Spectral brain mapping in children with cerebral palsy treated by the Masgutova Neurosensorimotor Reflex Integration method

Abstract

EEG Mapping Shows Changes in Brainwave Spectrum in Children with Cerebral Palsy During and After Masgutova Neurosensorimotor
Reflex Integration Therapy.

By: Prof. Witold Pilecki, Ph.D; Anna Pilecka-Kalamarz; Prof. Dariusz Kalka, Ph.D; Lech Kipinski, Ph.D; Svetlana Masgutova, Ph.D

Abstract
The rehabilitation of children with impaired motor function due to a damaged central nervous system can be performed using a variety of methods. One of them is Masgutova Neurosensorimotor Reflex Integration (MNRI®), often used with children suffering from cerebral palsy. The objective of this report is to demonstrate the effectiveness of MNRI® therapeutic methods by means of
mapping the brain. The paper presents the case of a 13-year-old boy with cerebral palsy. EEGs were performed using ASA Lab’s ANT Software BV data acquisition system. Nine measuring sessions were performed before, during, and following an MNRI® session of seven specific therapeutic exercises. Spontaneous EEG activity was recorded in a 10-20 electrode system using 32 leads. The recording automatically underwent an elimination of artifacts, after which
the software produced qualitative descriptions as well as mappings of the EEG frequencies. The fast Fourier transform (FFT) algorithm and ASA Lab’s mapping software were used in the computer analysis. Spectral maps were compared in relative and absolute scale for the measured signals obtained before, during, and after MNRI® rehabilitation. Pre- and post treatment results showed changes in the spatial distribution and relative amplitude of alpha and rapid beta activity taking place under the influence of the therapy. It is concluded that neurosensorimotor reflex integration (MNRI®) stimulated the child’s central nervous system by activating the cortical centers of his brain, thereby causing modification of spontaneous activity.

Reprinted in book: "Reflexes: Portal to Neurodevelopment and Lerning. Collective Work" with official permission.
This reprinted article is a variation of a publication by Pilecki W., Kipiński L., Szawrowicz-Pełka T., Kałka D., Masgutova S. (2013) under title: "Spectral Brain Mapping in Children with Cerebral Palsy Treated by the Masgutova Neurosensorimotor Reflex Integration Method". Journal of the Neurological Sciences: Volume 333, Issue (supplement 1), e550

Full text

© 2015, Svetlana Masgutova Educational Institute® for Neuro-Sensory-Motor and Reex Integration, SMEI (USA) » 107
PoRtal t o NeuRodeveloPmeNt aNd l eaRNiNg
EEG Mapping Shows Changes in
Brainwave Spectrum in Children with
Cerebral Palsy During and Aer
Masgutova Neurosensorimotor
Reex Integration erapy
sc i e N t i f i c R e s e a R c h B ehiNd mNRi®
Abstract
The rehabilitation of children with impaired
motor function due to a damaged central
nervous system can be performed using a va-
riety of methods. One of them is Masgutova
Neurosensorimotor Reex Integration (MNRI®), often
used with children suering from cerebral palsy. The
objective of this report is to demonstrate the eec-
tiveness of MNRI® therapeutic methods by means of
mapping the brain. The paper presents the case of
a 13-year-old boy with cerebral palsy. EEGs were per-
formed using ASA Lab’s ANT Software BV data acquisition system. Nine
measuring sessions were performed before, during, and following an
MNRI® session of seven specic therapeutic exercises. Spontaneous EEG
activity was recorded in a 10-20 electrode system using 32 leads. The re-
cording automatically underwent an elimination of artifacts, after which
the software produced qualitative descriptions as well as mappings of
the EEG frequencies. The fast Fourier transform (FFT) algorithm and ASA
Lab’s mapping software were used in the computer analysis. Spectral
maps were compared in relative and absolute scale for the measured
signals obtained before, during, and after MNRI® rehabilitation. Pre- and
post treatment results showed changes in the spatial distribution and
relative amplitude of alpha and rapid beta activity taking place under
the inuence of the therapy. It is concluded that neurosensorimotor reex integration (MNRI®) stimulated the
child’s central nervous system by activating the cortical centers of his brain, thereby causing modication of
spontaneous activity.
Prof. Witold Pilecki, Ph.D; Anna Pilecka-Kalamarz; Prof. Dariusz Kalka, Ph.D;
Lech Kipinski, Ph.D; Svetlana Masgutova, Ph.D
T
Reprinted with ocial permission. This article is a variation of a publication by Pilecki, W., Masgutova, S., Kowalewska, J., Masgutov,
D., Akhmatova, N., Poręba, M., Sobieszczanska, M., Kolęda, P., Pilecka, A., Kalka, D., (2012). The Impact of Rehabilitation Carried out Us-
ing the Masgutova Neurosensorimotor Reex Integration Method in Children with Cerebral Palsy on the Results of Brain Stem Auditory
Potential Examinations. Advances in Clinical and Experi mental Medicine, 21, 3, s. 363–371.
Anna Pilecka-Kalamarz
Svetlana Masgutova,
Ph.D
Prof. Witold Pilecki Prof. Dariusz Kalka
Lech Kipinski, Ph.D

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108 « © 2015, Svetlana Masgutova Educational Institute® for Neuro-Sensory-Motor and Reex Integration, SMEI (USA)
Introduction
Childhood cerebral palsy (CP, paralysis cerebralis infantum), also known as Little’s disease, is a syndrome
of various disorders of movement and posture resulting from permanent, non-progressive brain damage in
the early stages of development (Zablocki, 1998). Childhood cerebral palsy can be dened as a set of chronic
and non-progressive central nervous system disorders resulting from damage to the brain during pregnancy
(20%), during the perinatal stage (60%) or in the rst years of life (20%). Causes of CP include: abdominal in-
juries to the mother, chronic disease during pregnancy, malformations, fetal hypoxia, infection, the inuence
of ionizing radiation, drugs or toxins including cigarette smoking and alcohol consumption, perinatal trauma,
prematurity, brain injury, hypoxia after birth, severe neonatal jaundice, and neuro-infections (Zablocki, 1998).
Four main forms of the disease can be distinguished, depending on the area of damage and the symptoms:
spastic (pyramidal) and dyskinetic (extrapyramidal) forms, and atactic (brain) and mixed forms, which are the
most numerous (Michalowicz, 2001).
One of the most important tasks in treating a child with CP is nding an eective method of rehabilitation.
Among the many interventions for children with locomotor and nervous system disabilities are those of: Vo-
jta, Bobath, Doman-Delacato and the Wroclaw Improvement System (Vojta, 1964; Bobath, 1958; The Doman-
Delacato method, 1968). An alternative for these is the relatively new method of Masgutova Neurosensorimo-
tor Reex Integration (MNRI®), described in various works (Masgutova, 2005; Masgutova, Akhmatova, 2004;
Masgutova, Regner, 2009).
An assessment of the impact of dierent methods of rehabilitation on the central nervous system can be
made in an objective manner using electrophysiological procedures. Such tests are carried out by the Medical
University’s Department of Pathophysiology in Wroclaw, under the direction of Professor Witold Pilecki. With
regard to the eect of MNRI® on childhood cerebral palsy, examinations of brainstem auditory evoked poten-
tials have already been used, as presented in other research (Pilecki, Masgutova S., Kowalewska, Masgutov D.,
Akhmatova, Poręba, Sobieszczanska, Koleda, Pilecka, Kalka, 2012). In the present study we used a dierent
electoencephalographic technique – EEG mapping, introduced to the study of the brain by Duy et al (Duy,
Burchel, Lombroso, 1979).
Purpose
The object of this study is to demonstrate the eectiveness of MNRI® using mapping of brain wave frequen-
cies performed in a computer system, based on multi-channel EEGs recorded in a child with cerebral palsy.
Material
A 13-year-old boy diagnosed with mixed form CP, with a distinct component of the spastic form, but with-
out other chronic diseases, was treated with exercises from the MNRI® protocol. Choosing a child with chronic
spasticity as part of the MPD was important because this is the most common form associated with damage to
the cerebral cortex, and the EEG mapping allows one to observe the spatial distribution of bioelectrical activity
in precisely that part of the brain.
Rehabilitation involved the modication of a typical MNRI® therapeutic process and included 7 consecutive
progressive exercises chosen in such a way as to aect various parts of the body and to activate various motor
functions. MNRI® repatterning exercises relating to the following exercises were used:
1. Foot Tendon Guard Reex (automatic dorsal exion of the foot) – right limb
2. Foot Tendon Guard Reex (automatic dorsal exion of the foot) – left limb
3. Leg Cross Flexion-Extension Reex – right limb
4. Leg Cross Flexion-Extension Reex – left limb
5. Hands Supporting (parachute) reex – both sides
6. Asymmetric Tonic Neck Reex
7. Spinal Gallant
8. Breathing Reex for mobilizing the diaphragm.
The time spent conducting the exercises was only about 45 minutes, whereas typically a therapeutic ses-
sion would last more than an hour. This change was necessary due to technicalities involved in conducting
EEG measurements after each exercise. Three separate EEG recordings were done: phase 1: before exercises,
phase 2: after each of the seven exercises, and phase 3: a nal measurement a few minutes after the end of the

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PoRtal t o NeuRodeveloPmeNt aNd l eaRNiNg
session.
For the measurements, we used an ASA (Advanced Source Analysis) measurement and diagnostic system
from the Dutch rm A.N.T., which consists of the ASA-Lab’s computerized data acquisition system combined
with an electroencephalographic amplier from TSMI Refa-8 (ASA User Manual version 4.6, 2008). Thirty-two
silver-plated cupped electrodes were placed against the child’s scalp, in accordance with the international
standard of 10-20 leads (Fp1, Fpz, Fp2, AFz, Fz, F3, F4, Fc1, Fc2, Fc5, Fc6, F7, F8, C3, Cz, C4, Cp1, Cp2, Cp5, Cp6, T7,
T8, P7, P8, P3, Pz, P4, POz, O1, Oz, O2) and congured in a unipolar system with the reference electrode placed
on the left ear lobe and grounded on the forehead. During the measurements, the impedance was held be-
tween the skin and the electrode < 5k Ω. The sampling frequency was 625Hz.
Method
The study was carried out in four phases:
Phase 1: EEG measurement before rehabilitation
Phase 2: EEG measurements during rehabilitation (Immediately following each of the 7 MNRI® exercises.)
Phase 3: EEG measurement a few minutes after completion of the MNRI® session
Phase 4: Calculations, production of brain mapping images and analysis of results
The calculations were made o-line on the ASA computer system (ASA User Manual version 4.6, 2008). The
preprocessing of signals was applied through low-pass ltration with a Butterworth lter 0.53 Hz, providing
automatic detection of artifacts outside the range of amplitude -150 - 150μV. The elimination of interference
was necessary because artifacts of locomotive origin are a signicant problem during the rehabilitation of mo-
tor functions from an electrophysiological point of view. Only selected portions of the recording containing
undisturbed EEG activity were chosen for the calculations by opting for the fragmentation of signals on win-
dows 1s. Every 1s window containing artifacts detected by the software was eliminated (Fig. 1). A compromise
between the frequency resolution of the calculations, and records suciently clean of artifacts was agreed on.
Each EEG signal’s spectrum was calculated for the purpose of demonstrating the frequencies generated by
the brain’s neural structures in each stages of the study. For this purpose we used the so-called Discrete Fou-
rier Transform (DFT) as determined by introducing the ASA algorithm of Fast Fourier Transform (Fast Fourier
Transform, FFT) into the system (Brigham, Oran, 1988). The average spectrums were calculated in windows 1s.
The spectrum frequencies of EEG signals for each lead obtained in this way, after the application of the ap-
Figure 1: Detect ion of artifact s in the ASA pro-
gram. Inter ference marked on the right sid e
was automatic ally cut from EEG recording s
before per forming calculatio ns.

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110 « © 2015, Svetlana Masgutova Educational Institute® for Neuro-Sensory-Motor and Reex Integration, SMEI (USA)
propriate interpolation techniques, allowed for the creation of a spectral power map of EEG signals in selected
frequency bands, and thus imaging their spatial distribution in each phase of the study.
We analyzed the EEG spectrum from 0.5 Hz to 30Hz, divided into ve sub-ranges: 0.5 - 3.5 Hz (delta), 3.5 - 7.5
Hz (theta), 7.5 - 12, 5 Hz (alpha), 12.5 - 20 Hz (free beta, beta 1) and 20 - 30 Hz (fast beta, beta 2). For these sub-
bands we performed spectral distribution maps in 3D, using interpolation techniques and the standard model
of the head as found in the functions library of the ASA software. It was decided to create maps in two scales:
absolute [μV2/Hz] and relative [%]. Absolute maps allow one to compare the spatial distribution of signals in
the dierent ranges of the energy spectrum in terms of amplitude (the same as in research by the electroen-
cephalograph) and it shows areas that are mostly intensely activated in the sub-ranges of the spectrum. In con-
Figure 2: Maps of th e spatial distribution o f
the power spec trum expressed in a bsolute
units [μV2/Hz] for re sting EEG performe d at
the outset o f the experiment. In subs equent
columns dier ent projections are show n in
3D using a standar d model of the head for
the frequen cies: delta, theta, alpha, be ta 1
and beta 2.
Figure 3: Maps of the spa tial distribution of
the power spec trum expressed in a bsolute
units [μV2/Hz] for on e of the EEG recordings
made during phas e 2 (after MNRI® work
involving the lim bs). Subsequent column s
show the diere nt projections in 3D using a
standard mo del of the head for the frequ en-
cies: delta, th eta, alpha, beta 1 and beta 2.

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PoRtal t o NeuRodeveloPmeNt aNd l eaRNiNg
trast, representing the transmitted energy of the signal in a given frequency range relative to the energy of the
entire spectrum allows one to determine which areas are primarily responsible for EEG activity in a particular
sub-range. Images are presented in an automated scale using the RGB color scale.
Results
A qualitative analysis of EEG records from phases 1, 2, and 3 (before, during, and after rehabilitation by
MNRI®), show certain dierences among results. A symmetrical record was achieved, with no seizure activ-
ity, showing a mixture of slow delta-theta waves mainly in the frontal and parietal leads, alpha waves mainly
above the occipital leads, and a correct halting response. In addition, in the records taken during phase 2,
rehabilitation, a greater admixture of fast beta waves is found in fronto-temporo-parietal leads.
Through EEG frequency mapping we obtained images of the distribution of the spectral power of the EEG
signal for each of the 9 registrations. Due to the short scope of this study we present only selected images,
presenting the results for recordings done in phase 1 (Figs. 2 and 5), in phase 2 after the Galant Reex exercise
(Figs. 3 and 6) and in phase 3, after the whole rehabilitation session (Figs. 4 and 7). Figs. 2 to 4 present maps
in absolute scale [μV2/Hz] and Figs. 5 to 7 are drawn in relative scale [%]. Each series of images was analyzed
separately.
Mapping of the recorded output in an absolute scale shows the dominance of low frequencies in the upper
frontal leads, and the lowest amplitude in the vicinity of the temporo-occipital leads. Alpha activity is cor-
rectly located mainly in the occipital region. Fast frequencies from beta waves are of the highest amplitude in
the fronto-parietal leads. This information leads to presupposition that the quality of focusing, presence, and
awareness in the patient can increase.
During rehabilitation, the minor periodic voltage uctuations, delta and theta frequencies did not change
signicantly throughout all three phases.
Changes did occur in the alpha frequency range (7.5-12.5 Hz). Alpha activity initially observed (phase 1) in
the occipital leads disappeared at the beginning of rehabilitation (phase 2). Subsequent phases of the study
show clearly that there is a lack of alpha activity in this area, and at the same time the alpha activity did ap-
pear above the central leads (Fig. 3 middle column), mainly on the left side, although it had a relatively not
Figure 4: Maps of the sp atial
distribu tion of the power
spectr um expressed in absolu te
units [μV2/Hz] for an EEG
perfor med after MNRI® reha-
bilitation Sub sequent columns
show the various p rojections in
3D using a standar d model of
the head for the f requencies:
delta, theta, al pha, beta 1 and
beta 2.

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112 « © 2015, Svetlana Masgutova Educational Institute® for Neuro-Sensory-Motor and Reex Integration, SMEI (USA)
high amplitude. This eect proved to be permanent, as it held until phase 3, the end of the experiment (Fig. 4
middle column). The result again shows a possibility of more proper organization of brain wave spectrum in
the patient and a basis for improvement of the abilities to focus, increase awareness and critical thinking.
EEG frequencies in the range free beta (12.5 - 20Hz) did not change in spatial distribution or in average ab-
solute values. Certain changes were observed for fast beta (20 - 30Hz): this stronger voltage appeared during
phase 2, rehabilitation, (expiring after its completion) in the temporal leads of the dominant hemisphere (Figs.
3 and 4, last column).
Mapping in the relative scale brings additional information. As in the absolute scale, the dominance of
delta activity asserts itself in the frontal leads; however, the theta function has the highest percentage (17%)
in the central region, while low in the frontal leads. Clearly, this is related to the extremely high dominance of
lower brain waves (delta and theta) in this area, approaching up to 89% (Fig. 5, rst column). A second strongly
expressed frequency in phase 1 is alpha activity above the occipital leads, lling 48% of the signal spectrum at
this location (Fig. 5, middle column).
The percentage of free beta (12.5 - 20Hz) is strongly (13%) expressed in the frontal central leads. In contrast,
the fastest EEG output frequency (beta), is localized in the temporal regions, mainly on the right side. It is also
associated with low activity in the rest of the spectrum in these areas, which is not visible on the absolute scale
maps. The percentage of the total spectral power of EEG output in this (beta) frequency range is the lowest of
all and reaches just over 4% in channel T8.
During phase 2, rehabilitation, as before, no signicant changes in either distribution or amplitude of delta
and theta activity were shown. The disappearance of alpha activity in the occipital leads was conrmed. This
frequency range strongly (several dozen %) saturates spectrum signals measured in the temporo-parietal elec-
trodes, which is a new discovery. In contrast to the absolute scale maps, relative scale maps show a tendency of
occipital alpha activity to revert after the exercises. This discovery suggests that MNRI® treatments temporarily
arrest/inhibit alpha activity in its physiological location, while stimulating it in regions operationally linked to
the motor cortex system as it works in a healthy brain (Fig. 6, middle column). It is interesting that, at the same
time, it does not return to its phase 1 level immediately after phase 2. The absolute scale maps show this, as do
the maps in Fig. 7 where alpha activity represents a lower percentage of the spectrum in phase 3, after MNRI®,
Figure 5: Maps of the spa tial distribution
of the power spec trum, expressed i n the
relative scale a s the ratio of power in a
given range of f requency to the power
of the entire spe ctrum. Illustrati ons
were made for res ting EEG recorded
in phase 1. Subseque nt columns
show various pr ojections in 3D using
a standard mo del of the head for the
frequenci es: delta, theta, alpha, beta 1
and beta 2.

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PoRtal t o NeuRodeveloPmeNt aNd l eaRNiNg
than in phase 1, before. This can mean a tendency that MNRI® can reach a stable transition in the work of the
brain waves.
Free beta activity of 12.5-20Hz does not change its spatial properties or relative amplitude during the test.
However, maps in relative scale conrm the increase of rapid beta activity (20-30Hz) in the temporal leads (up
to several percent), expiring after completion of MNRI® exercises focused on the extremities (Figure 6, and
Figure 7, last column).
Conclusions
We conclude that rehabilitation using MNRI® in a child patient with cerebral palsy caused a reorganization
of spontaneous electrical activity in his brain. This is observed in the alpha frequency range as well as in fast
beta activity. Increased activity in these frequency bands during rehabilitation appears in the parietal and
temporal locations. This may be connected with the positive and stable therapeutic eect of this method on
motor disorders originating in the central nervous system. Our observations are consistent with the ndings
of other authors who have noticed changes in EEG mapping in children with CP during experiments related to
the motor system (Shin, Lee, Hwanh, You, Im, 2012).
Figure 6: Maps of the sp atial distribu-
tion of the power sp ectrum, express ed
in a relative sca le, as the ratio of
power represe nting a given range
of frequenc y to the power of the
entire spec trum signal. The illustra -
tions were made fo r one of the EEG
recordings t aken during phase 2 (after
stimulation o f the limbs). Subsequent
columns show vari ous projections
in 3D using a standar d model of the
head for the fr equencies: delta, thet a,
alpha, beta 1 and b eta 2.

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114 « © 2015, Svetlana Masgutova Educational Institute® for Neuro-Sensory-Motor and Reex Integration, SMEI (USA)
It should be made clear that although the results presented in this analysis are positive, they apply only
to a single case. Taking into account inter-individual variability and the inuence of many external factors on
EEG results, drawing objective conclusions about the physiological eects of MNRI® would require testing a
homogeneous group of children with childhood cerebral palsy, and comparing mapping results to the results
obtained in a control group of healthy children. A future publication by our team will be devoted to this issue.
Figure 7: Maps of the spat ial distribution
of the power spec trum, expressed i n
a relative scale, a s the ratio of power
represent ing a given frequency ra nge
to the power of the ent ire spectrum of
the signal. Illust rations were made for
the nal EEG (phase 3), per formed after
completion of t he entire MNRI® session.
Subsequent co lumns show the various
project ions in 3D using a standard
model of the head f or the frequencies:
delta, theta, al pha, beta 1, and beta 2.

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We thank all the families and the children participating in and supporting our brain research and
for scientically showing such great results with the MNRI® procedures! We appreciate your patience
during all test procedures and for tolerating the unpleasant electrodes on your heads as well as the
auditory and visual stimulus. You were the best and you helped uncover a new level of science! –
Authors