2<\/sup> = 0.69, F = 18.545, d.f. = 2, 16, P < 0.00007).<\/p>\n![](\"\/psychophysiology\/files\/images\/stories\/cikk13_fig03.png\")
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DISCUSSION<\/h3>\n
Hitherto intelligence di\ufb00erences have been associated with event-related neural activity patterns of the wakeful working brain (Gray et al., 2003) or physical (Andreasen et al., 1993; Thompson et al., 2001), chemical (Jung et al., 1999; Rae et al., 1996) and electrical (Jausovec and Jausovec, 2000) brain features measured during wakeful resting conditions.<\/p>\n
Our results show that general mental ability reliably correlates with fast spindle density as well as with the grouping of fast spindles by the slow oscillation. These \ufb01ndings cohere with the preliminary report of a positive correlation between the total number of sleep spindles for the night and performance IQ and full scale IQ respectively (Nader and Smith, 2001).<\/p>\n
As the sleep-EEG of the prefrontal derivations showed the most robust correlations with RPMT scores in our study, present results are in accordance with data supporting the role of the prefrontal cortex in intelligent behaviour (Duncan et al., 2000; Gray and Thompson, 2004; Gray et al., 2003) as well as with the relationship between prefrontal grey matter volume and IQ (Posthuma et al., 2002; Thompson et al., 2001). The deterioration of intellectual functions is an inherent property of dementia and demented patients were shown to have a reduced number of sleep spindles when compared with non- demented, age-matched controls (Petit et al., 2004). Hitherto changes in the topographical distribution of sleep spindles of demented patients have not been analysed, but we would hypothesize an association between the reduction in frontal fast spindles and cognitive impairments assessed by the RPMT. Although our results are in line with other reports showing a predominant anterior localization of slow spindles (Anderer et al., 2001; De Gennaro and Ferrara, 2003; Zeitlhofer et al., 1997), there was a lack of association between slow spindles and RPMT scores. This might relate to their di\ufb00erent physiological source as suggested by the predominance of frontal slow spindles during the period of immaturity of frontal lobes (Shinomiya et al., 1999). Moreover, there is evidence suggesting that slow spindles could be in fact anterior peaks of alpha activity during NREM sleep (De Gennaro and Ferrara, 2003). Despite the fact that fast spindles were more abundant in centroparietal derivations in our study and in other reports as well (Anderer et al., 2001; De Gennaro and Ferrara, 2003; Zeitlhofer et al., 1997), subjects characterized by relatively high frontal fast spindle density and grouping were shown to perform better on the RPMT. Thus, e\ufb00ective frontal triggering and grouping of fast spindles during NREM sleep correlates with general mental ability.<\/p>\n
Recently, Anderson and Horne (2003) reported signi\ufb01cant associations between 0.5 and 1.0 Hz power from the left frontal EEG channel in the \ufb01rst NREM period and performance at tasks more speci\ufb01c to the left prefrontal cortex in\u00a0healthy older people. A high level of slow (0.5\u20131.0 Hz) oscillatory activity during sleep was associated with a better performance in prefrontal cortical tasks. However, negative correlations between 0.5 and 1.0 Hz power from the right frontal EEG channel in the \ufb01rst NREM period, and \ufb02uid intelligence measured by the Cattell test. As there is strong evidence that the lateral prefrontal cortex supports intelligent behaviour (Duncan et al., 2000; Gray and Thompson, 2004; Gray et al., 2003), their results are somewhat inconsistent in this regard. We did not \ufb01nd signi\ufb01cant correlations between \ufb02u
\nid intelligence assessed by the RPMT and the amplitude spectra of the slow oscillation. This discrepancy could have a methodological basis. Only older individuals were examined in the Anderson and Horne (2003) study, while our results are based on a relatively younger sample. Power spectral density in the 0.5\u20131.0 Hz band was expressed as a percent of delta (0.5\u2013 4.5 Hz) power by Anderson and Horne (2003), while we used absolute and relative amplitude spectra in our study. Bipolar EEG recordings were used by Anderson and Horne (2003), while we used contralateral mastoid reference in the present study. Finally, the whole NREM sleep (both stage 2 and slow- wave sleep) of the \ufb01rst cycle was analysed in the Anderson and Horne (2003) report, while we used only slow-wave sleep in our analyses regarding the slow oscillation. However, we have found a distinct grouping of sleep spindles by the slow oscillation: fast sleep spindling during the positive phases of the slow oscillation was in general 1.3\u20132.0 or higher than fast sleep spindling during the negative phases of the slow oscillation. This is in accordance with the notion that the slow oscillation controls the synchronization of other sleep rhythms (Steriade, 2003), as well as with other studies reporting similar results (Fell et al., 2002; M\u00f6lle et al., 2002). Although there was no relationship between the amplitude spectra of the slow oscillation and RPMT scores in our study, individual di\ufb00erences in grouping of fast sleep spindles by the slow oscillation correlated positively with general mental ability. This could be interpreted as a relationship between e\ufb00ective cortical modulation of thalamocortical spindle oscillations and intelligence.<\/p>\n
As the daytime activity of our subjects was controlled in this study, correlations re\ufb02ect a relationship between baseline characteristics of individual-speci\ufb01c sleep EEG features and general mental ability.<\/p>\n
The density and grouping of sleep spindles, which are triggered by the slow oscillation (Steriade, 2003) depending on intracortical connectivity (Amzica and Steriade, 1995) correlate with general mental ability measured by the RPMT. Thus, general mental ability is related to intracortical connectivity of neural networks. This result is in accordance with the hypothesis of Anderson (1995) that brain connectivity is responsible for the biological correlates of IQ discovered so far.<\/p>\n
This does not mean that learning has no e\ufb00ect on corticothalamic and neocortico-hippocampal processes during night- time sleep. The latter hypothesis is supported by the study of Gais et al. (2002). However, there are some important issues to rise in this regard. The possibility exists that slow and fast sleep spindles are di\ufb00erentially involved in these processes. Despite the fact that there is evidence for the functional di\ufb00erentiation of slow and fast sleep spindles (De Gennaro and Ferrara, 2003; Shinomiya et al., 1999), these were not di\ufb00erentially measured in the Gais et al. (2002) study. Finally, learning performance correlated positively with baseline (prelearning) sleep spindle density in the Gais et al. (2002) report, which suggests a trait-like relationship between sleep and cognition similar to that observed in the present study.<\/p>\n
However, one cannot rule out the possibility that use dependency of spindles could be re\ufb02ected throughout the night. In motor learning, \ufb01nal stages of o\ufb00-line stage 2 sleep- dependent neuronal reprocessing and consolidation of memory traces are hypothesized to be related to sleep spindling in the second half of the night (Smith et al., 2004). Taking into consideration the above-mentioned theory and the fact that high IQ persons perform better on motor learning demanding their non-dominant hands than low IQ persons (Tan, 1989), the possibility exists that di\ufb00erences in spindle density of our subjects re\ufb02ect the brain correlate of some motor learning processes.<\/p>\n
Several studies suggest that sleep spindles have a sleep protective function playing an active role in inducing and maintaining sleep and a\ufb00ecting arousal by blocking the transmission of external stimuli through the thalamus to the cortex (De Gennaro and Ferrara, 2003). This could mean that general mental ability correlates not only with sleep spindle density, but also with a less disturbed sleep. However, there was quite weak evidence supporting this hypothesis in our results. Although percentage of time spent in stage 2 sleep correlated positively with performance in the RPMT, no relationship between RPMT scores and signs of disturbed sleep, like wake after sleep onset or stage 1 sleep was observed. Therefore, we agree with the view that to the extent to which sleep spindles can be considered to play a role in sleep preservation by inhibiting or attenuating potentially arousing stimuli, these e\ufb00ects appear to be restricted to endogenously generated stimuli and are passive rather than reactive in nature (Pivik et al., 1999).<\/p>\n
In conclusion, the above results suggest that cortical network e\ufb03ciency, particularly in prefrontal areas, ensuring intelligent behaviour during wakefulness is re\ufb02ected in e\ufb00ective triggering and grouping of fast sleep spindles during night-time NREM sleep. According to the e\ufb00ect size estimates for Pearson correlation coe\ufb03cients, correlations above 0.50 are of crucial practical importance (Hojat and Xu, 2004). Therefore, our results raise the possibility of the prominent signi\ufb01cance of sleep-EEG in unravelling the neural bases of human cognitive functions.<\/p>\n
ACKNOWLEDGEMENTS<\/h3>\n
The authors wish to thank Zolt\u00e1n Somogyv\u00e1ri for his helpful advices concerning data analysis. This work was supported by grants of the Hungarian Medical Research Council (ETT-162\/2003) and the National O\ufb03ce for Research and Technology (NKFP-1B\/020\/04).<\/p>\n
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<\/span> \t\t\t\t\t<\/p>\n","protected":false},"excerpt":{"rendered":"<\/a> Authors: R\u00d3BERT B\u00d3DIZS1, TAM\u00c1S KIS2, ALP\u00c1R S\u00c1NDOR L\u00c1Z\u00c1R1, LINDA HAVR\u00c1N3, P\u00c9TER RIG\u00d31, ZS\u00d3FIA CLEMENS4 and P\u00c9TER HAL\u00c1SZ4 1 Institute of Behavioural Sciences, Semmelweis University, Budapest, Hungary 2 Medical and Pharmaceutical University of T\u00e2rgu-Mure\u015f, T\u00e2rgu-Mure\u015f, Romania 3 Department of Psychology, K\u00e1roli G\u00e1sp\u00e1r University, Budapest, Hungary4 Department of Neurology, National Institute of Psychiatry and Neurology, Budapest, Hungary …<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[6],"tags":[],"class_list":["post-169","post","type-post","status-publish","format-standard","hentry","category-articles-in-professional-journals"],"acf":[],"_links":{"self":[{"href":"https:\/\/semmelweis.hu\/psychophysiology\/wp-json\/wp\/v2\/posts\/169","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/semmelweis.hu\/psychophysiology\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/semmelweis.hu\/psychophysiology\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/semmelweis.hu\/psychophysiology\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/semmelweis.hu\/psychophysiology\/wp-json\/wp\/v2\/comments?post=169"}],"version-history":[{"count":2,"href":"https:\/\/semmelweis.hu\/psychophysiology\/wp-json\/wp\/v2\/posts\/169\/revisions"}],"predecessor-version":[{"id":438,"href":"https:\/\/semmelweis.hu\/psychophysiology\/wp-json\/wp\/v2\/posts\/169\/revisions\/438"}],"wp:attachment":[{"href":"https:\/\/semmelweis.hu\/psychophysiology\/wp-json\/wp\/v2\/media?parent=169"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/semmelweis.hu\/psychophysiology\/wp-json\/wp\/v2\/categories?post=169"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/semmelweis.hu\/psychophysiology\/wp-json\/wp\/v2\/tags?post=169"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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