Reduced fronto-cortical brain connectivity during NREM sleep in Asperger syndrome: An EEG spectral and phase coherence study
Introduction
Sleep holds promise as a sensitive indicator of changes in brain neuronal organization in a large number of conditions, including psychiatric (Leistedt et al., 2009) and neurodevelopmental disorders (Lázár and Bódizs, 2008).
Asperger syndrome (AS) is a neurodevelopmental disorder characterised by impairment in social interactions, manifest repetitive and stereotyped behaviours and interests without significant delay in language or cognitive development (APA, 2000). Genetic, neurophysiologic, cognitive and behavioural data support the hypothesis that AS is a variant of autism located at the milder end of the spectrum of autistic disorders (Frith, 2004). This may imply that neurobiological findings for high functioning autistic patients can in part be generalized to AS.
Assessments of brain development based on measurement of head circumference, magnetic resonance imaging and post-mortem brain weight revealed that autism is characterised by a period of unusually rapid rate of early brain growth followed by abnormally slow or arrested growth (Redcay and Courchesne, 2005). It has been proposed that this early overgrowth interferes with the normal developmental trajectory of connectivity in the cortex (Courchesne and Pierce, 2005). Post-mortem neuropathologic studies of autistic brain have shown various abnormalities (Bailey et al., 1998, Casanova et al., 2002). These abnormalities include changes in the cortical microanatomy, mainly in frontal areas and this may provide evidence for cellular abnormalities and processes that may underlie the brain growth abnormalities which primarily affect the frontal lobe (Carper and Courchesne, 2005, Herbert et al., 2004).
It has been hypothesized that these altered patterns of brain neural development result in a local overconnectivity in the frontal cortex and a reduction in long-distance cortical–cortical coupling (Courchesne and Pierce, 2005). The frontal overconnectivity and the long-distance underconnectivity are thought to underlie the cognitive characteristics of autism spectrum disorders (ASD).
In accordance with these studies, functional imaging assessments showed that in autistic subjects brain activity was less synchronized across activated brain areas during a variety of cognitive tasks, including sentence comprehension (Just et al., 2004), executive functions (Just et al., 2007, Koshino et al., 2008), visuo-motor performance (Villalobos et al., 2005), social cognition (Castelli et al., 2002, Kana et al., 2008) and also during the baseline resting-state (Cherkassky et al., 2006). These findings imply that impairments in cognitive domains such as executive function (Hill and Bird, 2006), complex information processing (Minshew et al., 2002), theory of mind (Baron-Cohen et al., 1985) and empathy (Baron-Cohen, 2002) may all be related to abnormalities in neocortical connectivity in ASD.
EEG studies and analyses of the EEG during NREM in particular may provide further insight into functional brain connectivity (Miyamoto et al., 2003) also in ASD (Rippon et al., 2007); Several relevant EEG measures are available. An EEG spectral profile in NREM sleep is characteristic for an individual, possibly, reflecting individual traits of functional neuroanatomy (Finelli et al., 2001, De Gennaro et al., 2008). Developmental features and functional as well as structural characteristics of cortico-cortical and thalamo-cortical networks are likely to have a general effect upon a large range of EEG frequencies. The various frequencies in the EEG reflect different neural sources that subserve different brain functions (Buzsáki and Draguhn, 2004) and studying EEG spectra topographically and over a large range of frequencies may provide information about the underlying neurophysiological systems. Slow waves and sleep spindles are key EEG characteristics of NREM sleep and are functionally dependent on the thalamo-cortical network (Steriade, 2006), a system shown to be modified in autism (Tsatsanis et al., 2003, Hardan et al., 2006). These EEG activities have been shown to be affected in small groups of AS subjects (Godbout et al., 2000, Limoges et al., 2005). However, there are currently no data of spectral measures involving a broad range frequency bands and topographic patterns in subjects with ASD.
An EEG measure particularly sensitive to changes in connectivity is EEG coherence. Based on the principle ‘What is wired together, fires together’ it is assumed that EEG phase coherence indices, which are primarily measures of phase correlation, reflect synchronous co-activation of different brain areas (see a review in Sauseng and Klimesch, 2008).
Studies in control subjects have shown that both intrahemispheric and interhemispheric coherence of EEG activity reaches a high level in NREM sleep primarily in low delta frequency band (1–2 Hz) and is largely independent of the signal amplitude (Achermann and Borbély, 1998a) reflecting large-scale functional connectivity of brain regions (Achermann and Borbély, 1998b). NREM sleep specific inter- and intrahemispheric EEG coherence is enhanced by learning reflecting possible reactivation of learning induced brain connections (Mölle et al., 2004). Sleep coherence measures may be relevant for understanding the abnormal functional neuroarchitecture assumed to underline the particular behavioural and cognitive phenotype in ASD. It is important to mention that the neuroarchitectural substrate of the two types of coherences is different. The transcallosal fibres contribute to interhemispheric coherence while other types of fibres, including subcortical–cortical and cortico-cortical fibers, contribute to intrahemispheric coherence (Nielsen et al., 1993).
Some of the previous PSG sleep studies in subjects with high functioning autism (HFA) and AS reported alterations in sleep macrostructure such as longer sleep latency, more frequent nocturnal awakenings, lower sleep efficiency, increased duration of stage 1 sleep, and decreased slow-wave sleep (Godbout et al., 2000, Limoges et al., 2005). The results of other studies, however, are not consistent with these results (Bruni et al., 2007; Malow et al., 2006, Miano et al., 2007, Tani et al., 2004). This may be due to methodological issues related to the subjects’ age range, distinct subtypes of ASD or co-morbid mental retardation. More detailed analyses of NREM EEG identified a decreased number a visually detected sleep spindles (Godbout et al., 2000, Limoges et al., 2005), a non-significant decrease in delta and increase in theta power (Tani et al., 2004), and a significant increase (Bruni et al., 2007) of A1 type Cyclic Alternating Pattern, which supposedly reflects an altered thalamo-cortical and cortico-cortical connectivity pattern, in subject with AS and HFA (Bruni et al., 2007).
The current study was designed to extend previous sleep research in AS. Sleep macrostructure was analyzed to further explore PSG determined sleep patterns in AS, and to better understand the basis of the frequent sleep complaints in this population in this vulnerable period from childhood to late adolescence. The regional distribution of spectral activity across a wide range of frequency bands, and of inter- and intrahemispheric coherence was analyzed to gain a better insight into the functional brain architecture underpinning the peculiar cognitive, affective and behavioural phenotype in AS.
Based on the hypothesized changes of the thalamo-cortical system in ASD (Tsatsanis et al., 2003, Hardan et al., 2006, Limoges et al., 2005) we predicted an altered NREM sleep EEG spectral profile across a large range of frequencies in children and adolescents with AS.
Based on the neurobiological, neuropsychological, functional imaging data and on the long-distance underconnectivity hypothesis of the frontal region with other brain areas (Courchesne and Pierce, 2005, Just et al., 2004) in subjects with ASD we predicted a decreased inter- and intrahemispheric coherence of NREM EEG between the frontal region and other cortical areas in AS. Local overconnectivity could not be assessed by coherence analyses, because this requires high density EEG recordings (Grieve et al., 2003) which exceeded the technical resources available in the current study.
Section snippets
Subjects
Eighteen un-medicated subjects (all males) with AS frequenting the outpatient care of Vadaskert Child Psychiatric Hospital, Budapest and 14 control (CONT) subjects were recruited in a multicentre sleep study. Parents of the participating children and subjects above the age of 18 years signed the informed consent approved by the ethics committee of Semmelweis University, Budapest, Hungary. Principles of the Declaration of Helsinki were followed. Parents were interviewed extensively with respect
Sleep macrostructure
The AS group spent significantly more time in bed, had a longer sleep onset latency, a somewhat lower sleep efficiency and longer wake after sleep onset. Differences in SWS between the AS and controls were not statistically significant (Table 1). These differences between AS and controls were not statistically significant in the data set used for the qEEG analyses, although the numerical differences were in the same direction.
Absolute power spectrum density
Analyses of the absolute total PSD revealed neither a main effect for
Discussion
This is a first study in which whole night NREM sleep dependent EEG spectra and its topography as well as phase coherence in children and adolescents with AS is compared with age and IQ matched controls. Marked changes in both absolute and relative power density values as well as changes in intrahemispheric coherence were observed in AS. In the following we discuss our results in the light of the available data in ASD. Because AS represents only one segment of the autistic spectrum, our
Disclosure statement
This is not an industry supported study. The authors have indicated no financial conflicts of interest.
Acknowledgements
The first author thanks Dr. Derk-Jan Dijk for comments on the manuscript. This study was supported by the Hungarian National Scientific Research Fund (OTKA T-048927 and OTKA TS-049785) and the National Office for Research and Technology (NKFP-1B/020/04). Authors were supported by the Hungarian Academy of Sciences’ János Bolyai Research Fellowship (R.B.) as well as by the National University Research Council of Romania (Grant No. 27687/14.03.2005) and the Marie-Curie Grant No. MTKD-CT-2004-003134
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