Randomness of resting-state brain oscillations encodes Gray's personality trait

Hahn, Tim; Dresler, Thomas; Ehlis, Ann‐Christine; Pyka, Martin; Dieler, Alica C.; Saathoff, Claudia; Jakob, Peter M.; Lesch, Klaus‐Peter; Fallgatter, Andreas J.

doi:10.1016/j.neuroimage.2011.08.042

Cited by 46 publications

(47 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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] . Based on the above background, we speculate that individuals with higher extraversion scores would be more active in processing incoming information.…”

Section: The Resting-state Fingerprint Of Extraversionsupporting

confidence: 52%

See 1 more Smart Citation

Functional neuroimaging of extraversion-introversion

Lei

Yang

2015

Neurosci. Bull.

View full text Add to dashboard Cite

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...

show abstract

Section: The Resting-state Fingerprint Of Extraversionsupporting

confidence: 52%

“…Gray's impulsivity score is associated with a decrease of H [53] . Recen tly, we validated that resting-state spontaneous oscillations in RSNs encode personality traits.…”

Section: Scale-free Oscillations and Extraversionmentioning

confidence: 97%

Functional neuroimaging of extraversion-introversion

Lei

Yang

2015

Neurosci. Bull.

View full text Add to dashboard Cite

show abstract

“…Temporal autocorrelation is a well-known characteristic of spontaneous neural activity at both macroscopic (41)(42)(43)(44) and microscopic (45)(46)(47)(48)(49) scales and can explain why evidence-based neural decisions are partially predictable even before the evidence is provided (50). A backward selection bias (only epochs ending with an actual movement are subject to analysis) ensures that the spontaneous fluctuations that contributed to the threshold crossing are recovered in the average (8,9) or in the probability of classifying a small segment of data as coming from early or late in the epoch (ref.…”

Section: Discussionmentioning

confidence: 99%

“…If you are somewhat hungry and also somewhat thirsty, then spontaneous fluctuations might play a role in tipping the scales in favor of one or the other, as is the case with cued perceptual decisions (56). This notion might also help in understanding behavioral traits such as impulsivity, which can be predicted in individuals on the basis of a measure of longrange dependency (the Hurst exponent) applied to spontaneous fluctuations in brain activity (44).…”

Section: Discussionmentioning

confidence: 99%

An accumulator model for spontaneous neural activity prior to self-initiated movement

Schurger

Sitt

Dehaene

2012

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

362

549

View full text Add to dashboard Cite

A gradual buildup of neuronal activity known as the "readiness potential" reliably precedes voluntary self-initiated movements, in the average time locked to movement onset. This buildup is presumed to reflect the final stages of planning and preparation for movement. Here we present a different interpretation of the premovement buildup. We used a leaky stochastic accumulator to model the neural decision of "when" to move in a task where there is no specific temporal cue, but only a general imperative to produce a movement after an unspecified delay on the order of several seconds. According to our model, when the imperative to produce a movement is weak, the precise moment at which the decision threshold is crossed leading to movement is largely determined by spontaneous subthreshold fluctuations in neuronal activity. Time locking to movement onset ensures that these fluctuations appear in the average as a gradual exponential-looking increase in neuronal activity. Our model accounts for the behavioral and electroencephalography data recorded from human subjects performing the task and also makes a specific prediction that we confirmed in a second electroencephalography experiment: Fast responses to temporally unpredictable interruptions should be preceded by a slow negative-going voltage deflection beginning well before the interruption itself, even when the subject was not preparing to move at that particular moment.resting state | autocorrelation | volition | power-law T he role of ongoing spontaneous fluctuations in neural activity has been widely studied in the domain of perceptual decision making (1-5), but their potential role in self-initiated movement has been largely overlooked. This oversight probably arises because changes in neural activity that reliably precede the onset of self-initiated movements are attributed de facto to specific movement preparation, and thus the possibility that these might reflect random fluctuations simply never arises. The initiation of spontaneous, uncued movements raises some unique questions: If there is no external signal to trigger the movement, then what in the brain is responsible for triggering the movement, and how is the precise time of onset determined? These questions have been raised in the past (6, 7), but the possible role of ongoing spontaneous neural activity has not been considered.The term bereitschaftspotential or "readiness potential" (RP) refers to a slow buildup of electrical potential, measured using electroencephalography (EEG), that reliably precedes self-initiated movements (8). Discovered in 1965 by Kornhuber and Deecke (8), the RP appears in the average over many data epochs time locked to movement onset and may begin its negative deflection one full second or more before movement onset (depending upon the subject, task, and methods) (8-11). The RP is taken to be "the electro-physiological sign of planning, preparation, and initiation of volitional acts" (ref. 12, p. 14), echoing the widely held assumption that the gradual increase in firing r...

show abstract

“…They have been used in investigations in various areas of psychology, such as visual perception (Cardaci et al, 2009), aesthetics (Boon et al, 2011), development (Scibinetti et al, 2011;Pureza et al, 2013), sport (Audiffren et al, 2009), creativity (Zabelina et al, 2012), sleep (Heuer et al, 2005;Bianchi and Mendez, 2013), and obesity (Crova et al, 2013), to mention a few. In neuropsychology, random number generation tasks and other measures of behavioral or brain activity complexity have been used to investigate different disorders, such as schizophrenia (Koike et al, 2011), autism (Lai et al, 2010;Maes et al, 2012;Fournier et al, 2013), depression (Fernandez et al, 2009), PTSD (Pearson and Sawyer, 2011;Curci et al, 2013), ADHD (Sokunbi et al, 2013), OCD (Bédard et al, 2009), hemispheric neglect (Loetscher and Brugger, 2009), aphasia (Proios et al, 2008), and neurodegenerative syndromes such as Parkinson's and Alzheimer's disease (Brown and Marsden, 1990;Hahn et al, 2012).…”

mentioning

confidence: 99%

Algorithmic complexity for psychology: a user-friendly implementation of the coding theorem method

et al. 2015

View full text Add to dashboard Cite

Kolmogorov-Chaitin complexity has long been believed to be impossible to approximate when it comes to short sequences (e.g. of length 5-50). However, with the newly developed coding theorem method the complexity of strings of length 2-11 can now be numerically estimated. We present the theoretical basis of algorithmic complexity for short strings (ACSS) and describe an R-package providing functions based on ACSS that will cover psychologists' needs and improve upon previous methods in three ways: (1) ACSS is now available not only for binary strings, but for strings based on up to 9 different symbols, (2) ACSS no longer requires time-consuming computing, and (3) a new approach based on ACSS gives access to an estimation of the complexity of strings of any length. Finally, three illustrative examples show how these tools can be applied to psychology. Keywords Algorithmic complexity · Randomness · Subjective probability · Coding theorem method Randomness and complexity are two concepts which are intimately related and are both central to numerous recent developments in various fields, including finance (Taufemback et al., 2011, Brandouy et al., 2012, linguistics (Gruber, 2010;Naranan, 2011), neuropsychology (Machado et al., 2010;Fernández et al., 2011Fernández et al., , 2012, psychiatry (Yang and Tsai, 2012;Takahashi 2013), genetics (Yagil, 2009;Ryabko et al. 2013), sociology (Elzinga, 2010 and the behavioral sciences (Watanabe et al., 2003;Scafetta et al., 2009). In psychology, randomness and complexity have recently attracted interest, following the realization that they could shed light on a diversity of previously undeciphered behaviors and mental processes. It has been found, for instance, that the subjective difficulty of a concept is directly related to its "boolean complexity", defined as the shortest logical description of a concept (Feldman, 2000(Feldman, , 2003(Feldman, , 2006. In the same vein, visual detection of shapes has been shown to be related to contour complexity (Wilder et al., 2011).More generally, perceptual organization itself has been described as based on simplicity or, equivalently, likelihood (Chater, 1996;Chater and Vitányi, 2003), in a model reconciling the complexity approach (perception is organized to minimize complexity) and a probability approach (perception is organized to maximize likelihood), very much in line with our view in this paper. Even the perception of similarity may be viewed through the lens of (conditional) complexity (Hahn et al., 2003).Randomness and complexity also play an important role in modern approaches to selecting the "best" among a set of candidate models (i.e., model selection; e.g., Myung et Behav Res (2016) 48:314-329 315 al., 2006Kellen et al., 2013), as discussed in more detail below in the section called "Relationship to complexity based model selection".Complexity can also shed light on short term memory storage and recall, more specifically, on the process underlying chunking. It is well known that the short term memory span lies b...

show abstract

Randomness of resting-state brain oscillations encodes Gray's personality trait

Cited by 46 publications

References 24 publications

Functional neuroimaging of extraversion-introversion

Functional neuroimaging of extraversion-introversion

An accumulator model for spontaneous neural activity prior to self-initiated movement

Algorithmic complexity for psychology: a user-friendly implementation of the coding theorem method

Contact Info

Product

Resources

About