{"id":191,"date":"2020-09-10T20:32:25","date_gmt":"2020-09-10T18:32:25","guid":{"rendered":"http:\/\/www.hbimed.com\/wp-enfold-aug2020\/?page_id=191"},"modified":"2020-09-10T20:32:25","modified_gmt":"2020-09-10T18:32:25","slug":"research","status":"publish","type":"page","link":"https:\/\/hbimed.com\/en\/forschung\/","title":{"rendered":"Research"},"content":{"rendered":"<div id='av_section_1'  class='avia-section av-1m486-c4ba37123efc5011fae03206ec432ee7 main_color avia-section-default avia-no-border-styling  avia-builder-el-0  el_before_av_section  avia-builder-el-first  avia-bg-style-scroll container_wrap sidebar_right'  ><div class='container av-section-cont-open' ><main  role=\"main\" itemprop=\"mainContentOfPage\"  class='template-page content  av-content-small alpha units'><div class='post-entry post-entry-type-page post-entry-191'><div class='entry-content-wrapper clearfix'>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-15y6f-39312c453ec97aabad9ecafe7ad5e6d4\">\n#top .av-special-heading.av-15y6f-39312c453ec97aabad9ecafe7ad5e6d4{\npadding-bottom:10px;\n}\nbody .av-special-heading.av-15y6f-39312c453ec97aabad9ecafe7ad5e6d4 .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-15y6f-39312c453ec97aabad9ecafe7ad5e6d4 .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-15y6f-39312c453ec97aabad9ecafe7ad5e6d4 av-special-heading-h1 blockquote modern-quote  avia-builder-el-1  el_before_av_one_half  avia-builder-el-first'><h1 class='av-special-heading-tag'  itemprop=\"headline\"  >Research<\/h1><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-s82i-5e70d99c8233c51b6166d65407b030cd\">\n.flex_column.av-s82i-5e70d99c8233c51b6166d65407b030cd{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-s82i-5e70d99c8233c51b6166d65407b030cd av_one_half  avia-builder-el-2  el_after_av_heading  el_before_av_one_half  first flex_column_div av-zero-column-padding'     ><section  class='av_textblock_section av-kdoek3vo-272befba42fc51414855ade34c25da1f'   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div class='avia_textblock'  itemprop=\"text\" ><h2>Article<\/h2>\n<\/div><\/section><br \/>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-kex5kljy-40686d73d7d9a4f8362914a9905570cf\">\n#top .togglecontainer.av-kex5kljy-40686d73d7d9a4f8362914a9905570cf p.toggler{\nborder-color:#96c11f;\n}\n#top .togglecontainer.av-kex5kljy-40686d73d7d9a4f8362914a9905570cf .toggle_wrap .toggle_content{\nborder-color:#96c11f;\n}\n<\/style>\n<div  class='togglecontainer av-kex5kljy-40686d73d7d9a4f8362914a9905570cf av-minimal-toggle  avia-builder-el-4  el_after_av_textblock  avia-builder-el-last  toggle_close_all' >\n<section class='av_toggle_section av-nrb1j-8fed4d9cbbd34df673f2121156b34491'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-1' data-fake-id='#toggle-id-1' class='toggler  av-title-above av-inherit-border-color'  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-1' data-slide-speed=\"200\" data-title=\"Abkl\u00e4rung des ADHS - Durch Biomarker zu personalisierter Medizin\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Abkl\u00e4rung des ADHS - Durch Biomarker zu personalisierter Medizin\" data-aria_expanded=\"Click to collapse: Abkl\u00e4rung des ADHS - Durch Biomarker zu personalisierter Medizin\">Clarification of ADHD - Towards Personalized Medicine Through Biomarkers<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-1' aria-labelledby='toggle-toggle-id-1' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-border-color'  itemprop=\"text\" ><p><em>M\u00fcller, Andreas; Candrian, Gian<\/em><br \/>\n<strong>Clarification of ADHD - Towards Personalized Medicine Through Biomarkers<\/strong><\/p>\n<p>IN|FO|Neurologie &amp; Psychatrie; VOL. 10, Nr. 3, 2012.<\/p>\n<p>The main problem in diagnosing ADHD is the lack of objectivity. Incorporating biomarkers and understanding them from the perspective of actions, thoughts, and feelings, within the context of life circumstances and personal history, largely resolves this issue. Electrophysiological biomarkers (quantitative analysis of EEG and event-related potentials) are excellent for understanding neural dynamics, for diagnosis, and for determining interventions in the sense of personalized medicine. Studies demonstrate a high validity of event-related potentials.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-gl3in-8e9784e2d62ffbb8a2eb320dc8ad9692'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-2' data-fake-id='#toggle-id-2' class='toggler  av-title-above av-inherit-border-color'  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-2' data-slide-speed=\"200\" data-title=\"The QEEG theta\/beta ratio in ADHD and normal controls: Sensitivity, specificity, and behavioral correlates \" data-title-open=\"\" data-aria_collapsed=\"Click to expand: The QEEG theta\/beta ratio in ADHD and normal controls: Sensitivity, specificity, and behavioral correlates \" data-aria_expanded=\"Click to collapse: The QEEG theta\/beta ratio in ADHD and normal controls: Sensitivity, specificity, and behavioral correlates \">The QEEG theta\/beta ratio in ADHD and normal controls: Sensitivity, specificity, and behavioral correlates <span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-2' aria-labelledby='toggle-toggle-id-2' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-border-color'  itemprop=\"text\" ><p><em>Ogrim, Geir; Kropotov, Juri D; Hestad, Knut<\/em><br \/>\n<strong>The QEEG theta\/beta ratio in ADHD and normal controls: Sensitivity, specificity, and behavioral correlates<\/strong><\/p>\n<p>Psychiatry Research, 2012.<\/p>\n<p>The purpose of the present study was to determine whether the theta\/beta ratio, as well as theta and beta waves separately, correlate with behavioral parameters, and whether these measures can distinguish between children and adolescents with ADHD and normal gender- and age-matched controls. Sixty-two patients and 39 controls participated in the study. A continuous performance test (CPT), a GO\/NOGO test, and two rating scales were used to measure behavior in the patient group. EEG spectra were analyzed under eyes-closed and eyes-open conditions, and during a GO\/NOGO task in both groups. Neither the theta\/beta ratio at CZ nor theta and beta separately showed significant differences between patients and controls. When each individual was compared with the database, significant elevations in theta were found in 25.81% of the patients and in only one control subject (2.61%). In the ADHD group, theta at CZ was positively correlated with inattention and executive function problems and negatively correlated with hyperactivity\/impulsivity. Beta correlated with good attention levels in the control group, but with ADHD symptoms in the patients. Omission errors in the GO\/NOGO test distinguished between patients and controls with an accuracy of 85.1%. For theta at CZ, the accuracy was 62.1%. Significantly elevated theta characterized a subgroup of ADHD and correlated with inattention and executive problems.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-zr6n-172c56512c2bbf06ebee60957c80e0f5'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-3' data-fake-id='#toggle-id-3' class='toggler  av-title-above av-inherit-border-color'  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-3' data-slide-speed=\"200\" data-title=\"Discriminating between ADHD adults and controls using independent ERP components and a support vector machine: a validation study\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Discriminating between ADHD adults and controls using independent ERP components and a support vector machine: a validation study\" data-aria_expanded=\"Click to collapse: Discriminating between ADHD adults and controls using independent ERP components and a support vector machine: a validation study\">Discriminating between ADHD adults and controls using independent ERP components and a support vector machine: a validation study<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-3' aria-labelledby='toggle-toggle-id-3' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-border-color'  itemprop=\"text\" ><p><em>M\u00fcller, Andreas; Candrian, Gian; Grane, Venke Arntsberg; Kropotov, Yuri D.; Ponomarev, Valery A.; Baschera, Gian-Marco<\/em><br \/>\n<strong>Discriminating between ADHD adults and controls using independent ERP components and a support vector machine: a validation study<\/strong><\/p>\n<p>Nonlinear Biomedical Physics, 5:5, 2011.<\/p>\n<p><strong>Background:<\/strong><br \/>\nNumerous event-related potential (ERP) studies have been conducted on attention-deficit hyperactivity disorder (ADHD), and a substantial number of ERP correlates of the disorder have been identified. However, most of these studies are limited to group differences in children. Independent component analysis (ICA) separates a set of mixed event-related potentials into a corresponding set of statistically independent source signals, which are likely to represent different functional processes. Using a support vector machine (SVM), a classification method originating from machine learning, this study aimed to investigate the use of such independent ERP components in differentiating adult ADHD patients from non-clinical controls by selecting the most informative feature set. A second aim was to validate the predictive power of the SVM classifier using an independent ADHD sample recruited at a different laboratory.<\/p>\n<p><strong>Methods:<\/strong><br \/>\nTwo groups of age-matched adults (75 with ADHD, 75 controls) performed a visual two-stimulus go\/no-go task. ERP responses were decomposed into independent components, and a selected set of independent ERP component features was used for SVM classification.<\/p>\n<p><strong>Results:<\/strong><br \/>\nUsing a 10-fold cross-validation approach, the classification accuracy was 91.1%. The predictive power of the SVM classifier was verified using an independent ADHD sample (17 ADHD patients), resulting in a classification accuracy of 94.1%. The latency and amplitude measures that, in combination, best distinguished between ADHD patients and non-clinical subjects primarily originated from independent components associated with inhibitory and other executive functions.<\/p>\n<p><strong>Conclusions:<br \/>\n<\/strong>This study shows that ERPs can substantially contribute to the diagnosis of ADHD when combined with up-to-date methods.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-fkguf-9d48716a1bc33293d64b837be2f1d4be'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-4' data-fake-id='#toggle-id-4' class='toggler  av-title-above av-inherit-border-color'  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-4' data-slide-speed=\"200\" data-title=\"Analysis of EEG characteristic at early stages of depression using method of independent components\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Analysis of EEG characteristic at early stages of depression using method of independent components\" data-aria_expanded=\"Click to collapse: Analysis of EEG characteristic at early stages of depression using method of independent components\">Analysis of EEG Characteristics in Early Stages of Depression Using the Independent Components Method<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-4' aria-labelledby='toggle-toggle-id-4' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-border-color'  itemprop=\"text\" ><p><em>Grin\u2018-Iatsenko, VA; Baas, Ineke; Ponomarev, Valery A; Kropotov, Juri D<\/em><br \/>\n<strong>Analysis of EEG Characteristics in Early Stages of Depression Using the Independent Components Method<\/strong><\/p>\n<p>Human Physiology. Jan-Feb;37(1):45-55, 2011.<\/p>\n<p>Independent Component Analysis (ICA) was used for 19-channel resting EEG analysis of 111 patients at early stages of depressive disorder and 526 age-matched healthy subjects. Comparison of independent components' power spectra in depressed patients and healthy subjects in two states: eyes closed and eyes open, revealed significant differences between groups for three frequency bands: Theta (4-7.5 Hz), Alpha (7.5-14 Hz), and Beta (14-20 Hz). Increased power of alpha and theta activity in depressed patients at parietal and occipital sites may be caused by decreased cortical activation of these regions. Diffuse enhancement of beta activity level can correlate with anxiety symptoms, which play an important role in the clinical picture of depressive disorder at early stages. The use of the ICA method for comparing the spectral characteristics of EEG in groups of patients with different brain pathologies and healthy subjects allows for more precise localization of discovered differences compared to traditional analysis of EEG spectra.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-p3p3-0af4162a63a13b67699c90efa181d3ad'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-5' data-fake-id='#toggle-id-5' class='toggler  av-title-above av-inherit-border-color'  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-5' data-slide-speed=\"200\" data-title=\"Dissociating action inhibition, conflict monitoring and sensory mismatch into independent components of event related potentials in GO\/NOGO task\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Dissociating action inhibition, conflict monitoring and sensory mismatch into independent components of event related potentials in GO\/NOGO task\" data-aria_expanded=\"Click to collapse: Dissociating action inhibition, conflict monitoring and sensory mismatch into independent components of event related potentials in GO\/NOGO task\">Dissociating action inhibition, conflict monitoring, and sensory mismatch into independent components of event-related potentials in a Go\/No-Go task<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-5' aria-labelledby='toggle-toggle-id-5' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-border-color'  itemprop=\"text\" ><p><em>Kropotov, Juri D; Ponomarev, Valery A; Hollup, Stig; M\u00fcller, Andreas<\/em><br \/>\n<strong>Dissociating action inhibition, conflict monitoring, and sensory mismatch into independent components of event-related potentials in a Go\/No-Go task<\/strong><\/p>\n<p>NeuroImage 57, 565\u2013575, 2011.<\/p>\n<p>In previous studies, the anterior N2 and P3 waves of event-related potentials (ERPs) in the GO\/NOGO paradigm, during trials involving preparatory set violations, were inconsistently associated with either action inhibition or conflict monitoring operations. In the present study, a paired-stimulus GO\/NOGO design was used to experimentally control the preparatory sets. Three variants of the same stimulus task manipulated sensory mismatch, action inhibition, and conflict monitoring operations by varying stimulus-response associations. The anterior N2 and P3 waves were decomposed into components using independent component analysis (ICA). The ICA was performed on a collection of 114 individual ERPs across the three experimental conditions. Three of the independent components were selectively affected by the task manipulations, indicating an association of these components with sensory mismatch, action inhibition, and conflict monitoring operations. According to sLORETA, the sensory mismatch component was generated in the left and right temporal areas, the action suppression component was generated in the supplementary motor cortex, and the conflict monitoring component was generated in the anterior cingulate cortex.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-a2iav-88d82f2e4841f5146ecb47ca7ecbd5d2'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-6' data-fake-id='#toggle-id-6' class='toggler  av-title-above av-inherit-border-color'  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-6' data-slide-speed=\"200\" data-title=\"Classification of ADHD patients on the basis of independent ERP components using a machine learning system\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Classification of ADHD patients on the basis of independent ERP components using a machine learning system\" data-aria_expanded=\"Click to collapse: Classification of ADHD patients on the basis of independent ERP components using a machine learning system\">Classification of ADHD patients based on independent ERP components using a machine learning system<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-6' aria-labelledby='toggle-toggle-id-6' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-border-color'  itemprop=\"text\" ><p><em>M\u00fcller, Andreas; Candrian, Gian; Kropotov, Juri D; Ponomarev, Valery A; Baschera, Gian-Marco<\/em><br \/>\n<strong>Classification of ADHD patients based on independent ERP components using a machine learning system<\/strong><\/p>\n<p>Nonlinear Biomedical Physics, 4 (Suppl 1): S1, 2010.<\/p>\n<p>Background: In the context of sensory and cognitive processing deficits in ADHD patients, there is considerable evidence of altered event-related potentials (ERPs). However, most studies have been conducted on children with ADHD. Using the independent component analysis (ICA) method, ERPs can be decomposed into functionally different components. This study investigated whether features of independent ERP components can be used to differentiate between adults with ADHD and healthy subjects, employing the support vector machine classification method.<\/p>\n<p>Methods: Two groups of age- and sex-matched adults (74 with ADHD, 74 controls) performed a visual two-stimulus GO\/NOGO task. ERP responses were decomposed into independent components using ICA. A feature selection algorithm defined a set of independent component features that were entered into a support vector machine.<\/p>\n<p>Results: The feature set consisted of five latency measures within specific time windows, which were collected from four distinct independent components. The independent components included a novelty component, a sensory-related component, and two executive function-related components. Using a 10-fold cross-validation approach, the classification accuracy was 92.1%.<\/p>\n<p>Conclusions: This study was a first attempt to classify adults with ADHD using a support vector machine, indicating that classification with non-linear methods is feasible within the context of clinical groups. Furthermore, independent ERP components have been shown to provide features that can be used to characterize clinical populations.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-e4arb-372acf51276ffbc771c35d0510283743'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-7' data-fake-id='#toggle-id-7' class='toggler  av-title-above av-inherit-border-color'  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-7' data-slide-speed=\"200\" data-title=\"Beneficial Effects of Electrostimulation Contingencies on Sustained Attention and Electrocortical Activity\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Beneficial Effects of Electrostimulation Contingencies on Sustained Attention and Electrocortical Activity\" data-aria_expanded=\"Click to collapse: Beneficial Effects of Electrostimulation Contingencies on Sustained Attention and Electrocortical Activity\">Beneficial Effects of Electrostimulation Contingencies on Sustained Attention and Electrocortical Activity<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-7' aria-labelledby='toggle-toggle-id-7' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-border-color'  itemprop=\"text\" ><p><em>Chen, Max Jean-Lon; Thompson, Trevor; Kropotov, Juri D; Gruzelier, John H<\/em><br \/>\n<strong>Beneficial Effects of Electrostimulation Contingencies on Sustained Attention and Electrocortical Activity<\/strong><\/p>\n<p>CNS Neuroscience &amp; Therapeutics 00, 1\u201316, 2010.<\/p>\n<p>Introduction: Chinese acupuncture therapy has been practiced for more than 3000 years. According to neuroimaging studies, electroacupuncture has been demonstrated to be effective via control of the frequency parameter of stimulation, based on the theory of frequency modulation of brain function.<\/p>\n<p>Aims: To investigate the following: (1) possible sustained effects of acustimulation in improving perceptual sensitivity in attention by comparing before, during, and 5 min following stimulation; (2) relations between commission errors and the motor inhibition event-related potential (ERP) component measured with independent component analysis (ICA); (3) whether habituation would be demonstrated in the sham control group and would be mitigated by acustimulation in the experimental groups.<\/p>\n<p>Results: Twenty-seven subjects were divided into three groups (n = 9). d-Prime (d') derived from signal detection theory was used as an index of perceptual sensitivity in the visual continuous performance attention test. Increased d' was found during both alternating frequency (AE) and low frequency (LE) stimulation, but with no change in the sham control group (SE). However, only following AE was there a sustained poststimulation effect. Spatial filtration-based independent components (ICs) in the AE group revealed significantly decreased amplitudes of the motor inhibition ICs both during and poststimulation. There was a significant habituation effect from task repetition in the sham group with decreased amplitudes of ICs as follows: the visual comparison component difference between go (correct response) and nogo cues (correct withheld response), the P400 action monitoring and the working memory component in the nogo condition, and the passive auditory component on control trials.<\/p>\n<p>Conclusion: The results showed associations between acustimulation and improved perceptual sensitivity, with sustained improvements following AE but not LE stimulation. Improvements in commission errors in the AE group were related to motor inhibition IC. The activational effects of acustimulation apparently attenuated the across-task habituation that characterized the control group.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-bu2vr-db514295a14a4878ea9ab2225c2becf1'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-8' data-fake-id='#toggle-id-8' class='toggler  av-title-above av-inherit-border-color'  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-8' data-slide-speed=\"200\" data-title=\"The comparison of clustering methods of EEG independent components in healthy subjects and patients with post concussion syndrome after traumatic brain injury\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: The comparison of clustering methods of EEG independent components in healthy subjects and patients with post concussion syndrome after traumatic brain injury\" data-aria_expanded=\"Click to collapse: The comparison of clustering methods of EEG independent components in healthy subjects and patients with post concussion syndrome after traumatic brain injury\">A comparison of clustering methods for EEG independent components in healthy subjects and patients with post-concussion syndrome after traumatic brain injury<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-8' aria-labelledby='toggle-toggle-id-8' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-border-color'  itemprop=\"text\" ><p><em>Ponomarev, Valery A.; Gurskaia, O. E.; Kropotov, Yuri D.; Artiushkova, L. V.; M\u00fcller, Andreas<\/em><br \/>\n<strong>A comparison of clustering methods for EEG independent components in healthy subjects and patients with post-concussion syndrome after traumatic brain injury<\/strong><\/p>\n<p>Human Physiology. Mar-Apr;36(2):5-14, 2010.<\/p>\n<p>A comparison of three different clustering methods for 19-channel EEG independent components was performed in 518 healthy subjects and 87 patients with post-concussion syndrome after traumatic brain injury to more precisely define the location of pathological brain activity sources. The following grouping methods were used: clustering of independent component topographies, clustering of equivalent dipole source coordinates corresponding to independent component topographies, and sorting of independent components using extremes of equivalent source current density computed by Standardized Low Resolution Electromagnetic Tomography (sLORETA). The comparison of independent component power spectra revealed a statistically significant increase in EEG power located in frontal and temporal brain areas across delta, theta, and alpha frequency bands in patients with post-concussion syndrome after traumatic brain injury. The method of clustering independent component topographies appears to be the most sensitive when compared to the other two methods.<\/p>\n<\/div><\/div><\/div><\/section>\n<\/div><\/p><\/div>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-s82i-b7ee1b49f4240284e23b34917aff6499\">\n.flex_column.av-s82i-b7ee1b49f4240284e23b34917aff6499{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-s82i-b7ee1b49f4240284e23b34917aff6499 av_one_half  avia-builder-el-5  el_after_av_one_half  el_before_av_hr  flex_column_div av-zero-column-padding'     ><section  class='av_textblock_section av-kdoek3vo-272befba42fc51414855ade34c25da1f'   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div class='avia_textblock'  itemprop=\"text\" ><h2>Books<\/h2>\n<\/div><\/section><br \/>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-kex5kljy-40686d73d7d9a4f8362914a9905570cf\">\n#top .togglecontainer.av-kex5kljy-40686d73d7d9a4f8362914a9905570cf p.toggler{\nborder-color:#96c11f;\n}\n#top .togglecontainer.av-kex5kljy-40686d73d7d9a4f8362914a9905570cf .toggle_wrap .toggle_content{\nborder-color:#96c11f;\n}\n<\/style>\n<div  class='togglecontainer av-kex5kljy-40686d73d7d9a4f8362914a9905570cf av-minimal-toggle  avia-builder-el-7  el_after_av_textblock  el_before_av_hr  toggle_close_all' >\n<section class='av_toggle_section av-hcbgf-1af4d83aaa4ed45edf9aca1092e73c20'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-9' data-fake-id='#toggle-id-9' class='toggler  av-title-above av-inherit-border-color'  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-9' data-slide-speed=\"200\" data-title=\"ADHS \u2013 Neurodiagnostik in der Praxis\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: ADHS \u2013 Neurodiagnostik in der Praxis\" data-aria_expanded=\"Click to collapse: ADHS \u2013 Neurodiagnostik in der Praxis\">ADHD - Neuro-diagnostic Testing in Practice<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-9' aria-labelledby='toggle-toggle-id-9' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-border-color'  itemprop=\"text\" ><p><em>M\u00fcller, Andreas; Candrian, Gian; Kropotov, Juri D<\/em><br \/>\n<strong>ADHD - Neuro-diagnostic Testing in Practice<\/strong><\/p>\n<p>Springer-Verlag, 2011, ISBN: 139783642200618.<\/p>\n<p>The genesis of this book had several starting points. First, there was the growing unease regarding the objectivity and the limits of psychological-psychiatric diagnostics, which had developed over the course of our practical work in the last 30 years. Although the currently available psychometric testing methods have a long tradition and a multitude of studies concerning their validity criteria exist, they contribute little to the diagnosis in most cases. While it is possible to assess cognitive-behavioral functions such as various types of thinking and problem-solving, work tempo, memory, and attention with more or less original instruments and to open up avenues to emotions and behavior through questionnaires, the problem with all these findings is that they mostly have low ecological validity and additionally contribute little to understanding human difference. This leads to the fact that the clinically obtained psychostatus is largely generated by the descriptions of those affected and their relatives. The impressions of the assessor are situationally influenced and essentially dependent on the feelings of the expert themselves. This subjective imprint on diagnostics and the associated variability and inaccuracy were, so to speak, the \u00bbdriving force\u00ab for the search for new possibilities for more objective diagnostics.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-7sdif-b43f2f7232397a05292100a8e4d76df8'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-10' data-fake-id='#toggle-id-10' class='toggler  av-title-above av-inherit-border-color'  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-10' data-slide-speed=\"200\" data-title=\"Neurofeedback and Neuromodulation Techniques and Applications\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Neurofeedback and Neuromodulation Techniques and Applications\" data-aria_expanded=\"Click to collapse: Neurofeedback and Neuromodulation Techniques and Applications\">Neurofeedback and Neuromodulation Techniques and Applications<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-10' aria-labelledby='toggle-toggle-id-10' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-border-color'  itemprop=\"text\" ><p><em>Coben, Robert; Evans, James R. (Eds.)<\/em><br \/>\n<strong>Neurofeedback and Neuromodulation Techniques and Applications<\/strong><\/p>\n<p>Academic Press, 2010.<\/p>\n<p>It wasn't many years ago that the term \u201eneuromodulation\u201c would have been considered contradictory by many, at least with regard to modifying a damaged or dysfunctional central nervous system. Although it had generally been assumed that learning and memory somehow resulted in relatively permanent modifications of brain structure and\/or function, the notion persisted that neural function and structure were basically set by genetics and were relatively immune to change. However, within the past couple of decades, developments in neuroimaging have enabled scientific research providing evidence of neural plasticity far greater than previously imagined. Research on neural plasticity is burgeoning, along with a plethora of scientifically unsubstantiated claims by practitioners from many different professions for \u201ebrain-based\u201c methods for remediation of various medical, psychological, and educational problems.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-a5vfz-d6fb71ecf1274ae16e21e6f57b127b54'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-11' data-fake-id='#toggle-id-11' class='toggler  av-title-above av-inherit-border-color'  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-11' data-slide-speed=\"200\" data-title=\"Quantitative EEG, Event-Related Potentials and Neurotherapy\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Quantitative EEG, Event-Related Potentials and Neurotherapy\" data-aria_expanded=\"Click to collapse: Quantitative EEG, Event-Related Potentials and Neurotherapy\">Quantitative EEG, Event-Related Potentials, and Neurotherapy<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-11' aria-labelledby='toggle-toggle-id-11' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-border-color'  itemprop=\"text\" ><p class=\"tp_pub_author\"><em>Kropotov, Yuri D<\/em><br \/>\n<strong>Quantitative EEG, Event-Related Potentials, and Neurotherapy<\/strong><\/p>\n<p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_publisher\">Academic Press,\u00a0<\/span><span class=\"tp_pub_additional_year\">2008<\/span>,\u00a0<span class=\"tp_pub_additional_isbn\">978-0123745125<\/span>.<\/p>\n<p>While the brain is largely governed by chemical neurotransmitters, it is also a bioelectric organ. The combined study of \u201aQuantitative Electroencephalograms (QEEG\u2014the conversion of brainwaves into digital form to enable comparison between neurologically healthy and dysfunctional individuals), Event-Related Potentials (ERPs\u2014electrophysiological responses to stimuli), and neurotherapy (the process of actively retraining brain processes),\u2018 offers a window into brain physiology and function through computer and statistical analyses of traditional EEG patterns, suggesting innovative approaches to improving attention, anxiety, mood, and behavior. The volume provides a detailed description of the various EEG rhythms and ERPs, conventional analytical methods such as spectral analysis, and the emerging method utilizing QEEG and ERPs. This research is then related back to practice, and all existing approaches in the field of neurotherapy\u2014conventional EEG-based neurofeedback, brain-computer interfaces, transcranial direct current stimulation, and transcranial magnetic stimulation\u2014are covered in full. Additionally, software for EEG analysis is provided on a companion website so that the theory can be put into practice immediately, and a database of the EEG algorithms described in the book can be combined with algorithms uploaded by the user to compare dysfunctional and normative data. While it does not offer the breadth provided by an edited work, this volume does provide a level of depth and detail that a single author can deliver, as well as giving readers insight into the personal theories of one of the preeminent leaders in the field. Features and benefits include: provides a holistic picture of quantitative EEG and event-related potentials as a unified scientific field; presents a unified description of the methods of quantitative EEG and event-related potentials; gives a scientifically based overview of existing approaches in the field of neurotherapy; provides practical information for the better understanding and treatment of disorders, such as ADHD, schizophrenia, addiction, OCD, depression, and Alzheimer\u2019s disease; a companion website containing software that analyzes EEG patterns and a database of sample EEGs; and, readers can view actual examples of EEG patterns discussed in the book and upload their own library of EEGs for analysis.<\/p>\n<\/div><\/div><\/div><\/section>\n<\/div><br \/>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-kex65max-5e473eb3fb2656aa86ac10eb2603cbd1\">\n#top .hr.hr-invisible.av-kex65max-5e473eb3fb2656aa86ac10eb2603cbd1{\nheight:25px;\n}\n<\/style>\n<div  class='hr av-kex65max-5e473eb3fb2656aa86ac10eb2603cbd1 hr-invisible  avia-builder-el-8  el_after_av_toggle_container  el_before_av_textblock'><span class='hr-inner'><span class=\"hr-inner-style\"><\/span><\/span><\/div><br \/>\n<section  class='av_textblock_section av-kdoek3vo-272befba42fc51414855ade34c25da1f'   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div class='avia_textblock'  itemprop=\"text\" ><h2>Research reports<\/h2>\n<\/div><\/section><br \/>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-kex5kljy-40686d73d7d9a4f8362914a9905570cf\">\n#top .togglecontainer.av-kex5kljy-40686d73d7d9a4f8362914a9905570cf p.toggler{\nborder-color:#96c11f;\n}\n#top .togglecontainer.av-kex5kljy-40686d73d7d9a4f8362914a9905570cf .toggle_wrap .toggle_content{\nborder-color:#96c11f;\n}\n<\/style>\n<div  class='togglecontainer av-kex5kljy-40686d73d7d9a4f8362914a9905570cf av-minimal-toggle  avia-builder-el-10  el_after_av_textblock  avia-builder-el-last  toggle_close_all' >\n<section class='av_toggle_section av-7xeu7-f270ef92626cf5ef1bf1658ae5581477'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-12' data-fake-id='#toggle-id-12' class='toggler  av-title-above av-inherit-border-color'  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-12' data-slide-speed=\"200\" data-title=\"New tools for diagnosis and modulation of brain dysfunction\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: New tools for diagnosis and modulation of brain dysfunction\" data-aria_expanded=\"Click to collapse: New tools for diagnosis and modulation of brain dysfunction\">New tools for the diagnosis and modulation of brain dysfunction<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-12' aria-labelledby='toggle-toggle-id-12' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-border-color'  itemprop=\"text\" ><p><em>Kropotov, Yuri D<\/em><br \/>\n<strong>New tools for the diagnosis and modulation of brain dysfunction<\/strong><\/p>\n<p>0000.<\/p>\n<p>Suppose a boy comes to your door. His behavior looks like typical ADHD: he is extremely inattentive, impulsive, and hyperactive. He performs poorly on continuous performance tasks.<\/p>\n<p><strong>Resent research in neurophysiology of ADHD shows that there are several reasons why the boy behaves in this way:<\/strong><\/p>\n<p>1. The patient may have a focus in his cortex, which, without any overt symptoms of epilepsy, impairs information processing and consequently mimics attention deficit (see Aldenkamp, Arends, 2004);<br \/>\n2. The patient may have a lack of overall cortical activation due to dysfunction of the ascending reticular system of the brainstem (Sergeant, 2000);<br \/>\n3. a patient may have genetically determined hyperactive frontal lobes (Clarke et al., 2003);<br \/>\n4. The patient may have dysfunction of the prefrontal-striato-thalamic system due to structural abnormality (Silk et al., 2009; Busch et al., 2005; Castellanos et al., 1996); or an increase of dopamine reuptake dopamine transporters in the striatum (Krause et al., 2003).<br \/>\n5. The patient may have hypoactivation of the premotor cortex, which is compensated by an increase in motoric activity (Simmonds et al., 2007).;<br \/>\n6. The patient may have dysfunction in the anterior cingulate gyrus, which produces anxiety, emotional instability, and hyperactivation (Albrecht et al., 2008).<\/p>\n<\/div><\/div><\/div><\/section>\n<\/div><\/p><\/div><div  id=\"aposapos\"  class='hr &apos;av-kgkiedt6&apos;-6e09bbdc0abbbc35ecda3588a92a569a hr-&apos;invisible&apos;  avia-builder-el-11  el_after_av_one_half  el_before_av_button  aposapos'><span class='hr-inner'><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n<div  id=\"aposapos\"  class='avia-button-wrap &apos;av-kg6ego6a&apos;-4caa9273f534d4cbf3ce01fc393ef4df-wrap avia-button-&apos;center&apos;  avia-builder-el-12  el_after_av_hr  avia-builder-el-last  aposapos'>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-&apos;av-kg6ego6a&apos;-4caa9273f534d4cbf3ce01fc393ef4df\">\n#top #wrap_all .avia-button.&apos;av-kg6ego6a&apos;-4caa9273f534d4cbf3ce01fc393ef4df:hover{\nbackground-color:&apos;theme-color-highlight&apos;;\ncolor:#ffffff;\ntransition:all 0.4s ease-in-out;\n}\n#top #wrap_all .avia-button.&apos;av-kg6ego6a&apos;-4caa9273f534d4cbf3ce01fc393ef4df:hover .avia-svg-icon svg:first-child{\nfill:#ffffff;\nstroke:#ffffff;\n}\n<\/style>\n<a href=''  class='avia-button &apos;av-kg6ego6a&apos;-4caa9273f534d4cbf3ce01fc393ef4df av-link-btn avia-icon_select-&apos;no&apos; avia-size-&apos;large&apos; avia-position-&apos;center&apos; avia-color-&apos;theme-color&apos; avia-font-color-&apos;theme-color&apos;'  target=\"_blank\"  rel=\"noopener noreferrer\"  aria-label=\"&amp;apos;&amp;apos;\"><span class='avia_iconbox_title' >&apos;HBimed<\/span><\/a><\/div>\n\n<\/div><\/div><\/main><!-- close content main element --><\/div><\/div>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-&apos;av-jpr4h52r&apos;-ee4d9a51b9f5f0fe2e7ef1fa03246151\">\n.avia-section.&apos;av-jpr4h52r&apos;-ee4d9a51b9f5f0fe2e7ef1fa03246151{\nbackground-color:&apos;&apos;;\nbackground-image:unset;\n}\n.avia-section.&apos;av-jpr4h52r&apos;-ee4d9a51b9f5f0fe2e7ef1fa03246151 .av-section-color-overlay{\nopacity:&apos;0.5&apos;;\nbackground-color:&apos;#ffffff&apos;;\nbackground-image:url(&apos;&apos;);\nbackground-repeat:repeat;\n}\n<\/style>\n<div id='av_section_2' aria-label='&amp;apos;&amp;apos;' class='avia-section &apos;av-jpr4h52r&apos;-ee4d9a51b9f5f0fe2e7ef1fa03246151 &apos;main_color&apos; avia-section-&apos;huge&apos; avia-&apos;no-border-styling&apos;  avia-builder-el-13  el_after_av_section  avia-builder-el-last  aposapos avia-bg-style-&apos;parallax&apos; av-section-color-overlay-active av-minimum-height av-minimum-height-&apos;&apos; av-height-&apos;&apos;  av-section-with-video-bg container_wrap sidebar_right'   data-section-video-ratio='&apos;16:9&apos;'><div  class='avia-slideshow av_slideshow_obj-1-6a0aadcf8e073 avia-slideshow-featured av_slideshow avia-slide-slider av-slideshow-ui av-control-default av-slideshow-manual av-loop-once av-loop-manual-endless av-default-height-applied  av-section-video-bg avia-slideshow-1' data-slideshow-options=\"{&quot;animation&quot;:&quot;slide&quot;,&quot;autoplay&quot;:false,&quot;loop_autoplay&quot;:&quot;once&quot;,&quot;interval&quot;:5,&quot;loop_manual&quot;:&quot;manual-endless&quot;,&quot;autoplay_stopper&quot;:false,&quot;noNavigation&quot;:false,&quot;bg_slider&quot;:false,&quot;keep_padding&quot;:false,&quot;hoverpause&quot;:false,&quot;show_slide_delay&quot;:0}\"  itemprop=\"image\" itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/ImageObject\" ><ul class='avia-slideshow-inner' style='padding-bottom: 28.666666666667%;'><li  data-controls='disabled' data-mute='aviaTBaviaTBvideo_mute' data-loop='1' data-disable-autoplay=''  data-video-ratio='0' class='avia-slideshow-slide av_slideshow_obj-1-6a0aadcf8e073__0  av-video-slide  av-video-service-  av-hide-video-controls av-mute-video av-loop-video  av-single-slide slide-1 slide-odd'><div data-rel='slideshow-1' class='avia-slide-wrap'    itemprop=\"video\" itemtype=\"https:\/\/schema.org\/VideoObject\" ><img fetchpriority=\"high\" class=\"avia-img-lazy-loading-not-\"  src='' width='' height='' title='' alt=''  itemprop=\"thumbnailUrl\"   \/><div class=\"av-click-to-play-overlay\"><div class=\"avia_playpause_icon\"><\/div><\/div><\/div><\/li><\/ul><\/div><div class=\"av-section-color-overlay-wrap\"><div class=\"av-section-color-overlay\"><\/div><div class='container av-section-cont-open' ><div class='template-page content  av-content-small alpha units'><div class='post-entry post-entry-type-page post-entry-191'><div class='entry-content-wrapper clearfix'>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-&apos;av-kdoiz7vy&apos;-f327509d9f9bed6964d4fe2350313348\">\n.av_font_icon.&apos;av-kdoiz7vy&apos;-f327509d9f9bed6964d4fe2350313348{\ncolor:&apos;#354b6a&apos;;\nborder-color:&apos;#354b6a&apos;;\n}\n.avia-svg-icon.&apos;av-kdoiz7vy&apos;-f327509d9f9bed6964d4fe2350313348 svg:first-child{\nstroke:&apos;#354b6a&apos;;\nfill:&apos;#354b6a&apos;;\n}\n.av_font_icon.&apos;av-kdoiz7vy&apos;-f327509d9f9bed6964d4fe2350313348 .av-icon-char{\nfont-size:&apos;80px&apos;;\nline-height:&apos;80px&apos;;\nwidth:&apos;80px&apos;;\n}\n<\/style>\n<span  id=\"aposapos\"  class='av_font_icon &apos;av-kdoiz7vy&apos;-f327509d9f9bed6964d4fe2350313348 avia_animate_when_visible av-icon-style-&apos;&apos; avia-icon-pos-&apos;center&apos; aposapos avia-iconfont avia-font-&apos;hbimed-icon&apos;'><a href='&apos;&apos;'   class='av-icon-char' data-av_icon='' data-av_iconfont='&apos;hbimed-icon&apos;' aria-hidden=\"false\" ><\/a><span class='av_icon_caption av-special-font'>&apos;&apos;<\/span><\/span>\n<\/div><\/div><\/div><!-- close content main div --><\/div><\/div><\/div><div id='after_section_2'  class='main_color av_default_container_wrap container_wrap sidebar_right'  ><div class='container av-section-cont-open' ><div class='template-page content  av-content-small alpha units'><div class='post-entry post-entry-type-page post-entry-191'><div class='entry-content-wrapper clearfix'>","protected":false},"excerpt":{"rendered":"","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":250,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-191","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/hbimed.com\/en\/wp-json\/wp\/v2\/pages\/191","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hbimed.com\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/hbimed.com\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/hbimed.com\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/hbimed.com\/en\/wp-json\/wp\/v2\/comments?post=191"}],"version-history":[{"count":0,"href":"https:\/\/hbimed.com\/en\/wp-json\/wp\/v2\/pages\/191\/revisions"}],"wp:attachment":[{"href":"https:\/\/hbimed.com\/en\/wp-json\/wp\/v2\/media?parent=191"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}