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Muthuraman M, Moliadze V, Boecher L, Siemann J, Freitag CM, Groppa S, Siniatchkin M. Multimodal alterations of directed connectivity profiles in patients with attention-deficit/hyperactivity disorders. Sci Rep 2019; 9:20028. [PMID: 31882672 PMCID: PMC6934806 DOI: 10.1038/s41598-019-56398-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Accepted: 11/22/2019] [Indexed: 12/23/2022] Open
Abstract
Functional and effective connectivity measures for tracking brain region interactions that have been investigated using both electroencephalography (EEG) and magnetoencephalography (MEG) bringing up new insights into clinical research. However, the differences between these connectivity methods, especially at the source level, have not yet been systematically studied. The dynamic characterization of coherent sources and temporal partial directed coherence, as measures of functional and effective connectivity, were applied to multimodal resting EEG and MEG data obtained from 11 young patients (mean age 13.2 ± 1.5 years) with attention-deficit/hyperactivity disorder (ADHD) and age-matched healthy subjects. Additionally, machine-learning algorithms were applied to the extracted connectivity features to identify biomarkers differentiating the two groups. An altered thalamo-cortical connectivity profile was attested in patients with ADHD who showed solely information outflow from cortical regions in comparison to healthy controls who exhibited bidirectional interregional connectivity in alpha, beta, and gamma frequency bands. We achieved an accuracy of 98% by combining features from all five studied frequency bands. Our findings suggest that both types of connectivity as extracted from EEG or MEG are sensitive methods to investigate neuronal network features in neuropsychiatric disorders. The connectivity features investigated here can be further tested as biomarkers of ADHD.
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Affiliation(s)
- Muthuraman Muthuraman
- Department of Neurology, Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.
| | - Vera Moliadze
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, Germany
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Autism Research and Intervention Center of Excellence, University Hospital Frankfurt am Main, Goethe University, Frankfurt am Main, Germany
| | - Lena Boecher
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Autism Research and Intervention Center of Excellence, University Hospital Frankfurt am Main, Goethe University, Frankfurt am Main, Germany
| | - Julia Siemann
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, Germany
- Department of Child and Adolescent Psychiatry and Psychotherapy Bethel, Ev. Hospital Bielefeld, Bielefeld, Germany
| | - Christine M Freitag
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Autism Research and Intervention Center of Excellence, University Hospital Frankfurt am Main, Goethe University, Frankfurt am Main, Germany
| | - Sergiu Groppa
- Department of Neurology, Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Michael Siniatchkin
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, Germany
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Autism Research and Intervention Center of Excellence, University Hospital Frankfurt am Main, Goethe University, Frankfurt am Main, Germany
- Department of Child and Adolescent Psychiatry and Psychotherapy Bethel, Ev. Hospital Bielefeld, Bielefeld, Germany
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Song D, Chang D, Zhang J, Peng W, Shang Y, Gao X, Wang Z. Reduced brain entropy by repetitive transcranial magnetic stimulation on the left dorsolateral prefrontal cortex in healthy young adults. Brain Imaging Behav 2019; 13:421-429. [PMID: 29629499 DOI: 10.1007/s11682-018-9866-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Entropy indicates system irregularity and the capacity for information processing. Recent research has identified interesting voxel-wise entropy distribution patterns in normal brain and its changes due to aging and brain disorders. A question of great scientific and clinical importance is whether brain entropy (BEN) can be modulated using non-invasive neuromodulations. The purpose of this study was to address this open question using high-frequency repetitive transcranial magnetic stimulation (rTMS). BEN was calculated from resting state fMRI at each voxel acquired before and after applying 20 Hz rTMS or SHAM (control) stimulation. As compared to SHAM, 20 Hz rTMS reduced BEN in medial orbito-frontal cortex and subgenial anterior cingulate cortex (MOFC/sgACC), suggesting a reduced information processing therein, probably as a result of the enhanced top-down regulation by the left DLPFC rTMS. No significant changes were observed to the functional connectivity (FC) between the left DLPFC (the target site) to the rest of the brain, suggesting that rTMS may not affect FC though it might use FC to transfer its effects or the ad hoc information. Our data proved that rTMS can modulate BEN and BEN can be used to monitor rTMS effects.
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Affiliation(s)
- Donghui Song
- Center for Cognition and Brain Disorders, Department of Psychology, Hangzhou Normal University, 126 Wenzhou Road, Building 7, Zhejiang, Province, 310005, Hangzhou, China
| | - Da Chang
- Center for Cognition and Brain Disorders, Department of Psychology, Hangzhou Normal University, 126 Wenzhou Road, Building 7, Zhejiang, Province, 310005, Hangzhou, China
| | - Jian Zhang
- Center for Cognition and Brain Disorders, Department of Psychology, Hangzhou Normal University, 126 Wenzhou Road, Building 7, Zhejiang, Province, 310005, Hangzhou, China
| | - Wei Peng
- Center for Cognition and Brain Disorders, Department of Psychology, Hangzhou Normal University, 126 Wenzhou Road, Building 7, Zhejiang, Province, 310005, Hangzhou, China
| | - Yuanqi Shang
- Center for Cognition and Brain Disorders, Department of Psychology, Hangzhou Normal University, 126 Wenzhou Road, Building 7, Zhejiang, Province, 310005, Hangzhou, China
| | - Xin Gao
- Center for Cognition and Brain Disorders, Department of Psychology, Hangzhou Normal University, 126 Wenzhou Road, Building 7, Zhejiang, Province, 310005, Hangzhou, China
| | - Ze Wang
- Center for Cognition and Brain Disorders, Department of Psychology, Hangzhou Normal University, 126 Wenzhou Road, Building 7, Zhejiang, Province, 310005, Hangzhou, China. .,Department of Radiology, Lewis Katz School of Medicine, Temple University, 3401 N Broad Street, 1st Floor, Radiology, Philadelphia, PA, 19140, USA.
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Complexity Analysis of Resting-State fMRI in Adult Patients with Attention Deficit Hyperactivity Disorder: Brain Entropy. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2018; 2017:3091815. [PMID: 29379522 PMCID: PMC5742910 DOI: 10.1155/2017/3091815] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Revised: 10/25/2017] [Accepted: 11/21/2017] [Indexed: 11/28/2022]
Abstract
Objective Complexity analysis of functional brain structure data represents a new multidisciplinary approach to examining complex, living structures. I aimed to construct a connectivity map of visual brain activities using resting-state functional magnetic resonance imaging (fMRI) data and to characterize the level of complexity of functional brain activity using these connectivity data. Methods A total of 25 healthy controls and 20 patients with attention deficit hyperactivity disorder (ADHD) participated. fMRI preprocessing analysis was performed that included head motion correction, temporal filtering, and spatial smoothing process. Brain entropy (BEN) was calculated using the Shannon entropy equation. Results My findings demonstrated that patients exhibited reduced brain complexity in visual brain areas compared to controls. The mean entropy value of the ADHD group was 0.56 ± 0.14, compared to 0.64 ± 0.11 in the control group. Conclusion My study adds an important novel result to the growing literature pertaining to abnormal visual processing in ADHD that my ADHD patients had lower BEN values, indicating more-regular functional brain structure and abnormal visual information processing.
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Xiong W, Faes L, Ivanov PC. Entropy measures, entropy estimators, and their performance in quantifying complex dynamics: Effects of artifacts, nonstationarity, and long-range correlations. Phys Rev E 2017; 95:062114. [PMID: 28709192 PMCID: PMC6117159 DOI: 10.1103/physreve.95.062114] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Indexed: 11/07/2022]
Abstract
Entropy measures are widely applied to quantify the complexity of dynamical systems in diverse fields. However, the practical application of entropy methods is challenging, due to the variety of entropy measures and estimators and the complexity of real-world time series, including nonstationarities and long-range correlations (LRC). We conduct a systematic study on the performance, bias, and limitations of three basic measures (entropy, conditional entropy, information storage) and three traditionally used estimators (linear, kernel, nearest neighbor). We investigate the dependence of entropy measures on estimator- and process-specific parameters, and we show the effects of three types of nonstationarities due to artifacts (trends, spikes, local variance change) in simulations of stochastic autoregressive processes. We also analyze the impact of LRC on the theoretical and estimated values of entropy measures. Finally, we apply entropy methods on heart rate variability data from subjects in different physiological states and clinical conditions. We find that entropy measures can only differentiate changes of specific types in cardiac dynamics and that appropriate preprocessing is vital for correct estimation and interpretation. Demonstrating the limitations of entropy methods and shedding light on how to mitigate bias and provide correct interpretations of results, this work can serve as a comprehensive reference for the application of entropy methods and the evaluation of existing studies.
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Affiliation(s)
- Wanting Xiong
- School of Systems Science, Beijing Normal University, Beijing 100875, People’s Republic of China
- Keck Laboratory for Network Physiology, Department of Physics, Boston University, Boston, Massachusetts 02215, USA
| | - Luca Faes
- Bruno Kessler Foundation and BIOtech, University of Trento, Trento 38123, Italy
| | - Plamen Ch. Ivanov
- Keck Laboratory for Network Physiology, Department of Physics, Boston University, Boston, Massachusetts 02215, USA
- Harvard Medical School and Division of Sleep Medicine, Brigham and Women’s Hospital, Boston, Massachusetts 02115, USA
- Institute of Solid State Physics, Bulgarian Academy of Sciences, Sofia 1784, Bulgaria
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Gómez C, Poza J, Fernández A, Bachiller A, Gómez J, Hornero R. Entropy analysis of MEG background activity in attention-deficit/hyperactivity disorder. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2013:5057-60. [PMID: 24110872 DOI: 10.1109/embc.2013.6610685] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The aim of this study was to analyze the magnetoencephalography (MEG) background activity in Attention-Deficit/Hyperactivity Disorder (ADHD) using fuzzy entropy (FuzzyEn), an entropy measure that quantifies signal irregularity. Five minutes of recording were acquired with a 148-channel whole-head magnetometer in 14 ADHD patients and 14 control children. Our results showed that MEG activity was more regular in ADHD patients than in controls. Additionally, there were statistically significant differences (p < 0.01, Student's t-test with Bonferroni's correction) in the five analyzed brain regions: anterior, central, posterior, left lateral, and right lateral. Using receiver operating characteristic (ROC) curves, the highest values of accuracy (82.14%) and area under the ROC curve (0.9005) were achieved in anterior area. Our results support the hypothesis that ADHD is characterized by a delay of cortical maturation in the prefrontal cortex.
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Sokunbi MO. Sample entropy reveals high discriminative power between young and elderly adults in short fMRI data sets. Front Neuroinform 2014; 8:69. [PMID: 25100988 PMCID: PMC4107942 DOI: 10.3389/fninf.2014.00069] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2014] [Accepted: 07/03/2014] [Indexed: 01/31/2023] Open
Abstract
Some studies have placed Sample entropy on the same data length constraint of 10m–20m (m: pattern length) as approximate entropy, even though Sample entropy is largely independent of data length and displays relative consistency over a broader range of possible parameters (r, tolerance value; m, pattern length; N, data length) under circumstances where approximate entropy does not. This is particularly erroneous for some fMRI experiments where the working data length is less than 100 volumes (when m = 2). We therefore investigated whether Sample entropy is able to effectively discriminate fMRI data with data length, N less than 10m (where m = 2) and r = 0.30, from a small group of 10 younger and 10 elderly adults, and the whole cohort of 43 younger and 43 elderly adults, that are significantly (p < 0.001) different in age. Ageing has been defined as a loss of entropy; where signal complexity decreases with age. For the small group analysis, the results of the whole brain analyses show that Sample entropy portrayed a good discriminatory ability for data lengths, 85 ≤ N ≤ 128, with an accuracy of 85% at N = 85 and 80% at N = 128, at q < 0.05. The regional analyses show that Sample entropy discriminated more brain regions at N = 128 than N = 85 and some regions common to both data lengths. As data length, N increased from 85 to 128, the noise level decreased. This was reflected in the accuracy of the whole brain analyses and the number of brain regions discriminated in the regional analyses. The whole brain analyses suggest that Sample entropy is relatively independent of data length, while the regional analyses show that fMRI data with length of 85 volumes is consistent with our hypothesis of a loss of entropy with ageing. In the whole cohort analysis, Sample entropy discriminated regionally between the younger and elderly adults only at N = 128. The whole cohort analysis at N = 85 was indicative of the ageing process but this indication was not significant (p > 0.05).
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Affiliation(s)
- Moses O Sokunbi
- MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff School of Medicine, Cardiff University Cardiff, UK ; Imaging Science, Cardiff University Brain Research Imaging Centre, Cardiff University Cardiff, UK
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Sokunbi MO, Gradin VB, Waiter GD, Cameron GG, Ahearn TS, Murray AD, Steele DJ, Staff RT. Nonlinear complexity analysis of brain FMRI signals in schizophrenia. PLoS One 2014; 9:e95146. [PMID: 24824731 PMCID: PMC4019508 DOI: 10.1371/journal.pone.0095146] [Citation(s) in RCA: 83] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2013] [Accepted: 03/24/2014] [Indexed: 11/18/2022] Open
Abstract
We investigated the differences in brain fMRI signal complexity in patients with schizophrenia while performing the Cyberball social exclusion task, using measures of Sample entropy and Hurst exponent (H). 13 patients meeting diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM IV) criteria for schizophrenia and 16 healthy controls underwent fMRI scanning at 1.5 T. The fMRI data of both groups of participants were pre-processed, the entropy characterized and the Hurst exponent extracted. Whole brain entropy and H maps of the groups were generated and analysed. The results after adjusting for age and sex differences together show that patients with schizophrenia exhibited higher complexity than healthy controls, at mean whole brain and regional levels. Also, both Sample entropy and Hurst exponent agree that patients with schizophrenia have more complex fMRI signals than healthy controls. These results suggest that schizophrenia is associated with more complex signal patterns when compared to healthy controls, supporting the increase in complexity hypothesis, where system complexity increases with age or disease, and also consistent with the notion that schizophrenia is characterised by a dysregulation of the nonlinear dynamics of underlying neuronal systems.
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Affiliation(s)
- Moses O. Sokunbi
- Aberdeen Biomedical Imaging Centre, University of Aberdeen, Aberdeen, United Kingdom
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- Institute of Psychological Medicine and Clinical Neurosciences, Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Cardiff School of Medicine, Cardiff University, Cardiff, United Kingdom
- * E-mail:
| | - Victoria B. Gradin
- Medical Research Institute, University of Dundee, Dundee, United Kingdom
- Centre for Basic Research in Psychology, Universidad de la Republica, Montevideo, Uruguay
| | - Gordon D. Waiter
- Aberdeen Biomedical Imaging Centre, University of Aberdeen, Aberdeen, United Kingdom
| | - George G. Cameron
- Aberdeen Biomedical Imaging Centre, University of Aberdeen, Aberdeen, United Kingdom
| | - Trevor S. Ahearn
- Aberdeen Biomedical Imaging Centre, University of Aberdeen, Aberdeen, United Kingdom
| | - Alison D. Murray
- Aberdeen Biomedical Imaging Centre, University of Aberdeen, Aberdeen, United Kingdom
| | - Douglas J. Steele
- Medical Research Institute, University of Dundee, Dundee, United Kingdom
| | - Roger T. Staff
- Department of Nuclear Medicine, Aberdeen Royal Infirmary, Aberdeen, United Kingdom
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Sokunbi MO, Fung W, Sawlani V, Choppin S, Linden DEJ, Thome J. Resting state fMRI entropy probes complexity of brain activity in adults with ADHD. Psychiatry Res 2013; 214:341-8. [PMID: 24183857 DOI: 10.1016/j.pscychresns.2013.10.001] [Citation(s) in RCA: 106] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2013] [Revised: 09/30/2013] [Accepted: 10/01/2013] [Indexed: 10/26/2022]
Abstract
In patients with attention deficit hyperactivity disorder (ADHD), quantitative neuroimaging techniques have revealed abnormalities in various brain regions, including the frontal cortex, striatum, cerebellum, and occipital cortex. Nonlinear signal processing techniques such as sample entropy have been used to probe the regularity of brain magnetoencephalography signals in patients with ADHD. In the present study, we extend this technique to analyse the complex output patterns of the 4 dimensional resting state functional magnetic resonance imaging signals in adult patients with ADHD. After adjusting for the effect of age, we found whole brain entropy differences (P=0.002) between groups and negative correlation (r=-0.45) between symptom scores and mean whole brain entropy values, indicating lower complexity in patients. In the regional analysis, patients showed reduced entropy in frontal and occipital regions bilaterally and a significant negative correlation between the symptom scores and the entropy maps at a family-wise error corrected cluster level of P<0.05 (P=0.001, initial threshold). Our findings support the hypothesis of abnormal frontal-striatal-cerebellar circuits in ADHD and the suggestion that sample entropy is a useful tool in revealing abnormalities in the brain dynamics of patients with psychiatric disorders.
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Affiliation(s)
- Moses O Sokunbi
- MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff School of Medicine, Cardiff University, Cardiff, UK; Imaging Science, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK.
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Robinson SE, Mandell AJ, Coppola R. Spatiotemporal imaging of complexity. Front Comput Neurosci 2013; 6:101. [PMID: 23355820 PMCID: PMC3553423 DOI: 10.3389/fncom.2012.00101] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2012] [Accepted: 12/30/2012] [Indexed: 12/03/2022] Open
Abstract
What are the functional neuroimaging measurements required for more fully characterizing the events and locations of neocortical activity? A prime assumption has been that modulation of cortical activity will inevitably be reflected in changes in energy utilization (for the most part) changes of glucose and oxygen consumption. Are such a measures complete and sufficient? More direct measures of cortical electrophysiological activity show event or task-related modulation of amplitude or band-limited oscillatory power. Using magnetoencephalography (MEG), these measures have been shown to correlate well with energy utilization sensitive BOLD fMRI. In this paper, we explore the existence of state changes in electrophysiological cortical activity that can occur independently of changes in averaged amplitude, source power or indices of metabolic rates. In addition, we demonstrate that such state changes can be described by applying a new measure of complexity, rank vector entropy (RVE), to source waveform estimates from beamformer-processed MEG. RVE is a non-parametric symbolic dynamic informational entropy measure that accommodates the wide dynamic range of measured brain signals while resolving its temporal variations. By representing the measurements by their rank values, RVE overcomes the problem of defining embedding space partitions without resorting to signal compression. This renders RVE-independent of absolute signal amplitude. In addition, this approach is robust, being relatively free of tunable parameters. We present examples of task-free and task-dependent MEG demonstrating that RVE provides new information by uncovering hidden dynamical structure in the apparent turbulent (or chaotic) dynamics of spontaneous cortical activity.
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Affiliation(s)
- Stephen E Robinson
- MEG Core Facility, National Institute of Mental Health, National Institutes of Health Bethesda, MD, USA
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