Q-Pro

Human Brain Institute

Russia-Switzerland-USA

1.  Personal and clinical data –Questionnaire[1].

2.  Description of the procedure of EEG recording and analysis.

EEG was recorded by means of the Mitsar (Mitsar, Ltd.) amplifier[3] from 19 electrodes (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, O2 sites in the International 10-20 system) with 250 Hz sampling rate in 0.3 – 70 Hz frequency range in the following conditions:  1) eyes opened (EO) – at least 3 minutes, 2) eyes closed (EC) – at least 3 minutes, and 3) a modification of GO/NOO task (20 minutes).

The task consisted of 400 trials presenting to a subject every 3 seconds.  In the task we selected four categories of stimuli: 1) 20 different images of animals –referred to later as A, 2) 20 different images of plants - P, 3) 20 different images of humans presented together with an artificial sound - HS[4].  Trials consisted of presentation of a par of stimuli with interstimulus interval of 1.1 sec.  Four categories of trials were selected: A-A, A-P, P-P, and P-HS.  The trials were grouped into four sessions with one hundred trials each.  In each session a unique set of five A stimuli, five P and five HS stimuli was selected.  Each session consisted of a pseudo-random presentation of 100 pairs of stimuli with equal probability for each category and each stimulus. 

The data were stored on the hard disk in the linked ears reference montage and processed offline by means of WinEEG software.  The software is based on the 30 years experience obtained in the laboratory at the Institute of the Human Brain of Russian Academy of Sciences (director Prof. Dr. Juri Kropotov).

Absolute and relative magnitude spectra and coherences in all conditions computed and compared with the corresponding parameters from the Human Brain Institute (HBI) normative database.  The normative database includes data of about 1000 healthy people of 7-89 years old age.  EEG was recorded in Chur, Switzerland (under supervision of Dr. Andreas Mueller) in the Institute of the Human Brain, St. Petersburg, Russia (under supervision of Prof., Dr. Juri Kropotov).

Patient’s event related potentials are subjected to spatial filtration.  The spatial filters are computed on the basis of Independent Component Analysis preformed with the whole set of ERPs in healthy individuals.  The extracted components are further compared with the mean values averaged over the corresponding age group.  Each component corresponds to a specific psychological operation.

3.  Automated spike detection.

The method automated spike detection is based on temporal parameters of spikes as well on spatial location of the corresponding spike dipole[5]. The amplitude-temporal parameters have defined on the basis of comparison spike detection by the program and by experienced experts on the data base of more than 300 EEG recordings in epileptic patients.  There are three characteristics that define a spike or a sharp wave in EEG.  They are paroxysmal character, high degree of sharpness and short duration.  These parameters are presented in Fig[6].

The relative residual energy for dipole approximation of the detected spike is chosen less than 0.2.

For this client the automatic spike detection was performed on EEG in the common average montage for both eyes open and eyes closed conditions.  In total 266 spikes were detected and averaged.  The waveform, topography and dipole approximation of the averaged spike are presented below.

Note that spiky alpha activity is generated in the left occipital area.

4.  Eyes Open – background EEG rhythms and activities (page1)

Two fragments of EEG recorded in Eyes Opened (EO) condition in a reference-free montage - “weighted average according to Lemos” are presented below.  This montage will be further referred to as the data base montage.

Scale: 50 mcV/cm, speed – 30mm/sec, time constant – 0.3 sec, low frequency filter – 30 Hz. Vertical and horizontal eye movement artifact correction was done by means of Independent Component Analysis (ICA) (see Appendix 2)

4.  Eyes Open – background EEG rhythms and activities (page 2)

Spectra (EEG power vs. EEG frequency) in Eyes Open condition

for all 19 electrodes in the database montage are presented below.  The spectra are computed as follows[7]:  1) The interval in EO condition is divided into equal parts (epochs).  The length of an epoch is 4 s. Overlapping of the epochs is set to 50% so that the first 50% of each epoch overlaps the final 50% of the next epoch.  2)  To suppress energy infiltration through boundaries of epochs maxima, each epoch is filtered by the Hanning time window.  3) The power spectra(periodogram) are computed by means of "fast Fourier transformation" (FFT) algorithm. 4) Finally the averaged (over time of recording) spectra are calculated for each EEG channel separately.EEG rhythms are expressed in form of spectra peaks.  In this client the following rhythms can be separated (topographies and frequencies are depicted at the bottom).

4.  Eyes Open – background EEG rhythms and activities (page 3)

Comparison EEG spectra in eyes open condition with the normative database.

The comparison was made for absolute and relative magnitudes on EEG spectrums.

Relative amplitude was computed as a ratio of the EEG amplitude in the corresponding frequency to the EEG amplitude averaged over 3-30 Hz[8] range.

The bins with statistically significant (t-test) differences are marked by bars at the bottom of each curve.  The smallest ones correspond to p<0.05 (z-score >2), the largest ones - to p<0.001 (z-score>2.6), the medium ones – to p<0.01 (z-score>3).  Topographies of significant deviations from normality are presented at the bottom.

Note: excess of the left temporal-occipital alpha activity both for absolute and relative values.

Absolute magnitude.

Relative magnitude.

4.  Eyes Open – background EEG rhythms and activities (page 4)

The map of theta/beta ratio (Theta=4-8 Hz.  Beta – 13-21 Hz) is presented below (at the left). 

For comparison, at the right the corresponding map for a group of healthy subjects is presented.

Theta beta ratio is called “inattention index” because it negatively correlates with age and positively correlates with errors in continuous performance tasks (such as TOVA – Test for variances of attention or IVA ).  In ADHD patients this index is elevated in comparison to norms[9].

Note NO significant increase of this index at Cz in this patient in comparison to norms.

4.  Eyes Open – background EEG rhythms and activities (page 5)

Asymmetry maps of power spectra in eyes open conditions for 1 Hz bands.

Note that an asymmetry higher than 50% may be a sign of abnormality[10].

4.  Eyes Open – background EEG rhythms and activities (page 6)

Diagrams of excessive (in red) or reduced (in blue) coherence in sagital (top) and coronal (bottom) planes.

Note reduction of coherency in alpha band in frontal areas in coronal planes and between central-parietal areas in sagital planes.

5.  Eyes Closed – background EEG rhythms and activities (page1)

Two fragments of EEG recorded in Eyes Closed (EC) condition

in the data base montage.

Scale: 50 mcV/sm, speed – 30mm/sec, time constant – 0.3 sec, low frequency filter – 30 Hz. Vertical and horizontal eye movement artifact correction was done by means of Independent Component Analysis (ICA) (see Appendix 2)

5.  Eyes Closed – background EEG rhythms and activities (page 2)

Spectra (EEG power vs. EEG frequency) in Eyes Closed condition for all 19 electrodes in the database montage are presented below.  EEG rhythms are expressed in forms of peaks on spectra. 

In this client the following rhythms can be separated (topographies and frequencies are depicted at the bottom).

5.  Eyes Closed – background EEG rhythms and activities (page 3)

Difference spectra Eyes closed – Eyes open.

The results of subtraction spectrograms in Eyes Open condition from those in Eyes Closed condition are presented below.

Note that opening eyes produces increase of alpha activity at 9.3 Hz at the occipital areas.  This rhythm dominates on the spectra[11] and is named the dominant alpha rhythm.

5.  Eyes Closed – background EEG rhythms and activities (page 4)

Comparison EEG spectra in eyes closed condition with the normative database.

The comparison was made for absolute and relative amplitudes on EEG spectrums.

Relative amplitude was computed as a ratio of the EEG amplitude in the corresponding frequency

Absolute magnitude.

Relative magnitude.

5.  Eyes Closed – background EEG rhythms and activities (page 5)

Asymmetry maps of power spectra in eyes closed conditions for 1 Hz bands.

Note that an asymmetry higher than 50% may be a sign of abnormality[12].

6. GO/NOGO task –EEG rhythms and activities (page 1)

Spectra (EEG power vs. EEG frequency) in GO/NOGO task condition for all 19 electrodes in the database montage are presented below.  EEG rhythms are expressed in forms of peaks on spectra.  In this client the following rhythms can be identified:

6. GO/NOGO task –EEG rhythms and activities (page 2)

Comparison EEG spectra in GO/NOGO task condition with the normative database.

The comparison was made for absolute and relative magnitudes on EEG spectrums.

Absolute magnitude.

Relative magnitude.

(page 1).

Behavioral data (omission and commission errors, reaction times and variances) are presented below.

Note: too many omission errors.

      (page 2).

ERPs in the GONOGO task computed for GO, NOGO stimuli and ERP differences (NOGO-GO).  GO stimuli – green, NOGO stimuli (animal-plant) – red, NOGO-GO – blue.  Bars under ERPs represent levels of statistical significance of deviation from the pre-stimulus interval (small bar – p<0.05, medium – p<0.01, big – p<0.001).

Note: at the bottom ERPs topographies taken at the maximums of ERPs and ERPs differences.

7. Behavioral data and cognitive Event Related Potentials (ERPs) in GO/NOGO task

      (page 3).

ICA analysis of ERPs in GO/NOGO task – comparison with the normative data base.

Table of Normal Components.

The client’s components (green) in comparison to the normative components (red) and difference components (Client – Norm) are presented below.

Note strong deviations in sensory visual information processing component. 

8.  Event Related De/Synchronization - ERD/ERS (pages 1 and 2).

The results of wavelet analysis for GO/NOGO task are presented below.

The wavelet analysis provides the Power-Time-Frequency representations of EEG responses to GO and NOGO trials separately. 

X-axis – time (Zero corresponds to the first stimulus onset).  Y-axis – EEG frequency (from 4 to 40 Hz).  Color reflects increase (red) or decrease (blue) of EEG power in the corresponding frequency band.

The main components in this client are:

For GO+NOGO stimuli:

 9.  Conclusion:

I.  Spontaneous EEG abnormality.

Spectrograms of spontaneous EEG in all conditions (Eyes Open, Eyes Closed, GO/NOGO task) persistently show an abnormality manifested in:

1)      increase of occipital alpha.

2)      Spiky morphology in the occipital areas.

II.  ERPs abnormalities.

ERPs in GONOGO task show strong deviations from normality in sensory-related (not executive) components indicating the hyper-sensitivity of the ventral visual pathway.

III.  Event-related desynchronization (ERD) abnormalities.

ERD in GO/NOGO task show strong alpha desynchronization at the occipital areas.

IV.  Brain dysfunction associated with EEG abnormality.

These QEEG abnormalities are associated with the occipital lobe dysregulation.

This dysregulation is manifested in idling of the occipital area and delay of the sensory component in this area.  This pattern is seen in a dyslexic group.

10.  Recommendations for therapy.

a) Neurofeedback protocol.

Recommendations are made on the basis of the bulldozer principle of neurofeedback and on the client’s complaints.

In the case of memory problems the protocol might be as follows:

Inhibiting (training DOWN) alpha activity at the left occipital (O1).  The map of deviations from normality at 9 Hz in EEG power in Eyes Open condition in this client is presented below.

As you can see from the map electrodes position may be: O1, Pz bipolar.  A spectrogram and an EEG fragment in EO condition in this client in bipolar montage (O1, Pz) are presented below.

On the basis of spectrogram the neurofeedback suppression band for this client is chosen as:

Alpha =8.5-10 Hz.

Appendix 1.  Methodological principles for QEEG/ERP assessment.

Principle 1.

EEG oscillations and event related potentials reflect different qualities of brain electrophysiology. 

They are obtained by different computational algorithms:  EEG oscillations are assessed by means of Fourier and wavelet transformations.  ERPs are assessed by averaging techniques and independent component analysis.

Principle 2.

Different oscillating patterns of the background EEG (such as theta, alpha and beta rhythms) reflect distinct processes of modulation of information processing in neuronal networks.

In the healthy brain, alpha rhythms are generated in cortico-thalamic reciprocally connected networks and reflect idling states of sensory systems.  Beta rhythms are generated by the interplay between excitatory and inhibitory neurons in local cortical networks and serve as a reset following cortical activation.  Frontal midline theta rhythm is generated in septo-hyppocampal circuits and is associated with encoding of episodic memory.  Any type of impairment in the corresponding mechanisms can lead to excess or lack of a particular rhythm when compared with a normative data.

Normal rhythms of the human brain.

Principle 3.

ERPs components are associated with different psychological operations.

For decomposing ERPs or ERPs difference waves into separate components we use independent component analysis on a set of ERPs or ERPs difference waves computed for a large (about 1000) group of healthy subjects.  The extracted components are associated with distinct psychological operations on the basis of correlation with behavior and other imaging (such as PET, fMRI) techniques.

An example of grand averaged ERPs computed for a group of healthy adult (older than 20 years) subjects (N=796) is presented below.  ERPs were computed in a two stimulus task (see description above) for two conditions: GO (depicted as green) and NOGO (depicted as red).

ICA decomposition was performed as described in (Makage et al., 1999) after re-referencing to common average reference.  In Fig. we present the results of ICA analysis of the whole set of ERPs computed for a group of healthy adult subjects for GO and NOGO conditions.  Six independent components were separated.  Each of the component has a unique 2D topography (left), a unique s-LORETA generators (right), and unique temporal pattern (middle, right).  In the middle, left – vertically stalking (from younger age -20 years old – to older age up to 89 years old) thin-color-coded horizontal bars representing a component at a single subject.  Note the latency (but not topography) of some of the components increases with age.

It should be stressed that different psychological tasks may be associated with different components and, consequently, with different psychological operations.  The main of them are listed in the table below.

 Main ERPs components:

Principle 3.

Brain is decomposed into basic systems: sensory systems, affective system, executive system and memory systems. 

Each of the system is characterized by specific rhythms and by ERPs specific components.  Distinct systems are controlled by distinct neuro-modulators of the brainstem, and consequently, can be treated by pharmacological interventions in these systems, such as increasing (or decreasing) the level of neuromodulator by giving to patients pre-coursors of mediators, blocking the post-synaptic receptor, blocking the reuptake mechanism, or and changing the cellular mechanisms of transmission.  Association between psychiatric/neurological diseases and brain systems are presented in Table below.

Principle 4.

Any of the brain systems obeys to the inverted U law.  The law claims that responses of the system are largest if the activity of the system stays within the normal range and are abnormally smaller if the activity turns out to be below or higher than the normal range of activation.

Fig.   The inverted U law.

(Top) Schematic representation of the dependence of the overall activity of a hypothetical neuronal network on the input that drives this system.  (Bottom) Schematic representation of the dependence of the response of the system on its input.  The response is defined as a change in the activity of the system as reaction to a small and elementary increase of the input.

Note that increase of the input (for example, due to a stressful situation) at two points of the system (depicted by black dots with arrows) will lead to enhancing or deteriorating performance depending on the initial state.

The overall activity of the system can be assessed by spectral analysis of the spontaneous EEG while system responses are associated with the corresponding ERPs components.  For example, abnormally high amplitude of beta activity generated in the medial prefrontal cortex indicates over-activation of this part of the executive system while abnormally small P3 monitoring component indicates a reduced response of this system in situations that need monitoring of actions.

Principle 5.

Brain disorders can be classified in associated with impairment of brain systems and diagnosed according to deviations from normality in the corresponding EEG spectral and ERP parameters.

Scientists tried to classify brain disorders for centuries.  Nowadays, there are two parallel sets of diagnostic criteria.  One is American Psychiatric Association’s Diagnostic and Statistical Manual of Mental Disorders (the latest edition is DSM-IV).  The other one is World Health Organization’s International Classification of Disease (the latest version is ICD-10).  Any classification made on descriptive behavioral level is to some extent arbitrary.  The need for endophenotypes as objective indexes of disorders could not be overestimated.  Several components of QEEG and ERPs have be suggested as probable candidates for endophenotypes.  Although more systematic research is required the first results are very promising (see table below).

Table.  Classification of brain diseases and their endophenotypes.

Principle 6.

tDCS and neurofeedback provide electrophysiologically-based tools for activation or suppression of cortical neuronal networks. 

tDCS implies passive injection of small amount of DC currents that depolarize (anodal currents) and hyperpolarize (cathodal currents) cortical pyramidal cells under the stimulation electrode.  Neurofeedback implies active involvement of the subject in voluntarily changing the EEG parameters recorded from a given electrode.  Our recent studies have shown that the combination of these two techniques might be the best way of activating the brain.

Appendix 2.  Artifact correction by means of spatial filtration.

EEG is contaminated by various artifacts.  Eye movement artifacts are the largest ones.  They are generated by vertical and horizontal eye movements.  The main source of the artifacts is the potential of the eyeball.  The eyeball acts as an electric dipole with the positive pole oriented anteriorly.  Eye blink results in reflexive upward vertical eye movement that produces positive deflection at frontal areas with maximum at F_p1, F_p2 electrodes.  Eyes closing is associated with similar artifact, while eyes opening results in downward vertical eye movement and negative deflection at F_p1, F_p2 electrodes.  Horizontal e