{"id":164,"date":"2020-09-08T12:38:57","date_gmt":"2020-09-08T10:38:57","guid":{"rendered":"http:\/\/www.hbimed.com\/wp-enfold-aug2020\/?page_id=164"},"modified":"2020-09-08T12:38:57","modified_gmt":"2020-09-08T10:38:57","slug":"hbi-database","status":"publish","type":"page","link":"https:\/\/hbimed.com\/en\/hbi-datenbank\/","title":{"rendered":"HBi Database"},"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-164'><div class='entry-content-wrapper clearfix'>\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_full  avia-builder-el-1  avia-builder-el-no-sibling  first flex_column_div av-zero-column-padding'     ><p>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-av_heading-1c127246643dd8d86b4c2ef4c6281e68\">\n#top .av-special-heading.av-av_heading-1c127246643dd8d86b4c2ef4c6281e68{\npadding-bottom:10px;\n}\nbody .av-special-heading.av-av_heading-1c127246643dd8d86b4c2ef4c6281e68 .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-av_heading-1c127246643dd8d86b4c2ef4c6281e68 .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-av_heading-1c127246643dd8d86b4c2ef4c6281e68 av-special-heading-h1 blockquote modern-quote  avia-builder-el-2  el_before_av_tab_container  avia-builder-el-first'><h1 class='av-special-heading-tag'  itemprop=\"headline\"  >HBi Database<\/h1><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div><br \/>\n<div  class='tabcontainer av-ketu4spg-bc658a80ec1472fde6e0f982ec3d3db7 top_tab  avia-builder-el-3  el_after_av_heading  avia-builder-el-last'>\n<section class='av_tab_section av_tab_section av-z39r0-fef29d8db4dc4c493b6016b9f2702610'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div id='tab-id-1-tab' class='tab active_tab' role='tab' aria-selected=\"true\" tabindex=\"0\" data-fake-id='#tab-id-1' aria-controls='tab-id-1-content'  itemprop=\"headline\" >The product<\/div><div id='tab-id-1-content' class='tab_content active_tab_content' role='tabpanel' aria-labelledby='tab-id-1-tab' aria-hidden=\"false\"><div class='tab_inner_content invers-color'  itemprop=\"text\" ><h2>A fundamental innovation in diagnostic science<\/h2>\n<p>The HBi database is a revolutionary tool that allows professional users<\/p>\n<ul>\n<li>Assessing brain system malfunctions using biomarkers<\/li>\n<li>to make more precise diagnoses and clearer therapy indications (e.g., for subtypes of attention disorders)<\/li>\n<li>Creating protocols for individual treatments (personalized medicine)<\/li>\n<li>to investigate the influence of medications<\/li>\n<li>predicting response to medication<\/li>\n<li>to support the development of new medicines<\/li>\n<\/ul>\n<p>The HBi Database is approved as a medical device in the EU and the USA. You can order the HBi Database directly in our shop.<\/p>\n<h3>Targetedly address mental disorders using biomarkers<\/h3>\n<p><strong>Brainwaves as Biomarkers?<\/strong><br \/>\nWhile modern imaging techniques such as fMRI (functional magnetic resonance imaging) enable the investigation of the correlations between neurobiological processes in the brain and cognition, behavior, and emotions, much simpler and less expensive methods are needed for broad practical application. Thanks to modern signal processing and computer-aided analysis methods, information processing in the brain can now be derived with great precision from the recording of brain waves \u2013 the electroencephalogram (EEG). The systems required for this are priced comparably to, for example, blood analysis devices, making them affordable for every practice.<\/p>\n<p>The challenge, of course, is to identify patterns in brain waves that are typical for specific diseases. If the statistical significance of such patterns is sufficient, they can be defined as biomarkers as a basis for objective diagnosis and targeted therapy.<\/p>\n<p><strong>Database Analytics<\/strong><br \/>\nAccurate conclusions from observing brain activity require comparison with a norm database. It is essential that, in addition to the brain's self-organization processes, determined by analyzing EEG in a resting wakeful state (\u201eresting EEG\u201c), information processing processes are also mapped. These are determined from observing the activation of different brain regions (\u201eevoked potentials\u201c - ERP) during the repeated solving of standardized tasks (\u201etasks\u201c).<\/p>\n<p>The HBImed AG database contains self-organization and information processing processes from thousands of healthy individuals between 7 and 87 years of age, as well as from various patient groups, often with more than one task, making it the largest and most accurate of its kind. The analysis tools developed for this purpose enable, on the one hand, the identification of biomarkers and, on the other hand, the targeted interpretation of EEG recordings.<\/p>\n<p><strong>Measurement of Evoked Potentials<\/strong><br \/>\nDuring the EEG recording with an electrode cap, the patient observes a screen on which simple tasks are displayed at regular intervals (for example, comparing images or solving arithmetic problems). The response is given by pressing a button. The process takes about half an hour \u2013 enough tasks must have been processed to achieve sufficient contrast.<br \/>\nThe evaluation is then carried out either by appropriately trained personnel immediately on-site, or the report service offered by HBImed is used.<\/p>\n<p><strong>Outlook<\/strong><br \/>\nBiomarkers already developed to identify subtypes of attention disorders achieve a discrimination index of over 90% compared to healthy individuals. This enables more precise diagnoses and provides clearer guidance for treatment (e.g., in selecting medication). Biomarkers for other conditions, such as schizophrenia (including early detection), depression, and stress, are currently in development.<br \/>\nAlthough HBImed's database-based analytics are already in use today in many practices and clinics, the actual biomarker technology is still on the verge of a breakthrough. To date, HBImed AG has been able to gain approval for its database and analysis software as a medical device in both Europe and the USA, thus paving the way for further validation of the biomarkers and their subsequent approval.<\/p>\n<\/div><\/div><\/section>\n<section class='av_tab_section av_tab_section av-2tyng-a16c56ad9c962111509743f1a159b637'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div id='tab-id-2-tab' class='tab' role='tab' aria-selected=\"false\" tabindex=\"0\" data-fake-id='#tab-id-2' aria-controls='tab-id-2-content'  itemprop=\"headline\" >Progress begins<\/div><div id='tab-id-2-content' class='tab_content' role='tabpanel' aria-labelledby='tab-id-2-tab' aria-hidden=\"true\"><div class='tab_inner_content invers-color'  itemprop=\"text\" ><h2>Progress begins with innovation<\/h2>\n<h3>We are entering a new era of psychiatry and neurology.<\/h3>\n<p>The fifth, revised edition of \u201eThe Diagnostic and Statistical Manual of Mental Disorders\u201c (DSM-5) was initially intended to classify mental disorders based on biological markers. The reserved reaction from the field and too many current uncertainties prompted its creators to postpone the biomarker approach for the 6th revision. The new approach assumes that psychiatric diagnoses are not made solely on observable behavior but also on knowledge of which brain system is impaired. The biomarker approach is virtually certain to prevail, as it creates objectivity and traceability.<\/p>\n<h3>We are seeing a renaissance of EEG.<\/h3>\n<p>The Renaissance is linked to the development of new methods of analysis and groundbreaking discoveries in the field of neural mechanisms of EEG. The majority of the new methods (e.g., decomposition of EEG and evoked responses into independent components, and LORETA \u2013 Low Resolution Electromagnetic Tomography) were only initiated a few years ago under laboratory conditions. However, there is a strong drive to adopt these new methods into clinical practice. Unfortunately, none of the currently existing normative databases utilize these newly developed technologies.<\/p>\n<p><strong>This disadvantage of previous databases is eliminated with the new database, which is based on the method developed at the Human Brain Institute (HBI) of the Russian Academy of Sciences and the Institute for Experimental Medicine of the Russian Medical Academy of Sciences.<\/strong><\/p>\n<p>This method won the State Prize of the USSR (the highest scientific award of the former Soviet republic) and is officially recognized as a unique discovery in the field of human physiology. The database is now used in many scientific centers worldwide, as well as in clinics and practices in Europe and the USA.<\/p>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-9ccx8-0c41e42670a00e791785745dc28ca9dc\">\n#top .hr.hr-invisible.av-9ccx8-0c41e42670a00e791785745dc28ca9dc{\nheight:60px;\n}\n<\/style>\n<div  class='hr av-9ccx8-0c41e42670a00e791785745dc28ca9dc hr-invisible  avia-builder-el-4  el_before_av_hr  avia-builder-el-first'><span class='hr-inner'><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n<\/div><\/div><\/section>\n<section class='av_tab_section av_tab_section av-eozc4-26d744c46c89a1d25457a2a5a5f4a866'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div id='tab-id-3-tab' class='tab' role='tab' aria-selected=\"false\" tabindex=\"0\" data-fake-id='#tab-id-3' aria-controls='tab-id-3-content'  itemprop=\"headline\" >Specifications<\/div><div id='tab-id-3-content' class='tab_content' role='tabpanel' aria-labelledby='tab-id-3-tab' aria-hidden=\"true\"><div class='tab_inner_content invers-color'  itemprop=\"text\" ><h2>Specifications<\/h2>\n<p><strong>The HBI reference (normative) database includes multi-channel EEG recordings from the following groups:<\/strong><\/p>\n<ul>\n<li>Children\/Adolescents: Ages 7-17 (n=300)<\/li>\n<li>Adults: Ages 18-60 (n=500)<\/li>\n<li>Seniors: Age 61+ (n=200)<\/li>\n<\/ul>\n<p>Inclusion and exclusion criteria presuppose: an uncomplicated birth, no head injuries with cerebral symptoms, no history of neurological or psychiatric disorders, no seizures, normal mental and physical development, and average or above-average school grades. A 19-channel EEG will be recorded under two resting conditions with eyes open (minimum 3 minutes) and eyes closed (minimum 3 minutes), and five different task conditions, including two stimulus GO\/NOGO tasks, arithmetic and reading tasks, auditory detection, and auditory oddball tasks. The characteristics of the QEEG are normalized. Means and standard deviations for different age groups are obtained. Deviations from the \u201enorm\u201c are assessed by calculating z-scores\u2014standardized measures of the deviation of individual EEG parameters from normative values.<\/p>\n<h3>Image and Conditions of the Visual Continuous Performance Task<\/h3>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-184 size-full\" src=\"https:\/\/hbi-reports.com\/wp-enfold-aug2020\/wp-content\/uploads\/2020\/09\/hbimed-csm-qeeg-specifications.jpg\" alt=\"\" width=\"352\" height=\"264\" \/><\/p>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-6u9v8-858d1652ddaa86b0c83ab0bf0e366973\">\n#top .hr.hr-invisible.av-6u9v8-858d1652ddaa86b0c83ab0bf0e366973{\nheight:100px;\n}\n<\/style>\n<div  class='hr av-6u9v8-858d1652ddaa86b0c83ab0bf0e366973 hr-invisible  avia-builder-el-5  el_after_av_hr  el_before_av_hr'><span class='hr-inner'><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n<\/div><\/div><\/section>\n<section class='av_tab_section av_tab_section av-vq3o-d89913c34ee65050128b76a0a09b4eed'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div id='tab-id-4-tab' class='tab' role='tab' aria-selected=\"false\" tabindex=\"0\" data-fake-id='#tab-id-4' aria-controls='tab-id-4-content'  itemprop=\"headline\" >Potential<\/div><div id='tab-id-4-content' class='tab_content' role='tabpanel' aria-labelledby='tab-id-4-tab' aria-hidden=\"true\"><div class='tab_inner_content invers-color'  itemprop=\"text\" ><h2>Event-Related Potentials<\/h2>\n<p><strong>Brain responses (e.g., evoked potentials) to psychological tasks are decomposed into independent components.<\/strong><\/p>\n<p>The components are associated with unique psychological operations. By comparing the amplitude and latency of the components with normative data, new insights are gained into the different stages of information processing in the patient.<\/p>\n<p><strong>In clinical settings, the HBI database is a valuable aid for individualized treatment planning.<\/strong><\/p>\n<p>Ein Beispiel f\u00fcr eine solche Anwendung is in Bild 1 gezeigt. Die einfache Betrachtung des Roh-EEGs eines AD(H)S-Patienten (oben, links) l\u00e4sst keine Abnormalit\u00e4t erkennen; wenn man jedoch die Daten in Spektren komprimiert und sie mit den normativen Werten vergleicht, ergibt sich eine statistisch signifikante (p&lt;0.01) Abweichung von der Normalit\u00e4t im Theta-Frequenzbereich (oben, rechtes Spektrum) welche sich in den zentralen Bereichen zeigt (s. Brainmap unten). Die elektromagnetische Tomographie der Theta-Aktivit\u00e4t wird unten im Bild gezeigt. Auf Grundlage dieser Daten werden zwei alternative Behandlungsm\u00f6glichkeiten f\u00fcr den Patienten vorgeschlagen:<\/p>\n<ul>\n<li>Psychostimulants like Ritalin or Concerta and<\/li>\n<li>Training the Beta\/Theta ratio to correct inattention using Brain Computer Interface (BCI) methodology.<\/li>\n<\/ul>\n<h3>Deviations from the norm<\/h3>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-182\" src=\"https:\/\/hbi-reports.com\/wp-enfold-aug2020\/wp-content\/uploads\/2020\/09\/hbimed-csm-deviations-1.jpg\" alt=\"\" width=\"322\" height=\"336\" \/><\/p>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-6u9v8-858d1652ddaa86b0c83ab0bf0e366973\">\n#top .hr.hr-invisible.av-6u9v8-858d1652ddaa86b0c83ab0bf0e366973{\nheight:100px;\n}\n<\/style>\n<div  class='hr av-6u9v8-858d1652ddaa86b0c83ab0bf0e366973 hr-invisible  avia-builder-el-6  el_after_av_hr  el_before_av_hr'><span class='hr-inner'><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n<\/div><\/div><\/section>\n<section class='av_tab_section av_tab_section av-av_tab-6efeaee804e57c2af36d505303022ee5'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div id='tab-id-5-tab' class='tab' role='tab' aria-selected=\"false\" tabindex=\"0\" data-fake-id='#tab-id-5' aria-controls='tab-id-5-content'  itemprop=\"headline\" >ICA Components<\/div><div id='tab-id-5-content' class='tab_content' role='tabpanel' aria-labelledby='tab-id-5-tab' aria-hidden=\"true\"><div class='tab_inner_content invers-color'  itemprop=\"text\" ><h2>Independent Component Analysis \u2013 Analysis by Independent Components<\/h2>\n<p><strong>A comparison of the independent components of the evoked potentials with the database reveals:<\/strong><\/p>\n<ul>\n<li>which psychological operation is impaired in the patient and<\/li>\n<li>how the malfunction can be corrected.<\/li>\n<\/ul>\n<p>An example of a comparison of evoked potential components is shown in the image below\/to the right. The image shows the time dynamics of four different components for an ADHD patient (thin line) compared to norms (thick line). The components are associated with comparison, attention shifting, engagement, and monitoring operations. The maps of the components for this patient and the norm are also shown. Only one component is selectively reduced in this patient, as indicated by the red coloring. Our studies show that the entire ADHD population can be divided into specific categories, each characterized by the selective suppression of a particular component. Each of these ADHD categories responds to a specific medication.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-183\" src=\"https:\/\/hbi-reports.com\/wp-enfold-aug2020\/wp-content\/uploads\/2020\/09\/hbimed-csm-information-processing.jpg\" alt=\"\" width=\"449\" height=\"412\" \/><\/p>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-6u9v8-858d1652ddaa86b0c83ab0bf0e366973\">\n#top .hr.hr-invisible.av-6u9v8-858d1652ddaa86b0c83ab0bf0e366973{\nheight:100px;\n}\n<\/style>\n<div  class='hr av-6u9v8-858d1652ddaa86b0c83ab0bf0e366973 hr-invisible  avia-builder-el-7  el_after_av_hr  avia-builder-el-last'><span class='hr-inner'><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n<\/div><\/div><\/section>\n<section class='av_tab_section av_tab_section av-95v4k-487fb63c41fdfde98c6499251f983385'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div id='tab-id-6-tab' class='tab' role='tab' aria-selected=\"false\" tabindex=\"0\" data-fake-id='#tab-id-6' aria-controls='tab-id-6-content'  itemprop=\"headline\" >Processing<\/div><div id='tab-id-6-content' class='tab_content' role='tabpanel' aria-labelledby='tab-id-6-tab' aria-hidden=\"true\"><div class='tab_inner_content invers-color'  itemprop=\"text\" ><h2>Information processing<\/h2>\n<p><strong>The analysis consists of the following steps:<\/strong><\/p>\n<h3>1. Correction and removal of eye movement artifacts<\/h3>\n<p>a) by using spatial filtering techniques, based on setting the activation curves of individual Independent Component Analysis (ICA) components associated with horizontal and vertical eye movements to zero, and b) by discarding epochs with excessive EEG amplitudes and excessively fast and slow frequency activity;<\/p>\n<h3>2. Fast Fourier Transform (FFT)<\/h3>\n<p>... of the corrected EEG, to extract the EEG power and coherence for all narrow frequency band units in the range of 0.5 to 30 Hz;<\/p>\n<h3>3. Calculation of Evoked Potentials<\/h3>\n<p>... by averaging the EEG over multiple trials for each trial category and each channel with high temporal resolution;<\/p>\n<h3>4. Decomposition of an Individual ERP<\/h3>\n<p>... into independent components by applying spatial filters, extracted using ICA from the set of ERPs calculated for the respective group of healthy individuals;<\/p>\n<h3>5. Comparison of each extracted electrophysiological and behavioral variable<\/h3>\n<p>\u2026 with the corresponding variable, which was calculated for a carefully created and statistically controlled age-adjusted, normative database, in which the variables were transformed and tested for Gaussian distribution.<\/p>\n<p>The comparison is performed using parametric statistical procedures, which represent the differences between patients and their corresponding age-adjusted reference groups in the form of z-scores.<\/p>\n<p><strong>The results of the analysis and statistical comparison will be summarized in an individual report.<\/strong><\/p>\n<\/div><\/div><\/section>\n<\/div><\/p><\/div><\/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; 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