Relationships between prepulse inhibition and cognition are mediated by attentional processes

Relationships between prepulse inhibition and cognition are mediated by attentional processes

Behavioural Brain Research 205 (2009) 456–467 Contents lists available at ScienceDirect Behavioural Brain Research journal homepage: www.elsevier.co...

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Behavioural Brain Research 205 (2009) 456–467

Contents lists available at ScienceDirect

Behavioural Brain Research journal homepage: www.elsevier.com/locate/bbr

Research report

Relationships between prepulse inhibition and cognition are mediated by attentional processes Kirsty Elizabeth Scholes a,b,∗ , Mathew Thomas Martin-Iverson a,b a b

Centre for Clinical Research in Neuropsychiatry, Graylands Hospital, Perth, WA, Australia School of Medicine and Pharmacology, Faculty of Medicine and Dentistry, University of Western Australia, QEII Medical Centre, Nedlands, WA 6009, Australia

a r t i c l e

i n f o

Article history: Received 17 July 2009 Accepted 29 July 2009 Available online 7 August 2009 Keywords: Schizophrenia Prepulse inhibition Startle Neuropsychological tests Attention Cognition

a b s t r a c t Prepulse inhibition (PPI) is suggested to reflect a basic inhibitory mechanism which regulates sensory input to the brain, preventing sensory overload and cognitive fragmentation. However, studies directly investigating the relationship between PPI and cognition have produced inconsistent findings; this is likely to be due to the use of uninstructed PPI tasks, and limitations with the methods for measurement of PPI and startle. Therefore, the current study examined the relationship between cognitive performance and attentional modulation of PPI, using novel methods for the measurement of PPI. PPI was measured in 44 patients with schizophrenia and 32 healthy controls, across a range of startling stimulus intensities, under two attention set conditions. A range of neuropsychological tasks was also administered. Curves of best fit were fitted to the startle magnitudes, across the stimulus intensities, and a number of parameters were extracted from these curves, each of which reflects a different characteristic of the startle response. Correlations and quartile splits of the sample (highest versus lowest PPI) revealed that more PPI of response measures and less PPI of stimulus measures under the IGNORE condition was associated with superior performance in the colour-word subtest of the Stroop task. Further, more PPI of stimulus measures and less PPI of response measures under the ATTEND condition was associated with better performance on a memory task. These relationships appear to be mediated by common attentional processes active within both PPI and cognitive tasks, rather than by common underlying neurophysiological inhibitory processes. © 2009 Elsevier B.V. All rights reserved.

Prepulse inhibition (PPI) of the acoustic startle response (ASR) refers to the diminution in the magnitude of the startle response, occurring when a weak stimulus (the prepulse) precedes an intense auditory startling stimulus (pulse) by approximately 50–500 ms [28]. PPI was first proposed to be a pre-attentive and automatic inhibitory mechanism, elicited by the prepulse, acting to reduce the impact of the subsequent startling stimulus, thereby preserving the processing of the initial stimulus [28]. More recent hypotheses have expanded this concept, and suggested that PPI reflects the basic inhibitory process of sensorimotor gating, which regulates the input of sensory stimuli to the brain, thereby preventing sensory overload and cognitive fragmentation [4]. Although these hypotheses do not suggest that PPI is specifically a cognitive process, they do suggest that there is a relationship between cognitive processing and PPI, in which a disruption to PPI may lead to or be prognostic of complex disturbances to cognitive functioning [25].

∗ Corresponding author at: Centre for Clinical Research, Graylands Hospital, Post Office Private Bag No. 1, Claremont, WA 6910, Australia. Tel.: +61 8 9347 6492; fax: +61 8 9384 5128. E-mail address: [email protected] (K.E. Scholes). 0166-4328/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.bbr.2009.07.031

There are a number of lines of evidence that have further implicated a relationship between cognitive processing and PPI. Firstly, a multitude of studies have shown that PPI can be modulated by attentional processes [21,22,37,40,61]. Secondly, studies have shown activation of frontal brain regions during performance of both attentional modulation PPI tasks, where participants are instructed to attend to certain stimuli and ignore others [30,32], and uninstructed ‘passive’ PPI tasks where no attention instructions are given [12,44,45]. An association between PPI and grey matter volume in the dorsolateral prefrontal cortex, the orbital/medial prefrontal cortex and the middle frontal cortex has also been reported [46]. Further, patients with schizophrenia have been shown to exhibit deficits both in PPI [3,5–9,16,17], and also cognitive processing in a number of domains including attention, memory and executive functioning [for reviews see 38,48]. Although a relationship between PPI and cognitive performance is implied, studies which have attempted to directly examine the relationship between PPI and cognitive performance have been inconsistent. Early studies explored the relationship between PPI and other inhibitory control based cognitive tasks, such as the Stroop task and the negative priming task, in an attempt to identify common underlying inhibitory processes. Filion and McDowd

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[23] found a correlation between PPI and performance on a negative priming task, whereby those who exhibited the most PPI also exhibited the most negative priming. This study used an attentional modulation of PPI paradigm, and this correlation was only observed for the stimuli for which participants were instructed to selectively attend to and not for the stimuli which participants were instructed to ignore. On the other hand, studies using uninstructed PPI tasks have found no correlations between PPI and performance on both the Stroop [1,58] and negative priming [1] tasks. In contrast, a relationship between planning abilities and PPI has been implicated in a number of studies, although the reported direction of this relationship has been mixed, with one study suggesting that poorer planning was associated with greater PPI during an uninstructed PPI task [1], while other studies have suggested that greater PPI is associated with better planning efficiency [2,14,26]. Similarly, mixed findings have been reported in regard to the relationship between PPI and performance of the continuous performance task (CPT), a measure of attention. While two studies have found no correlation between PPI and behavioural measures from the CPT, in both healthy controls and patients with schizophrenia [31,33], another study found greater PPI to be associated with better CPT performance in healthy controls [49]. The former two studies suggested their negative findings to be associated with a restricted range of CPT performance in their samples. An early study reported an apparent relationship between PPI and Wisconsin Card Sorting Task (WCST) performance, a measure of executive functioning, whereby patients who showed increased perseverative errors on the WCST also exhibited reduced PPI for tactile-elicited, but not auditory-elicited stimuli; however, this claimed difference was not statistically significant [10]. A more recent study failed to find any significant correlations between PPI during an uninstructed task, and performance on a range of cognitive tasks including measures of working memory (letternumber sequencing), executive functioning (WCST) and verbal learning/memory (California Verbal Learning Task, CVLT) [59]. Inconsistency in the findings from such studies may be attributed to a number of factors. Firstly, the vast majority of studies used ‘passive’ PPI methods in which no attentional instructions were given to participants. These uninstructed tasks are confounded by unconstrained attention across trials and between individuals or groups. They should not be considered measures of ‘pre-attentive’ function simply because of a lack of attentional instructions; attention to the auditory stimuli is not absent with these uninstructed tasks, it is just unmeasured, and some participants may be actively listening out to the stimuli while others may be ignoring them. This complicates the interpretation of findings using such tasks. Secondly, in the case of examining the relationship between PPI and cognition in patients with schizophrenia, care should be taken to control for a variety of moderating variables which have been shown to affect both PPI and cognition, including use of substances such as nicotine, caffeine and illicit drugs [24,42,43,60]. Finally, there are large individual differences (and possibly group differences) in sensitivity to startle stimuli, and PPI varies as a function of this sensitivity [15,39,64]; thus, examination of PPI using a single startling stimulus intensity (as is common practise) can lead to large variation in PPI across individuals and between groups [15,39,64] resulting in inconsistency in findings. Examination of startle and PPI across a range of stimulus intensities has been suggested to be an effective method of dealing with this individual variation [14,39,56]. The relationship between startling stimulus intensity and startle response magnitude (SIRM relationship) can be characterised by a few specific parameters that reflect different processes involved in the startle reflex; these are: reflex capacity (RMAX ), stimulus potency (ES50 ), stimulus sensitivity (Threshold) and sensorimotor efficacy (Hillslope) [39,52,56]. Examination of these parameters may inform us of the effects

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of various manipulations on specific processes involved in startle and PPI. The aim of this current study was to directly examine the relationship between PPI (as measured during a novel attentional modulation PPI paradigm) and cognitive performance. PPI was assessed across a range of startle stimulus intensities in order to examine PPI as a function of the SIRM reflex parameters [39,52,56], and a sample comprising both patients with schizophrenia and controls was utilised in order to obtain a broad range of PPI and cognitive performance scores. This was in an attempt to provide further understanding on the relationship between PPI and cognition, given the above discussed inconsistencies and confounds. We have recently conducted a study examining the differences in PPI between patients with schizophrenia and healthy controls using this methodology [52], thus startle and PPI levels in these two groups, as well as comparisons between patients and controls have already been reported and, as such, will not be presented here. The focus of the current study is to examine any differences in cognitive performance between groups stratified according to their PPI levels (high or low), and to examine if there are any correlations between PPI and cognitive performance using different methodology for the measurement and characterisation of attentional modulation of PPI. 1. Materials and methods 1.1. Participants Thirty-eight healthy controls and 50 patients with a diagnosis of either schizophrenia (n = 48) or schizoaffective disorder (n = 2) were recruited for participation in the study. Healthy controls were recruited from the general community via newspaper advertisements, and from a database of potential willing volunteers. Patients were inpatients and outpatients of the major psychiatric hospital in Perth, Western Australia (Graylands Hospital), and were recruited via direct approach from the researcher (KES). Each patient’s treating psychiatrist was approached prior to their inclusion in the study to verify their ability to provide informed consent. All participants were screened prior to inclusion to ensure they satisfied the inclusion criteria, which included: no self-reported presence of any hearing disorders; no neurological disorders or head injury; no loss of consciousness for over 15 min; no current or past treatment for a substance use disorder, and no current use of illicit substances such as cannabis. Moreover, given that healthy relatives of patients with schizophrenia may share some endophenotypes of schizophrenia [11,54,55], control participants were excluded if they reported having a first degree relative with a diagnosis of schizophrenia or schizoaffective disorder. ICD-10 psychiatric diagnoses in patients were confirmed with the Diagnostic Interview for Psychoses (DIP-DM) [13]. Control participants were also administered the Mini International Neuropsychiatric Interview [53] to screen for the presence of Axis I disorders; as a consequence, two controls were excluded from analysis. Five participants (three patients and two controls) were classified as non-responders and were excluded from analysis; that is, they had a mean Threshold (defined below) under the pulse alone condition of greater than 115 dB, under either the ATTEND or IGNORE condition. One control and one patient were excluded as they did not provide urine samples; thus, absence of illicit drug use could not be confirmed. Further two patients and one control were excluded at random (based on their individual order of presentation of the two PPI attention conditions) in order to ensure that the order of presentation of the two PPI attention conditions was counter-balanced. Therefore, the final sample comprised 76 participants, of which 44 were patients and 32 were controls. All of the patients were taking antipsychotic medications, with 39 on atypical antipsychotics, 1 on typical antipsychotics, and 4 on both atypicals and typicals. The average daily chlorpromazine equivalent dose was 591.45 mg (S.D. = 335.60 mg). In addition to neuroleptics, 4 participants were taking anticholinergics, 16 were taking antidepressants, 9 were taking benzodiazepines and 10 were taking mood stabilizers. The average age of illness onset in the patients was 22 years (S.D. = 6.49 years). The demographic and substance use characteristics of the patient and control groups have been reported previously [52]. The demographic and substance use characteristics of the high and low PPI groups (groups of interest) for the current study can be found in Tables 3–6. This study was approved by the Western Australia North Metropolitan Area Mental Health Service Ethics Committee, and has therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki. 1.2. Substance use assessment Recent use of nicotine, alcohol and caffeine was assessed with a self-report questionnaire, as previously described [52]. In order to verify the absence of illicit

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drug use, and to quantify cotinine concentrations (nicotine metabolite) in the cigarette-smoking participants, urine samples were also obtained from all participants. Cloned-enzyme-donor-immunoassay, CEDIA, was used to screen the urine samples for the presence of opiates (cut-off 300 ␮g/L), amphetamines (300 ␮g/L cutoff), benzodiazepines (200 ␮g/L cut-off), cannabis metabolites (50 ␮g/L cut-off) and cocaine metabolites (300 ␮g/L cut-off). Gas chromatography–mass spectrometry (GCMS) was also performed to quantify the cotinine levels. 1.3. Neuropsychological testing 1.3.1. Stroop colour and word test The standardised version of the Stroop task was administered in order to measure attentional control [27]. The task comprised three pages: a ‘word’ page consisting of colour names written in black ink, where participants were required to read the words; a ‘colour’ page consisting of rows of XXXX’s printed in different colours, where participants were required to name the colours; and a ‘colour-word’ page consisting of words from the word page printed in the colours from the colour page, where the word and the ink colour were always incongruent, and where participants were required to name the ink colour and ignore the written word. Each page contained 100 items and participants were given 45 s on each page in which to complete as many items as possible. This procedure yielded three raw scores, reflecting the number of items completed on each page. For each raw score, a T-score was calculated, based on the individuals age and education predicted scores [27]. An ‘interference’ T-score was also calculated according to standard procedures [27], which provided a measure of the extent to which the words interfered with the colour naming during the colourword subtest, while also controlling for the speed of colour naming. The T-scores for each measure were the variables subjected to analysis; a low T-score indicates poorer performance. 1.3.2. Wechsler Memory Scale (WMS III): letter-number sequencing and spatial span The letter-number sequencing (LNS; as a measure of auditory working memory), and the spatial span (SS; as a measure of spatial working memory) [63] subtests of the WMS-III were administered. For the LNS task, participants were verbally presented with a series of randomly ordered numbers and letters and were required to first recount the numbers in ascending order, then the letters in alphabetical order. This yielded a score reflecting the number of items completed correctly, and this raw score was adjusted to account for the participant’s age [63]. In the SS task, participants were presented with a board featuring ten 3dimensional cubes. The task comprised two subtests. For the spatial span forward subtest (SS-F), a series of cubes were tapped in a specified sequence, and the participant was required to replicate this sequence. For the spatial span backwards subtest (SS-B), a series of cubes were tapped in specified sequence, and the participant was required to tap this sequence in the reverse order. This procedure yielded three raw scores, two reflecting the number of items completed correctly in each subtest (SS-F and SS-B), and one reflecting the sum of the items correctly completed across both subtests (SS-T). These three raw scores were then adjusted to account for the participants age [63]. The adjusted scores for LNS and SS-T were summed to give an overall measure of working memory (WM) [63]. 1.3.3. Wisconsin card sorting task (WCST) The computerised version of the WCST was administered [36]. Participants were presented with four key cards on the screen, each containing a different number of different coloured shapes. A pile of stimulus cards appeared at the bottom of the screen, each containing a different number of different coloured shapes (i.e. triangles, stars, crosses, circles), and participants were required to match each stimulus card that appeared with one of the key cards. However, participants were not instructed as to which dimension of the stimulus card should be matched. After each match the computer indicated whether this match was right or wrong, and another stimulus card appeared at the bottom of the screen. This continued until 10 cards had been matched correctly, in which case the matching principle changed. The task continued until six matching principle categories had been completed, or until the entire set of 128 cards had been used. Scoring was completed by the WCST computer scoring program [35] in accordance with the standardised scoring procedures [34]. The resulting raw score variables of interest were: total number of trials administered [Trials admin], number of categories completed [Categories], number of times the participant failed to maintain the set [Set failure], and trials taken to complete the first category [Trials to 1st]. Further, a number of variables were corrected to account for the participant’s age and education, and were converted to T-scores (low score reflects poorer performance) [34]. These were: % Errors, % perseverative responses [% P Responses], % perseverative errors [% P errors], % non-perseverative errors [% N-P errors], and % conceptual level responses [% C responses]. 1.4. Psychophysiological data collection and processing 1.4.1. Auditory stimuli and attention modulation tasks Startle testing was identical to our previous report [52]. Briefly, auditory stimuli were presented binaurally through a pair of stereo headphones. Background noise was 60 dB white noise presented continuously throughout the session, following a

two-minute acclimatization period. Pulse stimuli were 40 ms bursts of white noise which ranged between 80 and 115 dB (in 5 dB increments), and had nearly instantaneous rise and fall times. Prepulses were 20 ms bursts of 74 dB white noise, with 5 ms rise and fall times. Prepulse to pulse SOAs (stimulus onset asynchronies) were either 60 or 100 ms. The startle testing was completed under two conditions, one in which participants attended to the auditory stimuli (ATTEND), and one in which they ignored the auditory stimuli and focused on a visual task (IGNORE). During each attention condition participants were presented with 2 blocks of 26 trials. Each trial was 9 s long, and was separated by a random inter-trial interval (10–20 s, M = 15 s). Each block contained 8 pulse alone trials, 8 prepulse plus pulse trials with a 60 ms SOA, 8 prepulse plus pulse trials with a 100 ms SOA, one prepulse only trial, and one null trial in which no auditory stimuli were presented. The stimuli were presented in a random order in each block, and the order of presentation of the two attention conditions was counterbalanced. During the ATTEND task participants were instructed to attend to the auditory stimuli and indicate, on a joystick after each trial, whether they heard one (pulse alone) or two (prepulse plus pulse stimulus) sounds. During the IGNORE task participants were instructed to ignore the auditory stimuli and instead to attend to the computer screen where they had to find small ‘smiley faces’ hidden among neutral pictures. Onset and offset of a visual stimulus (picture in the IGNORE task, black screen in the ATTEND task) as well as response requirements were matched across the tasks. Further detail on these tasks can be found in our previous study [52]. As opposed to previous studies examining attentional modulation of PPI [17,18,31], this task was designed to measure attention sets, rather than dynamic selective attention processes. 1.4.2. Startle reflex recording and processing Recording and processing of the ASR was identical to our previous report [52]. Briefly, two miniature tin-cup surface electrodes filled with conductive paste placed beneath the right pupil, and a silver/silver chloride ground electrode on the left hand were used to measure EMG activity via a Standard National Instruments data acquisition (DAQ) card (DAQ 6062E; San Diego, USA). Recording began 600 ms prior to onset of the startling stimulus, and continued for 400 ms after its presentation. Hardware filtering (Grass instruments AC preamplifier, model CP511) consisted of high pass at 30 Hz, low pass at 1000 Hz and a notch filter at 50 Hz (the frequency of Australian mains power). The signal was amplified (by 50,000), converted from analog to digital, and sampled at a rate of 1000 Hz. A software bandpass filter was then applied (70–240 Hz), as well as a 60 Hz notch filter (the frequency at which the computer monitors operate). The signal was rectified by taking the absolute values. Smoothing was performed with a second order Chebyshev filter with a ripple of 0.03 dB. Computer algorithms then scored the EMG signal for each trial, and each trial was visually inspected (with the scorer blind to the group and trial type) in order to verify that it was scored appropriately. For each trial, the peak eyeblink magnitude was extracted according to the previously described methods [52]. Trials were excluded if they contained a blink in the baseline period (spontaneous or prepulse-elicited blink), or excessive artefact (>14 ␮V) (excluded trials were designated as a missing values). Trials in which no response was recorded were scored as zero magnitude. 1.5. Procedure Participants provided written informed consent on arrival at the research centre after the study procedures were fully explained to them. They then completed the substance use questionnaire and urine sample. Smoking was permitted ad libitum prior to the testing session to prevent any withdrawal effects on PPI [20]; however, acute effects of nicotine [43] were minimized as participants spent approximately 20 min with the researcher undergoing consent procedures and providing demographic and substance use information before the startle testing began. Participants were then seated in a comfortable chair in a shielded room and were prepared for recording. Instructions were provided for the completion of the first attention PPI task (either ATTEND or IGNORE). Completion of the two PPI tasks took approximately 40 min. Following this, the neuropsychological tests were administered. 1.6. Data analysis 1.6.1. Fitting of logistic functions Using the exact methods as previously described [52], non-linear regression was used to fit curves of best fit to the mean (across the two blocks) peak response magnitude for each individual, across the pulse stimulus intensities, for each condition, according to the following formula: y = RMAX +

(y0 − RMAX ) 1 + (x/ES50 )

Hillslope

;

(1)

where y is the ASR in microvolts, RMAX is the asymptote, y0 is the y intercept, x is the startling stimulus intensity in dB (ranging in intensity from 80 to 115 dB), ES50 is the stimulus intensity required to produce a half-maximal response, and Hillslope is the slope of the dynamic range at ES50 . RMAX , y0, ES50 and Hillslope were all ascertained with a non-linear regression Levenberg–Marquardt algorithm for iterative estimation, with the constraints that: ES50 > 80 dB; half of maximum observed response < the estimated RMAX < 2 times the maximum observed response; Hillslope > 0.

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Table 1 Summary of the principal components analysis for the startle, PPI and neuropsychological variables. Factor

Variance explained

Component

Primary loadings

(WCST)

18.43%

% errors % perseverative errors % perseverative responses % conceptual level responses % non-perseverative errors Trials administered Categories completed

0.98 0.91 0.89 0.93 0.89 −0.89 0.84

(WMS-III)

11.80%

Working memory Spatial span—total Spatial span—backward Spatial span—forward Letter-number sequencing

Stimulus measures under ATTEND

8.92%

Pulse alone ES50 ATTEND Pulse alone Threshold ATTEND PPI of ES50 ATTEND PPI of Threshold ATTEND

−0.87 −0.68 0.88 0.75

Stimulus measures under IGNORE

8.87%

Pulse alone ES50 IGNORE Pulse alone Threshold IGNORE PPI of ES50 IGNORE PPI of Threshold IGNORE

0.86 0.72 −0.90 −0.64

Response measures under ATTEND

8.32%

Pulse alone Hillslope ATTEND PPI of Hillslope ATTEND PPI of RMAX ATTEND

0.90 0.87 −0.52

Response measures under IGNORE

8.14%

Pulse alone Hillslope IGNORE PPI of Hillslope IGNORE PPI of RMAX IGNORE

0.88 0.86 −0.34

Attention

(Stroop)

6.21%

Stroop colour Stroop word Stroop colour-word

0.79 0.79 0.83

Maximum startle magnitude

5.66%

Pulse alone RMAX ATTEND Pulse alone RMAX IGNORE Stroop Interference

0.73 0.77 0.51

Attentional Set

4.74%

WCST set failure Trials to 1st category

0.76 0.50

Executive functioning

Working Memory

The Threshold parameter (minimum level of stimulation required to produce a startle response) was calculated according to the following formula, as previously described [52]. Threshold = ES50 −

RMAX − y0 ; Hillslope

(2)

While RMAX and Hillslope are measured in response units and are considered closer to the response/motor aspect of the startle reflex, Threshold and ES50 are measured in stimulus units and are considered closer to the stimulus or sensory component [56]. 1.6.2. Statistical analysis In order to replace missing SIRM parameter values, the mean difference between the pulse alone condition and each prepulse condition was calculated for each parameter, and then this was applied to the case with the missing value (i.e. known pulse alone condition ± the mean difference = missing value). It should be noted that

0.92 0.95 0.81 0.86 0.65

there are very few missing values using SIRM methodology, as curves are fitted to the raw peak magnitude data as long as there are at least 5/8 data points per curve. The Hillslope parameters were significantly skewed to the right, which were remedied with the application of common Log transformations. PPI of RMAX was calculated according to the formula:



%PPI =

P(RMAX ) − PP(RMAX ) P(RMAX )





× 100 ;

(3)

where P(RMAX ) = the RMAX from pulse alone trials, and PP(RMAX ) = the RMAX from prepulse plus pulse trials. Positive values indicate inhibition. PPI of ES50 and Threshold were calculated as difference scores (prepulse plus pulse − pulse alone) given that these parameters are on a logarithmic scale, and differences in log units are equivalent to arithmetic unit ratios. Positive difference scores for ES50 and Threshold indicate PPI (as increases in these parameters with prepulse presentation indicate increases in the intensity of the stimuli required to produce a startle response; that is, inhibition of sensitivity/potency). PPI of Hillslope was also calculated as a

Table 2 Frequency distribution of controls and patients in the high and low PPI quartiles for each PPI composite measure. PPI measurea

Quartileb

Patients (N)

X2

p

ST-AT

Q1 Q4

8 7

11 12

0.11

0.740

ST-IG

Q1 Q4

8 9

11 10

0.11

0.744

RS-AT

Q1 Q4

7 10

12 9

0.96

0.328

RS-IG

Q1 Q4

9 5

10 14

1.81

0.179

Controls (N)

a ST-AT: PPI of the stimulus measures under the ATTEND condition; ST-IG: PPI of the stimulus measures under the IGNORE condition; RS-AT: PPI of response measures under the ATTEND condition; RS-IG = PPI of the response measures under the IGNORE condition. b Q1: high PPI, Q4: low PPI.

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Table 3 Demographic and substance use characteristics of high and low PPI of ST-ATa groups.

N Male/female Cigarette smoker/non-smoker Alcohol drinkers/non-drinkers Caffeine drinkers/non-drinkers

Age Education Average caffeine (cups/day) Alcohol30 (days in last 30) Startle only ES50 ATTEND Startle only Threshold ATTEND PPI of ST-AT

Cotinine (units) Last cigarette (h) Cigarette on average (per day) Last alcoholic drink (h) Average alcohol (standard drinks/week) Last caffeinated drink (h) Caffeine30 (days in last 30) a

Q1—high PPI

Q4—low PPI

X2

Df

P

19 15/4 7/12 13/6 16/3

19 15/4 7/12 12/7 18/1

0.00 0.00 0.00 0.12 1.12

1 1 1 1 1

1.00 1.00 1.00 0.732 0.290

M (S.D.)

M (S.D.)

t

Df

P

35.21 (11.40) 12.05 (2.68) 2.53 (1.83) 7.85 (6.39) 89.39 (1.03) 86.84 (1.49) 34.94 (4.58)

35.84 (11.18) 13.26 (3.16) 3.06 (1.95) 7.67 (8.00) 100.14 (1.97) 96.39 (2.24) −0.53 (6.28)

−0.17 −1.27 −0.87 0.06 −4.84 −3.55 19.89

36 36 36 23 36 36 36

0.864 0.211 0.393 0.951 <0.0005* 0.001* <0.0005*

Median (range)

Median (range)

U

z

P

0 (0–2464) 0.25 (0.08–5.5) 25 (12–50) 72 (3.5–672) 5 (0.1–25) 4.5 (1–24) 30 (0–30)

0 (0–1825) 0.25 (0.08–1.5) 23 (10–35) 84 (10–336) 5 (0.1–20) 2.5 (0.08–168) 30 (0–30)

177.50 23.50 21.50 66.50 74.50 124.50 175.50

−0.10 −0.13 −0.39 −0.63 −0.19 −0.68 −0.16

0.923 0.898 0.699 0.529 0.848 0.500 0.873

ST-AT: PPI of stimulus measures under the ATTEND condition.

Table 4 Demographic and substance use characteristics of high and low PPI of ST-IGa groups.

N Male/female Cigarette smoker/non-smokers Alcohol drinkers/non-drinkers Caffeine drinker/non-drinkers

Age Education Average caffeine (cups/day) Alcohol30 (days in last 30) Startle only ES50 IGNORE Startle only Threshold IGNORE PPI of ST-IG

Cotinine (units) Last cigarette (h) Cigarette on average (per day) Last alcoholic drink (h) Average alcohol (standard drinks/week) Last caffeinated drink (h) Caffeine30 (days in last 30) a

Q1—high PPI

Q4—low PPI

X2

Df

P

19 18/1 5/14 13/6 17/2

19 15/4 8/11 10/9 18/1

0.00 2.07 1.05 0.99 0.362

1 1 1 1 1

1.00 0.150 0.305 0.319 1.00a

M (S.D.)

M (S.D.)

t

Df

P

36.74 (12.23) 13.00 (2.92) 2.16 (1.67) 10.62 (9.09) 86.88 (1.41) 85.14 (1.27) 27.62 (3.72)

40.58 (10.16) 12.26 (2.75) 3.22 (3.35) 11.50 (10.28) 106.92 (1.93) 98.34 (1.76) −4.37 (5.28)

−1.05 0.80 −1.23 −0.22 −8.38 −6.06 21.58

36 36 36 21 36 36 36

0.301 0.429 0.229 0.829 <0.0005* <0.0005* <0.0005*

Median (range)

Median (range)

U

z

P

0 (0–2544) 0.5 (0.16–1) 30 (20–47) 48 (3.5–672) 6 (0.1–25) 2 (0.83–120) 30 (0–30)

10 (0–2090) 0.25 (0.08–1.5) 20 (10–35) 32 (10.5–336) 5 (0.1–20) 3.3 (0.6–168) 30 (0–30)

160.00 12.5.00 8.50 63.00 62.00 148.00 179.50

−0.65 −1.10 −1.70 −0.12 −0.19 −0.17 −0.03

0.517 0.270 0.089 0.901 0.852 0.868 0.974

ST-IG: PPI of stimulus measures under the IGNORE condition.

difference score, given that it had been submitted to a log transformation. However, this difference score was calculated as [pulse alone − prepulse plus pulse] such that PPI of each SIRM parameter was expressed as a positive number. PPI of each parameter was calculated for each SOA under each attention condition, and the average of the two SOAs was then taken to be consistent with our previous report where no significant effect of SOA was evident in the current sample [52]. In order to reduce the number of variables, so as to enable splitting of the sample into those who show high versus low levels of PPI, the neuropsychological, startle and PPI variables were then submitted to principal components analysis (PCA) with varimax rotation. Given that the PPI variables were grouped into four distinct factors based on the type of measure (response/stimulus) and the attention condition (ATTEND/IGNORE) (see results section), the PCA loadings were used to determine PPI composite measures comprised of these variables (i.e. composite measures were: PPI of stimulus measures under ATTEND [ST-AT], PPI of response measures under ATTEND [RS-AT], PPI of stimulus measures under IGNORE [ST-IG], PPI of response measures under IGNORE [RS-IG]).

Quartile splits were then performed for each PPI composite measure to separate the sample into high PPI (quartile 1) and low PPI (quartile 4) for each measure. For each PPI composite measure, differences in demographic and substance use variables between the high and low PPI groups were investigated with t-tests, chi square tests or Mann Whitney tests, where relevant. Subsequently, repeated measures multivariate analysis of variance (RM MANOVA) for each PPI composite measure was conducted, with one between–subjects factor (PPI quartile: 1—high PPI; 4—low PPI) and the neuropsychological performance indices as the within–subjects factors. MANOVAs were followed up with planned pairwise comparisons with Sidak corrections (˛ = 0.05), regardless of the significance of the interaction term, due to the insensitivity of ANOVA to ordinal interactions [57]. Differences in behavioural performance of each PPI attention task between the high and low PPI groups were investigated in the same way. Further, relationships between PPI of each composite measure, and neuropsychological performance, were investigated with Pearson’s and Spearman’s correlations, where appropriate.

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Table 5 Demographic and substance use characteristics of high and low PPI of RS-ATa groups.

N Male/female Cigarette smoker/non-smokers Alcohol drinkers/non-drinkers Caffeine drinkers/non-drinkers

Age Education Average caffeine (cups/day) Alcohol30 (days in last 30) Startle only RMAX ATTEND Startle only Hillslope ATTEND PPI of RS-AT

Cotinine (units) Last cigarette (h) Cigarette on average (per day) Last alcoholic drink (h) Average alcohol (standard drinks/week) Last caffeinated drink (h) Caffeine30 (days in last 30) a

Q1—high PPI

Q4—low PPI

X2

Df

P

19 17/2 7/12 15/4 19/0

19 13/6 3/16 11/8 17/2

0.00 2.53 2.17 1.95 2.11

1 1 1 1 1

1.00 0.111 0.141 0.163 0.486a

M (S.D.)

M (S.D.)

t

Df

P

35.05 (10.73) 12.59 (3.36) 3.64 (2.10) 8.53 (8.13) 233.14 (28.93) 1.77 (0.20) 42.24 (8.08)

35.53 (12.29) 12.47 (2.17) 10.27 (9.29) 10.27 (9.29) 143.13 (20.84) 2.33 (0.14) −52.29 (47.89)

−0.13 0.12 1.61 −0.51 2.53 −2.29 8.55

36 36 36 24 36 33 19.04

0.900 0.909 0.117 0.616 0.016* 0.029* <0.0005*

Median (range)

Median (range)

U

z

P

10 (0–2369) 0.25 (0.01–14.5) 14 (4–25) 72.00 (6.5–336) 3 (0.10–12) 2.5 (0.6–96) 30 (4–30)

0 (0–2464) 0.08 (0.08–0.1) 25 (12–50) 72 (12–672) 5 (0.1–20) 2 (0.08–168) 30 (0–30)

137.00 3.50 5.00 78.00 63.00 154.50 143.50

−1.44 −1.61 −1.26 −0.24 −1.02 −0.22 −1.23

0.150 0.108 0.207 0.814 0.309 0.824 0.220

RS-AT: PPI of the response measures under the ATTEND condition.

2. Results

2.2. Quartile splits of PPI composite measures

2.1. Principal components analysis

Table 2 shows the number of controls and patients that made up the high and low PPI quartiles for each PPI composite measure. There were no significant differences in the frequency distributions of controls versus patients in each quartile group. This is not entirely surprising given that although the mean PPI observed across a sample of patients with schizophrenia may be decreased relative to a sample of controls [For example see 3,8,17,33], as we have demonstrated in this entire sample of patients [52], some patients can show normal levels of PPI, as is the case with cognition [29]. As can be seen in Tables 3–6, there were no significant differences in any of the demographic or substance use variables

A summary of the primary loadings from the principal components analysis is presented in Table 1. This analysis yielded 9 primary factors, which account for 81.07% of the variance, and which appear to reflect executive functioning, working memory, the stimulus measures under the ATTEND condition, the stimulus measures under the IGNORE condition, the response measures under the ATTEND condition, the response measures under the IGNORE condition, attention, maximum response magnitude, and attentional set.

Table 6 Demographic and substance use characteristics of high and low PPI of RS-IGa groups.

N Male/female Cigarette smoker/non-smokers Alcohol drinkers/non-drinkers Caffeine drinkers/non-drinkers

Age Education Average caffeine (cups/day) Alcohol30 (days in last 30) Startle only RMAX IGNORE Startle only Hillslope IGNORE PPI of RS-IG

Cotinine (units) Last cigarette (h) Cigarette on average (per day) Last alcoholic drink (h) Average alcohol (standard drinks/week) Last caffeinated drink (h) Caffeine30 (days in last 30) a

Q1—high PPI

Q4—low PPI

X2

Df

P

19 17/2 8/11 13/6 18/1

19 16/3 4/15 10/9 17/2

0.00 0.23 1.95 0.99 0.36

1 1 1 1 1

1.00 0.631 0.163 0.319 0.547

M (S.D.)

M (S.D.)

t

Df

P

36.21 (11.97) 12.11 (3.20) 3.84 (3.25) 11.15 (8.49) 146.72 (19.20) 1.66 (0.16) 34.38 (3.17)

35.11 (10.72) 12.84 (2.12) 2.43 (2.00) 9.70 (9.37) 148.16 (18.52) 2.05 (0.15) −20.92 (16.77)

3.00 −0.84 1.61 0.39 −0.05 −1.79 14.12

36 36 36 21 36 36 19.28

0.766 0.407 0.116 0.701 0.957 0.082 <0.0005*

Median (range)

Median (range)

U

z

P

10 (0–2369) 0.25 (0.08–14.5) 24 (12–35) 72 (12–336) 4 (0.1–12) 2 (0.75–27) 30 (0–30)

10 (0–2544) 0.21 (0.08–0.50) 32.5 (12–47) 84 (0.75–336) 5 (0.1–20) 2.5 (0.08–168) 30 (0–30)

150.5 14.50 10.00 59.00 62.5 137.50 155.00

−0.96 −0.26 −1.03 −0.37 −0.16 −0.51 −0.85

0.337 0.795 0.302 0.708 0.876 0.608 0.398

RS-IG = PPI of the response measures under the IGNORE condition.

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Table 7 Means and standard errors of measurement (SEM) of neuropsychological performance for high and low PPI quartiles of each PPI composite measure. Test

Measure

ST-ATa

ST-IG b

e

f

Q1 M (SEM)

Q4 M (SEM)

RS-ATc

Q1 M (SEM)

Q4 M (SEM)

RS-IG d

Q1 M (SEM)

Q4 M (SEM)

Q1 M (SEM)

Q4 M (SEM)

Stroop

Word Colour Colour-Word Interference

42.53 (2.42) 41.14 (2.23) 47.67 (2.57) 50.39 (1.87)

44.24 (2.42) 40.08 (2.23) 47.23 (2.57) 50.50 (1.87)

45.72 (2.93) 42.38 (2.79) 47.05 (2.45) 48.85 (1.84)

48.40 (2.93) 48.93 (2.79) 53.74 (2.45) 51.03 (1.84)

43.25 (2.56) 41.74 (3.12) 47.42 (2.51) 49.88 (1.78)

47.31 (2.56) 45.27 (3.12) 51.90 (2.51) 52.15 (1.78)

43.14 (2.50) 42.90 (2.67) 49.77 (2.21) 51.18 (1.75)

41.40 (2.50) 35.59 (2.67) 43.10 (2.21) 48.73 (1.75)

WMS

LNS WM SS-F SS-B SS-T

9.79 (0.71) 20.00 (1.07) 10.11 (0.66) 10.58 (0.52) 10.21 (0.55)

9.63 (0.71) 19.11 (1.07) 8.74 (0.66) 10.32 (0.52) 9.47 (0.55)

9.95 (0.63) 20.74 (1.13) 9.79 (0.73) 11.42 (0.65) 10.79 (0.69)

10.58 (0.63) 20.68 (1.13) 9.05 (0.73) 11.47 (0.65) 10.11 (0.69)

8.74 (0.63) 18.42 (1.22) 8.84 (0.68) 10.42 (0.72) 9.68 (0.68)

10.26 (0.63) 21.63 (1.12) 10.90 (0.68) 12.00 (0.72) 11.37 (0.68)

9.74 (0.75) 20.32 (1.24) 9.26 (0.65) 11.84 (0.64) 10.58 (0.63)

9.42 (0.75) 19.58 (1.24) 9.79 (0.65) 10.42 (0.64) 10.16 (0.63)

WCST

Trials admin % errors % P responses % P errors % N-P errors % C responses Categories Trials to 1st Set failure

106.21 (5.59) 45.79 (2.64) 49.42 (3.33) 48.11 (3.24) 45.05 (2.24) 45.32 (2.90) 4.32 (0.43) 24.90 (5.30) 1.05 (0.28)

104.63 (5.59) 45.74 (2.64) 49.47 (3.30) 48.05 (3.24) 44.90 (2.24) 48.11 (2.90) 4.68 (0.43) 16.95 (5.30) 1.05 (0.28)

103.84 (5.72) 43.32 (3.02) 46.90 (3.25) 45.95 (3.25) 43.21 (2.80) 42.42 (3.04) 3.84 (0.50) 25.26 (6.57) 0.74 (0.29)

108.47 (5.72) 44.79 (3.02) 48.42 (3.25) 47.42 (3.25) 43.79 (7.80) 45.00 (3.04) 4.16 (0.50) 17.47 (6.57) 1.21 (0.29)

114.21 (5.15) 40.58 (2.60) 46.11 (2.85) 44.58 (2.76) 39.47 (2.57) 39.68 (2.90) 3.63 (0.49) 18.16 (4.79) 1.00 (0.30)

102.00 (5.15) 48.53 (2.60) 53.37 (2.85) 51.79 (2.76) 46.63 (2.57) 50.42 (2.90) 4.47 (0.49) 21.68 (4.79) 1.37 (0.30)

102.84 (5.83) 47.00 (3.14) 49.84 (3.70) 48.42 (3.60) 47.58 (2.66) 46.95 (3.43) 4.21 (0.53) 21.26 (7.47) 0.74 (0.29)

101.21 (5.83) 44.79 (3.14) 47.00 (3.70) 46.16 (3.60) 45.42 (2.66) 46.42 (3.43) 4.00 (0.53) 26.90 (7.47) 1.05 (0.29)

a b c d e f

ST-AT = PPI of stimulus measures under ATTEND conditions. ST-IG = PPI of stimulus measures under IGNORE conditions. RS-AT = PPI of response measures under ATTEND conditions. RS-IG = PPI of response measures under IGNORE conditions. Q1 = Quartile 1, Highest PPI. Q4 = Quartile 4, Lowest PPI.

between the high and low PPI quartiles for each PPI measure. As would be expected, there were significant differences in PPI levels between the high and low quartiles for each PPI composite measure (Tables 3–6). The mean and standard error of measurement (SEM) of performance on each neuropsychological task, for the high and low PPI quartiles of each PPI composite measure, can be seen in Table 7. Stroop: For PPI of stimulus measures under the ATTEND condition there was no main effect of group (F(1,36) = 0.001, p = 0.976, observed power = 0.05) and no group by measure interaction (F(3,34) = 0.34, p = 0.797, observed power = 0.11). For PPI of stimulus measures under the IGNORE condition there was no main effect of group (F(1,36) = 2.44, p = 0.127, observed power = 0.33), and the group by measure interaction was not significant (F(3,34) = 2.43, p = 0.082, observed power = 0.56). For PPI of response measures under the ATTEND condition there was no main effect of group (F(1,36) = 1.51, p = 0.227, observed power = 0.22) and no group by measure interaction (F(3,34) = 0.58, p = 0.629, observed power = 0.16). For PPI of response measures under the IGNORE condition there was no main effect of group

(F(1,36) = 2.92, p = 0.096, observed power = 0.34) but a significant group by measure interaction (F(3,34) = 3.58, p = 0.024, partial 2 = 0.24). Pairwise comparisons with Sidak correction indicated that those participants with the most PPI of response measures under IGNORE conditions also showed the best performance on the colour-word measure of the Stroop task (F(1,36) = 4.58, p = 0.039, partial 2 = 0.11) as compared to those with the least PPI of response measures under IGNORE. WMS: For PPI of stimulus measures under ATTEND conditions there was no main effect of group (F(1,36) = 0.65, p = 0.425, observed power = 0.12) and no group by measure interaction (F(4,33) = 0.65, p = 0.628, observed power = 0.19). Similarly, for PPI of stimulus measures under IGNORE conditions there was no main effect of group (F(1,36) = 0.03, p = 0.870, observed power = 0.05) and no group by measure interaction (F(4,33) = 1.20, p = 0.328, observed power = 0.33). For PPI of response measures under ATTEND condition there was a significant main effect of group (F(1,36) = 4.43, p = 0.042, partial 2 = 0.11) but the group by measure interaction was not significant (F(4,33) = 1.89, p = 0.135, observed power = 0.51). Pairwise comparisons with Sidak correction indicated that those partic-

Table 8 Means and standard errors of measurement (SEM) of behavioural performance during the PPI attention tasks (total number of smiley faces detected in the IGNORE task, total number of correct auditory stimulus discriminations in the ATTEND task), under each stimulus condition, for high and low PPI quartiles of each PPI composite measure. Task

Stimulus condition

ST-ATa

ST-IGb

RS-ATc

RS-IGd

Q1e M (SEM)

Q4f M (SEM)

Q1 M (SEM)

Q4 M (SEM)

Q1 M (SEM)

Q4 M (SEM)

Q1 M (SEM)

Q4 M (SEM)

ATTEND

Pulse alone Prepulse + pulse

14.1 (1.2) 11.8 (1.2)

10.8 (1.2) 9.5 (1.2)

14.1 (1.0) 12.5 (1.2)

11.6 (1.2) 10.4 (1.2)

12.5 (1.0) 11.5 (1.1)

14.6 (1.0) 11.8 (1.1)

13.6 (1.0) 11.3 (1.3)

12.9 (1.0) 10.3 (1.3)

IGNORE

Pulse alone Prepulse + pulse

57.4 (2.9) 57.7 (3.2)

58.6 (2.9) 56.1 (3.2)

61.6 (2.0) 62.2 (2.3)

59.5 (2.0) 58.9 (2.3)

61.6 (2.9) 62.6 (3.0)

58.4 (2.9) 58.2 (3.0)

61.7 (2.1) 62.6 (2.3)

58.4 (2.1) 57.2 (2.3)

a b c d e f

ST-AT = PPI of stimulus measures under ATTEND conditions. ST-IG = PPI of stimulus measures under IGNORE conditions. RS-AT = PPI of response measures under ATTEND conditions. RS-IG = PPI of response measures under IGNORE conditions. Q1 = Quartile 1, Highest PPI. Q4 = Quartile 4, Lowest PPI.

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ipants with less PPI of response measures under the ATTEND condition showed better performance on the spatial span forward subtest (F(1,32) = 4.72, p = 0.037, partial 2 = 0.13), compared to those participants with high levels of PPI of response measures under ATTEND conditions. The same trends were evident in all the other working memory indices, but they did not quite reach pairwise significance (letter-number sequencing, p = 0.093; spatial span, p = 0.087; working memory, p = 0.051; spatial span backwards, p = 0.123). Finally, for PPI of response measures under IGNORE conditions there was no main effect of group (F(1,36) = 0.23, p = 0.637, observed power = 0.08) and no group by measure interaction (F(4,33) = 1.37, p = 0.266, observed power = 0.38). WCST: For PPI of stimulus measures under ATTEND conditions there was no main effect of group (F(1,36) = 0.13, p = 0.724, observed power = 0.06) and no group by measure interactions (F(9,28) = 0.52, p = 0.848, observed power = 0.20). There was also no main effect of group (F(1,36) = 0.81, p = 0.376, observed power = 0.14) and the group by measure interaction was not significant (F(9,28) = 1.68, p = 0.141, observed power = 0.64) for PPI of stimulus measures under IGNORE conditions. There was a significant main effect of group (F(1,36) = 5.88, p = 0.020, partial 2 = 0.14) and no group by measure interaction (F(9,28) = 0.83, p = 0.599, observed power = 0.32) for PPI of response measures under ATTEND conditions. Planned pairwise comparisons indicated that those participants with the most PPI of response measures under ATTEND conditions had the poorest performance on percent total errors (F(1,36) = 4.68, p = 0.037, partial 2 = 0.12), and percent conceptual responses (F(1,36) = 6.84, p = 0.013, partial 2 = 0.16). Percent perseverative responses (p = 0.080), percent perseverative errors (p = 0.073) and percent non-perseverative errors (p = 0.056) were all not quite statistically significant. For PPI of response measures under IGNORE conditions there was no significant main effect of group (F(1,36) = 0.19, p = 0.666, observed power = 0.07) and no group by measure interaction (F(9,28) = 0.92, p = 0.521, observed power = 0.36). Further, behavioural performance during each PPI attention task, can be found in Table 8. There were no significant differences in behavioural performance of either attention task, between the high and low PPI quartiles, for each PPI composite measure. 2.3. Correlations Correlations were consistent with the findings from the quartile splits of the PPI composite measures. Stroop: There was a significant negative correlation between the Stroop colour-word subtest and PPI of stimulus measure under IGNORE conditions (r = −0.25, p = 0.031), and a positive correlation between Stroop colour-word subtest and PPI of response measures under IGNORE conditions (r = 0.29, p = 0.012). Thus when ignoring, more PPI of stimulus measures was associated with poorer colour-word performance, whereas more PPI of response measures was associated with better colour-word performance. There was also a significant positive correlation between the Stroop colour subtest and PPI of the response measures under IGNORE conditions (r = 0.24, p = 0.034), where more PPI of the response measures was associated with better performance. See Fig. 1. WMS: There were significant negative correlations between PPI of the response measures under ATTEND conditions and the working memory score ( = −0.24, p = 0.038), and the spatial span forward subtest ( = −0.27, p = 0.017); indicating that more PPI of response measures when attending was associated with poorer working memory performance. There was also a significant positive correlation between PPI of stimulus measures when attending and the spatial span forward subtest ( = 0.24, p = 0.035), indicating that more PPI of the stimulus measures while attending was associated with better spatial span performance. See Fig. 2.

Fig. 1. Significant correlations between PPI and Stroop performance. (A) Correlation between PPI of response measures under IGNORE and Stroop Colour-Word score. (B) Correlation between PPI of stimulus measures under IGNORE and Stroop ColourWord score. (C) Correlation between PPI of response measures under IGNORE and Stroop Colour score.

3. Discussion The current study examined the relationship between attentional modulation of PPI and cognition, in a sample comprising both patients with schizophrenia and healthy controls, with the aim of improving understanding of the functional significance of PPI. Quartile splits of the whole sample, separating the sample into those with the highest PPI versus those with the lowest PPI, as well as correlations, revealed a number of relationships between neuropsychological performance and attention modu-

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Fig. 2. Significant correlations between PPI and WMS performance. (A) Correlation between PPI of response measure under ATTEND and WMS Working memory score. (B) Correlation between PPI of response measures under ATTEND and WMS Spatial span forward score. (C) Correlation between PPI of stimulus measures under ATTEND and WMS Spatial span forward score.

lated PPI. Specifically, more PPI of the response measures and less PPI of the stimulus measures under IGNORE conditions was associated with better performance in the colour-word subtest of the Stroop task. Additionally, more PPI of stimulus measures under ATTEND conditions was associated with better performance in the spatial span forward subtest, while more PPI of the response measures under ATTEND conditions was associated with poorer performance in the spatial span forward subtask and the working memory score. These relationships between PPI and cognition appear to be explained by common attentional processes activated during performance of both cognitive tasks and PPI paradigms.

Previous research on the relationship between PPI and other inhibitory control based neuropsychological tasks has been mixed [23,58]. Here we show that better performance on the colourword subtest of the Stroop, in which participants are required to selectively attend to and respond to the correct dimension of the stimulus, is associated with higher PPI of the SIRM response measures under IGNORE conditions, and lower PPI of the SIRM stimulus measures under IGNORE conditions. In healthy people, who presumably have no trouble ignoring the irrelevant auditory stimuli and focusing on the relevant visual stimulus during the IGNORE condition, more PPI of RMAX (primary response measure) and less PPI of the stimulus measures (ES50 and Threshold) is observed under IGNORE as compared to ATTEND [52]. Thus, the ability to selectively ignore a distracting stimulus is characterised by more PPI of the stimulus measures and less PPI of the response measure. Patients with schizophrenia do not show this pattern [52]. Thus, the relationship between PPI under IGNORE conditions and performance of Stroop colour-word observed here suggests that those who are better able to ignore the irrelevant auditory stimuli during the PPI IGNORE task (exhibiting better control of attention, and consequently more PPI of response measures but less PPI of stimulus measures) are better able to ignore the distracting words in the Stroop colour-word subtest (exhibiting better control of attention and consequently better colour naming performance). As such, the common underlying process observed here between PPI and Stroop colour-word appears to be attentional control. Similar relationships between PPI and cognition were not evident under the ATTEND condition, as control of attention from distraction is not required under this condition. Our findings are somewhat consistent with the findings of Filion and McDowd [23], who found a relationship between PPI and attentional control (as measured by a negative priming task), only when participants were instructed to selectively attend to the relevant auditory prepulse stimuli, and ignore the irrelevant prepulses using a different attentional modulation of PPI paradigm than that which was employed in the current study. As such, those who were better able to selectively attend to the correct stimulus (i.e. the ‘attend’ high pitched tone), were also better able to selectively attend to the correct stimulus in the negative priming task, resulting in better performance. The lack of significant correlations between inhibitory measures and PPI in previous studies [1,58] is likely due to the use of uninstructed PPI tasks, where varying attentional processes between subjects and across trials confound measurement of PPI. The current study’s findings also suggest that relationships between PPI and inhibitory control tasks are unlikely to be mediated by underlying neurophysiological or neurobiological inhibitory mechanisms, as such relationships were only observed under certain attentional conditions. As such, the commonality between these two tasks appears to lie in top-down attentional control processes occurring during both attentional modulation PPI tasks, and cognitive tasks. A number of studies have administered working memory tasks and shown relationships between PPI and measures of strategy formation during these tasks, but not between PPI and measures of memory performance itself (such as number of correct responses) [2,14]. In the present study, those who had more PPI of response measures when attending to the auditory stimuli had poorer performance on the SS-F subtask of the WMS, where participants were required to repeat a spatial sequence in the order in which it was presented. Similarly, more PPI of response measures when attending was associated with poorer performance on the working memory measure of the WMS, which is obtained by summing the letter-number sequencing and total spatial span scores. The opposite was true for PPI of stimulus measures, where more PPI was associated with better SSF performance. As with the Stroop findings, these relationships appear to be due to common atten-

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tional processes during the tasks. During the ATTEND PPI task in the current study, participants were required to sustain attention to the auditory stimuli in the face of little other distracting stimuli. When attending to the auditory stimuli in this ATTEND task, healthy controls show less PPI of RMAX response measure and more PPI of stimulus measures, as compared to when they are ignoring the auditory stimuli [52]. While the letter-number sequencing and spatial span backwards subtests require participants to remember and manipulate the information in order to produce a response, which are the essential features of working memory, the spatial span forward subtest simply requires participants to repeat the spatial sequence, and as such, is more a measure of simple spatial span, which is particularly affected by attention span [47]. Thus, both the PPI ATTEND task, and the spatial span forward task rely heavily on sustained attention or attention span. Therefore, the common process underlying PPI and memory measures here appears to be sustained attention. As such, those who are better able to sustain attention to the auditory stimuli (characterised by more PPI of stimulus measures and less PPI of response RMAX measure [52]) also are better able to sustain attention across the span of stimuli during the spatial span forward subtest (thus producing better performance). Relationships between PPI and the measures of memory were only observed under certain attention conditions; that is, when participants were required to sustain attention to the auditory stimuli throughout the task, with little external distraction. If the common underlying process were to do with PPI per se, or its inhibitory function, with more PPI indicating improved early information processing, then relationships between PPI and cognitive tasks should be observed regardless of whether participants are attending to or ignoring the auditory stimuli. Such a contention is supported by previous studies where no correlations between measures of working memory (number of errors, or number of correct responses) and PPI have been observed using uninstructed PPI tasks [2,14,59]. Thus, the common underlying process between PPI measured here and the memory measures appears to be top-down attention span, rather than a more fundamental bottom-up process associated with sensorimotor gating. It is interesting to note that in the quartile split analyses, for PPI of response measures under ATTEND, there was a main effect of group, and the pairwise comparisons between high and low PPI groups were close to significance for all working memory measures. It is appealing to speculate that this is because attention span underpins all these tasks to some degree, given that attention to the stimuli is required in increasing lengths throughout each task. However, these other measures also require more complex working memory and information manipulation processes, possibly weakening such underlying associations between attention span and PPI under the ATTEND condition. Larger sample sizes and inclusion of other tasks which index attention span or sustained attention may help to address whether this is in fact the case. In the current study, there were no significant differences in behavioural performance of both PPI attention tasks, between the high and low PPI groups for each PPI composite measure. There was very little variability in this data, and thus these measures may not be sensitive enough to pick up the subtle alterations in attention that appear to contribute to the differences in PPI between high and low PPI groups. Finally, a number of studies have reported relationships between planning and strategy performance and PPI [1,2,14,26]. The WCST is the classic neuropsychological measure of executive functioning, reflecting the function of a number of higher order cognitive control skills or processes that enable an individual to plan and execute goal-directed operations, and work to optimize performance on complex cognitive tasks [50,51,62]. However, the findings on the relationship between PPI and the WCST are mixed. While one study reported an apparent relationship between exec-

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utive function and PPI (though this was not confirmed statistically), whereby those who performed better on the WCST (less perseverative errors) exhibited more PPI [10]; another study found no such relationship [59]. In the current study, there were no significant correlations between PPI measures and WCST; however, quartile split analyses of PPI of response measure under ATTEND revealed a main effect of group, with pairwise comparisons indicating that, as compared to those participants with the least PPI of response measures, those participants with the most PPI of response measures under ATTEND conditions had the poorest scores in percent total errors and percent conceptual responses, with percent perseverative errors, percent perseverative responses and percent non-perseverative responses just off significance. Given that the differences between those with high and low PPI were only observed under the ATTEND condition, a fundamental inhibitory process mediating this relationship between PPI and WCST is unlikely. Further, given that apparent differences were evident across all measures, not any one specific measure reflecting any one specific process, it is unlikely that the group differences are due to differences in a specific process reflected by any one measure (such as perseveration). However, these findings could again be interpreted in light of attentional processes common to both the PPI task and the WCST. As mentioned, in healthy people, sustained attention to the auditory stimuli is associated with more PPI of stimulus measures and less PPI of RMAX response measure, as compared to when ignoring the auditory stimuli and selectively attending to a visual stimulus [52]. Therefore, it is possible that those who were better able to sustain attention across the entirety of the WCST (thus producing enhanced performance as measured across all WCST measures) were also better able to sustain attention to the auditory stimuli across the entire PPI ATTEND task (thus producing less PPI of response measures). However, given that corresponding correlations were not observed between response gating under ATTEND and WCST measures, these differences should be interpreted with caution. It is apparent that the relationships between PPI and cognition, and the differences in cognitive performance between high and low PPI groups, observed here, can be explained most parsimoniously by attention processes activated during each task, rather than by more basic bottom-up, inhibitory or gating processes. Inconsistencies in findings, and failure to find relationships between PPI and cognitive performance in previous research is likely to be due to the use of uninstructed PPI tasks, in which the role of various attentional processes cannot be assessed and can confound interpretation. It should be noted that in the current study, the correlations observed were relatively weak, and although the pairwise comparisons were corrected for multiple comparisons, the correlations were not. However, these correlations were entirely consistent with the findings obtained with the pairwise comparisons; this provides some confidence in the presence of these relationships. Nevertheless, these correlations should be interpreted with the lack of correction in mind. Aside from this, the fact that differences between those with high and low PPI appear to be due to differences in attention again provides further support to the hypothesis that PPI disturbances in patients with schizophrenia are secondary to impairments in attentional processes [17,19,33,41,52]. In the current study we utilised a mixed sample of patients with schizophrenia and healthy controls, in an attempt to provide a broad range of PPI and cognitive performance scores. This was important given that some studies have attributed a failure to find relationships between PPI and cognition to their restricted range of scores in control or patient groups [31,33]. Given that we have reported that this sample of patients, on average as a group, shows alterations in PPI, relative to the average of a healthy control group [52], this mixed sample may seem problematic. However,

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it should be noted that the proportion of patients and controls in the high and low PPI quartiles did not significantly differ, and the distribution of controls and patients in the scatter plots was relatively uniform with large overlap amongst both groups, indicating that the correlations were not driven by the two groups having distinctly different profiles. These observations suggest that our findings would not be adversely affected by the combination of both patients and controls in the sample. Although we [52] and many other studies have shown that patients, on average, show attentiondependant alterations in PPI [17,31,33,41] as well as disturbed cognitive performance [for a review see 38], not every patient with schizophrenia will show a deficit relative to a control group norm. For instance, two distinct cognitive subtypes with different genetic profiles have been proposed in schizophrenia, one which is referred to as cognitively deficit, and shows widespread deficits in cognitive performance, and one that is referred to as cognitively spared, and shows relatively spared cognitive performance [29]. The fact that there were no significant differences in the proportion of patients and controls in the high and low PPI quartiles in the current study is consistent with the contention that not every patient will show altered PPI or cognition, and highlights the fact that PPI is variable in both patients and controls. In conclusion, it appears that relationships between cognitive performance and PPI are mediated through attentional processes, rather than through more fundamental bottom-up functions. Using an attentional modulation of PPI paradigm, differences in cognitive performance between those with high and low PPI, as well as correlations between PPI and cognitive task performance, suggest that the underlying commonalties between PPI and cognition lie in processes of attention that are active during both cognitive tasks and PPI tasks, as relationships between PPI and cognition were only observed under certain attention conditions.

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