Long-Range Temporal Correlations of Patients in Minimally Conscious State Modulated by Spinal Cord Stimulation

Spinal cord stimulation (SCS) has been shown to improve the consciousness levels of patients with disorder of consciousness (DOC). However, the underlying mechanisms of SCS remain poorly understood. This study recorded resting-state electroencephalograms (EEG) from 16 patients with minimally conscious state (MCS), before and after SCS, and investigated the mechanisms of SCS on the neuronal dynamics in MCS patients. Detrended fluctuation analysis (DFA), combined with surrogate data method, was employed to measure the long-range temporal correlations (LRTCs) of the EEG signals. A surrogate data method was utilized to acquire the genuine DFA exponents (GDFAE) reflecting the genuine LRTCs of brain activity. We analyzed the GDFAE in four brain regions (frontal, central, posterior, and occipital) at five EEG frequency bands [delta (1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–45 Hz)]. The GDFAE values ranged from 0.5 to 1, and showed temporal and spatial variation between the pre-SCS and the post-SCS states. We found that the channels with GDFAE spread wider after SCS. This phenomenon may indicate that more cortical areas were engaged in the information integration after SCS. In addition, the GDFAE values increased significantly in the frontal area at delta, theta, and alpha bands after SCS. At the theta band, a significant increase in GDFAE was observed in the occipital area. No significant change was found at beta or gamma bands in any brain region. These findings show that the enhanced LRTCs after SCS occurred primarily at low-frequency bands in the frontal and occipital regions. As the LRTCs reflect the long-range temporal integration of EEG signals, our results indicate that information integration became more “complex” after SCS. We concluded that the brain activities at low-frequency oscillations, particularly in the frontal and occipital regions, were improved by SCS

Are intrinsic neural timescales related to sensory processing? Evidence from abnormal behavioral states

The brain exhibits a complex temporal structure which translates into a hierarchy of distinct neural timescales. An open question is how these intrinsic timescales are related to sensory or motor information processing and whether these dynamics have common patterns in different behavioral states. We address these questions by investigating the brain\u27s intrinsic timescales in healthy controls, motor (amyotrophic lateral sclerosis, locked-in syndrome), sensory (anesthesia, unresponsive wakefulness syndrome), and progressive reduction of sensory processing (from awake states over N1, N2, N3). We employed a combination of measures from EEG resting-state data: auto-correlation window (ACW), power spectral density (PSD), and power-law exponent (PLE). Prolonged neural timescales accompanied by a shift towards slower frequencies were observed in the conditions with sensory deficits, but not in conditions with motor deficits. Our results establish that the spontaneous activity\u27s intrinsic neural timescale is related to the neural capacity that specifically supports sensory rather than motor information processing in the healthy brain

Are intrinsic neural timescales related to sensory processing? Evidence from abnormal behavioral states

The brain exhibits a complex temporal structure which translates into a hierarchy of distinct neural timescales. An open question is how these intrinsic timescales are related to sensory or motor information processing and whether these dynamics have common patterns in different behavioral states. We address these questions by investigating the brain\u27s intrinsic timescales in healthy controls, motor (amyotrophic lateral sclerosis, locked-in syndrome), sensory (anesthesia, unresponsive wakefulness syndrome), and progressive reduction of sensory processing (from awake states over N1, N2, N3). We employed a combination of measures from EEG resting-state data: auto-correlation window (ACW), power spectral density (PSD), and power-law exponent (PLE). Prolonged neural timescales accompanied by a shift towards slower frequencies were observed in the conditions with sensory deficits, but not in conditions with motor deficits. Our results establish that the spontaneous activity\u27s intrinsic neural timescale is related to the neural capacity that specifically supports sensory rather than motor information processing in the healthy brain

Are intrinsic neural timescales related to sensory processing? Evidence from abnormal behavioral states

The brain exhibits a complex temporal structure which translates into a hierarchy of distinct neural timescales. An open question is how these intrinsic timescales are related to sensory or motor information processing and whether these dynamics have common patterns in different behavioral states. We address these questions by investigating the brain\u27s intrinsic timescales in healthy controls, motor (amyotrophic lateral sclerosis, locked-in syndrome), sensory (anesthesia, unresponsive wakefulness syndrome), and progressive reduction of sensory processing (from awake states over N1, N2, N3). We employed a combination of measures from EEG resting-state data: auto-correlation window (ACW), power spectral density (PSD), and power-law exponent (PLE). Prolonged neural timescales accompanied by a shift towards slower frequencies were observed in the conditions with sensory deficits, but not in conditions with motor deficits. Our results establish that the spontaneous activity\u27s intrinsic neural timescale is related to the neural capacity that specifically supports sensory rather than motor information processing in the healthy brain

Relation of spontaneous and evoked brain activity to language development in young children

Separate studies have shown connections between spontaneous alpha oscillations and language ability in 4-6 year-olds, and between auditory evoked potential (AEP) maturity and language in 7-10-year-olds. The thesis aimed to further our understanding of how these spontaneous and evoked neural measures relate to language development in younger children than previously studied. In this thesis, I first propose a method to investigate spontaneous alpha oscillations and language in 1-4-year-olds. Next, I examined alpha oscillations, AEP maturity, and language ability in 5-6-year-olds. Results revealed that AEP maturity did not predict language ability and correlated with alpha long-range-temporal-correlation but not with alpha power or flexibility. Next, I examined the possibility that this study did not have enough AEP trials. Using a new index called standardized measurement error, I found that AEP trial-by-trial noise decreases with age between 4-7 years, suggesting that future studies of AEP maturity may need more trials for younger ages

Self-Organized Criticality as a Neurodynamical Correlate of Consciousness: A neurophysiological approach to measure states of consciousness based on EEG-complexity features

Background and Objectives This thesis was based on the hypothesis that the physics-derived theoretical framework of self-organized criticality can be applied to the neuronal dynamics of the human brain. From a consciousness science perspective, this is especially appealing as critical brain dynamics imply a vicinity a phase transition, which is associated with optimized information processing functions as well as the largest repertoire of configurations that a system explores throughout its temporal evolution. Hence, self-organised criticality could serve as a neurodynamical correlate for consciousness, which provides the possibility of deriving empirically testable neurophysiological indices suitable to characterise and quantify states of consciousness. The purpose of this work was to experimentally examine the feasibility of the self-organized criticality theory as a correlate for states of consciousness. Therefore, it was aimed at answering the following research questions based on the analysis of three 64 channel EEG datasets: (i) Can signatures of self-organized criticality be found on the level of the EEG in terms of scale-free distribution of neuronal avalanches and the presence of long-range temporal correlations (LRTC) in neuronal oscillations? (ii) Are criticality features suitable to differentiate state of consciousness in the spectrum of wakefulness? (iii) Can the neuronal dynamics be shifted towards the critical point of a phase transition associated with optimized information processing function by mind-body interventions? (iv) Can an explicit relationship to other nonlinear complexity features and power spectral density parameter be identified? (v) Do EEG-based criticality features reflect individual temperament traits? Material and Methods (1): Re-analysis: Thirty participants highly proficient in meditation (mean age 47 years, 11 females/19 males, meditation experience of at least 5 years practice or more than 1000 h of total meditation time) were measured with 64-channel EEG during one session consisting of a task-free baseline resting, a reading condition and three meditation conditions, namely thoughtless emptiness, presence monitoring and focused attention. (2): 64-channel EEG was recorded from 34 participants (mean age 36.0 ±13.4 years, 24 females/ 10 males) before, during and after a professional singing bowl massage. Further, psychometric data was assessed including absorption capacity defined as the individual’s capacity for engaging attentional resources in sensory and imaginative experiences measured by the Tellegen-Absorption Scale (TAS-D), subjective changes in in body sensation, emotional state, and mental state (CSP-14) as well as the phenomenology of consciousness (PCI-K). (3): Electrophysiological data (64 channels of EEG, EOG, ECG, skin conductance, and respiration) was recorded from 116 participants (mean age 40.0 ±13.4 years, 83 females/ 33 males) – in collaboration with the Institute of Psychology, Bundeswehr University Munich - during a task-free baseline resting state. The individual level of sensory processing sensitivity was assessed using the High Sensitive Person Scale (HSPS-G). The datasets were analysed applying analytical tools from self-organized criticality theory (detrended fluctuation analysis, neuronal avalanche analysis), nonlinear complexity algorithms (multiscale entropy, Higuchi’s fractal dimension) and power spectral density. In study 1 and 2, task conditions were contrasted, and effect sizes were compared using a paired two-tailed t-test calculated across participants, and features. T-values were corrected for multiple testing using false discovery rate. To calculate correlations between the EEG features, Spearman’s rank correlation was applied after determining that the distribution was not appropriate for parametric testing by the Shapiro-Wilk test. In addition, in study 1, a discrimination analysis was carried out to determine the classification performance of the EEG features. Here, partial least squares regression and receiver operating characteristics analysis was applied. To determine whether the EEG features reflect individual temperament traits, the individual level of absorption capacity (study 2) and sensory processing sensitivity (study 3) was correlated with the EEG features using Spearman’s rank correlation. Results Signatures of self-organized criticality in the form of scale-free distribution of neuronal avalanches and long-range temporal correlations (LRTCs) in the amplitude of neural oscillations were observed in three distinct EEG-datasets. EEG criticality as well as complexity features were suitable to characterise distinct states of consciousness. In study 1, compared to the task-free resting condition, all three meditative states revealed significantly reduced long-range temporal correlation with moderate effect sizes (presence monitoring: d= -0.49, p<.001; thoughtless emptiness: d= -0.37, p<.001; and focused attention: d= -0.28, p=.003). The critical exponent was suitable to differentiate between focused attention and presence monitoring (d= -0.32, p=.02). Further, in study 2, the criticality features significantly changed during the course of the experiment, whereby values indicated a shift towards the critical regime during the sound condition. Both analyses of the first and second dataset revealed that the critical exponent was significantly negatively correlated with the sample entropy, the scaling exponent resulting from the DFA denoting the amount of long-range temporal correlations as well as Higuchi’s fractal dimension in each condition, respectively. In addition, the critical scaling exponent was found to be significantly negatively correlated with the trait absorption (Spearman's ρ= -0.39, p= .007), whereas an association between critical dynamics and the level of sensory processing sensitivity could not be verified (study 3). Conclusion The findings of this thesis suggest that neuronal dynamics are governed by the phenomena of self-organized criticality. EEG-based criticality features were shown to be sensitive to detect experimentally induced alterations in the state of consciousness. Further, an explicit relationship with nonlinear measures determining the degree of neuronal complexity was identified. Thus, self-organized criticality seems feasible as a neurodynamical correlate for consciousness with the potential to quantify and characterize states of consciousness. Its agreement with the current most influencing theories in the field of consciousness research is discussed

Dynamique de connectivité fonctionnelle et modulation expérimentale des oscillations cérébrales en sommeil dans le vieillissement

Le sommeil est un état de conscience faisant preuve d’un vaste potentiel au niveau clinique. Par exemple, le sommeil est devenu un outil dans le diagnostic précoce de certains processus dégénératifs au sein du cerveau, ainsi que dans le traitement de différents troubles physiologique et psychologiques. Son potentiel pourrait même être augmenté via la stimulation cérébrale. Bien que le cerveau soit un centre de communication majeur, la recherche en sommeil s’est principalement centrée sur des mesures statiques du sommeil. L’étude des patrons de communication entre les différentes régions du cerveau nous permet pourtant d’inférer sur leur utilité fonctionnelle chez l’humain. Et si ces patrons de communication permettaient une compréhension plus intégrée des changements du sommeil à travers la vie et de leurs conséquences au cours du vieillissement? Cette thèse permet d’étudier, sous un angle dynamique et novateur, l’interaction de l’activité neuronale et la modulation expérimentale du sommeil au cours du vieillissement normal. À l’aide de l’électroencéphalographie, la connectivité fonctionnelle cérébrale est évaluée à l’échelle des stades et des cycles de sommeil, de l’onde lente elle-même, ainsi que sous une perspective expérimentale grâce à la stimulation transcrânienne par courant alternatif. Les résultats des deux premières études démontrent les changements au niveau de la connectivité cérébrale en sommeil au cours du vieillissement, tandis que la troisième étude démontre la possibilité de moduler cette connectivité, ainsi que les oscillations cérébrales, chez la population âgée. À l’échelle des stades de sommeil, le cerveau des personnes âgées est plus connecté lors du sommeil lent profond et moins connecté lors du sommeil lent léger, comparativement aux jeunes adultes. Ces différences d’âge sont d’ailleurs plus importantes en début de nuit. Nos résultats démontrent aussi une diminution de connectivité associée à la phase de dépolarisation de l’onde lente chez la population âgée, comparativement aux jeunes adultes. Chez ces derniers, une augmentation marquée de la connectivité pendant l’onde lente est observée. La connectivité au cours de l’onde lente est aussi affectée par la présence d’un fuseau de sommeil en simultané, suggérant soit le mixte de leurs réseaux ou la mise en place de ceux du fuseau. Nous démontrons également, grâce à l’utilisation de métriques novatrices, la présence de deux types d’ondes lentes avec une dynamique de connectivité qui leur est propre, suggérant qu’elles soient impliquées dans des processus fonctionnels distincts. Pendant une sieste, l’utilisation de la stimulation transcrânienne par courant alternatif a aussi permis de moduler les fuseaux de sommeil, leur couplage avec l’onde lente ainsi que la connectivité fonctionnelle des individus âgés. Ces résultats, bien que modestes, démontrent l’aspect prometteur de la modulation non-pharmacologique du sommeil. Non seulement cette thèse fournit une vision intégrée des changements de connectivité fonctionnelle au cours du vieillissement, mais elle démontre qu’il est possible de moduler le sommeil des personnes âgées à des fins ultimement thérapeutiques. Le manque de flexibilité des différents réseaux des personnes âgées pourrait être à la base, entre autres, des changements au niveau de la consolidation de la mémoire. Les implications de nos résultats pourraient être pertinentes à l’étude des processus de plasticité ayant lieu au cours du sommeil.Sleep is a state of consciousness which shows a great potential in the clinical field. For instance, sleep has become a tool in the early diagnosis of certain neurodegenerative processes, as well as in the treatment of various physiological and psychological disorders. Its potential could even be increased via brain stimulation. Although the brain is a major communication center, sleep research has mainly focused on static measures of sleep. The study of the patterns of communication between the different regions of the brain nevertheless allows us to infer on their functional utility in humans. What if these patterns of communication allowed a more integrated understanding of sleep changes throughout life and their consequences during aging? This thesis investigates, from an innovative and dynamic angle, the interaction of neuronal activity and experimental modulation of sleep in normal aging. Using electroencephalography, functional connectivity is assessed at the scale of sleep stages and cycles, at the scale of the slow wave itself, and from an experimental perspective using the transcranial alternating current stimulation. The results in our first two studies demonstrate changes in EEG functional connectivity during sleep in aging while our third study showed the possibility of experimentally modulating functional connectivity as well as brain oscillations in the same population. At the sleep stage scale, the brain of older individuals is more connected during slow wave sleep and less connected during lighter sleep, compared to young adults. These age differences are predominant at the beginning of the night. Our results also demonstrate a decrease in functional connectivity associated with the slow wave depolarization phase in older individuals. In the young ones, brain connectivity associated to a slow wave is markedly increased. Functional connectivity during slow wave depolarization is also affected by the simultaneous presence of sleep spindles, suggesting either the admixture of their networks or the establishment of those underlying spindle occurrence. We also demonstrate, through the use of novel metrics, the presence of two types of slow waves, each endowed with specific connectivity dynamics. This suggests the presence of distinct functional implications. These slow waves types could also be inherently modulated by distinct physiological processes. During a nap, the use of transcranial alternating current stimulation has made it possible to experimentally modulate sleep spindles, their coupling with the slow waves, and functional connectivity in older individuals. These results, although modest, demonstrate the promising aspect of non-pharmacological sleep modulation. This thesis provides an integrated view of functional connectivity changes in aging and also demonstrates the feasibility of experimental sleep modulation in older individuals. The lack of network flexibility that we described in the older population in term of connectivity could underlie changes in sleep-dependent memory consolidation processes. The implications of our results is relevant to the study of sleep-dependent plasticity processes