www.elsevier.com/locate/ynimg
NeuroImage 26 (2005) 1052–1058
Structural brain correlates of prepulse inhibition of the acoustic
startle response in healthy humans
Veena Kumari,a,T Elena Antonova,a Elizabeth Zachariah,b Adrian Galea,b Ingrid Aasen,a,b
Ulrich Ettinger,c Martina T. Mitterschiffthaler,c and Tonmoy Sharmad
aDepartment of Psychology, PO78, Institute of Psychiatry, De Crespigny Park, London SE5 8AF, UKbDivision of Psychological Medicine, Institute of Psychiatry, London, UKcCentre for Neuroimaging Sciences, Institute of Psychiatry, London, UKdClinical Neuroscience Research Centre, Dartford, UK
Received 30 August 2004; revised 26 February 2005; accepted 2 March 2005
Available online 7 April 2005
Neural regions modulating prepulse inhibition (PPI) of the startle
response, an operational measure of sensorimotor gating, are well
established from animal studies using surgical and pharmacological
procedures. The limbic and cortico-pallido-striato-thalamic circuitry is
thought to be responsible for modulation of PPI in the rat. The
involvement of this circuitry in human PPI is suggested by observations
of deficient PPI in a number of neuropsychiatric disorders charac-
terized by abnormalities at some level in this circuitry and recent
functional neuroimaging studies in humans. The current study sought
to investigate structural neural correlates of PPI in a sample of twenty-
four right-handed, healthy subjects (10 men, 14 women). Subjects
underwent magnetic resonance imaging (MRI) at 1.5 T and were
assessed (off-line) on acoustic PPI using electromyographic recordings
of the orbicularis oculi muscle beneath the right eye. Optimized
volumetric voxel-based morphometry (VBM) implemented in SPM99
was used to investigate the relationship of PPI (prepulse onset-to-pulse
onset interval 120 ms) to regional grey matter volumes, covarying for
sex. Significant positive correlations were obtained between PPI and
grey matter volume in the hippocampus extending to parahippocampal
gyrus, basal ganglia including parts of putamen, globus pallidus, and
nucleus accumbens, superior temporal gyrus, thalamus, and inferior
frontal gyrus. These findings identify the relationship between PPI and
grey matter availability on a highly spatially localized scale in brain
regions shown to be activated in recent functional neuroimaging studies
in association with PPI in healthy humans and demonstrate the validity
of structural neuroimaging methods in delineating the neural mecha-
nisms underlying human PPI.
D 2005 Elsevier Inc. All rights reserved.
Keywords: Prepulse inhibition; Acoustic startle response; Magnetic
resonance imaging (MRI); Voxel-based morphometry (VBM); Volume;
Hippocampus; Nucleus accumbens; Thalamus
1053-8119/$ - see front matter D 2005 Elsevier Inc. All rights reserved.
doi:10.1016/j.neuroimage.2005.03.002
T Corresponding author. Fax: +44 207 848 0860.
E-mail address: [email protected] (V. Kumari).
Available online on ScienceDirect (www.sciencedirect.com).
Introduction
Prepulse inhibition (PPI) of the startle response, a cross species
phenomenon, refers to a reliable reduction in the amplitude of the
response to a strong sensory stimulus (pulse) if briefly (by 30–500
ms) preceded by a weak stimulus (prepulse) (Graham, 1975).
Presentation of the pulse at short lead intervals, while the prepulse is
still being processed, presumably causes disrupted processing of,
and thus an attenuated overt response to, this stimulus. The
paradigms both in human and animal studies most commonly
employ a loud noise as the pulse and a quieter noise as the prepulse.
In human subjects, PPI is usually quantified as reduction in the eye
blink response to a prepulse-plus-pulse trial compared to the
response to a pulse-alone trial. Most human studies involving
healthy as well as clinical populations have utilized passive task
conditions (i.e., no specific instruction to attend the prepulse or the
pulse) though some studies have required their subjects to
specifically attend to the prepulse (review, Braff et al., 2001a,b).
In animal studies, PPI is usually measured as reduction in the whole
body startle (jump) response to a prepulse-plus-pulse trial compared
to the response to a pulse-alone trial (review, Swerdlow et al., 1994).
However, a recent study (Arnfred et al., 2004) of pigs using the eye
blink startle response to assess PPI has confirmed PPI and its similar
modulation (i.e., disruption) by a dopaminergic challenge to that
seen with PPI of the whole body startle in rats (review, Swerdlow
and Geyer, 1998).
Animal studies have shown that PPI is mediated by brain stem
circuits involving the inferior colliculus, pedunculopontine tegmen-
tal nucleus, laterodorsal tegmental nucleus, substantia nigra pars
reticulata, and caudal pontine reticular nucleus (Fendt et al., 2001),
and modulated by forebrain circuits involving the prefrontal cortex,
thalamus, hippocampus, amygdala, nucleus accumbens, striatum,
ventral pallidum, globus pallidus, and subpallidal efferents to the
pedunculopontine nucleus (reviews, Koch and Schnitzler, 1997;
Swerdlow and Geyer, 1998; Swerdlow et al., 2001). Our recent
study (Kumari et al., 2003a) using blood-oxygenation-level-
V. Kumari et al. / NeuroImage 26 (2005) 1052–1058 1053
dependent functional magnetic resonance imaging (fMRI) of the
entire brain during a tactile PPI paradigm demonstrated activation in
the hippocampus, striatum, thalamus, and frontal and parietal
cortical regions in healthy subjects and significantly less activation
in all these regions in patients with schizophrenia (Kumari et al.,
2003a). Indirect information on the neural substrates of human PPI
is provided by observations of deficient PPI in a number of psy-
chiatric and neurological disorders, including schizophrenia (re-
view, Braff et al., 2001a), Huntington’s disease (Swerdlow et al.,
1995), obsessive compulsive disorder (Swerdlow et al., 1993a),
temporal lobe psychosis (Morton et al., 1994), and Tourette’s
syndrome (Castellanos et al., 1996), which are known to be as-
sociated with abnormalities at some level in the circuitry considered
to modulate PPI in animals (reviews, Koch and Schnitzler, 1997;
Swerdlow and Geyer, 1998; Swerdlow et al., 2001).
The structural neural correlates of PPI in humans remain
unexplored, although previous studies have utilized structural MRI
volumetry to study the neural correlates of higher order cognitive
functions (e.g., Ettinger et al., 2002, 2005; Maguire et al., 2000;
Sanfilipo et al., 2002). This approach stems from the assumption
that the volume of a brain region is an important tissue property
(Armstrong, 1983; Ringo, 1991), reflecting its size and shape as
well as the pattern of arrangement and densities of its cellular
components (Caviness et al., 1996), and is closely associated with
behavioral measures of its function. Recent data, for example,
demonstrating a relationship between the behavioral measures of
specific frontal lobe function and regional frontal lobe volumes
(Ettinger et al., 2005; Sanfilipo et al., 2002) and between memory
and spatial ability and hippocampal volumes (Maguire et al.,
2000), have provided empirical support to this approach.
The present study aimed to investigate structural brain correlates
of acoustic (passive) PPI in healthy human subjects using the voxel-
based morphometry (VBM) technique which, rather than limiting
the search to certain regions of interest (ROIs), allows the
examination of correlations between grey matter availability and
behavioral measures on a voxel-by-voxel basis across the entire
brain (Gaser and Schlaug, 2003). On the basis of available
(reviewed earlier) data, we predicted that PPI would be positively
correlated with grey matter volume in the hippocampus/temporal
lobe, striatum, thalamus, frontal, and possibly parietal regions.
Materials and methods
Subjects and design
Twenty-four right-handed subjects (10 men, 14 women; mean
age [years] = 29.29, SD = 10.02) took part. Subjects were clinically
screened for the exclusion criteria of DSM-IV Axis I and II
disorders (First et al., 1996a,b) and neurological abnormalities.
Subjects provided written, informed consent. The study procedures
had approval from the ethics committee of the Institute of
Psychiatry and Maudsley Hospital, London.
PPI and MRI data were acquired on the same day except for a
few subjects who underwent PPI assessment the day before or after
the scanning due to practical issues.
Psychophysiology data collection and analysis
A commercially available human startle response monitoring
system (Mark II, SR-Lab, San Diego, CA) was utilized to generate
and deliver the acoustic stimuli, and to record and score the
electromyographic (EMG) activity for 250 ms starting from the
onset of the acoustic startle stimulus. Acoustic stimuli were
presented to subjects binaurally through headphones. The pulse-
alone stimulus was a 40-ms presentation of 115-dB (A) white noise
and the prepulse stimulus a 20-ms presentation of 85-dB (A) white
noise, both over 70-dB (A) continuous background noise. The
noise levels were calibrated using the continuous noise on a
monthly basis. The session began with a 5-min acclimatization
period consisting of 70-dB (A) continuous white noise. Subjects
received four blocks of 18 trials each, after an initial pulse-alone
trial; each block consisted of three pulse-alone trials, three prepulse
trials with a 30-ms prepulse-to-pulse (onset to onset) interval, three
prepulse trials with a 60-ms prepulse-to-pulse interval, three pre-
pulse trials with a 90-ms prepulse-to-pulse interval, three prepulse
trials with a 120-ms prepulse-to-pulse interval, and three prepulse
trials with a 150-ms prepulse-to-pulse interval presented to subjects
in a pseudorandom order with a mean inter-trial interval of 15 s
(range 9–23 s).
The experimental procedures for recording and scoring the
startle reflexes were identical to those reported previously (e.g.,
Kumari et al., 2000, 2004). Eye blink component of the startle was
indexed by recording EMG activity of the orbicularis oculi muscle
directly beneath the right eye, by positioning two miniature silver/
silver chloride electrodes. Recorded EMG activity was band-pass
filtered, as recommended by the SR-Lab. A 50-hz filter was used to
eliminate the 50-Hz interference. The EMG data were at first
inspected on trial-to-trial basis offline (to exclude unusable trials
for a particular subject) and scored by the analytic program of this
system for response amplitude (in arbitrary analogue-to-digit
units).
Subjects were told that the experiment was to measure their
reactivity to a number of noise bursts, but no specific instructions
were given as to attend or ignore them. They were requested to
keep their eyes open during the experiment.
PPI was computed for each participant separately for each trial
type as (a � b/a) 100, where baQ = pulse-alone amplitude and bbQ =amplitude over prepulse trials. Percent of PPI, rather than absolute
amount of PPI (i.e., arithmetic difference between pulse-alone and
prepulse trials), was used since this procedure eliminates the
influence of individual differences in startle responsiveness. PPI at
the 120-ms prepulse-to-pulse interval was chosen as the main
dependent measure for hypothesis testing because this interval
produces the maximum PPI thus allowing the maximum power in
terms of range of scores and is also the most frequently used
interval in clinical studies (review, Braff et al., 2001a; Kumari et
al., 2004). We chose to study 120-ms PPI rather the mean PPI
across all prepulse-to-pulse intervals because (a) the neural
correlates of PPI at different intervals may somewhat differ, and
(b) focussing on a particular interval would facilitate a precise
comparison of the results of future studies investigating structural
brain correlates of human acoustic PPI with those of the present
study.
MRI acquisition
Subjects underwent scanning on a 1.5 T GE Signa Advantage
scanner (Milwaukee, WI). A high-resolution 3D inversion recovery
prepared spoiled GRASS volume dataset was acquired in the AC–
PC plane with TE = 5.3 ms, TI = 300 ms, TR = 12.2 s, in-plane
resolution = 0.94 mm, and slice thickness = 1.5 mm.
V. Kumari et al. / NeuroImage 26 (2005) 1052–10581054
MRI pre-processing
Structural images were converted into ANALYZE format
(ANALYZE software, BRU, Mayo Foundation, Rochester, MN;
Robb, 1990). A manual determination of the AC–PC line was
performed for all images prior to pre-processing. The images were
pre-processed as required for the optimized protocol for VBM
devised and validated by Good et al. (2001) within the statistical
parametric mapping software package (SPM99, Wellcome Depart-
ment of Cognitive Neurology, University College London,
London, UK; http://www.fil.ion.ucl.ac.uk/spm), running in Matlab
6.1 (MathWorks, Natick, MA). An overview of the procedures is as
follows.
Creation of customized templates
We created a template of the whole brain as well as
probability maps of grey and white matter and cerebrospinal
fluid (CSF) using the brain images of 40 healthy subjects
including those of the present study to yield optimal spatial
normalization and segmentation of the images. For the whole
brain template, the images were spatially normalized to T1-
weighted template (ICBM 152 standard, inherent to SPM99)
using 12-parameter affine transformation (Ashburner et al., 1997),
smoothed with 8 mm full width half maximum (FWHM)
isotropic Gaussian kernel, and averaged. For customized proba-
bility maps, images were segmented into grey matter, white
matter, and CSF compartments using grey matter, white matter,
and CSF probability maps inherent to SPM99. The resultant
tissue segments were automatically cleaned to remove non-brain
tissue, smoothed with 8-mm FWHM isotropic Gaussian kernel,
normalized using affine transformation with sinc interpolation
algorithm, and averaged to derive grey matter, white matter, and
CSF probability maps. To reduce the partial volume problem and
insure optimal tissue segmentation, all images for the templates
were written out with 1 � 1 � 1 mm voxel size.
Deriving and applying optimized normalization parameters
The original structural scans were processed using customized
whole brain and tissue probability templates. The first step entailed
a segmentation of the original images in native space, registering to
the customized tissue probability maps and correcting for image
inhomogeneity, followed by the automatic brain extraction and
cleaning procedure to remove non-brain tissue. The second step
involved spatial normalization of the original images to the
customized whole brain template using 12-parameter linear and
7 � 8 � 7 discrete cosine transform basis function non-linear
transformations (Ashburner and Friston, 1999), with parameters
determined from the images derived from the first step, and
resliced to 1 � 1 � 1 mm voxel size to yield more accurate
subsequent tissue segmentation. The spatially normalized images
were then segmented into three tissue compartments using the
Table 1
Mean (standard errors of the mean, SEM) and inter-correlations of PPI (percent i
Prepulse interval (ms) Mean (SEM) 30-ms r ( P) 60-ms
30 12.17 (4.41) –
60 29.42 (4.20) 0.362 (0.082) –
90 39.42 (4.41) 0.483 (0.017) 0.776
120 39.89 (3.65) 0.432 (0.035) 0.701
150 36.76 (4.02) 0.523 (0.009) 0.631
customized grey matter, white matter, and CSF templates. Brain
extraction and cleaning procedures were re-applied to the
segmented normalized grey matter images to further remove
extraneous brain tissue. Since the volume of some brain regions
may shrink or expand as a result of non-linear spatial normal-
ization, the cleaned grey matter images were modulated, i.e., the
voxel values of each segment were multiplied by the Jacobian
determinants of the deformation matrix derived during the spatial
normalization step, to drestoreT the original volume of each grey
matter segment. Finally, grey and white matter segments were
smoothed using 12-mm FWHM isotropic Gaussian kernel to make
the data conform to the Gaussian field model, underlying the
structural inferences as implemented in SPM 99; to render the data
more normally distributed (by the central limit theorem); and to
reduce the effects of individual variation in sulcal/gyral anatomy
(Ashburner and Friston, 2000).
MRI analysis
Simple linear regression of 120-ms PPI to grey matter volume
maps was performed at each voxel within SPM99. Given that PPI
is known to be influenced by sex (e.g., Kumari et al., 2003b, 2004;
Swerdlow et al., 1993b), sex was included into the regression
model as a confounding covariate. The resulting statistical para-
metric maps were thresholded at P b 0.001 uncorrected. Small
volume corrections (SVCs; sphere-shaped with search volume of
15 mm due to the anatomical extensions of the hypothesized
structures, P b 0.05) were applied during the multiple correction
procedure for the hypothesized ROIs (see Introduction). The
thalamus, one of our ROIs (see Introduction), did not emerge as a
correlate of PPI with these criteria (see Results), thus we re-
evaluated the results with statistical parametric maps thresholded at
P b 0.005 uncorrected and considered them meaningful for the
ROIs if significant at P b 0.05 with sphere-shaped search volume
of 5 mm during the multiple correction procedure to avoid a Type
II error.
Next, we extracted the values representing the percentage of
total grey/white matter volume under a smoothing kernel relative to
the total grey/white matter volume for each participant at the
maxima voxel of all the regions showing an association with PPI
and examined (a) the inter-relationships of these volumes using
Pearson’s r and (b) the total variance in PPI predicted jointly by
these structural volumes given their positive inter-relationships (see
Results) using a multiple regression model (enter method).
Results
Replicating numerous reports (review, Braff et al., 2001a), the
120-ms prepulse to pulse interval produced the maximum PPI and
was positively correlated with PPI at all other intervals (Table 1).
nhibition) variables
r ( P) 90-ms r ( P) 120-ms r ( P) 150-ms r ( P)
(0.001) –
(0.001) 0.857 (0.001) –
(0.001) 0.775 (0.001) 0.840 (0.001) –
V. Kumari et al. / NeuroImage 26 (2005) 1052–1058 1055
PPI was not significantly associated with age in this sample of
young healthy subjects (Ps N 0.20). There was apparently less
PPI in women compared to men at the 120-ms prepulse-to-pulse
interval but the difference failed to attain statistical significance
(P = 0.21). Descriptive statistics for all PPI variables are
presented in Table 1.
Fig. 1. Grey matter volumetric correlate of PPI. (a) Group correlation maps thresh
PPI and grey matter volumes in the hippocampus extending to the parahippocam
sample brain. Left hemisphere is shown on the left of the coronal view. Colors repr
(b) Correlation images thresholded at P b 0.01 within the SPM99 demonstrating a
superior temporal gyrus, and posterior cingulate imposed on the averaged brain. L
strength of the correlation with PPI. (c) Scatter plots showing the relationship bet
voxel of all the regions ( y-axis) showing an association with PPI (x-axis) for eac
Correlations with grey matter volume
Higher PPI was associated with greater grey matter volume in
the left hippocampal formation (hippocampus extending to para-
hippocampal gyrus, centered at x = �26, y = �28; z = �26; t =
4.98; P V 0.001 uncorrected, 0.022 corrected), left striatum
olded at P b 0.01 within the SPM99 showing positive relationship between
pal gyrus, the striatum, and inferior frontal gyrus imposed on the averaged
esent voxel level t values and represent strength of the correlation with PPI.
positive relationship between PPI and grey matter volumes in the thalamus,
eft hemisphere on left. Colors represent voxel level t values and represent
ween the percentage grey volumes (relative to group mean) at the maxima
h subject.
V. Kumari et al. / NeuroImage 26 (2005) 1052–10581056
including parts of putamen/globus pallidus and nucleus accumbens
(centered at x = �15, y = 9; z = �16; t = 4.52, P V 0.001
uncorrected, 0.05 corrected), and left inferior frontal gyrus (BA44;
centered at x = �51; y = 23; z = 1; t = 4.93, P = b 0.001
uncorrected, 0.024 corrected ) (see Fig. 1b). No other region across
the entire brain met our criteria (see MRI analysis) for a significant
association with PPI. No region showed a negative relationship
with PPI.
When we re-evaluated the results with lowered thresholds (see
MRI analysis), the thalamus (bilaterally; centered in the right at x =
3, y = �10, z = 16 but extending to the left, x = �14, y = �15, z =
11; t = 3.11, P = 0.003 uncorrected, 0.046 corrected), right
hippocampus extending to the parahippocampal gyrus (centered at
x = 31, y = �22; z = �25, t = 3.10, P = 0.003 uncorrected, 0.046
corrected), and a region in the right superior temporal lobe (BA22/
42; centered at x = 65, y = �29, z = 10; t = 3.50, P = 0.001
uncorrected, 0.023 corrected) showed positive relationships with
PPI (see Figs. 1a–c). A cluster with peak in the posterior cingulate
(BA23/31, x = 7; y = �83, z = 30 extending to x = 9, 0 = �87, z =
37; t = 4.40, P = 0.001 uncorrected) also showed a positive
relationship with PPI but only at a trend level (P = 0.06) when
SVC was applied (see Figs. 1b–c).
The grey matter volumes at the maxima voxel of all the regions
associated with PPI showed positive inter-relationships, though
failing to reach the conventional level of significance in two (out of
21) instances (see Table 2). Multiple regression analysis revealed
that 62% of the variance in PPI (F = 6.40, df = 7, 23, P = 0.001;
adjusted R2 = 0.622) was explained by these volumes.
Discussion
The present study examined the structural neural correlates of
PPI in healthy humans. It used VBM to explore the grey matter
correlates of PPI across the entire brain without being limited by
anatomically predefined ROIs and found meaningful association
between PPI and highly spatially localized regions in the hippo-
campus/parahippocampal gyrus, superior temporal gyrus, striatum,
thalamus, and the inferior frontal gyrus, which (combined)
explained 62% of the total variance in PPI. In general, these
observations are consistent with previous relevant data on neural
mechanisms of PPI as discussed further.
The positive association between PPI and the volumes of
hippocampal, striatal, and thalamic regions is compatible with the
evidence from animal studies (reviews, Koch and Schnitzler, 1997;
Swerdlow et al., 2001). Hippocampus is thought to be critically
involved in PPI in the rat (reviews, Bast and Feldon, 2003;
Swerdlow et al., 2001). In this study, however, the parahippocam-
pal gyrus also seemed strongly positively correlated with PPI. This
Table 2
Pearson’s inter-correlations ( Ps) of structural volumes associated with PPI
Region LH RH ST
Left hippocampal (LH) –
Right hippocampal (RH) 0.543 (0.006) –
Superior temporal (ST) 0.577 (0.003) 0.445 (0.029) –
Striatal 0.531 (0.008) 0.439 (0.032) 0.603 (0.
Thalamus 0.630 (0.001) 0.492 (0.015) 0.430 (0.
Inferior frontal (IF) 0.569 (0.004) 0.541 (0.006) 0.413 (0.
Posterior cingulate (PC) 0.542 (0.006) 0.582 (0.003) 0.543 (0.
is not surprising given that the areas immediately adjacent to the
hippocampus are considered functionally an extension of the
hippocampus and work in tandem in cognitive processing
(Lavenex and Amaral, 2000). In addition to animal studies
(reviews, Koch and Schnitzler, 1997; Swerdlow et al., 2001),
clinical studies also support the involvement of these regions in
PPI. As stated earlier in the Introduction, patients with schizo-
phrenia reliably show PPI deficits under the conditions employed
in the present experiment (Braff et al., 2001b; Kumari et al., 2004).
Abnormalities in the hippocampal formation (Amaral and Insausti,
1990) as well as basal ganglia have been considered central to the
pathophysiology of this disorder by several theorists (e.g., Grace,
2000; Gray et al., 1991; Scarr et al., 2001). There is also evidence
of reduced hippocampal volumes (reviews, Shenton et al., 2001;
Wright et al., 2000) in schizophrenia patients, including prodromal
and first episode patients (Bogerts et al., 1990; Joyal et al., 2002;
Lawrie et al., 1999; Lieberman et al., 2001; Pantelis et al., 2003;
Velakoulis et al., 1999). Similarly, patients with schizophrenia are
reported to display thalamic volume reductions (reviews, Csernan-
sky et al., 1994; McCarley et al., 1999; Shenton et al., 2001) even
during the first episode (Ettinger et al., 2001).
Recent data indicate that ventricular enlargement, one of the
most reliable findings in schizophrenia, is inversely related to the
volumes of the thalamus, striatum, and superior temporal cortex
(Gaser et al., 2004). The present observations, taken together with
those showing volumetric reductions in these regions and of
reliable PPI deficits in schizophrenia, suggest that structural
alterations of the hippocampal, striatal, and thalamic regions might
account for PPI deficit in at least a proportion of schizophrenia
patients.
The positive relationship between PPI and grey matter volumes
of hippocampal, striatal, and thalamic regions is also compatible
with the observations of our previous study (Kumari et al., 2003a)
which explored the neural correlates of PPI using functional MRI
and noted activation in the hippocampus (x = 20, y = �16, z =
�10) as well as in the striatum (x = �14, y = 14, z = �6) and
thalamus (z = �12, y = �14, x = 2) in close proximity to clusters
found in the present study. The correspondence between neural
mechanisms at the structural level of acoustic PPI in the present
study, and at the functional level in our previous study (Kumari et
al., 2003a) that used fMRI, tactile stimuli to elicit PPI, and an
entirely different pool of subjects, is impressive. It lends further
support to the premise that volumes of specific brain areas are
associated with behavioral measures that have been functionally
linked to them (e.g., Maguire et al., 2000; Sanfilipo et al., 2002). A
previous study (Hazlett et al., 2001) using fMRI with an ROI
approach also noted thalamic activation in association with
acoustic PPI. Activation in the inferior frontal and temporal lobes
was noted in our fMRI study (Kumari et al., 2003a), but in both
Striatal Thalamus IF PC
002) –
036) 0.366 (0.079) –
045) 0.543 (0.006) 0.425 (0.039) –
006) 0.445 (0.029) 0.334 (0.111) 0.707 (0.001) –
V. Kumari et al. / NeuroImage 26 (2005) 1052–1058 1057
cases on the opposite side to that observed in the current study.
However, a previous positron emission tomography (PET) study
(Hazlett et al., 1998) observed an association between higher
(120-ms) acoustic PPI and increased activity in the inferior frontal
and superior temporal lobes lobe bilaterally. The only region not
to have been hypothesized in advance and to emerge with a weak
(trend level) positive association with PPI (see Fig. 1c) was
localized in the posterior cingulate. This observation requires a
replication.
In conclusion, the present study examined, for the first time to
our knowledge, structural neural correlates of PPI and revealed
evidence of a positive association between PPI and grey matter
availability in the hippocampal, striatal, thalamic, and inferior
frontal regions in healthy humans. These observations confirm the
involvement of these regions in human PPI as suggested by
previous relevant data and also point to the validity of structural
MRI methods in delineating the neural correlates of PPI and other
cognitive functions. Given the evidence of deficient PPI, a number
of neurological and psychiatric patient populations (review, Braff
et al., 2001a), and of altered structure–function relationships in
schizophrenia (e.g., Antonova et al., 2004; Ettinger et al., 2004),
future research should extend the present methods to study clinical
populations.
Acknowledgment
Wellcome Trust (grant no. 067427/z/02/z to V.K.).
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