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ERPs studies of cognitive processing during sleep

Agustın M. Ibanez

Universidad Diego Portales, Santiago de Chile,

Chile and Heidelberg University, Heidelberg,

Germany

Rene San Martın

Universidad de Chile and Universidad Diego

Portales, Santiago de Chile, Chile

Esteban Hurtado

Universidad Diego Portales, Santiago de Chile, and

Pontificia Universidad Catolica de Chile, Chile

Vladimir Lopez

University of California San Diego, USA

I n the last few decades, several works on cognitive processing during sleep have emerged. The study of

cognitive processing with event related potentials (ERPs) during sleep is a topic of great interest, since ERPs

allow the study of stimulation with passive paradigms (without conscious response or behavioural response),

opening multiple research possibilities during different sleep phases. We review ERPs modulated by cognitive

processes during sleep: N1, Mismatch Negativity (MMN), P2, P3, N400-like, N300–N550, among others. The

review shows that there are different cognitive discriminations during sleep related to the frequency, intensity,

duration, saliency, novelty, proportion of appearance, meaning, and even sentential integration of stimuli. The

fascinating results of cognitive processing during sleep imply serious challenges for cognitive models. The studies

of ERPs, together with techniques of neuroimaging, have demonstrated the existence of cognitive processing

during sleep. A fundamental question to be considered is if these cognitive phenomena are similar to processing

that occurs during wakefulness. Based on this question we discussed the existence of possible mechanisms

associated with sleep, as well as the specific cognitive and neurophysiologic differences of wakefulness and sleep.

Much knowledge is still required to even understand the conjunction of dramatic changes in cerebral dynamics

and the occurrence of cognitive processes. We propose some insights based on ERPs research for further

construction of theoretical models for integrating both cognitive processing and specific brain sleep dynamics.

D ans les quelques dernieres decennies, plusieurs travaux sur le traitement cognitif durant le sommeil ont

emerge. L’etude du traitement cognitif avec des potentiels evoques lies a un evenement pendant le sommeil

est un sujet d’un grand interet vu que les ERPs permettent l’etude de la stimulation avec des paradigmes passifs

(sans reponse consciente ou de reponse comportementale), en ouvrant des possibilites de recherche pendant les

differentes phases du sommeil. Nous avons recense des potentiels evoques (ERPs) modules par des processus

cognitifs durant le sommeil: N1, negativite de discordance (MMN), P2, P3, N400-like, N300-N550; entre autres.

La recension indique qu’il existe des discriminations cognitives differentes pendant le sommeil liees a la frequence,

a l’intensite, a la duree, a la saillance, a la nouveaute, a la proportion de l’apparence, au sens et meme a

l’integration sententielle des stimuli. Les fascinants resultats du traitement cognitif pendant le sommeil impliquent

de serieux defis pour des modeles cognitifs. Les etudes des ERPs, ensemble avec les techniques de la neuro-

imagerie, ont demontre l’existence de traitement cognitif durant le sommeil. Une question fondamentale a

considerer est si ces phenomenes cognitifs sont similaires au traitement qui a lieu pendant l’etat d’eveil. En se

fondant sur cette question, nous avons discute de l’existence de possibles mecanismes associes au sommeil ainsi

que de specifiques differences cognitives et neurophysiologiques de l’etat d’eveil et du sommeil. Il est necessaire

d’acquerir davantage de connaissances pour meme comprendre la conjonction des changements dramatiques

dans des dynamiques cerebraux et la survenue des processus cognitifs. Nous avons propose quelques reflexions

# 2008 International Union of Psychological Science

http://www.psypress.com/ijp DOI: 10.1080/00207590802194234

Correspondence should be addressed to Dr A. Ibanez, Laboratory of Neuroscience, Universidad Diego Portales, Santiago de Chile,

Chile. (E-mail: amibanez@puc.cl).

The authors would like to thank Michele Dufey and Sebastian Bacquet for their participation in a previous version of manuscript.

This work was partially supported by a DAAD Grant (PKZ:A/07/71171) and Universidad Diego Portales Grant (Laboratorio de

Neurociencias) to A.I.

INTERNATIONAL JOURNAL OF PSYCHOLOGY

2008, iFirst Article, 1–15

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fondees sur la recherche des ERPs pour construire davantage de modeles theoriques pour integrer a la fois un

traitement cognitif et des dynamiques du sommeil du cerveau.

E n las ultimas decadas se han llevado a cabo varios estudios respecto del procesamiento cognitivo durante el

sueno. El estudio del procesamiento cognitivo durante el sueno con potenciales evocados relacionados a

eventos (ERPs) es un topico de gran interes, ya que los ERPs permiten el estudio a partir de paradigmas de

estimulacion pasiva (sin una respuesta consciente o conductual), lo cual ha abierto multiples oportunidades de

investigacion en las diferentes fases del sueno: N1, Negatividad de disparidad (MMN), P2, P3, N400-similar,

N300–N550, entre otras. La presente revision ha mostrado la presencia de diferentes discriminaciones cognitivas

durante el sueno que estan relacionadas con la frecuencia, la intensidad, la duracion, la particularidad, la

novedad, la proporcion de la aparicion, el significado, e incluso la integracion contextual de los estımulos. Los

fascinantes resultados del procesamiento cognitivo durante el sueno plantean serios desafıos para los modelos

cognitivos. Los estudios con ERPs, junto con tecnicas de neuroimagen, han demostrado la existencia de

procesamiento cognitivo durante el sueno. Una pregunta fundamental a ser considerada es si estos fenomenos

cognitivos son similares al procesamiento que ocurre durante la vigila. Tomando esta pregunta como base, se

discute la existencia de posibles mecanismos asociados con el sueno, ası como las diferencias especıficas tanto

cognitivas como neurofisiologicas entre vigilia y sueno. Mucho conocimiento se requiere aun para entender la

conjuncion de dramaticos cambios en la dinamica cerebral y el desarrollo de procesos cognitivos. Nosotros

proponemos algunas ideas claves basadas en la investigacion con ERPs para la futura construccion de modelos

teoricos que integren el procesamiento cognitivo con las dinamicas cerebrales especıficas del sueno.

Keywords: Cognitive neuroscience of sleep; Consciousness; ERP; Passive paradigms; Postlexical integration; Sleep.

Sleep is a highly complex, essential process for the

biological balance of the mammalian organism

(Benington, 2000). Nevertheless, much contro-

versy exists as to whether sleep is equally relevant

for cognitive processes. On the one hand, drastic

changes in cerebral dynamics that happen during

sleep suggest that the brain does not process

cognitive information in this state in the same way

that it does while awake. Diverse theories about

functional disconnection between the brain and

the external environment have been set forth

(Horne, 1989; Jones, 1991; Pompeiano, 1970).

Nevertheless, other data do not seem to support

this perspective. In the first place, sensorial stimuli

affect sleep differentially, suggesting that stimulus

relevance and saliency are processed during sleep,

and therefore there is not a complete disconnection

(Bonnet, 1982; Bradley & Meddis, 1974; Burton,

Harsh & Badia, 1988). Other studies have shown

that some cognitive phenomena happen during

sleep (Cipolli et al., 2003). Finally, the role of sleep

in the consolidation of cognitive processes, such as

memory at a cerebral level, is a known fact

(Stickgold & Walker, 2005).

The study of cognitive processing with event

related potentials (ERPs) during sleep is a topic of

great interest, since ERPs allow the study of

stimulation with passive paradigms (without con-

scious response or behavioural response), opening

multiple research possibilities during different sleep

stages. Additionally, the technique’s excellent

temporal resolution allows inferences to be made

about the type of processing that is taking place at

a specific moment. Thus, how the brain responds

to different stimuli properties, from variations in

intensity, salience, relevance, and frequency to

semantic aspects of cognition, can be studied.

In the first part of the present review, several ERP

components that have been studied as correlated to

cognitive processing of different types are reviewed:

N1, P1, MMN, P2, P3 family, N400, N300–N550.

Only those potentials that have been sensitive to

manipulations of cognitive aspects of the stimula-

tion are reviewed in this text. Many other compo-

nents of evoked potentials reflecting sensorial

processing have been studied during sleep, but their

significance is clearly beyond the scope of the

present review. In the second part, the role of these

studies in cognitive research is discussed. We

discussed the existence of possible mechanisms

associated with sleep, as well as the specific cognitive

and neurophysiologic differences of wakefulness

and sleep. Finally, we propose some insights based

on ERPs research for further construction of

theoretical models for integrating both cognitive

processing and specific brain sleep dynamics.

ERPS RELATED TO COGNITIVEPROCESSING

N1

Auditory N1 is a negative ERP elicited between 75

and 150 ms following the presentation of an

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auditory stimulus. It is specifically sensitive to the

flow of auditory stimulation (Loveless & Brunia,

1990). Thus, for example, its amplitude diminisheswhen the same stimulus appears repeatedly; this is

due, supposedly, to the incomplete recovery of

neuronal firing between one presentation and

another (Naatanen, 1992). Its generating sources

are mainly bilaterally located in the supratemporal

auditory cortex, although it seemingly receives

additional contribution from frontal regions

(Woods, 1995).Magnetoencephalographic (MEG) studies

suggest that in N1, neural mechanism is

involved the coding of specific characteristics

of auditory stimuli: frequency, intensity, and

location (Elberling, Bak, Kofoed, Lebech, &

Saermark, 1982); characteristics that would be

stored in a format that Naatanen and Winkler

(1999) call ‘‘sensory feature traces.’’ Based onadditional evidence, Atienza, Cantero, and

Escera (2001) suggest that sensory feature traces

would not be available to the mechanisms

responsible for voluntary sensorial discrimina-

tion, but could activate the mechanism of

involuntary attention, resulting in conscious

perception of the stimulus.

According to several studies, N1 would be verysensitive to the general state of the brain at any

given moment. A reduction in the amplitude of

N1 during sleep is observed, which can be

explained by changes in sleep stages, which affect

the pre-cortical information processing.

Observations of sleep-specific changes in

MERPs (middle-latency auditory ERPs) support

this idea. The amplitude of MERPs has beenreduced during sleep (Deiber, Bastuji, Fischer, &

Mauguiere, 1989), indicating that the previous

processing of specific characteristics of the

stimulus is sensitive to sleep. This difference in

the availability of information for successive

cerebral processes could explain the diminished

amplitude of latter components.

N1 has also shown a differential modulation foreach sleep stage. For example, an increase in latency

and a loss of amplitude have been observed during

non-REM sleep (Paavilainen et al., 1987) and a

slight recovery during REM sleep (Bastuji, Garcia-

Larrea, Franc, & Mauguiere, 1995). Results col-

lected on thalamic neurons of anesthetized guinea

pigs seemed to satisfactorily explain the N1

differential modulation during wakefulness—SlowWave Sleep (SWS) and REM sleep (Edeline,

Manunta, & Hennevin, 2000). To be exact, evoked

response diminished in SWS when compared with

wakefulness and REM sleep. This study suggests

that the information received by the cortex in

non-REM is less than that received in paradoxical

sleep, which would explain the increase in the

amplitude of N1 found in this study.

Mismatch Negativity

Mismatch Negativity (MMN) corresponds to a

negative auditory ERP that appears between 100

and 200 ms following stimulus presentation. It is

usually evoked when the repetition of a standard

sound gives way to one that differs in some

physical characteristic (frequency, duration, inten-

sity, or location). Thus, the MMN is understood

as the result of a perceptual process able to detect

changes in a repetitive sonorous sequence, based

on processes regularity detection (Winkler,

Karmos, & Naatanen, 1996), and automatic

comparison (Naatanen, 1992).

A series of studies has shown that MMN can be

found in stage I (Nittono, Momose, & Hori, 2001),

in stage II of non-REM sleep (Sallinen, Kaartinen,

& Lyytinene, 1994), and in REM sleep (Atienza &

Cantero, 2001). Studies such as these suggest the

pre-attentional change detection mechanism con-

tinues working during sleep, possibly based on the

neural activations constructed upon the analysis of

the physical characteristics of the stimuli, also

preserved as shown by N1 studies.

Most of the research on MMN has discussed its

quality to reveal the operation of the sensorial

memory. A pioneer study from Campbell, Bell,

and Bastien (1992) demonstrates that a great

frequency deviation between auditory stimuli

(1000 versus 200 Hz) can elicit a small MMN in

REM sleep and in stage II of non-REM sleep.

Nevertheless, these results could not be repeated

using the oddball paradigm for the last three stages

of non-REM sleep (Nashida et al., 2000). Sallinen

et al. (1994) achieved a certain level of success by

being able to register MMN wave type during

stage II, but this was evident only when the new

stimulation was followed by a K-complex (Kc

hereafter). Several studies report similar results to

those of Campbell et al. (1992) during REM sleep

under similar conditions. Specifically, in a study by

Nashida et al. (2000), MMN was elicited by a

deviant tone (one octave of difference) during

wakefulness, REM sleep and stage I of non-REM.

In this study the interstimulus interval (ISI) varied

between 450 and 600 ms. When testing slower

rhythms, MMN was not registered in response to

the same stimuli, adding to the idea that rhythm of

stimulation is a variable that determines the degree

of its activation in sensorial memory. Another

representative study is that of Atienza et al. (2001),

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with the stimulus train paradigm. In this study,

subjects were tested during wakefulness and REM

sleep, presenting simple tones at a relatively fastrhythm (SOA 5 650 ms) within trains separated by

a silent interval of 3, 6, or 9 s; the deviant stimuli

could on the other hand appear in position 1, 2, 4,

or 6 of each train. It was demonstrated that during

wakefulness, there is an MMN elicited in all

conditions, except the one in which the new

stimulus occupies the first position of the train

after an interval of 9 s. In this case, a context topredict the following acoustic events would not

exist; however, when the first tone corresponds to

at least the routine train, the off track stimulus

appears to contradict the generated expectation.

The results in REM sleep show that MMN was

elicited by the new stimulus, disregarding its

position in the acoustic sequence, but only when

the silence interval was the minimum (3 s). In anycase, the MMN appeared diminished in compar-

ison to the one found in wakefulness, supporting

the idea that storage in sensorial memory lasts less

in REM sleep than in wakefulness.

There are two compatible accounts for the

diminished MMN amplitude during sleep in com-

parison with wakefulness. The first takes a bottom-

up perspective, indicating that since the changedetection process to the base of the MMN rests

mainly in the activity of the ascending connections

of the auditory sensorial system, much of the MMN

amplitude reduction during sleep could be explained

by the inhibition of the routes that process the

auditory stimulus prior to it reaching its generators

in the temporal lobe (Atienza, Cantero, &

Dominguez-Marin, 2002). The second line ofexplanation comes from a top-down perspective,

suggesting that the main activity during sleep would

complicate the synchronization of auditory cortex

after the repetition of stimuli, which would also

affect the change detection process to the base of the

MMN (Atienza et al., 2002). In addition, the MMN

would be attenuated by deactivation during sleep,

revealed by PET (Maquet et al., 1996) and studies offMRI (Portas et al., 2000) of the prefrontal areas

that contribute to their elicitation.

Finally, it is worth pointing out that the MMN

generating system not only accedes to information

of the sensorial memory, but also to other more

lasting forms of memory. Atienza and Cantero

(2001) demonstrate that MMN can be elicited

during REM sleep by changes in complex auditorypatterns, which have previously been distinguished

through learning while awake. Prior to the

training, they found that none of the participants

presented MMN, not even in wakefulness; never-

theless, after being taught, an MMN was elicited

as much in wakefulness as in REM sleep, and with

similar amplitude. These results suggest that

initiated neural changes during wakefulness would

be accessible during REM sleep, even 2 days after

the training (Atienza et al., 2002).

P2 (P210–P220)

Certain findings suggest that during sleep it is

possible to obtain a wave sensitive to the novelty

and saliency of the auditory stimuli, similar to the

P3a of wakefulness, around 200 ms after stimulus

presentation. This wave would be distributed in the

frontal cortical region (Nittono et al., 2001).

Nielsen-Bohlman, Knight, Woods, and Woodward

(1991) found a wave of this type after 200 ms of the

presentation of novel and salient stimuli during

stage II. This study showed the amplitude of the

wave being greater in stage II than in wakefulness.

This wave was accompanied by a decrease in N1 and

MMN amplitude, which, according to Nielsen-

Bohlman et al., would indicate the improbability

that this wave corresponds to a P3a of sleep. Winter,

Kok, Kenemans, and Elton (1995) found a similar

wave, P210, in stages of somnolence and during

stage II, elicited as much by standard tones as by

frequency-deviant tones. Winter et al. indicate that

at the base of the P210 amplitude modulation there

would be different refractory patterns from those

elements involved in processing standard and

deviant tones.

P300 family

The P300 component has been described to engage

higher-order cognitive operations related to selec-

tive attention and resource allocation (Donchin &

Coles, 1988), and the amplitude of the P300 is

proportional to the amount of attentional

resources engaged in processing a given stimulus

(Johnson, 1988). There are some intrinsic difficul-

ties in obtaining a canonical P300 during sleep,

especially during stage II. One of them is the fact

that P300 is primarily sensitive to attentional

manipulations. Attentional resources are often

required to elicit this component in wakefulness

(Atienza et al., 2001). P300 has also been related to

a post-decisional ‘‘cognitive closure’’ mechanism

(Verleger, 1998); and to the access of information

by consciousness (Picton, 1992).

Nevertheless, some authors have successfully

used the P300 passive response paradigm during

sleep. For example, positive deflections similar to

P3b have been elicited using an oddball paradigm

in sleep (Bastuji et al., 1995; Nielsen-Bohlman et

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al., 1991; Pratt, Berlad, & Lavie, 1999). In stage

II, there is some evidence that suggests a positive

wave similar to P3a in wakefulness fulness

(Nielsen-Bohlman et al., 1991; Perrin, Bastuji,

Mauguiere, & Garcia-Larrea, 2000). Even today,

these results are subject to some degree of

controversy. Bastuji et al. (1995) found a P300

in stage I, which was not present in stage II.

Later, Bastuji and Garcia-Larrea (1999) found an

‘‘S II–P3’’ in stage II, close to 600 ms (different

from the Ps–P3 observed in REM and more similar

to wakefulness). There is some research which has

homologated a positivity around the 400 ms post-

stimulus presentation (P400) during stage I and II

with the P300 of wakefulness (Nielsen-Bohlman

et al., 1991); nevertheless, this homologation has

been criticized, suggesting that AEPs induced by

a rare stimulus in the tasks used do not

distinguish between those trials with and without

Kc (Niiyama, Fushimi, Sekine, & Hishikawa,

1995). In this sense, P400 has been categorized as

an important Kc component, more than an

analogue of the P300 of wakefulness, and, more-

over, it has been suggested that during non-REM

sleep, there is no presence of P3 (see a review by

Kotchoubey, 2005). The absence of P300 in stage

II, reported in several studies, could be explained

not only by functional changes in the thalamo-

cortical network in this stage, but also by the

presence, during stage II exclusively, of ‘‘N3’’ and

‘‘P4’’ potentials (Perrin et al., 2000), in the same

temporal window as P300. During REM sleep,

the latency and scalp topography of P300 is

similar to wakefulness (Atienza et al., 2001).

More recently, electrophysiological methods have

been used to demonstrate specific human brain

responses to semantic stimulation during sleep

related to P300. For example, two studies (Perrin,

Garcia-Larrea, Mauguiere, & Bastuji, 1999; Pratt,

Berlad, & Lavie, 1999) have, so far, recorded

ERPs to participants’ own names during sleep.

The ERP during wakefulness and REM sleep

(rapid eye movement sleep or paradoxical sleep)

was very similar in latency, amplitude, and scalp

topography related to the cognitive ‘‘P300’’

component recorded in target detection tasks. In

both cases a late positive wave at 400–600 ms

(pseudo P300) was selectively evoked by the

subject’s own name, with maximum amplitude

over the posterior scalp (Figure 1). The persis-

tence of a differential response to the subject’s

own name during REM sleep, relative to any

other proper name, suggests that the brain

remains able to discriminate an intrinsically

relevant word during these sleep stages.

N300 and N550

N300 and N550 are two negative ERPs typically

registered during stages I and II of sleep, and also

during SWS (Cote, de Lugt, Langley, & Campbell,

1999). N550 has been linked to the appearance of

Kc (Bastien & Campbell, 1992), and it has been

found that it is affected mainly by contextual

characteristics of the stimuli (Atienza et al., 2001),

as well as its novelty, saliency (Bastien &

Campbell, 1992), and stimulation proportion

(Colrain, Webster, & Hirst, 1999). Colrain, Di

Parsia, and Gora (2000) have discussed two factors

that make the interpretation of these results

difficult: (1) on the one hand, there is a prevalence

of P300 before new stimuli during wakefulness,

which limits the attribution to the target of a

special meaning for the subject, and (2) on the

other hand, research results do not systematically

discriminate between trials with and without Kc;

so it is difficult to evaluate if the N550 modulation

must be due to the Kc amplitude, to the

probability of eliciting it, or to both factors. A

late negative wave (LNW), with a peak between

500 and 650 ms, is observable in the form of the Kc

average wave; this wave is improved before

infrequent tones during REM sleep (Nordby

et al., 1996). Niiyama et al. (1995) have related

the functional value of N550 within the Kc to a

Figure 1. ERPs in wakefulness and paradoxical sleep inresponse to the subject’s first name and other firstnames, recorded from Fz, Cz and Pz. Positive voltagesare plotted down. Reproduced from Bastuji et al. (2002),with permission from Elsevier # 2002.

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negative potential of this type (long lasting

negative potential), whose amplitude tends to be

greater in response to infrequent stimuli, or in a

non-task night, and minor before the frequent

stimuli, or in a task night. The aforementioned

could reflect a certain level of information proces-

sing during stage II.

Cote et al. (1999) have found a topographic

distribution different from N550 in stage II and in

SWS, which they have interpreted as a reflection of

differentiated activity on course with different

intracranial sources. Some results indicate that

N550 reflects a sleep-dependent process, which

would respond more to interoceptive stimulation

than external stimulation (Colrain et al., 1999).

N300 has been associated with vertex sharp

waves in the EEG, and its amplitude tends to be

greater when elicited within the Kc (Bastien &

Campbell, 1992). N300 has been sensitive to the

intensity, novelty, and probability of appearance

of a stimulus (Bastien & Campbell, 1992; Nielsen-

Bohlman et al., 1991). Also, it has been highly

sensitive to the intrinsic meaning of a stimulus like

one’s own name (Perrin, Bastuji, & Garcia-Larrea,

1999), which supports the hypothesis that refers to

N300/P400 as a functional equivalent of N2/P3 in

wakefulness (Nielsen-Bohlman et al., 1991).

The generating mechanisms of N300 and N550

are not well known; however, Ujszaszi and Halasz

(1988) mention that both components would

correspond to two parallel types of processing.

According to them, N300 would be associated

with standard information processing, and N550

would be linked to processes dependent on sleep

stage (see also Perrin et al., 1999). This hypothesis

of Kc functional segregation has been confirmed

by diverse studies that have shown N300 elicited in

absence of N550, as much at the beginning of sleep

as during stage II (e.g., Bastien & Campbell, 1992;

Nielsen-Bohlman et al., 1991; Perrin et al., 2000),

contradicting previous research that suggested

N300 (N350) as a trigger for N550.

N400

N400 is a negative component that appears around

400 ms after the presentation of semantically

unrelated information between two words or

between a context and a word (Holcomb &

Neville, 1991; Kutas & Hillyard, 1980). N400

functions as a specific neurophysiological indica-

tor of semantic processes (linguistic, iconic, or

conceptual). Amplitude and latency of N400 can

be modulated by contextual semantic phenomena

(Cornejo et al., 2007; Kutas & Federmeier, 2000;

Van Berkum, Hagoort, & Brown, 1999), where

semantic incongruity is induced at the level of

lexical, sentential, and para-linguistic integration.N400 has been classically described as a negative

component with central-parietal topography

(Kutas & Hillyard, 1980). Recent studies indicate

that the topographic distribution of N400 could be

very wide, even to the whole anterior half of the

scalp. This data suggests that N400 is a polymodal

context-dependent effect, consisting of different

processes with multiple generating sources (Kutas& Federmeier, 2000). N400 has been reported

under nonconscious conditions; using prime or

attentional blink paradigms (Luck, Vogel, &

Shapiro, 1996; Maki, Frigen, & Paulson, 1997;

Vogel, Luck, & Shapiro, 1998). These results

support the notion that N400 reflects processes

at the semantic level with or without conscious

awareness.Recent studies have shown that the N400

component may also be elicited during stage II

and REM sleep, indicating preserved detection of

semantic congruency during these sleep stages.

Brualla, Romero, Serrano, and Valdizan (1998)

were the first to report that a negative deflection

similar to N400 persisted during stage II and REM

sleep in response to semantically unrelated words.In a parallel study, Lopez, Carmenate, and

Alvarez (2001) found that this effect was addi-

tionally identifiable during Slow Wave Sleep

(stages III and IV). Perrin et al. (2002) find that

the N400 response to unrelated words persisted

during stage II and REM sleep. Additionally, they

used words and pseudoword stimuli suggesting

that linguistic incongruity is processed in differentways during REM sleep, stage II, and waking. In

sleep stage II all signs of the hierarchical process of

linguistic discordance disappeared, while a quali-

tatively different hierarchy reappeared in para-

doxical sleep, whereby responses to pseudowords

did not differ from those to congruous words. In

another study, Ibanez, Lopez, and Cornejo (2006)

show that different degrees of semantic con-gruency at sentence level can be discerned by

N400 amplitude modulation, not only in wakeful-

ness but also during sleep, and even independently

of sleep stage (non-REM and REM sleep) (see

Figure 2). Furthermore, results of the N400-like

priming effect depend on the contextual informa-

tion at sentence level and not at word level. On the

other hand, the serial pre/post distinction does notfollow from the results obtained in these works.

This investigation shows, on the one hand, a

modulation of N400 based on the sentential

integration (a post-lexical feature) and, on the

other hand, that this modulation does not require

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conscious activity (a pre-lexical feature). In short,

these results contradict a serial model in which the

lexical processing precedes the post-lexical proces-

sing (but perhaps not a parallel model).

The negativity described in sleep studies is

congruent with the so-called N400-like effect that

has a slightly different spatial distribution across

the scalp (Van Petten & Luka, 2006). From this

perspective, the process reflected by N400 could be

understood as a stage in a wider process for

conflict or incongruence detection and defined as a

member of a large family of negativities. A variety

of negative deflections in ERP, in different time

windows, have been associated with conflict

detection or task complexity, even during sleep

(see Ibanez et al., 2006). This fact promotes the

consideration of an N400-like component during

sleep as a member of the automatic conflict

detection family of negativities.

DISCUSSION: THE RELEVANCE OF ERPSLEEP STUDIES ON COGNITIVE

PHENOMENA

The review conducted in the previous sections

shows that there are different cognitive discrimina-

tions during sleep related to the frequency,

intensity, duration, saliency, novelty, proportion

of appearance, meaning, and even sentential

integration of stimuli. The fascinating results of

cognitive processing during sleep imply serious

challenges for cognitive models. Not only basic

cognitive processes such as the detection ofinfrequent or novel stimuli, but also semantic

stimuli, produce differences in ERP components.

The participant’s name is distinguished electro-

physiologically and, moreover, the brain discrimi-

nates pseudowords and degrees of incongruence

during sleep at the semantic integration level of a

sentence. Without a doubt, several questions

regarding the relevance of sleep in cognitiveprocessing (i.e., memory, semantic integration,

learning) have to be considered, and call for

further research. For example, it is commonly

accepted that a process of post-lexical integration

(i.e., the integration of the semantic properties of a

sentence) requires conscious activity, since working

memory is required to maintain the different

semantic and lexical properties of each word, inorder to be able to determine the global property

of the sentence (i.e., different degrees of sentential

incongruence). The fact that post-lexical semantic

integration occurs during sleep suggests that an

analogous aspect of the waking state working

memory must be operational during sleep. Perhaps

this semantic working memory process operates

based on nonsemantic heuristics that can resolvethe degrees of incongruence presented in this

design (i.e., a general sequence processing cap-

ability that encodes sequential information and,

when combined with past elements in the sequence,

allows the prediction of successor elements).

Nevertheless, there is no model that explains how

this phenomenon takes place.

At a physiological level, equivalent questionsarise. It has been suggested that the fundamental

difference between sleep and wakefulness is the

activation of frontal lobes during wakefulness. In

general terms, a decrease in the prefrontal activity

exists during sleep, which would make the type of

processes that require prefrontal operation diffi-

cult (i.e., working memory, post-lexical integra-

tion). Although it is well known that certainprefrontal reactivation in presence of stimuli

during sleep exists, it is really difficult to conceive

a model that integrates functional cerebral changes

during sleep and the cognitive processes that have

been reported. A model is required that can

equally account for cognitive processing during

the different phases of sleep and the functional

changes at the electrophysiological and neuro-chemical levels. To our knowledge, such a general

model does not exist and the functions of sleep

remain unclear (Siegel, 2005), although some

partial explicative models related to cognitive

processing have been proposed (i.e., Activation

Figure 2. Different degrees of congruence elicit N400-like amplitude modulation in REM sleep. (A) Scalptopography of N400 for each category. (B) ERPwaveforms at Left Frontal Region (ROI centred overF3 electrode) showing N400 amplitude in each condi-tion. The bar and asterisk at the bottom represent thetime interval at which N400 amplitude exhibitedsignificant statistical differences between categories.Negative voltages are plotted down. Reproduced fromIbanez et al. (2006), with permission from Elsevier #

2006. To view this figure in colour, please visit the onlineversion of this issue.

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Syntheses Model, A. Hobson, 2002; the ‘‘reverse

learning’’ of Crick & Mitchison, 1983; and the

memory reactivation and consolidation model of

Wilson, 2002).

Without the intention of proposing an ERP

model of cognitive processing during sleep, we will

discuss two topics that must be considered in any

proposal: the effect of different neurophysiologic

states on ERPs and the nature of cognitive

phenomena during sleep.

Changes of neurophysiologic states andERPs

The usual distinction is between the two great

sleep stages which have been discussed in this

revision: REM sleep (also known as paradoxical

sleep) and non-REM sleep. Non-REM sleep is

subdivided into a continuum of four stages

reflecting the depth of sleep: stage I—sleep onset

period, characterized by decreasing EEG fre-

quency (from 15–60 Hz in waking state to 4–8 Hz

in stage I) and increasing amplitude (from ,30 mV

in waking state to 50–100 mV in stage I); stage II—

light sleep, characterized by 10–12 Hz oscillations

(50–150 mV) called spindles, which occur periodi-

cally and last for a few seconds; and SWS, which

consists of stage III—characterized by slower

waves at 2–4 Hz (100–150 mV); and stage IV—

defined by slow waves (also called delta waves) at

0.5–2 Hz (100–200 mV).

Non-REM sleep and ERPs

Unlike wakefulness and REM sleep, non-REM

sleep is characterized by low frequencies and

higher amplitude waves, oscillations reflecting

huge neuronal activity synchronization in the

thalamocortical network. These neuronal patterns

in the thalamocortical system, a burst-silence

mode during non-REM sleep versus a sustained

single-spike activity during waking and REM, are

under the control of generalized modulatory

systems originating in the brainstem, the hypotha-

lamus, and the basal forebrain (review in Jones,

2005; Pace-Schott & Hobson, 2002; Steriade, 2003;

Steriade & McCarley, 1990).

The ERPs during non-REM sleep differ greatly

from those found during wakefulness, as much

with respect to their morphology as to their

topography. These factors are seen to be strongly

influenced by sleep-specific cortical responses,

where the Kc stand as being the most prominent

cortical responses to the stimulation during non-

REM sleep. The Kc are characterized by a

negativity, whose latency varies between 500 and

600 ms and whose amplitude surpasses even

200 mV (Crowley, Trinder, & Colrain, 2004).

Importantly, Kc in stage II of non-REM sleep

are sensitive to the habituation by stimuli repeti-

tion (Bastien & Campbell, 1994) and have a larger

amplitude to rare stimuli than to frequent ones

(Bastuji et al., 1995; Pratt et al., 1999).

Certain exogenous components are registered in

non-REM sleep. N1 is greater in stage I, smaller in

SWS, and even smaller in stage II during spindles.

Also MMN has been observed in stage I–II of

non-REM sleep, although in the second case only

when a stimulus also elicited a Kc, and without

being able to respond to these results. Several

studies show a substantial decrease or even

disappearance of the MMN during drowsiness or

in stage I. This disparity of results shows that the

research around the MMN in non-REM sleep is

especially sensitive to methodological aspects such

as filtering, presentation rate, and modality.

The most consistent finding in non-REM sleep

is the lack of P3 (Afifi, Guilliminault, & Colrain,

2003; Bastuji et al., 1995; Cote & Campbell, 1999;

Hull & Harsh, 2001; Kotchoubey, 2005; Voss &

Harsh, 1998; Winter et al., 1995). There are other

positive components, possibly related to the Kc,

that inversely correlate with the probability of

stimuli as the P3 of wakefulness; nevertheless they

do not have the typical posterior topography and,

even in stage I, when subjects still produce

behavioural responses, they can have larger

amplitudes to nontargets than targets (Hull &

Harsh, 2001). As already indicated in the revision,

the absence of P3 in non-REM sleep could be

explained not only by functional changes in the

thalamocortical network in this stage, but also by

the presence, during stage II exclusively, of N3 and

P4 potentials in the same temporal window.

Semantic processing in stage II sleep may be

more similar to that in wakefulness. Thus, for

example, semantically inappropriate stimuli

(semantic mismatch) may be elicited N400 during

stage II and in SWS, and even this effect is related

to different degrees of semantic congruency at the

sentence level. Those results suggest a more

automatic processing of semantic stimuli, in agree-

ment with other N400 paradigms of priming, or

attentional blink. Neurophysiologic changes present

in non-REM sleep entails changes in the ERPs of

their classic properties, i.e., latency and amplitude,

and topographic distribution. Some typical ERP

components are difficult to obtain during non-REM

sleep. In stage II it seems that the cortex is able to

respond according to stimulus probability and

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according to semantic appropriateness of words, a

situation rather less clear in SWS.

REM sleep and ERPs

REM sleep is a neurophysiologic state that

varies greatly from non-REM; it is more similar to

wakefulness (J. A. Hobson & Pace-Schott, 2002;

Pace-Schott & Hobson, 2002), which is also

reflected in the studies of ERPs. For example,

the N1 component, besides being present, behaves

as a component of the orienting response, beinglarger in the first stimulus than in the following of

a stimuli run. The MMN shows a great frequency

deviation (1000 versus 200 Hz). Several studies

reported in this review suggest a significant P3 to

rare stimuli in REM sleep, although its magnitude

was reduced as compared to wakefulness, and its

elicitation required stimuli of higher intensity. The

latency and scalp topography of P300 is similar towakefulness. In wakefulness unexpected words

elicited an N400 greater than nonwords did.

Although N400 is also elicited in stage II, the

difference between unexpected words and non-

words disappeared. In REM sleep nonwords (in

contrast to unexpected words) no longer produced

N400, more similar to when N400 occurs during

wakefulness.

Unlike non-REM sleep, ERP effects in REM

sleep and wakefulness are more similar(Kotchoubey, 2005), but in REM sleep they are

usually reduced and delayed, and require stronger

and more salient stimuli than during wakefulness.

Both non-REM and REM studies of ERPs suggest a

basic cognitive processing during sleep, maybe with

more automatic processing. In order to discuss this

possibility, we now introduce some ERPs studies in

anesthesia, vegetative, and comatose states.

Anesthesia

The anesthetic state is composed of uncon-

sciousness, amnesia, analgesia, immobility, andreduction of autonomic nervous system response.

Despite a century of active research, its molecular

mechanisms are not well known (Barash, Cullen,

& Stoelting, 2006). According to Barash et al.,

plausible sites of action of general anesthetics

include the spinal cord, which does not explain

amnesia or unconsciousness; the brainstem, sup-

ported by increased latency and decreased ampli-tude of somatosensory evoked potentials; and the

cerebral cortex, which shows consistent changes in

surface EEG during anesthesia, possibly caused by

a blockage of thalamocortical communication due

to inhalation anesthetics.

Because of technical and ethical issues, ERP

studies in anesthesia are less common than in

sleep. Studies assessing cortical information pro-

cessing during general anesthesia, sleep, coma, and

vegetative state show that N1 and P1 components

are more likely to remain in these conditions than

endogenous components (Kotchoubey, 2005).

During an auditory oddball task, patients under-

going cardiac surgery exhibit a delayed P1–N1–P2

complex and larger P1–N1 and N1–P2 amplitudes

for infrequent stimuli. Both facts are interpreted as

a decreased, but still present, ability to process

information (Van Hoof et al., 1997).

While Van Hoof et al. (1997) suggest that the

mechanisms underlying auditory processing in

sleep and anesthesia are similar, Hennevin,

Huetz, and Edeline (2007) argue that what occurs

during the anesthetic state is different from what

occurs during natural sleep. Young, Ropper, and

Bolton (1998) support the latter view, stating that

electrophysiologic differences among wakefulness,

coma, sleep, and anesthesia imply that there are

basic differences in the underlying neural activities.

Coma and vegetative state

Coma is characterized by an impairment of the

arousal system, preventing the patient from becom-

ing conscious. The inability to reach wakefulness

makes it difficult to clinically assess higher mental

functions. On the other hand, patients in persistent

vegetative state (PVS) have wake and sleep cycles,

but show no evidence of cognition or awareness of

self or the environment (Young et al., 1998).

Individual case ERPs have been studied on

patients in coma and vegetative state. A single

criterion does not exist for establishing an ERP

component as being valid for an individual case.

Despite this fact, N1, mismatch negativity, and P3 are

consistently found in such patients, and the presence

of late ERP components is related to the severity and

outcome of the coma (Kotchoubey, 2005).

As a conclusion, evidence found by ERP studies

on anesthesia, coma, and vegetative state shows

that basic cognitive processing capabilities would

be present under those states, even though

important differences appear among them and in

relation to wakefulness. This supports the possi-

bility of automatic cognitive processing in states

different to wakefulness.

Cognitive activity during sleep

The studies of ERPs, together with neuroimage

techniques, have demonstrated the existence of

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cognitive processes during sleep. The fundamental

question that must be considered is whether these

cognitive phenomena are the same processes thathappen during wakefulness. To this end we will

discuss the cognitive and neurophysiologic exis-

tence of possible mechanisms associated with

sleep, and specific differences of cognition in

wakefulness and sleep.

The existence of online and offline mechanisms

associated to the cognitive phenomena during sleep.

First, there is indirect evidence on the role that

sleep has in the processes associated to memory.

Several studies have shown reactivated neuronal

populations (previously engaged in a learning

task) during sleep; and that this reactivation is a

key process for the consolidation of memory traces

during sleep (Hennevin et al., 2007). This demon-

strates an offline processing of cognitive phenom-ena during sleep associated to sleep-dependent

memory consolidation (Stickgold, 2005), present

in various cognitive processes such as motor-

sequence learning (Cohen, Pascual-Leone, Press,

& Robertson, 2005; Walker et al, 2002); visual-

discrimination learning (Stickgold, James, &

Hobson, 2000), perceptual learning of language

(Fenn, Nusbaum, & Margoliash, 2003), ordeclarative memory (Ellenbogen et al. 2006;

Stickgold, 2005). Those reports are complemented

by cellular and molecular models of sleep-depen-

dent plasticity (Cirelli & Tononi, 2001; Graves,

Pack, & Abel, 2001). Additionally, behavioural,

electrophysiological, and neuroimage studies

(Durmer & Dinges, 2005; Gais & Born, 2004;

Stickgold & Walker, 2005; Tassi et al., 2006) showthat the suppression of sleep produces deficits in

cognitive processing during wakefulness. Similar

alterations of cognition have been reported in

sleep disorders (Verstraeten & Cluydts, 2004).

Those sources of evidence suggest a specific and

differential role from the offline cognitive proces-

sing during sleep.

With respect to the evidence reviewed in thispaper, on the cognitive online processing some

considerations can be assumed. First, selective

changes in an ERP component seem to reveal

specific changes in certain cognitive processes.

Many basic cognitive processes seem to be processed

through a system similar to wakefulness.

Phenomena of infrequency detection, meaningful

stimuli, or the detection of semantic incongruencecould be explained on the basis of well-known

neurophysiologic processes, such as selective

activation (Hofle et al., 1997) and intermittent

gamma activity (J. A. Hobson, Pace-Schott,

Stickgold, & Kahn, 1998) during non-REM; or

gamma oscillations (Kahn, Pace-Schott, & Hobson,

1997; Steriade, 1996) and the increasing of certain

areas’ activation (Braun et al., 1997; Maquet et al.,1996; Nofzinger et al., 1997) during REM.

Additionally, basic cognitive processes that happen

during sleep could be processed in a similar way to

that of wakefulness, but with nonidentical neuro-

physiologic substrata. Finally, the existence of basic

cognitive processes during sleep does not necessarily

imply that wakefulness and sleep have similar

psychophysiological global states.

Specificity and psychophysiological differentia-

tion of cognitive processes during wakefulness and

sleep. Although basic cognitive processes seem to

be processed similarly during wakefulness and

sleep, evidence suggests that, globally, they are

psychologically and neurophysiologically different

processes. The cognitive processing during sleepseems to have a certain specificity. The processes

associated with memory and learning seems to

have unique dynamics. On the other hand, specific

sleep ERPs with possible specific functions have

also been reported (Colrain & Campbell 2007).

Additionally, cognitive phenomena during sleep

seemed to have differential characteristics related

to basic processes (during non-REM sleep) or abizarre cognition (during REM). Both character-

istics of the cognitive processing during sleep

demonstrate a greater difference with wakefulness

processing: The capacity of intentionally orches-

trating different cognitive activities around a

global cognitive process. This aspect of wakeful-

ness coincides with one of the multiple conceptua-

lizations of consciousness (‘‘consciousness as thewaking state’’: Zeman, 2001). The same is empha-

sized in various neurocognitive theories of the

consciousness such as the global workspace theory

(Baars, 2005): the neurodynamic core of con-

sciousness (Ibanez, 2007; Seth & Baars, 2005) or

neurophenomenology (Petitmengin, Navarro, &

Quyen Mle, 2007). Despite the specific differences

between those theories, all assume a globalcoordinating system, which comprises several

basic cognitive processes in a dynamic and

transient pattern. Although some degree of con-

sciousness cannot be discounted during sleep; if

conscious activity implies global coordination of

meaningful cognitive processing (Cosmelli &

Ibanez, 2008; Ibanez & Cosmelli, 2008), non-

conscious processing should be a consequence ofthe sleeping brain (Krueger & Obal, 2003).

Complex stimulus analysis such as semantic

processing can be carried out without conscious-

ness (Gaillard et al., 2006). These automatic

processes can occur too during sleep, processing

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the incoming information without conscious con-

trol (Hennevin et al., 2007); and the ERPs are a

correlation of such unconscious processing

(Campbell & Colrain, 2002; Colrain & Campbell,

2007). Similarly, residual cognitive functions in

comatose and vegetative states seem limited to

low-level cognition, and do not imply areas of

high-order integration that are considered neces-

sary for conscious perception (Laureys, 2005). The

waking state, REM sleep, and non-REM sleep

exhibit considerable differences with regard to

sensations, perceptions, thoughts, and movements,

as well as physiological and neurological signs

(Coenen, 1995). Therefore it can be stated that

there is specific and differential phenomena in

sleep and wakefulness.

The aforementioned differences also exist at a

neurophysiologic level, since those global states

of the brain during wakefulness and sleep stages

are different. Most neurons reduce their dis-

charge during sleep transition (Steriade, 2001).

During non-REM sleep, thalamocortical neurons

are globally inhibited. In sleep, ultradian rhythms

replace circadian ones (Borbely & Achermann,

1999; Czeisler& Khalsa, 2000). Loss of synchrony

in oscillations of gamma frequencies between

frontal and posterior cortex has been reported

during REM sleep (Gross & Gotman, 1999;

Perez-Garcıa et al., 2001). The presentation of

stimuli acted to reset the oscillation in wakeful-

ness but not in REM (Llinas & Ribary, 1993). In

this stage, a relative deactivation of the dorso-

lateral frontal cortex is observed, compared to

wakefulness (Braun et al., 1997; Maquet et al.,

1996). There is also evidence from fMRI

(Lovblad et al., 1999) indicating that activity in

limbic and paralimbic regions is increased during

REM sleep (Braun et al., 1997; Maquet et al.,

1996; Nofzinger et al., 1997). During non-REM

sleep, the executive areas (dorsolateral prefrontal

cortex) are deactivated (Hofle et al., 1997;

Maquet et al., 1996), and they are not reactivated

during REM sleep (Braun et al., 1997, 1998;

Maquet et al., 1996).

Summarizing the above, an explanatory model

of the cognitive processes during sleep would have

to consider certain heuristics:

1. That the existence of basic cognitive processes

during sleep is a fact, and that some of these basic

processes can be relatively well explained by well-

known neurophysiological mechanisms.

2. That there are specific and differential cogni-

tive phenomena of sleep with characteristics

different from wakefulness.

3. That the conscious coordination of different

cognitive processes (i.e., executive functions, con-

sciousness, working memory) is a fundamental

characteristic of wakefulness and, thus far, it has

not been clearly established in sleep. Many

cognitive processes during sleep could be executed

automatically and without conscious control.

4. That certain neurophysiologic differences

between wakefulness and sleep endorse this differ-

entiation.

Future studies, and the progressive integration of

electrophysiological techniques of neuroimagery

and behaviour will be able to establish the scope

and specificity of such heuristics.

Perhaps because sleep is associated with one

massive gating of stimulus input (thalamocortical

inhibitory loop) and to a reduction of the frontal

activity and areas of association, the platonic idea

that sleep is a partial death (Mansfield, Goddard,

& Moldofsky, 2003) has implicitly survived for a

long time, making it difficult to accept the concept

of an active mind during sleep (Feinberg & Evarts,

1969). Nowadays, the idea that little or no mental

activity occurs during sleep, endorsed by an

implicit identification between conscience and

cognition, cannot be accepted. It is time for an

active search of an unconscious cognition theory

during sleep. The investigation using ERPs repre-

sents a direct means of reaching the integration of

cognitive and physiological levels of this research

agenda.

Manuscript received October 2007

Revised manuscript accepted April 2008

First published online month/year

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