Respiratory Physiology & Neurobiology 131 (2002) 101– 120
www.elsevier.com/locate/resphysiol
High frequency oscillations in respiratory networks:
functionally significant or phenomenological?
Gregory D. Funk *, Marjorie A. Parkis
Department of Physiology, Faculty of Medicine and Health Science, Uni6ersity of Auckland, Pri6ate Bag 92019,
Auckland, New Zealand
Accepted 9 April 2002
Abstract
Inspiratory activities, whether recorded from medullary neurons, motoneurons or motor nerves, feature prominent
oscillations in high (50–120 Hz) and medium (15–50 Hz) frequency ranges. These oscillations have been extensively
characterized and are considered signatures of respiratory network activity. Their functional significance, however, if
any, remains unknown. Here we review the literature describing the nature and origin of these oscillations as well as
their modulation during development and by mechanoreceptive and chemoreceptive feedback, respiratory- and
non-respiratory-related behaviors, temperature and anesthesia. We then consider the potential significance of these
oscillations for respiratory network function by drawing on analyses of distributed motor and sensory networks of the
cortex where current interest in oscillatory activity, and the synchronization of neural discharge that can result, is
based on the increased efficacy with which synchronous inputs influence neuronal output, and the role that
synchronous activity may play in information coding. We speculate that synchronized oscillations at the network level
help coordinate activity in distributed rhythm and pattern generating systems and at the muscle level enhance force
development. Data most strongly support that oscillatory synaptic inputs play an important role in controlling timing
and pattern of action potential output. © 2002 Elsevier Science B.V. All rights reserved.
Keywords: Control of breathing; High and medium frequency oscillations; Oscillation; Respiratory network; Pattern generation;
Oscillatory networks; Rhythm generation
1. Introduction
Synchronized oscillations in neuronal activity
on the order of 10 – 150 Hz appear in many regions and systems of the brain (Gray, 1994).
Traditionally, these oscillations were considered
* Corresponding author. Tel.: +64-9-373-7599x6317; fax:
+64-9-373-7499
E-mail address: g.funk@auckland.ac.nz (G.D. Funk).
an epiphenomenon of network organization with
little functional relevance. More recently, based
on theoretical grounds and a growing body of
empirical data (Konig and Engel, 1995; Engel et
al., 1999), oscillations and the temporal synchrony
that can develop from them are proposed to serve
a variety of important functions. For example,
oscillations may make neurons selectively sensitive
to inputs arriving at specific times (Lampl and
Yarom, 1993). In thalamo-cortical pathways, os-
1569-9048/02/$ - see front matter © 2002 Elsevier Science B.V. All rights reserved.
PII: S 1 5 6 9 - 9 0 4 8 ( 0 2 ) 0 0 0 4 1 - 1
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cillations of different frequencies establish arousal
and the various sleep states (Steriade, 1999). In
cortico-motor regions, oscillations of approximately 40 Hz appear during attentive or exploratory behavior (Fetz et al., 2000), and in the
hippocampus they are thought to be involved in
forming memory (Fell et al., 2001). Within sensory systems, particularly the visual system, the
potential for oscillations and neuronal synchronization to solve the ‘binding problem’ (i.e. ‘bind’
together neuronal arrays for the perception of
correlated objects in an image) has also generated
great interest (Gray, 1994; Engel et al., 1997,
1999; Lestienne, 1999). The possibility that the
‘binding problem’ also arises in motor systems
and that short-term synchronization between neurons offers a solution to this problem has recently
been reviewed (Farmer, 1998).
One motor system featuring prominent oscillations, at frequencies much higher than the primary rhythm, is that controlling breathing (Cohen
et al., 1997). Oscillations in the range of 20– 140
Hz are present in the activity of respiratory muscles, nerves and neurons in all mammals studied,
including humans, dogs, pigs, cats, rabbits, rats,
and mice. Yet, as in other systems, their function
remains speculative. The observation that their
removal appears to be associated with minimal
effect on baseline respiratory rhythm (Richardson
and Mitchell, 1982; Gootman and Cohen, 1983;
Davies et al., 1986; Gootman et al., 1990; Bruce et
al., 1991; Romaniuk and Bruce, 1991) has led
many to consider them epiphenomenal. Here we
provide a detailed review on the nature, origin,
and factors affecting short time scale respiratory
oscillations (see also Cohen et al., 1997). Then,
based on recent work in sensory and motor systems, we speculate on the potential significance of
these oscillations for respiratory network function, motoneuron (MN) activation and generation
of muscle force.
activity of the phrenic nerve and diaphragm in
rabbits and dogs were synchronized on a much
shorter time scale than that associated with primary respiratory rhythm. Prominent oscillations
of 100 Hz, superimposed on the slower respiratory rhythm (0.5–1 Hz), were apparent under
visual inspection, bilaterally synchronized and
sensitive to changes in body temperature (Dittler
and Garten, 1912). Thus, they were considered
manifestations of central nervous system drive to
respiratory muscles. The more precise quantification of nerve and diaphragmatic activities made
possible with the introduction of the oscillograph
(Gasser, 1928; Wyss, 1939, 1955) confirmed these
findings and extended them to the vagus and
phrenic nerves of rabbit and cat in the late 1930’s
(Rijlant, 1937; Wyss, 1939). These oscillations became more apparent with increased inspiratory
drive (Wyss, 1939), were independent of the basic
inspiratory rhythm and were synchronized between different respiratory motor nerves (Rijlant,
1937).
Short time scale oscillations in respiratory
outflow received little further study until the 1970s
when cross-correlation analyses between the activities of medullary respiratory neurons and phrenic
nerve revealed that oscillations were synchronized
on a msec time scale (Cohen et al., 1974), providing evidence for monosynaptic connections between brainstem inspiratory neurons and phrenic
MNs. Cohen introduced the term ‘high frequency
oscillation’, or HFO, to describe respiratory-related oscillations in the range of 50–120 Hz. An
additional lower frequency, non-harmonic peak
was later revealed via spectral analysis of phrenic
and recurrent laryngeal (RL) nerve activities in
decerebrate cats (Richardson and Mitchell, 1982)
( 37 Hz for the phrenic nerve and 54 Hz for
the RL). Oscillations in this range were later
dubbed ‘medium frequency oscillations’ or MFOs
(Cohen et al., 1987b).
2. Medium and high frequency oscillations
2.2. Distinction between HFO and MFO
2.1. Early studies
Spectral analysis reveals that HFOs (50–120
Hz) and MFOs (15–50 Hz) are present together
in inspiratory activity in cats (Cohen et al., 1987b;
In 1912, Dittler and Garten documented that
G.D. Funk, M.A. Parkis / Respiratory Physiology & Neurobiology 131 (2002) 101–120
Bruce, 1988; Christakos et al., 1988, 1989; Webber, 1989; Sica and Gandhi, 1990; Christakos et
al., 1991; Romaniuk and Bruce, 1991; Christakos
et al., 1994; Masuda et al., 1995), rats (Kocsis and
Gyimesi-Pelczer, 1997; Marchenko et al., 2000),
rabbits (Bruce, 1988; Schmid and Bohmer,
1989b,a; Romaniuk and Bruce, 1991; Cairns and
Road, 1998), pigs (Gootman et al., 1985; Cohen
et al., 1987a; Sica et al., 1988a; Gootman et al.,
1990; Sica et al., 1991; Steele et al., 1993), and
humans (Ackerson and Bruce, 1983; Bruce and
Goldman, 1983; Bruce and Ackerson, 1986; Smith
and Denny, 1990).
The frequency ranges where HFOs and MFOs
occur vary widely between studies and species and
are affected by a variety of factors including the
103
type and state of the preparation (i.e. intact,
decerebrate, anesthetized, in vivo, in vitro), respiratory drive (hypercapnia, hypoxia), inspiratory
phase, and temperature (Section 2.3.3). Therefore
the criterion of frequency bandwidth alone is
often insufficient to distinguish between the HFO
and MFO. Instead, coherence between activities
of diverse neuronal populations provides a better
means for distinguishing between these two types
of oscillations. In general, where two spectral
peaks are found, those in the HFO range are
typically coherent between different respiratory
nerves and muscles, whereas those in the MFO
range are not (Fig. 1, and see Section 2.3.1).
Distinction between high and medium frequency oscillations is further confounded by de-
Fig. 1. Power spectra and coherences for activities of three efferent inspiratory nerves in cat (phrenic, Phr; recurrent laryngeal, RL;
and hypoglossal, Hyp) showing coherence between activities of different nerves in the HFO but not MFO bandwidths. Recordings
were taken at 0.08 end-tidal CO2. Power spectra of activities during 560 msec window in 23 inspiratory cycles (analysis was based
on cycles without lung inflation since autospectra of RL and Hyp activities often lacked clear peaks during inflation cycles).
Coherences between the three pairs of activities are shown on the right. Peak coherence values (maximum possible value of 1.0) were
0.68, 0.49 and 0.48 respectively. Vertical dashed lines indicate MFO and HFO spectral peaks at 47 and 86 Hz respectively.
Reproduced with permission (Cohen et al., 1987b).
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G.D. Funk, M.A. Parkis / Respiratory Physiology & Neurobiology 131 (2002) 101–120
velopmental changes in respiratory network activity. In adults the dominant peak is in the HFO
range (Cohen et al., 1997) (Fig. 1). In contrast, in
neonatal mammals in vivo (Cohen et al., 1987a;
Sica and Gandhi, 1990; Kocsis et al., 1999) and in
vitro (Liu et al., 1990; Smith et al., 1990; Kato et
al., 1996; Tarasiuk and Sica, 1997; Marchenko et
al., 2000; Bou-Flores and Berger, 2001), the dominant peak is in the 20–50 Hz (MFO) range and
shows coherence (Sica et al., 1988a,b, 1991; Steele
et al., 1993; Tarasiuk and Sica, 1997)— but see
(Kocsis et al., 1999). Studies showing that peak
oscillation frequency increases with development
(Suthers et al., 1977; Ackerson and Bruce, 1984;
Bruce, 1986; Cohen et al., 1987a; Sica and
Gandhi, 1990; Marchenko et al., 2000) suggest
that centrally-generated oscillations whose frequency occupies the HFO range in adults occur at
lower frequencies in immature animals. If so,
coherent ‘MFOs’ in neonates may be analogous
to HFOs in adults. The lability of these short time
scale oscillations emphasizes the importance of
defining them by criteria other than their frequency bandwidth. In this paper we regard MFOs
showing coherence between different nerves as
analogous to HFOs.
2.3. Characteristics of HFOs
2.3.1. Synchronization of acti6ities in respiratory
neurons, ner6es and muscles
It is important to emphasize that oscillation is
not synonymous with synchronization. For example, presence of HFOs in the hypoglossal and
phrenic nerves does not indicate that individual
MNs in the two pools are discharging synchronously. Cross-correlation and coherence spectral analyses are required to determine the degree
to which oscillatory activity is synchronized between respiratory neurons, nerves and muscles. A
detailed discussion of the application of spectral
and coherence analysis to various respiratory activities is provided elsewhere (Christakos et al.,
1991). In brief, a prominent, narrow peak in the
coherence spectrum indicates a high level of synchrony between the compared activities (Figs. 1
and 2).
HFOs (and some MFOs) in the phrenic nerve
(or C4/C5 spinal roots) are significantly correlated
with HFOs in cranial nerves, including the facial
(Kato et al., 1987), vagal (Wyss, 1955; Kato et al.,
1987; Bruce, 1988; Smith and Denny, 1990; Romaniuk and Bruce, 1991), RL (Cohen et al.,
1987b; Bruce, 1988; Christakos et al., 1988;
Richardson, 1988; Sica et al., 1988a; Christakos et
al., 1989; Huang et al., 1993; Steele et al., 1993;
Christakos et al., 1994; Nakazawa et al., 2000),
spinal accessory (Tarasiuk and Sica, 1997), and
hypoglossal nerves (Cohen et al., 1987b; Kato et
al., 1987; Sica et al., 1988b, 1991, 1992), as well as
the first cervical nerve (Tarasiuk and Sica, 1997)
and thoracic spinal nerves innervating accessory
respiratory and intercostal muscles respectively
(Davies et al., 1985; Tarasiuk and Sica, 1997;
Vaughan and Kirkwood, 1997) (Fig. 1). There is
corresponding synchronization of HFOs in activities of airway and accessory respiratory nerves
such as the hypoglossal, facial, glossopharyngeal,
and vagal nerves (Cohen et al., 1987b; Sica et al.,
1988a; Kato et al., 1996) (Fig. 1).
HFOs in respiratory nerves are also synchronized with HFOs in unit activities or membrane
potentials of brainstem inspiratory neurons
(Achard and Bucher, 1954; Cohen, 1973; Cohen
et al., 1974; Mitchell and Herbert, 1974; Sieck and
Harper, 1981; Feldman and Speck, 1983; Cohen
and Feldman, 1984; Davies et al., 1985; Christakos et al., 1988; Hukuhara et al., 1988; Christakos et al., 1989; Sica and Gandhi, 1990; Huang
et al., 1996; See et al., 1999), expiratory neurons
(where spectra are based on membrane potential
during inspiration) (Mitchell and Herbert, 1974;
Sieck and Harper, 1981; Ballantyne et al., 1988;
Anders et al., 1991; Huang et al., 1996; Cohen et
al., 1997) and individual MNs. For example, 50% of phrenic and RL (Christakos et al., 1991,
1994) MNs have HFOs in their discharge and
these are highly correlated with the HFOs in their
respective nerves, indicating high correlation in
discharge between MNs within the population
(Fig. 2).
Synchronization of oscillations in the medium
frequency range is seldom seen between different
respiratory nerves (Fig. 1), neurons or muscles in
adults. However, significant, but weak, coherence
G.D. Funk, M.A. Parkis / Respiratory Physiology & Neurobiology 131 (2002) 101–120
105
Fig. 2. Analyses of spectral properties of the phrenic nerve and an individual phrenic MN’s activity in cat showing: (i) HFO and
MFO in autospectra of phrenic nerve (Phr) and phrenic Unit activities; (ii) dependence of spectral properties on time in the
inspiratory phase, and; (iii) strong coherence between Unit-Phr nerve activity in the HFO range and weak (but significant) coherence
in the MFO range. (A) Cycle-triggered histograms (CTH) of Phr and an early-onset phrenic MN (Unit). The windows used to
distinguish early from late-inspiration for computation of the auto- and coherence-spectra in B are indicated by the vertical lines (I,
first half of inspiration; II, second half of inspiration; I + II, entire inspiratory phase, gate of 600 msec duration). (B) Autospectra
and coherence of Phr and Unit activities in different parts of the inspiratory phase. Computations were performed on activity in 32
inspiratory phases with lung inflation. Note strong HFO spectral and coherence peaks at 65 Hz in all portions of the inspiratory
phase. In I +II, note 2 MFO peaks (arrowhead and arrow) in the Unit autospectrum. Smaller peak at 21 Hz corresponds to activity
in the first half of inspiration (I, arrowhead); larger peak at 31 Hz corresponds to activity in second half of inspiration (II, arrow)
and coincides in frequency with the main nerve MFO peak. For both unit MFO peaks, the frequency is very close to the highest
discharge rate of the cell in the corresponding phase of inspiration (see CTH in A). MFO coherence is near zero in I and small but
significant in II. Reproduced with permission (Christakos et al., 1991).
is apparent in the MFO range when the activities
of individual phrenic (Fig. 2) or RL MNs are
compared with activities in their respective whole
nerves (Richardson and Mitchell, 1982; Christakos
et al., 1991, 1994). Thus, while there is little synchrony in the MFO range between the activities of
MNs from different pools, small numbers of MNs
within discrete pools discharge synchronously.
2.3.2. Effects of respiratory phase on HFOs and
MFOs
The amplitude, frequency and coherence of
HFOs and MFOs all vary with the phase of the
respiratory cycle. HFOs are predominantly associated with the inspiratory phase. However, they
have also been noted in post-inspiratory activity
(Schmid et al., 1990), and occasionally during the
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G.D. Funk, M.A. Parkis / Respiratory Physiology & Neurobiology 131 (2002) 101–120
expiratory phase in recordings of RL nerve activity (Huang et al., 1993; Huang and Cohen, 2000).
HFOs also vary between different portions of the
inspiratory phase. With few exceptions (Webber,
1989; Sica et al., 1991; Steele et al., 1993), HFO
(or coherent MFO) amplitude and coherence are
highest in the first half of the inspiratory cycle,
then decrease or disappear in late inspiration (Fig.
2) (Mitchell and Herbert, 1974; Bruce, 1986;
Christakos et al., 1988; Richardson, 1988; Christakos et al., 1989; Schmid and Bohmer, 1989b;
Schmid et al., 1990; Tarasiuk and Sica, 1997;
Cairns and Road, 1998; Marchenko et al., 2000).
MFOs generally either appear, or increase in
amplitude, frequency (Webber, 1989; Christakos
et al., 1991; Sica et al., 1991; Steele et al., 1993)
and coherence (Christakos et al., 1991) during late
inspiration (Fig. 2).
2.3.3. Factors affecting the strength, frequency
and synchronization
The influence on HFOs of many factors including chemical stimuli, mechanical stimuli, anesthetics, temperature, and descending drives for
behaviors that compete with breathing have been
extensively investigated. Three attributes of HFOs
are modulated in response to these influences:
amplitude; frequency; and the degree of synchrony (coherence) between the different components of the respiratory network.
In general, HFOs become faster, more prominent and more tightly synchronized when respiratory drive increases, whether as a result of
chemical stimuli, or voluntary increases in respiratory effort (Bruce and Ackerson, 1986). Factors
that depress respiratory drive, such as anesthetics,
reduce the amplitude of HFOs.
2.3.3.1. Chemical stimuli. Hypercapnia reliably reinforces the amplitude of the HFO (Wyss, 1939,
1955; Cohen, 1973; Richardson and Mitchell,
1982; Bruce and Ackerson, 1986; Cohen et al.,
1987b; Kato et al., 1987; Bruce, 1988; Schmid and
Bohmer, 1989a,b; Schmid et al., 1990; Bruce et
al., 1991; Romaniuk and Bruce, 1991). It also
increases HFO synchrony and frequency (Cohen,
1973; Kirkwood et al., 1982b; Richardson and
Mitchell, 1982; Bruce and Ackerson, 1986; Cohen
et al., 1987b; Kato et al., 1987; Bruce, 1988;
Schmid et al., 1990; Bruce et al., 1991; Romaniuk
and Bruce, 1991), but see (Richardson and
Mitchell, 1982) where frequency did not change).
Hypoxia also increases HFO amplitude and frequency (Bruce, 1988; Sica et al., 1988b; Sica and
Gandhi, 1990; Steele et al., 1993).
The effects of hypercapnia on MFOs are less
well studied, and variable. Actions of hypercapnia
on the phrenic MFO include no effect (Schmid
and Bohmer, 1989b), enhancement with low level
hypercapnia but loss of this effect with increased
CO2 levels (Schmid et al., 1990), and restoration
of MFO amplitude after its attenuation by anesthetic (Masuda et al., 1995). Hypercapnia has also
been reported to increase the amplitude of the RL
MFO (Cohen et al., 1987b). Hypoxia has no
effect on the phrenic MFO in rabbit (Schmid and
Bohmer, 1989b) but increased coherence between
the activities of the phrenic-RL nerves in the
medium frequency range in piglets (Steele et al.,
1993).
2.3.3.2. Mechanorecepti6e feedback. The influence
of pulmonary vagal afferent feedback on HFOs
appears minimal. HFOs are routinely reported in
vagotomized preparations, and HFOs in animals
with and without intact vagi are not notably
different (Bruce, 1986; Schmid and Bohmer,
1989b). In rabbits, withholding lung inflation during inspiration is without effect (Schmid and
Bohmer, 1989b), while in decerebrate cats, it either has no effect (Cohen et al., 1987b) or alters
the phase dependence of the HFO, keeping coherence high throughout inspiration (Christakos et
al., 1989). Artifically-induced lung inflation, on
the other hand, is associated with an increase in
HFO frequency (Richardson, 1988), or loss of the
HFO in the spectra of RL and hypoglossal, but
not phrenic, nerve activities (Cohen et al., 1987b;
Richardson, 1988), presumably reflecting graded
inhibition of these activities by pulmonary afferent input (Sica et al., 1984, 1985). Tracheal occlusion enhances HFO (and MFO) amplitude
(Schmid and Bohmer, 1989b; Cairns and Road,
1998), supporting the view that enhanced central
drive strengthens HFOs. However, the degree to
which this reflects increased mechanoreceptive
G.D. Funk, M.A. Parkis / Respiratory Physiology & Neurobiology 131 (2002) 101–120
versus chemoreceptive feedback is unclear since in
at least one of these cases, increases in alveolar
CO2 secondary to occlusion were not controlled
for (Cairns and Road, 1998).
2.3.3.3. Temperature. Raising or lowering central
temperature produces parallel changes in the frequency and amplitude of HFOs without altering
coherence. This is true whether the temperature is
changed in the entire body (Dittler and Garten,
1912; Richardson and Mitchell, 1982), the isolated
brainstem-spinal cord in vitro (Kato et al., 1996),
or in small areas on the ventral medullary surface
in whole animals (Bruce et al., 1991; Romaniuk
and Bruce, 1991). The magnitude of the effect of
temperature on HFO frequency in adult animals
is 5 Hz/°C (Richardson and Mitchell, 1982)
and possibly less in neonates (Kato et al., 1996).
The observations that cooling limited regions of
the medulla is sufficient to shift the HFO frequency and that the shift occurs without disruption of synchrony between widely distributed MN
pools suggest that the effects of temperature are
mediated through changes in the activity of
medullary networks generating the HFO.
2.3.3.4. Anesthetics. Anesthetics reduce HFO amplitude and frequency. While barbiturates are especially potent (Cohen, 1973; Kirkwood et al.,
1982b; Gootman and Cohen, 1983; Gootman et
al., 1990), others including fluothane (Cohen,
1973), ketamine, chloralose, urethane (Richardson
and Mitchell, 1982), sevoflurane, halothane (Masuda et al., 1995) and morphine (Kato, 1998)
produce similar effects in a variety of species.
Interesting variations from these findings are that
Saffan in piglets is not depressant (Sica et al.,
1988a; Gootman et al., 1990; Sica et al., 1991),
and that while ketamine depresses or eliminates
the HFO, it enhances MFOs in phrenic and RL
nerves (Richardson and Mitchell, 1982).
The depressive actions of anesthetics on oscillatory behavior could be brought about either
through specific actions on target regions of the
CNS (Gootman et al., 1990) or through generalized depression of synaptic activity. Distinction
between these alternatives is difficult since most
anesthetics are administered intravenously, in-
107
traperitoneally or via inhalation and have widespread actions. However, the fact that such
similar responses are elicited by a wide variety of
anesthetics that act in different ways on different
brain regions suggests that a key effect underlying
suppression of the HFO is generalized depression
of synaptic activity. By extension, enhancing
synaptic activity in the respiratory network, either
through additional excitatory inputs or potentiation of synaptic transmission, may enhance the
HFO. This is consistent with the potentiating
effects on the HFO of chemical stimuli, voluntary
hyperventilation (Bruce and Ackerson, 1986),
temperature and 4-aminopyridine an A-type
potassium channel blocker and respiratory stimulant that enhances synaptic transmission (Schmid
et al., 1990).
2.3.3.5. In 6i6o 6ersus in 6itro. The peak frequency
of respiratory oscillations recorded in vitro (Liu et
al., 1990; Smith et al., 1990; Kato et al., 1996;
Tarasiuk and Sica, 1997; Marchenko et al., 2000;
Bou-Flores and Berger, 2001) is consistently lower
than that observed in adults in vivo. This difference likely reflects that studies in vitro are performed at reduced temperature in neonatal tissue,
since lowering temperature lowers the HFO frequency (Section 2.3.3.4), and HFOs in vivo occur
at lower frequencies in neonates than adults (Section 2.2). Supporting this, the only report of
HFOs in neonatal rats in vivo (Kocsis et al., 1999)
indicates complete overlap with HFOs recorded in
vitro (Liu et al., 1990; Smith et al., 1990; Tarasiuk
and Sica, 1997; Marchenko et al., 2000; Bou-Flores and Berger, 2001), and the single measurement
of HFOs in kittens in vitro (Kato et al., 1996)
indicates overlap with HFOs recorded in kittens
in vivo (Sica and Gandhi, 1990; Kocsis et al.,
1999).
2.3.4. HFO: origin and underlying mechanism
The high degree of coherence between HFOs
recorded from widely dispersed medullary respiratory neurons, MNs, motor nerves and muscles
(Section 2.3.1) is generally accepted as indicating
that the HFO arises from a common source.
Persistence of the HFO in midcollicular decerebrate preparations establishes its location within
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G.D. Funk, M.A. Parkis / Respiratory Physiology & Neurobiology 131 (2002) 101–120
the brainstem-spinal cord. A pontine or spinal
origin is unlikely. Lesions to the pontine pneumotaxic center and midpontine transection do not
eliminate HFOs (Berger et al., 1978); oscillations
showing coherence between multiple nerves persist
in neonatal brainstem-spinal cord preparations
that lack the pons (Liu et al., 1990; Smith et al.,
1990; Kato et al., 1996; Tarasiuk and Sica, 1997;
Bou-Flores and Berger, 2001); and very few pontine neurons exhibit respiratory HFOs (Cohen,
1973; Sieck and Harper, 1981; Hukuhara et al.,
1988; Shaw et al., 1989). Spinal hemisection
causes only a slight decrease in the bilateral coherence in the phrenic nerve HFO, whereas cervical
transection at C3 removes most of the phrenic
HFO (Bruce, 1986).
The majority of data support that HFOs originate in the medulla. First, a large number of
medullary respiratory neurons in the dorsal and
ventral respiratory groups (Cohen, 1973; Sieck
and Harper, 1981; Hukuhara et al., 1988; Shaw et
al., 1989) display HFOs. Further, the HFO in
many of these neurons correlates with the HFO in
the phrenic nerve (Cohen, 1973; Mitchell and
Herbert, 1974; See et al., 1999). These data, combined with disappearance of the HFO but not
respiratory rhythm following electrical lesions in
the region of the nucleus tractus solitarius, bilateral aspiration of all dorsomedial structures in the
vicinity of obex (Richardson and Mitchell, 1982),
or midsagittal section of the medulla (Rijlant,
1937; Davies et al., 1986; Romaniuk and Bruce,
1991), implicate regions in the vicinity of the
dorsal respiratory group in HFO generation. A
discrete versus distributed network, however, has
not been established since changes in pattern associated with NTS lesion (Richardson and Mitchell,
1982) and damage to crossing fibers with midsagittal section (Davies et al., 1986; Romaniuk
and Bruce, 1991) could have contributed to the
disruption of the HFO (Cohen et al., 1997).
The ability to separately disrupt the HFO but
not the basic respiratory rhythm, by anesthetics
(Richardson and Mitchell, 1982; Gootman and
Cohen, 1983; Gootman et al., 1990), sagittal sectioning through the midline of the medulla (Rijlant, 1937; Davies et al., 1986; Romaniuk and
Bruce, 1991), bilateral or unilateral cooling of the
ventral medulla (Bruce et al., 1991), blockade of
fast inhibitory transmission (Schmid and Bohmer,
1989a; Bou-Flores and Berger, 2001), or systemic
application of MK801 (non-competitive NMDA
receptor antagonist) (Sica et al., 1992), indicates
that networks underlying these different rhythms
are at least partially independent. These data do
not support earlier hypotheses that the HFO
arises from ‘reexcitant’ connections between
medullary inspiratory neurons (Cohen, 1973), or
that the central respiratory rhythm generator supplies both the HFO and the primary respiratory
rhythm (Mitchell and Herbert, 1974). Completely
independent networks generating respiratory
rhythm and HFOs, however, is inconsistent with
observations that HFOs do not occur in the absence of respiratory activity and that they occur
specifically during inspiration. Thus, it appears
most likely that the primary respiratory rhythm
can be generated separately and that the HFO
emerges through activation of additional circuit
elements, including inhibitory circuits (Schmid
and Bohmer, 1989a; Bou-Flores and Berger,
2001), within the dorsomedial medulla.
2.3.5. MFO: origin and underlying mechanism
Like the HFO, the origin of MFOs is undetermined. However, there is general agreement that
MFOs arise from interactions within MN pools.
The observations that MFOs are rare in
medullary inspiratory neurons, that when present
they are not correlated between different respiratory nerves (Bruce, 1988; Christakos et al., 1988;
Sica and Gandhi, 1990), and that the MFO bandwidth varies between different nerves, all suggest
that MFOs are not medullary in origin, do not
have a single common source, and are generated
separately by each MN pool.
MFOs may arise due to the activity of late
recruited MNs (Webber, 1989) or augmenting inspiratory discharge patterns of individual MNs
(Cohen, 1969). This latter possibility is consistent
with the suggestion that the MFO reflects the
action potential discharge of individual MNs
(Christakos et al., 1991), which in turn is supported by the presence of the MFO, but not the
HFO, in activity of all phrenic and RL MNs
examined (Christakos et al., 1991, 1994), by the
G.D. Funk, M.A. Parkis / Respiratory Physiology & Neurobiology 131 (2002) 101–120
observation in phrenic MNs and nerves that the
main peak in the MFO corresponds closely to the
peak firing rate in the MN (or the population),
and by the fact that the MFO frequency in the
nerve increases during inspiration in parallel with
the augmenting discharge pattern of phrenic MNs
(Fig. 2). The broad nature of the peak in the
MFO spectrum is proposed to result from the
large distribution of MN firing rates (Christakos
et al., 1991).
Confirmation that MFOs reflect MN discharge
frequency should be obtainable by determining
whether manipulations that alter MN discharge
frequencies during inspiration cause parallel
changes in MFOs. Essential for such tests is that
firing frequency of individual MNs be directly
measured during inspiration, not inferred from
the magnitude of phrenic nerve output, since
nerve output may increase without increases in
the firing frequency of individual MNs, by increasing duration of MN firing or recruitment of
more MNs. If MFOs do reflect the rhythmic,
augmenting discharge of respective MN pools,
then the real source of MFOs is the combination
of factors that determine the firing frequency of a
MN population during inspiration, including intrinsic membrane properties, the dynamic pattern
of inspiratory synaptic inputs (Fig. 3), and the
influence of modulatory inputs on both of these
109
(Berger, 2000; Rekling et al., 2000; Powers and
Binder, 2001).
Low-level coherence between MN and nerve
activities in the MFO range (Christakos et al.,
1991, 1994; Cohen et al., 1997) is also of importance because it indicates that small numbers of
MNs in a nerve, and therefore motor units within
a muscle, discharge synchronously. Implications
of synchronous motor unit activation for development of muscle force are discussed later (Section
3.3). The mechanism underlying coherence in the
MFO range is not known. Similar to the HFO, it
may have a synaptic origin since oscillatory inputs
markedly increase reliability of spike timing and
facilitate synchronization (Konig and Engel, 1995;
Konig et al., 1995; Maldonado et al., 2000).
Blockade of electrical synapses facilitates, rather
than inhibits, synchronization between MNs in
newborns (Bou-Flores and Berger, 2001). Thus, if
this coherence is synaptically driven, it must be
through chemical synapses and have a source
outside the MN pool, which would require revision of the view that MFOs originate separately
within individual MN pools. One possible source
is divergent output from premotor neurons to
multiple MNs, as proposed to underlie broadpeak synchronization between intercostal MNs
(Kirkwood et al., 1982a).
Fig. 3. Inspiratory synaptic currents and potentials feature prominent oscillations. (A) Whole-cell voltage-clamp recording from a
phrenic MN in the brainstem-spinal cord preparation of neonatal rat showing the high-frequency large amplitude components of the
synaptic drive current indicative of synchronous synaptic input. (B) Power spectra showing frequency components of inspiratory
synaptic current and potential of a phrenic MN. Power spectra are the average of individual spectra computed from 3 inspiratory
bursts. Segments of drive potential without action potentials were analyzed to determine frequency components of the potential.
There is a dominant 20 Hz frequency component in both the drive current and potential. Adapted with permission (Liu et al., 1990).
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2.3.6. Influence of other beha6iors on respiratory
HFOs
Respiratory muscles, MNs, and premotoneuronal networks subserve a variety of behaviors
and reflexes (e.g. gasping, sighing, coughing,
sneezing, vocalization, chewing, swallowing, vomiting). Of interest are how respiratory HFOs and
MFOs are affected by other behaviors. Are they
characteristic of all motor activities employing the
respiratory musculature? Do multiple HFO generators exist with a specific generator for each behavior or are HFOs a unique signature of
respiratory-related behaviors (e.g. eupnea, gasping, sighing) that disappear when non-respiratory
demands are placed on the system?
At present, characterization of oscillatory phenomena during behaviors other than breathing is
minimal, but further analysis incorporating both
mammals and non-mammalian vertebrates would
be valuable for the potential insight it could
provide into the origin and function of HFOs and
MFOs in coordinating activities in distributed
motor networks.
2.3.6.1. Gasping and apneusis. HFOs in phrenic
nerve during apneusis are reduced in frequency
relative to eupnea (Berger et al., 1978). In contrast, with transitions from eupnea to gasping,
HFO frequency shifts from 80 Hz to 115– 120
Hz (Richardson, 1986; Tomori et al., 1995),
though the degree of HFO synchronization between phrenic, RL or hypoglossal nerves changes
little. Whether this frequency shift reflects activation of a novel HFO generator, inclusion of novel
elements, or reconfiguration of the respiratory
HFO generator is unclear. The possibility that it
reflects hypoxia must also be considered since
moderate hypoxia increases HFO frequency (Section 2.3.3).
2.3.6.2. Vocalization. Effects of vocalization on
respiratory HFOs have been examined during
speech and speech-like breathing in humans
(Smith and Denny, 1990), and during fictive vocalization in cats (Nakazawa et al., 2000). Since
vocalization entails deepened inspiratory effort
prior to sound production, and factors enhancing
inspiratory drive tend to enhance HFO activity,
one might predict enhanced inspiratory HFO.
However, during sound production, exhalation is
sustained and the rhythm of breathing is disrupted, suggesting that HFOs might be disrupted
as well. In humans, coherence between activities
of right and left diaphragm in the HFO, but not
the MFO, range falls significantly during speech.
During fictive vocalization in cat, power and frequency of HFOs in phrenic and RL nerve activities increase in the inspiratory phase and a 50–70
Hz expiratory rhythm appears that is coherent
between RL and superior laryngeal nerves
(Nakazawa et al., 2000).
2.3.6.3. Vomiting. During vomiting, despite profound increases in phrenic nerve discharge, the
HFO peak in the phrenic power spectra broadens
and coherence between HFOs in right and left
phrenic nerves drops significantly (Cohen et al.,
1992). Since the network underlying vomiting
does not overlap with that generating respiration,
and respiratory muscles are activated in a completely different pattern during vomiting than eupnea, it is not surprising that the respiratory HFO
is lost.
Limited analysis of behaviors other than respiration support few definitive conclusions. Data
indicate that the HFO is not a common feature of
all activities employing the respiratory musculature. However, the factors selecting for or against
development of HFOs in any particular motor act
are not known. It is also not known whether the
HFOs that are observed reflect activity of a single
generator or whether each motor network generates its own HFO. It has recently been suggested
that the respiratory-related behaviors of gasping,
eupnea, and sighing come about through reconfiguration of the same basic medullary network rather than from three separate rhythm
generators (Lieske et al., 2000). A similar hypothesis can be made for the networks underlying
HFOs that characterize the respiratory-related behaviors of eupnea and gasping. The observation
that overall coherence of activities in the various
respiratory nerves persists with transitions from
eupnea to gasping might support a common, reconfigured oscillator that acts to maintain spatiotemporal coordination of muscle groups
G.D. Funk, M.A. Parkis / Respiratory Physiology & Neurobiology 131 (2002) 101–120
required for producing significant airflow. Analyses of HFOs during sighs, another respiration-related behavior, would help address this question,
as would determining whether medullary lesions
that disrupt the eupneic HFO also disrupts the
HFOs associated with gasps and possibly sighs.
111
but that they increase the efficiency with which
synaptic input is transformed into action potential
output. (3) At the level of the respiratory muscles,
we propose that oscillations may underlie synchronous activation of multiple motor units, and
improve force transmission within the muscle by
synchronously activating serially arranged motor
units (or nearest neighbors).
3. Function: physiology or phenomenology?
3.1. Rhythm and pattern forming systems
The HFO and MFO that characterize inspiratory activities are generally considered signatures
of network organization with minimal functional
significance because they are not essential for
production of the basic rhythm of breathing. Until recently, similar views prevailed for oscillations
in other neural systems. However, experimental
and theoretical data are now emerging that challenge this view and support the possibility that
synchronized oscillations have an important role
in information processing within sensory-motor
systems (Konig and Engel, 1995; Farmer, 1998;
Engel et al., 1999). Renewed interest in neuronal
oscillations derives largely from the fact that they
appear to facilitate establishment of synchrony
between neurons (Konig and Engel, 1995; Konig
et al., 1995; Maldonado et al., 2000), that neurons
are more efficiently activated by synchronized inputs (Bernander et al., 1994; Murthy and Fetz,
1994; Stevens and Zador, 1998) and the possibility
that if neurons can function as coincidence detectors, then synchronous activity may provide an
additional dimension for information coding
within the CNS (Konig et al., 1996).
Based largely on analyses of non-respiratory
systems, we speculate on the potential significance
of these oscillations for respiratory network activity at three levels of organization. (1) At the level
of medullary rhythm and pattern forming networks, we ask whether oscillations might improve
efficacy of network function by enhancing the
spatiotemporal coordination between dispersed
network elements controlling the various respiratory muscles that subserve a variety of respiratory
and non-respiratory behaviors. (2) At the level of
single neurons, particularly MNs, we propose that
oscillations not only play an important role in
determining precise timing of neuronal output,
Neuronal synchronization and oscillations are
common features of sensory networks (Konig and
Engel, 1995). The possibility that these oscillations may help solve the binding problem has
generated widespread interest (Singer, 2001). In
essence binding problems arise because neurons,
or subsets of neurons, can contribute to multiple
sensory representations by being recruited into
different neuronal assemblies. Active cells contributing to a given representation must be unambiguously identified as belonging together; i.e.
they must be bound together. It is proposed that
oscillatory firing patterns facilitate synchronization and that it is the synchrony between cells in
distributed networks that ultimately solves the
binding problem (q.v. Konig and Engel, 1995;
Engel et al., 1999; Singer, 2001).
Similar computational problems exist for motor
systems, where the binding problem can be redefined as ‘the formation of associations between
the distributed motor systems necessary for the
spatiotemporal coordination of the activity of different muscles involved in the same motor task’
(Farmer, 1998). As in the sensory system, neurons
in the motor cortex can contribute to the production of many different movements/behaviors by
changing the populations with which they are
co-active (Konig and Engel, 1995). The same
holds for respiratory networks in the ventral
medulla that contribute to the generation and
modulation of multiple related behaviors and reflexes. For example, if one accepts the recent
proposal that a common medullary network is
reconfigured to produce eupnea, gasping, or sighing (Lieske et al., 2000), it follows that transition
between different behaviors will require the rapid
segregation of one assembly of medullary neurons
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G.D. Funk, M.A. Parkis / Respiratory Physiology & Neurobiology 131 (2002) 101–120
and the binding together of another. Synchronized oscillations at different frequencies may
provide a means for binding together the partially
overlapping neuronal assemblies that form these
different motor representations.
The challenge, however, is to demonstrate that
information is actually coded in the precise temporal correlations (indicated by zero or near-zero
phase lags) between neuronal activities and that
they are not simply a result of connectivity and
common inputs. Within cortical systems, external
stimuli, changes in state, or specific components
of a behavior all shift temporal correlations between neurons (Castelo-Branco et al., 2000; Mima
et al., 2001). These findings suggest that temporal
associations are not fixed by anatomical substrate,
but reflect a dynamic functional coupling (Konig
and Engel, 1995) and support a role for synchrony. Demonstrations that the correlation of
activity between cortical neurons changes systematically in relation to behavioral events, while the
activity levels of the respective neurons remains
unchanged, is even more significant, since these
data suggest that the synchronization of activity
between neurons is the important parameter (Vaadia et al., 1995).
Similar evidence in respiratory networks is minimal. Cross-correlation studies applied to respiratory networks were traditionally designed to
explore anatomical connectivity. As a result, they
were performed under stable baseline conditions
to eliminate variability rather than under conditions required to detect stimulus- or context-dependent changes in the temporal relationships
between neuronal discharges. Such analyses reveal
synchronization with near-zero phase lag between
a significant percentage of neuronal and nerve
activities (Cohen et al., 1997). Thus, precise correlations do exist between activities of different
respiratory neurons. The possibility that temporal
relationships between various respiratory neurons
shift in stimulus- or context-dependent manner is
suggested by shifts in the HFO that accompany
increased chemical drive (Section 2.3.3) or transitions from eupnea to gasping (Section 2.3.6).
More direct evidence supporting a role for synchrony in information processing within respiratory networks has recently come about through
the application of multi-array recording technology and computational methods of analysis, including the ‘gravity method’ and pattern detection
methods, by Lindsey et al. (q.v. Lindsey et al.,
2000 and references therein). These procedures
facilitate screening of large sets of data and have
identified assemblies of neurons whose activities
become transiently synchronized in specific phases
of the respiratory cycle in response to afferent
stimuli from baroreceptors, chemoreceptors, nociceptors and airway cough receptors. Of particular
importance are the observations that raphe neurons with no respiratory modulation in their individual firing rates show phase-dependent impulse
synchrony in response to specific afferent inputs
and that transiently synchronized assemblies recur
if stimuli are repeated. While the relationship
between neuronal synchrony established through
the gravity method and inspiratory HFOs remains
unclear, these data suggest that synchrony itself
can be an important coding parameter in respiratory networks.
The biggest counterargument to the proposal
that synchronized oscillations are important for
the normal functioning of medullary networks (or
MNs and muscles— see below) is that while the
HFO increases or decreases in parallel with the
overall strength of respiration, rhythmic inspiratory output persists in the absence of HFOs. This
apparent lack of a critical role for oscillations,
however, may simply reflect that our measurements of respiratory network activity (most commonly recordings of integrated phrenic nerve
activity) are not sensitive enough to detect a
deficit. For example, if oscillations increase efficiency of breathing by enhancing coordination
between distributed MN pools controlling the respiratory muscles, a reduction in efficiency following loss of the HFO is unlikely to be detected in
short term recordings of a single nerve. It may
only become apparent by comparing activities of
multiple nerves and muscles under conditions of
high respiratory demand over the long term.
To conclude, a role for correlated activity in
information processing within cortical networks is
gaining widespread support. A similar role in
respiratory-related neuronal assemblies remains
highly speculative, but is supported by common
G.D. Funk, M.A. Parkis / Respiratory Physiology & Neurobiology 131 (2002) 101–120
features in the oscillatory activities of cortical and
brainstem networks, including synchronization at
frequencies in the gamma bandwidth (Konig and
Engel, 1995; Cohen et al., 1997) and context- or
phase-dependent synchrony between respiratory
and non-respiratory modulated neurons of the
brainstem (Lindsey et al., 2000).
3.2. Motoneuronal excitability
We propose that respiratory HFOs (and
MFOs) play an important role in controlling
repetitive firing activity of MNs during breathing.
This hypothesis is based on the observations that
the influence of neurons on others is enhanced if
they fire in synchrony (Bernander et al., 1994;
Murthy and Fetz, 1994; Stevens and Zador,
1998), and that phrenic MNs receive synchronous
inputs. Although we focus on phrenic MNs, general principles apply to other MNs and inspiratory neurons whose inputs feature prominent
oscillations.
Synchronous activity of inspiratory premotor
neurons is supported by the presence of HFOs in
the activity of DRG and rVRG and of 50% of
individual phrenic MNs (Fig. 2) (Christakos et al.,
1991), which are coherent with HFOs in whole
phrenic nerve activity. It is also supported by the
presence of HFOs in inspiratory synaptic inputs
to phrenic MNs (Liu et al., 1990; Parkis et al.,
1998), which are evident in their membrane potential trajectories and synaptic current profiles
(Baumgarten et al., 1963; Liu et al., 1990; Funk et
al., 1997; Parkis et al., 1999) as large peaks superimposed on a slower DC envelope (Fig. 3).
How does the synchronous activity of premotor
neurons and the resultant oscillations in current
and membrane potential influence repetitive firing
behavior of MNs? The output of any neuron
results from an interaction between intrinsic membrane properties and synaptic inputs (Berger,
2000; Rekling et al., 2000; Powers and Binder,
2001). When synaptic current fluctuates, time- and
activity-dependent changes in the state of ion
channels alter synaptic current delivery to the
soma. Action potential threshold also varies dynamically with membrane potential, decreasing as
113
the rate of depolarization increases (Schlue et al.,
1974; Azouz and Gray, 2000). Activating neurons
with oscillatory inputs (Volgushev et al., 1998),
current transients simulating EPSCs (Stevens and
Zador, 1998), random synaptic activity (Mainen
and Sejnowski, 1995; Nowak et al., 1997; Tang et
al., 1997) or afferent stimuli (Bennett and Wilson,
1998), demonstrates the importance for firing behavior and action potential timing of dynamic
changes in membrane potential (Powers and
Binder, 2001).
The role of synchronized inputs and membrane
potential oscillations in controlling activity during
actual behaviors is largely unexplored (but see
Brownstone et al., 1992). This reflects the
difficulty of reproducing the dynamic patterns of
synaptic input that characterize drive to MNs
during behavior. To address this limitation, we
have developed a software-based method for activating neurons with endogenous inspiratory
synaptic current waveforms (Parkis et al., 2000).
This method expands on earlier methods developed for cortical neurons (Nowak et al., 1997). It
involves recording an inspiratory current waveform in voltage-clamp, storage of the waveform
to disk, then subsequent re-injection of the waveform into the same neuron under current-clamp
conditions. In this way neurons are not activated
with simulated waveforms, but with somatic current waveforms generated in that neuron by the
respiratory network.
Preliminary data obtained using this technique
indicate that it produces patterns of discharge
virtually indistinguishable from spontaneous inputs (Fig. 4). In addition, both the variability in
interspike interval (see Figs. 3 and 4 in Parkis et
al., 1998) and reliability of action potential timing
(Fig. 5) (Mainen and Sejnowski, 1995) within a
burst appears to be higher in response to endogenous input waveforms. The implication is that
dynamic features of the input waveform, presumably the oscillations (Liu et al., 1990; Parkis et
al., 1998), play a dominant role in controlling the
precise timing of MN discharge. It is therefore
tempting to speculate that by controlling the firing
frequency, oscillations may help prevent diaphragmatic fatigue and increase efficiency of diaphragmatic contraction by ensuring that muscle
114
G.D. Funk, M.A. Parkis / Respiratory Physiology & Neurobiology 131 (2002) 101–120
Fig. 4. Stimulation of a phrenic MN with an inspiratory synaptic current waveform that was generated endogenously in the MN
by the respiratory network produces responses indistinguishable from ongoing spontaneous synaptic drive potentials. Top trace:
current clamp recording showing the membrane potential response (VM) of a phrenic MN to an endogenously-generated synaptic
current waveform (Iinj) injected in the time between two spontaneous inspiratory bursts. Note: endogenous bursts are coincident with
bursts of activity in C1. Dashed lines represent 1.5 sec. Reproduced with permission (Parkis et al., 2000).
fibers are activated at an optimal frequency. In
this regard it is interesting that spectral peaks in
the HFO of synaptic inputs to neonatal rat
phrenic MNs (Liu et al., 1990; Parkis et al., 1998)
match the fusion frequency of neonatal diaphragm (Martin-Caraballo et al., 2000).
In addition to controlling action potential timing, we predict that endogenous inspiratory oscillations will increase input– output efficiency,
providing greater output power in the form of
action potentials for the same input power (Tang
et al., 1997), since synchronized EPSCs will more
readily elevate membrane potential above
threshold. Moreover, since coincident inputs
cause more rapid depolarization, and action potential threshold drops with rate of depolarization
(Schlue et al., 1974), coincidence can ‘functionally’ amplify synaptic inputs (Azouz and Gray,
2000). Given that respiratory MNs remain continuously active from birth until death, potential
energy savings are significant.
In summary, we propose that the HFOs (and
possibly coherent MFOs) play a dominant role in
controlling repetitive firing behavior of MNs.
They may prove important for efficient activation
of MNs and their influence on precise timing of
action potentials may prove important for efficient muscle activation and prevention of respiratory muscle fatigue.
3.3. Muscle function and force transmission
We next consider the significance of synchronized MN output for muscle function and intramuscular force transmission. Knowledge of
muscle microanatomy has increased rapidly over
recent years, fueling renewed interest in the subject of intramuscular force delivery (Sheard, 2000;
Monti et al., 2001; Sheard et al., in press). It is
increasingly clear that pathways and mechanisms
underlying transmission of tension from sarcomere to tendon are varied and complex. Two observations discussed in detail elsewhere (Sheard et
al., in press) are of particular interest in relation
to the functional significance of MN and motor
unit synchrony. First, with the exception of primates, muscle fibers are typically short ensuring
simultaneous activation of all sarcomeres along a
fiber. A consequence of this for long muscles is
that fibers do not consistently span the entire
muscle from tendon to tendon but instead are
arranged serially and must transmit tension to
other fibers and the extracellular matrix. Second,
even in muscles where fibers span the entire muscle, tension is not simply delivered axially from
the sarcomere to the tendon. Some tension is
delivered laterally away from the fiber of origin to
the extracellular matrix and neighboring fibers. In
both types of organization, force is delivered from
G.D. Funk, M.A. Parkis / Respiratory Physiology & Neurobiology 131 (2002) 101–120
active to inactive fibers during submaximal contractions. During normal breathing as well as
maximum inspiratory efforts, the diaphragm is
submaximally activated (Hershenson et al., 1988).
Thus, inactive fibers will increase elasticity of the
tension delivery pathway.
An important consequence of this anatomical
organization is that the transmission of force by
the motor unit varies dynamically with the activity patterns of its neighbors or serial partners
(Sheard et al., in press). The problem for motor
control is obvious: how to generate smooth, consistent gradations of force through recruitment of
motor units whose compliance varies depending
on activity patterns of neighboring fibers. Theoretically, this problem could be addressed by always synchronously recruiting a unit with the
same set of neighbors (Sheard et al., in press).
Fig. 5. Repeated injection of the same inspiratory synaptic
current waveform elicits highly reproducible responses. (A)
Current clamp recording showing the membrane potential
response (VM, top trace) of a phrenic MN to three different
injections of the same synaptic current (Iinj, bottom traces). (B)
Plot of instantaneous firing frequency versus time for the
responses of the current waveform shown in A. Reproduced
with permission (Parkis et al., 2000).
115
Within the respiratory system, a mechanism for
synchronous activation of anatomically coupled
motor units (‘functional units’ as defined by
Sheard et al. (in press) may exist in the HFO (and
MFO). If these functional units exist, the ability
of the muscle to generate fine gradations in muscle force will depend on the number of MNs/motor units that are coactive (i.e. the size of the
functional unit). It is therefore important to emphasize that the presence of the HFO in 50% of
phrenic MNs does not mean that all of these will
discharge synchronously. Coincident discharge
between only a percentage of MNs could account
for the HFO peak in the coherence spectrum
between unit and nerve activities (Richardson and
Mitchell, 1982; Christakos et al., 1991). Significant but low-level coherence between the MFO in
phrenic MN and nerve activities indicates that a
much smaller number of MNs are synchronously
active in this bandwidth. Thus, HFOs and MFOs
may support functional units of different sizes.
Whether functional units actually exist in the
diaphragm is not known. Fiber arrangement is
species dependent. In rat and rabbit (Gordon et
al., 1989) all fibers span from rib cage to the
central tendon, whereas in cat and dog (Gordon
et al., 1989; Boriek et al., 1998), fiber arrangement
is mixed with some in series and some in parallel.
Glycogen depletion studies indicate that motor
unit territories are highly delineated (Hammond
et al., 1989), but the parameter of real interest, the
spatial relationships between fibers of endogenously coactive motor units, is much more
difficult to assess since it requires endogenous
muscle activation rather than nerve stimulation.
In the sternocleidomastoid muscle of guinea pig, a
serially organized accessory respiratory muscle,
slow fibers are surrounded entirely by fast fibers
indicating that efficient coactivation of motor
units is only likely during moderate to high level
contractions when slow and fast fibers are both
recruited (Young et al., 2000).
From a functional perspective, the possibility
that synchronous MN output enhances force production is supported by computational models
demonstrating that muscle force increases in response to inputs with the same mean firing rate
but increasing synchrony (Murthy and Fetz, 1994;
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G.D. Funk, M.A. Parkis / Respiratory Physiology & Neurobiology 131 (2002) 101–120
Baker et al., 1999). It may also explain the finding
that during the hold phase of a precision grip
task, force remains constant or increases while
discharge of 18% of corticomotor neurons actually decreases (Maier et al., 1993). Indeed, synchrony does appear in the latter stages of this
behavior (Baker et al., 1999).
As mentioned above, while force production
may increase with synchronous activation of motor units, the ability to produce fine gradations in
muscle force or smooth contractions will be compromised (Yao et al., 2000). Thus, the degree of
synchronization between motor units may represent a balance between these competing requirements for fine control and efficient contraction.
For example, in a precision grip task that requires
fine motor control during the initial stages, synchronized oscillations between EEG and finger
EMG activities only appear during the final hold
phase (Baker et al., 1999). As the generation of
respiratory airflow does not require fine motor
control, but instead requires that muscle remain
rhythmically active virtually uninterrupted
throughout life, activation patterns may have
evolved to favor efficiency. In this context it is
interesting that the HFO is enhanced under conditions of increased ventilatory drive (Section 2.3.3).
The increased synchronization may increase efficiency of muscle contraction.
In summary, the tension delivered to the tendon
from any single fiber depends on the arrangement
(serial or parallel) and microarchitecture of muscle fibers within the muscle and whether its neighboring fibers are coactive. Evidence supporting
synchronous activation of serial or neighboring
fibers is sparse but the possibility that the HFOs
or MFOs in respiratory MNs serve to synchronously activate groups of functionally coupled motor units and increase efficiency of muscle
contraction is an intriguing possibility worthy of
further investigation.
Acknowledgements
Special thanks to Professor Morton Cohen and
Dr Philip Sheard for their helpful discussions.
This work was supported by the Marsden Fund,
Health Research Council of New Zealand, Lotteries Health, Auckland Medical Research Foundation, New Zealand Neurological Foundation and
the Paykel Trust.
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