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Individual Characteristics of the Sleep Electroencephalogram As Markers of Intelligence– Effects in a Broad Age and Intelligence Range

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Individual Characteristics of the Sleep Electroencephalogram As Markers of Intelligence– Effects in a Broad Age and Intelligence Range Individual Characteristics of the Sleep Electroencephalogram as Markers of Intelligence– Effects in a Broad Age and Intelligence Range Doctoral thesis Péter Przemyslaw Ujma Semmelweis University Doctoral School of Mental Health Sciences Advisor: Róbert Bódizs, PhD, chief research fellow Official Reviewers: Gábor Csukly, MD, Ph.D., lecturer Kristóf Kovács, Ph.D., chief research fellow President of the Qualification Committee: István Bitter, MD, DSc, professor Members of the Qualification Committee:Pál Czobor, MD, Ph.D., associate professor Dezs ő Németh, Ph.D., associate professor Budapest 2015 Table of Contents List of abbreviations ......................................................................................................... 3 1. Introduction .................................................................................................................. 3 1.1. Sleep as a Biological State ................................................................................................. 5 1.1.1. Basic Features and Regulation of Sleep ...................................................................... 5 1.1.2. Potential Functions of Sleep, Slow Waves and Spindles .......................................... 11 1.1.3. Methodological Problems – Measuring Spectra and Sleep Spindles ........................ 23 1.2. Intelligence ....................................................................................................................... 28 1.2.1. Traditional Views of Intelligence .............................................................................. 29 1.2.2. Non-Cognitive Correlates of IQ ................................................................................ 38 1.2.3. The K-factor .............................................................................................................. 41 1.3. Sleep, cognition and intelligence ..................................................................................... 45 1.3.1. Memory consolidation ............................................................................................... 45 1.3.2. Intelligence ................................................................................................................ 47 2. Aims ........................................................................................................................... 54 3. Methods ...................................................................................................................... 55 3.1. Study 1 – Children ........................................................................................................... 60 3.1.1. Recruitment, Ethics and Psychometric Testing ......................................................... 60 3.2.2. Polysomnography Recording and Scoring ................................................................ 61 3.2.3. Spectral Analysis, Sleep Spindle Detection and Statistics ........................................ 62 3.2. Study 2 – Adolescents ...................................................................................................... 63 3.1.1. Recruitment, Ethics and Psychometric Testing ......................................................... 63 3.3.2. Polysomnography Recording and Scoring ................................................................ 64 3.3.3. Spectral Analysis, Sleep Spindle Detection and Statistics ........................................ 65 3.3. Study 3 – Adults ............................................................................................................... 65 3.3.1. Recruitment, Ethics and Psychometric Testing ......................................................... 65 3.3.2. Polysomnography Recording and Scoring ................................................................ 67 3.3.3. Spectral Analysis, Sleep Spindle Detection and Statistics ........................................ 68 4. Results ........................................................................................................................ 70 4.1. Study 1 – Children ........................................................................................................... 70 4.1.1. Basic biological and psychometric data .................................................................... 70 1 4.1.2. Sleep macrostructure and sleep spindles ................................................................... 70 4.1.3. Correlations between EEG data and intelligence ...................................................... 72 4.2. Study 2 – Adolescents ...................................................................................................... 78 4.2.1. Basic biological and psychometric data .................................................................... 78 4.2.2. Sleep macrostructure and sleep spindles ................................................................... 79 4.2.3. Correlations between EEG data and intelligence ...................................................... 81 4.3. Study 3 – Adults ............................................................................................................... 86 4.3.1. Basic biological and psychometric data .................................................................... 86 4.3.2. Sleep macrostructure and sleep spindles ................................................................... 86 4.3.3. Correlations between EEG data and intelligence ...................................................... 89 5. Discussion ................................................................................................................... 95 Summary ....................................................................................................................... 103 Összefoglalás ................................................................................................................ 104 References .................................................................................................................... 105 Publications .................................................................................................................. 132 Acknowledgements ..................................................................................................... 133 2 List of abbreviations APM – Advanced Progressive Matrices BA – Brodmann area CPM – Coloured Progressive Matrices DTI – diffusion tensor imaging EEG – electroencephalography FDR – false discovery rate FFT – Fast Fourier Transform fMRI – functional magnetic resonance imaging IAM – individual adjustment method IQ – intelligence quotient MRI – magnetic resonance imaging NREM – non-rapid eye movement PET – positron emission tomography PTSD – post-traumatic stress disorder REM – rapid eye movement RPMT – Raven Progressive Matrices Test SD – standard deviation SWS – slow wave sleep WASO – wake after sleep onset 1. Introduction 3 A typical human being spends approximately one third of his life sleeping, which is hardly matched by any other activity typical for our lives. Despite its prominence, sleep has been an elusive subject to study, and its functional importance has not been known until the second half of the last century. Once a subject of religious speculations, a source of mystery and prophetic dreams, sleep has been revealed to be a very particular neurobiological state in which the central nervous system enters a drastically altered state of functioning compared to wakefulness. While not all questions regarding the functions and mechanisms of sleep have been completely elucidated, by now it is certain the changes in the functioning of the central nervous system sleep brings about are crucial for optimal functioning in wakefulness. Individual characteristics of sleep variables have also been revealed to correlate with intelligence. Single-factor intelligence has been repeatedly confirmed as a valid and reliable psychometric tool for over a century, and its importance is increased even further in new theories of its interpretation which stress that based on intelligence reliable predictions can be made not only of cognitive functioning or social status, but also about health and longevity. Therefore, the study of the relationship between sleep and intelligence links two fields – one from a neurobiological and one from a psychometric domain – which are of exceptional importance for human life. The introduction section of this thesis briefly presents the most important morphologic and functional aspects of sleep – particularly NREM sleep – and the mechanisms by which it can contribute to cognitive functioning. Sleep spindles – NREM oscillations often implicated in the relationship between cognition and sleep – will be described in detail. The introduction also reviews some of the most important theoretical and empirical results related to intelligence, demonstrating that intelligence, on the one hand, can be conceptualized as a single-factor construct and on the other hands its importance extends beyond the cognitive domain into basic aspects of life strategies. Furthermore, the introduction reviews previous studies about the relationship between sleep and cognition, and it also comments on some important methodological details which were considered in the studies presented in the Methods and Results section. Our investigations were performed on over two hundred subjects with a wide age range (4-70 years) and a similarly wide IQ range (85-160). Our results confirm the 4 relationship between individual EEG characteristics in sleep and intelligence,
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