Modulation of scale-free properties of brain activity in MEG

Zilber, Nicolas; Ciuciu, Philippe; Abry, Patrice; Wassenhove, Virginie van

doi:10.1109/isbi.2012.6235864

Cited by 30 publications

(38 citation statements)

References 15 publications

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“…In contrast, the dynamic range of the fMRI signal is the largest and the temporal dependence is the longest during the resting state. Measurement of Hurst exponents using magnetoencephalography has also confirmed this phenomenon when contrasting resting-state activity with evoked activity during a learning paradigm [56] . In a remarkable parallel to these findings in healthy individuals, the Hurst exponent of the fMRI signal in Alzheimer patients is larger than that in age-matched controls, suggesting that the efficiency of information processing in these patients is reduced [51,53] .…”

Section: The Resting-state Fingerprint Of Extraversionmentioning

confidence: 77%

Functional neuroimaging of extraversion-introversion

Lei

Yang

2015

Neurosci. Bull.

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Neuroimaging techniques such as functional magnetic resonance imaging and positron emission tomography have provided an unprecedented neurobiological perspective for research on personality traits . Evidence from task-related neuroimaging has shown that extraversion is associated with activations in regions of the anterior cingulate cortex, dorsolateral prefrontal cortex, middle temporal gyrus and the amygdala. Currently, resting-state neuroimaging is being widely used in cognitive neuroscience. Initial exploration of extraversion has revealed correlations with the medial prefrontal cortex, anterior cingulate cortex, insular cortex, and the precuneus. Recent research work has indicated that the long-range temporal dependence of the resting-state spontaneous oscillation has high test-retest reliability. Moreover, the long-range temporal dependence of the resting-state networks is highly correlated with personality traits, and this can be used for the prediction of extraversion. As the long-range temporal dependence refl ects real-time information updating in individuals, this method may provide a new approach to research on personality traits.Keywords: extraversion; neuroimaging; resting-state fMRI; default mode network ; scale-free dynamics ·Review· IntroductionIn personality psychology, personality traits are regarded as an individual's lasting and stable behavioral tendencies at different times and in different situations. As one of the most important dimensions of personality traits, extraversion is the best established and validated [1] . Moreover, extraversion is the most stable core trait and a universal component in personality theory. Extraverts are typically described in positive emotional terms such as excitement, engagement, and enthusiasm. In contrast, introverts are described as quiet, conservative, and insensitive to the environment.An individual with high extraversion is more talkative, outgoing, and excited than someone with low extraversion, but even people with low extraversion may occasionally experience talkative and outgoing states. Trait levels of extraversion are known to be highly heritable and are linked to vulnerability to anxiety and major depression disorder [2,3] .Although much is known about the behavioral correlates of extraversion, little is understood about its biological basis.Research on personality traits usually has the ambitious goal of providing a comprehensive understanding of a person's integrated framework [3] . To achieve this goal, various effective research methods have been adopted.Based on the premise that the whole person cannot be understood without understanding the brain, p ersonality neuroscience has emerged as an important direction in the study of personality traits [4] . Generally, personality traits are measured by questionnaire through self-report or peerrating. Then, correlations between personality traits and n eurobiological p arameters are calculated [5] . Currently, a number of techniques are available for personality neuroscience. Anatomical neuroima...

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Section: The Resting-state Fingerprint Of Extraversionmentioning

confidence: 77%

Functional neuroimaging of extraversion-introversion

Lei

Yang

2015

Neurosci. Bull.

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“…turbulence, stock market...) [1], this infraslow activity has long been attributed to sensor or neural noise and considered to be functionally irrelevant. However, since the last decade, a growing body of evidence has shown a modulation of the 1/f slope between contrasted cognitive states (awake vs phases of sleep [1,2], task vs rest [3,4]) and in pathologies [5] using different imaging techniques (ECoG [1,6], EEG [7][8][9], MEG [8,10] and fMRI [3,6,11,12]). Such results suggest that the infraslow activity also carries meaningful information for brain function.…”

Section: Introductionmentioning

confidence: 99%

“…Instead, another analysis, more robust to non-stationarity issues and more efficient in disentangling true scaling phenomena from superimposed drifts has been proposed since the last decade as the Wavelet Leader Based Multifractal Formalism (WLBMF) [14]. This method offers a richer and more flexible description of functional neuroimaging data and has been successfully applied to MEG in previous work [10] to demonstrate a modulation of scale-free properties of MEG time series at the sensor level between evoked and spontaneous activity.…”

Section: Introductionmentioning

confidence: 99%

Learning-induced modulation of scale-free properties of brain activity measured with MEG

Zilber

Ciuciu

Abry

et al. 2013

2013 IEEE 10th International Symposium on Biomedical Imaging

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Previous studies have suggested that infraslow brain activity could play an important role in cognition. Its scale-free properties (coarsely described by its 1/f power spectrum) are indeed modulated between contrasted conscious states (sleep vs. awake). However, finer modulations remain to be investigated. Here, we make use of a robust multifractal analysis to investigate the group-level impact of perceptual learning (visual (V), or audiovisual (AV), N = 12 subjects in each group) on the source reconstructed scale-free activity recorded with magnetoencephalography (MEG) during rest and task. We first observed a significant decrease of self-similarity in evoked activity during the task after both trainings. More interestingly, only the most efficient training (AV) induced a decrease of self-similarity in spontaneous activity at rest whereas only V training induced an increase of multifractality in evoked activity.

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“…Due to hemodynamic filtering, these arrhythmic dynamics lie below 0.2 Hz in fMRI data. Indeed, scale-free temporal dynamics were observed across several modalities, both at rest or during Work supported by ANR-16-CE33-0020 MultiFracs, France task performance, and under various conditions or pathologies [1][2][3][4][5][6][7][8][9][10][11][12]. It has also been documented that scale-free dynamics are functionally associated with neural excitability [3,13] and negatively correlates with power fluctuation in alpha-band (8−12 Hz) [14], hence explaining the modulation of scaling exponents with task engagement or pathologies.…”

Section: Introductionmentioning

confidence: 99%

“…Since scale-free analysis requires the data to be analyzed at several time scales jointly, relatively long time series are required for robust estimation. This limitation may explain why it leads to relevant characterizations and promising conclusions when applied to modalities such as M/EEG [6,11,12] while successes remain debated on (typically short length) fMRI time series. Another limitation stems from current scale-free analysis procedures being univariate: Despite several tens of thousands of voxels being recorded jointly in the brain with fMRI, the obviously rich and informative spatial structure of the data is not exploited and each time series is analyzed independently.…”

Section: Introductionmentioning

confidence: 99%

Spatially regularized wavelet leader scale-free analysis of fMRI data

Wendt

Abry

Ciuciu

2018

2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

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Slow brain dynamics has received considerable interest in the recent years, with the scale-free paradigm playing a crucial role for analysis of various neuroimaging modalities. However, assessing the role of slow arrhythmic fluctuations requires the use of a large continuum of time scales and thus of long time series, hence raising concerns regarding the use of scale-free tools on fMRI data. Further, scale-free analysis remained so far mostly univariate, that is, voxels are analyzed independently, hence neglecting their spatial organization. The present contribution aims to propose a spatially regularized estimation of the self-similarity parameter, based on a recently formalized formalism combining wavelet leaders and Bayesian models. The spatially regularized estimates permit to quantify the modulations of the scale-free dynamics from rest to a working memory task from fMRI data collected for 21 healthy volunteers. These modulations are significant in the default mode network and in some regions involved in task performance such as primary visual regions or the supplementary motor area.

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Modulation of scale-free properties of brain activity in MEG

Cited by 30 publications

References 15 publications

Functional neuroimaging of extraversion-introversion

Functional neuroimaging of extraversion-introversion

Learning-induced modulation of scale-free properties of brain activity measured with MEG

Spatially regularized wavelet leader scale-free analysis of fMRI data

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