Brain Topogr DOI 10.1007/s10548-014-0387-1

ORIGINAL PAPER

Narcoleptic Patients Show Fragmented EEG-Microstructure During Early NREM Sleep Alena Kuhn • Verena Brodbeck • Enzo Tagliazucchi Astrid Morzelewski • Frederic von Wegner • Helmut Laufs

•

Received: 4 December 2013 / Accepted: 20 July 2014 Ó Springer Science+Business Media New York 2014

Abstract Narcolepsy is a chronic disorder of the sleepwake cycle with pathological shifts between sleep stages. These abrupt shifts are induced by a sleep-regulating flipflop mechanism which is destabilized in narcolepsy without obvious alterations in EEG oscillations. Here, we focus on the question whether the pathology of narcolepsy is reflected in EEG microstate patterns. 30 channel awake and NREM sleep EEGs of 12 narcoleptic patients and 32 healthy subjects were analyzed. Fitting back the dominant amplitude topography maps into the EEG led to a temporal sequence of maps. Mean microstate duration, ratio total time (RTT), global explained variance (GEV) and transition probability of each map were compared between both groups. Nine patients reached N1, 5 N2 and only 4 N3. All healthy subjects reached at least N2, 19 also N3. Four dominant maps could be found during wakefulness and all NREM- sleep stages in healthy subjects. During N3, narcolepsy patients showed an additional fifth map. The mean microstate duration was significantly shorter in narcoleptic patients than controls, most prominent in deep sleep. Single maps’ GEV and RTT were also altered in narcolepsy. Being aware of the limitation of our low sample size, narcolepsy patients showed wake-like features during sleep A. Kuhn  V. Brodbeck  E. Tagliazucchi  A. Morzelewski  F. von Wegner  H. Laufs Department of Neurology and Brain Imaging Center, Goethe University, Frankfurt am Main, Germany A. Kuhn (&) Universita¨tsklinikum Frankfurt, Klinik fu¨r Neurologie, TheodorStern-Kai 7, 60590 Frankfurt am Main, Germany e-mail: [email protected] H. Laufs Department of Neurology, University Hospital Kiel, Kiel, Germany

as reflected in shorter microstate durations. These microstructural EEG alterations might reflect the intrusion of brain states characteristic of wakefulness into sleep and an instability of the sleep-regulating flip-flop mechanism resulting not only in pathological switches between REMand NREM-sleep but also within NREM sleep itself, which may lead to a microstructural fragmentation of the EEG. Keywords Narcolepsy  EEG microstates  Restingstate  Microstructure  NREM sleep

Introduction Narcolepsy is a chronic disorder of the sleep-wake cycle. Clinical features include excessive daytime- sleepiness, nocturnal sleep disruption, cataplexy and other REM sleep associated phenomena such as sleep paralysis and hypnagogic hallucinations (American Academy of Sleep Medicine 2005). Patients with narcolepsy reach all physiological vigilance states: wakefulness (W), non-rapid eye movement (NREM) and rapid eye movement (REM) sleep. However, their continuity is disturbed, resulting in frequent pathological transitions between vigilance states, which may either be reached incompletely or inappropriately in time. A deficiency of the hormone orexin (hypocretin) has been found to be crucial in the pathophysiology of narcolepsy (de Lecea et al. 1998; Sakurai et al. 1998; Burgess and Scammell 2012). Orexin-producing neurons promote wakefulness and suppress REM sleep by ‘‘fine-tuning’’ a sleep-regulating functional ‘‘flip-flop switch’’. This is formed by reciprocally inhibitory cell groups, which promote either sleep or wakefulness thus regulating the transition between these complementary states. In an analogous

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Brain Topogr

manner, the switching between REM and NREM sleep is instantiated (Methippara et al. 2000; Eggermann et al. 2001; Adamantidis et al. 2007; Saper et al. 2010; Lee and Dan 2012; Saper 2013). Hence, a destruction of orexinproducing neurons leads to a destabilization of this flip-flop mechanism, resulting in instability of different vigilance states. This instability is expressed in direct transitions from wakefulness to REM sleep (Dantz et al. 1994; Khatami et al. 2007; Saper et al. 2010), excessive sleepiness during daytime and fragmented nocturnal sleep, which is disrupted by numerous arousals (Nobili et al. 1995). Such pathological state transitions have mainly been examined at a macrostructural level where an altered sleep architecture with increased numbers of arousals, sleeponset REM periods, higher amounts of REM and N1 sleep and decreased sleep efficiency have been reported (Montplaisir et al. 1978; Hudson et al. 1992; Nobili et al. 1995). Using electroencephalography (EEG) microstate analysis, we aimed at investigating whether these pathological state transitions in narcolepsy are also expressed in microstructural alterations of the spontaneous EEG. EEG microstates exploit the entire spatio-temporal EEG information by characterizing fast, spontaneous fluctuations of the scalp potential field across time (PascualMarqui et al. 1995). Technically, the EEG is parsed into a time series of potential maps by means of a clustering algorithm (Pascual-Marqui et al. 1995). EEG microstates are time epochs in the sub-second range in which the scalp potential topographies appear as discrete segments of electrical stability, separated by sharp and short transitions, switching abruptly into the next period of stability (Lehmann et al. 2009). These EEG maps mirror the scalp potential and are a summation of all the currently active sources in the brain irrespective of their frequency. EEG microstates have been studied during wakefulness and four standard classes of EEG microstate maps have been identified (Lehmann et al. 1987; Wackermann et al. 1993; Koenig et al. 2002; Schlegel et al. 2012). A deviation of microstate features from the norm has been shown in different pathologies (Strik et al. 1995; Dierks et al. 1997; Strik et al. 1997; Koenig et al. 1999; Strelets et al. 2003; Lehmann et al. 2005; Kikuchi et al. 2011; Kindler et al. 2011). We recently established characteristic microstates for the NREM sleep stages and found the four maps known from wakefulness to be topographically preserved to a large extent in all NREM sleep stages in healthy subjects (Brodbeck et al. 2012). The four physiological maps occurring in wake and sleep have been labeled arbitrarily with the letters A, B, C and D (Koenig et al. 2002). For each of these maps, the specific topography, duration, occurrence rate, global explained variance and the transitions from one map to the next can be determined (Wackermann et al. 1993; Koenig et al. 2002; Schlegel et al. 2012).

123

We hypothesized that the destabilization of the sleepregulating flip-flop mechanism in narcolepsy results in altered microstructural state transitions, and that EEG microstate analysis is sensitive to this NREM sleep pathology.

Materials and Methods Subjects The study was performed comparing EEG microstates in NREM sleep of narcolepsy patients (patient group) with healthy subjects (control group). This study is part of a set of experiments initially designed as a simultaneous EEG-functional magnetic resonance imaging (fMRI) study on sleep physiology. Previous data of the same subjects as in our control group has been published (Brodbeck et al. 2012; Jahnke et al. 2012; Tagliazucchi et al. 2013). The data presented here are results of the EEG recordings only. Written informed consent was obtained, and the study was approved by the local ethics committee. Patient Group 14 right handed patients in whom the diagnosis of narcolepsy had been established (American Academy of Sleep Medicine 2005) were examined. Two of which had to be excluded due to artifacts in the EEG recordings. The remaining 12 patients had a mean age of 42.5 years (age range 18–61), 6 were male, 6 female (details see Table 1). The inclusion criteria for patients were adjusted regarding sleep depth and length of each sleep stage EEG epoch. Not all of the patients reached deep sleep stages (see Table 1). The mean length of the EEG epochs was 3 min (40–308 s). Control Group Recordings of 32 right handed subjects (age range 19–31, mean age 23 years) of a series of 149 healthy, not sleep deprived subjects were included in the study. Subjects were included only if at least sleep stage N2 was reached and if for each sleep stage (W, N1, N2, an optionally N3) an EEG epoch of at least 10 4500 duration was available. EEG Recordings 30-channel EEG was recorded via a cap (modified BrainCapMR, 10-10 system, Easycap, Herrsching, Germany) with an optimized polysomnographic setting (chin and tibial EMG, ECG, EOG recorded bipolarly, sampling rate 5 kHz, low pass filter 1 kHz) and FCz as the reference

Brain Topogr Table 1 Patient characteristics Patient

Sex

Age

Excessive daytime sleepiness

Cataplexy

Hypnagogic Hallucinations

Sleep paralysis

MSLT

HLA DQB1*06

Medication

Deepest sleep stage

1

M

28

?

-

?

?

?

?

-

W

2

F

48

?

?

?

?

?

?

-

N2

3 4

M F

38 30

? ?

?

? ?

? Unknown

? ?

? Unknown

-

N3 N3

5

F

46

?

-

-

-

?

-

Modafinil

N1

Venlafaxin 6

F

60

?

?

?

-

?

Unknown

Venlafaxin

N1

Reboxetin Bisoprolol 7

M

46

?

?

Unknown

Unknown

?

Unknown

Methylphenidat

W

Methotrexat Folsa¨ure Infliximab 8

M

18

?

?

-

-

?

?

Fluoxetin

N1

9

F

60

?

-

-

-

?

?

-

N3

10

M

52

?

?

?

?

?

Unknown

Clomipramin

N1

Modafinil

Reboxetin Methylphenidat Sodiumoxybate 11

F

23

?

-

?

?

?

-

Modafinil

N3

12

M

61

?

?

?

?

?

?

Clomipramin

W

Flupirtin Clopidogrel Ramipril Simvastatin Bisoprolol Pentaerythrityltetranitrat Excessive daytime sleepiness was reported by all subjects. Three patients showed all symptoms of the classical narcoleptic tetrad. Seven patients took medication. All patients fulfilled the criteria for narcolepsy according to the international classification of sleep disorders (ICSD). (? positive,- negative, m male, f female, MSLT multiple sleep latency test, CSF cerebrospinal fluid)

(sampling rate 5 kHz, low pass filter 250 Hz), facilitating sleep scoring (American Academy of Sleep Medicine 2007) during fMRI acquisition. Subjects were instructed to relax, remain motionless and to allow themselves to fall asleep. The experiments were performed in the evening (starting from approximately 7 p.m.) and room lightning was diminished during the recordings. EEG Scoring and Artifact Correction Sleep graphoelements (K-complexes, vertex sharp waves and sleep spindles) were manually marked. Artifact periods with eye blinks, muscle activation or movements visible in the EEG traces were also marked manually, and these EEG periods as well as sleep graphoelements were excluded

from further analysis. EEGs were sleep scored according to the AASM criteria (American Academy of Sleep Medicine 2007) and cut into segments for each sleep stage: wakefulness (W) and N1, N2 and N3 sleep. EEG Data Pre-Processing After MRI-artifact correction, the EEG was re-referenced to common reference, downsampled to 250 Hz and bandpass filtered between 1 and 40 Hz. The filter was applied in order to assure methodological comparison with other studies (i.e. Britz et al. 2010). For each subject, EEG epochs of similar length per sleep stage were exported. All local maxima of global field power (GFP) were identified and marked. The GFP is a measure of the

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Brain Topogr

strength of a scalp potential and is based on potential differences between all electrodes at each sampling point, leading to a scalar value of field strength for each sampling point (Skrandies 1989). vffiffiffiffiffiffiffiffiffiffiffi uP un 2 u ui t GFPu ¼ i¼1 n (n = number of electrodes, u = measured voltage, i = electrode i). In other words, the GFP quantifies the overall potential variance across the given set of electrodes. High GFP is associated with a stable EEG topography around its peak (Lehmann et al. 1987). Following established procedures (Lehmann et al. 1993; Wackermann et al. 1993; Koenig et al. 2002), the momentary amplitude values of each electrode at the time of a GFP peak were selected for further analysis. The momentary EEG potential fields at GFP maxima will be referred to as maps.

Microstate Parameters The sleep stage specific template maps of each group were fit competitively into the original corresponding EEG epoch of each subject/patient (Koenig et al. 1999; Brodbeck et al. 2012). At each GFP peak, the map with the highest spatial correlation was assigned, leading to a unique sequence of maps for each EEG epoch of each subject. For these concatenated sequences of maps the following parameters were calculated: –

–

–

– Topographical Atomize-Agglomerate Hierarchical Clustering (TAAHC) To obtain individual cluster maps, all exported GFP peak maps per subject and sleep stage were subjected to the topographical atomize-agglomerate hierarchical clustering (TAAHC) (Pascual-Marqui et al. 1995), a modified spatial cluster analysis, implemented in the Cartool Software (Brunet et al. 2011) (http://sites.google.com/site/cartoolcommunity). The cluster analysis allows the identification of the most dominant map topographies of a given set of maps irrespective of the GFP and the polarity of the map (Wackermann et al. 1993). The polarity of the maps can be ignored, as the underlying neuronal generators of the maps oscillate permanently and therefore produce maps with the same topography but inverted polarity (Lehmann et al. 1987). The optimal number of clusters was indicated by the minimum of the cross validation criterion, which was first introduced by Pasqual Marqui et al. (1995) as a modified version of the predictive residual variance (Murray et al. 2008). Its minimum defines the optimal number of clusters. Hereby we identified sleep stage specific maps for each group, leading to a specific set of template maps for each sleep stage (W, N1, N2, N3) and group. These sleep stage specific maps were used for calculating the parameters, as defined in the next section. Group specific clustering—as opposed to an overall clustering of all subjects, patients and healthy controls— was performed to account for possible topographic differences between the groups. As such differences were small but indeed present, we also decided to use the group specific maps for the fitting back procedure (see next and ‘‘Results’’ section).

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Mean microstate duration (MMD): Mean duration of epochs in milliseconds in which one template map is successively assigned, before switching to another. Ratio of total time covered (RTT): Percent of time covered by one template map within a given sleep stage EEG epoch. Global explained variance (GEV): Ratio of variance explained by each of the maps per sleep stage (at GFP peaks only). Transition probability (TP): Ratio (in percent) of the number of transitions from one microstate map to another over all transitions occurring within one sleep stage. To exploit map transitions we looked at each GFP maximum and the map it was assigned to. As the EEG topography can but does not need to change between two GFP peaks, a transition from one map to itself can occur and is then counted as a transition (Wackermann et al. 1993). Based on the four maps, 16 (4 9 4) pairs of transitions are possible.

As described above, all microstate parameters are based on a competitive fitting procedure at each local GFP maximum. Therefore, the frequency of GFP maxima influences the microstate parameter MMD. A high frequency of GFP peaks allows for short durations, whereas larger inter-peak intervals necessarily result in longer durations. Spatial Correlation of Map Topographies The spatial correlations of maps were compared using the following algorithm implemented in the cartool software: n P ðui  vi Þ i¼1 C ¼ sffiffiffiffiffiffiffiffiffiffiffi sffiffiffiffiffiffiffiffiffiffiffi n n P P u2i  v2i i¼1

i¼1

(c = spatial correlation, n = number of electrodes, u = measured voltage map u, v = measured voltage map v, i = electrode i). All sleep stage specific maps were labeled according to their highest similarity to one of the corresponding wake maps of the given group.

Brain Topogr

Control Group Maps

Narcolepsy Group Maps

W

W

N1

N1 1.00

0.80

0.97

0.92

0.99

0.82

0.96

0.96

0.93

0.66

0.95

0.88

0.89

0.91

0.97

0.96

D

A

0.89

N2

N2 0.98

0.88

0.76

0.70

N3

N3 0.72

A

0.94

B

0.92

C

B

C

D

E

Fig. 1 Comparison of sleep stage specific map topographies of control and patient group Microstate maps of wake and sleep stages N1 to N3 (rows) for both groups (left: control group, right: patient group) sorted in columns according to their similarity to wake maps. The spatial correlation to the corresponding group specific map in

wakefulness is given underneath each map (i.e. AN1_c to Aw_c, BN2_c to BW_C, AN1_p to Aw_p, BN2_p to BW_p etc.). During wakefulness, N1 and N2, four dominant maps were found in both groups. In N3, a fifth map (E) was present in the patient group but not in controls

Statistical Comparison

Spatial Correlations of the Clustered Maps over All Sleep Stages

Statistics on microstate parameters between both groups were performed using two-sample t tests. We applied Bonferroni correction because of multiple testing. The number of performed t tests was 16 for the parameters GEV, RTT and MMD and 4 for the parameter global-fieldpower Peaks per second (PPS).

Results EEG Healthy Controls All 32 subjects reached W, N1 and N2, and 19 of these also N3. None of the control subjects reached REM sleep within the recording of about one hour. On average, the duration of the analyzed EEG epochs was 30 2000 (±10 , Min: 10 4500 , Max: 50 ). Patient Group All 12 patients reached W, 9 N1, 5 N2 and 4 N3, and three patients REM sleep. The average length of the analyzed EEG segments was 3 min (±89 ms, Min: 40 s, Max: 50 800 ).

In the control group, the clustering revealed four maps for wakefulness, N1, N2 and N3. In the patient group, four maps were found in wakefulness, N1 and N2. In N3, however, five maps were identified (Fig. 1). Four maps were labeled as A, B, C, D according to their similarity to previously published data on EEG microstates (Koenig et al. 2002; Brodbeck et al. 2012), indicating the sleep stage (W, N1, N2, N3) and group (c: controls, p: patients) in subscript letters (i.e. AW_c, AW_p, BN1_c, BN1_p, CN2_c, CN2_p, DN3_c, DN3_p). The fifth maps was labeled E. The group and sleep stage specific maps and their spatial correlation to the corresponding map of wakefulness are given in Fig. 1. The additional Map E in narcoleptic patients’ N3 showed the highest correlation with map DN3_p (68 %). In both controls and patients, the N1-specific maps showed high similarity to the wake maps. In sleep stage N2 the dissimilarity to the corresponding wake maps increased in both groups, though to a higher extent in the control group. During N3, a high similarity to wakefulness was found in the patient group (average: 93 %), which was even higher than in N1 and N2, whereas the N3-specific maps of the control group showed a lower spatial correlation (average 87 %) with their corresponding maps of wakefulness.

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Brain Topogr Table 2 Summary of microstate parameters GEV (%) Average

RTT (%) SD

Average

MMD (ms) SD

Average

GFP peaks (n) SD

PPS (n/s)

Average

SD

Average

SD

6,182

1,872

31.2

3,6

6,342

2,086

31.8

3.1

5,476

1,706

27.6

2.7

4,656

1,534

22.6

4.2

4,560

2,956

27.8

3.2

4,708

2,306

28.1

4.6

4,997

1,610

28.1

2.1

6,333

2,086

26.2

1.8

Controls W A

13.4

4.8

23.1

5.3

42.3

5.4

B

13.1

6.4

22.7

7.4

42.3

7

C

25.6

8.5

32.1

7.8

51.2

10.8

D

11.8

4.2

22.1

5.7

43.2

9.2

N1 A

12.1

3

19.5

3.2

40.6

4.1

B

11.4

7.3

20.2

6.5

41

6.9

C

26.9

6.9

39.7

6.9

55.7

7.1

D

10.3

2.7

20.6

4.1

42.2

5.7

A

19.3

3.8

30.8

4.4

61.5

7.8

B

24.2

3.3

36.7

3.9

67.9

6.7

C

11.2

3.1

17.3

3.3

51.7

6.6

D

7.8

2.5

15.2

3.7

50.4

8

N2

N3 A

15.1

3.4

22.1

3.8

76.1

15.6

B

16.8

3.1

27.1

3.7

82.7

17

C

27.4

6.1

36.5

5.2

97.2

23.1

D

7.8

2.9

14.3

3.9

69.4

16.3

A

13.0

5.8

22.5

6.9

42.4

5.7

B

14.2

9.2

24.0

9.6

44.3

9.9

C

23.3

10.5

29.4

11.2

50.4

11.3

D

15.5

10.5

24.1

8.9

47.6

16.6

A

15.5

6.2

22.3

7.8

41.9

5.0

B

17.6

7.7

27.6

7.7

44.9

6.3

C

13.5

6.2

21.6

7.4

42.0

6.7

D

21.6

28.5

6.9

46.7

6.6

Patients W

N1

5,9

N2 A

15.6

7.5

28.5

8.9

51.4

5.4

B

14.9

9.9

25.2

11.2

48.2

7.4

C

9.4

4.7

17.6

6.4

46.8

8.5

D

25.3

7.1

28.6

7.6

52.2

9.5

N3 A

8.1

4.3

16.6

5.2

B

8.7

2.5

17.6

2.2

C

22.8

4.1

25.9

5.3

67.0

8.3

D

15.3

5.0

18.9

2.8

58.4

3.9

E

16.1

2.0

21.0

3.2

59.0

6.4

52.4 54,5

6.1 5.6

Mean microstate parameters per sleep stage and map including standard deviations are reported for both the patient and control group. The average number of GFP peaks and the GFP peak frequency (PPS) are listed as group averages per sleep stage

123

Brain Topogr

NS

NS

NS

NS

A

B

C

D

0.5

N1 NS

NS

A

B

**

NS

0.1 0.0

0.1

−0.1

0.0

C

D

NS

NS

NS

A

B

C

0.5

N3 NS

NS

*

NS

NS

C

D

0.3

0.3

0.4

0.4

N2

−0.1

0.0

0.0

0.1

0.1

0.2

0.2

Global Explained Variance

0.2

0.2

0.3

0.3

0.4

0.4

0.5

Wake

D

A

B

E

Map Fig. 2 Group comparisons of global explained variance (GEV) The ordinate indicates the percentage of GEV for each map and each sleep stage (control group in blue, patient group in red, NS not significant,

*p \ 0.05, **p \ 0.01 after Bonferroni correction). Map D explains more variance in narcolepsy patients (significant for N1), map BN3 explains more variance in controls

Microstate Parameters

significantly between the groups. Only map BN3_P explained significantly less of the EEG variance compared to map BN3_c (16*p = 0.03).

A complete summary of all parameters is given in Table 2 and Figs. 2, 3, 4, 5. The corresponding p-values are listed in Table 3. In the following section, the main results of the different parameters will be summarized. Global Explained Variance (GEV) In wakefulness, the GEV of all four maps was similar in both groups, with map CW explaining most of the data variance (Fig. 2). In N1, map CN1 was still dominant in controls, whereas map DN1 explained most of the variance in patients and also significantly more than in controls (22 % vs. 10 %, 16*p \ 0.01). In N2, again map DN2 was prominent in the patient group. In the control group, it was map B. In N3 sleep, map C dominated in both groups again. The additional N3-map E showed similar GEV as the other four maps; a consequent relative GEV decrease of the other four maps could thus have been expected. However, the GEV of the maps AN3, CN3 and DN3 did not differ

Ratio Total Time (RTT) The parameters RTT and GEV depend on one another: The more time a map covers within a given data set, the more variance it is likely to explain. However, whereas a map’s GEV reflects the topographical similarity to the data (e.g. how well the map correlates with the scalp topography at each GFP-peak it is assigned to), the RTT accounts for the map’s temporal presence, not taking into account how well the data is explained at each GFP maximum. Like for GEV, there was no significant RTT difference between both groups during wakefulness. In each group map C was dominant (see Fig. 3). During sleep stage N1 and N2, group differences in single maps’ RTT were present but did not reach significance after Bonferroni correction. While in the control group map BN2_c covered most of the time in N2, map AN2_p and DN2_p were the

123

Brain Topogr

NS

NS

NS

NS

A

B

C

D

NS

NS

NS

A

B

C

D

0.0

0.0

0.1

0.1

0.2

0.2

0.3

0.3

0.4

0.4

0.5

NS

NS

NS

NS

A

B

C

NS

0.5

N3 NS

**

NS

NS

C

D

0.4

0.5

N2

−0.1

0.0

0.0

0.1

0.1

0.2

0.2

0.3

0.3

0.4

Ratio Total Time

0.6

N1

0.5

0.6

Wake

D

A

B

E

Map Fig. 3 Group comparisons of ratio total time (RTT) The ordinate indicates the RTT for each map and each sleep stage (control group in blue, patient group in red, NS not significant, *p \ 0.05 after Bonferroni correction). Map D shows a higher ratio of occupied time

in patients than controls in all sleep stages, but the effect does not reach significance after Bonferroni correction anymore. Map BN3 occupies significantly more time in controls

prominent ones in the patient group (29 % each). Due to the additional map E, a comparison of the N3 RTT is difficult. However, map BN3 covered significantly less time in the patient group (16*p \ 0.01). In both groups, map CN3 was dominant with respect to RTT.

the patient group did not differ from the other maps in N3 (52–67 ms). During wakefulness both groups showed similar microstate durations (average duration control group: 45 ms, patient group: 46 ms). During N1 map CN1 was significantly shorter in patients (mean controls: 56 ms, mean patients: 42 ms, 16*p \ 0.01), during N2 map BN2_c (16*p = 0.04).

Mean Microstate Duration (MMD) Regarding the duration of the microstates, the most prominent difference between both groups was present in sleep stage N3 (see Fig. 4). The durations of the maps A, B, C and D in N3 were shorter in the narcolepsy group than in controls, reaching significance after Bonferroni correction for map AN3_P and CN3_P (16*p \ 0.01). The increase in microstate duration of all maps from wake to N3 was detectable in both groups, but much stronger in controls than in patients. While microstates lasted on average about 81 ms (69–97 ms) in the control group in N3, their mean duration in the patient group was on average only 58 ms (52–67 ms). The length of the additional map E (59 ms) in

123

Transition Probabilities (TP) The TP during wakefulness were very similar between the two groups (see Fig. 5). In each group, transition to map C was most probable. During sleep stage N1, transitions to map CN1 were still most likely in controls, but not in patients. In patients, transitions to map BN1 and DN1 were most probable during N1, independent of the preceding map. During N2, transition to map CN2 was least likely in both groups. Transition to map DN2 was more and transition to map BN2 less probable in patients. During N3, an

Brain Topogr

NS

NS

NS

NS

A

B

C

D

80

N1 NS

NS

A

B

NS

**

30

20

20

D

N3

*

NS

NS

C

D

80

NS

150

90

N2

C

NS

**

**

NS

0

30

40

50

50

60

100

70

Mean Duration [ms]

40

40

60

50

60

80

70

100

Wake

A

B

A

B

C

D

E

Map Fig. 4 Group comparisons of mean microstate duration (MMD) The ordinate indicates the MMD in milliseconds for each map and each sleep stage (control group in blue, patient group in red, NS not significant, *p \ 0.05, **p \ 0.01 after Bonferroni correction; note adapted scales for different sleep stages). An increase of MMD from wakefulness to deep sleep was detected in both groups, much stronger

in controls though. In N3, the durations of all maps were shorter in the narcolepsy group than in controls, reaching significance after Bonferroni correction for map AN3_p and CN3_p. Single maps’ durations were also shortened in N1 and N2. The duration of the additional map E (59 ms) in the patient group did not differ from the other maps in N3 (52–67 ms)

increased probability of one map to be followed by itself could be observed in both groups. This was the case for all maps, including map EN3.

These results are based on 12 patients, 9 of which reached N1, and only 5 N2 and 4 N3.

GFP-Peaks Per Second (PPS)

Summary of Main Results

The average number of GFP-peaks in the patient group was significantly lower in wakefulness (4*p = 0.02), but not during any of the sleep stages.

–

– Discussion – We compared EEG microstate characteristics between patients with narcolepsy and healthy controls, probing whether altered NREM sleep features in narcolepsy are reflected in the EEG. We found changes fitting well to the typically disturbed sleep macrostructure in narcolepsy.

–

In wakefulness, in both healthy controls and narcolepsy patients, four standard microstate maps were identified, resembling those previously described in healthy subjects. In N3, narcoleptic patients showed a fifth dominant map, not present in the control group. In N3, the duration of all microstate maps was shorter in narcolepsy patients than in controls, reaching significance after Bonferroni correction for map AN3_P and map CN3_P. Shorter durations of individual patients’ maps were also significant in N1 and N2.

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from B

from C

from D

from D

0.2

C

D

A

B

to

C

D

A

B

to

C

D

A

B

to

C

D

to

0.0

B

A

B

C

D

A

B

to

C

from C

0.8

0.8

from B

A

B

C

D

to

A

B

C

D

to

N3 from D

from A

from B

from C

from D

from E

0.6

0.6

from A

D

to

N2

0.4 A

B

C to

D

A

B

C to

D

A

B

C to

D

A

B

C

D

to

0.0

0.2

0.2 0.0

from C

0.4

0.6 0.4 0.2 0.0

A

0.4

Transition Probability

from B

from A

0.6

from A

N1 0.8

0.8

Wake

A B C D E A B C D E A B C D E A B C D E A B C D E to

to

to

to

to

Map Fig. 5 Transition probabilities (TPs) in both groups For the maps given at the top of a column the transition likelihood to each of the maps at the bottom is given on the ordinate axis (control group in blue, patient group in red). The TPs during wakefulness were similar in both groups with the transition to map C being the most probable. During N1, transition to map CN1 was also the most likely in controls but not in patients. In narcolepsy patients, transitions to map BN1 and

DN1 were most probable irrespective of the preceding map. During N2, a transition to map CN2 was least likely in both groups. A transition to map DN2 was more and a transition to map BN2 less probable in patients. During N3, an increased probability of one map to be followed by itself could be observed in both groups. This was the case for all maps, including map EN3

–

decreased alpha- power in narcoleptics during wakefulness (Daly and Yoss 1957; Alloway et al. 1997; Saletu et al. 2004, 2005; Smit et al. 2005) have been reported in several other studies. This has been linked to increased daytime sleepiness, one of the cardinal symptoms of narcolepsy, and probably reflects a latent state instability which makes wakefulness more susceptible to sleep in narcoleptics (Kim et al. 2009). Despite the differences in GFP frequency, the MMD of all microstates did not differ between the two groups in wakefulness (Fig. 4). This is remarkable, as a lower frequency of GFP-peaks in narcolepsy would have allowed for longer MMD, as switches of EEG microstates only take place between two GFP maxima. Longer microstate durations in narcolepsy therefore would have been expected, which were not present, though. This might indicate a latent but still balanced instability within the wake state in narcolepsy, but whatever the pathology in narcolepsy is, it is not reflected in the microstate parameters during wakefulness: brain states per se appear preserved as well as their proportional expression.

–

The parameters global explained variance, ratio total time and transition probability were similar in both groups during wakefulness. Narcolepsy patients showed less GFP-peaks per second than controls during wakefulness.

Wakefulness EEG microstate features of wakeful rest were very similar for healthy subjects and patients with narcolepsy. In each group, four cluster maps were identified which showed very similar topographies (Fig. 1). For none of the four wake maps significant group differences in terms of GEV, RTT or mean duration (Figs. 2, 3, 4) were detectable. The dominance of map C for each parameter was present in both groups and transitions to map CW were most probable in both controls and subjects (Fig. 5). We found a lower GFP-peak frequency in narcoleptics compared to controls (Fig. 6). Increased power of lower frequencies (Saletu et al. 2004, 2005; Smit et al. 2005) and

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Brain Topogr Table 3 Difference in microstate parameters between narcolepsy patients and controls GEV

RTT

MMD

PPS

A

NS

NS

NS

4*p = 4*0.0053 = 0.0212

B

NS

NS

NS

C

NS

NS

NS

D

NS

NS

NS

A

NS

NS

NS

B

NS

16*p = 16*0.027 = 0.432

NS

C

NS

16*p = 16*0.0162 = 0.2592

16*p = 16*0.0001 = 0.0016

D

16*p = 16*0 = 0.0048

16*p = 16*0.0086 = 0.1376

NS

W

N1 4*p = 4*0.048 = 0.192

N2 A

NS

NS

16*p = 16*0.0082 = 0.1312

B

NS

NS

16*p = 16*0.0023 = 0.0368

C

NS

NS

NS

D

16*p = 16*0.0052 = 0.0832

16*p = 16*0.016 = 0.256

NS

A

NS

NS

16*p = 16*0.0002 = 0.0032

B

16*p = 16*0.0016 = 0.0256

16*p = 16*0.0002 = 0.0032

16*p = 16*0.016 = 0.256

C

NS

16*p = 0.02*16 = 0.32

16*p = 16*0.0005 = 0.008

D

NS

16*p = 16*0.038 = 0.608

16*p = 16*0.017 = 0.272

NS

N3 4*p = 4*0.017 = 0.068

p values of the one-sided two-sample t tests for group comparisons of all microstate parameters individually for each sleep stage and map. p values have been Bonferroni corrected, based on the number of statistical tests performed per sleep stage. The parameters which still reached significance after Bonferroni correction are marked in red. (NS not significant)

Fig. 6 Group comparison of global-field-power-Peaks per second (PPS) The y-axis shows the number of PPS for each sleep stage (control group in blue, patient group in red, NS not significant, *p \ 0.05 after Bonferroni correction). A decrease in PPS with deepening sleep was observable in healthy subjects, whereas in patients the PPS remained rather stable

N1 Sleep The N1-map topographies of both groups showed high spatial correlations with their corresponding maps of wakefulness (mean: controls 91 %; patients 92 %), indicating similar neuronal network activity during sleep stage N1 and wakefulness (Fig. 1). Regarding the microstate parameters, slight differences were present when comparing both groups: While map CN1

explained most of the variance and covered most of the time in healthy subjects, a dominance of map DN1 was found in the patient group with regard to GEV and RTT (Figs. 2, 3). In healthy subjects a high similarity between microstate characteristics of N1 sleep and wakefulness has been shown (Brodbeck et al. 2012) and can be explained by the fact that N1 (frequently intruding W) is an intermediate state between wakefulness and deeper sleep stages. However, in narcolepsy patients we found the N1 results to be even more similar to

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wake, expressed in the shorter duration of map CN1 and a GFPfrequency in N1 which is more similar to wakefulness than in controls. In addition to that, a higher GEV explained by map DN1_p was found. In a study simultaneously recording EEG and fMRI at (wakeful) rest, microstate map D was related to fMRI-defined networks known to be involved in attentional tasks (Britz et al. 2010). It is conceivable that the emphasis of map D in narcolepsy patients might reflect a higher persistence of activity in (e.g. attentional) networks normally detected in wakefulness. All these findings might support the assumption that all vigilance states are unstable in narcolepsy and that wakefulness and N1 sleep are nearly indistinguishable in narcolepsy, with wakefulness fluctuating more towards light sleep and N1 to wakefulness than in healthy controls. N2 Sleep In healthy subjects, map BN2_c at the expense of map CN2_c was of increasing prominence during sleep stage N2 with respect to the parameters GEV, RTT, MMD and TP (Figs. 2, 3, 4, 5). In patients, however, especially map DN2_p gained importance in N2. The number of GFP-peaks/s did not differ between both groups during sleep stage N2. Still, MMD of map BN2_p was significantly shorter in narcolepsy patients than in controls (Fig. 6). Also map AN2_P and map CN2_p showed a tendency towards shorter durations in patients, although not reaching significance after Bonferroni correction (Fig. 4). As hypothesized for sleep stage N1, the lack of increasing MMD with increase in sleep depth in the narcolepsy group might reflect a higher degree of state instability in N2, e.g. a higher number of map switches.

As in healthy subjects (Brodbeck et al. 2012), transitions between different EEG microstates in narcolepsy patients showed higher probability for one map to be followed by itself and to remain in the same state during deep sleep compared to other sleep stages (Fig. 5). This reflects a gain in stability of microstates and potentially of neuronal networks active during deep sleep. MMDs were all shorter in narcolepsy patients than in controls, reaching significance after Bonferroni correction for map AN3_P and map CN3_P (Fig. 4, sample size in N3: only 4 patients compared to 19 healthy subjects). The shorter MMDs are unlikely the result of the additional map EN3_p alone as individual maps exhibited a shorter duration in N1 and N2, i.e. sleep stages in which the number of dominant maps was identical in the patient and control groups. To test whether the occurrence of a fifth map might have led to shortened MMD, we performed an additional analysis by fitting back the four control group’s N3-specific maps into the patients’ N3 EEG. Again, all maps showed shorter durations in patients compared to controls (Fig. 7). The shortened microstates in narcolepsy patients during N3 are therefore not purely a ‘‘statistical effect’’ caused by the fifth map in patients. Shorter MMD in narcolepsy patients might reflect the inability to maintain physiological vigilance states for as long as in the healthy condition. The gain in stability during N3 observable in healthy subjects (Brodbeck et al. 2012) is not achieved by narcolepsy patients. More frequent transitions between maps, which are the consequence of shorter MMD, may also lead to EEG fragmentation and both micro- and macrostructural changes in sleep architecture (Lamphere et al. 1989).

General Discussion N3 Sleep Being aware of the fact that only four patients reached deep sleep, the most striking difference between NREM sleep of patients and controls was the additional cluster map identified for N3 sleep in narcolepsy patients (Fig. 1). Healthy subjects showed four cluster maps with variable spatial similarity to the wake maps. The patients showed five maps, four of which were very similar to the wake maps. The dissociation into five dominant maps might represent the microstructural basis of the well-known macro-structurally fragmented nocturnal sleep typical for narcolepsy (Lamphere et al. 1989). The additional map EN3_p showed similar microstate parameters as map DN3_p. The GEV, RTT and MMD of these two maps were nearly identical. The networks underlying the generation of map DN3 might be variable and unstable in narcolepsy, leading to splitting of map DN3_p into two maps DN3_p and EN3_p. Maps BN3 was less prominent in narcolepsy patients with respect to the parameters GEV and RTT.

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While EEG microstates showed many commonalities between narcolepsy patients and healthy controls during wakefulness, differences increased with deepening of sleep, most prominent for MMD (Fig. 4). The additional fifth map in N3 sleep in the patient group reflects higher topographic variability and might hereby indicate general network instability, leading to the macrostructural symptom of fragmented nocturnal sleep. The parameters GEV and RTT showed only slight differences between the two groups (Figs. 2, 3). Especially map D seemed to play a more important role in narcolepsy patients than in healthy subjects, whereas the dominance of map C found in healthy subjects for the sleep stages N1 and N3 was not similarly obvious in patients. In a simultaneous EEG- fMRI study map C was correlated with increased frontal activity (Britz et al. 2010). Especially frontal cortical regions influence the generation of slow waves (\1 Hz) (Massimini et al. 2004; Dang-Vu et al. 2008), so that the loss of importance of map C could indicate a lower sleep depth

Brain Topogr

(corresponding to a lack of slow waves) in narcolepsy patients. Interestingly, frontal pathology was reported for narcolepsy patients and discussed as a neuroanatomical correlate of clinical features (Kaufmann et al. 2002; Brenneis et al. 2005; Joo et al. 2011). A simultaneous EEG-fMRI study on BOLD correlates of EEG microstate maps in wakeful rest linked Map D to attentional networks (Britz et al. 2010). The increased dominance of map D in narcolepsy patients during sleep could therefore indicate higher persistence of activity in attentional networks compared to healthy controls. The probability of one map to be followed by itself increased in deep sleep in both groups, reflecting a gain in stability of neuronal activity from wakefulness to deep sleep. However, in the patient group, this gain in stability was less prominent. This, together with the other main findings (shorter MMD, altered patterns of microstate parameters RTT and GEV and the additional map E in N3 sleep in narcoleptic patients) point to a generally higher similarity of NREM sleep to wakefulness in narcolepsy, probably due to an instability of all sleep states. With the caveat imposed upon the results by the limited sample size in deeper sleep, we interpret our results as a possible microstructural correlate of the fragmented nocturnal sleep and increased daytime sleepiness in narcolepsy and conclude that the pathology in narcolepsy is also present during NREM sleep resulting in pathological transitions not only between wakefulness and sleep or REM- and NREM-sleep, but also within NREM sleep itself. Limitations While we observed significant differences between patients and controls, the number of patients was low, especially of those reaching deep sleep stages (also inherent in the pathology of narcolepsy). Microstate parameters were also more widely scattered in the narcolepsy group compared to controls. A larger number of patients would have been desirable but is difficult to achieve due to the low prevalence of narcolepsy in Germany (26–50/100000; Deutsche Gesellschaft fu¨r Neurologie 2012) and often limited mobility precluding patient recruitment from a very large area. The restricted patient numbers might have led to false negative or false positive results and the interpretation of our findings need to be appreciated with care given the limited sample size. Patients’ characteristics were not homogeneous in all respects. Increased daytime sleepiness was reported by all of the subjects, other symptoms of narcolepsy however varied. This is not surprising as only about 15–30 % of narcolepsy patients show the classical constellation of the narcoleptic tetrad (Yoss and Daly 1957), i.e. cataplexy, sleep paralysis, hypnagogic hallucinations, and excessive daytime sleepiness. More problematic might be possible changes in sleep architecture due to centrally acting drugs (i.e. Foral et al.

2011) (see Table 1). A medication-naı¨ve patient group would have been desirable, but not all patients were willing to pause their drugs due to the associated high strain. The recruitment of patients turned out to be even more difficult than we had expected. Nevertheless, we were able to derive sensible conclusions from this data set which we hence chose to report including for when sample sizes were low, like in N3, which we explicitly mention in association with these results. To which extent the different therapies might have influenced our results remains unclear and poses a limitation. A possible interference of the different age in both groups cannot be ruled out (mean age control group: 23 years, patient group: 43 years). Changes in sleep macrostructure with age are well established. Especially total sleep time, sleep efficiency, percentage of slow wave and REM sleep decrease, whereas sleep latency and the percentage of sleep stage N1 and N2 increase with age (Ohayon et al. 2004; Kryger et al. 2011). Assuming an interaction between sleep micro- and macrostructure, agerelated changes in EEG microstate parameters are conceivable. Hence, we examined whether there is a correlation between the parameter MMD and age by performing a regression analysis for both the patient and control groups (Fig. 8). Of the 33 fitted lines, only one had a slope significantly (p \ 0.05) different from 0, and after correction for multiple testing, this slope was also not significant (Bonferroni: 33*p = 0.40). We conclude that there is no evidence that age differences contributed to the observed differences in MMD between the groups (Fig. 8). As this study is part of a set of experiments initially designed as a simultaneous EEG-fMRI study on sleep physiology, EEG recordings took place during MRI measurements. The setting therefore differs from usual sleep conditions, and changes in sleep architecture like longer sleep latency and more frequent interruptions of sleep might be possible. However, a previous analysis of our data of 71 healthy subjects showed that subjects sleep surprisingly well during fMRI scanning, and sleep propensity is high. One third of subjects lost wakefulness within the first 3 min of fMRI scanning, and the probability of healthy subjects entering N3 sleep is about 10 % after 20 min of scanning (Tagliazucchi and Laufs 2014). The MRI setting is also unlikely to explain group differences, as the experimental setting was identical for both groups. EEG quality within the MRI environment for the present purpose and in the present setting is sufficient given the experience and advancements in the field (Laufs et al. 2008; Laufs 2012) and that even high frequency information can be extracted reliably from EEG recorded during fMRI scanning (Giraud et al. 2007; Freyer et al. 2009; Tagliazucchi et al. 2012). Previous studies analyzing microstates of EEGs recorded during fMRI scanning (Britz et al. 2010; Musso et al. 2010; Brodbeck et al. 2012; Yuan et al. 2012) reported data also

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well in line with ‘‘canonical’’ microstate characteristics obtained from ‘‘conventional’’ EEG (Koenig et al. 2002). The use of a bandwidth filter from 1–40 Hz might also be a limitation as slow waves with frequencies below 1 Hz are important for the initiation and maintenance of deep sleep and we hence may have missed related effects. Therefore, the N3-results in particular need to be appreciated with this filtering in mind. However, like in the MRI setting, the use of the filter is unlikely to cause false positive group differences as the method for analyzing the EEG and performing microstate analysis was identical for both groups. Extending the analysis to REM-sleep would have been desirable as REM sleep is especially concerned in narcolepsy. However due to the experimental setup with simultaneous MRI recordings the recording time was limited to approximately 50 min which was not sufficient for the healthy control subjects to reach a first REM-sleep phase. We did not have hypocretin levels for all our patients and hence could not test for a link between the observed microstate parameters and hypocretin-deficiency, which is considered the relevant pathology in narcolepsy with cataplexy (de Lecea et al. 1998; Sakurai et al. 1998; Burgess and Scammell 2012). In addition, our cohort included also patients with narcolepsy without cataplexy, i.e. a sub-type of narcolepsy, when hypocretin-deficiency is sometimes not observed (Kanbayashi et al. 2002; Mignot et al. 2002). Hence, it remains enigmatic whether the alterations in microstate parameters we observed are a direct consequence of reduced hypocretin-levels or, alternatively, reflect a different underlying mechanism characteristic for all narcolepsy patients. However, our findings

mainly focus on pathological switches within NREM sleep itself and not between REM- and NREM sleep. This instability within NREM sleep might be independent of REMsleep associated phenomena such as cataplexies.

Fig. 7 Group comparison of mean microstate duration when fitting an equal number of maps to patients and controls In order to test whether shorter microstate durations resulted from the fitting of five instead of four maps, we performed an additional analysis and fitted back the control group’s 4 sleep stage specific maps into the corresponding EEG of narcolepsy patients: the control group’s wake maps were fitted back into the patients’ wake EEG, the control

group’s N1 maps into the patients’ N1 EEG and so on. The ordinate indicates the MMD in milliseconds for each map and each sleep stage (control group in blue, patient group in red). It can be seen that, again, during deep sleep all mean microstate durations were much shorter in patients compared to healthy controls. Therefore, the shorter MMDs in narcolepsy patients during N3 are not a pure effect of the additional map EN3_p, which is only present in patients

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Conclusion Narcolepsy can be understood as a model disease for pathological state transitions. In this condition, EEG microstates parameters of sleep are altered when compared to healthy controls. The observed changes are in line with macrostructural symptoms of sleep disturbance characteristic of this pathology. Beyond the insights into the specific condition studied here, our results demonstrate that EEG microstate analysis can yield patho-physiological insights into conditions with altered brain states. Acknowledgments This work was funded by the Bundesministerium fu¨r Bildung und Forschung (Grant 01 EV 0703) and the LOEWE Neuronale Koordination Forschungsschwerpunkt Frankfurt (NeFF). We thank Brooks Ferebee (Institute of Mathematics, Goethe University Frankfurt) for statistical support and are indebted to all our study participants. We especially thank Professor Geert Mayer for referring patients to our study. Ethical Standards Written informed consent was obtained by all subjects/patients and the study was approved by the local ethics committee.

Appendix See Figs 7 and 8

Brain Topogr

Fig. 8 Correlation between MMD and age of subjects/patients It shows the relationship between age and MMD for all combinations of sleep stage (first to fourth row), map (left to right), and subject group (blue: controls, red: patients). As can be seen, there is no indication of a correlation between age and MMD. As a check, we also fitted regression lines in each case. Of the 33 fitted lines, only one had a

slope significantly (p \ 0.05) different from 0, and after correction for multiple testing, this slope was also not significant (Bonferroni: 33*p = 0.40). We conclude that there is no evidence that age differences contributed to the observed differences in MMD between the groups

References

Brodbeck V, Kuhn A, von Wegner F, Morzelewski A, Tagliazucchi E, Borisov S, Michel CM, Laufs H (2012) EEG microstates of wakefulness and NREM sleep. Neuroimage 62:2129–2139 Brunet D, Murray MM, Michel CM (2011) Spatiotemporal analysis of multichannel EEG: CARTOOL. Comput Intell Neurosci 2011:813870 Burgess CR, Scammell TE (2012) Narcolepsy: neural mechanisms of sleepiness and cataplexy. J Neurosci 32:12305–12311 Daly DD, Yoss RE (1957) Electroencephalogram in narcolepsy. Electroencephalogr Clin Neurophysiol 9:109–120 Dang-Vu TT, Schabus M, Desseilles M, Albouy G, Boly M, Darsaud A, Gais S, Rauchs G, Sterpenich V, Vandewalle G, Carrier J, Moonen G, Balteau E, Degueldre C, Luxen A, Phillips C, Maquet P (2008) Spontaneous neural activity during human slow wave sleep. Proc Natl Acad Sci USA 105:15160–15165 Dantz B, Edgar DM, Dement WC (1994) Circadian rhythms in narcolepsy: studies on a 90 min day. Electroencephalogr Clin Neurophysiol 90:24–35 de Lecea L, Kilduff TS, Peyron C, Gao X, Foye PE, Danielson PE, Fukuhara C, Battenberg EL, Gautvik VT, Bartlett FS, Frankel WN, van den Pol AN, Bloom FE, Gautvik KM, Sutcliffe JG

Adamantidis AR, Zhang F, Aravanis AM, Deisseroth K, Lecea L (2007) Neural substrates of awakening probed with optogenetic control of hypocretin neurons. Nature 450:420–424 Alloway CE, Ogilvie RD, Shapiro CM (1997) The alpha attenuation test: assessing excessive daytime sleepiness in narcolepsycataplexy. Sleep 20:258–266 American Academy of Sleep Medicine (2005) The international classification of sleep disorders. Diagnostic and coding manual, 2nd edn. American Academy of Sleep Medicine, Darien American Academy of Sleep Medicine (2007) The AASM manual for the scoring of sleep and associated events: rules terminology and technical specifications. American Academy of Sleep Medicine, Darien Brenneis C, Brandauer E, Frauscher B, Schocke M, Trieb T, Poewe W, Ho¨gl B (2005) Voxel-based morphometry in narcolepsy. Sleep Med 6:531–536 Britz J, van de Ville D, Michel CM (2010) BOLD correlates of EEG topography reveal rapid resting-state network dynamics. Neuroimage 52:1162–1170

123

Brain Topogr (1998) The hypocretins: hypothalamus-specific peptides with neuroexcitatory activity. Proc Natl Acad Sci USA 95:322–327 Deutsche Gesellschaft fu¨r Neurologie (2012). S1-Leitlinie Narkolepsie Dierks T, Jelic V, Julin P, Maurer K, Wahlund LO, Almkvist O, Strik WK, Winblad B (1997) EEG-microstates in mild memory impairment and Alzheimer’s disease: possible association with disturbed information processing. J Neural Transm 104:483–495 Eggermann E, Serafin M, Bayer L, Machard D, Saint-Mleux B, Jones BE, Mu¨hlethaler M (2001) Orexins/hypocretins excite basal forebrain cholinergic neurones. Neuroscience 108:177–181 Foral P, Knezevich J, Dewan N, Malesker M (2011) Medicationinduced sleep disturbances. Consult Pharm 26:414–425 Freyer F, Becker R, Anami K, Curio G, Villringer A, Ritter P (2009) Ultrahigh-frequency EEG during fMRI: pushing the limits of imaging-artifact correction. Neuroimage 48:94–108 Giraud AL, Kleinschmidt A, Poeppel D, Lund TE, Frackowiak RS, Laufs H (2007) Endogenous cortical rhythms determine cerebral specialization for speech perception and production. Neuron 56:1127–1134 Hudson JI, Pope HG, Sullivan LE, Waternaux CM, Keck PE, Broughton RJ (1992) Good sleep, bad sleep: a meta-analysis of polysomnographic measures in insomnia, depression, and narcolepsy. Biol Psychiatry 32:958–975 Jahnke K, von Wegner F, Morzelewski A, Borisov S, Maischein M, Steinmetz H, Laufs H (2012) To wake or not to wake? The twosided nature of the human K-complex. Neuroimage 59:1631–1638 Joo EY, Jeon S, Lee M, Kim ST, Yoon U, Koo DL, Lee J-M, Hong SB (2011) Analysis of cortical thickness in narcolepsy patients with cataplexy. Sleep 34:1357–1364 Kanbayashi T, Inoue Y, Chiba S, Aizawa R, Saito Y, Tsukamoto H, Fujii Y, Nishino S, Shimizu T (2002) CSF hypocretin-1 (orexinA) concentrations in narcolepsy with and without cataplexy and idiopathic hypersomnia. J Sleep Res 11:91–93 Kaufmann C, Schuld A, Pollma¨cher T, Auer DP (2002) Reduced cortical gray matter in narcolepsy: preliminary findings with voxel-based morphometry. Neurology 58:1852–1855 Khatami R, Landolt H-P, Achermann P, Re´tey JV, Werth E, Mathis J, Bassetti CL (2007) Insufficient non-REM sleep intensity in narcolepsy-cataplexy. Sleep 30:980–989 Kikuchi M, Koenig T, Munesue T, Hanaoka A, Strik W, Dierks T, Koshino Y, Minabe Y (2011) EEG microstate analysis in drugnaive patients with panic disorder. PLoS ONE 6:e22912 Kim JW, Shin H-B, Robinson PA (2009) Quantitative study of the sleep onset period via detrended fluctuation analysis: normal versus narcoleptic subjects. Clin Neurophysiol 120:1245–1251 Kindler J, Hubl D, Strik WK, Dierks T, Koenig T (2011) Resting-state EEG in schizophrenia: auditory verbal hallucinations are related to shortening of specific microstates. Clin Neurophysiol 122:1179–1182 Koenig T, Lehmann D, Merlo MC, Kochi K, Hell D, Koukkou M (1999) A deviant EEG brain microstate in acute, neurolepticnaive schizophrenics at rest. Eur Arch Psychiatry Clin Neurosci 249:205–211 Koenig T, Prichep L, Lehmann D, Sosa PV, Braeker E, Kleinlogel H, Isenhart R, John ER (2002) Millisecond by millisecond, year by year: normative EEG microstates and developmental stages. Neuroimage 16:41–48 Kryger MH, Roth T, Dement WC (2011) Principles and practice of sleep medicine, 5th edn. Elsevier, Philadelphia Lamphere J, Young D, Roehrs T, Wittig RM, Zorick F, Roth T (1989) Fragmented sleep, daytime somnolence and age in narcolepsy. Clin Electroencephalogr 20:49–54 Laufs H, Daunizeau J, Carmichael DW, Kleinschmidt A (2008) Recent advances in recording electrophysiological data

123

simultaneously with magnetic resonance imaging. Neuroimage 40:515–528 Laufs H (2012) A personalized history of EEG-fMRI integration. Neuroimage 62:1056–1067 Lee S-H, Dan Y (2012) Neuromodulation of brain states. Neuron 76:209–222 Lehmann D, Ozaki H, Pal I (1987) EEG alpha map series: brain micro-states by space-oriented adaptive segmentation. Electroencephalogr Clin Neurophysiol 67:271–288 Lehmann D, Wackermann J, Michel CM, Koenig T (1993) Spaceoriented EEG segmentation reveals changes in brain electric field maps under the influence of a nootropic drug. Psychiatry Res 50:275–282 Lehmann D, Faber PL, Galderisi S, Herrmann WM, Kinoshita T, Koukkou M, Mucci A, Pascual-Marqui RD, Saito N, Wackermann J, Winterer G, Koenig T (2005) EEG microstate duration and syntax in acute, medication-naive, first-episode schizophrenia: a multi-center study. Psychiatry Res 138:141–156 Lehmann D, Pascual-Marqui RD, Michel CM (2009) EEG Microstates, Vol. 4, No. 3, 7632. http://www.scholarpedia.org/article/ EEG_microstates. Accessed 24 Nov 2013 Massimini M, Huber R, Ferrarelli F, Hill S, Tononi G (2004) The sleep slow oscillation as a traveling wave. J Neurosci 24:6862–6870 Methippara MM, Alam MN, Szymusiak R, McGinty D (2000) Effects of lateral preoptic area application of orexin-A on sleepwakefulness. NeuroReport 11:3423–3426 Mignot E, Lammers GJ, Ripley B, Okun M, Nevsimalova S, Overeem S, Vankova J, Black J, Harsh J, Bassetti C, Schrader H, Nishino S (2002) The role of cerebrospinal fluid hypocretin measurement in the diagnosis of narcolepsy and other hypersomnias. Arch Neurol 59:1553–1562 Montplaisir J, Billiard M, Takahashi S, Bell IR, Guilleminault C, Dement WC (1978) Twenty-four-hour recording in REMnarcoleptics with special reference to nocturnal sleep disruption. Biol Psychiatry 13:73–89 Murray MM, Brunet D, Michel CM (2008) Topographic ERP analyses: a step-by-step tutorial review. Brain Topogr 20(4):249–264 Musso F, Brinkmeyer J, Mobascher A, Warbrick T, Winterer G (2010) Spontaneous brain activity and EEG microstates. A novel EEG/fMRI analysis approach to explore resting-state networks. Neuroimage 52:1149–1161 Nobili L, Besset A, Ferrillo F, Rosadini G, Schiavi G, Billiard M (1995) Dynamics of slow wave activity in narcoleptic patients under bed rest conditions. Electroencephalogr Clin Neurophysiol 95:414–425 Ohayon MM, Carskadon MA, Guilleminault C, Vitiello MV (2004) Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals: developing normative sleep values across the human lifespan. Sleep 27:1255–1273 Pascual-Marqui RD, Michel CM, Lehmann D (1995) Segmentation of brain electrical activity into microstates: model estimation and validation. IEEE Trans Biomed Eng 42:658–665 Sakurai T, Amemiya A, Ishii M, Matsuzaki I, Chemelli RM, Tanaka H, Williams SC, Richarson JA, Kozlowski GP, Wilson S, Arch JR, Buckingham RE, Haynes AC, Carr SA, Annan RS, McNulty DE, Liu WS, Terrett JA, Elshourbagy NA, Bergsma DJ, Yanagisawa M (1998) Orexins and orexin receptors: a family of hypothalamic neuropeptides and G protein-coupled receptors that regulate feeding behavior. Cell 92:1 (page following 696) Saletu M, Anderer P, Saletu-Zyhlarz GM, Mandl M, Arnold O, Zeitlhofer J, Saletu B (2004) EEG-tomographic studies with LORETA on vigilance differences between narcolepsy patients and controls and subsequent double-blind, placebo-controlled studies with modafinil. J Neurol 251:1354–1363

Brain Topogr Saletu MT, Anderer P, Saletu-Zyhlarz GM, Mandl M, Arnold O, Nosiska D, Zeitlhofer J, Saletu B (2005) EEG-mapping differences between narcolepsy patients and controls and subsequent double-blind, placebo-controlled studies with modafinil. Eur Arch Psychiatry Clin Neurosci 255:20–32 Saper CB, Fuller PM, Pedersen NP, Lu J, Scammell TE (2010) Sleep state switching. Neuron 68:1023–1042 Saper CB (2013) The neurobiology of sleep. Continuum (Minneap Minn) 19:19–31 Schlegel F, Lehmann D, Faber PL, Milz P, Gianotti LRR (2012) EEG microstates during resting represent personality differences. Brain Topogr 25:20–26 Skrandies W (1989) Data reduction of multichannel fields: global field power and principal component analysis. Brain Topogr 2:73–80 Smit AS, Droogleever Fortuyn HA, Eling PATM, Coenen AML (2005) Diurnal spectral EEG fluctuations in narcoleptic patients during rest and reaction time tasks. J Sleep Res 14:455–461 Strelets V, Faber PL, Golikova J, Novototsky-Vlasov V, Koenig T, Gianotti LRR, Gruzelier JH, Lehmann D (2003) Chronic schizophrenics with positive symptomatology have shortened EEG microstate durations. Clin Neurophysiol 114:2043–2051 Strik WK, Dierks T, Becker T, Lehmann D (1995) Larger topographical variance and decreased duration of brain electric microstates in depression. J Neural Transm Gen Sect 99: 213–222

Strik WK, Chiaramonti R, Muscas GC, Paganini M, Mueller TJ, Fallgatter AJ, Versari A, Zappoli R (1997) Decreased EEG microstate duration and anteriorisation of the brain electrical fields in mild and moderate dementia of the Alzheimer type. Psychiatry Res 75:183–191 Tagliazucchi E, von Wegner F, Morzelewski A, Brodbeck V, Laufs H (2012) Dynamic BOLD functional connectivity in humans and its electrophysiological correlates. Front Hum Neurosci 6:339 Tagliazucchi E, Laufs H (2014) Decoding wakefulness levels from typical fMRI resting state data reveals reliable drifts between wakefulness and sleep. Neuron 82:695–708 Tagliazucchi E, Von Wegner F, Morzelewski A, Brodbeck V, Borisov S, Jahnke K, Laufs H (2013) Large-scale brain functional modularity is reflected in slow electroencephalographic rhythms across the human non-rapid eye movement sleep cycle. Neuroimage 70:327–339 Wackermann J, Lehmann D, Michel CM, Strik WK (1993) Adaptive segmentation of spontaneous EEG map series into spatially defined microstates. Int J Psychophysiol 14:269–283 Yoss RE, Daly DD (1957) Criteria for the diagnosis of the narcoleptic syndrome. Proc Staff Meet Mayo Clin 32:320–328 Yuan H, Zotev V, Phillips R, Drevets WC, Bodurka J (2012) Spatiotemporal dynamics of the brain at rest-exploring EEG microstates as electrophysiological signatures of BOLD resting state networks. Neuroimage 60:2062–2072

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