- physiological data recorded in the CHMP2Bintron5 model of frontotemporal de- mentia which exhibits neurodegeneration, synaptic loss, and behavioural impair- ments. Within a region of the brain (whisker barrel cortex), local functional net- works had a significantly increased average synchrony and synchronizable topol- ogy. Conversely, macro-scale functional networks derived from skull-screw EEG electrodes placed at six locations on the cortex demonstrated a reduction in syn- chrony, particularly in the frontal electrodes. A computational model of the mouse brain was used to study potential interplay between macro-scale and local syn- chrony. Whilst regimes did exist in which an increase in local coupling alone could explain reductions in macro-scale synchrony, the simulated EEG signals in these regimes exhibited unrealistic signal-to-noise ratios suggestive of locally hypersyn- chronous delta band activity that did not reflect the data. In regimes with more realistic dynamics, increases in local coupling resulted in increases in macro- scale synchrony. We therefore suggested that white matter impairments in these transgenic animals (Ghazi-Noori et al.,2012) reduced the long range coupling in the network and observed increases in local functional connectivity are potentially a compensatory mechanism by which long range functional connectivity may be restored (Abuhassan et al.,2014).

Chapter 4studied EEG recorded from human AD patients. Compared to con- trols, the AD patients exhibited increases in slow spectral power and decreases in fast power, with largest effect size in the theta frequency range. This spectral slowing was spatially heterogeneous, most predominantly affecting the frontal and parietal cortices. Functional networks had reduced small-worldness, sug- gesting less efficient topology for information transfer through the network. In the AD patients, small-worldness correlated with general MMSE scores and the language subscore. Interestingly, an analysis of local properties of the functional networks attributed the reduced small-worldness to decreased closeness central- ity of the temporal lobes, one of the regions of the brain responsible for language processing. To ensure that heterogeneous spectral slowing, a potentially local mechanism, was not solely responsible for these spatially distributed alterations to the functional network, a computational model of the whole brain was used. We found that slowing alone could not replicate alterations to the functional net- work structure, but reduced effective connectivity between the temporal lobes and rest of the brain was sufficient to accurately describe the empirical data. When combined, these results suggest that loss of synaptic connectivity between the temporal lobes and the rest of the brain is a potential mechanism by which cog-

Limitations and future work

nitive impairment, specifically language deficiencies, arise in AD.

Alterations to EEG microstates in AD were next studied inchapter 5. The to- pography of canonical class D, which is related to the frontoparietal working mem- ory/attention network (Britz et al., 2010), was found to be altered in AD. Cortical source localization suggested these alterations were due to reduced activation of the parietal lobe in class D. The duration of microstates significantly increased in AD, which may be related to the slowing of neuronal oscillations identified in

chapter 4(von Wegner et al., 2017). Finally, whilst no alterations were found to the Markovian transitioning matrix between AD and HOA, a novel application of the LZC algorithm identified less complex transitioning (i.e. more repetitive, with fewer distinct sequences of transitions).

Finally, chapter 6 combined the electrophysiological biomarkers of AD identi- fied inchapters 4and5to build a predictive classifier for AD. The data set studied inchapters 4-5was used to train a model using theta band relative power (θRP) and microstate LZC (C) as features, which had a classification rate of 85% when 10-fold cross validated on the training set. An independent and geographically distinct set of clinical EEG was used as a test set to validate the classifier and test generalizability and robustness. The accuracy of the classifier on the test set remained high, with classification rate of 81%. In a small preliminary cohort of 11 MCI patients, the classifier was additionally able to predict whether a patient would convert to AD within four years of EEG acquisition accurately for 10/11 pa- tients (91% accuracy), suggesting the EEG is potentially a powerful prognostic tool.