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© 2000 American Heart Association, Inc.
Integrative Physiology |
Distribution of Excitation Frequencies on the Epicardial and Endocardial Surfaces of Fibrillating Ventricular Wall of the Sheep Heart
From the Department of Pharmacology, SUNY Health Science Center, Syracuse, NY.
Correspondence to Alexey V. Zaitsev, Department of Pharmacology, SUNY Health Science Center, 750 East Adams St, Syracuse, NY 13210.
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Abstract |
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Abstract—Tissue heterogeneities may play an important role in the mechanism of ventricular tachycardia (VT) and fibrillation (VF) and can lead to a complex spatial distribution of excitation frequencies. Here we used optical mapping and Fourier analysis to determine the distribution of excitation frequencies in >20 000 sites of fibrillating ventricular tissue. Our objective was to use such a distribution as a tool to quantify the degree of organization during VF. Fourteen episodes of VT/VF were induced via rapid pacing in 9 isolated, coronary perfused, and superfused sheep ventricular slabs (3x3 cm2). A dual-camera video-imaging system was used for simultaneous optical recordings from the entire epi- and endocardial surfaces. The local frequencies of excitation were determined at each pixel and displayed as dominant frequency (DF) maps. A typical DF map consisted of several (8.2±3.6) discrete areas (domains) with a uniform DF within each domain. The DFs in adjacent domains were often in 1:2, 3:4, or 4:5 ratios, which was shown to be a result of an intermittent Wenckebach-like conduction block at the domain boundaries. The domain patterns were relatively stable and could persist from several seconds to several minutes. The complexity in the organization of the domains, the number of domains, and the dispersion of frequencies increased with the rate of the arrhythmia. Domain patterns on the epicardial and endocardial surfaces were not correlated. Sustained epicardial or endocardial reentry was observed in only 3 episodes. Observed frequency patterns during VT/VF suggest that the underlying mechanism may be a sustained intramural reentrant source interacting with tissue heterogeneities.
Key Words: ventricular fibrillation • optical mapping • frequency analysis
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Introduction |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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The spatial distribution of excitation frequencies during ventricular fibrillation (VF) is important for understanding the mechanism of this complex arrhythmia.1 2 3 It has been suggested that such a distribution reflects the dispersion of refractory periods, a feature that, according to the theory of Moe et al,4 plays a key role in the initiation and maintenance of fibrillation. Until recently, technical constraints have significantly limited our ability to measure the frequency distribution in any significant detail. In this study, we overcome these limitations by using the following combination of experimental techniques.
Instead of the traditional multiple-electrode mapping, we have made use of optical methods. With a high-resolution charge-coupled device video camera,5 6 we have recorded spatiotemporal variations in the fluorescence of a voltage-sensitive probe. In contrast to a conventional electrogram that reflects the complex distribution of extracellular currents, an optical signal is proportional to the cardiac action potential.7 This fact makes optical recordings particularly advantageous during VF when extracellular signals become complicated and hence difficult to interpret.
Another methodological difference that distinguishes this study from previous work on VF is our use of isolated coronary–perfused preparations of free ventricular wall8 9 10 capable of maintaining complex fibrillatory activity.11 12 An advantage of this experimental preparation is the accessibility of both endocardial and epicardial surfaces to optical recordings. By simultaneously using 2 video cameras, we were able to map the entire surface of the preparation with uniformly high spatial resolution and thus avoid ambiguity in the interpretation of experimental data. Clearly, such ambiguity is unavoidable in experiments using whole-heart preparations when significant areas of the heart remain unmapped.
Finally, to determine the local frequencies of excitation, we used a recently developed technique based on dominant frequency (DF) analysis13 instead of the traditional approach based on measuring local activation times. The frequency of excitation was derived from Fourier spectra calculated for each pixel of the epicardial and endocardial images. Using the DF method eliminated the need for manual selection of activation times, which is often required during analysis of VF based on more traditional approaches. The availability of fully automated algorithms was crucial for this study, given the fact that our typical frequency maps contain information from thousands of pixels.
The major finding of this study is that the spatial distribution of local frequencies of excitation during VF appears to be unexpectedly simple and organized. A typical frequency map (DF map) consists of few relatively large domains with uniform DFs within each domain. The ratios of DFs in adjacent domains are often close to 1:2, 3:4, or 4:5 as a result of intermittent Wenckebach-like propagation block at boundaries between domains. The domains persist for a relatively long time as compared with the excitation cycle. Our findings suggest the possibility that fibrillation in our experimental model is driven by a stable intramural high-frequency source of excitation.
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Materials and Methods |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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Isolated Coronary-Perfused Sheep Ventricular Preparation
All experimental protocols conformed to the Guide for the Care and Use of Laboratory Animals (NIH publication No. 85-23, revised 1996).
Young sheep (n=9) were heparinized (500 IU, IV) and subsequently
anesthetized with sodium pentobarbital (35 mg/kg IV). The heart
was rapidly removed and Langendorff-perfused with cold (4°C)
cardioplegic solution.14 The right (n=6) or left (n=3)
free ventricular wall was quickly excised, and one of the
large coronary arteries was cannulated. Nonperfused tissue was
removed, and the preparation was stretched on a plastic frame as shown
in Figure 1B
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Preparations were perfused with a standard oxygenated Tyrode solution15 at 80 mm Hg and superfused with the same solution at a rate of 40 mL/min. We added diacetyl-monoxime (DAM) to the Tyrode solution (15 mmol/L) to stop contractions. The voltage-sensitive dye di-4-ANEPPS (15 µg/mL) was added as described elsewhere.6 Areas with inadequate perfusion, if present, did not stain normally with fluorescent dye and could be immediately identified as dark regions in the background fluorescence images. The preparations with inadequate perfusion were excluded from the study.
The preparation was paced at a basic cycle length (BCL) of 500 ms at 2 times diastolic threshold. The shortest cycle length maintaining 1:1 ventricular capture (BClmin) was determined for each preparation. To induce tachycardia, decremental pacing at the stimulus intensity 1.0 to 4.0 mA was applied; the cycle length was progressively shortened from BCL in 5- or 10-ms steps. A bipolar electrogram was monitored on a digital storage oscilloscope (Hitachi) and recorded on videotape (Neuro-Corder DR-484).
Optical Setup and Signal Processing
The optical setup consisted of 2 identical video imaging
systems16 for simultaneous imaging of the
endocardial and epicardial sides of the preparation (Figure 1A
).
The magnification and field of view of both video cameras were adjusted
in a such a way that they observed the same area of the preparation
from opposite sides. The video images (typically 200x100 pixels) were
acquired at 120 frames per second, and the background
fluorescence was subtracted from each frame. After spatial
filtering,16 the effective spatial resolution of the
method was 0.4–0.8 mm, depending on magnification.
Fast Fourier transform (FFT) was applied to the 2.13-second segments of
the optical signal from each pixel of the epicardial and endocardial
images, which provided a spectral resolution of 0.47 Hz. The position
of the largest spectral peak (DF) was determined for each pixel, and
maps of spatial DF distribution were constructed for each
recording of arrhythmia (Figure 1C).
The complexity of the DF maps was characterized by the coefficient of
variation of DF over the mapped area
(
DF/DFmeanx100%). DF
maps were compared using coefficient of cross-correlation. The power of
the DF (PDF) of the individual spectra was used to measure the degree
of local periodicity. PDF was measured in the vicinity (±0.47 Hz) of
the dominant peak and was normalized to the total spectral power in the
range of 1 to 20 Hz (see Figure 1C). Time-space plots (TSPs) and
pseudo-ECGs were derived from optical recordings as described
elsewhere.6 17
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Results |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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Overview
Using the decremental pacing protocol (see Materials and Methods), we were able to induce sustained (>3 minutes) arrhythmias in all 9 preparations. A total of 14 tachyarrhythmias (25 recordings) of various degrees of complexity, ranging from monomorphic ventricular tachycardia (VT) to complex polymorphic tachycardias reminiscent of VF were analyzed. The characteristics of the sample are given in the Table
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Breakthrough pattern and the absence of a complete reentry on either
surface during arrhythmia were typical findings in our
preparations. Sustained reentrant circuits (see asterisks on the
Table) were observed in only 3 of 25 recordings (1 VT
and 2 VF). Figure 2 shows
representative examples of isochronal maps during
monomorphic VT and polymorphic VT (PVT)/VF. Panel A shows the
endocardial and epicardial propagation in control conditions during
epicardial pacing at BCL=500 ms. Epicardial longitudinal and transverse
conduction velocities were 0.84 and 0.40 m/s, respectively, which is
within the normal range for ventricular
myocardium.18 Panel B shows activation during
VT (cycle length=192 ms) in the same preparation. A multiple
breakthrough pattern was revealed on both surfaces, and no reentrant
activity was seen. Panel C shows isochronal maps after conversion
of VT shown in panel B into VF by burst pacing at cycle length of 130
ms. Multiple breakthroughs, incomplete reentrant circuits, and multiple
lines of conduction block can be seen. Note the marked difference
between the epicardial and the endocardial patterns of activation.
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The cycle length of monomorphic VT in our experiments was 196±18 ms.
This was significantly longer than the average cycle length during VF
(135±21 ms, P<0.0001) measured as the inverse value of the
average DF over the preparation (DFmean). It is
of interest that the average cycle length during VF was also much
shorter than the minimal pacing interval in control conditions
(BCLmin=180±10 ms, P<0.0001),
whereas the cycle length of monomorphic VT was very close to
BCLmin (see the Table).
BCLmin, DFmean, and
pseudo-ECG patterns for individual recordings are given in the
Table.
The spatial distribution of the DF was studied in all episodes of
arrhythmia. Epicardial and endocardial DF maps constructed for
an episode of monomorphic VT are shown in panel A of Figure 3 (the same episode as in Figure 2B). As one might expect, the DF was spatially uniform and
identical on both surfaces of the preparation, which reflects
homogeneous distribution of the cycle length. The DF was
5.2 Hz throughout the preparation, which corresponds to a cycle length
of 192 ms. The spectra of individual signals (Panel A, bottom) featured
a pronounced dominant peak at 5.2 Hz. The spectrum of the pseudo-ECG
(Panel A, top) was very similar to the local spectra.
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Surprisingly, the DF maps of VF also appeared very simple. They
consisted of few relatively large areas ("domains") of uniform DF.
Panel B of Figure 3 shows such maps for the episode of VF shown
in Figure 2C. A major part of the epicardial surface was
occupied by a continuous domain with a DF of 6.1 Hz. The rest of the
surface had a DF of 8.0 Hz, with a large domain in the upper-right
corner and small domains in the center and in the lower left corner of
the preparation. On the endocardium, the DFs were the same as on the
epicardium (6.1 and 8.0 Hz). However, the location and shape of the
domains were different from those on the epicardium.
The power spectrum of the pseudo-ECG (Panel B, top) also had 2 major peaks at 6.1 and 8.0 Hz, which corresponded to the 2 prevalent local DFs. Unlike monomorphic VT, during which all local power spectra contained only the DF and its harmonics, during VF the local spectra were more complex. For example, the power spectrum in epicardial point a1 (see panel B, bottom) had 2 peaks, one at 6.1 Hz (dominant) and the other at 8.0 Hz. Note that the second peak corresponds to the DF on the opposite point (a2) on the endocardial surface. Such bimodal spectra were usually observed near the boundaries between different DF domains (see below).
The average number of domains >3 mm2 in a
given VF episode was 8.0±3.6 on the epicardium and 8.27±3.9 on the
endocardium (NS). Given that the area of the preparation was 
3x3=9
cm2, the average area of a single DF domain was
1.1 cm2. The number of different frequencies
in a given VF episode was usually smaller (4.84±1.95) than the number
of domains, which means that there were several domains with the same
DF (on average, 1.6 domains per frequency). In addition to major
domains, there were also a number of small (<3
mm2) domains randomly scattered throughout the
preparation. Their location correlated to sites of low amplitude, such
as areas adjacent to lines of conduction block and low illumination at
the boundaries of the wedge. The origin of such domains was most likely
the result of noise. However, their relative contribution was always
<10% of either the epicardial or the endocardial surface. We did not
find a significant difference in domain organization of
arrhythmias in the left and right ventricles.
An important question is how stable the DF domain pattern is and how it
changes with time. To address this question, in 4 experiments we
compared DF maps of short (2.13-second) consecutive segments of the
same long (8.56-second) recording of VF. The analysis
shows that the spatial distribution of DF domains remained largely
unchanged over time intervals of 8 to 10 seconds. In all of these
experiments, the coefficient of cross-correlation between the
consecutive DF maps was >0.9. Figure 4A
shows the DF maps of 2 segments (I and II) separated by a 2.13-second
interval from the same episode of VF shown in Figures 2 and 3, as well as a map including the entire episode, as indicated
by the top thick lines. It is seen that the shape of the DF domains is
highly preserved. Moreover, the DF distribution during the whole
recording (III) is also remarkably similar to those of the
short segments (I and II).
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Temporal patterns of the DF changes at individual pixel locations are
shown in Figure 4B. The plots were obtained by shifting the
256-frame analysis time window (2.13 seconds) in 16-frame steps
(133 ms). In the majority of points, the DF either was stable (traces
b, e, f, and h) or flickered between the 2 main frequency levels, 6.1
and 8.0 Hz (traces a and c). The flickering was observed in those
points in which the spectra had 2 distinct peaks with comparable
amplitudes (see, for example, point a1 in Figure 3B). Fluctuations in the amplitude of these peaks could lead to
a change in the spatial extension of a given domain. In general, more
variability was observed closer to the boundaries between DF domains
and/or in the areas of conduction block.
Nature of the DF Domains
Analysis of impulse propagation near the boundary between
adjacent DF domains often revealed Wenckebach-like conduction block
patterns. A representative example is shown in Figure 5, which shows a fragment of endocardial
DF map from Figure 3B (map rotated 90°). To visualize the
propagation pattern, we constructed a TSP for a column of pixels
(vertical white line) across the boundary between 8.0- and
6.1-Hz domains (Figure 5A). The TSP (Figure 5B) reveals
3:4 conduction block with a Wenkebach-like activation pattern. We infer
that the excitation travels from the 8.0- to 6.1-Hz domain. Every
fourth impulse originating in the 8.0-Hz domain is blocked. The
position of the block (dashed line) coincides with the boundary of the
6.1-Hz domain. The individual recordings (a through e in Figure 5D) show a reduction in action potential amplitude from point e
to point c on every fourth cycle of points e and d. The block occurred
between points d and c. The spectra from points a through e (Figure 5C) show a bimodal distribution of power between peaks at 8.0
and 6.1 Hz. The amplitude of the 8.0 Hz peak gradually decreased from
point e to point a, whereas the amplitude of 6.1 Hz peak gradually
increased. The change of the dominance, demarcating the boundary
between the DF domains, occurred between point d and c. The activation
pattern (TSP) and spectral pattern showed a good agreement with respect
to the localization of the conduction block.
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In this, as well as in other experiments, we observed that domains with
lower DF often had longer action potential duration (APD). Figure 5E shows individual action potentials optically recorded
from different domains during regular pacing and during VF. One can see
that in both cases APD recorded in the domain with higher DF (point
f) was shorter than the one at point a located within a low frequency
domain (see Figure 5E). Although absolute difference in APD
between these 2 points was larger during regular pacing, the relative
difference (normalized to the APD) was larger during VF (27% versus
18%). Panel F shows the profile of APD across the domain boundary
during control pacing with a superimposed profile of 1/DF during VF.
Note the abrupt change in 1/DF despite a gradual variation in APD.
The most common DF ratios observed at boundaries between domains were
3:4, 1:2, and 4:5. The latter were found in 6, 5, and 3 of 17 VF
recordings, respectively. The ratios 2:3, 5:6, and 6:7 were
observed less often (in 2, 1, and 1 out of 17 VF recordings,
respectively). The resolution of our method (0.47 Hz) was not
sufficiently high to resolve ratios of higher order. TSP
analysis revealed Wenkebach patterns in 76% of cases with
simple DF ratios. Although Wenkebach block patterns were easily seen at
the boundaries of larger domains, they were less obvious between
smaller domains. The degree and localization of block could fluctuate
in time (see Figure 4B), causing shifts of domain boundaries.
Complexity of Arrhythmia as a Function of Its Rate
The complexity of the domain organization correlated with
frequency. To quantify this correlation, we calculated the coefficient
of spatial variation of the DF (see Materials and Methods) and plotted
it versus DFmean for each recording of
monomorphic VT and VF as shown in Figure 6A. A significant increase in
coefficient of variation was seen as DFmean
increased (r=0.79; P<0.001). The clusters of VT
points (open symbols) and VF points (closed symbols) practically do not
overlap; most of the VT points are to the left, whereas all VF points
are to the right of the fastest frequency in control (calculated as
1/BCLminx1000). Note that the complexity of VF
increases gradually with the average frequency of arrhythmia,
and no features can be identified that allow the selection of subranges
of PVT versus VF.
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As demonstrated in Figure 6B, the same conclusion can be drawn
from the analysis of the PDF (see Materials and Methods) that
shows an inverse correlation between the PDFmean
and DFmean (r=0.87;
P<0.001). Large PDF indicates a high degree of periodicity
in which most of the energy is contained in the dominant peak. A less
ordered process, with a broader spectrum, will have lower PDF. In the
same arrhythmia, the PDF usually decreased when the rate of the
arrhythmia increased. This was due to the appearance of
additional peaks and general broadening of the local spectra.
Despite the fact that, in general, an increase in frequency was
accompanied by a reduction in the degree of organization, in 70% of VF
recordings (12 out of 17), domains with the fastest frequency
preserved a high degree of periodicity. This is illustrated in Figure 7, which shows a standard DF map as well
as its corresponding PDF map. Individual signals shown in Figure 7 were selected from the domains with the highest (a),
intermediate (b), and the lowest (c) DF. Clearly, the highest DF is
associated with the most regular signal (Figure 7, trace a).
Sites with slower frequency (Figure 7, traces b and c) show
reduced PDF, which corresponds to increasing beat-to-beat variations in
amplitude and shape of the optical action potentials. Note that in this
example sites with the largest PDF formed a region that almost
coincided with the fastest DF domain.
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Domain Organization in the Absence of DAM
All experimental data presented above were obtained
in the presence of electromechanical uncoupler DAM. To evaluate effects
of DAM on VF, we conducted 3 additional experiments in which VF was
initially recorded in the absence of DAM. After a control
recording, DAM was added to perfusate and another
recording was obtained after 20 minutes of DAM perfusion. Our
study showed that DAM caused a small but statistically significant
decrease in DFmean during VF (from 7.99±0.49 to
7.11±0.36 Hz, P<0.05). However, the domain organization of
frequencies was preserved in the absence of DAM; namely, a limited
number of domains were relatively stable over 8- to 10-second
intervals. Figure 8 shows the DF maps of
3 consecutive segments (I–III) from a 10.24-second recording
of VF in the absence of DAM. There are 4 to 5 large DF domains on the
epicardium and 3 to 4 domains on the endocardium. It is seen that the
shape of the DF domains is relatively stable, especially during
segments I and II. The domain with the highest frequency (9.8 Hz) on
the epicardium (red "horseshoe") is the most stable; other domains
are more variable. The coefficient of cross-correlation
between maps I and II was 0.86; between maps II and III it was
0.77. A TSP in Figure 8 shows Wenkebach patterns at the
boundaries between 9.8- and 8.0-Hz domains in the upper part of the
epicardium. Note intermittent 5:4 and 6:5 Wenkebach patterns giving
rise to average frequency ratio 9.8 Hz/8.0 Hz11/9. It should be
noted, however, that experiments without DAM have certain limitations.
Despite significant reduction of mechanical activity during VF, which
made optical measurements possible, the contractions were not
completely eliminated. In certain areas, we still observed a noticeable
effect of mechanical activity on the shape of recorded action
potentials, which could have affected the domain patterns.
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Discussion |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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The major finding of this study is that the spatial distribution of local frequencies of excitation during VF appears to be much simpler and more organized than one might expect from the theory of Moe et al.4 A typical frequency map (DF map) consists of a small number (
8) of relatively large (1 cm2)
domains with uniform DFs within each domain. The ratios of DFs in
adjacent domains are often close to 1:2, 3:4, or 4:5. The domains
persist for a relatively long time as compared with the excitation
cycle. There is usually no significant correlation between domain
patterns on the epicardial and endocardial surfaces.
What do these data tell us about the mechanism of VF? Before attempting
to answer this important question, we discuss a mechanism by which DF
patterns similar to those described above can emerge. For the sake of
simplicity, we assume a 1-dimensional strip of myocardium
with a continuous distribution of refractory periods (see Figure 9), and we determine the DF
patterns that emerge in such a strip during high-frequency pacing. The
source with a constant period (T1) is
located on the left end (x=0) of the strip. It is easy to
see that if T1 is sufficiently small
(smaller than the maximum refractory period
T1<Rmax), a
frequency pattern with discrete domain organization will emerge.
Indeed, at the right of point x1 the
refractory period is greater than T1, and
1:1 propagation is impossible. This causes the development of an
intermittent Wenkebach-like block pattern with a stepwise reduction in
frequency of excitation at points distal to
x1 (see panel A). Hence, 2 domains emerge;
one is located proximally to x1, and the
other is located distally to point x1, with
frequencies
f1=1/T1 and
f2=1/T2,
respectively. The ratio of excitation frequencies (4:3 in this example)
is selected as the ratio
T2/T1 of the 2
smallest integers that satisfy the inequality
T2/T1
Rmax/T1
(the cycle length T2 must be greater than
Rmax).
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indicates conduction block. See
text for more detail.The mechanism described above explains why the domain organization becomes more complex when the frequency of excitation increases. Panel B shows a new domain pattern formed in the same refractory profile after a decrease in T1. Similar to what we observe experimentally, the number of domains increases and they become smaller. Now, instead of 2, 3 domains emerge. The left domain (0<x<x1) has a frequency f1=1/T1, the central domain (x1<x<x2) has a frequency f2=1/T2, and the right domain (x2<x) has a frequency f3=1/T3. The second domain is formed near the first small peak of the refractory profile. However, the increase in period that occurs as a result of the block is insufficient to overcome the second larger peak. As a result, a second block (3:2) develops near point x2, giving rise to a third domain with the slowest frequency.
The domain organization of VF may strongly depend not only on frequency
but also on the location of the hypothetical source. For example, if
the source were located in the "dip" of refractory profile between
points x1 and
x2, the fastest domain would be located
between 2 slower domains and the actual local frequencies in each
domain would be quite different from the examples presented in
Figure 9. Therefore, even with the same spatial distribution of
refractoriness, the domain organization may be dissimilar in different
episodes of VF.
The examples discussed above show that a simple model with a single
periodic source of excitation described above can reproduce
qualitatively the broad spectrum of the observed phenomena, including
the discrete domain organization, the stability of domain boundaries,
and the correlation of complexity with frequency. It is tempting to
hypothesize that fibrillation in our experimental model has a similar
mechanism and is driven by a stable high-frequency source of
excitation. Such a mechanism has been considered in relation to atrial
fibrillation15 19 20 as an alternative to the Moe et
al4 multiple-wavelet hypothesis and is termed
"fibrillatory propagation."21 Similar to previous
findings in atrial fibrillation,15 20 in our study the
area with the fastest frequency during VF often showed more orderly
activation than sites with slower frequencies (see Figure 7). In
a 3-dimensional myocardial wall, fibrillatory propagation can result in
very complex activation patterns away from the source. Multiple wave
breaks caused by intermittent block at the boundaries of refractory
domains can generate activity patterns indistinguishable from VF. The
fibrillatory propagation hypothesis is consistent with recent
phase-mapping data.22 The short-lived phase singularities,
reported on the surface of the fibrillating ventricles,22
could represent the formation of wave breaks at boundaries of
refractory domains.
Although the fibrillatory propagation hypothesis seems plausible, its validation will require the identification of the excitation source and its mechanism. An extremely high rate of excitation (significantly higher than the fastest possible rate in control) favors a reentrant mechanism. To the best of our knowledge, none of the known mechanisms of normal or abnormal automaticity can account for such a high rate of excitations. On the contrary, the spiral wave activity is capable of undergoing frequencies that are usually higher than those achievable during spontaneous pacemaker activity or rapid pacing. Recent computer simulations23 indicate that the core of the spiral wave may have a strong repolarizing influence on the surrounding tissues. Under these conditions, the APD of the cells near the core is shorter than the APD of the cells away from the core. In theory, this may account both for the extremely high rate near the excitation source and rhythm transformations away from that source, where the tissue cannot keep up with the rate of the spiral source.
In the majority of cases, we did not see sustained reentrant activity
on either the endocardial or the epicardial surface. This suggests the
possibility that, if the fibrillatory propagation hypothesis is
correct, the driving reentrant source should be intramural. Recent
theoretical analysis shows that 3-dimensional reentrant
activity in the myocardial wall tends to organize in space in such a
way that its rotation occurs around the long axis of the myocardial
fibers,24 which would predict that reentrant activity is
likely to be hidden inside the ventricular wall. Indeed, it
has been shown experimentally that intramural reentrant circuits do not
manifest on the surface.25 This may explain why sustained
reentrant activity is rarely seen in intact ventricular
preparations5 26 27 as opposed to epicardial
slices16 28 and the thin epicardial rim surviving
myocardial infarction.26 29 The fact that there are large
differences between endocardial and epicardial activation and DF
patterns (see Figure 3) is additional evidence for 3-dimensional
VF in our model.
Limitations
The major limitation of optical mapping is the interference of
mechanical contractions with the optical signal. This interference can
be eliminated by using electromechanical uncouplers such as DAM,
verapamil, or cytochalasin D.28 30 31 However,
uncouplers not only eliminate contractions but also alter the
electrical properties of cardiac myocytes.32 33 34 Quite
recently, it was shown that, in some species, DAM may significantly
affect the characteristics of VF; in coronary–perfused slabs
of the dog right ventricle, DAM can convert VF into VT.12
However, in our preparations, as well as in the isolated rabbit
heart,35 DAM does not eliminate complex fibrillatory
activity. Our data show that, although the domain organizations and
DFmean in the presence and absence of DAM may
differ, the main findings of this study are not affected by DAM.
Specifically, the number and stationarity of domains and the frequency
relationships between adjacent domains are similar in both cases. It
should be noted, however, that using uncouplers cannot be completely
avoided. Although mechanical artifact is significantly reduced during
VF, it still has noticeable effects on the optical recordings
in some areas and may influence the domain patterns. A more thorough
analysis of the influence of electromechanical uncouplers and
calcium blockers on the spatiotemporal organization of VF is
required.
Our explanation of the domain organization is based on spatial heterogeneity of refractoriness or APD. Indeed, in our experiments we observed general correlation between APD and the fibrillation cycle length (1/DF), which is consistent with previous reports.1 2 3 However, fiber organization may also influence the shape of DF domains and the position of interdomain boundaries, given that, at fast frequencies associated with VF, occurrence of block may depend on fiber orientation.36 37
Finally, we would like to emphasize that even though our data suggest fibrillatory propagation as a mechanism of VF, they do not exclude other mechanisms. Theoretically, the multiple-wavelet hypothesis4 and spiral wave–breakup mechanism38 may also result in similar DF patterns. A more quantitative comparison between modeling and experimental data are required before definite conclusions can be drawn. It appears from our results, however, that DF analysis will become a useful tool for such quantitative comparisons.
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Acknowledgments |
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These studies were supported by National Heart, Lung, and Blood Institute Grant HL 39707. O.B. is a fellow of the American Heart Association, New York State Affiliate. We thank Jiang Jiang and Fan Yang for technical assistance, Tatiana Yuzyuk for help in preparation of figures, and Clara Rubin Wu for administrative assistance. We also thank Drs Marcel Wellner, Jorge Davidenko, and Vadim Biktashev for discussion and valuable comments on this manuscript.
Received October 15, 1999; accepted December 2, 1999.
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M. Swissa, Z. Qu, T. Ohara, M.-H. Lee, S.-F. Lin, A. Garfinkel, H. S. Karagueuzian, J. N. Weiss, and P.-S. Chen Action potential duration restitution and ventricular fibrillation due to rapid focal excitation Am J Physiol Heart Circ Physiol, May 1, 2002; 282(5): H1915 - H1923. [Abstract] [Full Text] [PDF]
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M. Valderrabano, J. Yang, C. Omichi, J. Kil, S. T. Lamp, Z. Qu, S.-F. Lin, H. S. Karagueuzian, A. Garfinkel, P.-S. Chen, et al. Frequency Analysis of Ventricular Fibrillation in Swine Ventricles Circ. Res., February 8, 2002; 90(2): 213 - 222. [Abstract] [Full Text] [PDF]
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M. Valderrabano, M.-H. Lee, T. Ohara, A. C. Lai, M. C. Fishbein, S.-F. Lin, H. S. Karagueuzian, and P.-S. Chen Dynamics of Intramural and Transmural Reentry During Ventricular Fibrillation in Isolated Swine Ventricles Circ. Res., April 27, 2001; 88(8): 839 - 848. [Abstract] [Full Text] [PDF]
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F. H. Samie, O. Berenfeld, J. Anumonwo, S. F. Mironov, S. Udassi, J. Beaumont, S. Taffet, A. M. Pertsov, and J. Jalife Rectification of the Background Potassium Current: A Determinant of Rotor Dynamics in Ventricular Fibrillation Circ. Res., December 7, 2001; 89(12): 1216 - 1223. [Abstract] [Full Text] [PDF]
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