DISCHARGES IN HUMAN
MUSCLE AFFERENTS
DURING MANUAL TASKS
Michael Dimitriou
Department of Integrative Medical Biology
Section for Physiology
Umeå University
Sweden, 2008
© Michael Dimitriou 2008
Umeå University Medical Dissertations
New series no. 1239
ISSN 0346-6612
ISBN 978-91-7264-706-0
Printed at Print & Media, Umeå University
TO SOTIRIS AND ELENI DIMITRIOU
ABSTRACT
Dimitriou, M. (2008). Discharges in human muscle afferents during manual tasks. Doctoral
dissertation from the Department of Integrative Medical Biology, Section for Physiology,
Umeå University, S-901 87, Umeå, Sweden. ISBN: 978-91-7264-706-0
Muscle spindles are complex sensory organs that have been strongly implicated in the
control and perception of movements. Human muscle spindles in relaxed muscles behave as
stretch receptors, responding to the length and velocity of their parent muscles. However, it has
been unclear how they discharge during active movements since their discharges are also affected
by fusimotor activity and extrafusal contractions. The vast majority of neurophysiological
recordings of muscle afferents have been obtained under passive conditions, or active but
behaviourally restricted conditions. These restrictions prevent predictions of human muscle
afferent activity during purposeful multi-joint movements, naturally occurring during tasks such
as hand shaping, grasping or key-pressing.
An experimental protocol was therefore developed which allowed recordings of muscle
receptor afferent activity using microneurography during unrestrained wrist and digit
movements. Along with single afferent discharges, recordings were obtained of
electromyographic activity of major forearm muscles and the kinematics of the wrist and digits.
This approach allowed investigations of the factors shaping afferent discharge during everyday
manual tasks, i.e., block-grasping and pressing sequences of keys, and during active sinusoidal
joint movements. The afferents’ ability to encode information concerning the state of the muscle
and joint kinematics during these tasks was also assessed.
The responses of spindle afferents from load-bearing muscles were approximatelly 90
degrees more phase-advanced than expected on the length of their parent muscles. That is, the
discharges of primary muscle spindle afferents were significantly affected by both velocity and
acceleration, the discharges of secondary afferents by velocity, and neither afferent type was
particularly affected by static muscle length. Accordingly, these afferents failed to encode length,
encoded velocity well and acceleration poorly. The representation of muscle length and velocity
was, however, significantly improved when the discharge activity of Golgi tendon afferents was
taken into consideration along with muscle spindle activity. The discharge of primary afferents
during both key-pressing and block-grasping was best correlated to the muscle velocities observed
~100-160 ms in the future. This predictive ability went beyond what could be expected from the
spindles’ simultaneous sensitivity to velocity and acceleration, and could thus only be explained
by implicating the fusimotor drive. In addition, evidence is presented that the fusimotor control
of spindles was contingent on entire movement sequences during the key-pressing task.
It is proposed that the phase relationship between the discharge rate of spindle afferents
and the length of their parent muscles is load dependent. Moreover, muscle spindles seem to act
as forward sensory models of their parent muscle, which makes sensorial feedback control
possible despite neural delays.
Key words: motor control; muscle spindle; Golgi tendon organ; muscle afferent;
microneurography; proprioception; sensorimotor; fusimotor; hand; forward model.
ORIGINAL PAPERS
This doctoral thesis is based on the following articles. They will be referred
to by their roman numerals.
I.
Dimitriou, M. & Edin, B.B. (2008). Discharges in human muscle
spindle afferents during a key-pressing task. The Journal of Physiology
(London), 586, 5455-5470.
II. Dimitriou, M. & Edin, B.B. (2008). Discharges in human muscle
receptor afferents during block grasping. The Journal of Neuroscience,
28, 12632-12642.
III. Dimitriou, M. & Edin, B.B. (2008). Human muscle spindles act as
forward sensory models (Manuscript).
CONTENTS
INTRODUCTION .............................................................................................. 1
MUSCLE SPINDLES..........................................................................................................3
GOLGI TENDON ORGANS ...............................................................................................5
METHODOLOGICAL SUMMARY ................................................................... 6
NEUROPHYSIOLOGICAL TECHNIQUES ............................................................................6
Microneurography ......................................................................................................6
Afferent identification .................................................................................................7
EMG recordings .........................................................................................................7
KINEMATIC MEASUREMENTS..........................................................................................8
BEHAVIOURAL TASKS .....................................................................................................8
Block-grasping ............................................................................................................8
Key-pressing................................................................................................................9
DATA PROCESSING .......................................................................................................10
Kinematics ...............................................................................................................10
Afferent population responses .....................................................................................11
ANALYSES .....................................................................................................................12
Statistical methods ....................................................................................................12
PLS modelling..........................................................................................................13
SUMMARY OF STUDIES & RESULTS ........................................................... 16
STUDY I .......................................................................................................................16
STUDY II ......................................................................................................................19
STUDY III....................................................................................................................22
DISCUSSION .................................................................................................... 27
UNEXPECTED PHASE ADVANCES...................................................................................27
SPINDLES INFORM ON ACCELERATION .........................................................................29
SPINDLES CAN SIGNAL FUTURE SENSORY STATES ..........................................................30
EVIDENCE OF Α-Γ DISSOCIATION .................................................................................33
A ROLE FOR MUSCLE SPINDLES IN SENSORY CANCELLATION? .......................................34
CONCLUSIONS................................................................................................ 35
ACKNOWLEDGEMENTS................................................................................ 36
REFERENCES.................................................................................................... 37
INTRODUCTION
Any manual endeavour, such as reaching for a cup of coffee, relies heavily on
information concerning the mechanical state of the engaged limb. The task
of providing this information is charged to a wide range of sensory
mechanoreceptors, strategically placed in key areas such as muscle, tendon
and skin. In the past, considerable attention was directed to a particular class
of complex sensory receptors, tens or hundreds of which are tacked neatly
between the fibres of most skeletal muscles. These so-called muscle spindles
have been strongly implicated in the control and perception of movements.
Spindles have been proposed to play important roles in various functions
including in the reflex control of muscle stiffness (Houk, 1976; Sinkjaer et
al., 1988), in the establishment and maintenance of body schema (Lackner,
1988), in motor learning and adaptation (Prochazka, 1989), in inter-joint
coordination (Sainburg et al., 1993) and in proprioception (Goodwin et al.,
1972; Roll and Vedel, 1982). In fact, there is “…a kaleidoscope of
functions, in which proprioceptive feedback has been supposed to be
involved” (Windhorst, 2007).
Despite the numerous roles proposed for them, it has been hitherto impossible to confirm the contribution muscle spindles make during common
multi-joint movements. Technical constraints have discouraged the documentation of spindle afferent activity during ‘natural’ motor behaviours in
humans. The majority of neurophysiological recordings of spindle afferents
have been obtained under passive or behaviourally restricted conditions.
Hence, there has been no record of human spindle activity during everyday
actions such as grasping or typing.
In relaxed human muscles, spindle afferents respond to muscle stretch (Edin
and Vallbo, 1990a; Bergenheim et al., 2000). Muscle spindle responses to
movements imposed on passive muscles can be therefore easily predicted
from the anatomy of their parent 1 muscles (Roll et al., 2000; Ribot-Ciscar et
al., 2002, 2003). However, such findings may have limited applicability in
unravelling the functional role of muscle spindle afferents during active
movements. This is because spindle responses during passive stretch and
active movements may very well be different: during active movements the
muscle spindles are responsive to changes in muscle length, but are also
affected by the fusimotor system and by extrafusal muscle contractions
(Wilson et al., 1997). For example, it has been shown that human muscle
1
‘Parent’ refers to the muscle where the muscle spindle endings reside
2
spindle afferents discharge more in accordance to changes in contraction
strength than changes in muscle length during shortening contractions
against an external load (Burke et al., 1978). Similarly, muscle spindle
afferents from finger extensor muscles fail to represent joint positions during
visually guided finger movements conducted with isotonic loads (Vallbo et
al., 1981; Hulliger et al., 1982). During wrist movements, spindle afferents
from actively contracting muscles also show poor muscle length sensitivity
(Jones et al., 2001). Mathematical models of cat muscle spindles that use
length and velocity as main inputs account poorly for their responses in
load-bearing muscles (Prochazka and Gorassini, 1998). In short, while
spindle afferents from relaxed muscles can signal muscle length, this has not
been consistently shown for spindle afferents in actively contracting muscles.
It has been proposed that muscle spindles in the relatively relaxed and
lengthening antagonists may provide information concerning limb configurations (Jones et al., 2001). However, natural hand and finger movements
are typically associated with extensive co-contractions (Johansson and
Westling, 1988; Maier and Hepp-Reymond, 1995); that is, antagonistic
muscle may also be contracting during simple movements and this would
compromise the ability of spindle afferents to encode the length of these
muscles as well. Since previous human studies have focused on spindle
responses to isometric contractions or movements at single joints (Vallbo,
1971; Vallbo et al., 1981; Edin and Vallbo, 1990a-b; al-Falahe et al., 1990b;
Bergenheim et al., 2000; Jones et al., 2001; Cordo et al., 2002), there is
currently a lack of knowledge concerning the nature of muscle spindle
activity during important motor events of everyday life.
An experimental protocol was therefore developed which allowed recordings
of muscle receptor afferent activity during unconstrained wrist and digit
movements (Fig. 1). Along with single afferent activity, the electromyographic activity of all muscles engaged were recorded as well as the
kinematics of the wrist and digits. This approach allowed for the
investigation of the factors shaping afferent discharge during ‘everyday’
motor tasks such as block-grasping and pressing sequences of keys. In
addition, the afferents’ ability to encode information concerning the state of
the muscle and joint kinematics during the behavioural tasks was also
assessed.
3
Figure 1. The experimental protocol. Throughout the experiments, the participants were sitting
comfortably on a dentist’s chair, while microneurography from the radial nerve, surface EMG of major
forearm muscles and the joint angles of the wrist and digits were recorded. The joint angles were
recorded using the CybergloveTM and complimented by the use of four PolhemusTM 3D sensors, which
were placed on the wrist, thumb, index and middle finger. The participants’ forearm was resting on a
cushion and was clamped just proximal to the wrist to help prevent movements of the upper arm (i.e.
accidental dislocations of the electrodes). In this set-up, they could move their hand and digits freely.
Depending on the location of the recorded muscle receptor, the participants had to either conduct a blockgrasping task or a key-pressing task.
Muscle spindles
Muscle spindles are found in most striate muscles of all mammals and their
structure and efferent supply are well known (Matthews, 1972). The
spindles are encapsulated, fusiform in shape, and 5-10 mm in length. The
vast majority of spindles are mechanically situated in parallel with the
skeletal (i.e. ‘extrafusal’) fibres of the muscle they inhabit, although
exceptions have been observed microscopically (Swett and Eldred, 1960) and
functionally (Burke et al., 1987). The main components comprising the
muscle spindle organs are the receptor endings and their respective afferent
axons, the intrafusal fibres and the efferent axons innervating them. There
are three major types of intrafusal muscle fibres (Kucera and Dorovini-Zis,
1979; Taylor et al., 1999), the ‘dynamic’ bag1, ‘static’ bag2 and chain fibres,
whose central regions are mostly non-contractile. There are two kinds of
4
muscle spindle receptor endings, the primary receptor (or ‘annulospiral’
ending) and the secondary receptor or ‘flower-spray’ ending. The two types
of sensory ending are located around the non-contractile regions of the
intrafusal fibres, and respond with action potentials to the stretch of these
regions. Only the primary endings spiral around the bag1 fibre, however. A
typical muscle spindle may contain several bag1 and bag2 fibres, and 2-11
chain fibres. A number of muscle spindles, like some of those found in
compound spindles in the neck (Price and Dutia, 1989), lack bag1 fibres all
together. More recent research shows that human muscle spindles from deep
neck muscles may lack bag1 or bag2 fibres (Liu et al., 2003). There can also be
a considerable number of intrafusal fibres (i.e. ~10% in one muscle) which
display atypical features (Soukup et al., 2003).
In addition to external mechanical transients on the capsule itself, intrafusal
stretch may result from stretching the whole muscle or by the actions of
efferent motor axons. The efferent axons are of two main groups, γ and β,
with the former exclusively supplying intrafusal fibres whereas the latter
bifurcates to supply both intrafusal and extrafusal fibres. The γ and β axons
can be further divided into ‘static’ or ‘dynamic’ axons (Matthews, 1962;
Emonet-Dénand et al., 1975; Barker et al., 1976). The axons of the primary
muscle spindle endings constitute the primary or ‘type Ia’ afferents and the
axons of the secondary endings are the secondary or ‘type II’ afferents. A
single muscle spindle may give rise to 1-2 type Ia afferents and up to 5
secondary afferents.
The two types of afferent vary considerably in their responses. The current
consensus relates the discharges of type Ia afferents to both static muscle
length and the rate of muscle length changes (i.e. ‘velocity’), and those of
type II afferents to static length (Pearson and Gordon, 2000). The dynamic
fusimotor activity increases the gain of the afferents’ responses to muscle
stretch, whereas the static activity increases their base-line discharge but
decreases the gain to muscle stretch (Prochazka, 1996). Through fusimotor
activation of different intrafusal fibres, it has been possible to estimate the
effects of each intrafusal type on muscle afferent discharges. Chain fibres bias
afferent discharges and decrease stretch sensitivity, bag2 fibres have a biasing
effect but also reduce the stretch sensitivity of Ia afferents and increase it for
type II afferents, and activation of bag1 fibres increase the dynamic stretch
sensitivity of type Ia afferents (Emonet-Dénand et al., 1977; Taylor et al.,
1992, 2000; Prochazka, 1996; Durbaba et al., 2001, 2003).
5
Golgi tendon organs
Golgi tendon organs are encapsulated sensory organs, of 0.2-1 mm in length,
respond to tension in a small set of motor units, and are found near musculotendinous junctions, i.e. are placed in series with the extrafusal muscle
fibres (Prochazka, 1996). Accordingly, Golgi tendon organs are particularly
sensitive to contraction, and so their ‘type Ib’ afferents have been found to
primarily encode the actively produced muscle force (Crago et al., 1982).
Although the above is not always the case for individual type Ib afferents,
averaging across the responses of these afferents still provides a good
representation of contractile force (Horcholle-Bossavit et al., 1990).
6
METHODOLOGICAL SUMMARY
Neurophysiological techniques
Microneurography
Signals in single muscle receptor afferents were obtained using microneurography (Vallbo and Hagbarth, 1968). First, a thin non-insulated
electrode (200 μm diameter) was inserted percutaneously in the radial nerve
of the right arm, ~10 cm proximal to the elbow (Fig. 2). The precise location
of the nerve was progressively defined by providing low-intensity electrical
impulses through the electrode and monitoring the consequent reactions.
Subjective reports of elicited cutaneous sensations pertaining to the dorsum
of the hand or contractions of the extensor muscles were indicative of close
proximity to the nerve. At this stage, another electrode which was insulated
but for the tip, was used to penetrate the nerve. Once in the radial nerve,
small adjustments were made to the location of the insulated electrode until
the activity of a single afferent could be isolated in the neurogram i.e. single
action potentials with amplitudes substantially higher than that of the
background ‘multi-unit’ activity. Identification of single action potentials
was made semi-automatically under visual control, using criteria based on
the shape of single action potentials (Edin et al., 1988).
Throughout the microneurography recordings, the participants were seated
in a dentist’s chair with their right arm resting on a mobile ramp and
supported by a vacuum pillow around their forearm and a cushioned clamp
just proximal to the wrist (Figs. 1-2). In this position, the participants could
move their wrist and digits freely. This particular posture and the fixation of
the forearm were necessary precautions against dislocations of the electrode.
Even slight movements of the head or shoulders could cause an accidental
Figure 2. Microneurography. On the left, the tungsten electrodes used for microneurography; a pin is also
shown for visual reference. On the right, the location of the microneurographic recording is marked with a
rectangle.
7
dislocation of the electrode. Depending on the parent muscle of the recorded
muscle receptor afferent, the participants had to conduct one of two motor
tasks, i.e. a block-grasping task or a key-pressing task as described below (on
p.8).
Afferent identification
The parent muscle of an afferent was identified by palpation and by the
afferent’s responses to passive joint movements and active contractions.
Muscle spindle afferents were discriminated from Golgi tendon organ
afferents by their different responses to passive movements and isometric
contractions (Edin and Vallbo, 1990a). A lack of responses or inconsistent
responses to muscle stretch and static muscle length were indicative of a
Golgi tendon organ afferent. Higher discharge levels at the beginning and
during isometric contraction, rather than during muscle relaxation following
the contraction, were also indicative of a type Ib afferent (Paper II: Fig.
4AB). Muscle spindle afferents were subdivided into type Ia and type II
depending on the variability of their interspike intervals during passive
isometric conditions (Nordh et al., 1983), responses to lengthening of the
relaxed muscle (Edin and Vallbo, 1990a), and their response to a sudden
relaxation following isometric contraction (Edin and Vallbo, 1990b).
Specifically, a variable or poor response to static length but pronounced
responses to fast length changes imposed on the relaxed parent muscle were
indicative of a type Ia afferent (cf., Fig. 7AB). A clear sensitivity to static
muscle length increases and regular interspike intervals during static
conditions were indicative of a type II afferent (cf., Fig. 10, p.20). Single
afferents from skin receptors were discarded by moving the electrode. Unlike
muscle receptor afferents, skin afferents are not responsive to muscle
palpation or to voluntary muscle activation, but are very responsive to
localised cutaneous stimuli.
EMG recordings
Electromyographic (EMG) recordings were made from all major forearm
muscles involved in wrist and digit movements and accessible by surface
electrodes: the radial wrist extensor (m extensor carpi radialis), the radial wrist
flexor (m flexor carpi radialis), the ulnar wrist flexor (m flexor carpi ulnaris),
the ulnar wrist extensor (m extensor carpi ulnaris), the common finger
extensors (m extensor digitorum communis or m indicis proprius) and flexor (m
flexor digitorum superficialis), and the thumb abductor muscle (m abductor
pollicis longus). The optimal recording site for each muscle was identified
with help of a hand-held EMG recording probe during isometric
contractions. Custom-build surface electrodes (∅ 2 mm; 12 mm apart) were
8
coated with electrode jelly and attached to the skin at the optimal sites using
double-sided adhesive tape. The activity in the short and long RWE (m
extensor carpi radialis brevis et longus) cannot be distinguished by surface
electrodes although subtle differences can be demonstrated in intramuscular
EMG recordings (Riek et al., 2000). However, because the experimental
sessions lasted 4-5 hours, intramuscular EMG recordings were not feasible.
Kinematic measurements
The Polhemus FasTrakTM motion tracking system (Skill Technologies Inc.,
TM
Arizona, USA) and the CyberGlove (Immersion Corporation, CA, USA)
TM
were used for recording digit and wrist kinematics. The CyberGlove allows
recordings of wrist, metacarpophalangeal, and interphalangeal joint angles
(flexion, extension, abduction and adduction) with a nominal resolution of
~0.5°. Each of the 18 sensors of the CyberGloveTM was calibrated for each
participant according to predetermined angles obtained using custom-build
wooden blocks. Polhemus FasTrakTM sensors provide azimuth, elevation and
roll angles with an accuracy of 0.15 degrees, and can be fixated anywhere on
the hand or digits. The relevant null angles were defined as those occurring
when the participants kept the wrist and digits extended and the thumb fully
abducted.
Behavioural tasks
The participants were familiarized with the two behavioural tasks used in
this thesis – i.e., block-grasping and key-pressing – before the microneurography recordings commenced. Due to the simplicity of the tasks, only a few
minutes of practice were necessary before the participants achieved
proficiency. The participants were carefully instructed to execute the tasks at
speeds which they were comfortable with and felt represented the speeds
they would employ when conducting such tasks in everyday life.
Block-grasping
In the block-grasping task, the participants were asked to use the thumb and
their index or middle finger to grasp one of a total of seven wooden blocks
that was driven in their grasp by the experimenter (Fig. 3). Each block was
presented ~30 cm from the participant’s hand, and they performed the task
under full visual control. After driving the block into the participants hand,
the experimenter released the block when the participants made contact with
the object. They had received prior instructions to release the block when
the experimenter again grasped the block after several seconds of holding.
9
Figure 3. The block-grasping task. One the left, the participant wearing the CyberGlove™ and with
Polhemus FasTrak™ 3D sensors mounted on the wrist, the thumb and the index finger. The participant’s
hand is in the starting position, prepared to receive and grasp a wooden block. The experimenter is
holding the block prepared to drive it into the participant’s grasp. On the right, the recorded angles of the
wrist, index finger and thumb as well as the calculated muscle length changes of the finger extensor and
the thumb abductor muscles (bottom), when the participant grasped, held and then released a 60mm
block. MCP - Metacarpophalangeal; PIP - Proximal interphalangeal; IP - Interphalangeal.
Each trial started and ended with the finger and thumb in apposition. The
seven wooden blocks were presented in sequence and they differed only in
their widths in increments of 10 mm (30–90 mm).
Figure 3 also shows the digit and wrist angular changes as well as the
resulting muscle length changes while a single block was grasped, held and
then released. The participants’ grasping behaviours were very similar when
they received blocks as during the microneurography recordings, when they
themselves transferred the object from the left hand to the right hand, and
when they performed normal reaching-and-grasping movements (Paper II:
Fig. 2).
Key-pressing
In the key-pressing task, the participants were asked to use their wrist and
middle finger to press sequences of keys on a custom-build numeric key pad
(Fig. 4A; cf., Fig. 2). The keypad had nine buttons (∅ 5mm, arranged 3×3)
that required a normal force of ~1.5 N to be pressed. The position of the key
pad was adjusted so that, by having the wrist at ‘zero angles’, participants
could reach the middle key (‘5’) with the tip of the middle finger.
10
Figure 4. The key-pressing task. A, Throughout the key-pressing task, the hand was in a semi-pronated
position so that radial deviations of the wrist coincided with vertical movements against gravity (i.e. ‘UP’).
B, The key-pressing task involved pressing different sequences of three keys. Key ‘5’ was always the first
and last key in each sequence and never the middle key. Movements from key ‘5’ towards keys ‘2-3-6’
and movements towards key ‘5’ from keys ‘4-7-8’ required flexion and/or ulna deviation of the wrist joint,
and so induced elongation of the Radial Wrist Extensor (RWE) muscle. The Ulnar Wrist Extensor (UWE)
was mostly affected by radial deviations, so was elongated substantially during movements from key ‘5’
towards keys ‘1-4-7’, and during movements to key ‘5’ from keys ‘3-6-9’. C, For the purpose of further
analyses, each key sequence was divided in nine phases: 100 ms before and after each key and one
phase which included the actual keypresses.
All sequences were comprised of three keys, the first and last key was always
the 5 key, but the second key (i.e. ‘target’ key) could be any key but 5 (Fig.
4BC). All sequences were presented in a spatially congruent manner on a
visual display in front of the participants. Each sequence was presented 300
ms after pressing the terminal 5 key of the previous sequence, and the
concurrent sequence remained on display until the terminal 5 key was
pressed. Sequences were presented in blocks of eight. The interval between
blocks of sequences was about 10 s. A sound was triggered whenever an
incorrect key was pressed.
Data processing
Kinematics
Changes in muscle length, or more precisely tendon excursions, were
calculated from the measured joint angles by simple transformations of the
angular data using published coefficients (Elliot and McGrouther, 1986;
Smutz et al., 1998; Pigeon et al., 1996). The first and second derivatives of
muscle length (i.e. ‘velocity’ and ‘acceleration’), were calculated using moving
11
windows whose size corresponded to low-pass filtering DC~10Hz for the
wrist data of Study I and DC ~17Hz for the digit and wrist data of Study II.
All analyses of the key-pressing task focussed on the key sequences associated
with significant muscle length changes of the afferents’ parent muscles.
Therefore, different key sequences were associated with each of the two wrist
extensor muscles (cf., Fig. 4B). For both muscles there were three key
sequences with consistent initial muscle length increase and three key
sequences with initial length decrease.
Afferent population responses
From the spike trains recorded from each single afferent, a continuous signal
representing the instantaneous discharge was created. This signal
corresponded to the inverse of the duration of each interspike interval.
Afferent population responses were created from the empirically observed
discharges; such averaged responses are less affected by noise present at the
single afferent level (Faisal et al., 2008).
In Study I, for each discrete movement to and from all keys associated with
significant muscle length changes, 100 ensemble responses were generated by
averaging the discharge rates of single afferents. More specifically, the
ensemble responses were created by picking at random one of the recorded
responses of all single afferents for which at least two repetitions without
key-pressing errors had been obtained. The number of afferents contributing
to the ensemble responses thus varied slightly depending on the key of
interest. Of the 15 recorded Ia afferents from the RWE muscle, 12-14
contributed to the type Ia ensemble responses from the RWE muscle
whereas 4-8 of the 8 type II afferents contributed type II ensemble responses
from the UWE muscle. The degree to which the ensemble responses were
representative of the ‘true’ population responses was determined by
correlating the randomly generated responses for each movement (n=100,
N=1200) with the grand weighted mean. As Figure 5 shows, all ensemble
responses were good representations of ‘true’ populations except the UWE
type Ia ensemble. Since the validity of the latter was questionable, it was not
used when investigating the relationship between ensemble responses and
observed kinematics.
12
Figure 5. Validation of the ensemble responses pertaining to the key-pressing task. A, The histograms
represent r values when each randomly generated average was correlated with the grand mean. The
median r value obtained for the type Ia and type II ensemble were 0.87 and 0.90, respectively. B, The
same as in A but pertaining to the UWE. The type Ia and type II median r values were 0.68 and 0.95,
respectively. RWE - radial wrist extensor; UWE - ulnar wrist extensor.
As for the key-pressing task, the data of the block-grasping task (Study II)
were used to generate ’population responses’, averaged kinematic and EMG
variables. The averaged population responses were cross-validated by
employing a bootstrapping methodology. A ‘grasping’ and a ‘releasing’ phase
was defined for each single block-grasping trial (Fig. 11 on p.20). A data
matrix containing one data series for each recorded afferent, variable, block
size and phase was first compiled. From this matrix 100 averaged responses
of populations were created, each comprising a random sample of ~60% of
the available afferents (8/13 type Ia; 3/5 type II afferents; 5/9 type Ib; a total
of 3 afferent types × 2 phases × 7 block sizes × 100 replicates = 4,200
generated population responses). The median r obtained when correlating all
averaged population responses from a subset of type Ia afferents with the
corresponding average calculated from all type Ia afferents was 0.96 (range
between the upper and lower quartiles was 0.04), for type II 0.91 (range
0.17), and for type Ib 0.93 (range 0.09). Averaged kinematic and parent
EMG signals were generated in a similar way but based on participants not
on single afferents.
Analyses
Statistical methods
Statistical tests were performed using either STATISTICA® or MatLab®,
choosing a significance level of p<10-2 for all tests (applying compensations
for multiple tests when appropriate). In the key-pressing task, the
experimental paradigm included symmetrical movements around the central
13
5 key of the keyboard, and therefore muscle lengths, velocities and
accelerations should in theory all be pair-wise uncorrelated. Length and
velocity were indeed uncorrelated for both muscles (r2<0.02) but length and
acceleration were statistically significantly correlated but to such a small
degree that it was ignored in the regression analyses (r2<0.20). The data used
for regression analyses showed, however, a high degree of auto-correlation
which if not taken into account would have led to inflated p values. For the
data pertaining to both the block-grasping and key-pressing task, the
effective sampling rate was estimated to be ~20 Hz (Dawdy and Matalas,
1964). Accordingly, all regressions in Study I and II were performed on data
that was down-sampled to 20 Hz, i.e., each data point represented the
average in a 50 ms window. The impacts of kinematic variables and EMG
on the discharge rates were determined by means of non-linear regression
analyses under the assumption that if all other factors were constant, neither
length, nor velocity or acceleration could have had a negative impact on the
discharge of spindle afferents. The effect of EMG on the other hand was not
constrained and could thus have either a negative or a positive impact. The
non-linear regression model for the impact analyses is represented by the
following equation:
Discharge rate = k1 + |k2|·Len + |k3|·Vel + |k4|·Acc + k5·EMG
Eq. 1
‘Len’, ‘Vel’, and ‘Acc’ represent the instantaneous muscle length, velocity
and acceleration, respectively, and k1-k5 represent the constants determined
by the regression. Kinematic variables were recreated from the ensemble
discharges of the afferents using the following equation:
Kinematic variable = k1 + k2·Ia + k3·II + k4·Ib
Eq. 2
‘Kinematic variable’ corresponds to the instantaneous muscle length, velocity
or acceleration; ‘Ia’, ‘II’ and ‘Ib’ correspond to the instantaneous ensemble
discharge of type Ia, type II and type Ib afferents, respectively; k1-k4 represent
the constants determined by the regression. Different combinations of
populations were created by forcing one or two of the coefficients k2-k4 to
zero.
PLS modelling
To assess whether the fusimotor drive differed depending on the key
sequence, we determined if it was possible to calculate the functional endpoints of the key-pressing task, i.e., the spatial location of variable key ‘T’ of
each sequence (cf., Fig. 4B, p.10), from the muscle spindle afferent
responses. The mean discharge rate and standard deviation for each of the 9
phases depicted in Figure 4C were determined for each afferent and each key
sequence. Accordingly, 8×9 means and standard deviations corresponding to
14
8 target keys and 9 phases were tabulated for each recorded afferent. For a
specific phase of a specific key sequence a population response was created
by generating a vector containing random numbers with the same
population mean and standard deviation as the individual afferents. The
locations of the keys were represented by the true x-y coordinates of the keys
with an arbitrarily chosen origo (cf., Fig. 4A).
Modelling was performed using Partial Least Squares (also called Projection to
Latent Structures, PLS) analysis (Eriksson et al., 1999). This was a preferred
method for analysing the key-pressing data, because it can deal with
correlations among independent variables, missing data and multiple
dependent variables. The PLS method is a multivariate extension of
Principal Components Analysis (PCA). In PCA, a covariance matrix
pertaining to the standardised equivalents of the independent variables is
used for creating eigenvectors; in geometrical terms, this corresponds to a
rotation of the standardised axes, so that one axis essentially assumes the
dimension along which most data variance is observed (i.e., the rotated axis
constitutes a ‘principal component’ or ‘latent variable’). The dimension
where the second-largest variability is observed will become a second
principal component and is used along with the first principal component to
define a plane (additional principal components define a hyper-plane). The
coordinates of the points with respect to the newly formed plane are called
scores, and can be thought of as new and more ‘compact’ independent
variables. The required transformations between the original independent
variables and the scores is accomplished by the ‘loads’ or ‘weights’ (i.e.
coefficients); in geometric terms, weights describe the coordinate
transformations required to rotate the original axes in order to obtain the
final configuration (e.g. orientation of a plane).
In PLS, successive linear combinations of the independent variables are extracted and optimised to explain both dependent and independent variable
variation. More specifically, the axis rotations that take place in PLS seek to
account for both the variability in independent-variable and dependentvariable space. In contrast to the PCA, therefore, the PLS iteratively
maximises the covariance between latent independent variables and latent
dependent variables, and does not attempt to maximise the degree to which
it accounts for variance in independent variables. The dependent-variable
scores are in turn used to predict the original dependent values through
ordinary least squares procedures. In effect, PLS allows predictions of
multiple dependent variables from a set of independent variables. In our
case, the dependent variables were the x-y coordinates of the target keys.
Matlab® was used for data tabulations, generating simulated afferent
15
responses, probability calculations and data displays while PLS analyses were
performed using SIMCA-P®. Calculation of probabilities for correct and
false identification of key sequences was performed using Bayesian
probability calculus. The set of x-y coordinates calculated from the PLS
model represented the likelihood of observing a specific x-y coordinate given
a certain key (cf., Fig. 15 on p.25).
16
SUMMARY OF STUDIES & RESULTS
Study I
In this first study, we recorded the activity of muscle spindle afferents in the
radial wrist extensor (RWE) and ulnar wrist extensor (UWE), while
participants performed the key-pressing task which demanded precise control of the wrist. We simultaneously recorded the EMG of the four major
wrist muscles as well as the kinematics of the wrist and digits. During the
key-pressing task the RWE was contracting concentrically and acting as an
anti-gravity muscle, whereas the UWE was contracting eccentrically (Fig. 6).
Further analyses showed that the responses of the muscle spindle afferents
from the RWE were approximately 90 degrees more phased advanced than
expected on the length of their parent muscle. As such, only velocity and
acceleration significantly affected the discharges of single type Ia afferents
(Fig. 7) and of the population of type Ia afferents (Fig. 8) during the keypressing task. Type II afferent discharges from the same muscle were
significantly affected only by velocity (Fig. 8).
The same kinematic dependence was observed in both type Ia and type II
afferents from the RWE during voluntary sinusoidal wrist movements. On
the other hand, the discharges of type II afferents from the UWE were
affected by muscle length and velocity, while the type Ia afferents from this
muscle were too variable to allow reliable analyses (cf., Fig. 5B). Accordingly,
only the wrist ulnar-radial angular velocity was well encoded by the
Figure 6. Different activation patterns of wrist extensor muscles during the key-pressing task. The radial
wrist extensor (RWE) and the ulnar wrist extensor (UWE) fulfilled different mechanical roles during the
keypressing task. Namely, repeated ANOVAs indicated that RWE electromyographic activity was higher
when the muscle was at a short length and shortening (i.e. concentric contractions). The opposite effects
were seen for the UWE (eccentric contractions). The data used were of 19 participants and were ztransformed for each participant before analysis. Vertical bars represent ±SE.
17
Figure 7. Responses of a typical
type Ia muscle spindle afferent from
the radial wrist extensor. A, During
passive joint movements this Ia
afferent (65-02) showed poor
responses to static length but
pronounced dynamic responses particularly evident during small rapid
length changes (grayed area) shown
on an expanded time scale in (B)
and it discharged vigorously during
isometric contractions (C). D, During
key-pressing, however, the afferent’s
discharge was characterized by
bursting and pauses apparently
related to the second derivative of
muscle length ('acceleration'). In
particular, note the discharge onset
when acceleration reversed from
negative to positive (grayed areas).
18
Figure 8. Ensemble responses of muscle spindle afferents from the radial wrist extensor. A, Averaged
length, velocity, acceleration and EMG signals along with the corresponding ensemble discharges of type
Ia afferents (n=15) and type II afferents (n=8) from the radial wrist extensor (RWE), for a 'long' key 3 and
a 'short' key 7. EMG was z transformed before averaging. Shaded areas around means represent ±SD.
B, Qualitative comparisons of ensemble discharge profiles and kinematic variables having the most
significant impacts on the discharge rates. The kinematic variables were rescaled by eye to make a good
fit to the discharge profiles using red for acceleration and blue for velocity (as in A). C, Reconstructions of
the observed ensemble discharge rates shown in (A) by regression: solid lines represent the observed
values and dots the values predicted from linear regressions using the kinematic signals and RWE EMG
as independent predictors. For the key sequences shown, both acceleration and velocity significantly
affected type Ia ensemble discharges from the RWE, and velocity alone sculpted the type II discharges
from the same muscle.
19
combination of afferent populations, and ulnar-radial angle itself was well
encoded only during periods of UWE lengthening (Paper I: Fig. 7). Joint
angular acceleration was rather poorly encoded throughout.
Study II
The second study describes the discharges of muscle receptor afferents
recorded from the digit extensor and thumb abductor muscles whilst the
participants performed a block-grasping task (cf., p.8). EMG activity of all
pertinent muscles was recorded along with wrist and digit kinematics. The
discharges of single muscle afferents during block-grasping were more phaseadvanced than expected from previous studies and from the afferents’ own
responses to imposed stretches of their relaxed parent muscle (Figs. 9-10).
As for the key-pressing task, the type Ia ensemble responses from the finger
extensor and thumb abductor muscles were found to be significantly
sensitive to both acceleration and velocity (Fig. 11). Similarly, type II
afferents responded mostly to velocity. Golgi tendon organ afferents were
Figure 9. Responses of a type
Ia muscle spindle afferent from
the finger extensor.
A, During length changes
imposed on the relaxed parent
muscle by the experimenter, the
type Ia afferent showed poor
responses to static length but
clear dynamic responses.
B, The afferent also responded
to voluntary near-isometric contractions.
C, The afferent’s responses
when the participant grasped,
held and subsequently released
wooden blocks of increasing
size (40, 50 and 60mm). Instead
of discharging preferentially
during phases of muscle stretch,
the afferent displayed high discharge rates when the second
derivative of muscle length
(’acceleration’) was positive
(signified by the vertical gray
bars). The highest discharge
rates were observed when both
’acceleration’ and ‘velocity’ were
positive.
20
also recorded during block-grasping, and they were significantly affected
only by EMG activity of their parent muscle.
In addition, the study showed that the encoding of muscle length and
velocity by afferents was significantly improved when the ensemble activity
of Golgi tendon afferents was taken into consideration along with muscle
spindle activity (Fig. 12).
Study II study also demonstrated that both single afferents and ensemble
discharges failed to signal the moment of coming into contact with the
Figure 10. Responses of a type II muscle spindle afferent from the thumb abductor. A, The type II
afferent responded to passive muscle stretch of the relaxed muscle and also displayed a certain degree
of sensitivity to static length increases B, The afferent also responded during voluntary near-isometric
contractions C, The afferent’s responses when the participant grasped, held and subsequently released
wooden blocks of increasing size (40-50-60mm). This afferent discharged preferentially during phases of
muscle stretch (signified by the vertical gray bars). No consistent relationship between static muscle
length increase and discharge rate could be noted by simply observing the behaviour of the afferent
during block-grasping.
21
object during block-grasping (Paper II: Fig. 8). Lastly, it was shown that
afferents were unable to represent object size. As with Study I, the results of
this study were discussed in terms of the interactions between tendon
compliance, muscle fascicle length changes and the type of load the muscle is
acting upon.
Figure 11. Afferent population responses during the block-grasping task. Superimposed averaged
ensemble responses across all type Ia, II and Ib afferents during the grasping (left column) and releasing
phases (right column) across all block-sizes. The grasping phase included fingertip spacing and grasping
a block and the release phase included the release of the block and the return of the digits into apposition.
Both periods were 800 ms long and were fixed in time with respect to the peak negative and positive
muscle velocity, respectively, with the grasping phase starting 400 ms before and the release phase 300
ms before this event. The displayed kinematic and EMG signals were averaged across all participants
whom contributed with an afferent recording. The periods between the vertical dashed lines mark the 800
ms periods used in the regression analyses. Note the apparent lack of any significant length dependence
of the afferent discharges.
22
Figure 12. Population encoding of muscle length and velocity. A, Muscle velocity versus instantaneous
discharge rates of afferent populations (top row) and observed velocity versus that predicted from linear
regression with various combinations of populations as the independent variables (bottom row). Data from
both the grasping and releasing phases were included. Solid lines represent significant regression lines
(p<10-6). The r values for combinations which included Ib afferents were significantly higher than those of
any other combination including, in particular, the combination of only type Ia and II. B, As in A, but
relating to muscle length instead of muscle velocity. The r2 value of the combination comprised of Ia and Ib
afferents was significantly higher than that for both the combination of Ia and II and the combination of II
and Ib afferents (p<10-4).
Study III
In the third study we investigated the possibility that spindle Ia discharges
were more closely related to future than to current sensory states. This
exploration was inspired by the fact that muscle spindles are complex organs
whose output is shaped by both the current sensory state of the muscle and
an efferent command; i.e. the ‘inputs’ to the spindle match those of the socalled ‘forward sensory model’ (Wolpert and Miall, 1996; Wolpert and
Ghahramani, 2000). Therefore, if fusimotor activity biases the spindle
responses as an ‘efferent copy’ would affect a forward model, one may
hypothesise that spindle afferent discharge relates more to future sensory
23
states (e.g. in terms of length, velocity etc.) than to concurrent states. Using
simple cross-correlation, we re-examined the data presented in Study I and II
and showed that type Ia discharges do indeed encode future muscle velocity
significantly better than current velocity. Importantly, this capability went
beyond what could be expected from kinematic autocorrelations and the
ability of type Ia afferents to simultaneously signal acceleration and velocity
(Fig. 13).
Afferent discharges were well correlated with future muscle states during
some phases of the key-pressing task, but there were some occasions where
deviations in discharge rates could not be attributed to current muscle state
or to immediate future states. At certain phases during the key-pressing task,
for example, the 5 key was pressed to terminate one sequence and then again
to begin the following sequence. Although there were no notable changes in
muscle length, velocity or EMG during these periods, there were distinct
changes in the single afferent discharge (Fig. 14).
Figure 13. Real-time prediction of future muscle velocity from type Ia discharges observed during blockgrasping. A, The discharges of a type Ia afferent population (n=13; ‘grasping’ phase; cf., Fig. 11) plotted
against the velocity of their parent muscles at the same time as the discharge (0 ms) and at other times in
the future. The ensemble discharge showed the maximum correlation with the velocity ~160 ms in the
future (r=0.82). B, Correlation between type Ia ensemble discharge at time t and muscle velocity for time
advances 0-400 ms, i.e., v(t+tΔ); filled circles correspond to data in A. Also shown is the correlation
between the actual v(t+Δt) and v(t)+a(t)·Δt, i.e., the best correlation expected if the spindle output was a
mere consequence of velocity and acceleration sensitivity. At 160 ms, the r value obtained using the
ensemble discharge was significantly higher than with v(t)+a(t)·Δt, p<10-4. C, Auto-correlations of the
muscle velocity and acceleration observed during the block-grasping task. Colored areas correspond to
90% confidence intervals.
24
Figure 14.
15. Unaccounted discharge patterns during the key-pressing task. A, A type II afferent’s
discharges during a complete block of key sequences. The grey vertical bars mark key presses. Despite
different wrist postures, this type II afferent tended to increase its discharge just prior to key pressing. This
was particularly evident for the periods during which the participant performed the simple task of releasing
the final '5' key of one key sequence and again pressing the '5' key that commenced the following key
sequence ('repeat periods' in yellow).
yellow). B,
B, Ov
Overlaid
repeat periods
periods between
between sequential
sequential presses
presses ofofkey
key'5’
5
erlaid repeat
which were time-normalized to assist comparisons (UWE: Ulnar wrist extensor, RWE: radial wrist
extensor).
Further analyses showed that during a 100 ms period immediately before
pressing the first 5 key and immediately after the final 5 key of a sequence
(i.e., phases 1 and 9; cf., Fig. 4C on p.10), the discharge of several muscle
spindle afferents differed depending on the target key of the sequence, and
this was apparent immediately before executing each sequence and immediately after executing the sequence (Paper III: Fig. 3A,B).
The above findings suggested that some afferents were affected by the fusimotor system in a sequence-specific manner; in other words, afferent
discharges appeared to be under fusimotor control contingent on entire
movement sequences. In order to investigate whether or not this was true for
populations of afferents, we compared the predicted x-y coordinates of the
25
target key of each sequence generated from the afferent discharges of a
muscle spindle population (n=36) against predictions made from the
kinematic state and EMG levels of their parent muscles (cf., ‘PLS modelling’
on p.13). The predictive abilities of the two PLS models are shown in Figure
15A.
To summarise, clearly better predictions of the finger-tip coordinates were
obtained from the model based on spindle discharges (‘PLS-MS’) rather than
the model based on kinematic and EMG signals (‘PLS-MLVE’). This was
especially apparent during the beginning and end of each key sequence (i.e.
phases 1 and 8-9). The performance of the PLS models was further assessed
by comparing the models’ predictive errors and sensitivity estimates (Fig.
16). As expected from inspecting Figure 15, the PLS-MLVE model
produced more erroneous responses than the PLS-MS model.
Figure 15. Predicting fingertip coordinates from muscle spindle afferent responses and muscle states. A,
Mean x-y coordinates of the finger-tip when pressing the variable target key of a sequence (i.e. middle key
of each sequence, i.e. phase 5, cf. panel B) predicted from a PLS model which was based on muscle
spindle afferent discharges (top row) and another based on muscle length, velocity and EMG (bottom row)
during phases 1-9 (cf., inset in panel B). B, PLS model predictions from muscle spindle discharges for
phases 1-5; from top to bottom: histograms of predicted fingertip coordinates during phase 5, area
containing 90% of the predicted coordinates, and at the bottom, the areas with a probability of > 0.9 that
the target key of the sequence was correctly predicted; inset at the top of panel B depicts the colour
coding of target keys.
26
Figure 16. Assessing the performance of PLS models. Constant (A) and variable prediction error (B), and
prediction sensitivity, i.e., the probability of correct identification of the variable target key (C) of the PLS
model based on muscle spindle afferents (PLS-MS) and on muscle length, tendon velocity and EMG (PLSMLVE; solid lines represent median values; coloured zones 50% of the distribution). D, Median maximum
difference in discharge depending on the variable target key 'T', as well as the median discharge rate, and
median coefficient of discharge variation across all afferents.
Whereas the PLS-MS sensitivity around the time the target key was pressed
(phases 4-6; Fig. 16C) was understandable given the afferents’ dependence
on the muscle states, this did not explain the high sensitivity during phases
where the muscle lengths, velocities and EMG activation levels were similar
irrespective of the target-key pressed during phase 5. During the period with
highest sensitivity, the afferents showed both their largest contrast in
discharge rates between keys and their lowest coefficient of variation (Fig.
16D), both factors that should promote good predictions. Periods with low
sensitivity were associated with the lowest overall discharge rates and the
largest coefficient of variation. This relationship between the overall
discharge rates and discharge variation did not, however, explain the patterns
of sensitivity for the periods immediately before, during and after pressing
the target key 'T', nor when the 5 key following the target key was pressed.
In short, the quality of prediction appears not to have been a simple function
of the contrast in discharge rates among keys or of discharge variability.
27
DISCUSSION
This thesis is based on a compilation of three studies that generated several
new findings concerning muscle afferent responses. The discharge rates of
primary muscle spindle afferents can represent accelerations in addition to
velocities, and the discharge of primary and secondary afferents does not
necessarily represent muscle length. These findings challenge the role of
muscle spindles as the main source of hand posture information. However,
combining type Ib discharges together with muscle spindle afferent
discharges significantly improves the representation of kinematic signals.
The findings also counter the widely held belief that muscle spindles provide
unambiguous information concerning the properties of manipulated objects.
Most importantly, a novel role for muscle spindles in sensory prediction is
supported, which makes possible the use of sensory signals in feedback
control despite sensorimotor delays. Since the pertinent afferent activity was
recorded during the execution of common manual actions under
‘physiological’ conditions, the applicability of such findings to everyday life
is straight forward. In what follows, I shall discuss the aforementioned
findings and their implications.
Unexpected phase advances
Taken together, the results of Study I and II showed that spindle afferent
responses from load-bearing muscles were roughly 90 degrees more phaseadvanced than expected on the length of their parent muscle. These results
contradict the consensus according to which type Ia afferents signal muscle
length and velocity and type II signal length during ‘natural’ movements
(Pearson and Gordon, 2000). Instead, three unexpected phenomena were
observed as a direct result of the additional spindle phase advance: type II
afferents signalled velocity on par or better than type Ia afferents, neither
afferent type could substantially signal muscle length, and type Ia afferents
were able to signal acceleration and velocity. Although the majority of
muscle spindle morphological studies and afferent recordings have been
conducted in cats, there is no reason to believe that the encoding capabilities
of human muscle spindle endings differ fundamentally to those of other
mammals (Poppele and Kennedy, 1974; Kakuda, 2000). In order to explain
the presence of the unexpected phase advances therefore, one must look
beyond the encoding capabilities of the isolated muscle spindle ending.
A likely reason for the unexpectedly large phase advance in spindle responses
must be common to the primary and secondary spindle endings of a muscle,
and take into account the novel conditions under which the recordings were
28
made (i.e. varying shortening and lengthening velocities and contraction
levels of the parent muscles). Fusimotor activity alone does not seem to
provide a plausible explanation, as it modifies the spindles’ sensitivity to
length and velocity but not the phase relationship of either type Ia or type II
afferents (Matthews and Stein, 1969; Hulliger et al., 1977; Cussons et al.,
1977; Baumann and Hulliger, 1991). There have been reports of increases in
spindle phase advance when stretching relaxed muscles that had just
previously been exposed to fusimotor activity (Brown et al., 1969). While
this phenomenon has been called upon to explain spindle discharge patterns
during chewing in alert monkeys (Goodwin and Luschei, 1975), it does not
explain the phase-advanced responses of spindle afferents from the radial
wrist extensor, which was continuously acting against gravity in the keypressing task.
Another possible explanation is that the muscles were acting against a viscous
load constituted by antagonists, the skin and connective tissues. Given that
the mechanical properties of both contracting fascicles and tendons are
dominated by stiffness, their lengths will be proportional to the tendon
force. But if the muscle is acting on a viscous load, the force will be 90
degrees phase advanced on the length, and hence both type Ia and II
discharges should appear phase advanced. Future comparative studies will
hopefully shed more light on this issue. Nevertheless, it is now well
established that the muscle fascicle compartment ‘measured’ by the spindle
afferents is not always in phase with the whole muscle-tendon complex
(Hoffer et al., 1989; Griffiths, 1991; Fukunaga et al., 1997; Kawakami et al.,
2002). Indeed, even slight contractions alter the relationship between fascicle
lengths and whole muscle-tendon length (Herbert et al., 2002).
If series elasticity represented by some tendons acts so that changes in overall
muscle-tendon length differ significantly from those of muscle fascicles
(Loram et al., 2005), estimates of muscle lengths cannot rely solely on
receptors encoding the length of muscle fascicles. In retrospect, therefore, it
should not be particularly surprising that afferent predictions of length and
velocity significantly improved when the responses of Ib afferents were
incorporated in the predictive models (cf., Fig. 12). Such analyses have a
distinct physiological underpinning in spinal cord circuitry; namely, spinal
inter-neurons and spino-cerebellar neurons are known to receive both
“length-related” and “force-related” sensory inputs, creating what has been
interpreted as mixed kinematic-kinetic representations (Windhorst, 2007).
In such a framework, the role of afferent inputs from Golgi tendon organs
would be to disambiguate spindle inputs. Representations of static and
dynamic joint positions can also be provided by stretch-sensitive skin
29
mechanoreceptors as shown in neurophysiological (Edin and Abbs, 1991;
Edin, 1992, 2001, 2004; Aimonetti et al., 2007) and behavioural studies
(Edin and Johansson, 1995; Collins and Prochazka, 1996; Collins et al.,
2005).
In humans, there is currently no reason to doubt that the representation of
movement parameters is multi-modal (Collins et al., 2000, 2005;
Weerakkody et al., 2007). Given the abundance of muscle spindles and
cutaneous receptors in mammalian bodies, a quest for a ‘dominant’
mechanoreceptor in proprioceptive signalling seems misguided (Stein et al.,
2004). However, the relative weight of populations of different receptors
may vary systematically, even under ‘natural’ circumstances. For example,
one approach may be to explore the extent to which different receptor
groups contribute to the proprioception of different body parts. The
distribution of mechanoreceptor densities around the body should carry a
major clue (e.g. dependence on cutaneous receptors for lip proprioception
and a heavy reliance on muscle spindles for neck/head proprioception).
Tendon vibration (Goodwin et al., 1972) and cutaneous vibration
(Weerakkody et al., 2007) can be used in conjunction with psychophysical
measurements in order to address such issues.
Spindles inform on acceleration
A novel observation made in this thesis is that spindle afferent responses can
also be associated with acceleration. Thus far, several models of spindle
afferents have taken into account muscle length and velocity but not
acceleration and yet the simulated responses match well experimentallyobtained spindle responses (Hasan, 1983; Prochazka and Gorassini, 1998;
Mileusnic et al., 2006). However, these models have not been shown to
work well for muscle spindles residing in muscles actively working against
loads (Prochazka and Gorassini, 1998). There is evidence that signals related
to limb acceleration play a role in motor control. For example, in a study of
the reflexes evoked by postural and vestibular disturbances, Fitzpatrick et al.
(1996) found unexpectedly large phase advances which they suggested might
be attributed to central nervous processes. The current findings indicate that
some of this additional phase advance may in fact be attributed to phase
advances in the muscle afferent discharges themselves. Phase advanced
signals are beneficial for feedback control, since they can counteract phase
lags that are introduced by external loads or that are inherent in the control
apparatus (e.g. sluggish muscle contraction dynamics).
30
In addition, recent evidence has surfaced which documents the use of
acceleration-related afferent signals in more advanced sensorimotor transformations. Specifically, by studying the responses of cats to a sudden loss of
balance, Lockhart and Ting (2007) successfully reproduced the cats’ muscle
activation patterns by considering the position, velocity and acceleration of
the cats’ centre of mass. The estimated feedback gains obtained with healthy
cats were compared to those intoxicated with pyridoxine, known to destroy
large diameter afferents (Stapley et al., 2002). The main difference was a
significantly reduced acceleration feedback gain in the intoxicated cats,
which the authors suggested was likely due to a loss of type Ia afferents.
Spindles can signal future sensory states
In Study III, muscle spindles were shown to fulfil the criteria for neurophysiologically identifying a ‘forward sensory model’ (Wolpert and Miall,
1996). Their inputs were the current kinematic state of their parent muscle
and a corollary 2 command related to the α-motor command (‘efference
copy’, α-γ coactivation or β innervation); their output was an estimate of a
future kinematic state, beyond what was expected from autocorrelations and
multiple kinematic sensitivities. Although no similar results have been
previously reported, the apparent prerequisites for such a predictive
capability – i.e., simultaneous responsiveness to more than one kinematic
variable and fusimotor influence – are known to exist for muscle spindles of
various species (Prochazka, 1996).
The signals of muscle spindles have long been associated with movement
regulation. Physiological delays, however, speak against the use of sensory
information in feedback control. Yet, behavioural evidence has shown that
on-line error feedback control does occur (e.g. Cordo, 1990; Sainburg et al.,
1999). The lack of proprioceptive input has been associated with increased
variability of rapid single-joint movements (Forget and Lamarre, 1987), and
severe deficits in the coordination of multi-joint movement (Sainburg et al.,
1993). The coordination of discrete movement sequences of durations 210
ms and longer requires proprioceptive information concerning velocity and
position (Cordo et al., 1994). Furthermore, vibrating the biceps branchii
tendon during forearm movements (Cordo et al., 1995) and the wrist
extensor tendons during wrist movement (Verschueren et al., 1998) strongly
implicate muscle spindles in feedback control.
2
‘Corollary’ is used throughout to mean any efferent signal which can influence sensory
processing (Crapse and Sommer, 2008)
31
Effective feedback control becomes feasible by means of forward models.
That is, forward models circumvent sensorimotor delays because reactions to
motor errors are based on the state of the limb close to the time of reaction,
rather than on the state of the limb prevalent at the time the error occurred
(Desmurget and Grafton, 2000). Behavioural evidence of precisely this has
been recently made available in a study of perturbed arm movements
(Wagner and Smith, 2008), leading the authors to suggest the existence of a
forward model which predicts limb velocity in ‘real-time’. Regardless of
whether muscle spindles should be literally considered as ‘forward models’,
their velocity estimates are sufficiently advanced to match the minimum
delay required for trajectory corrections during reaching movements (i.e.,
80-100 ms; Paillard, 1996).
Forward models are currently believed to reside in central structures such as
the cerebellar cortex (Scott, 2004) and the cerebellum (Miall et al., 1993).
Evolution may have also favoured placing forward sensory models within
muscles for several reasons. First, the incorporation of multiple forward
models in the peripheral nervous system can certainly ease the computation
requirements placed on central structures. Second, unlike a corollary send
from a central to another central structure 3 , the muscle spindle is more likely
to receive a reliable corollary of the motor command entering the final
common pathway. In the case of β fibres, for example, the efferent axon
reaching the spindle is simply a bifurcation of the α-motor axon reaching the
skeletal muscle fibres; such corollaries should be less concerned with trial-totrial variability in transmission and noise (Faisal et al., 2008). The main
advantage of placing a forward sensory model within an effector, however, is
simply that current sensory state can be directly encoded and incorporated
into future predictions rather than estimated in a feed-forward manner
through a probably less accurate structure called a ‘forward dynamic model’
(Wolpert and Miall, 1996). Dynamic models use a copy of the motor
command and knowledge of system dynamics to produce estimates of
current state, which are then fed to forward sensory models as inputs. Such
models aim to “compensate for sensorimotor delays and to reduce the
uncertainty in the state estimate that arises because of noise inherent in both
sensory and motor signals” (Wolpert and Ghahramani, 2000). But
predicting and controlling the dynamic state of a multi-joint system is a
notoriously difficult task (e.g., Hogan et al., 1987), especially so due to joint
interaction torques (Hollerbach and Flash, 1982). Nevertheless, even with
3
As is proposed to occur in the cortico-ponto-cerebellar system (Ugolini and Kuypers,
1986; Ramnani, 2006).
32
impeccable knowledge of system and environmental dynamics, forward
models in the CNS can still be inaccurate, since their output ultimately
depends on expected motor outcomes and not on the actual forces acting on
effectors; as mentioned above, the accuracy of central predictions is subject
to “motor noise”, owing to variability in neuronal transmission 4 and muscle
fibre function. Therefore, by directly encoding current state as it is
immediately affected by system and environmental dynamics, as well as
using more reliable corollaries of the motor commands reaching the
periphery, muscle spindles justify their role as forward sensory models.
It should be emphasised that any predictions stemming from muscle spindles
only concern the state of the parent muscle, which in turn may relate to joint
angles, future movement directions etc. Higher-level neural circuits are
necessary for creating representations of a more elaborate nature. In the case
of hand position and velocity, for example, central structures may not create
sensory predictions from a ‘dynamic model’, but may use proprioceptive
information to construct a more pertinent or detailed neural representation.
Indeed, proprioceptive information from the hand is known to be necessary
for up-dating internal models (Ariff et al., 2002). Thus, an interesting
implication is that the CNS may create a representation of the sensorimotor
system which is directly based on predictive proprioceptive information,
advanced 100 ms or so in time.
Most importantly, however, predictive proprioceptive signals can be used for
feedback control only if such information is contrasted with an expected or
desired state. The discharge rates of cerebellar Purkinje cells signal
information related to limb position and velocity present at approximately
100 ms in the future (Coltz et al., 1999; Roitman et al., 2005), which has
been promoted as evidence of the cerebellum’s role as a forward sensory
model (Pasalar et al., 2006; Ebner and Pasalar, 2008). A proposed site for
comparisons of proprioceptive and cerebellar signals is the inferior olive
(Ramnani, 2006), as it receives proprioceptive afferent information from the
spinal cord and corollary information from the cerebellum. If the
proprioceptive and cerebellar systems are intimately linked in their
sensorimotor feedback roles, it should be of no surprise that cerebellar and
sensory neuropathy patients display similar motor deficits (Diener and
Dichgans, 1992; Gordon et al., 1995), and a distinct inability to counter the
effects of joint interaction torques (Sainburg et al., 1993; Bastian et al.,
4
Noise and variability certainly also affect afferent signals, but averaging across populations
of afferents counters such effects (Faisal et al., 2008), which is the approach adopted in
Study I and II.
33
1996); healthy individuals on the other hand compensate for such effects
(Sainburg et al., 1995; Gribble and Ostry, 1999).
Since muscle spindles under fusimotor control can act as forward sensory
models, a role for fusimotor activity may be to adjust the sensitivity to
length, velocity and acceleration in order to incorporate the effects of
intended actions on future sensory states, given the current sensory state. In
its simplest form, a modulation of spindle gain can even be simply adjusted
to skeleto-motor activation. In a lengthening muscle, for example, decreasing
the spindles’ overall gain in accordance with future parent muscle force
probably facilitates a rough estimation of future kinematics. More accurate
predictions are to be created if spindle sensitivity to length, velocity and
acceleration are finely tuned; however, fusimotor actions cannot adjust
spindle sensitivity to a particular kinematic variable alone, but rather adjust
the overall spindle gain (Matthews, 1981). If this extends to common
manual tasks, humans may learn to control the fusimotor system in order to
find optimal combinations of sensitivities to length, velocity and
acceleration, even if the three cannot be independently adjusted.
Evidence of α-γ dissociation
There were some occasions where deviations in discharge rates could not be
attributed to current muscle state or to immediate future states (cf., Fig. 14).
In addition, fusimotor activity during the key-pressing task was found to be
contingent on entire key-sequences (cf., Figs. 15-16). That the afferents
discharged differently depending on subsequent or previous keystrokes – in
the absence of differentiated surface EMG activity – suggests that the
fusimotor activity was dissociated from skeletomotor activity (i.e. ‘α-γ
dissociation’). The main reason for the signs of α-γ dissociation during keypressing may be the nature of the task itself. During both key-pressing and
block-grasping, the participants performed fast and unconstrained
movements, had to position their fingers accurately and apply force, and
were making and braking contact between their fingertips and objects.
Although the participants grasped blocks of different sizes in the one task,
the movement sequence was the same across trials (i.e. grasp opening
followed by grasp closing).
As recently pointed out, a universal role of corollary discharges across species
is to facilitate motor sequences (Crapse and Sommer, 2008). A parsimonious
explanation for the presence of α-γ dissociation in the key-pressing study,
therefore, relates to the serial execution of different movement sequences,
requiring different temporal and spatial coordination of several muscles. If
34
so, this may explain the failure to demonstrate significant dissociations
between fusimotor and skeletomotor activity in previous microneurography
studies with humans, even after increasing the precision requirements
(Kakuda et al., 1996), or learning to cope with disturbances (al-Falahe et al.,
1990a). Also, this may explain why we found evidence for sequence-specific
fusimotor drive in a task that was simple to execute once the participants
understood it but still obviously required clever coordination of the muscles
actuating the digits and the wrist. Given the spindles’ ability to signal future
kinematic states, it is tempting to speculate whether the purpose of the
sequence-specific and probably α-γ dissociated fusimotor activity was to
refine state predictions. Since this fusimotor activity was evidently unrelated
to the current state of the muscle, it may have represented the influence of
the synergists associated with each particular sequence; that is, the
sensorimotor system may account for the effects of motor commands send to
a parent muscle and to its synergists on the future state of the parent muscle.
A role for muscle spindles in sensory cancellation?
While position and movement sense is currently believed to result from
multi-modal sensory integration, the specific role of muscle spindle afferents
in this respect is, in fact, still unclear. The main evidence linking primary
spindle afferent responses to movement sense has been based on tendon
vibration studies. However, the illusions evoked by vibrating passive muscles
are slow and small in amplitude (Roll and Vedel, 1982), depend on context
(Rabin and Gordon, 2006) and show significant latencies following the
onset of vibrations (Cordo et al., 2005). If primary spindle afferents, on the
other hand, contribute by communicating future rather than current states,
this may explain why vibratory stimuli of relaxed muscles induce illusory
movements with significant delays.
Nevertheless, in addition to a role in motor control, forward sensory models
have also been proposed to facilitate our sense of self. That is, the process by
which sensation produced by the self (‘re-afference’) and sensations induced
by environmental influences (‘ex-afference’) has been suggested to involve a
comparison between predicted and actual sensory states (Wolpert and
Flanagan, 2001; Cullen, 2004). Hence, in establishing velocity re-afference,
both types of muscle spindle ending are likely contributors towards this goal.
35
CONCLUSIONS
1.
During active movements, muscle spindle responses were found to be
~90° more phase advanced on muscle length than expected from
previous studies in animals and humans. Consequently, the discharge of
type Ia afferents was correlated to velocity and acceleration of their
parent muscle, whereas that of type II afferents was correlated with
velocity.
2.
A parsimonious explanation for this phase advance is that muscle
fascicle length (as ‘measured’ by the muscle spindles) is proportional to
the force generated by the muscle, and that the muscles were acting on a
predominantly viscous load. This entails that information from muscle
spindle afferents is load-dependent.
3.
Combining muscle spindle and Golgi tendon organ activity generated
better representations of muscle kinematics than using spindle activity
alone. The discharge of type Ib afferents is related to contractile force.
Since this force is proportional to the length of in-series elastic elements
of muscles, this explains why type Ib responses may disambiguate
muscle spindle signals.
4.
Muscle spindles under fusimotor control can act as forward sensory
models. Their inputs are the current kinematic state of their parent
muscle and a corollary motor command, whereas their output is an
estimate of a future kinematic state. We demonstrate that type Ia
afferents predict muscle velocity ~100-160 ms in the future.
5.
The fusimotor input to the muscle spindles was found to be contingent
on planned movement sequences and not strictly coupled to the αmotor activity.
6.
A likely role of the γ motor system is to adjust spindle sensitivity to
incorporate the effects of imminent action on future muscle states,
thereby enabling muscle spindles to act as forward models.
7.
Finally, the ability of muscle spindles to act as forward sensory models
implies that sensorimotor learning includes learning to control the
fusimotor system.
36
ACKNOWLEDGEMENTS
I would like to express my appreciation to all the people that have
contributed -one way or another- to the successful completion of this
doctoral project. However, the contribution of quite a few individuals
warrants particular mention:
I would first like to thank my supervisor, Benoni B. Edin, for initiating me
into the art of microneurography and to the science of neurophysiology, and
for being enthusiastic, patient and optimistic throughout. Next is Anders
Bäckström, for bringing stoicism back into fashion but mostly for his
technical help. I would also like to thank Micael Andersson, for all the
matlab tips and his advice on physics, for occasionally being a gym buddy
and for always being a friend. Sven-Olof Johansson, for creating excellent
microneurography electrodes and the key-pressing apparatus. Roland
Johansson for impeccable advice on scientific and presentational matters.
Göran Westling for his technical help and for building gadgets of sorts. My
comrade in arms Daniel Säfström, I would like to thank for his advice, for
all of the stimulating discussions on various topics and for his contagious
gentlemanly disposition. Anna Theorin, for all of her help and support over
the years. Inga-Lill Bäckström, for help with all administrative issues.
Herbert Silfvenius, Erika Dahlin and CJ Olsson for all of their help and
encouragement. My appreciation also goes out to all the participants in my
microneurography experiments, for their discomfort was not in vain. To my
beloved wife, Linnéa Dimitriou, for her love and support but mostly for
showing me that life can be without compromise. Last but not least, I would
like to thank my family in Cyprus, in Sweden, in Athens and in London for
all of their encouragement and love.
Sincerely,
Michael Dimitriou
December - 2008
37
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