University of Groningen Neurophysiological signature(s) of visual hallucinations across neurological and perceptual Dauwan, Meenakshi

University of Groningen

Neurophysiological signature(s) of visual hallucinations across neurological and perceptual

Dauwan, Meenakshi

IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.

Document Version

Publisher's PDF, also known as Version of record

Publication date: 2019

Link to publication in University of Groningen/UMCG research database

Citation for published version (APA):

Dauwan, M. (2019). Neurophysiological signature(s) of visual hallucinations across neurological and perceptual: and non-invasive treatment with physical exercise. Rijksuniversiteit Groningen.

Copyright

Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).

Take-down policy

If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum.

neurological and perceptual disorders

And non-invasive treatment with physical exercise

Meenakshi Dauwan

Neurophysiological signature(s) of visual hallucinations across neurological and perceptual disorders

ISBN (print version): 978-94-034-1698-4 ISBN (ebook/digital version): 978-94-034-1697-7

Financial support for the publication of this thesis was kindly provided by: - Stichting Alkemade-Keuls - Alzheimer Nederland - MedCaT B.V. - OSG Bvba - Biosemi B.V. - Parkinson Vereniging

Copyright © 2019 Meenakshi Dauwan

All rights reserved. No part of this thesis may be reproduced, stored or transmitted in any way or by any means without the prior permission of the author, or when DSSOLFDEOHRIWKHSXEOLVKHUVRIWKHVFLHQWLÀFSDSHUV

Cover photo: Dominique Hekner

Layout and design: Eduard Boxem | www.persoonlijkproefschrift.nl. Printing: Ridderprint BV | www.ridderprint.nl

hallucinations across neurological and

perceptual disorders

And non-invasive treatment with physical exercise

Proefschrift

ter verkrijging van de graad van doctor aan de Rijksuniversiteit Groningen

op gezag van de

UHFWRUPDJQLÀFXVSURIGU(6WHUNHQ en volgens besluit van het College voor Promoties.

De openbare verdediging zal plaatsvinden op

Promotores

Beoordelingscommissie

It always seems impossible until it’s done

-TABLE OF CONTENTS

Understanding hallucinations in probable Alzheimer’s disease: very low prevalence rates in a tertiary memory clinic

39 Chapter 3 5DQGRPIRUHVWFODVVLÀFDWLRQWRGLIIHUHQWLDWHGHPHQWLDZLWK/HZ\ERGLHVIURP Alzheimer’s disease 55 Chapter 4

EEG-based neurophysiological indicators of hallucinations in Alzheimer’s disease: comparison with dementia with Lewy bodies

79

Chapter 5

EEG-directed connectivity from posterior brain regions is decreased in dementia with Lewy bodies: a comparison with Alzheimer’s disease and controls

105

Chapter 6

Aberrant resting-state oscillatory brain activity in Parkinson’s disease patients with visual hallucinations: An MEG source-space study

129

Chapter 7

Disrupted functional connectivity and brain network organization in Parkinson’s disease patients with visual hallucinations: An MEG source-space study

199

Chapter 8

Changes in brain network organization are related to visual hallucinations in visually impaired patients: A high-density EEG study

275

PART II 297

Chapter 9

Exercise improves clinical symptoms, quality of life, global functioning and depression in schizophrenia: a systematic review and meta-analysis

301

Chapter 10

Physical exercise improves quality of life, depressive symptoms and cognition across chronic brain disorders: a transdiagnostic systematic review and meta-analysis of randomized controlled trials

345

Chapter 11

Summary and general discussion

References 422 Nederlandse Samenvatting 476 Dankwoord 484 List of publications 492 Curriculum Vitae 496 List of Abbreviations 497

CHAPTER

10 Chapter 1

+DOOXFLQDWLRQV DUH GHÀQHG DV D SHUFHSWLRQ H[SHULHQFHG LQ DQ DZDNH VWDWH DQG LQ absence of an external stimulus (Waters et al., 2014). Hallucinations can be present in different sensory modalities such as visual, auditory, olfactory, tactile and gustatory hallucinations, but can also occur in multiple sensory modalities at the same time; the so-called multimodal hallucinations.

Hallucinations are common and stressful in many psychiatric, neurologic and perceptual disorders disturbing daily life, reducing quality of life of patients and caregivers, and increasing mortality (Blom and Sommer, 2011), while current treatment of hallucinations is far from optimal. In current clinical practice, treatment with medication is selected in accordance with guidelines for the underlying diagnostic entity, not on the underlying mechanism of hallucinations per se (Sommer et al., 2018). However, one subtype of hallucinations may occur in several different disorders, while patients with the same diagnosis may experience different subtypes of hallucinations (Figure 1). Understanding the pathophysiological mechanism of (subtypes of) hallucinations may provide new opportunities for treatment and enable rational choice of pharmacotherapy in a SHUVRQDOL]HGPDQQHULQFUHDVLQJWUHDWPHQWHIÀFDF\DQGVDIHW\6RPPHUHWDO

Figure 1. &ODVVLÀFDWLRQRIKDOOXFLQDWLRQVEDVHGRQXQGHUO\LQJSDWKRSK\VLRORJLFDOPHFKDQLVP

Left: different subtypes of hallucinations are present within one disorder, which are treated according WRWKHJXLGHOLQHVRIWKHXQGHUO\LQJGLVRUGHU5LJKWVXEW\SHVRIKDOOXFLQDWLRQVDUHFODVVLÀHGEDVHGRQWKH underlying pathophysiological mechanism. This may aid discovery of treatment options, irrespective of the underlying disorder, per subtype of hallucinations.

Part I of this dissertation builds on a wealth of studies exploring neurophysiological signatures of visual hallucinations across neurological and perceptual disorders by using electroencephalography (EEG-) and magnetoencephalography (MEG-) based functional connectivity and network analysis, and machine learning algorithms. The

12 Chapter 1

of life, and depressive symptoms, the effect of physical exercise as a therapeutic intervention on these general sequelae is also evaluated.

PART I

Visual hallucinations

Visual perceptual experiences can be divided into three broad categories: veridical perception, illusion, and hallucination. A given percept is veridical when it can be judged as such within the context of our social world that manifests sense-data (i.e. mind-dependent objects with exactly the properties they appear to have, and of which we are directly aware in perception) in our mind (Stanford Encyclopedia of Philosophy: https:// plato.stanford.edu/entries/sense-data/). An illusion means seeing things differently than they actually are, and a visual hallucination means seeing something that is not there (Blom and Sommer, 2011).

7KHSKHQRPHQRORJ\RIYLVXDOKDOOXFLQDWLRQV9+FDQEHFODVVLÀHGLQWRVLPSOHVXFKDV VHHLQJGRWVEOREVÁDVKHVJHRPHWULFIRUPVOLNHVSLUDOVWXQQHOVDQGFRPSOH[VXFK as seeing people, animals and/or objects) hallucinations. An important feature of VH is that they are generally experienced against the background of a veridical perception, which means for example that a hallucinated person is perceived as standing against an DFWXDOZDOORIWKHURRPLQVWHDGRIZDONLQJWKURXJKLW7KHVHÀQGLQJVLQGLFDWHWKDWWKH hallucinator is (partly) aware of his or her actual surroundings (Collerton et al., 2015). Alzheimer’s disease

AD, the most common type of dementia, is a progressive neurodegenerative disorder characterized by extracellular deposition of beta-amyloid plaques, intracellular DFFXPXODWLRQRIQHXURÀEULOODU\WDQJOHVDQGORVVRIV\QDSVHV-DFNHWDO&OLQLFDOO\ AD is often characterized by loss of episodic and working memory, impairment in executive functioning, problem solving, and language, and neuropsychiatric symptoms (McKhann et al., 2011). Hallucinations and delusions can complicate the course of AD, with prevalence rates ranging from 6% to 41% for hallucinations and 9% to 59% for delusions (Zhao et al., 2016a). Both delusions and hallucinations in AD are associated with increased risk of cognitive and functional decline (Fischer et al., 2015; Scarmeas

et al., 2005), whereas hallucinations are associated with higher caregiver burden, institutionalization, and increased mortality (Scarmeas et al., 2005; Wadsworth et al., 2012; Yaffe et al., 2002). It remains unclear why some AD patients develop hallucinations while others remain unaffected. Likewise, the neurobiological substrate RIKDOOXFLQDWLRQVLQ$'UHPDLQVHOXVLYH0RUHVSHFLÀFDOO\(O+DMHWDOSURSRVHGWKDW reduced activity in the frontal inhibitory control regions hampers memory retrieval as an explanation for the presence of hallucinations in AD (El Haj, 2016), whereas Burke et al (2016) showed that apolipoprotein (APOE) İ4 allele, a genetic risk factor for AD dementia, is associated with hallucinations (Burke et al., 2016). Also, serotonin and dopamine receptor polymorphisms have been related to hallucinations in AD (Tampi et al., 2011). Moreover, imaging studies examining the correlates of hallucinations in $'UHSRUWPL[HGÀQGLQJVVXFKDVDQDVVRFLDWLRQEHWZHHQKDOOXFLQDWLRQVDQGUHGXFHG cortical thickness in the lateral parietal cortex (Donovan et al., 2014), involvement of the right anterior-posterior network and the anterior insula (Blanc et al., 2014), visual cortex atrophy (Holroyd et al., 2000), and parietal as well as frontal and temporal lobe G\VIXQFWLRQ/RSH]HWDO5DÀLHWDODPRQJVWRWKHUV1HXURSDWKRORJLFDO studies, on the other hand, describe a poor correlation between hallucinations in $'DQGWKHSUHVHQFHRIQHXULWLFSODTXHVDQGQHXURÀEULOODU\WDQJOHVEXWDVWURQJ correlation with the presence of Lewy bodies (Ballard et al., 2004; Fischer et al., 2015; Jacobson et al., 2014), which suggests that AD patients with hallucinations might be cases of atypical presentation of DLB (McKeith et al., 2016).

Parkinson’s disease

14 Chapter 1

with preserved insight, with prevalence rates ranging from 22-38% (Fenelon and Alves, 2010). As the disease progresses, patients develop delusions (i.e. false beliefs) and insight becomes impaired (Ravina et al., 2007). Hallucinations in other sensory modalities (i.e. multimodal hallucinations) have been reported in both early and late phases of the disease (Fénelon, 2008; Goetz et al., 2011; Rivka Inzelberg et al., 1998; Katzen et al., 2010; Kulick et al., 2018). The hallucinations tend to occur several times a day, lasting seconds to minutes at a time, and occur during times of low ambient stimulation, mostly in the evenings (Ffytche et al., 2017; Ravina et al., 2007).

For decades, VHs in PD were considered to be a drug-induced phenomenon, the so called ‘dopamine-induced psychosis’. However, the relationship between anti-PD medication and VHs remains a controversial issue with lack of direct causal relationship EHWZHHQWKHWZRVXJJHVWLQJWKDWDQWL3'PHGLFDWLRQLVUDWKHUDPRGLÀHUWKDQDFDXVH of VHs (Ffytche et al., 2017; Ravina et al., 2007). Several mechanisms and involvement of multiple brain regions and networks have been proposed in the pathophysiology of VH in PD (Ffytche et al., 2017; Shine et al., 2014a, 2011). First, cognitive impairment has been strongly associated with and the most consistently reported risk factor for VH in PD (Fenelon and Alves, 2010; Hepp et al., 2013; Lenka et al., 2017b). In addition, excessive daytime sleepiness and the presence of Rapid Eye movement (REM) sleep Behavior Disorder has been associated with VH in PD (Lenka et al., 2017a). Second, in neuropathological studies, the presence of higher Lewy bodies load in the amygdala and parahippocampal gyrus, but also in the frontal, temporal and parietal cortex is noted in PD patients with VHs (Harding et al., 2002), which points towards the presence of Lewy bodies disease in these patients. Third, structural, functional and metabolic QHXURLPDJLQJVWXGLHVUHSRUWFRQWUDVWLQJÀQGLQJVZLWKUHVSHFWWR9+LQ3'6WUXFWXUDO imaging studies report atrophy of the visual cortex extending into the ventral and dorsal visual stream (Goldman et al., 2014; Ramírez-Ruiz et al., 2007; Watanabe et al., 2013), and posterior cortical areas (Goldman et al., 2014). However, gray matter reduction in frontal (Gama et al., 2014; Ibarretxe-Bilbao et al., 2010; Sanchez-Castaneda et al., 2010; Shin et al., 2012; Watanabe et al., 2013; Zhong et al., 2013), hippocampal (Ibarretxe-Bilbao et al., 2008) and other subcortical brain regions (Shin et al., 2012;

Watanabe et al., 2013) have also been found. In addition, inconsistent results have been found in functional imaging studies, with both functional MRI and metabolic imaging studies reporting hypo- and hyperactivation (Holroyd and Wooten, 2006; Meppelink et al., 2010; Ramírez-Ruiz et al., 2008; Stebbins et al., 2004), and hypo- and hypermetabolism in occipital, frontal and temporal brain areas (Boecker et al., 2007; Gasca-Salas et al., 2016; Nagano-Saito et al., 2004; Oishi et al., 2011; Okada et al., 1999; Park et al., 2013), respectively. Moreover, VHs in PD have been associated with greater connectivity in the default mode network (Yao et al., 2015, 2014). Finally, cholinergic GHÀFLWKDVIUHTXHQWO\EHHQSURSRVHGDVWKHQHXUDOVXEVWUDWHRI9+LQ3'%RHFNHU et al., 2007; Dagmar H. Hepp et al., 2017; Matsui et al., 2006; Meppelink et al., 2009; Park et al., 2013; Stebbins et al., 2004).

Dementia with Lewy bodies

Like Parkinson’s disease dementia (PDD), dementia with Lewy bodies (DLB) belongs to the spectrum of Lewy bodies dementia, signifying underlying alpha-synuclein deposits in neurons accompanied by neuronal loss (Z. Walker et al., 2015a). The clinical distinction between PDD and DLB is based on the timing of the onset of dementia and parkinsonism according to the one-year rule: DLB is diagnosed when dementia appears before, or no longer than one year after the development of parkinsonism (McKeith HWDO'/%LVFKDUDFWHUL]HGE\WKHFRUHFOLQLFDOIHDWXUHVFRJQLWLYHÁXFWXDWLRQV with variations in alertness and attention, formed VHs, REM sleep behavior disorder, and one or more features of parkinsonism (McKeith et al., 2017). VHs occur in up to 80% of DLB patients and typically feature people or animals (McKeith et al., 2017).

16 Chapter 1

et al., 2013). Moreover, reduced occipital glucose metabolism has been associated with the frequency and severity of VHs in DLB (Firbank et al., 2016).

Visual impairment

Patients with visual impairment may also experience VH, known as the Charles Bonnet 6\QGURPH&%6QDPHGDIWHUWKH6ZLVVSKLORVRSKHUZKRÀUVWGHVFULEHGLWII\WFKH 2005). CBS is characterized by (complex) VH in the presence of acquired vision loss in cognitively intact individuals with retained insight in the unreal nature of their hallucinations. CBS is considered a diagnosis of exclusion, ruling out other etiologies FDXVLQJ9+7HXQLVVHHWDO&%6LVSUHYDOHQWLQRISDWLHQWVZLWKVLJQLÀFDQW vision loss (Gordon, 2016; Kinoshita et al., 2009; Teunisse et al., 1996) and associated with negative outcome in terms of quality of life, functional ability, and distress in approximately one third of the patients (Cox and Ffytche, 2014). Again, it remains unclear why some patients with visual impairment develop hallucinations while others remain unaffected. To date, little work has been done to investigate the neural basis of hallucinations in visual impairment (Carter and ffytche, 2015; ffytche et al., 1998). 0RUHVSHFLÀFDOO\DV\HWWKHUHDUHQRVWUXFWXUDOLPDJLQJVWXGLHVLQYHVWLJDWLQJ9+LQ visual impairment (Carter and ffytche, 2015), whereas only one fMRI study has been done in this patient group in which a correlation was found between the contents of hallucinations and the functional anatomy of the occipital cortex (ffytche et al., 1998). Interestingly, VH acuity has been proposed as one of the most important risk factors for VH in eye disease indicating the presence of two possible theories that might explain the occurrence of VH in visual impairment, the deafferentiation (Painter et al., 2018) and the cortical release phenomenon (Kazui et al., 2009) theory (discussed below).

Hypotheses underlying visual hallucinations

‘…or art thou but, A dagger of the mind, a false creation, Proceeding from the heat-oppressed brain?’

- Macbeth, Act II Scene I -

$VGHVFULEHGDERYH9+RFFXULQDUDQJHRIGLVRUGHUVDQGWKXVDUHQRWVSHFLÀFWR any one of them. Although the underlying disorders giving rise to VH are different, underlying mechanisms of these hallucinations might be similar. Understanding the QHXUDOPHFKDQLVPVXQGHUO\LQJWKHVH9+UHPDLQVDVLJQLÀFDQWFKDOOHQJHDQGPDNHVLW GLIÀFXOWWRGUDZFRQFOXVLRQVZKHWKHUWKHUHH[LVWVDFRPPRQPHFKDQLVPWKDWXQGHUOLHV all VH. Considering the above-mentioned disorders, an explanation of VH would at least need to encompass disturbances in different neurotransmitter systems and impairment in mechanisms controlling attention and arousal. Several different models or theories have been proposed to explain the origin of VH, which will be discussed below:

Deafferentiation theory

The deafferentiation theory indicates the process in which brain regions that receive input from the peripheral visual system lower their detection threshold for neuronal ÀULQJDVDUHVXOWRIGHFUHDVHGYLVXDOLQSXW$VDUHVSRQVHWKHVHQVLWLYLW\RIQHXURQVLQ WKHUHFHLYLQJDUHDVLQFUHDVHVWRZDUGVLQFRPLQJVLJQDOVOHDGLQJWRQHXURQDOÀULQJWKDW might be false positive and perceived as a vision without the presence of an external source; a VH (Burke, 2002; Painter et al., 2018).

18 Chapter 1

Top-down and bottom-up processing

One main characteristic of the human brain is its hierarchical organization, thoroughly seen in the sensory systems such as the visual system involving primary visual areas that receive sensory input and secondary or higher visual areas that are involved in visual information processing (Friston and Kiebel, 2009). According to the free-energy principle, introduced by Karl Friston, the brain attempts to minimize the amount of free-energy or ‘surprise’ in the perception of sensory input by continuously updating and optimizing predictions of incoming sensory input (Friston and Kiebel, 2009). In case of discrepancy between the actual and the expected sensory input (i.e. ‘prediction errors’ in bottom-up information), the prediction error causes an update in the prior expectations (i.e. top-down predictive information) to reach a state of equilibrium between hierarchical predictions and sensory input (Friston and Kiebel, 2009). Visual attention and selection has been conceptualized to be controlled by the integration of goal-oriented top-down (Kloosterman et al., 2015; Sarter et al., 2001), and sensory-driven bottom-up attentional mechanisms (Sarter et al., 2001). Top-down mechanism refers to an active or ‘feedback’ attentional process that is driven by prior information from higher brain areas involving memory, individual goals and prior expectations. Bottom-up processing is a passive or ‘feedforward’ attentional process that is driven by the characteristics of the external stimulus and its sensory context (Sarter et al., 2001).

The basal forebrain cholinergic system, consisting of medial septal nucleus, the diagonal band nuclei, the nucleus basalis of Meynert (NBM), and the substantia innominate (SI), plays a crucial role in attention (Figure 2). The NBM and SI serve as the major sources of cholinergic projection to the neocortex and amygdala (Ballinger et al., 2016; D.H. Hepp et al., 2017). The NBM can be subdivided into three divisions with each division LQQHUYDWLQJVSHFLÀFFRUWLFDOUHJLRQVLHWKHDQWHULRUGLYLVLRQLQQHUYDWHVWKHIURQWDO and cingulate cortex, the intermediate division innervates the parietal and occipital cortex, and the posterior division innervates the temporal pole and the superior WHPSRUDOJ\UXV/LXHWDO7KHSUHIURQWDOFRUWH[3)&UHFHLYHVDVLJQLÀFDQW part of the cholinergic input and plays a key role in attentional performances exerting

top-down control over sensory cortical areas (Ballinger et al., 2016; Bloem et al., 2014). In addition to the PFC, the parietal cortex has been shown to exert top-down control over the visual cortex (Beck and Kastner, 2009).

Figure 2. The cholinergic systems of the brain.

The basal forebrain cholinergic system includes the medial septal nucleus (MS), the diagonal band nuclei (DB), the nucleus basalis of Meynert (NBM), and the substantia innominate (SI). Major cholinergic projections from the basal forebrain project to the cerebral cortex and the amygdala (not shown here). The brainstem also contains a cholinergic system, of which the pedunculopontine nucleus (PPN) forms the largest part. Cholinergic projections from the brainstem project mainly to the thalamus. Figure adapted from (Newman et al., 2012).

20 Chapter 1

processes. Correct functioning of the top-down attentional processing allows only one proto-object to enter conscious visual attention. In case of VH, impaired attentional control and poor sensory activation of the correct proto-object biases selection of an incorrect object. Inhibition of cholinergic input, modulator of the interaction between top-down and bottom-up processing, increases the risk of incorrect proto-object selection and thus hallucinations (Collerton et al., 2005). In line with the PAD model, neither impaired attention nor impaired sensory activation alone will result in hallucinations (Collerton et al., 2005). The neurophysiological counterpart of the PAD model is the so called framework of ‘global neuronal workspace’ (Dehaene and Naccache, 2001). This framework postulates that conscious perception of information occurs only when incoming information enters a neuronal workspace (i.e. information gains access to conscious processing). The entrance of incoming information into a global brain state is mediated by top-down attentional processing, which involves coherent activity of many distributed brain areas, in mainly the prefrontal, parieto-temporal and cingulate cortices (Dehaene et al., 2003; Dehaene and Changeux, 2011; Dehaene and Naccache, 2001).

In an attempt to explain the presence of visual misperceptions and hallucinations in PD, Shine et al proposed a novel attentional network model that VH in PD might arise from the failure to activate the goal-directed dorsal attention network (DAN) in the presence of an ambiguous percept (Shine et al., 2011). During normal vision, the DAN is involved in top-down voluntary orientation of attention towards externally driven percepts (e.g. locations of features) and deactivation of the default mode network (DMN), whereas the ventral attention network (VAN, also named salience network) is involved in rapidly re-orienting attention towards unattended or unexpected salient stimuli (Corbetta and Shulman, 2002). The DAN and VAN densely interact to enable ÁH[LEOHDWWHQWLRQDOSURFHVVLQJ9RVVHOHWDO7KH'01LVDWDVNLQGHSHQGHQW network, which is active in the so called ‘resting-state’ condition during which recollection and manipulation of episodic memory occurs (Andreasen et al., 1995; Raichle, 2015). According to the attentional network model, in the presence of a salient stimuli, the brain undergoes a rapid transition from a resting (involving the DMN) to an

active state (involving the VAN). Impaired signaling between the DMN and VAN results in perceptual errors, which are normally corrected by the DAN. However, according to the model, failure to activate the DAN would result in reinforcement of ‘incorrect’ perceptions as the DAN fails to ‘check’ these perceptions (Shine et al., 2011). In spite of the presence of VH as a shared symptom across different types of disorders and the presence of various underlying hypotheses for VH, imaging research has not EHHQDEOHWRÀQGXQDPELJXRXVUHVXOWVERWKZLWKLQDQGDFURVVGLVRUGHUVWRH[SODLQ the underlying mechanism(s) of VH. In order to further our understanding of VH DQG WR ÀQG WUHDWPHQW RSWLRQV WKDW PD\ HIIHFWLYHO\ DOOHYLDWH 9+ DFURVV GLVRUGHUV more knowledge on the underlying pathophysiological mechanisms of VH is required. Therefore, we need a method that can objectively and directly measure brain activity, and thus can provide insight into functional processes underlying VH.

Electroencephalography (EEG) and Magnetoencephalography (MEG)

((*ZDVWKHÀUVWLPDJLQJWHFKQLTXHWKDWPDGHLWSRVVLEOHWRPHDVXUHKXPDQEUDLQ cortical activity systematically (Hari and Puce, 2017; Schomer and Da Silva, 2012). EEG DQG0(*UHVSHFWLYHO\PHDVXUHWKHHOHFWULFDODQGPDJQHWLFÀHOGVSHUSHQGLFXODUWR each other) induced by the same electrical current in the human brain (Lopes da Silva, 2013). Both EEG and MEG are noninvasive techniques that directly measure neuronal activity with high temporal resolution (Hari and Puce, 2017). EEG measures differences in electrical potentials (i.e. voltage differences) on the scalp that are generated by post-synaptic electric currents of synchronously activated neuronal populations.

22 Chapter 1

sources (Hari and Puce, 2017). Whereas EEG is sensitive to extracellular currents and both tangential and radial components of a current source, MEG is more sensitive to primary intracellular currents and tangential components of a current source (Figure 3) (Lopes da Silva, 2013).

Figure 3. Radial and tangential oriented currents.

Red: radial current. Blue: tangential current (orthogonal or at 90o _{to the radial current)}

((*DQG0(*VLJQDOVDUHXVXDOO\DQDO\]HGLQÀYHIUHTXHQF\EDQGVGHOWD+] theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz) and gamma (30-48 Hz, mainly analyzed in MEG) band (Figure 4). These rhythms are ideally suited to study human brain activity related to different cognitive and behavioral processes as frequencies are often related to the transmission time of a signal coming from a certain brain area: low frequency oscillations tend to modulate activity from long-distance connections between brain regions, whereas high-frequency oscillations modulate activity in localized cortical regions (Buzsaki, 2006; Hari and Puce, 2017; von Stein and Sarnthein, 2000). However, it is also important to know where in the brain a signal of interest is generated, the so-called inverse problem. In EEG and MEG multiple neighboring recording sensors PD\SLFNXSVLJQDOIURPDVLQJOHVRXUFHNQRZQDVÀHOGVSUHDGVLJQDOVIURPVSDWLDOO\

separated sources may be mixed in a single sensor, and passive spread of current may occur due to the electrical conduction properties of the human head (known as volume conduction). Together, these factors can result in erroneous estimates of functional connectivity and complicate interpretation of results (Hari and Puce, 2017). Possible solutions to this problem are to project sensor-space data into source-space with source reconstruction methods while taking into account the spatial resolution (i.e. how well two sources can be separated) of an imaging technique, and to use methods that correct for signal leakage (Colclough et al., 2016). The spatial resolution of EEG and MEG varies from centimeters for EEG to millimeters for MEG (Hari and 3XFH%HDPIRUPLQJLVDVSDWLDOÀOWHULQJWHFKQLTXHZKLFKUHFRQVWUXFWVQHXURQDO activity in a target brain area as the weighted sum of all the sensors, passing brain activity from a certain location while attenuating activity from all the other locations. One main assumption of beamforming is that spatially distant neuronal sources are considered linearly independent (Hillebrand and Barnes, 2005). In this dissertation, for MEG, the beamformer approach is used to localize brain activity. For EEG, analysis are performed at sensor-level.

24 Chapter 1

Spectral analysis

2QHRIWKHPRVWRIWHQXVHGPHWKRGVRIREMHFWLYHTXDQWLÀFDWLRQRI((*DQG0(* comprises the frequency or spectral analysis. In spectral analysis, an EEG or MEG signal is transformed from the time domain to the frequency domain, and the power (amplitude squared) of a signal is represented as a function of the frequency (Engels et al., 2017). Relative power (i.e. power in a certain frequency band divided by the total power) and peak frequency (i.e. the frequency with the highest amplitude in the spectrum) form the hallmark of spectral analysis. In healthy individuals, the peak frequency is within the alpha band, around 10 Hz (Schomer and Da Silva, 2012). With aging, a decrease of the alpha peak below 8.0 Hz, a more than 5% increase in delta activity, or more than 10-15% increase in theta activity are considered to be abnormal (Klass and Brenner, 1995; Schomer and Da Silva, 2012).

Functional connectivity and brain networks

The human brain is a highly complex communication system with billions of neurons (Bullmore and Sporns, 2009). This complex neural system can be investigated with functional connectivity analysis to gain insight in how different brain regions are (functionally) interconnected, and with graph theory to study the organization of the brain as a network (known as network topology). Given the high temporal resolution of EEG and MEG, neurophysiological assessment of functional connectivity with EEG and MEG provides a direct measure of neuronal activity (Stam and van Straaten, 2012). Importantly, these functional connectivity analyses are needed to construct functional brain networks (Stam and van Straaten, 2012). Disruption in functional brain networks are associated with neurological diseases (Stam and van Straaten, 2012).

Functional and effective connectivity

)XQFWLRQDOFRQQHFWLYLW\DPRGHOIUHHFRQFHSWLVGHÀQHGDVVWDWLVWLFDOLQWHUGHSHQGHQF\ (or synchronization) between neural activity of spatially distant brain areas during WDVNRUUHVWLHUHVWLQJVWDWHDVVXPLQJWKDWWKLVVWDWLVWLFDOLQWHUGHSHQGHQF\UHÁHFWV communication between these brain regions (Friston, 2011). Effective connectivity is a model-based concept that refers to the causal effects of one neural system over DQRWKHUDQGWKHUHIRUHSURYLGHVLQVLJKWLQGLUHFWHGLQIRUPDWLRQÁRZEHWZHHQEUDLQ

regions (Friston, 2011). Both functional and effective connectivity are time-dependent. Over the past few decades, several functional and effective connectivity measures have been developed, see (Bastos and Schoffelen, 2016) and (Colclough et al., 2016) for an overview. Deciding on the most appropriate connectivity measure to be used in EEG and MEG analysis requires consideration of several methodological issues (van Diessen et al., 2015). In this dissertation, the Phase Lag Index (PLI) and the leakage-corrected Amplitude Envelope Correlation (AEC-c) have been used as functional connectivity measures to study functional interactions between brain regions in a resting-state condition. See the different chapters in this dissertation for an explanation of the PLI and AEC-c.

Functional brain networks

Graph theory, a branch of mathematics, represents the brain as a graph or network consisting of a set of nodes which are linked to each other by edges (links or connections) (Bullmore and Sporns, 2009). Graphs can be subdivided into different categories: a binary graph indicates only the presence or absence of an edge (connection) between two nodes, whereas a weighted graph represents the strength of functional interaction between two nodes. When edges indicate a direction of causal effect between two nodes, the graph is called directed (Figure 5) (Stam and van Straaten, 2012).

26 Chapter 1

Figure 5. Visualization of graph elements and different categories of graphs

A graph or network consists of two elements: nodes (upper left) and edges or links between nodes (upper right). (A) A binarized and undirected version of the graph or network. All edges have the same thickness, indicating only the presence or absence of a connection between two nodes. (B) A directed version of the binarized graph depicted in A. Directionality between nodes is represented with arrowheads. (C) A weighted version of the undirected graph shown in A. Weight or strength of the connection between nodes is represented by variations in edge thickness. (D) A directed version of the network depicted in C. Arrowheads between nodes represent directionality. Edge thickness between nodes represent weight or connection strength.

A large number of measures have been developed to characterize the organization of EUDLQQHWZRUNVLQYROYLQJPHDVXUHVWKDWGHÀQHWKHOHYHORIVHJUHJDWLRQDQGLQWHJUDWLRQ in a brain network, and measures that characterize node importance (Bullmore and Sporns, 2009). See (Rubinov and Sporns, 2010) for an overview of frequently used network measures. However, as described by (van Wijk et al., 2010) and (Hallquist and Hillary, 2018), comparison of network topologies is accompanied by several methodological issues which can lead to misinterpretation of results (Hallquist and Hillary, 2018; van Wijk et al., 2010). Differences in network size (i.e. number of nodes in a network), average degree (i.e. number of connections of a node), and network density (i.e. percentage of links present in the network) can cause spurious comparisons between networks, which cannot be completely solved by normalization procedures or the use of weighted networks (Hallquist and Hillary, 2018; van Wijk et al., 2010). One promising solution to this problem is the so-called minimum spanning tree (MST). The MST represents a subgraph of the network containing the strongest connections without forming loops (Stam et al., 2014). In other words, an MST includes all nodes (= N) of the original network but only N-1 edges. In a weighted network, all edges are

sorted from the lowest to the highest weight and edges are added to the tree working from lowest weight edge upwards until one MST is formed. In the MST itself, the edge weights are discarded making the MST binary in which an edge does or does not exist. In this way, weighted networks with same N but with unique weights result in unique MSTs that are comparable (Stam et al., 2014).

Machine learning

0DFKLQH OHDUQLQJ LV D ÀHOG RI FRPSXWHU VFLHQFH WKDW IRFXVHV RQ FRQVWUXFWLRQ RI algorithms that can learn from and make predictions on data (Flach, 2012). It can EHVXEFODVVLÀHGLQWRWZRFDWHJRULHVNQRZQDVVXSHUYLVHGOHDUQLQJDQGXQVXSHUYLVHG learning. Supervised learning involves prediction of a known output and mainly focuses RQFODVVLÀFDWLRQZKHUHDVLQXQVXSHUYLVHGOHDUQLQJWKHODEHOVRIWKHRXWSXWDUHQRW NQRZQDQGWKHDLPLVWRÀQGQDWXUDOO\RFFXUULQJSDWWHUQVZLWKLQWKHGDWD'HR Kotsiantis et al., 2006). Both types of learning require input variables, also known as features. In particular, supervised learning provides predicting algorithms that can learn from observations making supervised learning suitable to deal with highly experimental GDWDIURPWKHÀHOGRIPHGLFLQH*HXUWVHWDO$PRQJDOOVXSHUYLVHGOHDUQLQJ methods, decision trees are a popular group of nonlinear machine learning methods. Decision trees classify data by sorting them based on the feature values (Geurts et al., 2009).

In this dissertation, an ensemble-learning method that constructs more than one decision tree, known as random forest algorithm, was applied. Random forest, developed by L. Breiman, constructs a multitude of decision trees, and each decision

28 Chapter 1

Figure 6. Random forest algorithm.

Each decision tree in a random forest is constructed using a random sample of features from the original set of features (i.e. training set), after which the features are put back in the original training set. This ERRWVWUDSDJJUHJDWLRQSURFHGXUHUHVXOWVLQPXOWLSOHXQFRUUHODWHGWUHHVVRWKDWDSUHGHÀQHGQXPEHURI trees can be grown for each forest.

PART II

Treatment for hallucinations Current clinical practice

Hallucinations are common in chronic brain disorders. Currently, clinical treatment of hallucinations, regardless of the underlying modality, is far from optimal (Sommer et al., 2018). Cognitive behavioral therapy (CBT), one of the most frequently investigated psychological interventions, does not reduce the presence or frequency of hallucinations, but rather aims to improve insight and modify behavior thereby reducing the emotional reaction and distress in response to the hallucinations (Thomas et al., 2014). CBT has been proven effective for auditory verbal hallucinations (AVH) with an effect size of 0.4, while evidence for the effectiveness of CBT for VH is lacking (Lincoln and Peters, 2018; Waters et al., 2014). Brain stimulation in the form of transcranial magnetic stimulation (TMS) or transcranial direct current stimulation (tDCS), has EHHQSURYHQHIIHFWLYHDJDLQVW$9+LQVFKL]RSKUHQLDZKHUHDVHIÀFDF\DJDLQVW9+KDV only been reported in two cases (Koops and Sommer, 2017; Shiozawa et al., 2013). Pharmacological treatment for hallucinations targets different neurotransmitter systems including dopamine, serotonin, and cholinergic system, but is largely based on trial-and-error rather than deeper understanding of the underlying mechanisms (Sommer et al., 2018).

Like hallucinations, several other symptoms are common in chronic brain disorders. 7KHVHV\PSWRPVDUHQRWGLVHDVHVSHFLÀFEXWLQFOXGHJHQHUDOVHTXHODHVXFKDVTXDOLW\RI OLIH4R/DQGFRJQLWLYHLPSDLUPHQW6SHFLÀFDOO\FKURQLFEUDLQGLVRUGHUVDUHDVVRFLDWHG with reduced QoL (Berrigan et al., 2016; Karow et al., 2016; Ready et al., 2008; van Uem et al., 2015), high prevalence of depressive symptoms, and cognitive dysfunction (Feinstein, 2011; Pfeiffer, 2016). These sequelae are interdependent, as depressive mood DQGFRJQLWLYHLPSDLUPHQWLQÁXHQFH4R/%HUULJDQHWDO%ULVVRVHWDO Conde-Sala et al., 2016; Feinstein, 2011; Pfeiffer, 2016; Ready et al., 2008; van Uem HWDOZKLOHGHSUHVVLRQKDVDQHJDWLYHLQÁXHQFHRQFRJQLWLRQ3LURJRYVN\7XUN et al., 2016). Moreover, these general sequelae are associated with various adverse consequences such as poor treatment compliance, loss of independence and even mortality (Adamson et al., 2015). Whereas the current clinical practice tends to IRFXVRQLPSURYLQJGLVHDVHVSHFLÀFV\PSWRPVHJWUHPRUDQGULJLGLW\LQ3DUNLQVRQ·V disease, psychosis in schizophrenia), patients regard QoL and depressive mood as more LPSRUWDQWIRUWKHLUKHDOWKVWDWXVWKDQGLVHDVHVSHFLÀFSK\VLFDODQGPHQWDOV\PSWRPV (Fayers and Machin, 2013). Therefore, improvement of these common features should also become an important target in treatment of chronic brain disorders.

Physical exercise

Physical exercise (PE) has been reported to protect and restore the brain by inducing changes in neuroplasticity (van Praag, 2009; Voss et al., 2013c). Neuroplasticity is the capacity of the nervous system to change its structural and functional organization in response to changes in normal development, changes in environment and damage to

30 Chapter 1

&KLHIÀHWDO9RVVHWDOF3(LQFUHDVHVSHULSKHUDOEORRGOHYHOVRI%'1) (Huang et al., 2014). Blood levels of BDNF are highly correlated with BDNF levels in the central nervous system (CNS) as BDNF easily crosses the blood-brain barrier (Pan HWDO3(LQGXFHGXSUHJXODWLRQRI%'1)SOD\VDVLJQLÀFDQWUROHLQQHXURJHQHVLV neuronal survival and synaptic plasticity of the cerebral cortex and the hippocampus HQKDQFLQJFRJQLWLYHIXQFWLRQLQJ&KLHIÀHWDO&RWPDQHWDO+XDQJHW al., 2014; Voss et al., 2013c, 2010). PE mediated increase in VEGF, a hypoxia-induced angiogenic factor, promotes the proliferation of brain endothelial cells and angiogenesis (Van Praag, 2008; Voss et al., 2013c). IGF-1 is involved in maintenance of neuronal cells through its antiapoptotic properties, inhibiting neuronal cell death (Cassilhas et al., 2016).

3(DOVRPRGXODWHVQHXURLQÁDPPDWLRQ6YHQVVRQHWDO3(HOHYDWHVWKHH[SUHVVLRQ RI DQWLLQÁDPPDWRU\ F\WRNLQHV LH LPPXQRPRGXODWRU\ VLJQDOLQJ PROHFXOHV DQG UHGXFHVWKHH[SUHVVLRQRISURLQÁDPPDWRU\F\WRNLQHVLQWKLVZD\FKDQJLQJWKHSUR LQÁDPPDWRU\VWDWHRIWKHEUDLQLQWRDQDQWLLQÁDPPDWRU\PRGHDQGWKXVPHGLDWLQJ neuroprotection (Svensson et al., 2015). Additionally, microglia cells, the immune cells of the CNS, are also affected by PE. PE switches the expression of microglia cells from WKHSURLQÁDPPDWRU\0VXEW\SHVWRWKHDQWLLQÁDPPDWRU\0VXEW\SHV'L%HQHGHWWR et al., 2017). IGF-1 has been related to the activation of this M2 subtype of microglia cells and thus involved in neuroprotection (Di Benedetto et al., 2017; Svensson et al., 2015).

PE in animals has been associated with increased neurogenesis and synaptic plasticity in the hippocampus (Cotman et al., 2007; van Praag, 2009; Voss et al., 2013c). These HIIHFWVDUHPHGLDWHGYLDWKHH[HUFLVHUHODWHGLQFUHDVHRI%'1)&KLHIÀHWDO Cotman et al., 2007; Voss et al., 2013c). In healthy aging populations, physical activity has been reported to improve cognitive functioning and depressive symptoms, and delay neurodegeneration (Cotman et al., 2007; van Praag, 2009). Exercise has also been associated with changes in regional brain volume and integrity (Voss et al., 2013c) The above-mentioned exercise-induced effects on the brain have been replicated in both healthy and clinical populations, showing increased brain volume in grey and

white matter regions, increased white matter integrity in frontal and temporal lobes, increases in BDNF, reduction in depressive symptoms, and improvement in cognitive functioning in young and old (Cerrillo-Urbina et al., 2015; Colcombe et al., 2006; Erickson et al., 2011; Groot et al., 2016; Hirsch et al., 2016; Knapen et al., 2015; Knöchel et al., 2012; Smith and Blumenthal, 2010; Stroth et al., 2009; Voss et al., 2013b). The above-mentioned studies suggest that physical exercise as intervention might SRVLWLYHO\DIIHFWGLVHDVHVSHFLÀFV\PSWRPVVXFKDVKDOOXFLQDWLRQVEXWDOVRJHQHUDO sequelae such as QoL, mood, and cognition across disorders. However, current HYLGHQFHIRUHIÀFDF\RIH[HUFLVHWKHUDS\LVVWLOOGLVSXWHGDQGKHQFHH[HUFLVHLVQRWSDUW of the regular care offer for patients with chronic brain disorders in most countries.

32 Chapter 1

AIMS AND OUTLINE

PART I

General aim

The general aim of part I of this dissertation is to expand our understanding of the XQGHUO\LQJPHFKDQLVPVRIYLVXDOKDOOXFLQDWLRQVDQGWRÀQGQHXURSK\VLRORJLFDOVLJQDWXUHV of visual hallucinations by using EEG and MEG, and state of the art methods for neuroimaging analysis comprising functional connectivity and network analysis, and machine learning algorithms.

Research questions

1. Are there changes in EEG- and MEG-based spectral analysis, functional connectivity DQG IXQFWLRQDO QHWZRUN RUJDQL]DWLRQ SRLQWLQJ WRZDUGV VSHFLÀF SDWKRSK\VLRORJLFDO mechanisms that may underlie VH in the following disorders?:

a. Alzheimer’s disease

b. Dementia with Lewy bodies c. Parkinson’s disease

d. Visual impairment

2. To what extent are these neurophysiological changes common across the above-mentioned disorders?

Outline

7KHÀUVWTXHVWLRQZDVDVVHVVHGLQFKDSWHUWR7KHVHFRQGTXHVWLRQZDVDVVHVVHG in chapter 11.

Since part I of this dissertation studies four different disorders (AD, DLB, PD, visual impairment), the chapters are ordered per disorder.

*LYHQWKHRYHUODSLQFOLQLFDOV\PSWRPVDQGSDWKRORJ\EHWZHHQ'/%DQG$'ÀUVWRI all in chapter 2, we assessed the prevalence and characteristics of hallucinations in AD in a tertiary memory clinic.

In Chapter 3, we aimed to investigate the diagnostic value of quantitative EEG in discriminating DLB from AD by using a state-of-the-art machine learning technique, random forest algorithm.

After evaluating the discriminative ability of EEG, in chapter 4, we used 21-channel EEG to investigate neurophysiological indicators of hallucinations in AD patients with hallucinations, and compared them with non-hallucinating AD and DLB patients. In chapter 5ZHH[SORUHGZKHWKHUDWWHQWLRQDOGHÀFLWVZKLFKPLJKWSOD\DUROHLQ VH) in DLB could be caused by disruption in the direction and strength of causal LQÁXHQFHVLHGLUHFWHGFRQQHFWLYLW\EHWZHHQIURQWDODQGSDULHWDOEUDLQUHJLRQV:H used 21-channel EEG and investigated directed connectivity in DLB with a novel phase-based measure for directed connectivity, Phase Transfer Entropy (PTE), and compared WKHSDWWHUQRILQIRUPDWLRQÁRZZLWK$'SDWLHQWVWRH[SORUHWKHVSHFLÀFLW\RISRVVLEOH group differences to the pathophysiology of DLB.

7KHÀUVWTXHVWLRQIRUWKHGLVRUGHU3'LVH[SORUHGLQFKDSWHUDQG

In chapter 6, we used source-space MEG and performed spectral analysis to study IUHTXHQF\VSHFLÀFRVFLOODWRU\DFWLYLW\LQ3'SDWLHQWVZLWK9+DQGFRPSDUHGWKDWZLWK PD patients with multimodal hallucinations and PD patients without hallucinations. In chapter 7, we elaborated further on the MEG dataset described in chapter 6. We

34 Chapter 1

and explored changes in brain network organization between visually impaired patients with and without hallucinations.

PART II

The general aim of part II of this dissertation is to evaluate the potential role of physical exercise as therapeutic intervention in reducing hallucinations, and improving cognition, QoL and depressive symptoms in chronic brain disorders.

Research questions

1. Is physical exercise an effective therapeutic intervention in reducing hallucinations? 2. Is physical exercise an effective therapeutic intervention in improving QoL, GHSUHVVLYHV\PSWRPVDQGFRJQLWLYHGHÀFLWVLQFKURQLFEUDLQGLVRUGHUV"

3. Is physical exercise safe as a therapeutic intervention? Outline

In chapter 9ZHDGGUHVVWKHÀUVWUHVHDUFKTXHVWLRQZLWKDTXDQWLWDWLYHUHYLHZRU PHWDDQDO\VLVWRV\QWKHVL]HHYLGHQFHRQWKHHIÀFDF\RISK\VLFDOH[HUFLVHDVDWKHUDSHXWLF intervention in reducing hallucinations in patients with schizophrenia spectrum disorder. Since hallucinations are by far more common in schizophrenia and reduced physical capacity in patients with schizophrenia is strongly related to clinical symptoms, we investigated the therapeutic role of physical exercise in this patient group. Chapter 10 addresses the second and third research question. We performed a transdiagnostic systematic review and meta-analysis of randomized controlled trials to evaluate the therapeutic potential of physical exercise in improving cognition, QoL, and depressive symptoms in six chronic brain disorders: Alzheimer’s disease, Huntington’s disease, Multiple Sclerosis, Parkinson’s disease, Schizophrenia, and Unipolar Depression. We performed both transdiagnostic and within disorder analysis. In addition, we investigated the safety of physical exercise. Given the transdiagnostic character of the quantitative review we also evaluated the effect of several continuous

and categorical moderators such as the type of exercise, intensity of exercise, exercise time (in minutes/week), total length of the exercise period (in weeks), and age. In Chapter 11WKHPDLQÀQGLQJVRIWKLVGLVVHUWDWLRQDUHVXPPDUL]HGIROORZHGE\D general discussion, implications for clinical practice and recommendations for future research.

2

CHAPTER

Understanding

hallucinations in probable

Alzheimer’s disease: very

low prevalence rates in a

tertiary memory clinic

40 Chapter 2

ABSTRACT

Introduction: Averaging at 13.4%, current literature reports widely varying prevalence rates of hallucinations in patients with probable Alzheimer’s disease (AD), and is still inconclusive on contributive factors to hallucinations in AD.

Methods: This study assessed prevalence, associated factors and clinical characteristics of hallucinations in 1227 patients with probable AD, derived from a tertiary memory clinic specialized in early diagnosis of dementia. Hallucinations were assessed with the Neuropsychiatric Inventory.

Results: Hallucination prevalence was very low, with only 4.5% (n=55/1227) affected patients. Hallucinations were mostly visual (n=40/55) or auditory (n=12/55). Comorbid delusions were present in over one-third of cases (n=23/55). Hallucinations were associated with increased dementia severity, neuropsychiatric symptoms, and a lifetime history of hallucination-evoking disease (such as depression and sensory impairment), but not with age or gender.

Discussion: In the largest sample thus far, we report a low prevalence of hallucinations in probable AD patients, comparable to rates in non-demented elderly. Our results suggest that hallucinations are uncommon in early stage AD. Clinicians that encounter hallucinations in patients with early AD should be sensitive to hallucination-evoking comorbidity.

1. INTRODUCTION

Hallucinations occur in a variety of psychiatric, neurologic, and somatic disorders, as well as in the general population (Sommer et al., 2012). Their presence can induce distress and impair daily functioning toward a stage that professional help is necessary (Johns et al., 2014). Better understanding of hallucinations can improve both clinical assessment and treatment (Johns et al., 2014; Sommer et al., 2012).

Reported prevalence rates of hallucinations in patients with probable Alzheimer’s disease (AD) vary widely from 7% to 35% (Zhao et al., 2016b), averaging at 13.4% (“Research in context”; Supplementary Fig. 1 , Supplementary Tables 1ab ). Their presence has been repeatedly associated with more severe cognitive and functional decline, earlier institutionalization, higher burden of disease, and increased mortality (El Haj et al., 2017). It is therefore essential to better understand hallucinations in AD. However, heterogeneity between studies on hallucinations in probable AD is large and complicates comparability of study results (Zhao et al., 2016b). As such, current literature is not conclusive on potentially contributive factors, such as dementia severity (Zhao et al., 2016b). Also, the possibility of other diagnoses and medication use as alternative contributing factors to hallucinations in patients with probable AD is often underexposed.

The present study tries to improve the understanding of these uncertainties by studying hallucinations in a large sample of patients with probable AD, derived from a tertiary research memory clinic specialized in early detection of dementia (Van Der Flier et al., 2014). We assessed the prevalence and phenomenology of hallucinations and

42 Chapter 2

GXULQJEDVHOLQHGLDJQRVWLFDVVHVVPHQWRIFRJQLWLYHFRPSODLQWV$OOSDUWLFLSDQWVIXOÀOOHG criteria for probable AD as formulated by the National Institute for Neurological and Communicative Disorders/Alzheimer’s Disease and Related Disorders Association (McKhann et al., 2011) and had been diagnosed within 30 days from their initial visit. Diagnosis was based on standardized multidisciplinary assessment, including patients’ history, neurological examination, vital functions, neuropsychological assessment, whole-brain magnetic resonance imaging, electroencephalography and routine serum ODERUDWRU\DQGFHUHEURVSLQDOÁXLGVDPSOLQJLQDVXEVDPSOH9DQ'HU)OLHUHWDO

NPI assessment was conducted in patients’ caregivers, by a specialized dementia research nurse during the study day. A participant was considered “hallucinating” if he/she had a frequency score of ³1 on the NPI hallucination subscale. Further details on hallucination phenomenology were retrieved with hallucination items of the NPI, and, if necessary, by reviewing patients’ charts. The overall presence and severity of neuropsychiatric symptoms were based on total NPI scores.

Subjects’ medical history was dichotomously marked as relevant if one or more diagnoses had ever been present, in which hallucinations are reportedly part of the associated symptomatology, as stated by recent overview articles (Sommer and Kahn, 2014; Sommer et al., 2012) (listed in Table 1). Similar dichotomization was applied if patients used one or more drugs with hallucinations listed as a side effect (Farmacotherapeutisch Kompas n.d. Available at: https://farmacotherapeutischkompas. nl. Accessed February 15, 2018), referred to as hallucination-inducing medication (Table 1). Ranking of relevant history and medication was performed independently by two authors (M.D. and M.M.J.L.); discrepancies were solved by consensus. Dementia severity was based on scores from the Mini-Mental State Examination (MMSE) (27–30 no dementia, 20–26 mild dementia, 10–19 moderate, and 0–9 severe) (Perneczky et al., 2006) and the Clinical Dementia Rating (CDR) (Hughes et al., 1982).

&RQÀGHQFHLQWHUYDOVIRUSUHYDOHQFHUDWHVRIKDOOXFLQDWLRQVZHUHFDOFXODWHGXVLQJ Clopper-Pearson’s exact method in R, version 3.2.0, package PropCIs. Hallucinating and non-hallucinating subjects were compared using chi-square tests for categorical

variables and Mann-Whitney U-tests for continuous variables, using IBM SPSS Statistics, YHUVLRQ7KHOHYHORIWZRWDLOHGVLJQLÀFDQFHZDVVHWDW3

Table 1: Comparison of demographic and clinical characteristics between AD patients with (+) and without (-) hallucinations (n=1227)

Hall (+) n=55

Hall (-) n=1172

Statistics

Factor Median (IQR) Median (IQR) p Z U

Age (yrs) 67.2 (62.5 - 72.6) 66.7 (60.5 - 72.3) .44 .78 34,226.5 MMSE score*‡ 19 (13-22) 21 (17-24) <.001 -3.7 20,674.0 CDR score*‡ 1 (1-2) 1 (0.5-1) .003 3.0 31,829.5 Total NPI-score* 24 (13-34) 8 (3-16) <.001 7.1 48,802.0 Total NPI-score (excl. hallucination items)* 22 (10-29.5) 8 (3-16) <.001 5.8 45,475.5 n (%) n (%) p x2 _df Female gender 27 (49.1) 602 (51.4) .74 .11 1 Presence of comorbid delusions (NPI)* 22 (40.0) 84 (7.2) <.001 72 1 History of hallucination-associated disease*‡§ 21 (38.2) 299 (25.2) .036 4.4 1 Use of hallucination-inducing medication†‡¶ 31 (56.4) 517 (44.1) .074 3.2 1

Abbreviations: AD, Alzheimer’s disease; CDR, clinical dementia rating; IQR, interquartile range; MMSE, Mini-Mental State Examination; NPI, Neuropsychiatric Inventory.

127(5HVXOWVWKDWDUHVWDWLVWLFDOO\VLJQLÀFDQW3DUHOLVWHGLQEROG

44 Chapter 2

3. RESULTS

Out of 1545 patients diagnosed with probable AD during baseline screening between January 2005 and January 2018, 1227 subjects (79.4%) had NPI data available, with a mean age of 66.6 (standard deviation 7.9) (Supplementary Fig. 2). Supplementary Table 3 shows basic characteristics of the included sample (n=1227). There were no substantial differences between the group with and without NPI data (Supplementary Table 2). +DOOXFLQDWLRQVRFFXUUHGLQRXWRISDUWLFLSDQWVFRQÀGHQFHLQWHUYDO 3.4%–5.8%).

The 55 hallucinating subjects mainly reported experiences in the visual (n=40; 73%) or auditory modality (n=12; 22%). A smaller group reported olfactory (n=5; 9%) and tactile hallucinations (n=3; 5%); hallucination modality was unknown in 10 participants (18%). According to the NPI, delusions were present in 23 hallucinating participants (42%), of which paranoia (n=9), home intruders (n=10) and theft (n=12) were reported most frequently.

3.1 Associated factors

+DOOXFLQDWLQJVXEMHFWVVKRZHGVLJQLÀFDQWO\KLJKHUSHUFHQWDJHVRIFRPRUELGGHOXVLRQV than non-hallucinating subjects and had higher total NPI scores (Table 1). The percentage of subjects with a history of hallucination-associated disease was higher LQWKRVHZLWKKDOOXFLQDWLRQV7DEOH$WWUHQGOHYHOVLJQLÀFDQFHWKHSHUFHQWDJHRI hallucination-inducing medication use appeared higher inthe hallucinating group. +DOOXFLQDWLQJVXEMHFWVKDGVLJQLÀFDQWO\ORZHU006(VFRUHVDQGDVLJQLÀFDQWO\LQFUHDVHG &'5LQFRPSDULVRQZLWKWKHQRQKDOOXFLQDWLQJVXEMHFWV7DEOH6WUDWLÀFDWLRQIRU VHYHULW\RIGHPHQWLDUHVXOWHGLQVWDWLVWLFDOO\VLJQLÀFDQWGLVWULEXWLRQVIRUERWK006( (x2 _{12.3, p .006, df 3) and CDR (x}2 _{11.7, p .020, df 4) and an increasing percentage}

of hallucination prevalence with dementia severity (Fig. 1, Supplementary Fig. 3). No differences were observed with regard to age or gender (Table 1).

Figure 1.3UHYDOHQFHRIKDOOXFLQDWLRQVDVVWUDWLÀHGIRUGHPHQWLDVHYHULW\EDVHGRQ006( VFRUHVWRWDOQ (UURUEDUVLQGLFDWHORZHUDQGXSSHUERUGHUVRIFRQÀGHQFHLQWHUYDOV MMSE data were missing in 15 subjects, 4 of which reported hallucinations. Distribution was VWDWLVWLFDOO\VLJQLÀFDQW[2 _{12.3, p.006, df 3). Abbreviation: MMSE, Mini-Mental State Examination.}

4. DISCUSSION

In the largest sample of patients with probable AD to date, consisting predominantly of patients with early stage disease and relatively young age, we observed a remarkably low prevalence of hallucinations (4.5%) in comparison with existing literature (Supplementary Fig. 1). In studies from

46 Chapter 2

(van der Linde et al., 2010). Because our sample is derived from a research clinic specialized in diagnosis of early stages of dementia (Van Der Flier et al., 2014), the ÀQGLQJRIDQHDUQRUPDOSUHYDOHQFHRIKDOOXFLQDWLRQVZLWKLQWKLVVDPSOHVXJJHVWVWKDW hallucinations should not be considered a common symptom in early stage AD (in contrast to dementia with Lewy bodies).

,QGHHGZHREVHUYHGVLJQLÀFDQWDVVRFLDWLRQVEHWZHHQWKHSUHVHQFHRIKDOOXFLQDWLRQV and both decreased MMSE scores and an increased CDR. Hallucination prevalence rates increased with intensifying categories of dementia severity, with percentages up to 10% in the group with MMSE scores of 10 or less. These observations correspond with previous studies suggesting the uncommonness of hallucinations in early stage AD (Bassiony and Lyketsos, 2003; Devanand et al., 1992; Jost and Grossberg, 1996) and an increase in cumulative hallucination prevalence with disease progression (Devanand et al., 1992). The mean age of our sample was younger than that of other cohorts (Zhao et al., 2016b), but, in our sample, hallucinating and non-hallucinating subjects’ age did QRWGLIIHUVLJQLÀFDQWO\

Thirty-eight percent of hallucinating subjects reported a lifetime history of KDOOXFLQDWLRQHYRNLQJGLDJQRVHVVLJQLÀFDQWO\KLJKHUWKDQWKHQRQKDOOXFLQDWLQJFRQWURO group (25%). A similar trend was observed with regard to the use of hallucination-LQGXFLQJPHGLFDWLRQ$OVRKDOOXFLQDWLQJVXEMHFWVKDGDVLJQLÀFDQWO\HOHYDWHG13,VFRUH regardless of the hallucination score, indicating an increased overall presence and severity of neuropsychiatric symptoms. These observations imply that the presence of hallucinations in our sample does not necessarily have to be attributed to a diagnosis of AD alone but may also be evoked by other diagnoses or medication use. This implication stresses the importance of proper hallucination assessment in patients with AD. Clinicians who encounter hallucinations in patients with AD should consider the broad diagnostic spectrum in which hallucinations occur, such as dementia with Lewy bodies, delirium, psychotic or affective disorders, and sensory impairment, so that treatment options can be properly adjusted (Sommer et al., 2012). As such, we recommend clinicians who encounter hallucinations in early stage AD patients to

actively approach them within the context of medication use and current and prior disease.

4.1 Limitations

Most included subjects were not seen for follow-up visitations. Hence, we were not able to study the course and occurrence of hallucinations and AD diagnosis longitudinally. As hallucinations are based on subjective experiences, using caregiver-based assessment may have led to underreporting of hallucinations. It would be of added value to replicate hallucination assessment in a similar sample with an alternative but equally valid patient-based questionnaire and compare this to caregiver-patient-based results. The screener version of the Questionnaire for Psychotic Experiences may be a promising alternative for this purpose (Sommer et al., 2018).

NPI data were not available for all patients seen during the inclusion period. Although WKHLQFOXGHGVDPSOHUHPDLQVODUJHWKLVPD\KDYHLQÁXHQFHGJHQHUDOL]DELOLW\

Finally, due to the retrospective study design, assessment of hallucination-associated diagnoses was limited to incorporation of lifetime medical history. As a result, we cannot attribute any time-related associations to the occurrence of hallucinations and potentially relevant diagnoses in our sample. Ideally, future studies on hallucinations LQSUREDEOH$'VKRXOGLQFRUSRUDWHFXUUHQWFRPRUELGLW\WRDVVHVVLWVLQÁXHQFHPRUH extensively.

5. CONCLUSION

48 Chapter 2

SUPPLEMENTAL MATERIAL

6XSSOHPHQWDU\ÀJXUHMeta-analysis on hallucination prevalence in probable AD. Detailed

information on methodology and results is listed in supplementary tables 1ab.

6XSSOHPHQWDU\ÀJXUH+DOOXFLQDWLRQSUHYDOHQFHVWUDWLÀHGIRUVHYHULW\RIGHPHQWLDEDVHG

RQ&'5VFRUH(UURUEDUVLQGLFDWHORZHUDQGXSSHUERXQGDU\RIFRQÀGHQFHLQWHUYDO CDR-data was missing in 118 subjects, of which were hallucinating. Distribution was statistically VLJQLÀFDQW[2 _{11.7, p.020, df 4).}

50 Chapter 2

Supplementary table 1a. Systematic search: methods and results

Search terms “(Hallucination* OR Psychotic* OR Psychosis) AND Alzheimer*” in title, abstract

Search period September 30th_{, 2014 – October 4}th_{, 2017} a

Database Pubmed/Medline

Total studies 249

Inclusion criteria Original research b

Cross-sectional or longitudinal design b

5HSRUWLQJSUHYDOHQFHRIKDOOXFLQDWLRQVLQ$'25VXIÀFLHQW information to calculate an estimate b

Sample size at least 50 b

Published in English b

Subjects diagnosed with probable AD according to NINCDS-ADRDA criteria c

Included studies 7 (new search) d

14 (Zhao et al., 2016) e

Hallucination assessment

Neuropsychiatric Inventory (NPI): 12 studies Behavioral Pathology in Alzheimer’s Disease Rating Scale (BEHAVE-AD): 4 studies

Diagnostic and Statistical Manual of Mental Disorders III or IV (DSM III/IV): 4 studies

Supplementary table 1b. Meta-analysis: results

Average prevalence Fixed model: 15.2% (95% C.I. 14.3 - 16.2) Random model: 13.4% (95% C.I. 10.5 - 16.9) Heterogeneity (I2_{) 93.2%}

a. Articles published before September 30th_{, 2014: as listed in a recently published meta-analysis by Zhao}

et al.; J Affect Disord. 2016 Jan 15;190:264-271.

b. Criterion directly overlaps with those formulated by Zhao et al. (2016)

c. Criterion is more strict than those formulated by Zhao et al. (2016). Of all selected studies by Zhao et al. (2016), only those that included subjects with probable AD were incorporated in current meta-analysis.

d. List of references via systematic search, listed alphabetically: Burke et al.; Arch Gerontol Geriatr. 2016 Jul-Aug;65:231-8. Fischer et al.; J Alzheimers Dis. 2016;50(1):283-95. Hall et al.; Alzheimers Res Ther. 2015 May 1;7(1):24.

Quaranta et al.; Dement Geriatr Cogn Disord. 2015;39(3-4):194-206. Tanaka et al.; Psychogeriatrics. 2015 Dec;15(4):242-7.

Wadsworth et al.; Dement Geriatr Cogn Disord. 2012;34(2):96-111. Crossreference via Donovan et

al., Am J

Geriatr Psychiatry. 2014 Nov;22(11):1168-79.

Yoon et al.; J Geriatr Psychiatry Neurol. 2017 May;30(3):170-177. e. List of references via Zhao et al. (2016), listed alphabetically:

Bassiony et al.; Int J Geriatr Psychiatry. 2000 Feb;15(2):99-107. Chiu et al.; Int Psychogeriatr. 2012 Aug;24(8):1299-305.

Fuh et al.; J Neurol Neurosurg Psychiatry. 2005 Oct;76(10):1337-41. Gormley et al; Int J Geriatr Psychiatry. 1998 Feb;13(2):109-15. Hart et al.; Int J Geriatr Psychiatry. 2003 Nov;18(11):1037-42. Haupt et al.; Dement Geriatr Cogn Disord. 2000 May-Jun;11(3):147-52. Hirono et al.; J Neurol Neurosurg Psychiatry. 1998 May;64(5):648-52. Lopez et al.; Neurocase. 2005 Feb;11(1):65-71.

Lyketsos et al.; J Neuropsychiatry Clin Neurosci. 1997 Winter;9(1):64-7. Mega et al.; Neurology. 1996 Jan;46(1):130-5.

Mirakhur et al.; Int J Geriatr Psychiatry. 2004 Nov;19(11):1035-9. Mizrahi et al.; Am J Geriatr Psychiatry. 2006 Jul;14(7):573-81. Moran et al.; Sleep Med. 2005 Jul;6(4):347-52.

Van der Mussele et al.; J Alzheimers Dis. 2014;38(2):319-29.

52 Chapter 2

Supplementary table 2. Characteristics and comparison of included (NPI) and excluded participants (no NPI)

Characteristics NPI (n=1227) No NPI (n=318) Statistics Female gender a _{629 (51.3%) 179 (56.3%) p.11, c}2 _{2.6, df 1} Age (years) b _{66.7 (60.6 – 72.4) 66.6 (60.6 – 72.9) p.67, Z-.43, U 192063} Education (years) b,c _{10 (9 – 13) 10 (9 – 13) p.29, Z-1.1, U 174270} MMSE b,c _{21 (17 – 24) 21 (17 – 24) p.83, Z-.22, U 183346} CDR b,c _{1 (0.5 – 1) 1 (0.5 – 1) p.070, Z-1.8, U 112472} a _{variable displayed as n (%); comparative statistical analysis with Pearson’s chi-square.} b _{variables displayed as median (interquartile range); comparative statistical analysis with}

Mann-Whitney U test.

c _{missing data in the following amounts of subjects: years of education n=58 (NPI), n=8 (no NPI);}

MMSE n=15 (NPI), n=13 (no NPI); CDR n=118 (NPI), n=100 (no NPI).

Abbreviations: NPI, Neuropsychiatric Inventory; df, degrees of freedom; MMSE, Mini Mental State Examination; CDR, Clinical Dementia Rating

Supplementary table 3. Demographic and clinical characteristics of the total included sample (n=1227).

Demographic characteristics Total sample with NPI (n=1227)

Measure n

Age (years) 66.6 (7.9) Mean (SD) 1227

Female gender 629 (51.3) n (%) 1227

Education level (years) 10 (9 - 13) Median (IQR) 1169 Clinical characteristics

MMSE-score 21 (17 - 24) Median (IQR) 1212

CDR 1 (0.5 - 1) Median (IQR) 1109

APOE-İ4 genotype positive 760 (61.9) n (%) 1163 MRI markers

Hippocampal atrophy a _{326 (35.6) n (%) 892}

Global cortical atrophy b _{257 (28.1) n (%) 887}

AD Biomarkers

Ab42 (pg/ml) 543.4 (241.4) Mean (SD) 856

Total tau (pg/ml) 742.1 (408.9) Mean (SD) 849

Phosphorylated tau-181 (pg/ml) 89.5 (37.9) Mean (SD) 855

a _{Measures for hippocampal atrophy are based on the Medial Temporal Atrophy (MTA) visual}

UDWLQJVFDOHUDQJLQJIURPWRLQZKLFKKLJKHUVFRUHVUHÁHFWPRUHVHYHUHIROORZLQJ6FKHOWHQV et al., J Neurol. 1995 Sep;242(9):557-60). The mean of the left and right MTA was dichotomized LQWRSUHVHQFHóRUDEVHQFHRIKLSSRFDPSDODWURSK\

b _{Measures for global cortical atrophy are based on visual rating, ranging from 0 to 3 in which}

KLJKHUVFRUHVUHÁHFWPRUHVHYHUHDWURSK\IROORZLQJ3DVTXLHUHWDO(XU1HXURO 72). This rating was then dichotomized into presence (score 2, 3) or absence (score 0, 1) of global cortical atrophy.

Abbreviations: NPI, Neuropsychiatric Inventory; SD, standard deviation; IQR, interquartile range; MMSE, Mini Mental State Examination; CDR, Clinical Dementia Rating; APOE, apolipoprotein E; Ab42, amyloid-b_1-42

CHAPTER

Random forest

FODVVLÀFDWLRQWR

differentiate dementia

with Lewy bodies from

Alzheimer’s disease

3

56 Chapter 3

ABSTRACT

Introduction: 7KH DLP RI WKLV VWXG\ ZDV WR EXLOG D UDQGRP IRUHVW FODVVLÀHU WR improve the diagnostic accuracy in differentiating dementia with Lewy bodies (DLB) from Alzheimer’s disease (AD) and to quantify the relevance of multimodal diagnostic measures, with a focus on electroencephalography (EEG).

Methods: 66 DLB, 66 AD patients, and 66 controls were selected from the Amsterdam Dementia Cohort. Quantitative EEG (qEEG) measures were combined with clinical, QHXURSV\FKRORJLFDOYLVXDO((*QHXURLPDJLQJDQGFHUHEURVSLQDOÁXLG&6)GDWD Variable importance scores were calculated per diagnostic variable.

Results: For discrimination between DLB and AD, the diagnostic accuracy of WKH FODVVLÀHU ZDV  %HWD SRZHU ZDV LGHQWLÀHG DV WKH VLQJOH PRVW LPSRUWDQW discriminating variable. qEEG increased the accuracy of the other multimodal diagnostic data with almost 10%.

Discussion: Quantitative EEG has a higher discriminating value than the combination of the other multimodal variables in the differentiation between DLB and AD.