Resting state fMRI entropy probes complexity of brain activity in adults with ADHD

Sokunbi, Moses O.; Fung, Wilson; Sawlani, Vijay; Choppin, Sabine; Linden, David Edmund Johannes; Thome, Johannes

doi:10.1016/j.pscychresns.2013.10.001

Cited by 138 publications

(130 citation statements)

References 53 publications

(71 reference statements)

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“…Future research may need to explore further these networks with probabilistic independent component analysis or other approaches such as fractal analysis, entropy and complexity measurements and frequency analysis techniques, which recently provided interesting brain behavior correlations in ADHD (Zang et al 2007;An, Cao, Sui, et al 2013;Sokunbi et al 2013). Finally, it is important to note that our group division suggests that a continuum exists between the different symptoms dimension in ADHD.…”

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confidence: 88%

“…Previous rs-fMRI studies of ADHD have reported both hypo-and hyper-activation of fronto-striatal, fronto-parietal and other networks (see Konrad et al 2010 for review). Also, whole brain voxel-based analyses revealed decreased entropy (Sokunbi et al 2013) and decreased amplitude of low-frequency fluctuation (Zang et al 2007;An, Cao, Sui, et al 2013) in the frontal and the occipital lobes. These inconsistencies are likely to be due to a combination of methodological factors, including the method of analysis employed, micro-movements (Fair et al 2012), variability in the subtype diagnosis and the age range of subjects.…”

Section: Introductionmentioning

confidence: 95%

“…In children, psychostimulants have been reported to be associated with reduced inferior frontal lobe activation during inhibition related tasks (Pauls et al 2012). Further, in children with ADHD, increased impulsivity has been reported to be associated with atypical fronto-striatal function (Durston et al 2003), task related reduced activations (Cubillo et al 2010), decreased entropy (Sokunbi et al 2013), and increased rs-fMRI connectivity (Costa Dias et al 2013). Therefore the increased connectivity reported in our study may contribute to the overall lack of response control (i.e.…”

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

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Double-dissociation between the mechanism leading to impulsivity and inattention in Attention Deficit Hyperactivity Disorder: A resting-state functional connectivity study

Sanefuji

Craig

Parlatini

et al. 2017

Cortex

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Two core symptoms characterize Attention Deficit Hyperactivity Disorder (ADHD) subtypes: inattentiveness and hyperactivity-impulsivity. While previous brain imaging research investigated ADHD as if it was a homogenous condition, its two core symptoms may originate from different brain mechanisms. We, therefore, hypothesized that the functional connectivity of cortico-striatal and attentional networks would be different between ADHD subtypes. We studied 165 children (mean age 10.93 years; age range, 7-17 year old) diagnosed as having ADHD based on their revised Conner's rating scale score and 170 typical developing individuals (mean age 11.46 years; age range, 7-17 year old) using resting state functional fMRI. Groups were matched for age, IQ and head motion during the MRI acquisition. We fractionated the ADHD group into predominantly inattentive, hyperactive-impulsive and combined subtypes based on their revised Conner's rating scale score. We then analyzed differences in resting state functional connectivity of the cortico-striatal and attentional networks between these subtypes. We found a double dissociation of functional connectivity in the cortico-striatal and ventral attentional networks, reflecting the subtypes of the ADHD participants. Particularly, the hyperactive-impulsive subtype was associated with increased connectivity in cortico-striatal network, whereas the inattentive subtype was associated with increased connectivity in the right ventral attention network. Our study demonstrated for the first time a right lateralized, double dissociation between specific networks associated with hyperactivity-impulsivity and inattentiveness in ADHD children, providing a biological M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 2 basis for exploring symptom dimensions and revealing potential targets for more personalized treatments.

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confidence: 88%

Section: Introductionmentioning

confidence: 95%

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

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Double-dissociation between the mechanism leading to impulsivity and inattention in Attention Deficit Hyperactivity Disorder: A resting-state functional connectivity study

Sanefuji

Craig

Parlatini

et al. 2017

Cortex

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“…Let us consider a time-series (y 1 , y 2 , ..........., y P ) of length P for which the SEnt can be computed as [37]:…”

Section: Sample Entropymentioning

confidence: 99%

Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework

Kumar

Pachori

Acharya

2017

Entropy

122

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Myocardial infarction (MI) is a silent condition that irreversibly damages the heart muscles. It expands rapidly and, if not treated timely, continues to damage the heart muscles. An electrocardiogram (ECG) is generally used by the clinicians to diagnose the MI patients. Manual identification of the changes introduced by MI is a time-consuming and tedious task, and there is also a possibility of misinterpretation of the changes in the ECG. Therefore, a method for automatic diagnosis of MI using ECG beat with flexible analytic wavelet transform (FAWT) method is proposed in this work. First, the segmentation of ECG signals into beats is performed. Then, FAWT is applied to each ECG beat, which decomposes them into subband signals. Sample entropy (SEnt) is computed from these subband signals and fed to the random forest (RF), J48 decision tree, back propagation neural network (BPNN), and least-squares support vector machine (LS-SVM) classifiers to choose the highest performing one. We have achieved highest classification accuracy of 99.31% using LS-SVM classifier. We have also incorporated Wilcoxon and Bhattacharya ranking methods and observed no improvement in the performance. The proposed automated method can be installed in the intensive care units (ICUs) of hospitals to aid the clinicians in confirming their diagnosis.

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“…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).…”

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confidence: 99%

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

et al. 2015

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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...

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Resting state fMRI entropy probes complexity of brain activity in adults with ADHD

Cited by 138 publications

References 53 publications

Double-dissociation between the mechanism leading to impulsivity and inattention in Attention Deficit Hyperactivity Disorder: A resting-state functional connectivity study

Double-dissociation between the mechanism leading to impulsivity and inattention in Attention Deficit Hyperactivity Disorder: A resting-state functional connectivity study

Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework

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

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