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The Journal of Neuroscience, July 1, 1999, 19(13):5632-5643
Spatial Attention Deficits in Patients with Acquired or
Developmental Cerebellar Abnormality
Jeanne
Townsend1, 3,
Eric
Courchesne1, 3,
James
Covington3,
Marissa
Westerfield2, 3,
Naomi Singer
Harris3,
Patrick
Lyden1,
Timothy P.
Lowry3, and
Gary A.
Press4
Departments of 1 Neurosciences and
2 Cognitive Science, University of California, San Diego,
La Jolla, California 92093-0217, 3 Laboratory for Research
on the Neuroscience of Autism, Children's Hospital Research Center, La
Jolla, California 92037, and 4 Department of
Neuroradiology, San Diego Kaiser Permanente, San Diego, California
92120
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ABSTRACT |
Recent imaging and clinical studies have challenged the concept
that the functional role of the cerebellum is exclusively in the motor
domain. We present evidence of slowed covert orienting of visuospatial
attention in patients with developmental cerebellar abnormality
(patients with autism, a disorder in which at least 90% of all
postmortem cases reported to date have Purkinje neuron loss), and in
patients with cerebellar damage acquired from tumor or stroke. In
spatial cuing tasks, normal control subjects across a wide age range
were able to orient attention within 100 msec of an attention-directing
cue. Patients with cerebellar damage showed little evidence of having
oriented attention after 100 msec but did show the effects of attention
orienting after 800-1200 msec. These effects were demonstrated in a
task in which results were independent of the motor response. In this
task, smaller cerebellar vermal lobules VI-VII (from magnetic
resonance imaging) were associated with greater
attention-orienting deficits.
Although eye movements may also be disrupted in patients with
cerebellar damage, abnormal gaze shifting cannot explain the timing and
nature of the attention-orienting deficits reported here. These data
may be consistent with evidence from animal models that suggest damage
to the cerebellum disrupts both the spatial encoding of a location for
an attentional shift and the subsequent gaze shift. These data are also
consistent with a model of cerebellar function in which the cerebellum
supports a broad spectrum of brain systems involved in both nonmotor
and motor function.
Key words:
cerebellum; visual attention; orienting attention; spatial attention; autism; lesion
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INTRODUCTION |
Evidence from brain imaging and
clinical studies has aroused considerable new interest in the function
of the cerebellum. Although cognitive impairment has been reported in
patients with cerebellar disorders since the nineteenth century, the
dominant view remains that the cerebellum is associated exclusively
with motor function (for review, see Schmahmann, 1997 ). Now that view is challenged by functional imaging [positron emission tomography and
functional magnetic resonance imaging (fMRI)] studies reporting activation in the cerebellum that is associated with nonmotor cognitive
processes including attention, perception, language, and working memory
(for review, see Cabeza and Nyberg, 1997; Courchesne and Allen, 1997).
For example, fMRI studies have reported activation of the posterior
cerebellum in normal subjects during a visual selective attention task
with no motor component (Allen et al., 1997), an attention-shifting
task (Le et al., 1998), and spatial or temporal cuing tasks (Coull and
Nobre, 1998).
Because the cerebellum has primary or higher order connections to brain
systems controlling motor, social, and cognitive function (for review,
see Akshoomoff et al., 1997; Courchesne and Allen, 1997; Schmahmann,
1997), it is well placed to affect a variety of motor and nonmotor
behaviors. Cerebellar abnormalities have been identified as an
important component of the pathology of autism, a neurological disorder
in which social and cognitive development is severely affected. Autopsy
studies have reported substantial developmental reduction in Purkinje
cell numbers (20-95% across individuals) in the cerebellar vermis and
hemispheres in 17 of 19 autism cases examined (Williams et al., 1980;
Bauman and Kemper, 1985, 1986, 1990, 1994; Ritvo et al., 1986; Arin et al., 1991; Fehlow et al., 1993; Bailey et al., 1998) (for the two cases
in which Purkinje cell loss was not found, in one the Purkinje cells
were abnormal, and in the other the diagnosis may have been Rett
syndrome, not autism). Quantitative magnetic resonance imaging
(MRI) studies from eight independent research groups have also found
abnormally small vermal lobules VI-VII in the majority of the several
hundred total autistic subjects studied (for review, see Courchesne et
al., 1994b; Courchesne, 1997a). Inadequate or uncontrolled signals from
the cerebellum could disrupt many if not all of the brain systems that
contribute to dysfunctional behaviors in autism (Courchesne, 1995;
Yeung-Courchesne and Courchesne, 1997). Among the most salient of these
is impaired manipulation of attentional resources. For example,
autistic individuals and patients with acquired cerebellar damage are
slow to shift attention between and within sensory modalities
(Courchesne et al., 1994c; Akshoomoff and Courchesne, 1992, 1994).
Patients with damage to the cerebellum are also slow to orient visual
attention in space (Townsend et al., 1992, 1996a,b). Using a spatial
cuing task (Posner et al., 1984) in which attentional function is
indexed by response time and a task in which attention is indexed by
accuracy of performance, we have demonstrated that subjects with autism
who have developmental cerebellar abnormality require nearly a second
to benefit from a spatial cue. Our data from small samples have
suggested that those with greater hypoplasia of cerebellar vermal
lobules VI-VII have more severe attention-orienting deficits.
Here we compare spatial attention orienting in patients with autism to
those with acquired cerebellar lesions. Evidence from two different
tasks suggests slowed spatial attention orienting is associated with
structural cerebellar abnormality.
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MATERIALS AND METHODS |
Research participants
Study participants were 18 nonretarded individuals with autism,
62 normal control subjects, and nine patients with cerebellar lesions
from tumor or stroke (five left, one right, and three asymmetric
bilateral). Individual lesions are shown in Figure 1, and regions of overlap are shown in
Figure 2. Thirty-five of the control
subjects (mean age, 41.5 ± 24; range, 16-82), seven of the
autism subjects (mean age, 27.9 ± 12; range, 13-42), and nine of
the lesion subjects (mean age, 45.8 ± 21; range, 15-75) participated in the spatial detection task. Thirty-one of the control
subjects (mean age, 34.3 ± 20; range, 15-75), 13 of the autism
subjects (mean age, 27.7 ± 8; range, 13-42), and seven of the
lesion subjects (mean age, 42.1 ± 22; range, 15-75) participated in the spatial discrimination task. Four of the autism subjects were
part of a sample whose data from the spatial detection task were
previously reported (Townsend et al., 1996a). One of these and three
other of the 18 autism subjects were part of a sample whose data from
the spatial discrimination task were previously reported (Townsend et
al., 1996b).
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Figure 1.
MRI images showing the most extensive section of
lesion for each cerebellar stroke or tumor patient. Displayed MRI
images for subjects J.C. and B.M. illustrate bilaterality of the
lesions and do not necessarily show the largest lesion area. Images are
T2-weighted 3-mm-thick slices acquired in the axial plane. The
cerebellar slice for subject D.L. is from the film of a clinical MRI
study using gadolinium contrast to enhance the lesion site.
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Figure 2.
Tracings show the degree of lesion overlap among
all nine cerebellar lesion patients. Lesion tracings used the selected
cerebellar slice (relative position indicated in the figure as
1, 2, 3, and 4)
from each patient. Right side lesions were transposed to the left side
to better illustrate the degree of lesion overlap in the cerebellar
hemispheres and vermis. Lesion tracings were overlaid on cerebellar
slices traced from an MRI film of a nonlesioned normal control
subject.
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Participants with autism all met DSM-III-R (American Psychiatric
Association, 1987) criteria for autistic disorder. Thirteen subjects
also received the Autism Diagnostic Interviews (ADI) (Le Couteur et
al., 1989), the Autism Diagnostic Observation Schedule (ADOS) (Lord et
al., 1989), and 15 subjects received the Childhood Autism Rating Scale
(CARS) (Schopler et al., 1980). Scores for these diagnostic tests are
presented in Table 1.
None of the autistic subjects had additional psychiatric or
neurological diagnoses. All participants with autism were screened for
the presence of fragile X syndrome, and all were found to be negative.
Normal control participants were volunteers recruited from the
community. Controls had no history of substance abuse, special
education, major medical or psychiatric illness, or developmental or
neurological disorder. Ages and intelligence quotient (IQ) scores for the subject groups are presented in Table
2. We obtained IQ data for all of the
autism and lesion subjects, and for 35 of the 62 control subjects.
Brain measures from MRI
Participants had MRI according to previously described protocols
(Courchesne et al., 1988, 1994b). All subjects were cooperative and
were imaged without sedation. Images were acquired on a 1.5 T GE
magnet. Protocols used for measurement of cerebellar vermis and for
brain volume are described below. In addition to these protocols,
subjects received high resolution scans (1.2-1.6 mm slice thickness)
in sagittal and coronal planes from which three-dimensional reconstructions of the whole brain were done.
Individuals with autism are from a group with bilateral cerebellar
abnormalities that have been previously reported (Courchesne et al.,
1988, 1994b). Vermal area and brain volume measures for autism, lesion,
and normal control groups are shown in Table
3. For all brain measures, images were
coded, and normal and non-normal images were randomly intermixed so
that area and volume estimates were calculated by researchers who were
blinded to the subject's group. MRI films from all lesion patients
were read by a neuroradiologist (G.A. Press) who identified lesion
sites and examined the images for any additional structural damage or
abnormalities. We were unable to image one lesion subject (D.L.), but
the lesion site for this subject was verified from previous MRIs and is
included in those displayed in Figure 1. Because we had films only, we were unable to complete quantitative analysis for brain structures on
this lesion subject. We obtained complete brain data for 43 normal
control subjects and for 15 autism subjects. We were unable to image
one autism subject and were able to obtain only partial sets for two
others (vermis measures only).
Cerebellar vermal area
Vermal lobules I-V (lingula, centralis, culmen) and VI-VII
(declive, folium, tuber) were traced by hand from midsagittal
T1-weighted spin-echo magnetic resonance images [repetition time (TR),
600 msec; echo time (TE), 12-25 msec; 256 × 256 matrix; field of
view (FOV), 16; 5 mm slice thickness]. To obtain midsagittal sections that were comparable across subjects, axial localizers were used to
determine a plane of section that passed through the rostral and caudal
convexities at the levels of the superior, middle, and inferior
positions of the vermis. If a line drawn through the two convexities
differed by more than 1 mm in the right/left position between the three
transverse levels or was rotated or torqued relative to the sagittal
plane, the subject's head was repositioned, and the process was
repeated. Precise alignment is critical for accurate vermis measurement
and cannot be determined using extracerebellar structures such as the
corpus callosum (Courchesne et al., 1994d).
Vermal lobules were traced on images magnified to provide adequate
resolution to determine anatomic landmarks. Tracings were done at
Silicon Graphics workstations using software that computed the area in
traced regions. The boundary between vermal lobules I-V and lobules
VI-VII was defined as the line joining the anterior aspect of the
primary fissure to the apex of the fourth ventricle. The boundary
between lobules VI-VII and lobule VIII was defined as the line joining
the anterior aspect of the prepyramidal fissure to the apex of the
fourth ventricle. For additional details of imaging protocol, alignment
of vermis, and anatomic landmarks, see Courchesne et al. 1988, 1989,
and 1994a-d.
For correlational analyses, vermal measures were divided by
intracranial brain volume (see below) to control for overall brain size.
Brain and intracranial volumes
These volumes were measured using 3 mm axial interleaved T2- and
PD-weighted images (TR, 3000 msec; TE, 25 and 90 msec; 1 number of
excitations; FOV, 20 cm). First, skull and extracranial matter were
removed from the T2-weighted images using a semiautomated procedure
that used both thresholding and manual tracing for each slice. The
lowest slice matching the external morphology of the inferior medulla
was chosen as the inferior boundary of the brain. All brain tissue and
CSF spaces at and above the lowest slice level as well as the pituitary
and infundibulum were included in the brain volume. Excluded from the
intracranial measures were: skull, fat, mastoid, and nasal sinuses,
venous sinuses, blood vessels, and vessel artifact beyond the brain
surface, bony protuberances, and the cranial nerve roots as they extend
beyond the brain surface.
Next, gray matter, white matter, and CSF pixels were classified using
an automatic segmentation algorithm developed in our laboratory (E. Courchesne, H. J. Chisum, J. Townsend, A. Cowles, J. Covington, B. Egaas, S. Hinds, T. Lowry, and G. A. Press, unpublished observations). This approach was analogous to previous
approaches using feature space in the semiautomated segmentation of
PD/T2 protocols nearly identical to our image protocol (Jackson et al., 1994; Matsumae et al., 1996). The principle difference is that our
algorithm is fully automated and does not require the user to choose
pixel clusters in PD/T2 feature space. Our algorithm used all pixels in
the image set to form a global histogram in PD versus T2 "feature
space".
Using a maximum likelihood criteria algorithm (Vannier et al., 1985),
pixel clusters were classed as parenchyma (gray and white), CSF,
nonbrain, and partially volumed nonbrain and CSF. All parenchyma pixels
were then automatically separated into gray and white matter pixels
using a three-dimensional local contrast algorithm. In this
three-dimensional local contrast algorithm, the local threshold for
gray and white matter pixels was computed from pixel statistics within
a cube 2 cm on a side surrounding the pixel being classified (cube = 29 pixels × 29 pixels × 7 slices = 5887 surrounding
voxels). The use of this local contrast makes the segmentation
relatively insensitive to the signal inhomogeneities intrinsic to
scanning that plague simpler methods of segmentation, such as uniform
thresholding over an entire slice.
This automated classification has been validated using in
vivo magnetic resonance brain images from nine subjects
(Courchesne, Chisum, Townsend, Cowles, Covington, Egaas, Hinds, Lowry,
and Press, unpublished observations). For eight of these brain
datasets, all gray matter, white matter, and CSF spaces within the
right hemibrain were manually traced from the T2 and PD images at three different slice locations: the level of the centrum semiovale, the
level of the thalamus and basal ganglia, and the level of the pons. The
areas designated by this expert-based manual tracing as gray matter,
white matter, and CSF within each slice were calculated. Statistical
analyses showed that correlations between manual and automatic
classification were 0.982 for gray matter, 0.961 for white matter, and
0.887 for CSF. For the ninth brain in the validation dataset, the
expert neuroanatomist manually traced cortical gray matter on 20 slices
using the PD and T2 images; measures from those manual tracings and
from the automated classification algorithm correlated >99%.
Each two-dimensional pixel in the images represents a three-dimensional
volume (a voxel), and the intracranial volume (ICV) was determined by
summing volumes of all voxels designated as gray, white, and CSF, plus
half of the volume of all voxels designated as CSF partially volumed
with skull. Total brain volume was determined in a similar manner using
gray and white matter voxels only. Total CSF volume was constructed
from fully volumed CSF voxels plus half the volume of all voxels
designated as CSF partially volumed with skull.
Spatial target detection task (Townsend et al., 1996a)
This spatial task is patterned after a widely used spatial cuing
task (Posner et al., 1984). Subjects were seated in a testing room, 90 cm from a 36 cm monitor. The basic visual display was a central
fixation cross flanked on the left and right by 4 cm square boxes at
6° of visual angle (Fig.
3,top). An asterisk 2.5 cm in diameter was the target stimulus. Each trial began with a
stimulus-directing cue (one of the boxes brightened), or by a null cue
in which no information was provided about the subsequent target
location (i.e., both boxes brightened or neither box brightened). After
a delay of 50, 100, or 800 msec, the target was presented in either the
left or the right box. The subject's task was to maintain fixation on
the central cross and to press the button when the target was detected.
The target was displayed until the subject responded or for a maximum
of 2 sec. If a subject failed to respond within the allotted time or
responded <100 msec after the target onset, the trial was repeated.
The intertrial interval was 1 sec. Targets were preceded by valid cues
in two thirds of the trials, by invalid cues in one sixth, and by null
cues in one sixth. Within a cue condition, cues and targets occurred at random and with equal probability on the right or the left and with
equal probability in each of the three delay intervals. The total
number of trials was 432 (there were 48 valid trials in each of the six
conditions, and there were 12 invalid and 12 null trials in each of the
six conditions). Response time was evaluated as a function of
cue-to-target delay and cue type (valid, invalid, or null).
Spatial target discrimination task (Townsend et al., 1996b)
This task was similar in design to the spatial detection task,
but required a target discrimination rather than simple target detection (Fig. 3, bottom). The basic visual display was as
described above. The target was a block figure "E" that could be
oriented up, down, left, or right. As in the detection task, each trial began with an attention-directing cue (the box on either the left or
the right was brightened). After a delay of 100, 800, or 1200 msec the
target was presented in either the cued location (80% probability) or
the uncued location (20% probability). Within a cue condition, cues
and targets occurred at random and with equal probability on the right
or the left and with equal probability in each of the three delay
intervals. The total number of trials was 288 (there were 36 valid
trials in each of the six conditions; there were 12 invalid in each of
the six conditions). Chance performance for a trial was 25%.
After 50 msec duration, the target was masked by a figure that included
all features of the target in any orientation. Three lesion patients
(L.S., J.C., and C.K., ages 17, 53, and 75, respectively) and
five older control subjects (ages 71, 73, 73, 75, and 78) received a
version of the task in which target duration was 100 msec, because they
were unable to perform the task at greater than chance (25% accuracy)
with 50 msec target duration, regardless of the attentional
cue-to-target delay. This difficulty may reflect reduced visual acuity
and/or slowed visual processing in these subjects. Although it is
certainly not ideal to have differences in this experimental parameter,
it was unavoidable. It is, of course, the case that increasing the
target duration also increases the time to orient attention. Because,
however, that increase was constant across the orienting delay
conditions, it did not affect the primary results or conclusions based
on comparisons between delay conditions.
The subject's task was to move a joystick lever to indicate the
direction of the target orientation (up, down, left, or right). Both
response times and accuracy of response were recorded. Accurate performance in this task depended on speed of processing the target, not motor speed of the response. That is, subjects had only the brief
delay between target onset and mask in which to process the target
information but had several seconds in which to execute a motor
response. Subjects were instructed to respond accurately, not quickly.
This design effectively separates the speed of attention orienting
(indexed by the cue-to-target delay) from the speed of perceptual
processing (the target-to-mask duration) and speed of the motor response.
Data analysis
Repeated measures ANOVA and t tests were used
to evaluate statistical significance of effects (BMDP statistical
software; Dixon et al., 1990). These analyses are relatively
insensitive to heterogeneity of variance, even with small samples
(Edwards, 1985). If variances and sample sizes are unequal, however,
the statistical test may be either too conservative or too liberal (i.e., too conservative if larger variance is associated with larger
sample, see Glass et al., 1972; Kirk, 1982). For this reason, t tests were computed using separate rather than pooled
estimates of variability (Welch Test) if Levene F-tests for differences in variability in groups were significant. Separate variance estimates also use computed degrees of freedom (Dixon et al., 1990; appendix B.6). In repeated measures ANOVAs, Greenhouse-Geisser corrections were
used if sphericity assumptions were violated. To avoid confusion, the
standard degrees of freedom are reported rather than the adjusted degrees of freedom associated with the Welch test and the
Greenhouse-Geisser test.
Response time measures. Median response times
(RTs) were computed for each subject for each condition and then
averaged across groups.
Index of attention orienting. In these tasks, the more
quickly attention is directed to a cued location, the faster (detection task) or more accurate (discrimination task) will be the response. Short cue-to-target delays provide little time to orient attention; longer delays provide more. An index of the speed with which attention can be oriented to the cued location is the difference in RT or accuracy at the validly cued location as a function of the
cue-to-target delay (orienting deficit detection task = RT at 800 msec delay  RT at 100 msec delay; orienting deficit
discrimination task = percent correct at 100 msec delay percent correct at 1200 msec delay).
Index of attention cost. The effect of precuing when the cue
is invalid is expressed as the difference in RT or accuracy to targets
appearing at the validly cued compared with the invalidly cued location
(validity deficit detection task = RT at the validly cued
location RT at the invalidly cued location; validity deficit discrimination task = percent correct at the invalidly cued
location percent correct at the validly cued location).
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RESULTS |
IQ Measures
Table 2 presents verbal and performance IQ measures for all
subject groups. The verbal and performance subscales (vocabulary, comprehension, block design, and object assembly) represent relative strengths (block design and object assembly) and weaknesses (vocabulary and comprehension) in the autism cognitive profile (Dawson, 1983; Lincoln et al., 1988, 1995). Autism subjects had significantly lower
scores than control subjects on all IQ measures
(p < 0.05 for all) and lower scores than
cerebellar lesion patients on overall verbal IQ (VIQ), performance IQ
(PIQ), and on the verbal subscales (p < 0.05 for all). For autism subjects, VIQ and verbal subscales also fall in
the below normal range based on Wechsler normative data. However, PIQ
and performance subscales are within the range of normal performance
(VIQ and PIQ mean of 100, SD of 15; all subscales mean of 10, SD of 3;
Wechsler, 1981). Cerebellar lesion subjects had significantly lower
scores than control subjects on overall verbal and performance IQ
(p < 0.05). The overall IQ scores for the
lesion subjects fall within the range of normal performance based on
the Wechsler normative data.
MRI measures
Table 3 presents cerebellar vermal and brain volume measurements
for all subject groups. In groups of subjects for whom complete MRI
data were available, subjects with autism were significantly younger
than normal control subjects (t(56) = 2.34;
p < 0.025). The area measures of vermal lobules
VI-VII were smaller in autism subjects than in control subjects
(t(56) = 2.12; p < 0.04).
Area measures of vermal lobules VI-VII adjusted for intracranial
volume were also significantly smaller in autism subjects than in
normal control subjects (t(56) = 2.77;
p < 0.008). CSF volume was greater in cerebellar
lesion subjects than in normal control subjects (t(49) = 2.62; p < 0.015)
and greater in lesion subjects than in subjects with autism
(t(21) = 3.53; p < 0.0065). There were no other significant differences in brain measure
comparisons between subject groups.
Performance at the validly cued location (attention
orienting/performance facilitation)
Spatial target detection task
The following ANOVAs are group × cue-to-target delay
repeated measures analyses comparing RT to targets at the validly cued location at the three delay intervals (50, 100, or 800 msec) for the
three groups (control, autism, or cerebellar lesion). Overall, normal
control subjects had faster response times at the validly cued location
than did cerebellar lesion subjects
(F(1,48) = 9.35; p < 0.004). All subjects were faster with longer cue-to-target delays
(F(4,96) = 50.44; p < 0.0001). The rate of change was greater for subjects with autism
(F(2,96) = 13.12; p < 0.0004) and for cerebellar lesion subjects
(F(2,96) = 4.68; p < 0.03)
than for normal control subjects. Follow-up analyses showed that
compared with control subjects, lesion subjects' rate of change was
greater to both ipsilesional targets
(F(2,96) = 5.12; p < 0.025) and to contralesional targets
(F(2,96) = 3.79; p < 0.05;
Fig. 4A). At 50 msec
compared with 800 msec cue-to-target delay, normal control subjects'
responses were 27 msec slower, and autism subjects' responses were 122 msec slower. Cerebellar lesion patients' responses were 92 msec slower
to ipsilesional targets and 73 msec slower to contralesional targets.
These orienting deficits were significantly larger for autism subjects
than for normal control subjects (t(40) = 5.50; p < 0.0001; Fig. 4B).
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Figure 4.
A, RT at the validly cued location
for normal control subjects (filled square
symbols, solid line), autism subjects
(filled circle, long dashed line),
and cerebellar lesion subjects for targets in the ipsilesional
(filled triangles, short dashed
line) and contralesional (open triangles,
short dashed line) visual fields as a function of the
amount of time to orient attention to that location (50 and 800 msec
cue-to-target delays). Normal control subjects achieved the fastest
response speed with only 50 msec to orient attention and did not
improve performance significantly with longer cue-to-target intervals.
Autism and cerebellar lesion subjects improved significantly with
longer cue-to-target intervals (i.e., more time to orient attention).
B, RT-orienting deficits (RT at validly cued location at
800 msec cue-to-target delay RT at validly cued location at 50 msec cue-to-target delay) for all groups. Autism and cerebellar lesion
subjects showed significantly faster RT with more time to orient
attention. C, Accuracy (percent correct) at the validly
cued location for normal control subjects (filled square
symbols, solid line), autism subjects
(filled circle, long dashed line),
and cerebellar lesion subjects for targets in the ipsilesional
(filled triangles, short dashed
line) and contralesional (open triangles,
short dashed line) visual fields as a function of the
amount of time to orient attention to that location (100 and 1200 msec
cue-to-target delays). Normal control subjects achieved the most
accurate performance with only 100 msec to orient attention and did not
improve performance significantly with longer cue-to-target intervals.
Autism and cerebellar lesion subjects improved significantly with
longer cue-to-target intervals (i.e., more time to orient attention).
Chance performance accuracy was 25%. D,
Accuracy-orienting deficits (percent correct at validly cued location
at 100 msec cue-to-target delay percent correct at validly cued
location at 1200 msec cue-to-target delay) for all groups. Autism and
cerebellar lesion subjects showed significantly greater accuracy with
more time to orient attention.
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Differences in RT to targets after null cues as a function of the
cue-to-target delay were smaller for the patient groups than
differences after valid cues, but the patterns were similar. At 50 msec
compared with 800 msec cue-to-target delay, normal control subjects'
responses after null cues were 32 msec slower, and autism subjects'
were 89 msec slower. Cerebellar lesion patients' responses were 73 msec slower to ipsilesional targets and 44 msec slower to
contralesional targets. These orienting deficits after null cues were
only marginally significantly larger in patient groups than in controls
(autism vs controls, p < 0.13; lesion vs controls,
p < 0.08). However, the magnitude of the improvement in RT with increasing delays in the absence of an attentional cue
suggests the possibility that some general factor such as motor
preparation may also contribute to attention-orienting effects at the
validly cued location.
Spatial target discrimination task
The following ANOVAs are group × cue-to-target delay
repeated measures analyses comparing RT to targets at the validly cued location at the three delay intervals (100, 800, or 1200 msec) for the
three groups (control, autism, and cerebellar lesion). Overall, normal
control subjects were more accurate at the validly cued location than
were subjects with autism (F(1,48) = 14.10; p < 0.0005). There was no significant difference in
overall accuracy of performance at the validly cued location between
normal control and cerebellar lesion subjects. All subjects were more
accurate with longer cue-to-target delays
(F(2,96) = 7.37; p < 0.0015). The rate of improvement in accuracy over the three
cue-to-target delays was greater for subjects with autism
(F(2,96) = 5.84; p < 0.005) and for cerebellar lesion subjects
(F(2,96) = 4.49; p < 0.015) than for normal control subjects. Follow-up analyses showed that
compared with control subjects, lesion subjects' rate of change was
greater to both ipsilesional targets
(F(2,96) = 4.49; p < 0.015) and to contralesional targets
(F(2,96) = 4.37; p < 0.02;
Fig. 4C). At 100 msec compared with 1200 msec cue-to-target delay, normal control subjects were 1.6% more accurate, and autism subjects were 14% less accurate. Cerebellar lesion patients were 15%
less accurate to ipsilesional targets and 12% less accurate to
contralesional targets at 100 msec compared with 1200 msec cue-to-target delay. These orienting deficits were significantly larger
for autism subjects (t(42) = 3.14;
p < 0.0035) and cerebellar lesion subjects to
ipsilesional targets (t(36) = 2.67;
p < 0.012) and to contralesional targets
(t(36) = 2.24; p < 0.032)
than for normal control subjects (Fig. 4D).
In the spatial discrimination task, larger orienting deficits
(difference in accuracy at the validly cued location with 100 and 1200 msec cue-to-target delay using ipsilateral orienting deficits for
cerebellar lesion patients) were associated with smaller vermal lobules
VI-VII (r = 0.44; F(1,37) = 8.78; p < 0.0054). This correlation is slightly
larger when the area measure for vermal lobules VI-VII is divided by
intracranial brain volume to control for overall brain size
(r = 0.47; F(1,37) = 10.62; p < 0.0025; Fig. 5).
Because brain measures in some of the lesion and autism subjects were
extreme and may have inflated the magnitude of the correlation
coefficient, a Spearman rank order correlation was also computed for
the association between orienting deficits and vermal lobules VI-VII.
With this nonparametric test, smaller vermal lobules VI-VII were also
associated with larger orienting deficits
(r(39) = 0.40; p < 0.01).
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Figure 5.
Correlation of orienting with vermal lobules
VI-VII in 22 normal control subjects, 10 autism subjects, and 7 cerebellar lesion subjects. Orienting deficit is an index of time to
orient attention computed from response at the cued location as
follows: (percent correct with 100 msec cue-to-target interval percent correct with 1200 msec cue-to-target interval). Vermal lobule
VI-VII area measures in each subject were divided by that subject's
intracranial brain volume to control for overall size of brain.
|
|
Larger orienting deficits (more negative orienting scores) were also
associated with larger intracranial brain volume (r = 0.36; F(1,37) = 5.63; p < 0.025) and with lower performance IQ (r = 0.44;
F(1,37) = 8.68; p < 0.0056), but not with age or other brain measures, including cerebellar
vermal lobules I-V. Performance IQ was also significantly correlated
with a measure of overall performance, the average percent correct at
the validly cued location (higher performance IQ associated with
overall better performance; r = 0.42;
F(1,37) = 7.70; p < 0.009). None of the MRI brain measures were correlated with this
measure of overall performance accuracy. There were also significant
intercorrelations among size of cerebellar vermal lobules and amount of
CSF. More CSF was associated with smaller vermal lobules VI-VII
(r = 0.66; F(1,37) = 28.33; p < 0.0001) and I-V (r = 0.41; F(1,37) = 7.49; p < 0.0095).
Partial correlations were computed to estimate the unique association
with the orienting index of vermal lobules VI-VII, ICV, and PIQ. With
the linear effect of the other variables removed, the correlation of
vermal lobules VI-VII with the orienting score was 0.39 (i.e., smaller
lobules VI-VII associated with greater orienting deficits), whereas
correlations for ICV and PIQ were reduced to 0.12 and 0.39, respectively. When the overall measure of performance accuracy was
added to the model, partial correlations for vermal lobules VI-VII and
ICV changed little (0.35 and 0.14, respectively), and the partial
correlation for PIQ was reduced to 0.27.
A subset of the normal control subjects in which there were no
significant age or PIQ differences from the cerebellar lesion patients
(n = 10; age range, 16-71; mean age, 27 ± 17;
PIQ = 106.6 ± 10) were analyzed to further examine the
possible effect of PIQ on orienting differences. Results were identical
to those from comparisons using the entire control sample. Compared to this PIQ-matched control sample, cerebellar lesion patients had significantly greater orienting deficits (0.03 vs 0.20;
t(15) = 2.43; p < 0.029).
It was, of course, not possible to form a similarly matched group for
the autism subjects because their PIQ scores were below the normal range.
Cerebellar lesion patients with lesions that included the vermis (L.S.
and J.C., Fig. 1) had the largest orienting deficits (55 and 37%
change, respectively). Altogether, five of the seven cerebellar lesion
patients showed orienting deficits that were >3 SEs greater than those
of normal control subjects. One lesion subject (S.P.) had orienting
deficits that were within the range of those shown by normal control
subjects. The remaining lesion subject (G.Y.) had orienting deficits
that were >2.5 SEs smaller than those of control subjects. This
reversed pattern indicates a failure to sustain attention at the cued
location (i.e., better performance at the incorrectly cued location
with longer cue-to-target delays). This lesion patient had the smallest
of the cerebellar lesions (lacunar white matter infarcts specifically
affecting the left dentate nucleus and superior cerebellar peduncle).
Performance at the invalidly cued location (attention
orienting/performance cost)
Spatial target detection task
The following ANOVAs are group × cue-to-target delay × cue validity repeated measures analyses comparing RT to targets at the
three delay intervals (50, 100, or 800 msec) for the three groups
(control, autism, or cerebellar lesion) in the two cue conditions
(valid or invalid). Normal control subjects were faster overall at
valid and invalid cued locations than were autism subjects (F(1,48) = 5.48; p < 0.025) or cerebellar lesion subjects
(F(1,48) = 9.39; p < 0.005). Normal control subjects showed the largest difference in RT at
valid compared with invalid locations when the cue-to target delay was
short, whereas subjects with autism and cerebellar lesion subjects
showed the largest difference in accuracy at valid compared with
invalid locations when the cue-to-target delay was long (group × cue-to-target delay × cue validity interaction; F(4,96) = 7.76; p < 0.0002; Fig. 6A).
View larger version (40K):
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Figure 6.
A, RT at the validly
(filled squares, solid line) and
invalidly (filled circles, long dashed
line) cued locations for normal control, autism, and cerebellar
lesion (for targets in the ipsilesional and contralesional visual
fields) subjects as a function of the amount of time to orient
attention to that location (50 and 800 msec cue-to-target delays).
Normal control subjects showed maximum cue-related performance
facilitation after 50 msec (greatest difference in RT at the cued
compared with the uncued location). Autistic subjects showed maximum
cue-related performance facilitation after 800 msec. Cerebellar lesion
patients showed maximum cue-related performance facilitation to
contralesional targets after 50 msec, but showed maximum cue-related
facilitation after 800 msec to ipsilesional targets. B,
RT validity deficits (RT at validly cued location RT at
invalidly cued location) for all groups after 50 msec (white
bars) or 800 msec (striped bars) cue-to-target
delays. Normal control subjects and cerebellar lesion subjects (to
contralesional targets) showed largest validity deficits (i.e.,
greatest effect of attentional cue) at the 50 msec delay interval.
Autism subjects and cerebellar lesion subjects (to ipsilesional
targets) showed largest validity deficits (i.e., greatest effect of
attentional cue) at the 800 msec delay interval. C,
Accuracy of response at the validly cued (filled
squares, solid line) compared with the invalidly
cued (filled circles, long dashed
line) location as a function of the amount of time to orient
attention to that location (cue-to-target delay). Normal control
subjects showed greatest cue-related response differences with only 100 msec to orient attention. Autism and cerebellar lesion subjects (to
ipsilesional targets) showed the greatest performance facilitation
after 1200 msec to orient attention. Chance performance accuracy was
25%. D, Accuracy validity deficits (percent correct at
invalidly cued location percent correct at validly cued
location) for all groups at 100 msec (white bars) and
1200 msec (striped bars) cue-to-target delays. Normal
control subjects showed largest validity deficits (i.e., greatest
effect of attentional cue) at the 100 msec delay interval. Autism and
cerebellar lesion subjects (to ipsilesional targets) showed maximum
validity deficits after 1200 msec to orient attention.
|
|
Figure 6B shows validity deficits (difference in RT
at valid compared with invalid locations) at the shortest and longest cue-to-target delays. Normal control subjects showed maximal validity deficits at the 50 msec delay, whereas autism subjects showed maximal
validity deficits at the 800 msec delay (group × cue-to-target delay interaction; F(1,40) = 20.80;
p < 0.0001). Like autism subjects, cerebellar lesion
subjects showed maximal validity effects at the 800 msec delay to
ipsilesional targets (lesion subjects vs controls, group × cue-to-target delay interaction; F(1,42) = 5.79; p < 0.025). Like control subjects, lesion
subjects showed maximal validity effects at 50 msec to contralesional
targets. Overall, autism subjects had larger validity deficits than
control subjects (F(1,40) = 16.49;
p < 0.0002). There was no difference in the magnitude
of validity deficits to contralesional or ipsilesional targets between
cerebellar lesion subjects and controls.
Spatial target discrimination task
The following ANOVAs are group × cue-to-target delay × cue validity repeated measures analyses comparing RT to targets at the
three delay intervals (100, 800, or 1200 msec) for the three groups
(control, autism, or cerebellar lesion) in the two cue conditions
(valid or invalid). Normal control subjects were more accurate overall
at valid and invalid cued locations than were subjects with autism
(F(1,48) = 10.42; p < 0.0025), but not different from cerebellar lesion subjects. Normal
control subjects showed the largest difference in accuracy at valid
compared with invalid locations when the cue-to target delay was short,
whereas subjects with autism and cerebellar lesion subjects showed the
largest difference in accuracy at valid compared with invalid locations when the cue-to-target delay was long (group × cue-to-target
delay × cue validity interaction;
F(4,96) = 3.45; p < 0.02;
Fig. 6C).
Figure 6D shows validity deficits (difference in
accuracy at valid compared with invalid locations) at the shortest and
longest cue-to-target delays. As in the spatial detection task, normal control subjects showed maximal validity deficits at the shortest, 100 msec, delay whereas autism subjects showed maximal validity deficits at
the longest, 1200 msec, delay (autism vs controls; group × delay
interaction; F(1,42) = 7.60;
p < 0.009). Also, as in the spatial detection task,
cerebellar lesion subjects showed maximal validity effects at the
longest, 1200 msec, delay to ipsilesional targets (lesion subjects vs
controls; group × delay interaction; F(1,36) = 3.78; p < 0.06),
but maximal validity effects at 100 msec to contralesional targets.
There was no difference between normal control subjects and autism or
cerebellar lesion subjects in the overall size of validity deficits in
this task.
Results from analyses of validity deficits comparing the cerebellar
lesion patients to the subset of normal control subjects matched for
age and PIQ (described above) were again the same as those from
comparisons with the entire control sample. Like the entire control
sample, the lower PIQ control subjects showed maximal validity deficits
at the shortest cue-to-target delay interval (validity deficit at 100 msec delay = 23, at 1200 msec delay = 3). As in analyses
with the entire control sample, the group × delay interaction
comparing the PIQ-matched controls to lesion patients was marginally
significant for ipsilesional targets (F(1,15) = 4.42; p < 0.053) and not different for contralesional targets.
| |
DISCUSSION |
In two different spatial attention tasks, patients with autism and
those with acquired lesions of the cerebellum were slow to orient
attention in space. In both tasks, with only 50-100 msec to orient
attention, control subjects showed optimal performance (shorter RTs or
greater accuracy) at a location to which their attention had been cued
and maximal costs of that attention shift if the target did not occur
at the cued location. Patients with cerebellar abnormality however,
showed optimal performance at a cued location and maximal costs of the
attentional cue only after 800-1200 msec.
In the target detection task, an attention-directing cue was followed
by a target to be detected. The effect of the attentional cue was
assessed by differences in response time to targets that were cued
correctly or incorrectly. The times to orient attention to the cue,
detect and respond to the target were all reflected in response time,
but were not separable. Results from this task showing slower RTs with
shorter cue-to-target delays in cerebellar patients suggests slowed
attention orienting, but could also reflect slowed response preparation.
In the target discrimination task, an attention-directing cue was
followed by a stimulus whose orientation was identified. The effect of
the attentional cue was assessed by accuracy of response as a function
of the cue validity. Masking the stimulus to be discriminated after 50 msec effectively limited the time to process the target. Using accuracy
rather than RT as the dependent measure eliminated concerns about
slowed response preparation or execution in subjects with neurological
disorders. The design of this task separated time to orient attention
(time between cue onset and target onset) from target processing (time
between target onset and mask onset) and response preparation and
execution (variable of interest is accuracy not speed of response).
With only 100 msec to orient attention to the cued location, control subjects were as accurate as they were with longer cue-to-target intervals. Moreover, with only 100 msec to orient attention, normal controls showed the largest increases in accuracy at the cued compared
with the uncued locations. In contrast, patients with autism and
patients with cerebellar lesions improved performance significantly
with more time to orient attention and showed maximal performance
facilitation after the longest cue-to-target intervals. In this task,
slowed orienting cannot be caused by motor preparation or execution.
In the discrimination task, larger attention-orienting deficits were
significantly correlated with smaller cerebellar lobules VI-VII (from
MRI measures). Although orienting deficits were also correlated with
measures of performance IQ and ICV, partial correlations suggested that
orienting deficit variance associated with lobules VI-VII was unique,
whereas the association with ICV was secondary to a correlation between
CSF (a component of ICV) and lobules VI-VII. PIQ was not uniquely
associated with orienting deficits, but was associated more generally
with overall performance (mean accuracy across all conditions). This is
not an unexpected result because processing speed and competence are
among the cognitive operations assessed by PIQ scales that could affect
overall task performance. Evidence that slowed attention orienting in
patients with cerebellar pathology is not secondary to lowered PIQ
comes from analyses comparing attention orienting in cerebellar lesion patients to an age- and PIQ-matched normal control group. These analyses yielded results that were identical to those done with the
entire control sample.
The size of vermal lobules VI-VII were associated specifically with
orienting deficits and not with overall measures of performance competency. Although this correlation with orienting deficits was
specific to lobules VI-VII, patients from both clinical groups had
cerebellar abnormalities extending beyond this region. Because Purkinje
cell loss has been reported throughout the cerebellum in autism (Ritvo
et al., 1986; Bailey et al., 1998), it is likely that our autistic
subjects have cerebellar abnormalities beyond those measured in the
vermis. The cerebellar patients in this sample whose lesions involve
the posterior vermis had the largest orienting deficits. However, the
greatest overlap in lesions (Fig. 2) for these subjects was paramedial
and in the lateral posterior cerebellar hemispheres. Lesions or
abnormalities in these regions may involve deep cerebellar nuclei that
control cerebral-cerebellar communication. Damage to these nuclei
could disrupt vermal function even in the absence of vermal structural impairment.
Because vermal lobules VI-VII may be part of the oculomotor network
that controls saccadic eye movement (for review, see Noda, 1991), one
final concern about the interpretation of these results is that slowed
orienting in patients with cerebellar abnormality could be caused by
disruption of eye movements (for discussion, see Akshoomoff et al.,
1997). Although eye movements may also be disrupted in the patients
with cerebellar damage, the attention-orienting deficits observed in
our patients occur too early to be the result of abnormal gaze
shifting. Normal control subjects orient attention within 100 msec of
the onset of a peripheral stimulus. It is at this short interval that
control subjects and cerebellar-damaged patients differ most. That is,
normal control subjects are able to use an attentional cue effectively
within 100 msec, but those with cerebellar damage are not.
Additionally, if cerebellar patients improved use of an attentional cue
with longer time intervals by moving their eyes to the attended
location, they would be expected to show an associated decrease in
performance at the incorrectly cue location. As Figure 6 shows, this is
not the case for either task.
Although it is likely that covert attention and gaze shift processes
use overlapping brain systems, there is ample evidence that these
systems can be manipulated independently (Posner et al., 1980; Goldberg
and Segraves, 1987; Corbetta et al., 1993; Ladavas et al., 1997) (for
review, see Goldberg and Colby, 1992). Covert attention shifts may in
fact be used to direct a gaze shift (Posner and Cohen, 1984; Fischer
and Breitmeyer, 1987). Single cell recordings in alert monkeys have
demonstrated that activity in parietal cortex precedes an intended eye
movement to predict the location of expected visual input (Duchamel et
al., 1992). In our patients, covert orienting deficits could precede
similar deficits in gaze orienting.
Studies of orienting gaze shifts in the cat after muscimol inactivation
of the cerebellar fastigial nucleus (Goffart and Pelisson, 1994)
suggest that cerebellar contribution to spatial orienting may be both
motor and nonmotor. Visually triggered gaze shifts in the treated
animals were hypermetric (with constant error) when directed
ipsilateral to the injected side, and hypometric (with error that
increased as a function of the required movement amplitude) when
directed contralateral to the injected side. The authors conclude that
the adaptive error in the hypometric shifts may reflect an inability to
control gain of the movement. The constant hypermetric overshoots,
however, suggest that the target location of the saccade is fixed and
erroneous, perhaps reflecting a cerebellar-dependent faulty perception
of the target location that precedes the decision to execute an eye movement.
Neural connections suggest pathways by which the cerebellum may
influence both nonmotor and motor aspects of spatial orienting. Efferent fibers from the fastigial nucleus project to the ventral thalamus. There are reciprocal projections from the ventral thalamus to
other brain regions known to be involved in spatial attention, including posterior parietal and precentral frontal cortex (Ito, 1984;
Carpenter, 1985; Nieuwenhuys et al., 1988; Middleton and Strick, 1994).
These same cortical regions are activated by electrical stimulation of
the fastigial nucleus (Steriade, 1995). Additionally, fibers from the
fastigial, dentate, and interposed nuclei terminate in deep layers of
the superior colliculus via the superior cerebellar peduncle. The
thalamus has been suggested to be a critical component of systems that
control covert orienting of attention (Rafal and Posner, 1987) (for
review, see Desimone and Duncan, 1995), whereas the superior colliculus
may be more closely related to the programming and subsequent execution
of saccadic movement (Posner and Petersen, 1990; Goldberg and Colby,
1992). Damage to the cerebellum could disrupt spatial encoding and
cortical activation via a cerebellothalamocortical circuit and disrupt
programming to direct a gaze shift via cerebellocolliculocortical pathways, thus delaying both covert and subsequent overt orienting to a
salient location.
A growing number of studies indicate that the traditional model of the
cerebellum as a brain structure whose sole purpose is to support motor
function needs modification. Building on previous suggestions that the
cerebellum plays a role in sensory tracking, prediction, association,
and anticipatory learning (Bower and Kassel, 1990; Miall et al., 1993;
Paulin, 1993; Coenen and Sejnowski, 1996; Bell et al., 1997),
Courchesne has proposed an anticipatory model of cerebellar function.
The cerebellum may serve to prepare internal systems for upcoming
events based on predictions computed from continuous tracking of and
learning from sensory, cognitive, and motor information. In this way,
the cerebellum supports a broad spectrum of brain systems involved in
both motor and nonmotor function (Courchesne and Allen, 1997;
Courchesne, 1997b). Data from the present study are consistent with
this model. Continuous tracking of sensory information in space may
allow the cerebellum to compute and relay to other brain systems the
predictions that guide optimal attentional responses.
| |
FOOTNOTES |
Received Dec. 7, 1998; revised April 13, 1999; accepted April 14, 1999.
This work was supported by National Institute of Mental Health Grant
2RO1-MH36840-11 and National Institute of Neurological Diseases and
Stroke Grant 1RO1-NS34155-01. We thank Janet Werner, RN, Clinical
Nurse, Department of Neurosciences, University of California, San Diego
for her invaluable assistance.
Correspondence should be addressed to Jeanne Townsend, Department of
Neurosciences, MC-0217, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0217.
| |
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TOP
ABSTRACT
INTRODUCTION
Autism
