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: v.kumari@iop.kcl.ac.uk (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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