Brain complexity increases in mania

Bahrami, Bahador; Seyedsadjadi, Reza; Babadi, Baktash; Noroozıan, Maryam

doi:10.1097/00001756-200502080-00025

Cited by 53 publications

(35 citation statements)

References 12 publications

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“…Fractal dimension measures the degree of complexity of EEG fluctuations, providing an estimate of the activation of neuronal cell assemblies during a particular task (Lutzenberger et al, 1992a(Lutzenberger et al, ,b, 1995(Lutzenberger et al, , 1997Liu et al, 2005). This measure has been applied, for instance, to monitor the depth of anesthesia, mood changes in affective disorders, or force modulation during handgrip, showing that highest values were associated with awakening (Klonowski, 2007), manic episodes among patients with bipolar disorders (Bahrami et al, 2005), or increased handgrip force (Liu et al, 2005), respectively. In this sense, our results might indicate a more enhanced activation of cell assemblies over the right than over the left parietal cortex in chronic pain patients when non-painful stimulation was applied under negative mood conditions.…”

Section: Discussionmentioning

confidence: 99%

Linear and nonlinear analyses of EEG dynamics during non-painful somatosensory processing in chronic pain patients

Sitges

Bornas

Llabrés

et al. 2010

International Journal of Psychophysiology

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Section: Discussionmentioning

confidence: 99%

Linear and nonlinear analyses of EEG dynamics during non-painful somatosensory processing in chronic pain patients

Sitges

Bornas

Llabrés

et al. 2010

International Journal of Psychophysiology

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“…Many studies have characterized the fractal properties of local aspects of EEG temporal dynamics, namely of amplitude modulations at single electrodes (37)(38)(39)(40); these properties have been linked to cognitive tasks (41), sleep (42)(43)(44), and clinical conditions such as epilepsy (45,46), Alzheimer's disease (47,48), mania (49), dementia (50), and schizophrenia (51). Whereas these studies indicate that the fractal characterization of electric potential time series can be a useful measure of local brain activity, they cannot be related to the temporal properties of global brain networks.…”

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confidence: 99%

EEG microstate sequences in healthy humans at rest reveal scale-free dynamics

Ville

Britz

Michel

2010

Proc. Natl. Acad. Sci. U.S.A.

500

422

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Recent findings identified electroencephalography (EEG) microstates as the electrophysiological correlates of fMRI resting-state networks. Microstates are defined as short periods (100 ms) during which the EEG scalp topography remains quasi-stable; that is, the global topography is fixed but strength might vary and polarity invert. Microstates represent the subsecond coherent activation within global functional brain networks. Surprisingly, these rapidly changing EEG microstates correlate significantly with activity in fMRI resting-state networks after convolution with the hemodynamic response function that constitutes a strong temporal smoothing filter. We postulate here that microstate sequences should reveal scale-free, self-similar dynamics to explain this remarkable effect and thus that microstate time series show dependencies over long time ranges. To that aim, we deploy wavelet-based fractal analysis that allows determining scale-free behavior. We find strong statistical evidence that microstate sequences are scale free over six dyadic scales covering the 256-ms to 16-s range. The degree of long-range dependency is maintained when shuffling the local microstate labels but becomes indistinguishable from white noise when equalizing microstate durations, which indicates that temporal dynamics are their key characteristic. These results advance the understanding of temporal dynamics of brain-scale neuronal network models such as the global workspace model. Whereas microstates can be considered the "atoms of thoughts," the shortest constituting elements of cognition, they carry a dynamic signature that is reminiscent at characteristic timescales up to multiple seconds. The scale-free dynamics of the microstates might be the basis for the rapid reorganization and adaptation of the functional networks of the brain.critical state | microstates | resting-state networks | self-similar processes | wavelet fractal analysis T he human brain is intrinsically organized into interconnected neuronal clusters that form large-scale neurocognitive networks (1, 2). These networks have to dynamically and rapidly reorganize and coordinate on subsecond temporal scales to allow the execution of mental processes in a timely fashion (3, 4). Precise timing is crucial for the government of the continuous information flow from multiple sources to ensure perception, cognition, and action and ultimately consciousness. The anatomical architecture of several large-scale networks is well known and has been studied with different methods ranging from tracer studies to resting-state fMRI (5, 6). However, much less is known about their underlying temporal dynamics.Multichannel electroencephalography (EEG) is a key method to access real-time information about the function of large-scale neuronal networks with high temporal resolution. Traditionally, spontaneous EEG analysis relies mainly on the power variation in different frequency bands at a subset of electrodes; however, observing this variation inherently sacrifices temporal accuracy due t...

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“…This hypothesis has received increasing support by evidence indicating significant abnormalities in regional or global EEG complexity in patients with neuropsychiatric disorders compared to controls. Examples include Alzheimer's disease [12,13], Parkinson's disease [22,31], schizophrenia [8,15,25], post-traumatic stress disorder [7], and mania [4]. The clinical and diagnostic utility of complexity measures in these and related contexts remains to be explored.…”

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confidence: 99%

Genetic influences on dynamic complexity of brain oscillations

Anokhin

Müller

Lindenberger

et al. 2006

Neuroscience Letters

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Human electroencephalogram (EEG) consists of complex aperiodic oscillations that are assumed to indicate underlying neural dynamics such as the number and degree of independence of oscillating neuronal networks. EEG complexity can be estimated using measures derived from nonlinear dynamic systems theory. Variations in such measures have been shown to be associated with normal individual differences in cognition and some neuropsychiatric disorders. Despite the increasing use of EEG complexity measures for the study of normal and abnormal brain functioning, little is known about genetic and environmental influences on these measures. Using the pointwise dimension (PD2) algorithm, this study assessed heritability of EEG complexity at rest in a sample of 214 young female twins consisting of 51 monozygotic (MZ) and 56 dizygotic (DZ) pairs. In MZ twins, intrapair correlations were high and statistically significant; in DZ twins, correlations were substantially smaller. Genetic analyses using linear structural equation modeling revealed high and significant heritability of EEG complexity: 62-68% in the eyes-closed condition, and 46-60% in the eyes-open condition. Results suggest that individual differences in the complexity of resting electrocortical dynamics are largely determined by genetic factors. Neurophysiological mechanisms mediating genetic variation in EEG complexity may include the degree of structural connectivity and functional differentiation among cortical neuronal assemblies. © 2005 Elsevier Ireland Ltd. All rights reserved.Keywords: Electroencephalogram; Complexity; Nonlinear dynamics; Twins; Heritability Neural activity underlying cognition and behavior is characterized by a high degree of functional differentiation and, at the same time, functional integration achieved through rapid binding of spatially distributed and functionally specialized neuronal groups [32]. To some extent, these fundamental properties of large-scale brain dynamics are reflected in the electroencephalogram (EEG) recorded from the scalp. The EEG results from the summation of postsynaptic activity of a large number of spatially distributed but functionally connected and interacting cortical neurons and neuronal assemblies. Accordingly, the EEG time series has a complex structure reflecting the complexity of the underlying neural generators [16,26]. A greater number of independent processes contributing to the EEG results in a greater complexity of EEG time series [18]. EEG complexity may reflect the number of states of a system resulting from the interaction * Corresponding author. Tel.: +1 314 286 2201; fax: +1 314 286 0092.E-mail address: andrey@matlock.wustl.edu (A.P. Anokhin).among its elements, with higher complexity reflecting a larger number of separable oscillatory networks [32].In recent years, cortical oscillations have been increasingly viewed from the perspective of nonlinear dynamic systems theory, which investigates complex, aperiodic systems capable of self-organization [9,26]. The behavior of complex syste...

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Brain complexity increases in mania

Cited by 53 publications

References 12 publications

Linear and nonlinear analyses of EEG dynamics during non-painful somatosensory processing in chronic pain patients

Linear and nonlinear analyses of EEG dynamics during non-painful somatosensory processing in chronic pain patients

EEG microstate sequences in healthy humans at rest reveal scale-free dynamics

Genetic influences on dynamic complexity of brain oscillations

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