26A novel method for deriving composite, non-redundant measures of non-rapid eye 27 movement (NREM) sleep electroencephalogram (EEG) is developed on the basis of the 28 power law scaling of the Fourier spectra. Measures derived are the spectral intercept, the 29 slope (spectral exponent), as well as the maximal whitened spectral peak amplitude and 30 frequency in the sleep spindle range. As a proof of concept, we apply these measures on a 31 large sleep EEG dataset (N = 175; 81 females; age range: 17-60 years) with previously 32 demonstrated effects of age, sex and intelligence. As predicted, aging is associated with 33 decreased overall spectral slopes (increased exponents) and whitened spectral peak 34 amplitudes in the spindle frequency range. In addition, age associates with decreased sleep 35 spindle spectral peak frequencies in the frontal region. Women were characterized by higher 36 spectral intercepts and higher spectral peak frequencies in the sleep spindle range. No sex 37 differences in whitened spectral peak amplitudes of the sleep spindle range were found. 38 Intelligence correlated positively with whitened spectral peak amplitudes of the spindle 39 frequency range in women, but not in men. Last, age-related increases in spectral exponents 40 did not differ in subjects with average and high intelligence. Our findings replicate and 41 complete previous reports in the literature, indicating that the number of variables describing 42 NREM sleep EEG can be effectively reduced in order to overcome redundancy and Type I 43 statistical errors in future electrophysiological studies of sleep. 44 45 46 47 48 Given the tight reciprocal relationship between sleep and wakefulness, the objective 49 description of the complex neural activity patterns characterizing human sleep is of utmost 50 importance in understanding the several facets of brain function, like sex differences, aging 51 and cognitive abilities. Current approaches are either exclusively based on visual impressions 52 expressed in graded levels of sleep depth (W, N1, N2, N3, REM), whereas computerized 53quantitative methods provide an almost infinite number of potential metrics, suffering from 54 significant redundancy and arbitrariness. Our current approach relies on the assumptions that 55 the spontaneous human brain activity as reflected by the scalp-derived electroencephalogram 56 (EEG) are characterized by coloured noise-like properties. That is, the contribution of 57 different frequencies to the power spectrum of the signal are best described by power law 58 functions with negative exponents. In addition, we assume, that stages N2-N3 are further 59 characterized by additional non-random (non-noise like, sinusoidal) activity patterns, which 60 are emerging at specific frequencies, called sleep spindles (9-18 Hz). By relying on these 61 assumptions we were able to effectively reduce 191 spectral measures to 4: (1) the spectral 62 intercept reflecting the overall amplitude of the signal, (2) the spectral slope reflecting the 63 constan...

A kognitív epidemiológia az intelligencia és az egészségi állapot összefüggésének tudo- mánya. A modern, sokszor több százezer fős, teljes populációkon végzett kognitív epide- miológiai vizsgálatok eredményei alapján a magasabb premorbid intelligencia gya- korlatilag valamennyi mentális betegség, illetve pszichiátriai probléma alacsonyabb kockázatával függ össze. A magasabb premorbid intelligencia a halálozás, a szív- és ér- rendszeri betegségek, a metabolikus betegségek, a rossz egészség-magatartás és számos kisebb népegészségügyi jelentőségű betegség előfordulásával is negatívan függ össze; a légzőszervi betegségekkel és a dohányzáshoz nem köthető daganatokkal azonban gyen- ge vagy hiányzik az összefüggés. A mentális betegségekkel való összefüggést nem, a szo- matikus betegségekkel és a mortalitással való összefüggést azonban részben mediálják a felnőttkori szocioökonómiai státusz mutatói. A speciális vizsgálati elrendezések – úgymint ikerkontroll-vizsgálatok, pszeudoexperimentális vizsgálatok, valamint a mendeli ran- domizáció módszerét használó molekuláris genetikai vizsgálatok – eredményei arra utal- nak, hogy az intelligencia és az egészség közötti kapcsolat jelentős részét genetikai ténye- zők közvetítik, de a szomatikus egészségre a magasabb intelligencia következményeként elérhető jobb szocioökonómiai státusz is szerény hatást gyakorol.Cognitive epidemiology is the science of the relationship between intelligence and health. Modern studies of cognitive epidemiology, often with samples of several hundreds of thousands of individuals, have revealed that higher premorbid intelligence is associated with a lower risk of virtually all of mental illnesses and psychiatric problems. Higher premorbid intelligence is also associated negatively with the incidence of mortality, circulatory illness, metabolic illness, poor health behavior and many diseases of lower epidemiological significance, but its relationship to respiratory illness and non-smoking related cancers is weaker or non-existent. Indicators of adult socioeconomic status do not mediate the association between intelligence and mental illness, but they do partially mediate the relationship with somatic illness and mortality. Studies with special designs -twin control studies, pseudo-experimental studies and molecular genetic studies using Mendelian randomization – suggest that the relationship between intelligence and health is heavily mediated by genetic factors, but somatic health may be modestly but causally improved by better social status as a consequence of higher intelligence.

All sleep EEG recordings can be contaminated by artifacts. Both visual and automatic methods have been developed to mark such erroneous segments of EEG data. Here we systematically explore the effect of artifacts on the sleep EEG power spectrum density (PSD), and we compare gold-standard visual detections to a simple automatic detector using Hjorth parameters to identify artifacts. We find that most distortions in the all-night average PSD occur because of a small minority of highly anomalous artifacts, which mainly affect the beta and gamma frequency ranges and NREM delta. Visual and automatic detections only show moderate agreement in which data segments are artefactual. However, the resulting all-night average PSD is highly similar across all methods, and PSDs calculated with all methods successfully recover the known correlations of PSD with age and sex. No parameter settings of the automatic detector clearly outperformed others. Additionally, we show that accurate average PSD estimates can be recovered from just a fraction of available data epochs. Our results suggest that artifacts represent a minor and easily solvable problem in sleep EEG recordings. Most visually identified artifacts do not seriously distort estimates of mid-frequency activity in the sleep EEG spectrum, and distortions to low and high frequencies can be eliminated using a simple automatic detection method nearly as well as with visual detections. These findings show that the visual inspection of EEG data is not necessary to eliminate the effects of artifacts, which is encouraging for the expected performance of automatic preprocessing in large sleep EEG databases.

Slow waves are major pacemakers of NREM sleep oscillations. While slow waves themselves are mainly generated by cortical neurons, it is not clear what role thalamic activity plays in the generation of some oscillations grouped by slow waves, and to what extent thalamic activity during slow waves is itself driven by corticothalamic inputs. To address this question, we simultaneously recorded both scalp EEG and local field potentials from six thalamic nuclei (bilateral anterior, mediodorsal and ventral anterior) in fifteen epileptic patients (age-range: 17-64 years, 7 females) undergoing Deep Brain Stimulation Protocol and assessed the temporal evolution of thalamic activity relative to scalp slow waves using time-frequency analysis. We found that thalamic activity in all six nuclei during scalp slow waves is highly similar to what is observed on the scalp itself. Slow wave downstates are characterized by delta, theta and alpha activity and followed by beta, high sigma and low sigma activity during subsequent upstates. Gamma activity in the thalamus is not significantly grouped by slow waves. Theta and alpha activity appeared first on the scalp, but sigma activity appeared first in the thalamus. These effects were largely independent from the scalp region in which SWs were detected and the precise identity of thalamic nuclei. Our results indicate that while small thalamocortical neuron assemblies may initiate cortical oscillations, especially in the sleep spindle range, the large-scale neuronal activity in the thalamus which is detected by field potentials is principally driven by global cortical activity, and thus it is highly similar to what is observed on the scalp.