Complexity Analysis of Resting-State fMRI in Adult Patients with Attention Deficit Hyperactivity Disorder: Brain Entropy

Akdeniz, Gülsüm

doi:10.1155/2017/3091815

Cited by 6 publications

(4 citation statements)

References 41 publications

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“…Sample entropy is well suited for short data sets such as fMRI data (Sokunbi, ). Entropy in the brain is variable among healthy individuals of different age (Smith, Yan, & Wang, ; Yao et al, ) and sex (Yao et al, ) and among individuals with different neurological diseases, such as multiple sclerosis (Zhou, Zhuang, et al, ), Alzheimer's disease (Liu et al, ; Niu et al, ; Wang et al, ), attention deficit hyperactivity disorder (ADHD) (Akdeniz, ; Sato, Takahashi, Hoexter, Massirer, & Fujita, ; Sokunbi et al, ), and schizophrenia (Sokunbi et al, ; Yang et al, ). The present study used an rs‐fMRI time series to calculate brain entropy as a parameter of complexity and regularity.…”

Section: Introductionmentioning

confidence: 99%

Resting‐state brain entropy in right temporal lobe epilepsy and its relationship with alertness

et al. 2019

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BackgroundTo date, no functional MRI (fMRI) studies have focused on brain entropy in right temporal lobe epilepsy (rTLE) patients. Here, we characterized brain entropy (BEN) alterations in patients with rTLE using resting‐state functional MRI(rs‐fMRI) and explored the relationship between BEN and alertness.MethodThirty‐one rTLE patients and 33 controls underwent MRI scanning to investigate differences in BEN and resting‐state functional connectivity (rs‐FC) in regions of interest (ROIs) between patients and controls. Correlation analyses were performed to examine relationships between the BEN of each ROI and alertness reaction times (RTs) in rTLE patients.ResultsCompared with controls, the BEN of rTLE patients was significantly increased in the right middle temporal gyrus, inferior temporal gyrus, and other regions of the left hemisphere and significantly decreased in the right middle frontal gyrus and left supplementary motor area (p < .05). The rs‐FCs between the ROIs (at p < .01, with the left superior parietal lobule and right precentral gyrus defined as ROI1 and ROI2, respectively) and the whole brain showed an increasing trend in rTLE patients. In addition, the BEN of ROI2 was associated with the intrinsic alertness and phasic alertness RTs of patients with rTLE.ConclusionsOur findings suggest that BEN is altered in patients with rTLE and that decreased BEN in the right precentral gyrus is positively related to intrinsic and phasic alertness; the abnormal FC in the brain regions with altered entropy suggests a reconstruction of brain functional connectivity. These findings suggest that BEN mapping may provide a useful tool for probing brain mechanisms related to TLE.

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Section: Introductionmentioning

confidence: 99%

Resting‐state brain entropy in right temporal lobe epilepsy and its relationship with alertness

et al. 2019

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“…Meanwhile, Sohn et al in their study of adolescents with ADHD showed reduced complexity in the right frontal region during a cognitive task, but not at rest, compared to healthy adolescents ( 80 ). In contrast, all studies focusing on adults with ADHD showed reduced brain complexity in frontal and occipital regions compared to healthy peers ( 81 , 82 ). In summary, it seems that the disturbance in the complexity pattern of brain activity in ADHD is affected by age, changing from lower complexity values in childhood to higher complexity values in adulthood ( Figure 1 ).…”

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confidence: 83%

Brain Complexity and Psychiatric Disorders

Hernández,

Ponce-Meza,

Saavedra-López

et al. 2023

IJPS

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Objective: In recent years, researchers and neuroscientists have begun to use a variety of nonlinear techniques for analyzing neurophysiologic signals derived from fMRI, MEG, and EEG in order to describe the complex dynamical aspects of neural mechanisms. In this work, we first attempted to describe different algorithms to estimate neural complexity in a simple manner understandable for psychiatrists, psychologists, and neuroscientists. Then, we reviewed the findings of the brain complexity analysis in psychiatric disorders and their clinical implications. Method: A non-systematic comprehensive literature search was conducted for original studies on the complexity analysis of neurophysiological signals such as electroencephalogram, magnetoencephalogram, and blood-oxygen-level-dependent obtained from functional magnetic resonance imaging or functional near infrared spectroscopy. The search encompassed online scientific databases such as PubMed and Google Scholar. Results: Complexity measures mainly include entropy-based methods, the correlation dimension, fractal dimension, Lempel-Ziv complexity, and the Lyapunov exponent. There are important differences in the physical notions between these measures. Our literature review shows that dementia, autism, and adult ADHD exhibit less complexity in their neurophysiologic signals than healthy controls. However, children with ADHD, drug-naïve young schizophrenic patients with positive symptoms, and patients with mood disorders (i.e., depression and bipolar disorder) exhibit higher complexity in their neurophysiologic signals compared to healthy controls. In addition, contradictory findings still exist in some psychiatric disorders such as schizophrenia regarding brain complexity, which can be due to technical issues, large heterogeneity in psychiatric disorders, and interference of typical factors. Conclusion: In summary, complexity analysis may present a new dimension to understanding psychiatric disorders. While complexity analysis is still far from having practical applications in routine clinical settings, complexity science can play an important role in comprehending the system dynamics of psychiatric disorders.

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“…Several methods of measuring such autonomic responses are available and used in the literature, such as: fMRI (Akdeniz, 2017); electroencephalography/EEG (Gao, Wang, & Zhang, 2016); magnetoencephalography/MEG (Haumann, Parkkonen, Kliuchko, Vuust, & Brattico, 2016); positron emission tomography (PET); eye‐tracking and pupil dilatation (Zhang, Liu, Yuan, & Lin, 2017); electrodermal activity (Guerreiro et al, 2015)); electrocardiography; electromyography (Walla, Brenner, & Koller, 2011); analysis of blush, blinking, breathing or heartbeat (Camerer, Loewenstein, & Prelec, 2004; Fisher, Chin, & Klitzman, 2010). For this research, arousal was measured through electrodermal response (EDA) and its valence was measured by using the SAM (Bradley & Lang, 1994).…”

Section: Methodsmentioning

confidence: 99%

Autonomic emotional responses to food: Private label brands versus National Brands

Rita

Guerreiro

Omarji

2020

J of Consumer Behaviour

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Private label brands (PLB) have been increasing both perceived quality and consumer acceptance in the last decades, which has compelled national brands (NBs) to invest in maintaining consumer confidence and preference. Recent economic crisis led consumers to become more price sensitive and more worried about cutting on daily needs, such as food consumption. However, taste is still one of the most important factor of decision for consumers regarding food products. Despite such importance of taste in decision‐making, most research on consumer senses still relies on traditional methods of surveying consumers, which are unable to measure the consumers' autonomic emotional reactions. The current paper uses a psychophysiological method to measure emotional arousal–electrodermal activity (EDA) and a self‐assessment manikin to measure pleasure. Emotions are then tested to assess their influence on perceived quality and willingness to buy (WTB). Findings show that NB are still perceived by consumers as superior, but consumers' WTB is not higher for NB. These results show that consumers' decisions are not always in favor of the product which is perceived to have a better quality which suggests that the “quality gap” is fading and the “branding gap” is gaining momentum.

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Complexity Analysis of Resting-State fMRI in Adult Patients with Attention Deficit Hyperactivity Disorder: Brain Entropy

Cited by 6 publications

References 41 publications

Resting‐state brain entropy in right temporal lobe epilepsy and its relationship with alertness

Resting‐state brain entropy in right temporal lobe epilepsy and its relationship with alertness

Brain Complexity and Psychiatric Disorders

Autonomic emotional responses to food: Private label brands versus National Brands

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