Low-resolution brain electromagnetic tomography (LORETA) identifies brain regions linked to psychometric performance under modafinil in narcolepsy

Psychiatry Research: Neuroimaging 154 (2007) 69 – 84 www.elsevier.com/locate/psychresns

Low-resolution brain electromagnetic tomography (LORETA) identifies brain regions linked to psychometric performance under modafinil in narcolepsy Michael Saletu a , Peter Anderer b , Heribert V. Semlitsch b , Gerda Maria Saletu-Zyhlarz b , Magdalena Mandl b , Josef Zeitlhofer a , Bernd Saletu b,⁎ a b

Department of Neurology, Medical University of Vienna, Vienna, Austria Department of Psychiatry, Medical University of Vienna, Vienna, Austria

Received 11 January 2006; received in revised form 15 March 2006; accepted 2 April 2006

Abstract Low-resolution brain electromagnetic tomography (LORETA) showed a functional deterioration of the fronto-temporo-parietal network of the right hemispheric vigilance system in narcolepsy and a therapeutic effect of modafinil. The aim of this study was to determine the effects of modafinil on cognitive and thymopsychic variables in patients with narcolepsy and investigate whether neurophysiological vigilance changes correlate with cognitive and subjective vigilance alterations at the behavioral level. In a double-blind, placebo-controlled crossover design, EEG-LORETA and psychometric data were obtained during midmorning hours in 15 narcoleptics before and after 3 weeks of placebo or 400 mg modafinil. Cognitive investigations included the Pauli Test and complex reaction time. Thymopsychic/psychophysiological evaluation comprised drive, mood, affectivity, wakefulness, depression, anxiety, the Symptom Checklist 90 and critical flicker frequency. The Multiple Sleep Latency Test (MSLT) and the Epworth Sleepiness Scale (ESS) were performed too. Cognitive performance (Pauli Test) was significantly better after modafinil than after placebo. Concerning reaction time and thymopsychic variables, no significant differences were observed. Correlation analyses revealed that a decrease in prefrontal delta, theta and alpha-1 power correlated with an improvement in cognitive performance. Moreover, drowsiness was positively correlated with theta power in parietal and medial prefrontal regions and beta-1 and beta-2 power in occipital regions. A less significant correlation was observed between midmorning EEG LORETA and the MSLT; between EEG LORETA and the ESS, the correlation was even weaker. In conclusion, modafinil did not influence thymopsychic variables in narcolepsy, but it significantly improved cognitive performance, which may be related to medial prefrontal activity processes identified by LORETA. © 2006 Elsevier Ireland Ltd. All rights reserved. Keywords: Sleepiness; Psychostimulant; Electroencephalography; Cognition; Prefrontal activity

1. Introduction ⁎ Corresponding author. Section of Sleep Research and Pharmacopsychiatry, Department of Psychiatry, Medical University of Vienna, Währinger Gürtel 18-20, A-1090 Vienna, Austria. Tel.: +43 1 40400 3637/3683; fax: +43 1 4025909. E-mail address: [email protected] (B. Saletu).

Neuroimaging studies, including positron emission tomography (PET) (Wu et al., 1991; Thomas et al., 2000) and functional magnetic resonance imaging (FMRI) (Portas et al., 1998), suggest that activity levels

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in brain systems involved in arousal and attention may influence cognitive performance following total sleep deprivation (TSD). The serial addition/subtraction task used by Thomas et al. (2000) required both arithmetic working memory and attentional resources, and it showed decreased activation in related regions such as the prefrontal cortex, the inferior parietal lobe and the anterior cingulate gyrus. This suggests that brain regions involved in working memory and arithmetic might be vulnerable to vigilance alterations. In narcolepsy, excessive daytime sleepiness (EDS) is the most disabling feature at the behavioral level and responsible for an overall disruption of normal daytime functioning (Overeem et al., 2001). At the neurophysiological level, it is based on a deterioration of vigilance. Vigilance has been defined as the availability and grade of organization of man's adaptive behavior, which is in turn dependent upon the dynamic state of the neuronal network (Head, 1923). Since the early 1980s, several studies have attempted to demonstrate the relationship between sleepiness and cognitive performance in narcoleptic subjects (Broughton et al., 1982; Aguirre et al., 1985; Levander and Sachs, 1985; Godbout and Montplaisir, 1986; Ollo et al., 1987; Rogers and Rosenberg, 1990; Pollack et al., 1992; Smith et al., 1992; Henry et al., 1993). The findings obtained generally failed to demonstrate significant performance differences to controls and remained inconclusive in the question of whether performance decrements in narcolepsy are explained by attentional or organic cognitive mechanisms. Investigations using functional neuroimaging techniques, including PET (Cohen et al., 1988; Buchsbaum et al., 1990; Pardo et al., 1991), single photon emission computed tomography (SPECT) (Rezai et al., 1993) and regional cerebral blood flow (rCBF) (Roland and Friberg, 1985; Deutsch et al., 1987; Posner and Petersen, 1990; Pardo et al., 1991), concluded that the right hemisphere (and in particular the right parietal, temporal and prefrontal regions) plays a prominent role in the maintenance of a vigilance state (Posner and Petersen, 1990). In the last decade, electrophysiological neuroimaging techniques such as EEG low-resolution brain electromagnetic tomography (LORETA) were developed to identify brain regions that are involved in neuropsychiatric disorders and are the targets of therapeutic drug action (Pascual-Marqui et al., 1999; Saletu et al., 2002; Weber et al., 2005). Modafinil is a central wake-promoting psychostimulant with a lower risk of CNS, cardiovascular or gastrointestinal adverse events, abuse and dependence (Mitler et al., 2000). As early as in 1986, the first human

pharmaco-EEG studies in normal elderly subjects demonstrated a vigilance-promoting effect of modafinil (CRL 40476), characterized by an increase in alpha and slow beta activity and a decrease in delta, theta and very fast beta activity as compared with placebo, which was also demonstrated behaviorally by psychometry (Saletu et al., 1986) and confirmed later by clinical trials in alcoholic organic brain syndrome (Saletu et al., 1993). The efficacy, safety and tolerability of modafinil in narcolepsy patients have been demonstrated in controlled trials (US Modafinil in Narcolepsy Multicenter Study Group, 1998, 2000). In a recent study, LORETA objectified a functional deterioration of the frontotemporo-parietal network of the right hemispheric vigilance system in narcolepsy and a therapeutic effect of modafinil on the left hemisphere, which is less affected by the disease (Saletu et al., 2004). The aim of the present study was (1) to examine the effects of modafinil compared with placebo on cognitive performance and thymopsychic variables in narcoleptic patients, (2) to correlate significant and clinically relevant psychometric changes in midmorning hours with neurophysiological alterations measured by EEG LORETA at the same time, and (3) to explore the relation between these EEG-LORETA findings and MSLT results reflecting objective sleepiness over 1 day as well as ESS data indicating subjective sleepiness over a period of 1 week. 2. Methods 2.1. Study design, patients, and inclusion and exclusion criteria In the double-blind, placebo-controlled crossover study, 16 drug-free patients (10 males, 6 females; aged 21–59 years; mean age 39.1 ± 13.3 years; all righthanded) with the ICD-10 diagnosis of narcolepsy (G 47.4) were included. Fifteen completed the study; one patient had to be excluded because of noncompliance with the protocol requirements (did not appear for scheduled visits). Screened patients complaining of excessive daytime sleepiness first underwent neuropsychiatric, physical and laboratory examinations (including HLA typing for DQB1⁎0602 or dopamine receptor 2 positivity) and then spent 2 recording nights in the sleep laboratory (adaptation and baseline night). Inclusion criteria called for patients of either sex, satisfying the criteria of the International Classification of Sleep Disorders (ICSD) (American Sleep Disorders Association, 1997) for narcolepsy. In addition, the

M. Saletu et al. / Psychiatry Research: Neuroimaging 154 (2007) 69–84

symptoms were required to have been stable for 2 weeks before the beginning of the study. The following groups were excluded from the study: patients with evidence of a medical or psychiatric disorder that might account for the primary complaint; patients with sleep apnea, restless legs syndrome (RLS) or periodic limb movement disorder; pregnant or lactating women; patients with a history of drug abuse or dependency, including alcohol; patients requiring psychoactive medication or unwilling to temporarily discontinue anticataplectic medication or any other drug that might interfere with the study assessments; and patients who were unable or unwilling to comply with the protocol; and patients who worked at night. The study was performed in accordance with the relevant guidelines of the Declaration of Helsinki, 1964, as amended in Tokyo, 2004. A written informed consent was obtained. The study protocol was approved by the Institutional Ethics Committee. 2.2. Drug administration The study was designed as a 3-week, double-blind, randomized, placebo-controlled crossover trial. The two randomized treatment periods were separated by a washout phase of 1 week. Doses of 100 mg modafinil and placebo were prepared in capsules that looked identical. According to the fixed titration schedule, in week 1 patients received one capsule in the morning and one capsule at noon, in week 2 two capsules in the morning and one capsule at noon, and in week 3 two capsules in the morning and two capsules at noon. At the beginning and at the end of each 3-week treatment block, quantitative EEG recordings were performed and a psychometric test battery was completed in midmorning hours. 2.3. Psychometric tests Mental performance was tested by means of a computerized version of the Pauli Test (Pauli and Arnold, 1953; Arnold, 1975): Within a time interval of 20 min, subjects had to add as many single digit numbers as possible and to react to two-digit solutions by pressing the last digit only. According to Bartenwerfer (1983), the test results reflect attention, concentration, mnestic aspects and volition, which in turn are dependent upon the individual's ability to keep activation at an adequate level over 20 min. Complex reaction time was measured by a computerbased test device (Schuhfried, 1997). A multiple choice paradigm was chosen: yellow and red light blinking in

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randomized order, sometimes associated with a short acoustic stimulus. Subjects were instructed to press the spacebar on the keyboard if the yellow light and the acoustic stimulus were presented simultaneously. Eight target stimuli were presented during a period of 3 min. Correct answers, errors of commission and omission, and the mean value and S.D. of the reaction time (in milliseconds) were calculated. Thymopsychic variables included midmorning subjective well-being, based on the von Zerssen BF-S Scale (von Zerssen et al., 1970), the State-Trait Anxiety Inventory (Spielberger et al., 1970), the Beck Depression Inventory (BDI) (Beck and Beamesderfer, 1974), and the Symptom Checklist 90 (SCL-90) (Derogatis et al., 1976) as well as drive, mood, affectivity and drowsiness in the morning, measured by means of 100mm visual analog scales (VAS). The Epworth Sleepiness Scale (ESS) covering subjective sleepiness during the last week was completed as well (Johns, 1991). As a psychophysiological measure, critical flicker fusion frequency (CFF, descending threshold) (Grünberger et al., 1984) was assessed. 2.4. Electrophysiological investigations Subsequent to two polysomnographic all-night recordings, a 3-min vigilance-controlled EEG (V-EEG) and a 4-min resting EEG (R-EEG) were obtained at 11.00 h by means of a 21-channel Nihon Kohden 4321G polygraph, as described in detail by Saletu et al. (2004). For the present correlational analyses, only the R-EEG was used because it represents spontaneous vigilance fluctuations. The EEG recordings from 19 leads (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1 and O2 to averaged mastoids) as well as the vertical and horizontal electro-oculographic recordings were digitized on-line with a sampling frequency of 102.4 Hz. Artifact-free 5-s epochs not containing sleep were selected after minimizing ocular artifacts by regression analysis in the time domain by an automatic artifact-identification method with subsequent visual control (Anderer et al., 1992, 2002). Spectral analyses were performed for 5-s epochs (512 sample points), resulting in a frequency resolution of 0.2 Hz (Anderer et al., 1987). LORETA was used to estimate the three-dimensional intracerebral current density distribution (Pascual-Marqui et al., 1994, 1999). The LORETA Key Institute for Brain-Mind Research software was used for brain tomographic analysis (http://www.unizh.ch/keyinst/). The three-shell spherical head model (Ary et al., 1981) was registered to the human brain Talairach atlas (Talairach and

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Tournoux, 1988) available as a digitized MRI from the McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University. The EEG electrode coordinates reported by Towle et al. (1993) were used for registration between spherical and realistic head geometry. Based on the digitized Probability Atlas corresponding to the digitized MRI and also available from the McConnell Brain Imaging Centre (Montréal Neurological Institute, McGill University), a voxel was included in the solution space if its probability of being gray matter was higher than 33% and higher than its probability of being either white matter or cerebrospinal fluid. Finally, the solution space was restricted to cortical and hippocampal gray matter. The EEG lead field was computed numerically with the boundary element method without excessive "smoothing", i.e. the regularization parameter was set to zero (PascualMarqui et al., 1999). EEG cross-spectra were computed based on average referencing without normalization for the seven frequency bands: delta (1.5–6 Hz), theta (6–8 Hz), alpha-1 (8–10 Hz), alpha-2 (10–12 Hz), beta1 (12–18 Hz), beta-2 (18–21 Hz) and beta-3 (21–30 Hz) according to Kubicki et al. (1978). For each recording, LORETA spectral powers of six 5-s epochs of artifactfree, vigilance-controlled and resting EEG were averaged. In a previous study, Anderer et al. (1998) demonstrated the high retest reliability of 3D-LORETA estimates of neural generators of classical frequency bands, based on six 5-s epochs, which confirmed similar results for single EEG leads by Gasser et al. (1985). Thus, the presented LORETA images represent the power in 2394 voxels with a spatial resolution of 7 mm (Pascual-Marqui et al., 1999). The Multiple Sleep Latency Test (MSLT) (Carskadon et al., 1986) was performed at baseline and at the end of each crossover sequence, but not on the same day as the EEG recordings to avoid interference of the two vigilance test procedures. 2.5. Statistical analysis In the exploratory statistical analyses, Wilcoxon's signed rank test was used to compare cognitive and thymopsychic variables under modafinil and placebo. The drug-induced changes in the EEG, MSLT and ESS have been described in detail elsewhere (Saletu et al., 2004, 2005). For the correlation analyses between midmorning behavioral and neurophysiological variables, only two psychometric variables–mental performance over a period of 20 min (Pauli Test) and subjectively experienced wakefulness (100-mm VAS)–were selected in a hy-

pothesis-driven way. To explore the functional relationship between the EEG and other measures of vigilance, correlations with the MSLT and the ESS were calculated as well. Spearman rank correlations determined by means of the SPSS 11.0 software package were based on R-EEG and psychometric data obtained during three different experimental conditions (pretreatment, placebo and modafinil). The resulting voxelby-voxel correlation coefficients were displayed as statistical parametric maps (SPMs). To correct for the α-inflation due to the multiple tests, an omnibus significance test based on the binomial theorem was performed (α = 0.05) (Cross and Chaffin, 1982). Thus, to reject the global null hypothesis, more than 33 of the total of 2394 test results had to be significant at P b 0.01. On the basis of the Structure-Probability Maps Atlas, the number of significant voxels in each lobe (frontal, parietal, occipital, temporal, limbic and sub-lobar), gyrus and Brodmann area (BA) of the left and the right hemisphere was computed. 3. Results 3.1. Psychometric findings 3.1.1. Mental performance Concerning the total number of calculations in the Pauli Test, patients performed significantly (P b 0.01) Table 1 Differences between modafinil and placebo in mental performance and thymopsychic tests Placebo

Modafinil

Mean (S.D.)

Mean (S.D.)

Pauli Test (number of 790.1 (249.2) 835.9 (243.5) calculations) Pauli Test (errors) 3.9 (3) 4.6 (3) Pauli Test 0.6 (0.5) 0.6 (0.4) (errors in % of total score) Pauli Test (range) 9.3 (10.2) 5.7 (3.7) Reaction time (cognitive 454.3 (72.6) 453.9 (87.4) component) Reaction time (motor 192.0 (55.5) 190.3 (174.0) component) CFF (Hz) 43.2 (4.2) 43 (3.3) ASES Drive ⇓ 46.6 (28.7) 43.2 (25.8) Mood ⇑ 63.5 (25.1) 61.8 (23.5) Emotional rapport ⇑ 62.6 (25.1) 62.8 (21.1) Wakefulness ⇓ 49.9 (28) 44.9 (28.9) Well-being Zersen ⇓ 18.3 (12.2) 16.9 (14.6) Beck Depression Inventory 9.1 (7.7) 8 (8.5) STAI state anxiety 38.8 (12.5) 36.6 (13.5) STAI trait anxiety 39.0 (13.9) 37.9 (14.1) ⇑ ⇓ = direction of improvement.

U-test P b 0.01 n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s.

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better under modafinil than under placebo (836 versus 790 calculations) (Table 1). The other aspects of the test, reaction time and critical flicker fusion frequency (CFF), did not reveal any significant differences between the drug and placebo. 3.1.2. Thymopsyche No differences between modafinil and placebo were seen in the von Zerssen BF-S Scale measuring subjective well-being, the State-Trait Anxiety Inventory, the SCL-90, or the 100-mm VAS scales on drive, mood, affectivity and drowsiness (Table 1). The ESS score decreased from a median of 14.5 after placebo to 12.5 after modafinil (P b 0.05) (Saletu et al., 2004, 2005). 3.2. Electrophysiological findings As compared with placebo, modafinil increased fast alpha-2 power in the frontotemporal and sub-lobar cortical regions of the left hemisphere, with the maximum in Brodmann area (BA) 11 of the inferior frontal gyrus (Saletu et al., 2004). Beta-1–3 power showed an increase in a number of brain regions, predominantly in the left hemispheric temporoparietal and limbic cortices. Delta power and theta power were decreased by modafinil in the frontal lobes; theta power was increased in the left temporoparietal regions. In the MSLT, sleep latency to sleep stage S1 increased significantly from a median of 3.2 min after placebo to 6.6 min after modafinil (P b 0.05) (Saletu et al., 2004). 3.3. Correlations 3.3.1. Correlations between mental performance and EEG-LORETA power Spearman rank correlations between the total number of calculations in the Pauli Test and EEG-LORETA power measured in seven frequency bands demonstrated significant negative correlations in the delta (1.5–6 Hz), theta (6–8 Hz) and slow alpha-1 (8–10 Hz) frequency range: The less power in these slow frequency bands, the better performance in the arithmetic task (Fig. 1). The omnibus significance test demonstrated that out of a total of 2394 voxels, 84 showed significant correlations in the delta, 652 in the theta and 481 in the alpha-1 frequency band (P b 0.05, binomial test). Regarding delta power the highest negative correlation coefficient (r = − 0.45) was observed in the anterior cingulum in BA 24 (X = 4, Y = 38, Z = 8). Correlations were mainly found over the limbic and medial frontal lobes of both hemispheres. Concerning theta power the highest negative correlation coefficient (r = − 0.65) was

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found in the medial frontal gyrus in BA 10 (X = − 3, Y = 59, Z = 15). Interestingly, in the frontal cortex of the left hemisphere, 100% of the voxels of BA 11 (orbitofrontal cortex), BA 10 and BA 46, as well as 78% of the voxels of BA 9, 91% of those of BA 8 (all prefrontal cortex) and 94% of those of BA 47 showed significant negative correlations with the Pauli Test, while in the respective regions of the right hemisphere the number of voxels demonstrating significant negative correlations was much smaller (98%, 89%, 0%, 56%, 77% and 6%, respectively). BA 44 and 45 covering the cortical area of Broca (motor speech), as well as BA 47, showed significant correlations only in the left hemisphere. Regarding alpha-1 power the highest negative correlation (r = − 0.55) was found in the medial frontal gyrus in BA 10 (X = 11, Y = 59, Z = 8) (Fig. 2). Interestingly, in the prefrontal cortex of the left hemisphere, regional voxel-by-voxel correlation analysis between cognitive performance and LORETA alpha-1 power demonstrated that 100% of the voxels of BA 8, 9, 10 and 11 were involved, while in the right hemisphere the percentages were only 49, 29, 52 and 69, respectively (Table 2). Moreover, in BA 45 and 44, 100% of the left hemispheric voxels showed significant correlations with cognitive performance, as opposed to 0% of the right hemispheric voxels (Table 2). In BA 32 of the limbic lobe (anterior cingulate gyrus), 90% of the voxels revealed a negative correlation between alpha-1 power and mental performance in both hemispheres. 3.3.2. Correlations between subjective drowsiness and EEG-LORETA power Spearman rank correlations between subjectively rated drowsiness (VAS) and EEG-LORETA power measured in seven frequency bands demonstrated significant positive correlations in the delta (1.5–6 Hz), theta (6–8 Hz), alpha-1 (8–10 Hz), beta-1 (12–18 Hz) and beta-2 (18–21 Hz) frequency bands: The greater the power in these frequency bands, the more pronounced the subjective drowsiness (Fig. 3). The omnibus significance test showed that the number of significant correlations out of 2394 was 204, 160, 43, 422 and 496 for delta, theta, alpha-1, beta-1 and beta2, respectively (P b 0.05, binomial test). Concerning delta power the highest positive correlation coefficient (r = 0.47) was observed in the prefrontal cortex in BA 10 (X = 11, Y = − 45, Z = 1) (Fig. 3). Correlations were mainly found in the frontal lobe (BA 11 and 10). In the right hemisphere, 98% (BA 11) and 74% (BA 10) of the voxels were involved; in the left hemisphere in the same regions, only 59% and 37%, respectively. In the limbic

74 M. Saletu et al. / Psychiatry Research: Neuroimaging 154 (2007) 69–84 Fig. 1. Correlations between mental performance and EEG-LORETA power based on surface-rendered LORETA images in narcolepsy. Images depicting statistical parametric maps (SPMs) seen from different perspectives are based on voxel-by-voxel Spearman rank correlation analysis between the total number of calculations in the Pauli Test and EEG-LORETA power in seven frequency bands (delta, theta, alpha-1, alpha-2, beta-1, beta-2 and beta-3 bands) in narcolepsy patients before and after treatment with placebo or modafinil (n:3×15). Structural anatomy is shown in gray scale (L: left, R: right, A: anterior, P: posterior). Significant negative correlations (blue color: P b 0.01) between EEG-LORETA power in the delta (1.5–6 Hz), theta (6–8 Hz) and slow alpha-1 (8–10 Hz) frequency range and the total score in the Pauli Test may be seen in different brain regions: the less the power in these slow frequency bands mainly over frontal regions, the better the performance in the arithmetic task.

M. Saletu et al. / Psychiatry Research: Neuroimaging 154 (2007) 69–84 Fig. 2. Correlations between subjective drowsiness and EEG-LORETA power based on surface-rendered LORETA images. For a technical description of the statistical parametric maps (SPMs), see Fig. 1. Significant positive correlations (red color: P b 0.01) between subjectively rated drowsiness and the EEG-LORETA power in the delta (1.5–6 Hz), theta (6–8 Hz), alpha-1 (8–10 Hz), beta-1 (12–18 Hz) and beta-2 (18–21 Hz) frequency bands may be seen in different brain regions: The greater the power in these frequency bands mainly in occipital and frontal regions, the more pronounced the level of subjective drowsiness.

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Table 2 Regional voxel-by-voxel correlation analysis between cognitive performance and LORETA alpha-1 (8–10 Hz) power in narcolepsy patients (n:3 × 15) Region

(N)

Left

(In %)

(N)

Right

(In %)

Frontal lobe BA 25: BA 11: BA 10: BA 9: BA 8: BA 6: BA 47: BA 46: BA 45: BA 44: Limbic lobe BA 24: BA 32: Sub-lobar BA 13:

(403) (10) (54) (70) (49) (34) (102) (34) (16) (6) (19) (146) (20) (20) (43) (47)

286 2 54 70 49 34 25 24 16 6 9 29 7 18 9 8

(71) (20) (100) (100) (100) (100) (25) (71) (100) (100) (47) (20) (35) (90) (21) (17)

(415) (13) (55) (73) (48) (35) (97) (34) (18) (11) (20) (148) (23) (20) (37) (41)

129 3 38 38 14 17 19 0 0 0 0 28 8 18 0 0

(31) (23) (69) (52) (29) (49) (20) (0) (0) (0) (0) (19) (35) (90) (0) (0)

The number of significant voxels in the left and right hemispheres, the total number of voxels (N) and the percentage of significant voxels (in %) are given for each affected brain region.

lobe, almost the same number of voxels was involved in both hemispheres. Theta power showed the highest positive correlation (r = 0.44) in the superior frontal gyrus in BA 11 (X = 25, Y = 59, Z = − 13) (Fig. 3). Correlations were located in BA 10 of the right hemisphere (44% of the voxels) and BA 6 in both hemispheres.

Regarding alpha-1 power the highest positive correlation (r = 0.44) was observed in the superior frontal gyrus in BA 10 (X = 25, Y = 66, Z = −13) (Fig. 3). Significant positive correlations were found in BA 10 and BA 11 of the right hemisphere and BA 6 of the left hemisphere. Concerning beta-1 power, the highest positive correlation (r = 0.52) was seen in the fusiform gyrus of the temporal lobe in BA 37 (X = −31, Y = 17, Z = −20), the auditory visual association area (Fig. 3). Significant positive correlations were found in BA 19, BA 18 and BA 17 of the occipital lobe, with over 90% of the voxels involved on both sides. In the temporal lobe, 80% and 84% of the voxels of the left hemispheric BA 39 and BA 37 were involved, as compared with only 4% and 5% in the right hemisphere. In BA 30 of the limbic lobe, over 90% of the voxels showed significant correlations on both sides. Regarding beta-2 power, the highest positive correlation (r = 0.47) was observed in the fusiform gyrus of the occipital lobe in BA 19 (X = − 24, Y = − 53, Z = − 6) (Fig. 3). BA 19, BA 18 and BA 17 of the occipital lobe showed significant positive correlations, with 70–100% of the voxels involved on both sides. In the temporal lobe, 80% of the voxels of the right hemispheric BA 39 showed significant correlations, as compared with 28% in the left hemisphere. 3.3.3. Correlations between MSLT/ESS findings and EEG-LORETA power Spearman rank correlations between MSLT findings and EEG-LORETA power measured in seven frequency

Fig. 3. LORETA slices through the voxel with the highest correlation between mental performance and alpha-1 power in narcolepsy. Images depicting statistical parametric maps (SPMs) seen from different perspectives are based on voxel-by-voxel Spearman rank correlation analysis between the total number of calculations in the Pauli Test and alpha-1 power in narcolepsy patients before and after treatment with placebo or modafinil (n:3×15). Axial, sagittal and coronal slices through the voxel of the extreme t-value at the (X = 11, Y = 59, Z = 8) Talairach coordinate are displayed. Structural anatomy is shown in gray scale (L: left, R: right, A: anterior, P: posterior). The less the alpha-1 power in the dorsolateral and medial prefrontal cortex and anterior cingulate gyrus, the better the performance in the arithmetic task.

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bands demonstrated significant positive correlations in the alpha-2 (10–12 Hz) and beta-1 (12–18 Hz) frequency bands: The greater the power in these frequency bands, the longer the sleep latency to sleep stage S1 in the MSLT. The omnibus significance test showed that the number of significant correlations out of a total of 2394 was 95 in the alpha-2 and 72 in the beta-2 frequency band (P b 0.05, binomial test). Concerning alpha-2 power, the highest positive correlation (r = 0.42) was found in the middle occipital gyrus in BA 19 (X = − 59, Y = − 67, Z = − 6) (Fig. 4). Significant positive correlations were found in BA 19,

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BA 18 and BA 17 of the occipital and temporal lobe only in the left hemisphere (Fig. 4). Concerning beta-1 power, the highest positive correlation (r = 0.48) was seen in the fusiform gyrus of the temporal lobe (X = − 52, Y = −53, Z = − 1). Correlations were found over the temporal and occipital lobe of the left hemisphere. In tests of correlations between objective EEGLORETA data and subjective ESS findings at all time periods by means of a Spearman rank correlation analysis, no relationship between the two data sets was found (P b 0.01).

Fig. 4. LORETA slices on correlations between MSLT sleep latency and alpha-2 power. Horizontal brain slices depicting statistical parametric maps (SPMs) are based on voxel-by-voxel Spearman rank correlation analysis between MSLT sleep latency and alpha-2 power. The color key shows significant correlation coefficients (red colors represent positive correlations at P b 0.01). The greater the alpha-2 power in the left occipital and temporal lobe, the longer the MSLT sleep latency to sleep stage S1.

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4. Discussion 4.1. Cognitive performance and modafinil in narcolepsy Our study demonstrated that the cognition-enhancing effects of modafinil are also relevant for patients with narcolepsy, as in an arithmetic task they performed better on the drug than on placebo. There is a paucity of empirical studies objectively assessing the cognitive effects of modafinil in narcolepsy. In the major investigations in this field, daytime sleepiness was mainly measured by means of conventional methods (US Modafinil in Narcolepsy Multicenter Study Group, 1998, 2000). Becker et al. (2004) found that modafinil treatment in narcolepsy was associated with significantly reduced fatigue and significantly improved vigor and cognition, as assessed by the Profile of Mood States (POMS). In laboratory models of acute sleep loss, modafinil has been shown to preserve various aspects of cognition, such as concentration and sustained attention (Baranski et al., 1998). Further support for the cognition-enhancing effects of modafinil in healthy volunteers without sleep deprivation can be drawn from a recently published study using a comprehensive battery of neuropsychological tests (Turner et al., 2003). Modafinil significantly enhanced cognitive performance in tests of digit span, visual pattern recognition memory, spatial planning, and stop-signal reaction time (Turner et al., 2003). Subjects reported feeling more alert, attentive and energetic after taking modafinil. In another sleep-deprivation study in healthy volunteers, 300 mg modafinil was shown to ameliorate the effects of sleep deprivation stress on cognitive performance in a perceptual judgment task and a complex mental addition task (Baranski and Pigeau, 1997). Modafinil also improved performance in a four-choice serial reaction time task in humans for up to 6 h when administered after 47 h of sleep deprivation (Pigeau et al., 1995). In our studies, modafinil (as compared with placebo) increased fast alpha-2 power in the frontotemporal and sub-lobar cortical regions and beta-1–3 power in the temporoparietal and limbic cortices, predominantly in the left hemisphere (Saletu et al., 2004). Concerning delta and theta power, modafinil induced a decrease in the frontal lobes. Investigating healthy subjects during 60 h of sustained wakefulness, Chapotot et al. (2003) reported a modafinil-induced power decrease in low EEG frequencies, as well as a circadian delay in the theta-2 frequency band, which may be due to amphet-

amine-like dopaminergic effects. As compared with placebo and in contrast to d-amphetamine, modafinil also pronouncedly increased EEG power in the alpha-1 frequency range. Consequently, in contrast to those of damphetamine, the nootropic properties of modafinil may be related to its selective effects on the alpha-1 frequency band, which is intimately related to the maintenance of wakefulness. Both modafinil and amphetamine promote wakefulness primarily by increasing dopaminergic tone and not by stimulating hypocretin transmission (Wisor et al., 2001). 4.1.1. Attention and executive function in narcolepsy Our investigations concerning the relationship between mental performance and brain function measured by LORETA demonstrated that neurophysiological vigilance changes correlate with cognitive and subjective vigilance alterations at the behavioral level. Reduced vigilance or the ability to sustain full alertness during normal activity is frequently cited as a deficit associated with narcolepsy (Naumann and Daum, 2003). Tests that showed deficits in narcolepsy have tended to be those that are relatively long and repetitious, while in shorter, more challenging attentional tasks, such as the Paced Auditory Serial Addition Test (PASAT) or the Digit Span Test, the performance of narcoleptics was generally unimpaired (Mitler et al., 1982; Rogers and Rosenberg, 1990). The Pauli Test utilized in the present study requires attention and executive function over a relatively long period of 20 min. The shorter reaction time tasks did not show any significant findings. Aguirre et al. (1985), Ollo et al. (1987) and Rogers and Rosenberg (1990) would argue that performance decrements in narcolepsy have no organic base, but occur as a consequence of diminished attentional resources that are secondary to narcoleptic sleepiness. Performance decrements can therefore be compensated for by increased attentional effort. This attentional model is supported by the finding that narcoleptic patients perform as effectively as controls in stimulating test environments (Aguirre et al., 1985; Rogers and Rosenberg, 1990), but demonstrate performance decrements for repetitive and monotonous tasks, such as vigilance tasks, where there is minimal motivation to apply compensatory effort (Valley and Broughton, 1983). Executive function has been associated with dissociable mental operations including planning ahead and problem solving, shifting between actions easily, initiating goal-directed behavior, and regulating attention in order to complete tasks. Deficits in executive control have been classically associated with acquired

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damage to the prefrontal cortex, but recent findings show that executive dysfunctions should not be assumed to reflect damage specifically to the frontal lobes, as such deficits may also arise from damage to interconnected cortical and subcortical brain structures or from more diffuse brain damage (Fuster, 2000; Chayer and Freedman, 2001). Valley and Broughton (1981) examined narcoleptic patients in a classical working memory task (Digit Span Backwards) and observed no significant performance difference compared with healthy controls. This finding was replicated by Aguirre et al. (1985) as well as Rogers and Rosenberg (1990). In addition, comparisons between narcoleptic patients and controls were nonsignificant in a verbal fluency test, a complex verbal reasoning task and a working memory task that examined coding and representation of information in relation to time and place of occurrence (Aguirre et al., 1985; Pollack et al., 1992; Henry et al., 1993). In contrast, using Sternberg's STM Scanning Task, a short-term as well as a working memory task, Henry et al. (1993) showed selective cognitive deficits in narcoleptics. However, as a result of the small number of studies, the limited data and the small sample sizes, as well as the different methodological procedures, the evaluation of executive function in narcolepsy remains difficult. More studies concerning the functionality of executive processes in narcolepsy would be useful. 4.2. Thymopsyche and modafinil in narcolepsy In the present study, modafinil as compared with placebo showed no significant group effect on thymopsychic variables, which sets it apart from the classical psychostimulants. This is of clinical relevance, as narcolepsy patients have a higher risk of exhibiting psychiatric disorders (e.g. major depression, altered personality or other psychiatric problems) than patients without narcolepsy (Roy, 1976; Broughton et al., 1981; Krishnan et al., 1984; Mosko et al., 1989). Amphetamines and other psychostimulants have long been a mainstay of treatment for excessive sleepiness associated with narcolepsy. However, it has been argued that they may compound some psychiatric and interpersonal problems of patients with narcolepsy (Douglas, 1998). Well-documented side effects of psychostimulants include increased feelings of anxiety and agitation, erectile dysfunction, insomnia, decreased libido and, in some cases, mania (Palfai and Jankiewicz, 1991). All of these side effects might exacerbate existing or underlying psychiatric conditions and thereby worsen interpersonal relationships (Horrigan and Barnhill, 2000).

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Modafinil has been shown to be safe and effective in the management of narcolepsy, with significantly fewer side effects being observed in patients on modafinil relative to placebo (Billiard et al., 1994) and with little evidence of dependence (Jasinski, 2000). 4.3. Neuroimaging studies on executive function after sleep deprivation We may assume that TSD in normal subjects, with consequent alterations in alertness and cognitive performance, and vigilance decrements in narcolepsy, which are either due to the neurodegenerative disorder itself or to reduced sleep quality, show similar alterations in neuroimaging studies. The negative effects of TSD on alertness and cognitive performance suggest decreases in brain activity and function, primarily in the thalamus, a subcortical structure involved in alertness and attention, and in the prefrontal cortex, a region subserving alertness, attention and higher order cognitive processes. Serial addition/subtraction arithmetic tasks are sensitive to the effects of sleep deprivation, even when performed over short time periods. They are more complex than tests of simple reaction time or vigilance and involve not only sustained attention, but also working memory and arithmetic processing. All of these mental processes have been attributed, in large part, to the prefrontal cortex (e.g. Cohen et al., 1988; Coull et al., 1996; Dolan et al., 1997; Roland and Friberg, 1985; Dahaene et al., 1996, 1999). In an FMRI study in normal subjects, Dehaene et al. (1999) observed an activation in bilateral prefrontal and parietal lobe arithmetic working memory regions during an arithmetic task after a night of normal sleep. Following TSD, however, only the left superior parietal lobe and left premotor area remained significantly responsive to task demands (Drummond and Brown, 2001). In a PET study, Thomas et al. (2000) found significant decreases in the absolute and relative regional cerebral metabolic rate for glucose throughout the prefrontal cortices, including the anterior cingulate gyrus, and the posterior parietal cortices, including the posterior cingulate gyrus and precuneus. Following TSD, compensation for increased sleepiness also seems necessary. In all cognitive tasks, increased levels of sleepiness after TSD were associated with increased activation responses in the left ventral prefrontal cortex. Other authors found increased EEG activity following sleep deprivation in frontal regions during both waking rest (Cajochen et al., 1999) and cognitive performance (Smulders et al., 1997). This

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increased activity, especially in the theta range, is thought to reflect increased sleepiness. Cajochen et al. (1999) reported that theta EEG power in the frontal derivation showed a significant positive correlation with subjective sleepiness during TSD, which is in line with our present LORETA data in narcoleptic patients.

coefficient as well as the highest percentage of involved voxels was seen in BA 10 of the medial frontal gyrus. Concerning hemispheric differences, the left side showed more correlations, although due to the low resolution of LORETA and the anatomical closeness of the two medial frontal lobe regions, one has to be careful in the spatial interpretation.

4.4. Pathophysiology and the “right hemisphere vigilance system” in narcolepsy

4.5. EEG tomography and sleep

A cholinergic/monoaminergic imbalance underlying the symptomatology of narcolepsy has been established and is most likely caused by an absence of hypocretin signalling (Overeem et al., 2001). However, at present, there are no data available that directly show functional links between the hypocretin system and the involved cholinergic and monoaminergic cell groups. The widespread projections of hypocretin neurons make it difficult to elucidate its exact functional importance. Several hypotheses have been put forward concerning hypocretin pathways potentially responsible for the maintenance of a normal sleep/wake architecture. In contrast to the small area in which the hypocretin-containing neurons lie, these cells project widely throughout the brain (Overeem et al., 2001). Projection sites include the cerebral cortex, basal forebrain structures such as the diagonal band of Broca, the amygdala and brainstem areas including the reticular formation, raphe nuclei and the locus ceruleus. In a recent voxel-based morphometric MRI study, significant gray matter loss was found in the right prefrontal and frontomesial cortex of patients with narcolepsy, indicating a disease-related atrophy pattern, which may also be responsible for the reported prefrontal dysfunctions (Brenneis et al., 2005). In our recent EEG-tomographic studies with LORETA on vigilance differences between narcolepsy patients and controls, we observed a significant decrease in alpha-2 power, mainly in the frontal, temporal and parietal cortices of the right hemisphere, along with a global decrease in beta power, also accentuated over the right cortical brain areas (Saletu et al., 2004), which demonstrates a deterioration of the fronto-temporoparietal network of the “right hemisphere vigilance system” (Arruda et al., 1999) in narcolepsy. On the other hand, the maximal difference (text = − 3.0) was seen in the left BA 10 of the medial frontal gyrus. In the present study, we found a negative correlation between mental performance and EEG-LORETA power. The less theta and slow alpha-1 power in the midmorning EEG, the better arithmetic performance in the Pauli Test. In both frequency bands, the highest correlation

Sleep is generally regarded as a global brain process. Recently, however, regional aspects of sleep have gained increasing attention. Early reports that the location of the recording electrodes affects the pattern of the sleep EEG (Findji et al., 1981; Buchsbaum et al., 1982; Hori, 1985) were re-examined by means of contemporary methods of quantitative EEG analysis. Power spectra along the antero-posterior axis were shown to exhibit frequencyspecific and state-dependent gradients (Werth et al., 1996, 1997), with a frontal predominance in the 2-Hz band during the initial part of sleep. This hyperfrontality of low-frequency activity was accentuated by sleep deprivation (Cajochen et al., 1999). It may be associated with the reduction of regional cerebral blood flow, which is known to occur during slow-wave sleep (Finelli et al., 2000; Maquet, 2000) as well as in the course of prolonged waking (Thomas et al., 2000). An intriguing implication of the results of the current functional neuroimaging studies on sleep deprivation, when compared with results for sleep, is that the larger decreases in activity in the prefrontal and posterior parietal heteromodal association cortices may indicate a greater biological vulnerability of these areas to extended wakefulness. Several neuroimaging studies have shown absolute and relative decreases in dorsolateral prefrontal and inferior parietal cortical activity (as measured by cerebral blood flow) during light sleep, slow-wave sleep and REM sleep (e.g. Buchsbaum et al., 1989; Andersson et al., 1998; Maquet et al., 1996; Kajimura et al., 1999). Thus, the same higher order cognitive areas differentially affected by sleep deprivation are also differentially affected by sleep, indicating that these areas may be more susceptible to sleep deprivation and consequently have a greater need for the recuperative processes underlying sleep. Investigating topographic changes in EEG spectral power during pre- and post-nap wakefulness as well as stages 1 (S1) and 2 (S2) non-REM sleep in 12 subjects, Luo et al. (2001) observed an increase in delta and theta power in the frontal and central regions during S1 and S2, with an increase in inter- and intra-hemispheric correlations. Beta power significantly increased in the

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frontal, central and parietal regions during S2, with an increase in interhemispheric correlation. In contrast, alpha power significantly decreased in the parietal– occipital regions during S1 and S2, with a decrease in interhemispheric correlation. A recent topographic study after sleep deprivation showed that prolonged waking enhanced power in the low-frequency range (0.75–10.5 Hz) and reduced power in the high-frequency range (Finelli et al., 2001). The delta and alpha bands typically showed a frontal maximum and a bilateral minimum over parietal regions. In our correlational analyses between subjective drowsiness and EEG-LORETA power, we demonstrated significant positive correlations in the delta, theta and superimposed beta activity bands, reflecting vigilance changes in the sense of a drowsiness state. This supports the idea that sleep, in our case the process of falling asleep, has local aspects. Concerning the regional distribution of delta and theta power, the highest correlations and greatest amount of involved voxels were seen in the prefrontal cortex in BA 10 and the superior frontal gyrus in BA 11, with a preponderance of the right hemisphere. Regarding beta power, a positive correlation with drowsiness was found in both hemispheres in BA 19, 18 and 17 of the occipital lobe, with over 90% of the voxels involved. Further analyses also showed significant correlations between midmorning EEG tomography and the widely used MSLT—although in a smaller number of voxels. However, the latter may be due to the fact that the EEGLORETA data set obtained over a short period of time (3 min) was correlated with MSLT data averaged over a whole day. Finally, it seems understandable that the correlations between midmorning EEG-LORETA and subjective ESS data covering the last week did not reach the 1% level of statistical significance (as seen with all the other correlations), although at the 5% level, again significant correlations were found on the basis of these two very different data sets (objective versus subjective, short-term versus long-term sampling). Overall, our present correlation analyses demonstrated that the less the EEG-LORETA power in the delta, theta and beta bands, the better the subjective wakefulness. These present three-dimensional LORETA findings impressively corroborate our very early pharmaco-EEG reports on modafinil based on single lead analysis over 20 years ago (Saletu et al., 1986). Acknowledgments The authors would like to express their thanks to Uwe Maschek, MD, Alexandra Martin and Rod Hughes, PhD,

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