6. ESTUDIO DE LA FUNCIONES SIMBÓLICAS DEL ESPACIO
6.3. Composición, dinámica del espacio y figuración del yo
In this chapter, we propose long range synaptic disconnection in the temporal lobes as a potential mechanism directly underpinning cognitive and language deficits in AD. The results presented here suggest that this synaptic disconnec- tion manifests itself in the form of reduced effective connectivity between the tem- poral lobes and other anatomical regions of the brain. This reduced effective con- nectivity results in reduced functional integration of the temporal lobes and less efficient global network organization (reduced ‘small-worldness’), in turn leading to cognitive deficits in the form of language dysfunction. Increased mean degree is also observed in the networks, but computational modelling suggests that this change, unlike the topological network measures, can be well described by het- erogeneous power spectral slowing; in fact, global disconnection was required to accurately replicate the mean degree of each network. The results presented here give key insight into a specific link between disconnection, namely functional and effective disconnection of the temporal lobes, and language deficiencies in the early stages of AD. The anatomically localized nature of the findings and links with a specific cognitive domain mean that these results have the potential to inform treatments and therapies for people diagnosed with Alzheimer’s disease.
Chapter 5
Reduced EEG microstate sequence
complexity and altered cortical
generators in Alzheimer’s disease
The work presented in this chapter was performed in collaboration with Dr George Stothart (data acquisition), Dr Nina Kazanina (data acquisition), Dr Jon T Brown (supervision), and Dr Marc Goodfellow (supervision). The author’s contribution to this chapter includes preprocessing and analysis of the data, formulation of hypotheses and methodological design (analysis), interpretation of results, visu- alization of the data, and writing of the chapter.5.1
Introduction
Spectral slowing and functional disconnection between EEG time series are widely reported in AD (Babiloni et al. (2016); section 1.3; chapter 4), and the focus of the previous chapter. These data are typically measured on a time scale of the order of seconds to minutes in order to obtain reliable estimates (Gudmundsson et al., 2007; Fraschini et al., 2016). However, it is believed that information pro- cessing in the brain happens on a millisecond scale (Koenig et al.,2002;Khanna et al.,2015;Michel and Koenig,2018), and thus the brain’s resting state is com- posed of rapid transitioning between a number of distinct resting state networks corresponding to different cognitive domains (Lehmann et al., 1998; Britz et al.,
2010; Michel and Koenig,2018). EEG microstate analysis is a method proposed to study this switching behaviour of the resting state (Khanna et al.(2015);Michel and Koenig(2018);section 1.8.4).
Microstate analysis involves studying the instantaneous topographic maps of the EEG (Lehmann et al., 1987; Koenig et al., 1999). Past studies of EEG mi- crostates have remarkably found the EEG to be comprised of only a small num- ber of topographic classes, such that the EEG remain stable in a given class
for periods of the order tens or hundreds of milliseconds before rapidly switching to another class (Koenig et al., 1999; Khanna et al., 2015; Michel and Koenig,
2018). These rapidly switching periods of quasi-stability are hypothesised to be the electrophysiological correlates of the brain’s resting state networks relating to different functions underpinning information processing (Lehmann et al.,1987;
Michel et al., 2001; Koenig et al., 2005b; Britz et al., 2010; Musso et al., 2010;
Khanna et al.,2015;Milz et al.,2016), earning microstates the nickname “atoms of thought” (Lehmann et al., 1998). Alterations to microstates have been ob- served in healthy development and aging (Koenig et al., 2002) and a range of neurological disorders including dementia (Ihl et al., 1993; Dierks et al., 1997;
Strik et al.,1997;Stevens and Kircher,1998;Nishida et al.,2013), schizophrenia (Koenig et al.,1999;Lehmann et al.,2005;Nishida et al.,2013), and depression (Strik et al.,1995;Atluri et al.,2018) - seeKhanna et al.(2015) for a comprehen- sive review.
In Alzheimer’s disease research, EEG microstates have received relatively lit- tle attention. Early in the history of microstates, a number of studies identified alterations to EEG microstate statistics in people with Alzheimer’s disease (Ihl et al.,1993;Dierks et al.,1997;Strik et al.,1997;Stevens and Kircher,1998), but these studies used adaptive windows as opposed to the more modern clustering methods to define microstates (see subsection 1.8.4 for a discussion on adap- tive windowing vs clustering tools), meaning that alterations to the well studied ‘canonical maps’ (Khanna et al.(2015);Michel and Koenig(2018);section 1.8.4) have not been characterised in AD. Since the functional significance of these maps in cognition have been uncovered in recent years (Britz et al.,2010; Brod- beck et al., 2012; Milz et al., 2016; Seitzman et al., 2017), characterization of EEG microstates using clustering methods can potentially give crucial insight into the mechanisms underpinning impaired cognition in AD.
Much interest has been given to how properties such as duration of a mi- crostate, percentage of time within a class (coverage of the class), and topogra- phy of microstate maps are altered in neurological disorders (Strik et al., 1995,
1997; Dierks et al., 1997; Stevens and Kircher,1998; Koenig et al., 1999,2002;
Lehmann et al.,2005;Irisawa et al.,2006;Kikuchi et al.,2007;Kindler et al.,2011;
Nishida et al.,2013;Andreou et al.,2014;Tomescu et al.,2015;Gao et al.,2017;
Zappasodi et al., 2017). Patterns of transitions between classes have also been shown to alter in neurological disorders (Lehmann et al., 2005; Nishida et al.,
2013; Tomescu et al., 2015), suggesting that studying transitioning behaviour of microstates may give further mechanistic insights into cognition and neurological disorders as well as increase sensitivity of electrophysiological biomarkers. How- ever, the ‘syntax analysis’ (Lehmann et al., 2005; Nishida et al., 2013) used to analyse transitioning behaviour in these studies assumes stationary and Marko-
Materials and methods
vian transitioning, whilst recent work has brought into question the validity of these assumptions (Van De Ville et al.,2010;von Wegner et al.,2017). Therefore novel methodologies for studying the transitioning behaviour of EEG microstates that do not rely on these assumptions could give new insights into alterations to informa- tion processing and switching between active networks in AD on the millisecond scale, and potentially act as a more sensitive neurophysiological signature of AD in the EEG.
In this chapter, we analyse EEG microstates in unmedicated people with early stage AD with the objectives to characterize alterations to microstates such as altered topographies, coverage, and duration. Furthermore, we present a novel measure of the transitioning behaviour of EEG microstates which does not as- sume this to be Markovian or stationary, by applying the Lempel-Ziv complexity (LZC) algorithm (Lempel, 1976) to the microstate sequences. We hypothesise that there will be alterations to the transitioning dynamics and topographies of the canonical maps in AD. Since these maps are related to information process- ing in different cognitive domains, this can give crucial insight into mechanisms underpinning cognitive impairment in AD.