Acute sleep deprivation induces a local brain transfer information increase in the frontal cortex in a widespread decrease context

Sleep deprivation (SD) has adverse effects on mental and physical health, affecting the cognitive abilities and emotional states. Specifically, cognitive functions and alertness are known to decrease after SD. The aim of this work was to identify the directional information transfer after SD on scalp EEG signals using transfer entropy (TE). Using a robust methodology based on EEG recordings of 18 volunteers deprived from sleep for 36 h, TE and spectral analysis were performed to characterize EEG data acquired every 2 h. Correlation between connectivity measures and subjective somnolence was assessed. In general, TE showed medium-and long-range significant decreases originated at the occipital areas and directed towards different regions, which could be interpreted as the transfer of predictive information from parieto-occipital activity to the rest of the head. Simultaneously, short-range increases were obtained for the frontal areas, following a consistent and robust time course with significant maps after 20 h of sleep deprivation. Changes during sleep deprivation in brain network were measured effectively by TE, which showed increased local connectivity and diminished global integration. TE is an objective measure that could be used as a potential measure of sleep pressure and somnolence with the additional property of directed relationships.Postprint (published version

Acute sleep deprivation induces a local brain transfer information increase in the frontal cortex in a widespread decrease context

Sleep deprivation (SD) has adverse effects on mental and physical health, affecting the cognitive abilities and emotional states. Specifically, cognitive functions and alertness are known to decrease after SD. The aim of this work was to identify the directional information transfer after SD on scalp EEG signals using transfer entropy (TE). Using a robust methodology based on EEG recordings of 18 volunteers deprived from sleep for 36 h, TE and spectral analysis were performed to characterize EEG data acquired every 2 h. Correlation between connectivity measures and subjective somnolence was assessed. In general, TE showed medium-and long-range significant decreases originated at the occipital areas and directed towards different regions, which could be interpreted as the transfer of predictive information from parieto-occipital activity to the rest of the head. Simultaneously, short-range increases were obtained for the frontal areas, following a consistent and robust time course with significant maps after 20 h of sleep deprivation. Changes during sleep deprivation in brain network were measured effectively by TE, which showed increased local connectivity and diminished global integration. TE is an objective measure that could be used as a potential measure of sleep pressure and somnolence with the additional property of directed relationships.Postprint (published version

The effect of concurrent cognitive-visuomotor multitasking and task difficulty on dynamic functional connectivity in the brain

This thesis investigated the effect of visuomotor and working memory 1) task difficulty and 2) multitasking on dynamic functional connectivity in the brain. Studies have only recently begun to investigate functional connectivity within the scope of concurrent dual task or varying task difficulty conditions (Cocchi, Zalesky, et al. 2011; Rietschel et al. 2012). A series of EEG recordings were conducted during execution of visuomotor or working memory tasks within a novel paradigm using BCI2VR custom MATLAB toolbox. Functional connectivity was correlated with task-related coherence (TRCoh) analysis between two task conditions involving either variation in task difficulty or concurrent execution during multitasking within the delta (0 – 4 Hz), theta (4 – 8 Hz), alpha (8 – 12 Hz), beta1 (12-16 Hz), beta2 (16 – 20 Hz) and beta3 (20 – 24 Hz) frequency bands. An increase in coherence was observed with increased cognitive load, during both increased task difficulty and multitasking, in all frequency bands except beta1 and beta2. This may suggest that the psychomotor efficiency hypothesis also applies to multitasking as well as task difficulty. Decreases in beta coherence were observed with increased performance error, indicating that interregional beta coherence may not follow the PEH trend. The increased coherence between brain regions in the alpha, delta and theta bands contributes to the growing volume of research on quantifying cognitive workload and may serve as a future basis on increasing multitasking efficiency during high stress environments. Further research recording multitasking effects on individuals over regular intervals during an extended period of time (months or years) will be required to better understand changes in functional connectivity within the brain

The cognitive control of emotional distraction in working memory: from behaviour to functional connectivity

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Psicología, Departamento de Psicología Básica II (Procesos Cognitivos), leída el 04-07-2014Depto. de Psicología Experimental, Procesos Cognitivos y LogopediaFac. de PsicologíaTRUEunpu

The neurophysiology of intersensory selective attention and task switching

Our ability to selectively attend to certain aspects of the world and ignore others is fundamental to our day-to-day lives. The need for selective attention stems from capacity limitations inherent in our perceptual and cognitive processing architecture. Because not every elemental piece of our environment can be fully processed in parallel, the nervous system must prioritize processing. This prioritization is generally referred to as selective attention. Meanwhile, we are faced with a world that is constantly in flux, such that we have to frequently shift our attention from one piece of the environment to another and from one task to another. This process is generally referred to as task-switching. Neural oscillations in the alpha band (~8-14 Hz) have been shown to index the distribution of selective attention, and there is increasing evidence that oscillations in this band are in fact utilized by the nervous system to suppress distracting, task-irrelevant information. In order to elaborate on what is known of the function of alpha oscillations as well as current models of both intersensory selective attention and task switching, I investigated the dynamics of alpha amplitude modulations within the context of intersenory selective attention and task switching in neurologically typical young adults. Participants were alternately cued to attend to either the visual or auditory aspect of a compound audio-visual stimulus while high-density electroencephalography was recorded. It is typically found that alpha power increases over parieto-occipital cortices when attention is directed away from the visual modality and to the auditory modality. I report evidence that alpha oscillations play a role in task-switching (e.g., when switching from attending the visual task versus repeating this task), specifically as biasing signals, that may operate to re-weight competition among two tasks-sets. I further investigated the development of these same processes in school-aged children and adolescents. While exhibiting typical patterns of alpha modulations relevant to selective attention, Young school-aged children (8-12 years), compared to older participants, did not demonstrate specific task switching modulation of alpha oscillations, suggesting that this process does not fully develop until late adolescence. Finally, children and adolescents on the autism spectrum failed altogether to exhibit differentiation of alpha power between attend-visual and attend-auditory conditions--an effect present in age and IQ matched controls--suggesting that ASD individuals may have a deficit in the overall top-down deployment of alpha oscillatory biasing signals. This could result in an inability to ignore distracting information in the environment, leading to an overwhelming, disordered experience of the world, resulting in profound effects on the both social interaction and cognitive development. Altogether, these findings add to growing evidence that alpha oscillations serve as domain general biasing signals and are integral to our flexible goal-oriented behavior. Furthermore, the flexible use of these biasing signals in selective attention and task switching develops over a protracted period, and appears to be aberrant in autism spectrum disorder

Cerebro-Cerebellar Pathways for Verbal Working Memory

The current study examined the structural and functional connectivity of the cerebro-cerebellar network of verbal working memory as proposed by Chen and Desmond (2005a). Diffusion spectrum imaging was employed to establish structural connectivity between cerebro-cerebellar regions co-activated during a verbal working memory task. The inferior frontal gyrus, inferior parietal lobule, pons, thalamus, superior cerebellum and inferior cerebellum were used as regions of interest to reconstruct and segment the contralateral white matter cerebro-cerebellar circuitry. The segmented pathways were examined further to establish the relationship between structural and effective connectivity as well as the relationship between structural connectivity and verbal working memory performance. No direct relationship between structural and effective connectivity was found but the results demonstrated that structural connectivity is indirectly related to effective connectivity as DCM models that resembled more closely with underlying white matter pathways had a higher degree of model inference confidence. Additionally, it was demonstrated that the structural connectivity of the ponto-cerebellar tract was associated with individual differences in response time for verbal working memory. The findings of the study contribute to further our understanding of the relationship between structural and functional connectivity and the impact of variability in verbal working memory performance

Cerebro-Cerebellar Pathways for Verbal Working Memory

open access articleThe current study examined the structural and functional connectivity of the cerebrocerebellar network of verbal working memory as proposed by Chen and Desmond (2005a). Diffusion spectrum imaging was employed to establish structural connectivity between cerebro-cerebellar regions co-activated during a verbal working memory task. The inferior frontal gyrus, inferior parietal lobule, pons, thalamus, superior cerebellum and inferior cerebellum were used as regions of interest to reconstruct and segment the contralateral white matter cerebro-cerebellar circuitry. The segmented pathways were examined further to establish the relationship between structural and effective connectivity as well as the relationship between structural connectivity and verbal working memory performance. No direct relationship between structural and effective connectivity was found but the results demonstrated that structural connectivity is indirectly related to effective connectivity as DCM models that resembled more closely with underlying white matter pathways had a higher degree of model inference confidence. Additionally, it was demonstrated that the structural connectivity of the ponto-cerebellar tract was associated with individual differences in response time for verbal working memory. The findings of the study contribute to further our understanding of the relationship between structural and functional connectivity and the impact of variability in verbal working memory performance

Dynamics of large-scale brain activity in health and disease

Tese de doutoramento em Engenharia Biomédica e Biofísica, apresentada à Universidade de Lisboa através da Faculdade de Ciências, 2008Cognition relies on the integration of information processed in widely distributed brain regions. Neuronal oscillations are thought to play an important role in the supporting local and global coordination of neuronal activity. This study aimed at investigating the dynamics of the ongoing healthy brain activity and early changes observed in patients with Alzheimer's disease (AD). Electro- and magnetoencephalography (EEG/MEG) were used due to high temporal resolution of these techniques. In order to evaluate the functional connectivity in AD, a novel algorithm based on the concept of generalized synchronization was improved by defining the embedding parameters as a function of the frequency content of interest. The time-frequency synchronization likelihood (TF SL) revealed a loss of fronto-temporal/parietal interactions in the lower alpha (8 10 Hz) oscillations measured by MEG that was not found with classical coherence. Further, long-range temporal (auto-) correlations (LRTC) in ongoing oscillations were assessed with detrended fluctuation analysis (DFA) on times scales from 1 25 seconds. Significant auto-correlations indicate a dependence of the underlying dynamical processes at certain time scales of separation, which may be viewed as a form of "physiological memory". We tested whether the DFA index could be related to the decline in cognitive memory in AD. Indeed, a significant decrease in the DFA exponents was observed in the alpha band (6 13 Hz) over temporo-parietal regions in the patients compared with the age-matched healthy control subjects. Finally, the mean level of SL of EEG signals was found to be significantly decreased in the AD patients in the beta (13 30 Hz) and in the upper alpha (10 13 Hz) and the DFA exponents computed as a measure of the temporal structure of SL time series were larger for the patients than for subjects with subjective memory complaint. The results obtained indicate that the study of spatio-temporal dynamics of resting-state EEG/MEG brain activity provides valuable information about the AD pathophysiology, which potentially could be developed into clinically useful indices for assessing progression of AD or response to medication

EEG alpha power is modulated by attentional changes during cognitive tasks and virtual reality immersion

Variations in alpha rhythm have a significant role in perception and attention. Recently, alpha decrease has been associated with externally directed attention, especially in the visual domain, whereas alpha increase has been related to internal processing such as mental arithmetic. However, the role of alpha oscillations and how the different components of a task (processing of external stimuli, internal manipulation/representation, and task demand) interact to affect alpha power are still unclear. Here, we investigate how alpha power is differently modulated by attentional tasks depending both on task difficulty (less/more demanding task) and direction of attention (internal/external). To this aim, we designed two experiments that differently manipulated these aspects. Experiment 1, outside Virtual Reality (VR), involved two tasks both requiring internal and external attentional components (intake of visual items for their internal manipulation) but with different internal task demands (arithmetic vs. reading). Experiment 2 took advantage of the VR (mimicking an aircraft cabin interior) to manipulate attention direction: it included a condition of VR immersion only, characterized by visual external attention, and a condition of a purely mental arithmetic task during VR immersion, requiring neglect of sensory stimuli. Results show that: (1) In line with previous studies, visual external attention caused a significant alpha decrease, especially in parieto-occipital regions; (2) Alpha decrease was significantly larger during the more demanding arithmetic task, when the task was driven by external visual stimuli; (3) Alpha dramatically increased during the purely mental task in VR immersion, whereby the external stimuli had no relation with the task. Our results suggest that alpha power is crucial to isolate a subject from the environment, and move attention from external to internal cues. Moreover, they emphasize that the emerging use of VR associated with EEG may have important implications to study brain rhythms and support the design of artificial systems