HBi Database

The product

A fundamental innovation in diagnostic science

The HBi database is a revolutionary tool that allows professional users

Assessing brain system malfunctions using biomarkers
to make more precise diagnoses and clearer therapy indications (e.g., for subtypes of attention disorders)
Creating protocols for individual treatments (personalized medicine)
to investigate the influence of medications
predicting response to medication
to support the development of new medicines

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.

Targetedly address mental disorders using biomarkers

Brainwaves as Biomarkers?
While 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 – the electroencephalogram (EEG). The systems required for this are priced comparably to, for example, blood analysis devices, making them affordable for every practice.

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.

Database Analytics
Accurate 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 („resting EEG“), information processing processes are also mapped. These are determined from observing the activation of different brain regions („evoked potentials“ - ERP) during the repeated solving of standardized tasks („tasks“).

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.

Measurement of Evoked Potentials
During 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 – enough tasks must have been processed to achieve sufficient contrast.
The evaluation is then carried out either by appropriately trained personnel immediately on-site, or the report service offered by HBImed is used.

Outlook
Biomarkers 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.
Although 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.

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-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.

We are seeing a renaissance of EEG.

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 – 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.

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.

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.

Specifications

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

Children/Adolescents: Ages 7-17 (n=300)
Adults: Ages 18-60 (n=500)
Seniors: Age 61+ (n=200)

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 „norm“ are assessed by calculating z-scores—standardized measures of the deviation of individual EEG parameters from normative values.

Image and Conditions of the Visual Continuous Performance Task

Potential

Event-Related Potentials

Brain responses (e.g., evoked potentials) to psychological tasks are decomposed into independent components.

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.

In clinical settings, the HBI database is a valuable aid for individualized treatment planning.

An example of such an application is shown in Figure 1. A simple examination of the raw EEG of an AD(H)D patient (top, left) reveals no abnormality; however, when the data is compressed into spectra and compared with normative values, a statistically significant (pless than 0.01) Deviation from normality in the theta frequency range (top, right spectrum), which is evident in the central areas (see Brainmap below). The electromagnetic tomography of theta activity is shown in the image below. Based on this data, two alternative treatment options are proposed for the patient:

Psychostimulants like Ritalin or Concerta and
Training the Beta/Theta ratio to correct inattention using Brain Computer Interface (BCI) methodology.

Deviations from the norm

ICA Components

Independent Component Analysis – Analysis by Independent Components

A comparison of the independent components of the evoked potentials with the database reveals:

which psychological operation is impaired in the patient and
how the malfunction can be corrected.

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.

Processing

Information processing

The analysis consists of the following steps:

1. Correction and removal of eye movement artifacts

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;

2. Fast Fourier Transform (FFT)

... 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;

3. Calculation of Evoked Potentials

... by averaging the EEG over multiple trials for each trial category and each channel with high temporal resolution;

4. Decomposition of an Individual ERP

... into independent components by applying spatial filters, extracted using ICA from the set of ERPs calculated for the respective group of healthy individuals;

5. Comparison of each extracted electrophysiological and behavioral variable

… 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.

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.

The results of the analysis and statistical comparison will be summarized in an individual report.