Slow spindles’ cortical generators overlap with the epileptogenic zone in temporal epileptic patients: An electrical source imaging study

Clinical Neurophysiology 124 (2013) 2336–2344

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Slow spindles’ cortical generators overlap with the epileptogenic zone in temporal epileptic patients: An electrical source imaging study Alessandra Del Felice a,⇑, Chiara Arcaro b, Silvia Francesca Storti a, Antonio Fiaschi a,b,c, Paolo Manganotti a,b,c a b c

Department of Neurological, Neuropsychological, Morphological and Movement Sciences, Section of Neurology, University of Verona, Italy Department of Neurophysiology, IRCCS Fondazione Ospedale San Camillo, Venice, Italy Clinical Neurophysiology and Functional Neuroimaging Unit, AOUI of Verona, Italy

a r t i c l e

i n f o

Article history: Accepted 6 June 2013 Available online 10 July 2013 Keywords: Electrical source imaging (ESI) High density EEG (256 channels) Epilepsy Sleep Spike generators Spindles generators

h i g h l i g h t s  Both controls and temporal lobe epilepsy patients display multiple and concomitant cortical sleep

spindles generators.  Slow sleep spindles, but not fast ones, are mainly generated in the affected temporal lobe in temporal

epilepsy.  At least one of slow spindles’ generators in temporal lobe patients coincided with the patients’ epilep-

togenic zone.

a b s t r a c t Objective: To determine whether temporal epileptic patients and normal volunteers display similar sleep spindles’ cortical generators as determined by electrical source imaging (ESI), and whether such generators overlap in epilepsy patients with the epileptogenic zone identified by ESI. Methods: Twelve healthy subjects and twelve temporal lobe pharmaco-resistant epileptic patients underwent a 256-channel EEG recording during a daytime nap. Sleep spindles were analyzed off line, distinguishing slow (10–12 Hz) and fast (12–14 Hz) ones, and the final averaged signal was projected onto a MNI (Montreal Neurological Institute) space to localize cortical generators. The same procedure was performed for averaged epileptic spikes, obtaining their cortical source. Intra- and inter-group statistical analyses were conducted. Results: Multiple, concomitant generators were detected in both populations for slow and fast spindles. Slow spindles in epileptics displayed higher source amplitude in comparison to healthy volunteers (Z = 0.001), as well as a preferential localization over the affected temporal cortices (p = 0.039). Interestingly, at least one of slow spindles’ generators overlapped with the epileptogenic zone. Conclusion: Slow spindles, but not fast ones, in temporal epilepsy are mainly generated by the affected temporal lobe. Significance: These results point to the strict relation between sleep and epilepsy and to the possible cognitive implications of spikes arising from memory-encoding brain structures. Ó 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

1. Introduction Sleep spindles are bursts of waxing and waning oscillations in the frequency of 10–15 Hz, lasting from 0.5 to 2 s, and considered ⇑ Corresponding author. Address: Department of Neurological, Neuropsychological, Morphological and Motor Sciences, University of Verona, Policlinico GB Rossi, Piazzale LA Scuro, 10, 37134 Verona, Italy. Tel.: +39 045 8124285; fax: +39 045 8124873. E-mail address: [email protected] (A. Del Felice).

to be the hallmark of sleep stage N2. Since the beginning, spindles have been recognized in a double appearance (Gibbs and Gibbs, 1950; Anderer et al., 2001; Schabus et al., 2007), as slower frontal (10–12 Hz) and faster posterior (12–14 Hz) ones and this holds true both in healthy subjects and epileptic patients (Nir et al., 2011; Peter-Derex et al., 2012). Originally believed to be synchronous, global events (Contreras et al., 1997; Traub et al., 2005) due to the rhythmic firing of thalamo-cortical neurons that depolarize cortical pyramidal cells generating postsynaptic currents, spindles have been recently demonstrated to be restricted to specific corti-

1388-2457/$36.00 Ó 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.clinph.2013.06.002

A. Del Felice et al. / Clinical Neurophysiology 124 (2013) 2336–2344

cal regions (Andrillon et al., 2011; Del Felice et al., unpublished data). Interestingly, a study by Andrillon et al. (2011) exploiting the in-depth recordings obtained from stereo-EEG (sEEG) in pharmaco-resistant epileptic patients undergoing presurgical evaluation demonstrated spindles occurring across multiple neocortical regions, and less frequently also in the parahippocampal gyrus and hippocampus. Which generators are responsible for the spindles cortical distribution is still debated. Source analysis of magnetoencephalography (MEG) sleep recordings found that multiple equivalent current dipoles are necessary to account for field pattern (Liu et al., 1998; Shih et al., 2000; Urakami, 2008). Although a study by Gumenyuk et al. (2009) estimated overlapping sources for faster and slower components, different MEG estimation source techniques placed the generators, the number of which remains unclear, in deep parieto-central and fronto-central regions bilaterally (Ishii et al., 2003; Manshanden et al., 2002; Shih et al., 2000; Urakami, 2008). Dehghani and colleagues (2010) compared the simultaneously acquired EEG signal (60 channels) with the electromagnetic field (MEG) of seven sleeping healthy adults, demonstrating multiple asynchronous generators active during human sleep spindles detected through MEG signal. An alternative method deployed to study this issue in healthy volunteers has been fMRI (Laufs et al., 2007; Schabus et al., 2007, 2012; Tyvaert et al., 2008; Caporro et al., 2012), that, although lacking the high temporal resolution of the above-mentioned techniques, permits an insight into deep brain structures. Overall, fMRI results showed a blood oxygenation level dependent (BOLD) activation in the thalamus, cingulate, paracentral gyrus and often also in the bilateral superior temporal lobes. To sum up, data on spindles cortical generators have been collected only from healthy individuals, whilst studies focusing on epileptic patients investigated mainly patterns of spindles distribution over the cortex or their relation with epileptiform discharges (Malow et al., 1999; Nakabayashi et al., 2001; Asano et al., 2007). The latter point bears particular interest, given the strict bi-directional relationship between sleep spindles and epileptic discharges. A pioneer work by Prince and Farrell described the dose-dependent transformation of spindles into generalized spike-wave discharges during gradual injection of penicillin in cat, suggesting that a diffuse increase in cortical excitability makes the cortex respond to afferent thalamo-cortical volleys by generating spike-waves rather than spindles (Prince and Farrell, 1969). A similar effect has been described in humans by Terzano and colleagues, observing that rapid EEG frequency shifts, among which spindles and K complexes are comprised, often unleash generalized spike waves in idiopathic generalized epilepsy patients (Terzano et al., 1989). Nonetheless, different neural mechanisms for the generation of high voltage spindles and frontal spikes and epileptic discharges induced after local intracortical application of a GABA metabolite in a murine model have been proposed (Brankack et al., 1993), leaving the exact relation between focal spikes and spindles still to be clarified. To our knowledge, studies focusing on the possible spindles – temporal spikes interconnections are lacking, either in experimental designs or in vivo. Moreover, electrical source imaging (ESI) has only sporadically been deployed to estimate sources of sleep spindles (Anderer et al., 2001), but never, to our knowledge, with a 256 EEG channels signal. ESI can estimate the localization within the brain volume of electric source(s) generating an activity recorded with scalp electrodes. The main limitation to the application of this method has been the limited number of EEG electrodes, that allowed only imprecise source localization. The introduction of dense array EEG (256 channels) almost a decade ago (Cremades et al., 2004; Suarez et al., 2000) has dramatically improved the quality of data and enhanced the precision of the inverse solution (Michel et al., 2004). The main application of ESI so far has been the identification

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of spike cortical generators in epilepsy abnormalities (Lantz et al., 2003; Brodbeck et al., 2010, 2011; Kaiboriboon et al., 2012; Storti et al., 2012) and neuropsychology studies (Cremades et al., 2004; Suarez et al., 2000), although potential of this technique are beginning to be appreciated also applied to sleep (Murphy et al., 2009; Siniatchkin et al., 2010; Landesness et al., 2011; Plante et al., 2012). The aim of our study was to determine cortical spindles generators location in temporal epileptic patients, and compare them with those identified in healthy volunteers. Identifying the spindles’ source generators in healthy volunteers and temporal lobe epileptic subjects could shed new light on the supposed physiologic role of spindling and possibly contribute to our understanding of the bi-directional relation between sleep components and epilepsy. 2. Materials and methods 2.1. Subjects Twelve pharmaco-resistant temporal epileptic patients (7M, 5F; mean age 34, min 18, max 67) undergoing presurgical evaluation and seventeen healthy subjects (9M, 8F; mean age 31.5, min 23, max 47) underwent a 256-channel EEG recording during a daytime nap. Control subjects were required to have no neurological or sleep disturbance, and not to be on therapy with any drug. Patients were affected by temporal lobe epilepsy with a mean duration of epilepsy of 18.8 years and were all treated with antiepileptic drugs (see Table 1 for patients demographic characteristics). The drugs’ morning dose was suspended on the day of the recording and they were partially sleep deprived (waked up at 3 A.M.), in order to facilitate the recording of sleep N2. All individuals were requested not to take any stimulating substance from awakening until the recording. Experimental procedures were conform to the Declaration of Helsinki indication, and participant had to sign an informed consent. 2.2. EEG-recordings EEG recording was performed using 256 channels (Electrical Geodesic, Inc., Eugene, OR, USA). The net was adjusted so that Fpz, Cz, Oz and the pre-auricular points were correctly placed according to the international 10/20 system. Due to the geodesic tension structure of the net all electrodes were evenly distributed on the scalp at approximately the same location across subjects. The data were recorded against a vertex electrode reference (Cz) (Michel et al., 2004) at a sampling rate of 250 Hz and filtered offline with a band-pass filter (0.1–70 Hz). Electrooculogram (EOG) channels were mounted on the left and right eye cantus, with a sampling rate of 250 Hz, bandpass filtered at 0–100 Hz, with sensitivity below 5 mV. The recordings were performed in an electrically shielded, sound proof, darkened laboratory room. 2.3. EEG source imaging 2.3.1. Spindles and spikes selection and averaging First, spindles were visually scored according to the AASM guidelines (Silber et al., 2007), and marked at the exact time point of their beginning with a common marker, independently from their frequency and major topographical appearance. EEG signal analysis was performed according to the following procedure. A bi-dimensional, 256-channel topographical representation of spindle distribution over the scalp was first obtained (Fig. 1A for slow spindles and 1B for fast spindles). Successively, a visual selection on a monopolar montage, although intensive and time-consuming, was preferred in this study over an automated approach

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Table 1 Demographic characteristics of epileptic patients included in the study, with localization of cortical source of epileptic spikes as calculated by ESI. Pt.

Sex

Age

Etiology

RMN

Drugs

Epileptogenic zone as by ESI (gyrus)

1

F

27

Post infectious

CBZ + VAP

2

M

47

CBZ + VAP

L middle temporal gyrus, R inferior temporal gyrus R uncus

39

3 4

M F

18 20

CBZ CBZ

R inferior temporal L middle temporal gyrus

14 5

5

F

21

Bilateral hyppocampal hyperintensity

CBZ

R middle temporal gyrus

4

6

M

19

Right temporo-polar encephalocele

LEV, OXCBZ

R middle temporal gyrus

4

7

M

18

Bilateral hyppocampal hyperintensity

CBZ, VPA

M

43

Right temporal post-surgical cavity

9

F

67

VPA, LTG, CLZ CBZ, LTG

R middle temporal gyrus, L middle temporal gyrus L middle temporal gyrus

7

8

10

M

30

11 12

M F

33 64

Right hippocampal sclerosis Right temporal dysplasia Left hyppocampal sclerosis Right hyppocampal sclerosis Right temporo-polar encephalocele Bilateral hyppocampal sclerosis Right temporal angioma resection Bilateral hyppocampal sclerosis Right hyppocampal sclerosis Post traumatic Left hyppocampal sclerosis

Right fronto-orbital and left hippocampal hyperintensity Right hippocampal volume reduction and hyperintensity Norrmal Left hyppocampal hyperintensity

Bilateral hyppocampal hyperintensity, right > left Right temporal asimmetry (R < L) Right fronto-temporal malacic area Left hyppocampal hyperintensity

(Adjouadi et al., 2004, 2005): as with spike selection (Storti et al., 2012), this approach was maintained also for sleep spindles., and was independently performed by two experienced scorers (ADF, CA). Beginning of a spindle was considered the initial deflection of the EEG signal from baseline: this procedure was performed expanding the time window at 5 mm/s. Only concordant spindles were further processed. Only spindles were selected that appeared not to be related to other sleep figures (i.e., vertex waves, minimum distance 2 ms), and spindles not containing nor being immediately preceded or followed by any epileptic discharge. EEG was segmented according to the marker position, comprising the 500 ms before and after the marker: only the beginning of the spindle was the time point we were interested in localizing its source, and time windows were consequently fitted. The approach was derived from ESI studies in epilepsy (Brodbeck et al., 2010; Lantz et al., 2003; Storti et al., 2012) which consider the initial deflection from baseline of the spike as the beginning of the epileptic phenomenon: its generators thus represent the epilepto-

LEV, OXCBZ, LCM LEV, OXCBZ LEV

R middle frontal gyrus, L inferior temporal gyrus R middle frontal gyrus, L middle temporal gyrus R superior temporal L middle temporal gyrus

Years since diagnosis 6

29 40 26 8 43

genic zone. Single segments were thereafter visually checked on the 1 s segment window expanded at 5 mm/s, allowing a more detailed visualization of the marker position, so that segments in which the marker did not exactly correspond to the initial deflection were discarded. All the remaining segments were then independently bandpass filtered at 10–12 Hz for slow spindles and at 12–14 Hz for fast spindles. Inclusion in each category was confirmed by fast Fourier Transform (FFT) analysis (Fig. 2). Only spindles within a clear-cut frequency range were included in further analysis. For each epileptic patient, the peak of the spike was used as a trigger for averaging in epochs of ±500 ms (Storti et al., 2012). For both analysis, single electrodes containing artifacts were manually selected, and were interpolated using a three-dimensional spline interpolation algorithm (Perrin et al., 1989) to keep the number of electrodes the same for all epochs. At visual inspection, the whole segments contaminated by ocular, muscular or movement artefacts were rejected, as well as, in each spindle anal-

Fig. 1. Single patients’ slow (A) and fast spindles’ (B) distribution over the scalp. Topographical waveform representation of slow (10–12 Hz) and fast (12–14 Hz) spindles’ average. This plot shows a 1 s epoch for each of the 256 channels, arrayed in their approximate positions on the head surface. Cortical source/sources were calculated taking the time point at the transition from baseline to the initial deflection of the spindle. Each channel depicted in its position over the scalp (nose pointing forwards, lateral spaces ears), signal bandpass filtered at 10–12 Hz for slow and 12–14 Hz for fast spindles, and averaged. Note the antero-central distribution of slow spindles and centro-posterior distribution of fast ones, and their rendering over the zygomatic derivations.

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Fig. 2. Steps of analysis: (a) High-density EEG signal, on which spindles are visually marked. The EEG is visualized in bipolar montage. (b) Filtering (bandpass 10–12 Hz) and segmentation based on marker position (the example contains 25 one-second segments). (c) Fast Fourier transform on filtered and segmented traces for each of the 256 EEG channels (grid view) to confirm the exact frequency range and topography of selected elements; (d) Grand average of the spindle activities in the range of 10–12 Hz.

ysis, segments containing spindles in the other frequency range. For each patient an average of segments was done for fast and slow; a grand average calculated over all subjects for each spindle category was subsequently obtained. 2.3.2. ESI localization A standardized source imaging procedure, low-resolution brain electromagnetic tomography (LORETA) was applied to the averaged spindles (Pascual-Marqui et al., 1994). LORETA minimizes the squared norm of the Laplacian of the weighted 3D current density vector field. It incorporates the ‘‘smoothness assumption’’ selecting the inverse solution of the measured data with the smoothest distribution in space (Ossenblok and Spekreijse, 1991). The LORETA inverse solutions were implemented within the GeoSource software package (Electrical Geodesics, Inc. EGI) using the probabilistic cortical gray matter locations from the Montreal Neurological Institute (MNI) (Talairach and Tournoux, 1988) probabilistic atlas (http://www.bic.mni.mcgill.ca). Thus sources were only projected on the cortical gray matter. For each subject and for each spindle frequency, and for each epileptic and its spike average, cortical sources were calculated taking the time point at the transition from baseline to the initial deflection of the spindle/spike, and identified in terms of Brodmann coordinates and maximum source intensity (Michel et al., 2004; Michel and Murray, 2012). 2.4. Statistical analysis We tested if a difference between source intensities expressed in nA (nanoAmpére) existed between volunteers and epileptic patients via a Mann–Whitney t test for independent samples (Z < 0.05), as well as if slow and fast spindles displayed different

intensities in the same group by applying a Wilcoxon matched pairs test, given the non-parametric characteristics of data (Z < 0.05). Source generators’ localizations were statistically compared between epileptics and controls via a X2 test. Overlap of spindles generators and spike generator was performed via a binomial distribution test (p < 0.05).

3. Results Of the seventeen healthy subjects enrolled in the study design, 3 failed to fall asleep even during repeated recording sessions. Of the remaining 14 EEG recordings, 2 had to be discarded due to excessive noise and artifacts (1 case) or to technical problems (1 case). All the remaining EEG recordings (8 males, 4 females controls; 7 males, 5 females epileptics) were scored according to AASM guidelines (Silber et al., 2007), and showed clear-cut sleep stage N2, and at least 25 min of N2 sleep in each recording. The total mean number of spindles per healthy participant was 79 (min 39, max 100), of which 36 slow and 43 fast ones. Patients slept for a mean of 53 min, of which at least 25 min of N2 sleep with a total mean number of spindles per participant of 71 (min 29, max 89), of which 37 slow and 34 fast ones. The number of spikes in epileptic patients used for ESI ranged from 27 to 66 (mean 36). A mean of 10 channels’ traces were interpolated for each participant (min 3; max 14). Slow and fast spindles source generators were identified through the LORETA algorithm for individual subjects/patients (Figs. 3 and 4) and for grand average (Figs. 5 and 6). Individual results are summarized in Table 2.

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Fig. 3. Slow spindles cortical generators in and healthy control (A) and temporal lobe epileptic patients patient No. 3 (B) with the LORETA algorithm from a 256channel EEG recording. The crosshairs denote a point of maximal source. Slices were adjusted in order to show the maximum intensity and its area, and therefore differ for each individual. Time point at the exact half of the time window; EEG source imagine at the peak of the spike and in the rising phase of the spindles. Note the multiple, equipotent sources in different lobes. The maximum of the estimated source of the average slow spindles is indicated in yellow. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 5. Fast spindles cortical generators in a healthy control (A) and temporal lobe epileptic patients patient No. 7 (B) with the LORETA algorithm from a 256-channel EEG recording. The crosshairs denote point of maximal source. Slices were adjusted in order to show the maximum intensity and its area, and therefore differ for each individual. Time point at the exact half of the time window; EEG source imagine at the peak of the spike and in the rising phase of the spindles. Note the multiple, equipotent sources in different lobes. The maximum of the estimated source of the average slow spindles is indicated in yellow. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 4. Grand average of slow spindles’ source localization in healthy controls (A) and temporal lobe epileptic patients (B). The crosshairs denote point of maximal source. Slices were adjusted in order to show the maximum intensity and its area. Note the prevalent localization of slow spindles generators over the temporal cortex in patients, and their overall higher current intensity.

Fig. 6. Grand average of fast spindles’ source localization in healthy controls (A) and temporal lobe epileptic patients (B). The crosshairs denote point of maximal source. Slices were adjusted in order to show the maximum intensity and its area. Note the prevalent localization of slow spindles generators over the temporal cortex in patients, and their overall higher current intensity.

All generators, regardless of their location, showed comparable mean current intensity values in controls (Z = 0.175). On the contrary, slow spindles generators in epileptic patients showed higher intensities compared to fast sources (Z = 0.011). The overall comparison of generators’ intensities in volunteers and patients yielded higher values for the patient population (Z = 0.001).

spindles generators in patients displayed up to four sites, with a strong location in the frontal lobes (75%) associated with temporoparietal sources, and in the temporo-parieto-occipital areas (25%). Overall, temporal epileptic patients displayed higher source amplitude for the slow spindles in comparison to healthy volunteers (Z = 0.011). Analyzing the congruence of spindles generators and spike source in patients, slow spindles exhibited in the majority of cases (83.3%) at least one generator overlapping with the supposed epileptic focus. The same phenomenon was observed in 41.7% of fast spindles (p = 0.039), indicating a strong congruence of slow spindles generators with spike cortical source.

3.1. Slow spindles cortical sources In healthy individuals, slow spindle signal processing (LORETA) identified multiple sources (two to three) in all cases except one (Fig. 3A; Fig. 4A for grand average). A third of these subjects presented generators localized exclusively within the frontal lobe, while the remaining showed the persistence of an anterior generator accompanied by other/s in more posterior cortical areas (precuneus, inferior parietal gyrus, uncus, temporal lobe). Slow

3.2. Fast spindles cortical sources LORETA identified multiple cortical sources also for fast spindles, both in controls (two to three concomitant, equipotent

Table 2 Localization, MNI coordinates and intensities of each source identified in the healthy volunteers and temporal epileptic patients. Spindle 10–12 Hz Lobe

Spindle 12–14 Hz Gyrii, Brodmann area

MNI coordinates

Intensity (nA)

( ( ( (

3, 45, 13) 3, 39, 57) 3, 74, 22) 3, 4, 64)

0.003 0.003 0.002 0.007

Gyrii, Brodmann area

MNI coordinates

Intensity (nA)

1

Frontal Frontal

R inferior frontal, 10 R middle frontal, 10

(39, 51, 1) (32, 52, 1)

0.005 0.004

2

Frontal Frontal Temporal Parietal Frontal Frontal Frontal Frontal Temporal Parietal Temporal Frontal Parietal Temporal Occipital Parietal Temporal Parietal Occipital Temporal Temporal Parietal Frontal Temporal Parietal Occipital Temporal Parietal

Mesial medial frontal, 6 R and L medial frontal, 10 R inferior temporal, 20 R inferior parietal lobule, 40 Mesial medial frontal, 11 Mesial medial frontal, 6 Mesial medial frontal, 10 R middle temporal, 21 R middle frontal, 10 L parietal operculum, 43 L middel temporal, 21 medial frontal, 11 R and L precuneus, 7 R superior temporal, 39 R lingual, 18 R precuneus, 7 R middel temporal, 37 R inferior parietal lobule, 40 Lingual, 18 L middle temporal gyrus, 21 L inferior temporal, 37 L inferior parietal lobue, 40 Medial frontal, 11 R superior temporal, 39 R and L precuneus, 7 R lingual, 18 L inferior temporal, 37 R inferior parietal lobule, 40

(4, 11, 57) (4, 52, 8) (53, 4, 34) 846, 46, 43) ( 3, 45, 13) ( 3, 11, 13) ( 3, 52, 6) (46, 3, 34) (32, 52, 1) (46, 3, 34) (40, 117, 86) ( 3, 38, 20) ( 3, 74, 36) (53, 60, 22) ( 3, 88, 6) ( 3, 60, 50) (53, 53, 13) (46, 46, 43) ( 3, 81, 1) ( 45, 10, 34) ( 45, 67, 6) ( 45, 46, 50) ( 3, 38, 20) ( 3, 74, 36) (53, 60, 22) ( 3, 88, 52) ( 45, 67, 69) (46, 46, 43)

0.004 0.004 0.007 0.004 0.003 0.003 0.003 0.003 0.004 0.003 0.004 0.007 0.008 0.009 0.01 0.009 0.02 0.004 0.005 0.003 0.003 0.005 0.007 0.009 0.008 0.01 0.003 0.003

Temporal Temporal Limbic Frontal Temporal Temporal Temporal Occipital Limbic

R inferior temporal, 20 L inferior temporal, 20 Anterior cingulate, 32 R medial frontal, 6 R inferior temporal, 20 R middle temporal, 6 R middle frontal, 20 Lingual, 18 Anterior cingulate, 10

(53, 4, 34) ( 52, 4, 34) ( 3, 38, 29) ( 3, 4, 64) (25, 11, 27) ( 3, 3, 57) (32, 52, 1) ( 3, 81, 1) ( 3, 52, 1)

0.007 0.007 0.006 0.002 0.006 0.003 0.003 0.002 0.005

5

Parietal Occipital

Precuneus, 7 Lingual, 18

( 3, ( 3,

0.001 0.002

6

Frontal Temporal

Medial temporal, 10 R middle temporal, 37

( 3, 59, 6) (53, 53, 13)

0.004 0.005

7

Temporal Temporal Parietal

L middle temporal , 21 L inferior temporal, 37 L inferior parietal lobule, 40

( 45, 10, 34) ( 45, 67, 6) ( 45, 46, 50)

0.002 0.002 0.002

A 1

Control subjects Frontal

2

Occipital Frontal

Mesial medial frontal gyrus, 11 Mesial and L paracentral lobule, 5 L cuneus, 18 Mesial medial frontal, 6

Frontal Parietal Frontal Parietal

Mesial medial frontal, 6 Precuneus, 31 R inferior frontal, 45 R inferior parietal, 40

( 3, 4.57) ( 3, 74, 22) (53, 10, 22) (53, 10, 22)

0.003 0.002 0.003 0.013

3

5

Frontal

R inferior frontal, 46

(39, 45, 8)

0.002

5

6

Frontal Frontal

( 3, ( 3, ( 3,

4, 57) 37, 56) 4, 57)

0.002 0.003 0.005

6

7

Medial frontal, 6 Mesial medial frontal, 5 Medial frontal, 6

Limbic

R uncus, 20

(39,

11,

0.007

Frontal Parietal Frontal

R inferior frontal, 46 R inferior parietal lobule, 40 Medial frontal, 6

(39, 45, 8) (39, 53, 50) ( 3, 4, 57)

0.004 0.009 0.004

10

Frontal Parietal

R inferior frontal, 46 R insula,13

(39, 45, 8) (46, 10, 1)

0.007 0.008

10

11

Frontal Temporal

R inferior frontal, 46 R insula, 13

(39,45,8) (46, 10, 1)

0.007 0.006

11

12

Frontal Temporal

R inferior frontal, 46 R middle temporal, 37

(39, 45, 8) (53, 53, 13)

0.009 0.002

12

B 1

Epileptic patients Frontal Temporal Temporal Temporal Temporal Occipital Temporal Occipital Frontal Frontal Temporal Frontal Parietal Temporal Frontal Frontal Temporal Frontal Temporal Temporal

R inferior frontal, 10 R inferior temporal, 19 R insula, 13 R middle temporal, 21 L middle temporal, 37 Lingual, 18 R middle temporal, 6 Lingual, 18 R middle frontal, 9 Medial frontal, 6 L middle temporal, 21 Medial frontal, 6 R inferior parietal lobule, 40 R middle temporal, 37 Medial frontal, 10 Inferior frontal, 10 R middle temporal, 21 R middle frontal, 9 L middle temporal, 21 R inferior temporal, 20

(39, 52, 1) (46, 60, 6) (46, 10, 1) (39, 4, 34) ( 52, 60, 3) ( 3, 81, 6) ( 3, 3, 57) ( 3, 81, 1) (46, 10, 29) ( 3, 10, 56) ( 45, 10, 34) ( 3, 11, 57) (39, 53, 57) (53, 53, 13) ( 3, 52, 8) (39, 52, 1) (39, 4, 34) (46, 10, 29) ( 45, 10, 34) (53, 11, 34)

0.001 0.008 0.009 0.005 0.005 0.004 0.003 0.002 0.003 0.003 0.003 0.005 0.005 0.004 0.006 0.005 0.005 0.005 0.003 0.003

1

3 4

8 9

2

3 4

5

6

7

34)

4

7

8 9

2 3

4

67, 43) 81, 1)

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Lobe

( 45, 46, 50) (39, 10, 34) ( 3, 81, 1) L inferior parietal lobule, 40 R superior temporal, 38 Lingual, 18 Parietal Temporal Occipital 12

11

10

9

0.004 0.005 0.003

Intensity (nA)

0.003 0.003 0.005 0.004 0.004 0.002 0.002 0.002 0.003 0.003 0.003

MNI coordinates

12

11

10

9

Gyrii, Brodmann area Lobe

8

0.004 0.005 0.004 0.005 0.004 0.003 0.002 0.003 0.003 0.004 0.003 0.005 0.006 0.006 ( 3, 52, 8) ( 45, 10, 34) ( 38, 52, 8) ( 3, 53, 57) ( 45, 10, 34) ( 38, 52, 8) ( 3,52, 13) (32, 52, 1) (46, 4, 34) ( 3, 10, 56) (39, 52, 1) (39, 4, 34) ( 45, 10, 34) ( 3, 59, 6) Medial frontal, 10 L middle temporal, 21 L middle frontal gyrus, 10 Precuneus, 7 L middle temporal gyrus, 21 L middle frontal gyrus, 10 Medial frontal gyrus, 11 R middle frontal gyrus, 9 R inferior temporal gyrus, 20 Medial frontal, 6 R inferior frontal, 10 R middle temporal, 21 L middle temporal, 21 Medial temporal, 10 Frontal Temporal Frontal Parietal Temporal Frontal Frontal Frontal Temporal Frontal Frontal Temporal Temporal Temporal 8

Spindle 12–14 Hz

Intensity (nA) MNI coordinates Gyrii, Brodmann area Lobe

Spindle 10–12 Hz

Table 2 (continued)

Temporal Occipital Frontal Temporal Temporal Frontal Temporal Temporal Limbic Temporal Parietal

( 45, 67, 6) ( 3, 81, 1) (39, 52, 1) (39, 10, 34) (39, 17, 1) ( 38, 52, 1) ( 45, 10, 34) (39, 4, 34) (39, 11, 34) (39, 4, 34) ( 3, 67, 43)

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L inferior temporal, 37 Lingual, 18 R inferior frontal, 10 R superior temporal, 38 R insula, 13 L inferior frontal , 10 L middle temporal , 21 R middle temporal, 21 R uncus, 20 R middle temporal, 21 Precuneus, 7

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sources: individual analysis (Fig. 5A) and grand average (Fig. 6A) and well as in epileptics (up to four sources, Fig. 5B; Fig. 6B). Results are summarized in Table 2. Controls displayed persistent generators within the frontal lobe, although generally coupled with parieto-temporal concomitant sources. One case showed also an occipital generator. Six cases (50%) presented sources all outside the frontal lobe. In patients, fast spindles originated in all cases from a temporo-parietal or limbic sources, associated either with frontal (33.3%), or occipital (33.3%) generators. No significant deviations from normals were detected for fast spindles localization (p = 0.98).

4. Discussion Our results identified multiple cortical spindles’ generators as independent, equipotent sources both in healthy individuals and in temporal lobe epileptic patients. Interestingly, while fast spindles have a persistent generator over temporo-parietal cortices in both populations, slow spindles in patients appeared to originate mainly from the temporal lobes instead of fronto-central areas as in controls, with a substantial overlap with the epileptogenic zone as identified through ESI. Moreover, slow spindles in epileptic brain are generated by higher mean currents than the ones of controls. The first consideration we can draw is that, according to source localization, cortical generators of spindles are multiple and independent; the intensity of generators each group (healthy subjects and epileptics) did not differ, implying that spindles are synchronous, equivalent events scattered over the cortex. Interestingly, in temporal lobe epileptic patients, slow spindles appear to derive mainly from the mesial temporal cortex: previous papers (Brazier, 1972; Montplaisir et al., 1981; Malow et al., 1999) described the appearance of spindles over these areas, but did not distinguish nor the spindles type or the generator proper. Of note, in all our patients the epileptogenic zone as identified through ESI coincided with one of the slow spindles’ temporal generators. If mesial temporal spindles are either epileptic or just physiological phenomena has long been matter of debate: Malow proposed that such graphoelements contribute to the activation of epileptiform discharges in adult patients. Asano and colleagues (2007) observed in temporal epileptic children investigated with extra-operative electrocorticography (ECoG) that these young patients had spindles originating in the medial temporal regions, and that this area coincided in the majority of them with the seizure onset zone. Nonetheless, no one of such spindles evolved into an ictal discharge, leading the authors to postulate that medial temporal spindles are a physiological activity. From our data we derive the overlap of spikes generator and at least of one slow spindles generator, as well as higher currents implicated in the genesis of slow spindles: although purely on speculative grounds, this strict relation could point to a lower activation threshold of an epileptic cortex, as already demonstrated by transcranial magnetic stimulation (TMS)-EEG studies (Del Felice et al., 2011), that could facilitate also the appearance of spindles. Why this happens preferentially, although not uniquely restricted to, for slow spindles, though, remains still to be clarified. In this regard, an interesting future direction for research would be to link the scalp appearance of spikes with the occurrence of slow spindles by means of automatic detection methods, such as those developed by Adjouadi and colleagues (2004), in order to identify their respective sources and possible overlaps, also in a pre-surgical investigation perspective.

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The peculiar and preferential location of slow spindles generators in temporal lobes instead of frontal ones as in controls raises other interesting considerations. Spindles have been in recent years postulated to be the brain signature of memory encoding (Fogel and Smith, 2006, 2011; Mölle and Born, 2011; Barakat et al., 2011) and Born et al. (2006) report how thalamocortical spindles stimulate early gene expression and glutamate receptors, thereby creating optimal conditions for longterm potentiation (LTP) in the neocortex. Spindles are moreover supposed to represent the cortical equivalent of hippocampal ripples (Ji and Wilson, 2007; Clemens et al., 2011). Ripples are fast frequency discharges (110–300 Hz) interpreted as electric discharges promoting synaptic rearrangement and a replay of neural sequences learned during encoding (Ferrara et al., 2012). Cortical spindles and ripples appear to be entrained by slow waves, acting as synchronizer of the two discharges, so that the sequence replay in the hippocampus takes place at the moment of maximum readiness of the cortex, thus consolidating memories (Born, 2010; Mölle and Born, 2011). The counterpart of such data are observations of altered spindles density, amplitude and duration in conditions with defective cognition, such as schizophrenic patients (Ferrarelli et al., 2007), neurodegenerative diseases (Petit et al., 2004), or even in normal ageing (Martin et al., 2013). Cognitive dysfunctions have recently been recognized as being an undeniable part of the epilepsy picture, encompassing memory and language, but also executive functions in some instances (Akanuma et al., 2003; Giovagnoli et al., 2005), peculiarly in focal epilepsies, mainly temporal and frontal. Some authors even posed the question of whether chronic temporal epilepsy could be defined as a progressive dementing disease (Helmstaedter and Elger, 2009), given the substantial cognitive deterioration seen over time in such patients. While the strict relation between sleep and epileptic discharges has since long been recognized (Terzano et al., 1989), an effect of sleep elements on learning and cognition in such a population would be intriguing, and has up to now provided incongruent results based on neuropsychological testing mainly in children (Baglietto et al., 2001; Deak et al., 2011; Urbain et al., 2011). Although our experimental design was not aimed at any neuropsychological evaluation, we have to note that in our temporal lobe patients slow spindles had a preferential genesis in the temporal, mainly mesial lobes, that are, areas involved in memory formation (Lacruz et al., 2010; Ferrier et al., 2000). Since mainly fast spindles are responsible for information retention (Barakat et al., 2011), the predominance of slow spindles generators over the temporal cortices seems to suggest a defective mechanism of memory encoding in temporal epileptics, thus offering a possible neurophysiologic base for cognitive dysfunctions. In this study we did not compare spindles density between healthy volunteers and treated epileptic patients: although it could have provided interesting information on different spindles’ frequency and topographic distribution, we know that AEDs slow the appearance of spindles (Drake et al., 1991), thus providing possibly spurious results. A limitation of our study is the relatively small number of spindles averaged, but since we were interested in the pure spindle generators, we were strict in excluding any spindle meddled with vertex sharp waves, arousal or epileptiform discharges, in order to obtain the present sources. Moreover, since up to now ESI has mainly been used to localize the irritative regions in epileptic patients (Michel et al., 2004), our study is one of the first attempts to apply electrical source imaging to sleep figures. In conclusion, electrical source imaging of sleep recorded with a dense array EEG (256 channels) identified in temporal lobe epilepsy patients prevalent slow spindles generators in the temporal cortex, instead

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of fronto-central areas as in healthy individuals, with a marked overlap with the epileptogenic zone as identified through ESI. This may partly explain the cognitive decline associated with temporal lobe epilepsy and this subject is the line of future research. Acknowledgements We want to thank Prof. Göran Lantz for his useful technical suggestions. No financial support was received for conducting the study. No author has any conflict of interest to disclose. References Adjouadi M, Sanchez D, Cabrerizo M, Ayala M, Jayakar P, Yaylali I, et al. Interictal spike detection using the walsh transform. IEEE Trans Biomed Eng 2004;51:868–73. Adjouadi M, Cabrerizo M, Ayala M, Sanchez D, Jayakar P, Yaylali I, et al. Detection of interictal spikes and artifactual data through orthogonal transformations. J Clin Neurophysiol 2005;22:53–64. Akanuma N, Alarcón G, Lum F, Morris RG, Koutroumanidis M, Adachi N, et al. Lateralising value of neuropsychological protocols for presurgical assessment of temporal lobe epilepsy. Epilepsia 2003;44:408–18. Anderer P, Klösch G, Gruber G, Trenker E, Pascual-Marqui RD, Zeitlhofer J, et al. Lowresolution brain electromagnetictomography revealed simultaneously active frontal and parietal sleep spindlesources in the human cortex. Neuroscience 2001;103:581–92. Andrillon T, Nir Y, Staba RJ, Ferrarelli F, Cirelli C, Tononi G, et al. Sleep spindles in humans: insights from intracranial EEG and unit recordings. J Neurosci 2011;31:17821–34. Asano E, Mihaylova T, Juhász C, Sood S, Chugani HT. Effect of sleep on interictal spikes and distribution of sleep spindles on electrocorticography in children with focal epilepsy. Clin Neurophysiol 2007;118:1360–8. Baglietto MG, Battaglia FM, Nobili L, Tortorelli S, De Negri E, Calevo MG, et al. Neuropsychological disorders related to interictal epileptic discharges during sleep in benign epilepsy of childhood with centrotemporal or Rolandic spikes. Dev Med Child Neurol 2001;43:407–12. Barakat M, Doyon J, Debas K, Vandewalle G, Morin A, Poirier G, et al. Fast and slow spindle involvement in the consolidation of a new motor sequence. Behav Brain Res 2011;217:117–21. Born J. Slow-wave sleep and the consolidation of long-term memory. World J Biol Psychiatry 2010;11(Suppl. 1):16–21. Born J, Rasch B, Gais S. Sleep to remember. Neuroscientist 2006;12:410–24. Brankack J, Lahtinen H, Koivisto E, Riekkinen PJ. Epileptogenic spikes and seizures but not high voltage spindles are induced by local frontal cortical application of gamma-hydroxybutyrate. Epilepsy Res 1993;15:91–9. Brazier M. Interactions of deep structures during seizure in man. In: Petsche U, Brazier M, editors. Mechanisms of synchronization in epileptic seizures. Vienna: Springer; 1972. p. 409. Brodbeck V, Spinelli L, Lascano AM, Pollo C, Schaller K, et al. Electrical source imaging for presurgical focus localization in epilepsy patients with normal MRI. Epilepsia 2010;51:583–91. Brodbeck V, Spinelli L, Lascano AM, Wissmeier M, Vargas MI, Vulliemoz S, et al. Electroencephalographic source imaging: a prospective study of 152 operated epileptic patients. Brain 2011;134:2887–97. Caporro M, Haneef Z, Yeh HJ, Lenartowicz A, Buttinelli C, Parvizi J, et al. Functional MRI of sleep spindles and K-complexes. Clin Neurophysiol 2012;123:303–9. Clemens Z, Mölle M, Eross L, Jakus R, Rásonyi G, Halász P, Born J. Fine-tuned coupling between human parahippocampal ripples and sleep spindles. Eur J Neurosci 2011;33:511–20. Contreras D, Destexhe A, Sejnowski TJ, Steriade M. Spatiotemporal patterns of spindle oscillations in cortex and thalamus. J Neurosci 1997;17:1179–96. Cremades G, Sanchez D, Adjouadi M, Barreto A. Human-computer interfaces with regional lower and upper alpha frequencies as on-line indexes of mental activity. Comput Hum Behav 2004;20:569–79. Deak MC, Stickgold R, Pietras AC, Nelson AP, Bubrick EJ. The role of sleep in forgetting in temporal lobe epilepsy: a pilot study. Epilepsy Behav 2011;21:462–6. Dehghani N, Cash SS, Rossetti AO, Chen CC, Halgren E. Magnetoencephalography demonstrates multiple asynchronous generators during human sleep spindles. J Neurophysiol 2010;104:179–88. Del Felice A, Fiaschi A, Bongiovanni GL, Savazzi S, Manganotti P. The sleep-deprived brain in normals and patients with juvenile myoclonic epilepsy: a perturbational approach to measuring cortical reactivity. Epilepsy Res 2011;96:123–31. Drake Jr ME, Pakalnis A, Padamadan H, Weate SM, Cannon PA. Sleep spindles in epilepsy. Clin Electroencephalogr 1991;22:144–9. Ferrara M, Moroni F, De Gennaro L, Nobili L. Hippocampal sleep features: relations to human memory function. Front Neurol 2012;3:57. Ferrarelli F, Huber R, Peterson MJ, Massimini M, Murphy M, Riedner BA, et al. Reduced sleep spindle activity in schizophrenia patients. Am J Psychiatry 2007;164:483–92.

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