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Do agility and skull architecture influence thegeometry of the mammalian vestibulo-ocular reflex?Nathan Jeffery and Philip G. Cox

Division of Human Anatomy and Cell Biology, School of Biomedical Sciences, University of Liverpool, Liverpool, UK

Abstract

The spatial arrangement of the semicircular canals and extraocular muscles of the eye has been of considerable

interest, particularly to researchers working on adaptations of the vestibulo-ocular reflex. Here we offer the

first, extensive comparative analysis of the spatial relationships between each extraocular muscle and the canal

providing its primary excitatory stimulus. The sample consisted of 113 specimens, representing 51 extant mam-

malian species. Hypotheses tested included that variations in the spatial alignments are linked with differences

of skull morphology and with differences of agility during locomotion. Internal morphologies were visualized

with magnetic resonance imaging and were measured with landmark-based vectors and planes. Values for body

mass and agility were taken from the existing literature. Data were investigated for trends and associations

with standard bivariate and multivariate statistical methods as well as with phylogenetically adjusted bivariate

methods. The findings clearly show that species differences in the alignment of each extraocular muscle relative

to the canal providing its primary excitatory stimulus are closely associated with changes of orbit morphology.

The results also indicate that the actions of the oblique muscles interchange with those of the superior and

inferior recti muscles when comparing lateral-eyed (rabbit) with frontal-eyed species (cat). There was only weak

evidence to support the notion that canal–muscle alignments differ significantly among species according to

how agile they are. The results suggest that semicircular canal morphology is arranged primarily for detecting

head movements and then secondarily, if at all, for diminishing the burden of transforming vestibulo-ocular

reflex signals in the most agile species.

Key words agility; convergence; extraocular; frontation; orbit; semicircular.

Introduction

Gaze stabilization by the vestibulo-ocular reflex (VOR)

depends on the ability of a chain of neurons to transform

signals representing the planes of the semicircular canals

into signals that represent the pull directions of the extraoc-

ular muscles (e.g. Graf et al. 1993; Brettler & Baker, 2001;

Raphan & Cohen, 2002). The complex functional network

of interconnections that governs this transformation is

underpinned by a set of six primary couplings, each consist-

ing of an extraocular muscle and the canal that provides its

primary excitatory stimulus (Szentagothai, 1950; Fritzsch,

1998; Graf & Klam, 2006; Fig. 1). Numerous researchers have

investigated the functional and structural associations of

these six primary pairs to gain insights into the basic

workings of the VOR and its evolutionary adaptations (e.g.

Simpson & Graf, 1981; Ezure & Graf, 1984a,b; Daunicht &

Pellionisz, 1987; Spoor & Zonneveld, 1998; Rabbitt, 1999;

Raphan & Cohen, 2002; Cox & Jeffery, 2007, 2008). Here we

employ a comparative analysis of the spatial alignment of

primary canal-muscle pairs to address some fundamental

questions regarding the influence of skull architecture on

the alignments and the possible link with interspecific dif-

ferences of agility during locomotion.

Previous studies have shown that the primary canal–

muscle alignments vary between canals, between species as

well as during development (e.g. Ezure & Graf, 1984a; Cox &

Jeffery, 2008). For example, in the cat, the arrangements

found with regard to the lateral canal are closer to parallel

than the equivalent alignments found in relation to the

anterior canal, though this is not the case for all species (see

Cox & Jeffery, 2008). Current data for interspecific differ-

ences only cover a small number of species, including

humans, cats and rabbits, as well as a few rodents (e.g.

Simpson & Graf, 1981; Ezure & Graf, 1984a,b; Daunicht &

Pellionisz, 1987; Cox & Jeffery, 2008). Recently, Cox & Jeffery

(2008) increased the number of specimens sampled to

53 adults representing seven mammalian species. They

Correspondence

Nathan Jeffery, Division of Human Anatomy and Cell Biology, School

of Biomedical Sciences, University of Liverpool, Sherrington Build-

ings, Ashton Street, Liverpool L69 3GE, UK. T: + 44 151 7945514;

F: + 44 151 7945517; E: [email protected]

Accepted for publication 7 January 2010

Article published online 23 February 2010

ªª 2010 The AuthorsJournal compilation ªª 2010 Anatomical Society of Great Britain and Ireland

J. Anat. (2010) 216, pp496–509 doi: 10.1111/j.1469-7580.2010.01211.x

Journal of Anatomy

reported that the posterior canal – contralateral inferior rec-

tus (PSC-cIR) angle is about 30� in the rabbit but closely

aligned in the guinea pig (� 0�) and misaligned in the oppo-

site direction by 15� in humans. This amounted to a 45�range of variation across the seven species studied. The rab-

bit and human also occupied the extremes for measure-

ments of the anterior canal – ipsilateral superior rectus (ASC-

iSR; )21� rabbit to +13� human), the anterior canal – contra-

lateral inferior oblique (ASC-cIO; )15� rabbit to +30� human)

as well as the posterior canal – ipsilateral superior oblique

(PSC-iSO; )13� rabbit to +17� human) and lastly, the lateral

canal – contralateral lateral rectus (LSC-cLR; )25� rabbit to

+15� human). The sequence for the lateral canal – ipsilateral

medial rectus (LSC-iMR) is slightly different, with a range of

variation marked by the rat ()13�) at one end and humans

(+19�) at the other, with cats showing the closest alignment

(� 0�). In an earlier paper, Cox & Jeffery (2007) also

observed that these angles are not fixed during early in ute-

ro development. The angles varied throughout most of the

prenatal period from a state of misalignment towards, but

never actually reaching, a more parallel geometry.

Although it is not entirely clear what influences changes

in the alignment of the primary canal–muscle pairs, Cox &

Jeffery (2008) favour Simpson & Graf’s (1981) hypothesis

that differences of skull architecture are primarily responsi-

ble, particularly changes of orbit position within the skull.

Across adult mammals, and during prenatal development,

there is a trend in which the bony orbits, and presumably

the eye and extraocular muscles, shift position towards the

midline (orbital convergence) and towards the front of the

skull (orbital frontation) (see Noble et al. 2000; Jeffery et al.

2007; Heesy, 2008; see also Fig. 2). Another architectural

feature to consider is the orientation of the petrous bones

that encapsulate the semicircular canals. The angle between

the long axes of the petrous bones has been shown to vary

significantly across adult extant primates and fossil homi-

nids, as well as during primate fetal development (Spoor,

1997; Jeffery, 2003). For instance, Spoor (1997) documented

that in adult modern humans the petrous axes are more

coronally orientated than in other extant great apes and

fossil hominids. Jeffery & Spoor (2002) showed a similar

coronal re-orientation of the petrous bones with increasing

human fetal age. Assuming the arrangement of the canals

is fixed within the petrous bone, any petrous re-orientation

will shift the positions of the semicircular canals contained

therein relative to the axes of the extraocular muscles. Such

changes may compensate or exacerbate misalignments due

to concomitant changes of orbit morphology and extraocu-

lar muscle geometry. Because of the limited range of spe-

cies studied so far, Simpson & Graf (1981) and, more

Fig. 1 Illustration showing the primary

functional couplings between each

extraocular muscle and the canal providing

the excitatory stimulus. SO, superior oblique;

IO, inferior oblique; SR, superior rectus; IR,

inferior rectus; MR, medial rectus; LR, lateral

rectus; ASC, anterior semicircular canal; PSC,

posterior semicircular canal; LSC, lateral

semicircular canal; VIII, vestibular part of

vestibulo-cochlear nerve; VN, vestibular nuclei;

MLF, medial longitudinal fasciculus; III,

oculomotor nucleus; IV, trochlear nucleus; VI,

abducent nucleus.


Geometry of the mammalian vestibulo-ocular reflex, N. Jeffery and P. G. Cox 497

recently, Cox & Jeffery (2008) were unable to establish sta-

tistically the nature of the interspecific differences of align-

ment in relation to changes of skull morphology. Here we

examine the potential influence of skull architecture by

testing the following hypothesis with a larger and more

diverse sample of mammals:

Hypothesis 1 – skull architecture

This states that interspecific changes in the angle between

each extraocular muscle and the canal providing its primary

excitatory stimulus are due to alterations of orbital fronta-

tion, orbital convergence and petrous orientation. The null

prediction is that changes of canal–muscle angles are inde-

pendent of changes in orbit and petrous position.

The overall functional demands placed on the VOR trans-

formation can be simplified in terms of the frequency and

erraticism of head movements, collectively referred to here

as agility, together with the extent of any spatial mismatch

in the primary canal–muscle planes. This basic formulization

raises an interesting question: does the greater functional

challenge of integrating divergent canal–muscle axes place

any tangible limit on the range of head movements that a

species can effectively transform into extraocular muscle

pull directions? Or, can the neuron chain adequately trans-

form any spatial mismatch for any range of agility, implying

that the spatial arrangement of the canals and muscles is

inconsequential (see for example work on frogs by Pantle &

Dieringer, 1998)? Previous studies have linked canal orienta-

tions and relative canal sizes to differences of agility (e.g.

Spoor & Zonneveld, 1998; Spoor et al. 2007; Yang & Hullar,

2007). More specifically, Spoor & Zonneveld (1998) pro-

posed that the empirical association of canal morphology

with agility partly results from the functional and spatial

requirements of the vestibulo-ocular reflex. Here we test

the following hypothesis in order clarify the notion of a

spatial constraint on the ability of the VOR to compensate

for agile movements:

Hypothesis 2 – agility

This suggests that among fast-moving, agile species the

additional functional demands of erratic and high fre-

quency head movements has favoured a closer alignment

of canal–muscle planes. This predicts that the misalignment

measured among agile species will be significantly less than

that in slower moving species. The prediction will be tested

by comparing published data on agility (e.g. Spoor et al.

2007) against alignment angles. Note that the wording of

the hypothesis is a matter of convenience and does not

purport to distinguish cause from effect. In other words, if

we do observe a link, we cannot determine whether it is

agility constraining canal–muscle orientation, canal–muscle

orientation constraining agility or some other undefined

influence.

Methods

Sample and imaging

Image data were collected and collated for 113 specimens, rep-

resenting 51 extant mammalian species from 15 eutherian

orders (as defined by Wilson & Reeder, 2005). Specimens were

A B

C D Fig. 2 Sketches of a European rabbit skull

(Oryctolagus cuniculus; A,C) and a chimp

skull (Pan troglodytes; B,D) in superior (A,B)

and lateral (C,D) profile illustrating differences

of orbital convergence towards the midline

(A,B) and orbital frontation towards the

horizontal plane (C,D) (See Noble et al. 2000

& Heesy, 2004).


Geometry of the mammalian vestibulo-ocular reflex, N. Jeffery and P. G. Cox498

Tab

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kindly provided by numerous institutions and were imaged on

seven different systems (see Table 1 and Acknowledgements for

details). The majority of specimens were imaged with the

4.7 Tesla imaging and spectroscopy unit (Sisco-Varian, USA) at

Queen Mary, University of London, with a T2-weighted spin-

echo multi-slice sequence (TE = 20–50 ms; TR = 8000–16 000 ms).

Data were zero-filled to between 256 · 256 and 512 · 512 data

points, Fourier transformed and exported as raw binary files

representing fields of view (FOV) from 30.8 to 115.2 mm. Slice

thickness ranged from 0.24 to 0.90 mm. Similar apparatus and

comparable sequences were used to acquire data on the other

systems (see Table 1 for details). Data for 13 specimens, includ-

ing all the modern humans, were provided by James Rilling

(Emory University), Dirk Bartz (University of Leipzig) and the

NIH Virtual Human Project (http://www.nlm.nih.gov/research/

visible/). Slices for all 113 specimens were interpolated to form

isometric voxels (vertices ranging from 0.05 to 0.9 mm) with the

bicubic spline function in IMAGEJ (W. Rasband, National Institute

of Mental Health, Bethesda).

Measurements

Landmarks representing the semicircular canals, extraocular

muscles, orbits, midline and petrous bones were taken from

each set of the images using AMIRA 5.2 (Mercury Systems Inc.,

Chelmsford, MA, USA). Each extraocular muscle [ipsilateral supe-

rior rectus (iSR), medial rectus (iMR) and superior oblique (iSO),

and contralateral inferior rectus (cIR), lateral rectus (cLR) and

inferior oblique (cIO)] was represented by two landmarks, the

origin and an insertion of that muscle. Vectors representing

each muscle in a three-dimensional space were calculated from

the landmark co-ordinates for the origin and insertion. The ves-

tibular apparatus was defined by a series of 3D landmarks again

using AMIRA 5.2. Each landmark series traces the voxels at the

centre of the canal lumen and the voxels that continue the arc

of the canal through the vestibule. Landmarks were positioned

with reference to at least three slice directions and 3D represen-

tations. The number of points recorded varied between eight

and 42 per canal depending on the resolution of the image and

the relative size of the canal, but in all cases they were close

enough to capture accurately the entire circumference of the

canal including the entire duct, ampulla and utricle. Planes of

best fit for the anterior, posterior and lateral semicircular canals

(ASC, PSC, LSC) were calculated from the landmark sets using a

principal components analysis. Further groups of landmarks

were recorded to define the midsagittal plane, the long axis of

the petrous bone, the cranial base, and the convergence and

frontation of the orbit. Angles were calculated between each

canal plane and the vectors of the two muscles principally acti-

vated by that canal using the dot product and the right-hand

rule (see Table 2). Further details concerning the measurements

are given in Table 2 and Cox & Jeffery (2008).

Species were coded by P.G.C. for agility from one to six with

reference to the values published by Spoor et al. (2007), one

being the least agile and six the most agile. Only one species,

Choloepus didactylus, fell within agility category 1 and was

grouped with category 2. Primate body masses were taken from

Smith & Jungers (1997), Galidia elegans was from Macdonald

(2001), and all others were from Silva & Downing (1995). Where

the sex was known, mean values for that sex were calculated.

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Analysis

Significant deviations from univariate normality in the angular

data were tested for with the Shapiro–Wilk function in PAST

v1.89 (Hammer et al. 2001). In the present sample, absolute

error could in theory increase with body size, as voxels are typi-

cally larger for the larger species due to the physical limitations

of accommodating and imaging these samples. At least two

voxels are required to identify the canal lumen, but in practice

a cube of 3 · 3 · 3 voxels is needed to visualize the lumen in 3D

and then identify the central voxel correctly. To give an indica-

tion of the potential influence of resolution, and indirectly body

size, on the landmarking of the correct voxel we took one large

(giraffe) and one small (mouse) specimen and randomly altered

each x, y and z co-ordinate value by either +1, )1 or 0, repre-

senting the 3 · 3 · 3 voxel matrix. We then recalculated the

angles. The process was repeated 10 times per specimen. In

addition, a one-way ANOVA was calculated for each angular mea-

surement in Microsoft EXCEL 2007 to determine whether the

interspecific variance between species that are represented by

Table 2 Measurement details.

Abbreviation Description

Landmarks

BA Basion: midline point on the anterior margin of the foramen magnum

cIOI Centroid of the contralateral inferior oblique muscle as it inserts on the eyeball

cIOO Centroid of the contralateral inferior oblique muscle near its origin on the orbital wall

cIRI Centroid of the contralateral inferior rectus muscle as it inserts on the eyeball

cLRI Centroid of the contralateral lateral rectus muscle as it inserts on the eyeball

cO Centroid of the contralateral optic nerve as it passes through the optic foramen

cPETap Anteriormost and medialmost point of the contralateral petrous bone

DS Apex of the dorsum sellae in the midsagittal plane

FC Foramen caecum: midline point marking the pit between the crista galli and the endocranial wall

of the frontal bone

iMRI Centroid of the ipsilateral medial rectus muscle as it inserts on the eyeball

iO Centroid of the ipsilateral optic nerve as it passes through the optic foramen

iPETap Anteriormost and medialmost point of the ipsilateral petrous bone

iSOI Centroid of the ipsilateral superior oblique muscle as it inserts on the eyeball

iSOO Point on the medial orbital wall at which the ipsilateral superior oblique muscle abruptly changes

direction (trochlea)

iSRI Centroid of the ipsilateral superior rectus muscle as it inserts on the eyeball

ORBi Inferiormost point on the bony orbital margin

ORBm Medialmost point on the bony orbital margin

ORBs Superiormost point on the bony orbital margin

PETaA Apex of petrous bone in anteriormost coronal slice in which cochlea is visible

PETaM Apex of petrous bone in coronal slice in which internal auditory meatus is visible

PETaP Apex of petrous bone in posteriormost coronal slice in which posterior semicircular canal is visible

PETbA Base of petrous bone in anteriormost coronal slice in which cochlea is visible

PETbM Base of petrous bone in coronal slice in which internal auditory meatus is visible

PETbP Base of petrous bone in posteriormost coronal slice in which posterior semicircular canal is visible

PPS Posterior midsagittal point on the presphenoideum

Muscle vectors

cIO Contralateral inferior oblique muscle: cIOO to cIOI

cIR Contralateral inferior rectus muscle: cO to cIRI

cLR Contralateral lateral rectus muscle: cO to cLRI

iMR Ipsilateral medial rectus muscle: iO to iMRI

iSO Ipsilateral superior oblique muscle: iSOO to iSOI

iSR Ipsilateral superior rectus muscle: iO to iSRI

Semicircular canal planes

ASC Anterior semicircular canal plane of best fit

LSC Lateral semicircular canal plane of best fit

PSC Posterior semicircular canal plane of best fit

Other planes

ORB Orbital convergence plane: plane containing iO, ORBi and ORBs

CRB Cranial base plane: plane containing PPS, cPETap and iPETap

FRO Orbital frontation plane: plane containing ORBi, ORBm and ORBs

PBP Petrous bone plane: plane of best fit of PETaA, PETaM, PETaP, PETbA, PETbM and PETbP

MSP Midsagittal plane: plane containing BA, DS, FC and PPS



more than one individual (n = 18) was greater than the intra-

specific variance due to, amongst other things, landmarking

error, sexual dimorphism, and population differences.

A product-moment correlation coefficient matrix was

produced to explore the statistical relationships among the

data. Following convention, agility scores, which are ordinal cat-

egorical values, were treated as forms of continuous measure-

ment data (see Spoor et al. 2007). It is important to appreciate

that closely related species can resemble one another in a hier-

archical fashion due to their shared phylogenetic history as well

as due to shared functional demands. This can lead to increases

of type I, false-positive errors where phylogenetic relationships

are misinterpreted as functional relationships and vice versa

(Garland et al. 2005). Here we repeated the correlation matrix

with standardized phylogenetic independent contrasts (Garland

& Ives, 2000; Garland et al. 2005) computed with MESQUITE v2.5

(Maddison & Maddison, 2008) and the PDAP macro (Midford

et al. 2003). The tree used in the analysis (see Table 1) was

created using topological information and branch length data

taken from Bininda-Emonds et al. (2007).

Tests for significant differences between categories of agility

were carried out initially with Student’s t-tests in Microsoft EXCEL

2007 (two-tailed, equal variance) and then with multiple analy-

sis of covariance (MANCOVA) in SPSS version 16 (SPSS Inc., Chicago,

IL, USA). Agility scores were treated as forms of ordinal data.

With regard to the second hypothesis (see Introduction), the

direction of the angle is not important since, for example, an

alignment of )15� is presumed to place the same functional

demands on the VOR as an angle of +15�. Consequently angles

were standardized to unsigned values.

Results

None of the Shapiro–Wilk tests showed any significant devi-

ation of angular data from a normal distribution (W ranged

from 0.96 to 0.99, P > 0.05). The randomization of co-ordi-

nates showed that variations of angles ranged on average

by 2.3� for the mouse and by 4.2� for the giraffe. The series

of ANOVA computations showed that the interspecific

variance was significantly greater than the intraspecific

variance for all angles among the 18 species represented by

more than one individual (P < 0.001). These findings indi-

cate that interspecific trends are not significantly distorted

by noise from, for example, landmark error and variations

of spatial resolution.

The correlation matrix given in Table 3 highlights several

statistical relationships among the data collected. The phy-

logenetically adjusted coefficients indicate that some of the

standard correlations, particularly the weaker ones, were

influenced by the phylogeny of the sample. Results reveal

that the strongest corrected associations include those

between orbital convergence and the angles involving the

vertical canals (e.g. ASC < iSR, ASC < cIO, PSC < iSO and

PSC < cIR) as well as between orbit frontation and the lat-

eral canal angles (LSC < iMR and LSC < cLR).

Hypothesis 1 predicts that changes of primary pair orien-

tations correlate with changes of orbit and petrous orienta-

tion. Results for the corrected product moment correlations

Tab

le3

Pear

son’s

pro

duct

mom

ent

corr

elat

ion

mat

rix

for

unco

rrec

ted

and

phyl

ogen

etic

ally

corr

ecte

ddat

a.

log

BM

Ag

ilit

yA

SC<

iSR

ASC

<cI

OPSC

<iS

OPSC

<cI

RLS

C<

iMR

LSC

<cL

RM

SP<

OR

BM

SP<

PB

PFR

O<

CR

B

log

BM

)0.3

9)

0.0

20.1

40.1

50.3

80.1

3)

0.0

2)

0.1

5)

0.0

10.4

7

un

corr

ect

ed

corr

ela

tio

ns

Ag

ilit

y)

0.4

7)

0.2

6)

0.3

0)

0.2

9)

0.2

4)

0.2

5)

0.0

60.3

1)

0.3

7)

0.2

2

ASC

<iS

R)

0.0

9)

0.2

10.6

70.4

90.5

50.1

20.3

4)

0.8

70.4

70.1

6

ASC

<cI

O)

0.0

2)

0.0

60.3

50.5

00.6

50.1

00.2

6)

0.8

30.4

00.2

4

PSC

<iS

O0.1

9)

0.2

10.2

30.1

90.2

10.3

90.3

6)

0.5

80.2

00.3

6

PSC

<cI

R0.2

0)

0.1

60.2

30.5

1)

0.1

5)

0.1

00.0

6)

0.7

00.4

00.2

5

LSC

<iM

R0.1

4)

0.0

4)

0.1

0.0

90.4

7)

0.1

10.6

5)

0.1

50.0

10.5

2

LSC

<cL

R)

0.0

40.0

90.0

80.1

70.2

6)

0.1

40.6

1)

0.3

10.2

00.5

6

MSP

<O

RB

)0.1

00.3

3)

0.7

1)

0.5

4)

0.4

7)

0.4

7)

0.0

8)

0.0

6)

0.5

6)

0.3

0

MSP

<PB

P0.0

3)

0.4

00.1

3)

0.2

)0.0

20.0

2)

0.2

4)

0.1

9)

0.1

50.1

8

FRO

<C

RB

0.3

9)

0.0

6)

0.1

1)

0.0

80.3

70.2

40.5

40.4

50.0

10.0

1

Ph

ylo

gen

eti

call

yad

just

ed

corr

ela

tio

ns

To

pri

gh

t–

pro

du

ctm

om

en

tco

rrela

tio

nco

effi

cien

tsfo

rst

an

dard

,u

nco

rrect

ed

data

;b

ott

om

left

–p

rod

uct

mo

men

tco

rrela

tio

nco

effi

cien

tsfo

rp

hyl

og

en

eti

cin

dep

en

den

tco

ntr

ast

s;

sig

nifi

can

tco

rrela

tio

ns

(P<

0.0

5)

are

hig

hli

gh

ted

inb

old

.



are presented in the last three rows of Table 3. Significant

corrected relationships were observed between orbital fron-

tation and the lateral canal–muscle angles (LSC < iMR and

LSC < cLR) and, to a lesser extent, in relation to the poster-

ior canal ipsilateral superior oblique angle (PSC < iSO). In

addition, orbital convergence was significantly correlated

with the vertical canal–muscle angles (ASC < iSR, ASC < cIO,

PSC < iSO, and PSC < cIR). None of the adjusted compari-

sons involving petrous orientation was significant.

Figures 3 and 4 show plots of the vertical canal–muscle

angles against orbital convergence. Regression lines are

shown for illustrative purposes only. In all four plots there is

a similar trend: the canal–muscle angles are divergent in lat-

eral-eyed species, the canal–muscle angles are close to par-

allel alignment among species like Cyclopes didactylus, and

then the canal–muscle angles diverge in the opposite direc-

tion in frontal-eyed species (e.g. modern humans). A similar

trend was observed in plots of lateral canal angles against

orbital frontation (Fig. 5). These reveal that, as the orbits tilt

forward, the lateral canal–muscle angles initially converge

to parallel alignment and then diverge again as the orbits

continue to frontate. These findings support the hypothesis

that interspecific variations in the spatial relationships of

the VOR are associated with differences of orbit morphol-

ogy but not petrous orientation.

Hypothesis 2 predicts that each category of agility can be

distinguished in terms of primary pair orientations. A simple

pair-wise Student’s t-test was used to determine whether

any of the unsigned angles differ significantly between the

different categories of agility. Results given in Table 4 show

that nine t-tests revealed significant differences between

categories of agility on the basis of canal–muscle angles.

However, only in two cases did the more agile species

have significantly closer alignments than the less agile

species (ASC < cIO, 2 vs. 4 and 2 vs. 6). Taking a more

stringent, Bonferroni-adjusted view of significance levels

A

B

Fig. 3 Bivariate plots against orbital

convergence of (A) the angle between the

anterior semicircular canal and the ipsilateral

superior rectus (ASC < iSR) and (B) the angle

between the anterior semicircular canal and

the contralateral inferior oblique muscle

(ASC < cIO). Data points are coded according

categories of agility. Refer to Table 1 for

abbreviations.



(i.e. P < 0.005 rather than < 0.05 as the threshold) leaves

only two significant comparisons involving canal–muscle

angles (ASC < cIO 2 vs. 4 and LSC < cLR 2 vs. 5). Predictions

were also tested with a multiple analysis of covariance

(MANCOVA) with agility set as the independent variable and

primary pair angles as the dependent variables. Table 3 sug-

gests that agility and primary pair angles can also covary

with orbit convergence. Hence, orbit convergence was

included in the MANCOVA as a covariate. Results given in

Table 5 show that there is only a weakly significant

(P = 0.028) differentiation among categories of agility in

terms of the angle LSC < cLR.

Discussion

This paper set out to resolve several enduring questions

regarding the spatial relationships of the semicircular

canals and extraocular muscles that constitute the major

anatomical components of the vestibulo-ocular reflex. Over

100 specimens, representing 51 extant mammalian species

from 15 eutherian orders, were investigated. Findings from

the randomization tests and ANOVAs indicate that noise due

to, for example, differences of image resolution has only a

minimal influence on our findings. Nevertheless, it is impor-

tant to keep in mind that by their very nature, these data

will contain noise that may possibly lead to false-negative

results. We have tried to limit this by carefully formulating

the hypothesis and by approaching the analysis with several

methods (e.g. bivariate, multivariate and phylogenetic inde-

pendent contrasts).

Our results clearly support the view that most of the spa-

tial variation in the VOR system corresponds to shifts in the

position of the extraocular muscles with the surrounding

orbits by way of convergence and frontation but not with

changes of canal position due to petrous re-orientation (see

Cox & Jeffery, 2008). The lack of any significant link of

A

B


convergence of (A) the angle between the

posterior semicircular canal and the

contralateral inferior rectus (PSC < cIR) and

(B) the angle between the posterior

semicircular canal and the ipsilateral superior

oblique muscle (PSC < iSO). Data points are

coded according categories of agility. Refer to

Table 1 for abbreviations.



canal–muscle alignment with petrous angle, despite the

notable interspecific differences of petrous angle reported

here and elsewhere (Spoor, 1997), is intriguing. It is possible

that a trend exists but is overshadowed by the stronger

association with orbit position or by sample noise. Alterna-

tively, the spatial link could remain hidden if the canals had

shifted position to compensate for rotation of the sur-

rounding petrous bones. It seems unlikely that the canals

can move about sufficiently to compensate for movements

of the surrounding petrous bones. Rather, given how

tightly regulated and conserved canal morphology can be,

it seems most likely that the position of the developing

canals is already offset in the embryo to take into account

subsequent rotations of the petrous bones later in ontog-

eny. This is consistent with Cox & Jeffery’s (2007) findings

that during fetal life the canal–muscle alignments converge

towards, but never reach, a parallel arrangement (Cox &

Jeffery, 2007) as the petrous bones re-orientate towards the

coronal plane (Jeffery & Spoor, 2002). If the arrangement of

the developing canals were genetically predetermined to

account for petrous rotation later in life, then associations

between canal–muscle alignments and petrous orientation

among adults would be explained in terms of shared

genetic inheritance. Findings reported here for the angles

ASC < iSR, ASC < cIO and PSC < cIR show correlations with

petrous orientation that are largely dependent on phylog-

eny. Further morphological studies of the developing inner

ear should help answer the question of whether the posi-

tion of the developing inner ear is fixed or moving within

the re-orientating petrous bone. However, the problems of

defining a rotating canal framework, within a rotating pet-

rous framework, potentially within a rotating skull frame-

work, are especially challenging and will require more

sophisticated methodologies such as geometric morpho-

metrics (e.g. O’Higgins, 2000).

The greatest canal–muscle misalignments are typically

found amongst the most lateral-eyed species, such as the

rabbit (Oryctolagus cuniculus) and the pika (Ochotona

A

B


frontation of (A) the angle between the

lateral semicircular canal and the ipsilateral

medial rectus (LSC < iMR) and (B) the angle

between the lateral semicircular canal and the

contralateral lateral rectus muscle (LSC < cLR).

Data points are coded according categories of

agility. Refer to Table 1 for abbreviations.



macrotis), and amongst the most frontal-eyed species such

as modern humans and the grey seal (Halichoerus grypus).

In general the closest alignments between canals and mus-

cle planes are found among species with intermediate

forms of orbit orientation (approximately 40–60� relative

to the midline and 70–90� relative to the anterior cranial

base). Such species include the West European hedgehog

(Erinaceus europaeus) and the silky anteater (Cyclopes

didactylus). The intermediate position of the orbit is

similar to the primitive condition seen in, for example,

Zalambdalestes, a Late Cretaceous fossil from Mongolia

that is generally considered to be a stem eutherian

Table 4 Means and pair-wise t-test P-values representing differences between unsigned means within each agility category (two-tailed, equal

variance).

Log BM ASC<iSR ASC<cIO PSC<iSO PSC<cIR LSC<iMR LSC<cLR MSP<ORB MSP<PBP FRO<CRB

Means

2 (n = 6) 3.6 12.0 26.5 16.5 10.8 10.3 3.5 30.5 55.8 88.0

3 (n = 2) 4.7 12.0 10.0 15.5 9.0 13.0 12.5 47.5 42.5 93.5

4 (n = 27) 3.9 12.0 12.3 9.6 9.5 9.3 11.9 45.0 40.7 82.2

5 (n = 6) 3.2 14.7 14.5 19.5 18.5 11.7 22.0 54.5 41.3 67.7

6 (n = 10) 2.4 9.7 13.2 11.8 14.9 7.9 10.4 45.8 35.1 80.0

t-Test

2 vs. 3 0.319 1.000 0.104 0.932 0.824 0.703 0.019 0.105 0.273 0.688

2 vs. 4 0.595 0.992 0.001 0.056 0.680 0.735 0.010 0.012 0.024 0.440

2 vs. 5 0.548 0.615 0.079 0.657 0.169 0.765 0.000 0.015 0.046 0.009

2 vs. 6 0.010 0.366 0.010 0.414 0.506 0.417 0.083 0.017 0.001 0.293

3 vs. 4 0.360 0.995 0.677 0.315 0.915 0.465 0.911 0.782 0.874 0.392

3 vs. 5 0.251 0.785 0.590 0.732 0.221 0.862 0.184 0.619 0.919 0.078

3 vs. 6 0.009 0.608 0.570 0.701 0.548 0.303 0.763 0.847 0.406 0.335

4 vs. 5 0.203 0.497 0.555 0.008 0.006 0.447 0.005 0.119 0.926 0.055

4 vs. 6 0.000 0.411 0.755 0.449 0.092 0.525 0.610 0.857 0.264 0.721

5 vs. 6 0.110 0.218 0.765 0.187 0.542 0.257 0.019 0.233 0.237 0.102

P-values less than 0.05 are shown in bold.

Table 5 MANCOVA of the five agility categories

whilst controlling for differences due to

orbit convergence.Source

Dependent

variable

Type III sum

of squares df

Mean

square F Significance

Corrected Model ASC<iSR 491.54 5 98.31 1.81 0.12998

ASC<cIO 1911.65 5 382.33 7.63 0.00003

PSC<iSO 684.89 5 136.98 1.57 0.18719

PSC<cIR 613.96 5 122.79 1.69 0.15647

LSC<iMR 105.32 5 21.06 0.48 0.79038

LSC<cLR 1162.56 5 232.51 4.35 0.00257

Intercept ASC<iSR 7.41 1 7.41 0.14 0.71361

ASC<cIO 2991.92 1 2991.92 59.69 0.00000

PSC<iSO 1018.37 1 1018.37 11.69 0.00134

PSC<cIR 959.87 1 959.87 13.21 0.00071

LSC<iMR 527.72 1 527.72 11.97 0.00119

LSC<cLR 139.61 1 139.61 2.61 0.11288

Convergence

(MSP<ORB)

ASC<iSR 397.19 1 397.19 7.32 0.00962

ASC<cIO 864.92 1 864.92 17.25 0.00014

PSC<iSO 42.03 1 42.03 0.48 0.49088

PSC<cIR 86.75 1 86.75 1.19 0.28031

LSC<iMR 22.53 1 22.53 0.51 0.47803

LSC<cLR 104.22 1 104.22 1.95 0.16925

Agility ASC<iSR 123.72 4 30.93 0.57 0.68594

ASC<cIO 492.84 4 123.21 2.46 0.05902

PSC<iSO 657.83 4 164.46 1.89 0.12903

PSC<cIR 603.16 4 150.79 2.08 0.09980

LSC<iMR 92.11 4 23.03 0.52 0.71922

LSC<cLR 642.29 4 160.57 3.01 0.02783



(Novacek et al. 1997; Wible et al. 2004). Analysis of the

reconstruction of Zalambdalestes lechei given in Wible

et al. (2004, p.89) indicates an orbital convergence of

between 35� and 45�. It could be speculated therefore

that the extreme forms of orbital convergence and diver-

gence are derived features from an underlying, and per-

haps biophysically optimal, condition in which the canal

and muscle planes are closer to parallel.

Differences of canal–muscle planes between a lateral-

and a frontal-eyed species (rabbit and cat) were previously

reported by Simpson & Graf (1981) and later by Ezure &

Graf (1984b). Their results are not directly comparable

because the authors reflected planes onto the ipsilateral

side and in the latter paper the authors consolidated func-

tionally distinct muscle vectors into three muscle planes (iIR-

iSR, iSO-iIO, and iMR-iLR). Nonetheless, their findings

showed that alignments are similar between the cat and

rabbit, with the possible exception of the angles PSC < iSO

and PSC < iIR. These two angles showed species differences

of approximately 20� and the closest alignment was seen in

the rabbit. Simpson & Graf (1981) also reiterate previous

observations that pitching the head back and forth can lead

to vertical compensatory eye movements in frontal-eyed

species but torsional movements in lateral-eyed species.

From this, and from their own observations that the pri-

mary pair geometries are roughly similar in the cat and the

rabbit, the authors infer that differences of compensatory

movements must be due to differences in the secondary

actions of the extraocular muscles and related neuronal

connections (see also Ezure & Graf, 1984b). Our results, rep-

resenting samples of eight or more individuals per species,

do not completely concur with these interpretations, show-

ing, for instance, that the angle ASC < iSR is much closer to

parallel alignment in the cat (13�) than in the rabbit ()24�).We also show that all of the remaining canal–muscle angles

differ between cats and rabbits, with the possible exception

of LSC < iMR. Indeed, our findings reveal a possible reversal

in the relative positions and primary actions of the extraoc-

ular muscles with respect to the anterior canal in frontal- vs.

lateral-eyed species. In, for example, the cat, grey seal,

modern human and other great apes, the plane of the

anterior canal is more closely aligned with the superior rec-

tus than with the inferior oblique. By contrast, in, for exam-

ple, the rabbit, the elephant shrew (Rhynchocyon cirnei)

and the sheep (Ovis aries), the plane of the anterior canal is

closer to that of the inferior oblique than the superior rec-

tus. This could explain, in terms of primary muscle actions,

the differences in the compensatory movements of the eyes

seen between lateral- and frontal-eyed species (Simpson &

Graf, 1981).

We have shown that there are no tangible differences of

agility in species with divergent or with close to parallel

vertical canal–muscle pairs. Findings do suggest that differ-

ences of agility are only weakly associated with differences

in the spatial arrangement of the lateral canal and the

contralateral lateral rectus muscle. The pair-wise t-tests indi-

cate that this distinction occurs primarily between agility

categories 2v5 and 4v5. However, contrary to hypothesis 2,

the closest alignments were found in the least agile species.

The observed discrimination probably reflects several nested

trends representing phylogeny, differences due to orbit ori-

entation and perhaps also differences in the function of the

canals. For example, Fitzpatrick et al. (2006) suggests that

the lateral canal commands navigation, whereas the ante-

rior and posterior canals command reflex adjustments in

response to movement. Such functional differences could

explain why we find the only distinction among agility cate-

gories with the lateral canal alignment LSC < cLR and why

Spoor et al. (2007) have found that the strongest compara-

tive link of agility is in relation to the size of the lateral

canal.

The observed differentiation between categories of agil-

ity in terms of orbit and petrous orientation is worth dis-

cussing briefly (refer to Tables 3 and 4). Findings from our

sample indicate that the least agile species are more likely

to exhibit cranial morphologies at either end of the range

compared with the most agile species, which typically span

the middle range of observed skull morphologies. Given

the results reported above, it seems unlikely that these

skull–agility associations are simply the spatial by-products

of VOR function. Rather, as the orbits form an integral part

of the face and are carried forward in the direction of

movement, it seems likely that the observed distinction of

agility in terms of orbit orientation reflects a stronger,

underlying link with how the head is held during locomo-

tion. Similarly, the petrous bones occupy a region of the

skull that defines head orientation in relation to the axial

skeleton. These findings imply that the link between head

orientation during movement and the geometry of the

semicircular canals is of far greater importance than the

spatial arrangement of the VOR system.

Our results support the view that VOR circuitry has the

capacity, whether that be inherent or learned, to respond

to the range of functional challenges found among extant

mammals and that the spatial arrangement of the extraocu-

lar muscles relative to the semicircular canals is of limited

functional significance. Findings indicate that the canal

morphology is arranged primarily for perceiving canal–head

movements and then secondarily, if at all, for yielding accu-

rately encoded spatial signals for compensatory movements

of the eyes (see also Spoor et al. 2007; Yang & Hullar, 2007).

The next step is to elaborate further the spatial relation-

ships of the canals with respect to the rest of the skull and

how these relationships correspond to functional differ-

ences.

Acknowledgements

First of all we would like to thank the Biotechnology and Bio-

logical Sciences Research Council for funding this work (grant



no. BBD0000681). For allowing access to specimens we would

like to thank the following: Liz Chadwick (Cardiff University);

Rob Asher, Adrian Friday, Ray Symonds and Matt Lowe

(University Museum of Zoology, Cambridge); Chris Faulkes and

Haidee Price-Thomas (Queen Mary, University of London); Fay

Penrose (University of Liverpool Veterinary School); Fred Spoor

(University College London); Andrew Kitchener, Jerry Herman

and Phil Howard (National Museums of Scotland); Judith Chupa-

sko (Museum of Comparative Zoology, Harvard University); Bob

Connolly (University of Liverpool); Rod Penrose (UK Cetacean

Strandings Investigation Programme). We also thank Dirk Bartz

(University of Leipzig) and Jim Rilling (Emory University) for pro-

viding additional MR datasets of modern humans and apes. For

assistance with the imaging we would like to thank: Bill Bimson

and Valerie Adams (Magnetic Resonance and Image Analysis

Research Centre, University of Liverpool); Karen Davies and

Steve Williams (Imaging Science and Biomedical Engineering,

University of Manchester); Alasdair Preston [Queen Mary, Uni-

versity of London, and lately Preclinical Imaging Unit, Institute

of Psychiatry, King’s College London (funded by the British

Heart Foundation)]; Franklyn Howe (Cardiac and Vascular Sci-

ences, St George’s, University of London); Andrew Webb and

Thomas Neuberger (Huck Institute Magnetic Resonance Centre,

Penn State University). Finally, we thank two anonymous refer-

ees and the editor, Professor Dan Lieberman, for helpful com-

ments and suggestions for improvements to this paper.

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