Source Extraction Representing Thumb and Little Finger

Cortical Networks and Intra-Hand-Area Evoked

Synchrony

Franca Tecchio

1-2

, Sara Graziadio

, Giulia Barbati

, Roberto Sigismondi

Filippo Zappasodi

1-2

, Camillo Porcaro

, Giancarlo Valente

, Marco Balsi

ISTC-CNR, Unità MEG, Osp Fatebenefratelli, Isola Tiberina, 00186 Roma, Italy

AFaR, Ospedale Fatebenefratelli, Isola Tiberina,

00186 Roma, Italy

Dip. Fisica e

Dip. Ingegneria Elettronica,

Univ. ‘La Sapienza’, Roma, Italy

Abstract. A novel cerebral source extraction method is proposed (Functional

Source Separation, FSS) starting from extra-cephalic magnetoencephalographic

(MEG) signals in humans, based on source functional reactivity to the external

stimulation. Their activity is obtained all along the rest state and during proc-

essing of a simple separate sensory stimuli of thumb or little finger. This

method provides cerebral sources describing thumb and little finger primary

representations, as demonstrated by their higher responsiveness to the corre-

sponding finger stimuli and their different positions consistent with the homun-

cular organization. A dynamical index describing intra-regional synchrony was

introduced, which showed higher levels when stimulating the thumb with re-

spect to little finger stimulation, selectively in the gamma band ([33, 44]Hz).

This indicates that the stimulation of a functionally prevalent finger (thumb) ac-

tivates a cortical network more synchronized in the gamma band than a non-

prevalent one (little finger).

1 Introduction

1.1 Synchronization phenomena

Brain processes mainly rely on changes in the synchronization of the recruited neu-

ronal firing (functional binding and unbinding, [5]). Not only the synchronization

among cerebral regions processing different stimuli features involving quite distant

cortical areas are mandatory, but also the synchronization within a restricted cortical

area.

Starting from extra-cephalic data, direct signals from recording channels are often

used: if synchronization across distant areas is studied, this method is quite reliable,

Tecchio F., Graziadio S., Barbati G., Sigismondi R., Zappasodi F., Porcaro C., Valente G. and Balsi M. (2005).

Source Extraction Representing Thumb and Little Finger Cortical Networks and Intra-Hand-Area Evoked Synchrony.

In Proceedings of the 1st International Workshop on Biosignal Processing and Classiﬁcation, pages 12-18

DOI: 10.5220/0001192400120018

 SciTePress

but in the present study the limited region devoted to the hand control is under inves-

tigation. In this case all channels are significantly sensitive to the activity from the

same neuronal pools. For this reason, it is mandatory to achieve synchronization

indexes between neuronal source activities instead of between channel signals.

To extract cerebral sources starting from the recorded magnetic fields, the MEG

community main approach is to solve the so called ‘inverse problem’ [4], which iden-

tifies cerebral currents on the base of their spatial positions. In our region of interest,

the neural networks are highly interconnected and superimposed (for a review see

[12]) pulling the ‘inverse problem’ to its spatial resolution boundaries. Moreover, it

implies to fix the source model, which suitability changes across different steps of the

cerebral processing, and this limits the analysis when a neuronal group is followed

from the rest state to task involvement. For this reason, blind source separation meth-

ods (BSS, Comon 1994; [1]) could be more suitable, discriminating different sources

on the base of their generated signal statistical properties.

In particular, an ad hoc BSS algorithm was implemented, based on a priori infor-

mation about sources functional properties. Once obtained single fingers cortical

representation sources, a dynamic synchrony index was defined, related to the cortical

network including both finger representations. This intra-regional index was studied

when stimulating each finger separately.

2 Methods

2.1 Experimental Paradigm

Magnetoencephalographic (MEG) data were recorded from 15 healthy volunteers

(mean age 31±2 years, 7 females and 8 males) during separate electrical stimulation

of their right thumb or little finger. Ring electrodes were used to deliver around 300

stimuli with standard features (0.2-ms-long electric pulses, 631 ms inter-stimulus

interval, stimulus intensities set at about twice the subject’s sensory threshold, [9],

Tecchio et al 1997).

A 28-channel MEG system (developed by our own group, Tecchio et al, 1997) op-

erating in a magnetically shielded room (Vacuumschmeltze GMBH) was used, the

active channels being regularly distributed on a spherical surface (13.5 cm of curva-

ture radius) and covering a total area of about 180 cm

. Brain magnetic fields were

recorded from the left rolandic region, i.e. contralaterally to the stimulation, after

positioning the central sensor of the MEG apparatus over the C3 site of the Interna-

tional 10–20 electroencephalographic system. The noise spectral density of each

magnetic sensor was 5–7 fT/√Hz at 1 Hz. Data were analogically bandpass filtered

through a [0.16–250] Hz and gathered at 1000 Hz sampling rate.

2.2 Functional source separation (FSS)

Some of the authors (Valente et al submitted) have developed a modified Independent

Component Analysis (ICA) procedure, that explicitly uses additional information to

bias the decomposition algorithm towards solutions that satisfy physiological assump-

tions, instead of extracting sources only on the base of their signals statistical inde-

pendence. The method is based on optimizing a modified contrast function:

HJF

(1)

where J is any function as normally used for ICA, while H accounts for the prior

information we have on sources. Parameter

is used to weigh the two parts of the

contrast function. If

is set to zero, maximization of F leads to pure independence.

The optimization is performed by simulated annealing, so that function H can have

any form (e.g. it does not need to be differentiable).

To identify neural networks devoted to individual finger central representation, the

sources ‘reactivity’ to the stimuli was taken into account. This was defined as fol-

lows: the evoked activity (EA) was computed separately for the two sensorial stimu-

lations, by averaging signal epochs centered on the corresponding stimulus (EA

thumb; EA

, little finger) and subtracting the mean of the values in the ‘no response’

baseline interval [-30.-10] ms, t=0 corresponding to the stimulus arrival.

The reactivity coefficient (R) was computed as:

∑∑

−

−==

−=

)(EA)(EA

ttR

(2)

with X = T, L. The time interval ranging from 20 to 40 ms includes the maximum

activation.

The constraint function H

is then chosen as:

(

)

kRH

where

()

⎩

⎨

⎧

≤

else 1

when

kRkR

(3)

and k is a suitable parameter quantifying the required response

The shape of function

is such that the constraint is inactive when response is

greater than k, so as to define an admissible region where the optimization is only

driven by J. Parameter

is chosen large enough H

dominates the search. Therefore,

a constrained optimization procedure is obtained.

To obtain the time behavior of the neural networks devoted to different finger corti-

cal representations during different activation states, each functional source was ex-

tracted using data recorded during the two fingers separate stimulation.

To separate contributions representing individual fingers, we extracted a single

component by using the constraint H

, obtaining the functional source describing the

time evolution of the thumb cortical representation (FS

). Then, the procedure was

repeated using H

to obtain FS

2.3 Functional source characteristics

Functional source evoked activities. To demonstrate that the extracted functional

sources are more responsive to the stimulation of the district they represent, each

source was observed when stimulating both the fingers. In particular the above de-

fined indexes R

and R

describing respectively the responsiveness when stimulating

thumb and little finger, were calculated for each of the two functional sources FS

and FS

Functional source localizations. To describe spatial characteristics of the sources

and FS

, they were separately retro-projected. To be noted that when a single

component is retro-projected, as in our case, the field distribution is time independent,

up to the scaling factor FS

(t); this means that the localization algorithm produces

fixed positions and directions along time, and only the source strength varies with

time proportionally to FS

(t). As localization procedure a moving Equivalent Current

Dipole (ECD) model inside an homogeneous best-fitted sphere was applied. The

ECD characteristics (explained variance = e.v.; spatial position) were calculated for

each functional source. ECD coordinates, if e.v.

> 85%, were expressed in a right-

handed Cartesian coordinate system defined on the basis of three anatomical land-

marks.

For comparison, the positions of the known markers of signal arrival in the pri-

mary sensory cortex, occurring at around 20 ms from the stimulus (M20), were calcu-

lated by standard procedure (Tecchio et al 1997, 2002) of averaging original channel

signals.

2.4 Dynamic intra-regional synchrony index

The intra-regional synchrony index was obtained by the following 4 steps and exam-

ined in the two different conditions of thumb and little finger stimulations.

1) The FS

(t) and FS

(t) signals were forward-backward band pass filtered in

the classical alpha (α= [7, 13]Hz), beta (β = [14, 32]Hz) and gamma (γ= [33,

44]Hz) bands (FS

(t), where X = T, L; b = α, β, γ) by a Butterworth filter

of the second order;

2) the FS

(t) analytic signals were calculated, defined as:

)()()(

bbb

tihtFSta

XXX

(4)

where h

(t) is the FS

(t) Hilbert transform;

3) the module of the two analytical signals in each band were calculated and

averaged across all stimuli epochs separately as:

∑

aveXX

|)(|

(5)

where t

∈ [T

, T

], with T

-T

the intervals length and N

is the number of

considered stimuli for each finger X = {T, L} and aveX indicates that the av-

eraged is performed for each analytical signal across T and L finger stimuli

separately.

4) the indexes calculated at steps (3) were finally averaged across the two

sources, obtaining a whole region of interest dynamic synchronization index

(Syn

(t); it was estimated during both fingers separate stimulation. In par-

ticular the maximal value following each stimulation (Syn

) was chosen to

compare synchronization levels during thumb and little finger stimulations,

in the 3 frequency bands.

3 Results

3.1 Functional extracted source characteristics

Functional source evoked activities. The activity of the source representing a finger,

is compared when stimulating the finger itself with respect to when an other finger is

stimulated. To do this, the defined indexes R

and R

, describing respectively the

responsiveness to thumb and little finger stimulations, were both considered for each

of the two functional sources FS

and FS

. The evoked activity of the two extracted

sources, resulted significantly higher when the finger that source represents was

stimulated (Table 1, R

> R

for S

, p <.001 ; R

> R

for S

, p <.001).

Table 1. Average and s.d. across subjects of FS

and FS

characteristics: spatial position (x-

axis passing through the two preauricolar points directed rightward, positive y-axis passing

through the nasion, positive z-axis consequently) with their explained variance (e.v.); the

evoked activity indexes (R

and R

). Mean M20

and M20

positions are reported with their

mean latency (lat) .

FSS

lat

(ms) e.v.

(mm)

(mm) R

0.97±0.04 -38±11 10±13 89±10 13.4±4.8 6.2±5.2

0.95±0.07 -34±11 6±12 99±14 7.4±5.6 12.6±4.9

M20

24±2 0.96±0.18 -42±8 11±11 91±10

M20

24±2 0.94±0.06 -33±10 6±13 100±10

Functional source localization. Dipole coordinates (x,y,z) were computed

for the two sources FS

and FS

in our 15 subjects group.

A General Linear Model (GLM) for repeated measures was estimated to test dif-

ferences in source localization: as dependent variables the 3-dimensional coordi-

nates vectors obtained for each subject were used, with the two levels Finger

(Thumb, Little) as within-subjects factor. Factor Finger resulted significant

(F(3,12)=8.675, =0.001, Table 1), corresponding to FS

position significantly lat-

eral, anterior and lower with respect to FS

one. This was in agreement with M20

ECD positions when stimulating respectively thumb and little finger evaluated in

same subjects (Table1).

3.2 Dynamic intra-regional synchronization index

The index Syn

showed strongly different properties in the different bands follow-

ing stimulation of the two fingers. In fact, in alpha and beta bands no effect was ob-

served (paired t-test p>.200), while in gamma band the Syn

was significantly

higher for the thumb stimulation than for the little finger (0.039 ± 0.020 and 0.026 ±

0.021 respectively, p=.013, fig. 1).

-0,04

-0,03

-0,02

-0,01

0,01

0,02

0,03

0,04

Thumb

Li ttle f i ng e r

0 100 200 300 400 500

600

-100

Syn

-0,04

-0,03

-0,02

-0,01

0,01

0,02

0,03

0,04

Thumb

Li ttle f i ng e r

0 100 200 300 400 5000 100 200 300 400 500

600600

-100-100

Syn

Fig. 1. Temporal dynamic of the synchronization index in the gamma band, being t=0 the

sensory stimulus arrival on thumb (black) or little finger (grey).

4 Conclusion

The two functional extracted sources related to thumb (FS

) and little finger (FS

)

cortical representations, resulted significantly different both with respect to their

spatial positions and their activation properties. In fact FS

position was significantly

lateral, anterior and lower with respect to FS

one, as physiologically expected. This

positioning of the hand sensory representation boundaries completely agrees with the

classical homuncular somatotopy [10] [15], as also recently confirmed by other tech-

niques (e.g. functional Magnetic Resonance Imaging, [7], optical imaging, [14]).

The thumb cortical representation showed strongly higher activity evoked by

thumb than little finger stimulation and the same selective properties showed the little

finger cortical representation. This strengthens that extracted sources are significantly

different and suitable representing cortical networks specifically connected to differ-

ent fingers.

Using these functional extracted sources activities, an intra-regional synchroniza-

tion index was introduced. In particular it resulted finger-dependent in the gamma

band [5] [6], whereas in beta and alpha bands the synchronization index was similar

for thumb and little finger stimulations. Oscillatory activity within alpha and beta

frequency bands is well known to react to sensorimotor tasks in the whole rolandic

region [11]; the gamma band is instead characteristic of focal network activations [3].

Gamma band is moreover indicated with increasing evidences as playing a pivotal

role in perceptive [8] and cognitive motor tasks [13]. Present result, of gamma band

synchronization higher following stimulation of the thumb than of the little finger,

will suggest a new phenomenon: taking into account that thumb is functionally preva-

lent with respect to little finger, it could be hypothesized that the synchronization in

the gamma band codes for functional prevalence in the sensory hand representation,

strengthening previous results of our group [16].

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