HBi Database

The product

A Fundamental Change in Diagnostic Science

The HBI database is a revolutionary tool enabling the professional

to assess dysfunctions of brain systems by means of biomarkers
to make a more precise diagnosis and define more clear therapy indications (e. g. for AD(H)D subtypes)
to construct protocols of individual treatment (personalized medicine)
to monitor drug effects
to predict medication responses
to support the development of new drugs

The HBi database is approved as a medical device in the EU and the USA. You can order the HBI database in our shop:

Target mental disorders with biomarkers

Brain waves as biomarkers?
While modern imaging techniques such as fMRI (functional magnetic resonance imaging) allow to study the relationship between the neurobiological processes in the brain and cognition, behavior and emotions, for a broad implementation in practices much easier and cheaper methods are required. Due to modern signal processing and computer-aided analysis procedures, information processes in the brain can now be derived with great precision from the electroencephalogram (EEG). Such systems are now affordable for every practice.

The challenge is, of course, to identify patterns in the brainwaves that are typical for certain diseases. If the statistical significance of such patterns is sufficient, then they can be defined as biomarkers as a basis for an objective diagnosis and targeted therapy.

Database-based analysis
In order to make accurate conclusions from the observation of brain activity, the comparison with a normative database is required. Here, it is essential that in addition to the self-organization processes of the brain, determined by analysis of the EEG in awake resting state (“resting EEG”), also the information processing processes are analysed. These are determined from the observation of the activation of different brain regions (“evoked potentials” – ERPs) during the repeated solving of standardized tasks.

The HBImed AG database contains self-organization and information processing processes of several thousand healthy persons between 7 and 87 years old as well as different patient groups, usually with even more than one task, making it the largest and most accurate of its kind. The analysis tools developed for it allow on the one hand to identify biomarkers and on the other hand a targeted interpretation of the EEG recordings.

Practical application
During the recording of the EEG using an electrode cap, the patient observes a screen that displays simple tasks at regular intervals (for example, the comparison of images or solving math problems). The answer is given by pressing a button. The process takes about half an hour – enough tasks must be completed and processed in order to gain a sufficient contrast.
The analysis is then either performed by trained personnel on the spot, or the report service offered by HBImed can be used.

Prospect
Already developed biomarkers for the detection of subtypes of attention deficit disorders achieve an index of discrimination against healthy subjects of over 90%. This enables a more precise diagnosis and also a more clear indication for therapy (e.g. in the selection of an appropriate medication). Biomarkers for other diseases such as schizophrenia (including early detection), depression and stress are in preparation.
Although the database-based analysis by HBImed is used in many practices and clinics already, the breakthrough for biomarkers is still yet to come. HBImed AGs database and analysis software has been approved as medical device in Europe and the USA. Therefore, the path for the further validation of biomarkers and their approval is cleared.

Progress begins

Progress begins with innovation

We are entering a new era of psychiatry and neurology

The fifth revised edition of The Diagnostic and Statistical Manual of Mental Disorders (DSM-V) in the beginning aimed to classify mental disorders according to biological markers. Due to the reluctant reactions of practices and the fact that there are still several uncertainties, the makers of the biomarker approach decided to postpone the inclusion of the approach into the DSM until the 6th revision. The new approach assumes that a psychiatric diagnosis is made not only from behavior, but also from the knowledge of which brain system is impaired. It is almost certain that the biomarker approach will become widely accepted because it leads to objectivity and transparency.

We are facing a renaissance of EEG

The renaissance is associated with the development of new methods of analysis and breakthrough discoveries pertaining to the neuronal mechanisms of EEG. The majority of the new methods (e.g. the decomposition of EEG and evoked responses into independent components, and LORETA – Low Resolution Electromagnetic Tomography) were initiated in laboratory settings only a few years ago. There is an urgent need however to introduce these new methods into clinical practice. Unfortunately, none of the existing normative data bases use the newly developed technologies.

This flaw of the current databases is resolved in a new database built up on the methodology developed in the Human Brain Institute (HBI) of the Russian Academy of Sciences and the Institute for Experimental Medicine of the Russian Medical Academy of Sciences.

This methodology was awarded the USSR State Prize (the highest scientific award in the former Soviet Union) and is officially recognized as a unique discovery in the field of human physiology. The database is now used in many scientific centers abroad as well as in clinical practises in Europe and the USA.

Specifications

The HBI reference (normative) database includes multi-channel EEG recordings in the following groups:

children/adolescents: age 7-17 (n=300)
adults: age 18-60 (n=500)
seniors: age 61+ (n=200)

Inclusion/exclusion criteria presume an uneventful perinatal period, no head injury with cerebral symptoms, no history of neurological or psychiatric diseases, no convulsions, normal mental and physical development, and average or better grades in school. A 19-channel EEG is recorded in two resting conditions with eyes open (minimum 3 minutes), eyes closed (minimum 3 minutes), and five different task conditions, including two stimulus GO/NOGO tasks, arithmetic and reading tasks, auditory recognition and auditory oddball tasks. The characteristics of QEEG are normalized. The mean values and standard deviations for separate age groups are obtained. Deviations from “normality” are assessed by computing zscores – standardized measures of deviation of individual EEG parameters from the normative data.

Picture and conditions of visual continuous performance task

Potentials

Event-Related Potentials

Brain responses (i.e. evoked potentials) to psychological tasks are decomposed into independent components. The components are asso – ciated with distinctive psychological operations. Comparing the amplitude and latency of the components with the normative data gives new insights into the different stages of information processing in patients.

In clinical settings the HBI Database is a valuable resource for individualized treatment planning. An example of such an application is presented in Fig. 1. Simply viewing the raw EEG of an ADHD patient (top, left) does not reveal any abnormality; however, compression of the data into spectra and comparing the spectra with the normative data shows statistically significant (p<0.01) deviation from normality in the theta frequency range (top, right spectra) which is reflected in the central areas (see map at bottom). The electromagnetic tomography of the theta activity is presented at the bottom of the Figure. On the basis of these data, two alternatives of therapy are suggested for this patient:

psychostimulant medication such as Ritalin or Concerta and
training beta/theta ratio to correct inattention using the methodology of the brain computer interface (BCI).

Deviations from normality

ICA Components

Independent Component Analysis

A comparison of the independent components of ERPs with the database indicates:

what psychological operation is impaired in the patient, and
how the dysfunction can be corrected.

An example of comparing the components of evoked potentials is presented in the figure beside/below. The figure depicts time dynamics of four different components for an ADHD patient (thin line) in comparison to norms (thick line). The components are associated with comparison operation, attention shift, engagement, and monitoring operations. The maps of the components for these patients and norms are presented at the right. Only one component is selectively reduced in this patient, as shown by the red fill-in. Our studies show that the entire ADHD population can be separated into distinct categories, each characterized by selective suppression of a distinct component, and each responding to a specific medication.

Processing

Information processing

The analysis consists of the following steps:

1. Eye movement artifact correction and elimination

a) using spatial filtration technique based on zeroing the activation curves of individual Independent Component Analysis (ICA) components corresponding to horizontal and vertical eye movements, as well as b) excluding epochs with excessive amplitude of EEG and excessive faster and slower frequency activity;

2. Fast-Fourier Transformation (FFT)

… of the corrected EEG for extracting EEG power and coherence for all narrow bins in the frequency band from 0.5 to 30 Hz.

3. Computation of event related potentials

… by averaging EEG over trials for each category of trial and each channel with high time resolution.

4. Decomposition of an individual ERPs

… into independent components by applying spatial filters extracted by means of the ICA from the collection of ERPs computed for the corresponding group of healthy subjects.

5. Comparison of each extracted electrophysiological and behavioral variable

… against the corresponding variable computed for a carefully constructed and statistically controlled age-regressed, normative database in which the variables have been transformed and confirmed for their Gaussian distribution.

The comparison is made using parametric statistical procedures that express the differences between the patients and their appropriate age-matched reference group in the form of Z-scores.

The results of analysis and statistical comparison are compiled into an individual report.