behavioral speed and variability in patients with minimal hepatic encephalopathy

Clinical Neurophysiology 125 (2014) 287–297

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Insight into the relationship between brain/behavioral speed and variability in patients with minimal hepatic encephalopathy S. Schiff a,b,c,⇑, C. D’Avanzo d, G. Cona e, A. Goljahani d, S. Montagnese a,b, C. Volpato c, A. Gatta a,b, G. Sparacino b,d, P. Amodio a,b,1, P. Bisiacchi b,e,1 a

Department of Medicine, University of Padua, Italy C.I.R.M.A.ME.C., University of Padua, Italy IRCCS San Camillo, Lido di Venice, Italy d Department of Information Engineering, University of Padua, Italy e Department of General Psychology, University of Padua, Italy b c

a r t i c l e

i n f o

Article history: Accepted 8 August 2013 Available online 10 September 2013 Keywords: Intra-individual variability of RTs Single-trial P300 Minimal hepatic encephalopathy Simon task

h i g h l i g h t s  Single-trial P300 latency increases and amplitude decreases along reaction times (RTs) distribution.  The relationship between P300 and RTs disappears in patients with Minimal hepatic encephalopathy

(MHE).  Temporal overlap between stimulus and response selection is related to both RTs speed and

variability.

a b s t r a c t Objective: Intra-individual variability (IIV) of response reaction times (RTs) and psychomotor slowing were proposed as markers of brain dysfunction in patients with minimal hepatic encephalopathy (MHE), a subclinical disorder of the central nervous system frequently detectable in patients with liver cirrhosis. However, behavioral measures alone do not enable investigations into the neural correlates of these phenomena. The aim of this study was to investigate the electrophysiological correlates of psychomotor slowing and increased IIV of RTs in patients with MHE. Methods: Event-related potentials (ERPs), evoked by a stimulus–response (S–R) conflict task, were recorded from a sample of patients with liver cirrhosis, with and without MHE, and a group of healthy controls. A recently presented Bayesian approach was used to estimate single-trial P300 parameters. Results: Patients with MHE, with both psychomotor slowing and higher IIV of RTs, showed higher P300 latency jittering and lower single-trial P300 amplitude compared to healthy controls. In healthy controls, distribution analysis revealed that single-trial P300 latency increased and amplitude decreased as RTs became longer; however, in patients with MHE the linkage between P300 and RTs was weaker or even absent. Conclusions: These findings suggest that in patients with MHE, the loss of the relationship between P300 parameters and RTs is related to both higher IIV of RTs and psychomotor slowing. Significance: This study highlights the utility of investigating the relationship between single-trial ERPs parameters along with RT distributions to explore brain functioning in normal or pathological conditions. Ó 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

1. Introduction The study of within-person behavioral variability is an emerging topic in cognitive neuroscience (MacDonald et al., 2006). Indeed, a ⇑ Corresponding author. Address: Department of Medicine – DIMED, University of Padova, Via Giustiniani, 2, 35128 Padova, Italy. Tel.: +39 049 8218675; fax: +39 049 8754179. E-mail address: [email protected] (S. Schiff). 1 PA and PB joint senior authorship.

common observation in most cognitive and neuropsychological studies is that results based on subject samples cannot afford individual predictions. This is because data on variability within the same person are largely overshadowed by conventional measures of central tendency. Increased intra-individual variability (IIV) of response speed is detectable in many clinical conditions and could be considered as a marker of brain dysfunction (Hultsch et al., 2000). For example, increased IIV of reaction times (RTs) was previously observed: (i) in dementia (Hultsch et al., 2000; Murtha et al.,

1388-2457/$36.00 Ó 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.clinph.2013.08.004

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2002; Christensen et al., 2005), (ii) in traumatic brain injury (Burton et al., 2002; Stuss et al., 1994, 2003), (iii) in attention-deficit hyperactivity disorder (ADHD; Leth-Steensen et al., 2000; Castellanos and Tannock, 2002; Castellanos et al., 2006), and (iv) in schizophrenia (Kaiser et al., 2008). Minimal hepatic encephalopathy (MHE) refers to the initial subclinical phase of hepatic encephalopathy which is a condition characterised by quantifiable neurophysiological (Amodio et al., 1999a,b, 2001; Marchetti et al., 2011) and neuropsychological abnormalities including executive/attentional dysfunction and psychomotor slowing and reduced vigilance (Amodio et al., 2005a, 2010; Schiff et al., 2005a) related to liver cirrhosis and/or porto-systemic blood shunting (Del Piccolo et al., 2003). Two studies have previously investigated IIV of RTs in patients with cirrhosis on a behavioral basis (Elsass et al., 1985; Schiff et al., 2006). Specifically, Elsass et al., (1985) demonstrated that patients with cirrhosis exhibited a higher IIV of RTs during an auditory simple reaction time task not only compared with healthy individuals, but also with patients with traumatic brain injury. In the second study, Schiff et al., (2006) used a visual choice reaction time task and observed both psychomotor slowing and higher IIV of RT in patients with cirrhosis, both with and without MHE. However, the neural correlates of psychomotor slowing and increased IIV of RTs have never been investigated in patients with cirrhosis so far. Thus, the scope of the present work is to explore the neural substrate of psychomotor slowing and IIV of response speed in patients with MHE. Event-related brain potentials (ERPs) have higher temporal resolution (i.e. milliseconds) than some other neuroimaging methods (e.g., function MRI) and are preferred when the time course of mental operations is under study, in contrast to the precise neural location. In particular, a specific ERP component that seems to be particularly suited for investigating the neural locus of RT variability and the relationship between RTs and brain activity is the P300 component (Sutton et al., 1965). P300 is generally assumed to be a reliable neurophysiological correlate of the stimulus evaluation process (Magliero et al., 1984; Rugg and Coles, 1995; Polich, 2007). However, it was recently suggested that P300 component is not only related to stimulus evaluation but also to response selection, or at least to some initial decisions on stimulus–response (S–R) association (Verleger, 1997; Verleger et al., 2005). In agreement with this view, Nieuwenhuis et al. (2005) suggested a key role of the coeruleus–noradrenergic system in the decision process and in the modulation of cortical P300 responses. In addition, a link between catecholamines and the IIV of response speed was recently suggested (Castellanos et al., 2005). Indeed, catecholamines seem to modulate the signal-to-noise ratio (SNR) of neural information processing when an organism is engaged in tasks requiring attention and it contributes towards sustaining internally generated decision processes (Aston-Jones and Cohen, 2005). Thus, the relationship between signal modulation at the neural level and P300 suggests a possible link between P300 and IIV of response latency and psychomotor slowing. Interestingly, changes in P300 amplitude and/or latency are frequently observed in populations that also exhibit prolongation and increased IIV of RTs. For example, IIV of RTs changes throughout the lifespan from childhood to old age. Indeed, IIV decreased from childhood to adulthood and subsequently increased in older subjects (Hultsch et al., 2002; Bunce et al., 2004; Williams et al., 2005; MacDonald et al., 2006). On the other hand, together with increased IIV of RTs, older adults show longer RTs, delayed P300 latency and reduced P300 amplitude when compared to younger adults (Walhovd et al., 2008; Schiff et al., 2008; Fjell et al., 2009). Segalowitz and colleagues (1997) studied patients with head injury and found increased IIV of RTs was well explained by P300 amplitude reduction.

Usually, P300 amplitude decreases and P300 latency increases with increasing RTs (Holm et al., 2006; Li et al., 2008); however, if latency jittering of an ERP component increases, the amplitude of the wave obtained with the conventional averaging technique (i.e., the usually adopted technique) decreases, and its latency is no longer equivalent to the mean latency recorded in each trial (see e.g., Mouraux and Iannetti, 2008; D’Avanzo et al., 2013 for clinical and methodological remarks). Recently, single-trial analysis of ERPs was used to determine the brain activity expressed in each trial during a cognitive task, providing newer information about the IIV in both normal subjects (Walhovd et al., 2008; Fjell et al., 2009; Li et al., 2009; D’Avanzo et al., 2011; Saville et al., 2011) and clinical populations (Roth et al., 2007; Fell, 2007; De Lucia et al., 2010; Hu et al., 2010). For example, (Lorenzo-Lopez et al., 2007) showed that low-performing older adults manifest lower P300 amplitude and increased variability of P300 latency compared to both younger adults and age-matched high-performing older adults. Nevertheless, Walhovd et al., (2008) showed that even if older adults manifest higher variability of P300 latency, the negative correlation between age and average-based P300 amplitude persists after the correction of P300 latency jittering. Likewise, Saville et al., (2011) showed that healthy individuals with high IIV of RTs manifest both higher P300 latency jittering and reduced P300 amplitude also after adequate single-trial correction. In patients with cirrhosis, studies adopting the classic oddball paradigm showed that P300 is frequently prolonged in latency and reduced in amplitude (Klüger, 1996; Saxena et al., 2002; Amodio et al., 2005b). Thus, since both patients with and without MHE exhibit increased variability of RTs and psychomotor slowing, the single-trial approach seems to be the most adequate method for proper investigation of the neural correlates of these phenomena in patients with cirrhosis. In addition, single-trial analysis allows investigation of whether the increased variability of single-trial P300 latencies, or the reduction of P300 amplitude in each single-trial response, contribute to the reduction of average-based P300 amplitude observed in patients with cirrhosis (e.g., Amodio et al., 2005b). In the present study, a Bayesian technique (D’Avanzo et al., 2011) was adopted to estimate single-trial P300 parameters (i.e., latency and amplitude) evoked by the Simon task, a choice reaction times task widely used to investigate spatial S–R compatibility, from cirrhotic patients with and without MHE. In this task, participants are asked to respond using spatially arranged keys to a nonspatial stimulus feature (i.e., color) of lateralised targets (Simon and Rudell, 1967; Simon, 2011). The Simon task was previously used to study the temporal dynamic of S–R compatibility in normal individuals (Vallesi et al., 2005) and in patients with cirrhosis (Schiff et al., 2006). The Simon effect refers to the finding that response choices are usually faster and more accurate when the stimulus and the response-hand positions correspond spatially compared to when they do not, even if stimulus position is taskirrelevant. The standard deviation (SD) is usually used to assess withinsubjects IIV of RTs. A more sophisticated approach exploits distributional analysis providing information on the shape and the skewness of RTs distribution, which offers additional insight into IIV (Ratcliff, 1979; Castellanos et al., 2006). RT distributions are well described by an ex-Gaussian distribution: a Gaussian-like distribution with a longer right-sided tail. Balota and Yap (2011) suggested that a difference in the slopes of RTs distribution is a measure of a difference in s a parameter that defines the exponential component of the ex-Gaussian distribution, or in other terms, a difference in the skewness of the two distributions. The trial-bytrial association between behavioral responses and P300 parameters provides a clear description of the neural correlates of RT speed and variability along RTs distribution. Since temporal over-

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lap between early decision processes on S–R association and response selection may contribute to fast motor responses (Kornblum et al., 1990), it can be hypothesised that, using the distribution analysis, a relationship between P300 parameters and RTs may be detected in healthy individuals: i.e., amplitude decreases and latency increases with increasing RTs. In contrast, in patients with psychomotor slowing and higher IIV variability of RTs, this relationship would be weaker due to a reduced overlap between the S–R association processes. In summary, in the present paper we will specifically assess: (i) if P300 amplitude reduction that has been observed in patients with cirrhosis and MHE is explained, at least in part, by an increased variability in single-trial P300 latency, and (ii) if P300 is related to RTs and to their distribution in both patients with cirrhosis and in healthy individuals. 2. Methods 2.1. Participants A total of 43 participants were enrolled in the study: 29 patients with liver cirrhosis (age 51 ± 9 years, mean ± SD; males 66%; education level 9 ± 4 years), of whom 14 had MHE and 15 did not, and 14 age-matched healthy controls (age 49 ± 10 years, males 57%, education 13 ± 5 years). Patients and healthy controls did not differ in age or education level. None of the participants had a history of neurological or neuropsychiatric disorders, or significant cardiovascular, respiratory or renal impairment; none had taken psychotropic medication or had an uncorrectable impairment of visual acuity or was color blind. One of the patients had a history of alcohol misuse, however he was abstinent for at least 6 months. The diagnosis of cirrhosis was made on the basis of historical, clinical, laboratory, endoscopic and radiological findings. The aetiology of liver cirrhosis, the degree of hepatic failure according to Child–Pugh (Pugh et al., 1973) and the laboratory variables are reported in Table 1. None of the patients had overt hepatic encephalopathy and all of them showed disorientation for time or space at the time of the study (Bajaj et al., 2011). All the participants provided informed consent to take part in the study which was approved by the local Hospital University ethical committee. 2.2. Detection of MHE MHE was diagnosed based on spectral electroencephalographic (EEG) features (Amodio et al., 1999a,b, 2001) and performance in

Table 1 Clinical characteristics of the patients. Clinical characteristics Aetiology (%)

Hepatitis B Hepatitis C Alcohol Other

24.1 48.3 3.5 24.1

Child–Pugh classification (%)

A B C

16.7 55.6 27.8

Biochemical variables Total Bilirubin (mmol/L) Albumin (g/L) Protrombin time [PT] (%) Venous ammonia (mmol/L) Na (mmol/L) Urea (mmol/L) Creatinine (mmol/L) Glucose (mmol/L)

Median (SE) 48.4 (6.9) 46.6 (14.6) 60.6 (3.6) 59.7 (13.5) 139.4 (0.6) 6.7 (8.9) 87.6 (4.4) 17.8 (8.1)

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age-and education-adjusted validated paper and pencil psychometric tests, including the Trail-Making test (TMT part A and B), Symbol-Digit test, and the computerised SCAN test (Amodio et al., 1998, 2008). Patients were considered to have MHE if at least one measure (psychometric tests and/or EEG) was abnormal (Ferenci et al., 2002). On the basis of this criterion, 14 patients were classified as having MHE. 2.3. Simon task The experiment took place in a dimly light and sound attenuated room. Each participant (control or patient) was presented with a red or green target checkerboard on the right or the left side of the computer screen while a ‘neutral’ black and white checkerboard always appeared on the other side. Checkerboards were presented for 176 ms and the inter-trial interval ranged from 800 to 1200 ms. The distance between the eyes and the screen was fixed at 80 cm. A total of 300 experimental trials, balanced for stimulus color (red or green) and position (left or right), were presented to the participants in a completely randomised sequence. Before starting the experimental task, 40 trials were presented for practise. The participant was asked to press, as fast and accurately as possible, one of the two keys corresponding to the color of the checkerboard. Half of the participants were told that button ‘M’ (right side of the keyboard) corresponds to the red checkerboard and button ‘Z’ (left side of the keyboard) corresponds to the green checkerboard. The other half of the participants received the opposite S–R association. Speed and accuracy were equally emphasised when providing the instructions. 2.4. Electrophysiological recordings The electroencephalogram (EEG) was continuously recorded (Micromed System Plus, Mogliano Veneto, Italy) by 30 Ag/AgCl electrodes mounted on an elastic cap and positioned according to the international 10/20 system. The Fpz was used as the ground and the reference was provided by the linked right and left mastoid electrodes. Two electrodes were placed on the external canthus and under the left eye to monitor horizontal and vertical eye movements (EOG), respectively. Each channel had its own analogue-to-digital converter. The EEG and EOG signals were digitalised on-line with a frequency rate of 512 Hz and a conversion resolution of 0.19 lV/digit. Impedance was kept lower than 5 kX. Signals were band-pass filtered between 0.03 and 30 Hz. The EEG epochs were extracted using a time-window analysis time of 2300 ms (800 ms pre-stimulus and 1500 post-stimulus) and baseline corrected using a 200 ms pre-stimulus time interval. Epochs with excessive drift or containing eye movements/blinks were corrected using an independent component analysis algorithm (Jung et al., 2001; Delorme and Makeig, 2004; Makeig et al., 2004). A visual inspection was then performed in order to reject epochs with significant artefacts and only adequate epochs were subsequently considered for the analysis. Average-based P300 parameters (amplitude and latency) were calculated by identifying the maximum, in the time-window between 250 and 600 ms after stimulus onset, of the ERP profile obtained by conventional averaging. 2.5. Estimation of single-trial P300 responses During the last few years, different mathematical approaches have been proposed in order to derive ERP parameters, such as amplitude and latency, from single-trial EEG responses (Jaskowski and Verleger, 1999, 2000; Jung et al., 2001; Li et al., 2009; Rushby and Barry, 2009; D’Avanzo et al., 2013). Here, a single-trial P300 estimate was made using a Bayesian approach originally proposed by Sparacino et al. (2002) and further

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developed by D’Avanzo et al. (2011), to which we refer the reader for further details. Briefly, the method is formulated in two stages. In the first stage, each of the available raw sweeps is processed by an individual ‘optimal’ filter, determined using statistical information on the background EEG and on the unknown ERP obtained from pre-stimulus and post-stimulus data, respectively. This avoids critical assumptions regarding EEG and ERP stationarity. Then, a mean ERP is determined as the weighted average of the filtered sweeps, where each weight is proportional to the expected reliability of the sweep. In the second stage, single-sweep estimation is dealt within the same framework by using the average ERP estimated in the previous stage as an a priori expected response. The P300 latency and amplitude were determined for each single-trial by identifying the maximum of the ERP profile in the time-window between 250 and 600 ms after stimulus onset. The assessment of the method made in D’Avanzo et al. (2011) on synthetic data demonstrated its ability in estimating the latency of the P300 component and its variability (e.g., the correlation coefficient between the estimated and the true single-trial latency was 0.74 when the SNR was ‘‘high’’ and 0.47 when the SNR was ‘‘low’’; see the cited paper above for details). However, detecting latency and amplitude of the P300 component remains a challenging task: occasional trials with estimated P300 latency resulting at the boundary of the 250–600 ms time-window (e.g., because of monotonic increasing or decreasing behaviour of the estimated ERP) were considered not sufficiently robust and were excluded from the analysis. Notably, these trials occurred in less than 5% of the total for each subject, except for one subject who had 10% of the trials excluded from the analysis. Finally, the mean of P300 latencies of all of the trials performed by each participant was computed as well as the SD, which was used as an index of IIV in P300. Mean latency and amplitude of P300 from each RTs quintile were analysed in order to determine the relationship between physiological measures and the RTs distribution.

2.6. Statistics A series of analyses of variance (ANOVA) for repeated-measures were run with the group (patients with MHE, patients without MHE and healthy controls) as a between-subjects factor and the task condition (Corresponding vs. non-Corresponding) as a within-subjects factor. For behavioral analyses, mean RTs, percentage of correct response and RTs SDs were considered as dependent variables. Individual RTs were also ordered from the fastest to the slowest and were divided into five quintiles (i.e., bins) in order to study RTs distribution. In this case, the ANOVA also included the quintile factor as within-factor variable. For the study of both average-based and single-trial P300 amplitudes and latencies, the ANOVA also included the factor derivation (Pz, Cz, Fz) as a further within-subject factor. Pearson’s correlations were computed to evaluate the goodness of fit of the P300 estimates given by the Bayesian single-trial method and with those obtained using conventional averaging. Linear multiple regression analysis was run to assess the role of P300 latency jittering and of

the mean single-trial P300 amplitude as predictors of the averagebased P300 amplitude. The mean of single-trial P300 parameters (latency and amplitude) was calculated for each RTs quintile to evaluate the relationship between behavioral and electrophysiological responses along RTs distribution. Two ANOVAs were computed with the group as between – subjects factor and task condition, quintile and derivation as within – subjects factors, to investigate the relationship between P300 parameters (latency and amplitude) and RTs distribution. Post-hoc analyses were carried out using Bonferroni correction for multiple comparisons. 3. Results 3.1. Behavioral analysis 3.1.1. RTs RTs analysis highlighted the main effects of the task condition [F(1, 41) = 33.39, p < 0.001; g2p = 0.45] and of the group [F(2, 40) = 10.89; p < 0.001; g2p = 0.35]. Post-hoc comparisons revealed slower RTs in non-corresponding than corresponding condition (i.e., Simon effect) and that patients with MHE were slower than both healthy controls and patients without MHE (all post hoc p < 0.05). No interaction was observed between group and task condition [F(1, 41) = 1.42; p = 0.2; g2p = 0.03] (see Table 2). 3.1.2. Response accuracy The same main effect of task condition and group [F(2, 40)=5.64, p < 0.01; g2p = 0.2] were found when response accuracy was considered [F(1, 41) = 9.09, p < 0.005; g2p = 0.18]. Post-hoc tests showed lower accuracy in the non-corresponding compared to corresponding condition and that patients with MHE were less accurate than healthy controls (all p < 0.05). Also, in this case, the interaction between group and task condition was not significant [F(1, 41) = 0.49; p = 0.6; g2p = 0.02]. 3.1.3. IIV in RTs The IIV of RTs was greater in the corresponding compared to the non-corresponding condition [F(1, 40) = 23.96, p < 0.0001; g2p = 0.34] (Table 2). Patients with MHE showed higher IIV of RTs than both healthy controls and patients without MHE [F(2, 40) = 10.96, p < 0.001; g2p = 0.22, post hoc p’s < 0.05]. No difference in IIV of RTs was observed between healthy controls and patients without MHE (Fig. 1 left panel). No interaction was observed between group and task condition [F(1,41) = 0.22; p < 0.64; g2p = 0.005]. 3.1.4. Distribution analysis of RTs A dynamic image of IIV was provided by the study of RT distribution via quintile analysis. Together with the effect of group [F(2,40) = 10.86, p < 0.001; g2p = 0.35], quintile [F(4, 164) = 304.00, p < 0.0001; g2p = 0.87] and task condition [F(1, 41) = 39.4, p < 0.00001; g2p = 0.47], the interaction between task condition and quintile [F(4, 160) = 22.29, p < 0.00001; g2p = 0.33] was signfi-

Table 2 Behavioral measures obtained from the Simon task in controls and patients. Mean (standard deviation).

Mean RT corresponding (ms) Mean RT non-corresponding (ms) IIV. RT corresponding (ms) IIV. RT non-corresponding (ms) Response accuracy corresponding (%) Response accuracy non-corresponding (%)

Controls N = 14

All Patients N = 29

Patients without MHE N = 13

Patients with MHE N = 16

487 (63) 510 (67) 110 (25) 90 (18) 97 (2) 95 (4)

602 (119) 637 (117) 144 (41) 128 (37) 90 (12) 87 (12)

553 (103) 582 (105) 130 (45) 107 (30) 95 (6) 91 (8)

641 (119) 681 (109) 156 (35) 144 (35) 87 (15) 84 (13)

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Fig. 1. Left panel shows IIV of RTs of the three groups of subjects. The IIV of RTs progressively increases from healthy controls to patients with MHE (Bonferroni post hoc test, p < 0.05). Right panel shows the IIV of single-trial P300 latencies of the three groups of subjects. Patients with MHE showed a higher variability in P300 latency than healthy controls (post hoc test p < 0.05).

Fig. 2. Distribution analysis of RTs showed that the Simon effect (difference between non-corresponding and corresponding conditions) decreased with slower reaction times (solid line). In contrast, as concerning P300 latency, the magnitude of the Simon effect did not change with longer RTs (dashed line).

cant, revealing – as expected – that the Simon effect decreased with slower RTs (Fig. 2 continuous line). The quintile per task condition effect interaction was observed in all groups whereas no interaction was observed between group, task condition and quintile [F(8, 160) = 0.50, p = 0.85; g2p = 0.02]. A more relevant group per quintile interaction was found [F(8, 160) = 5.61, p < 0.00001; g2p = 0.21] and post hoc comparisons revealed that the RTs were more scattered in patients with MHE compared to healthy controls (all p’s < 0.05) from the 2nd to the 5th quintile (Fig. 3). 3.2. P300. analysis 3.2.1. Average-based P300 measures Overall, a trend of shorter latency of P300 in the corresponding compared to the non-corresponding condition was found [F(1, 41) = 3.71, p = 0.06; g2p = 0.8] (Table 3). No difference was observed in P300 latency between healthy controls and patients with and without MHE [F(1, 41) = 0.09, p = 0.74; g2p = 0.003], taking into ac-

Fig. 3. The distribution analysis of the reaction times (RTs) shows an interaction between group and quintile. A larger variability in RTs, from faster to slower quintiles, can be seen in individuals with MHE (squares) compared to healthy controls (diamonds). Asterisks denote significant differences between patients with MHE and healthy controls (Bonferroni post-hoc test, p’s < 0.05).

count both task condition [F(1, 41) = 1.51, p = 0.17; g2p = 0.04] and derivation [F(1, 41) = 0.11; p = 0.9; g2p = 0.003]. P300 amplitude was lower in the non-corresponding compared to the corresponding condition [F(1, 41) = 18.77, p < 0.0001; g2p = 0.32]; furthermore, an effect of derivation was also found [F(2, 82) = 3.32, p < 0.05; g2p = 0.07], showing that P300 was greater in the Pz compared to the Fz site (p < 0.05). The interaction between task condition and derivation [F(2, 82) = 3.20, p = 0.05; g2p = 0.07] showed that the difference between the corresponding and non-corresponding conditions was greater in the Pz compared to the Fz site (p < 0.05) (Table 3). A main effect of group was detected [F(2, 40) = 8.19; p < 0.005; g2p = 0.33] revealing that P300 was smaller in patients with MHE compared to healthy controls but not compared to patients without MHE (post hoc p < 0.05) (Figs. 4 and 5 left panel). No interaction of task condition or derivation with the factor group was found (Table 3).

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Table 3 Average-based and single-trial estimated P300 latencies and amplitudes in Pz, Cz, Fz Mean (standard deviation). Group Derivation

Controls

All patients

Patients without MHE

Patients with MHE

Controls

All patients

Patients without MHE

Patients with MHE

Amplitude (lV)

Latency (ms) AVG-P300 corresponding

Pz Cz Fz

346 (56) 359 (30) 364 (20)

361 (71) 374 (79) 379 (74)

352 (52) 354 (55) 362 (42)

369 (69) 390 (93) 393 (92)

9.8 (4.6) 9.7 (4.3) 9.0 (3.4)

6.3 (3.2) 5.5 (3.1) 6.0 (3.0)

7 (3.1) 7 (3.2) 7 (3.0)

5.8 (3.3) 4.5 (2.6) 4.5 (1.9)

AVG-P300 non-corresponding

Pz Cz Fz

360 (65) 388 (37) 373 (32)

365 (103) 377 (76) 379 (86)

347 (104) 366 (41) 367 (40)

378 (102) 386 (95) 388 (111)

9.0 (3.5) 8.8 (3.7) 7.9 (2.9)

5.4 (2.8) 4.7 (2.9) 5.1 (2.8)

6.0 (3.0) 5.8 (3.0) 6.4 (2.9)

4.9 (2.6) 3.9 (2.7) 4.0 (2.3)

Single-trial P300 corresponding

Pz Cz Fz

365 (31) 375 (27) 366 (20)

377 (36) 387 (32) 382 (34)

370 (37) 377 (27) 370 (25)

383 (36) 394 (37) 392 (38)

12.6 (4.2) 12.7 (4.3) 11.4 (3.6)

9.6 (3.4) 8.9 (2.9) 8.9 (2.6)

9.8 (3.4) 9.6 (3.3) 9.7 (3.3)

9.4 (3.4) 8.0 (2) 8.3 (1.7)

Single-trial P300 non-corresponding

Pz Cz Fz

385 (40) 393 (34) 384 (26)

391 (43) 405 (34) 394 (34)

388 (53) 396 (29) 383 (31)

397 (34) 412 (37) 403 (33)

12.0 (3.4) 12.4 (3.8) 11.3 (3.3)

9.1 (3) 8.7 (3) 8.8 (2.7)

9.4 (3.2) 9.4 (3.3) 9.0 (3.2)

8.8 (3.0) 8.1 (2.6) 8.2 (2.2)

Fig. 4. Event-related potentials in Pz, Cz and Fz are plotted separately for corresponding (on the left) and non-corresponding condition (on the right) and group (healthy controls, pts without MHE, pts with MHE). P300 was larger in amplitude in healthy controls (solid line) compared to patients with MHE (dotted line) but not compared to patients without MHE (dashed line).

3.2.2. Single-trial-based P300 measures Contrary to average based analysis, single-trial P300 latency was clearly shorter in the corresponding compared to the non-corresponding condition [F(1, 41) = 76.54, p < 0.00001; g2p = 0.65]. In

agreement with the results observed using the averaged-based P300 analysis, single-trial analysis also revealed a significant effect of derivation [F(2, 41) = 3.57, p < 0.05; g2p = 0.08], showing that P300 latency was higher in the Pz compared to the Fz site (post

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Fig. 5. Left panel: P300 scalp voltage distributions in the three groups are shown for the corresponding and non-corresponding trials. Right panel: Colour plots of single-trial P300 responses in Pz are depicted in order of RTs speed (black curves), from the fastest to the slowest, in the three groups. Patients with MHE show an increased P300 latency jittering, a reduction in P300 amplitude (both in the corresponding and non-corresponding conditions), and a loss of the relationship between P300 responses and RTs.

hoc p < 0.05). Also in this case, P300 latency did not differ between healthy controls and the patients with cirrhosis, with or without MHE [F(2, 82)=1.29, p = 0.26; g2p = 0.03] (Table 3) and no interaction was found between task condition or derivation with the factor group. Overall, P300 amplitude was lower in the non-corresponding condition compared to the corresponding conditions [F(1, 41) = 5.57, p < 0.05; g2p = 0.12). A significant derivation per task condition interaction was found [F(2, 82) = 7.51, p < 0.005; g2p = 0.16], showing that the difference between corresponding and non-corresponding conditions was greater in Pz compared to both Fz and Cz (all p’s < 0.001) (Table 3). The main effect of group [F(2, 40) = 5.07, p < 0.05; g2p = 0.19] revealed that patients with MHE had lower P300 amplitude compared to healthy controls (p < 0.05) (Table 3). No interaction was found between task condition or derivation and the factor group. 3.2.3. Relationship between average-based and single-trial P300 measures In Pz, both average-based latency and amplitude correlated with the mean of their estimated single-trials counterparts (r = 0.72, p < 0.0001 and r = 0.90, p < 0.0001, respectively). These results confirm that the estimated single-trial P300 latencies were distributed around a value that was very close to the P300 latency

determined by using the ERP waveform obtained with the conventional averaging. In fact, the obtained correlation coefficient was not surprising since, in the second stage of the method described in Section 2.5, single-trial estimates were obtained by exploiting an ‘‘average’’ obtained in stage 1. The relationship between IIV of single-trial P300 latency and average-based P300 amplitude was deducible by the inverse correlation between these two measures (r = 0.73, p < 0.0001), showing that the higher the IIV P300 latency, the lower the averagebased P300 amplitude. However, on closer inspection, multivariate regression analysis showed that the averaged-based P300 amplitude was predicted by both the IIV of single-trial P300 latency (b = 0.25 ± 0.04 (SE); p < 0.0001) and single-trial P300 amplitude (b = 0.78 ± 0.04; p < 0.0001). This was also true considering healthy controls and patients with cirrhosis separately: averagedbased P300 amplitude vs. IIV of single-trial P300 latency (patients with cirrhosis and healthy controls: b = 0.33 ± 0.06, p < 0.0001 and b = 0.16 ± 0.06, p < 0.05, respectively), averaged-based P300 amplitude vs. single-trial P300 amplitude (patients with cirrhosis and healthy controls b = 0.73 ± 0.06, p < 0.0001, and b = 0.85 ± 0.07, p < 0.0001, respectively). Notably, the ratio between the Beta’s and their SE (i.e., a measure of the strength of the association between the dependent variable and its predictor), was twice as high in patients with cirrhosis compared to healthy

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Table 4 IIV of P300 latency obtained by single trial estimate in the derivations Pz, Cz, Fz. Mean (standard deviation). Electrode

Controls

All patients

Pz Pz Cz Cz Fz Fz

77 83 79 82 80 85

94 (20) 106 (22) 98 (19) 104 (20) 93 (20) 100 (20)

Patients without MHE

Patients with MHE

Latency (ms) IIV IIV IIV IIV IIV IIV

P300 P300 P300 P300 P300 P300

corresponding non-corresponding corresponding non-corresponding corresponding non-corresponding

(23) (21) (18) (16) (17) (15)

87 97 89 97 84 92

(19) (23) (17) (17) (18) (15)

99 (20) 112 (19) 104 (18) 110 (21) 101 (20) 106 (22)

Fig. 6. Left panel: The relationship between P300 latency and RTs shows an interaction between group and quintile, proving that in healthy controls (diamonds), P300 latency increased with slower RTs. This relationship can also be observed in patients without MHE (circles) but only with faster RTs (i.e. 1st, 2nd and 3rd). In contrast, the relationship between P300 latency and RTs disappears in patients with MHE (squares). Right panel: The relationship between P300 amplitude and RTs shows an interaction between group and quintile proving that P300 amplitude decreased with increasing RTs in healthy controls and in patients without MHE, but not in those with MHE.

controls: 5 and 2.3, respectively. This result showed that the contribution of P300 latency jittering to average-based P300 amplitude was higher in patients with cirrhosis compared to healthy controls. Thus, not only IIV of RTs but also increased P300 latency jittering should be considered as pathophysiological markers of brain dysfunction in patients with cirrhosis.

3.2.4. IIV of P300 The IIV in P300 latency was higher in the non-corresponding compared to the corresponding condition [F(1, 41) = 11.23, p < 0.005; g2p = 0.21]. Furthermore, a derivation per task condition interaction was found [F(2, 82) = 5.45, p < 0.01 g2p = 0.12], showing that the difference in P300 latency jittering between the corresponding and non-corresponding trials was greater in Pz site (post hoc p < 0.05). The main effect of group [F(2, 39) = 7.8721, p = 0.005; g2p = 0.28] revealed higher IIV in P300 latency in patients with MHE compared to healthy controls, (p < 0.05; see Table 4 and Fig. 1 right panel); in contrast, a post hoc test between healthy controls and patients without MHE did not reach statistical significance (p = 0.08). No interaction was found between task condition and group [F(1, 41) = 1.1, p = 0.3; g2p = 0.02]. Considering IIV in P300 amplitude, a significant main effect of site was found [F(2, 82) = 7.95, p < 0.001; g2p = 0.16]. Post-hoc comparisons revealed that IIV of P300 amplitude was higher in Cz compared to both Pz and Fz sites (all p’s < 0.05). The ANOVA did not reveal any other significant effects of IIV in P300 amplitude concerning the factors task condition [F(1, 41) = 0.37, p = 0.85; g2p = 0.001] and group [F(1, 41) = 0.35, p = 0.54; g2p = 0.01].

3.2.5. Relationship between single-trial P300 and RT distribution Regarding single-trial P300 latency, the main effects of task condition [F(1, 41) = 75.78, p < 0.00001; g2p = 0.65] and quintile [F(4, 164) = 8.49, p < 0.005; g2p = 0.17] were significant. The effect of quintile revealed that P300 latency increased with longer RTs (1st vs. 2nd, 3rd, 4th quintiles; all p’s < 0.05). Unlike with RTs, no interaction was found between task condition and quintile [F(4, 164) = 0.97, p = 0.4; g2p = 0.02]; see Fig. 2 (dashed line). A relevant group per quintile interaction was found [F(8, 160) = 5.14, p < 0.00001; g2p = 0.20], showing that the relationship between P300 latency and RTs was closer and steeper in healthy controls than in patients with cirrhosis (Fig. 6 right). Post-hoc tests revealed that P300 latency increased along RT distribution (1st vs. 3rd, 4th and 5th quintiles; all p’s < 0.05) in healthy controls, but not in patients with cirrhosis, either with or without MHE. Indeed, in patients with cirrhosis the relationship between RTs and P300 latency was completely lost (see Fig. 6 left panel). The relationship between single-trial P300 amplitude and RTs distribution highlighted the main effects of task condition [F(1, 41) = 4.47, p < 0.05; g2p = 0.11], quintile [F(4, 164) = 37.92, p < 0.0001; g2p = 0.48], and derivation [F(2, 82) = 4.98, p < 0.01; g2p = 0.48]. The effect of quintile showed that the P300 amplitude decreased with increasing RTs (1st vs. 2nd, 3rd, 4th and 5th; 2nd vs. 3rd, 4th and 5th; and 3rd vs. 5th quintiles; all p’s < 0.05). A site per quintile interaction was also found, showing that the decrease in amplitude with slower RTs was greater for the Pz and Cz sites compared to the Fz site (all p’s < 0.05). Furthermore, the significant effect of group [F(1, 41) = 8.71, p < 0.01; g2p = 0.18] and the significant interaction between group and quintile [F(8, 160) = 2.7,

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p < 0.01; g2p = 0.12], revealed that the P300 amplitude was smaller in patients with MHE and decreased along RTs distribution in healthy controls (1st vs. 3rd, 4th and 5th quintiles) and in patients without MHE (1st and 2nd vs. 4th and 5th; 2nd vs. 5th quintiles; all p’s < 0.05); such a relationship was lost in patients with MHE (1st vs. 4th quintile; p < 0.05; see Figs. 5 and 6 right panels).

4. Discussion In the present study, a Bayesian approach was used to estimate single-trial P300 parameters evoked by a conflict S–R Simon task in patients with and without minimal metabolic brain dysfunction due to liver cirrhosis and in a group of healthy controls. In line with our hypothesis, patients with liver cirrhosis, especially those with MHE, showed longer RTs and reduced response accuracy as compared with healthy controls. Furthermore, IIV in RTs was greater in patients with cirrhosis, both with and without MHE, than in healthy controls. This latter result was confirmed by distribution analysis of RTs. Patients with cirrhosis had a greater difference between slower and faster quintiles when compared to healthy controls. Therefore, our data confirm previous observations regarding increased variability in reaction speed and changes in the slope of RTs distribution in patients with cirrhosis compared to healthy controls (Elsass et al., 1985; Schiff et al., 2006). The first aim of the present study was to investigate whether patients with or without MHE exhibit increased variability in single-trial P300 parameters and to evaluate the relationship between P300 latency jittering and average-based P300 amplitude. The pattern of results showed that patients with cirrhosis had smaller P300 amplitude compared to healthy controls; in contrast, P300 latency did not differ between groups. Our data revealed a greater IIV of P300 latency in patients with cirrhosis, mainly those with MHE, compared to healthy controls. Furthermore, even if average-based P300 amplitude was related to both single-trial P300 amplitude and P300 latency jittering, the contribution of IIV of P300 latency was double in patients compared to controls. These results suggest a specific link between P300 amplitude, IIV of P300 latency, and the metabolic brain dysfunction that characterises patients with cirrhosis. The second aim of the study was to evaluate the relationship between RTs distribution and P300 parameters. Our analysis revealed that in healthy participants, P300 latency increased and amplitude decreased with increasing RTs. In contrast, the relationships between RTs and P300 parameters were weaker in patients without MHE and absent in patients with MHE. These results suggest that, in normal conditions, the temporal overlap between early decision processes on S–R associations and response-related processes leads to faster RTs and is well described by the changes in within-subjects P300 parameters along the RTs distribution: lower P300 amplitude and delayed P300 latency for slower compared to faster RTs. In contrast, in patients with cirrhosis both psychomotor slowing and increase of IIV of RTs might be explained by the loss of the temporal overlap between the early decision on S–R association and late response selection processes. From a pathophysiological point of view, the results described above suggest that the increased variability in RTs is related to the altered brain functioning and neurotransmission observed in patients with cirrhosis (Zafiris et al., 2004; Shah et al., 2008; Poveda et al., 2010). The metabolic brain dysfunction in patients with cirrhosis seems to depend, at least in part, on increased blood/cerebral ammonia levels. These lead to changes in cerebral energy metabolism and neurotransmission, in particular within the glutamatergic system (Wilkinson et al., 2010). At the synaptic level, astrocytes play an important role in removing glutamate (Glu) from synapses. Glutamate is released by pre-synaptic neurons

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and astrocytes convert it into glutamine (Gln) before it is returned to the neurons and recycled. From a neurobiological point of view, astrocyte swelling with the accumulation of Gln is considered to be a key mechanism related to hepatic encephalopathy. It can be hypothesised that this metabolic dysfunction is implicated in increasing variability in the synchrony of the firing rate of large neuronal populations, as a consequence increasing the variability of post-synaptic neural responses, P300 and behavioral response. Interestingly, a similar mechanism linking neurones and astrocyte functioning was recently suggested in order to explain increased IIV in behavioral speed and RTs slowness in subjects with ADHD (Russell et al., 2006). Following a different perspective on brain functioning, fMRI studies showed an incomplete deactivation of the default mode network during task execution, which was correlated with increased variability in behavioral speed (Kelly et al., 2008). Within this framework, it is important to note that in individuals with ADHD, a lack of default network suppression was found and linked to increased response speed variability (Fassbender et al., 2009). Similarly, an abnormal activation of the default network was also found in patients with cirrhosis when engaged in a cognitive task (Zhang et al., 2007). Another observation derived from the present study concerns the functional meaning of P300. Using a single-trial approach, we have evaluated the dynamics of the relationship between P300 latency and S–R correspondence along the RTs distribution. Usually, the Simon effect is explained in terms of competition between a direct visuo-motor transmission route carrying the response linked to the spatial position of the stimulus, and an indirect route holding task demands. The activation/suppression model suggests that, in the non-corresponding condition, the early activation of the incorrect response spatially corresponding with stimulus position is proactively suppressed, at a response selection stage, by a late inhibitory mechanism. This mechanism needs time to be effective and, for this reason, it affects the magnitude of the Simon effect, mainly for slower RTs (Ridderinkhof, 2002). In agreement with this model, the Simon effect usually decreases with longer RTs. In the present study, the Simon effect (i.e., slower RTs and lower accuracy in the non-corresponding condition than in the corresponding condition) was detected both in healthy controls and patients with cirrhosis. Distribution analysis of RTs showed that the Simon effect decreased in magnitude as RTs increased, both in healthy controls and patients with cirrhosis, regardless of the presence of psychomotor slowing. Electrophysiological results confirm that P300, obtained during the Simon task, is sensitive to S–R correspondence (Valle-Inclan, 1996; Wascher and Wauschkuhn, 1996). A longer P300 latency and a smaller P300 amplitude in the non-corresponding condition compared to the corresponding condition were observed both in healthy controls and in patients with cirrhosis; however, in contrast to behavioral data, when P300 latency was considered, a suppression of the Simon effect with longer RTs was not observed. These results suggest that P300 is related to S–R association but not to late response inhibition. Indeed, P300-like responses have been recorded intra-cortically from several brain sites (Halgren et al., 1995a,b). However, parietal P300 (P3b) is plausibly generated mainly in the temporal-parietal junction (Verleger, 1997). Several pieces of evidence suggest that these regions of the brain are functionally involved in the integration of visual information and action plans (Rushworth et al., 2001; Schiff et al., 2011). On the other hand, Forstmann et al., (2008a,b) showed, using model-based fMRI, that in the Simon task the early activation of the prepotent incorrect response correlates with activity of the anterior cingulate cortex and the pre-supplementary motor area. In contrast, the late inhibition of the activated incorrect response was associated with the activity of the inferior frontal cortex. These

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results suggest that inhibition can be considered a two-stage process acting on both the early phase of response activation and on the late inhibition of an incorrect response (Bar et al., 2006). In agreement with this view, Wylie et al., (2010) showed that, in patients with Parkinson’s disease, deep brain stimulation of the sub-thalamic nucleus affects both the early activation and late inhibition of a prepotent incorrect response. Thus, in agreement with Verleger et al., (2005), our results suggest that P300 is sensitive to early coding of the S–R association but independent of inhibitory processes acting on later response-selection stage. In conclusion, the present study: (i) supports the convincement that behavioral and electrophysiological measures of withinsubject variability are useful indicators of brain dysfunctions and single-trial ERP analysis is a powerful technique both in clinical studies and basic research especially when within-subject brain responses are investigated together with RTs distribution, (ii) proves that in patients with MHE, the higher IIV of RTs is associated with both a reduction in P300 amplitude and an increase in P300 latency jittering, and (iii) shows that the loss of a temporal overlap between P300 and behavioral responses in patients with MHE is associated with psychomotor slowing, but is independent of late response inhibition. The present study further suggests that new technological approaches to EEG signal analysis, such as singletrial ERPs estimation or automatic detection of individual alpha frequency (Goljahani et al., 2012), may reveal further electrophysiological peculiarities uniquely related to the MHE condition. These peculiarities may allow development of non-invasive reliable diagnostic tools. Acknowledgment The study was partially supported by the Bial Foundation Grant No. 146/2008 to P.B., by the Regione Veneto FSE Grants ‘‘Development of a system for the analysis of the intraindividual EEG variability for the early identification of cognitive deficiencies’’ and ‘‘Implementation of methodologies for the quantification of electrophysiological signals in mild cognitive disorders’’ to C.D.A. and A.G., and by the University of Padova Grant ‘‘Quantitative understanding of the human brain functioning through advanced EEG and NIRS signal processing’’ to G.S. References Amodio P, Ridola L, Schiff S, Montagnese S, Pasquale C, Nardelli S, et al. Improving detection of minimal hepatic encephalopathy using the inhibitory control task. Gastroenterol 2010;139:510–8. Amodio P, Campagna F, Olianas S, Iannizzi P, Mapelli D, Penzo M, et al. Detection of minimal hepatic encephalopathy: normalization and optimization of the psychometric hepatic encephalopathy score. A neuropsychological and quantified EEG study. J. Hepatol. 2008;49:346–53. Amodio P, Schiff S, Del Piccolo F, Mapelli D, Gatta A, Umilta C. Attention dysfunction in cirrhotic patients: an inquiry on the role of executive control, attention orienting and focusing. Metab. Brain. Dis. 2005a;20:115–27. Amodio P, Valenti P, Del Piccolo F, Pellegrini A, Schiff S, Angeli P, et al. P300 latency for the diagnosis of minimal hepatic encephalopathy: evidence that spectral EEG analysis and psychometric tests are enough. Dig. Liver Dis. 2005b;37:861–8. Amodio P, Del Piccolo F, Petteno E, Mapelli D, Angeli P, Iemmolo R, et al. Prevalence and prognostic value of quantified electroencephalogram (EEG) alterations in cirrhotic patients. J. Hepatol. 2001;35:37–45. Amodio P, Del Piccolo F, Marchetti P, Angeli P, Iemmolo R, Caregaro L, et al. Clinical features and survivial of cirrhotic patients with subclinical cognitive alterations detected by the number connection test and computerized psychometric tests. Hepatol 1999a;29:1662–7. Amodio P, Marchetti P, Del Piccolo F, de Tourtchaninoff M, Varghese P, Zuliani C, et al. Spectral versus visual EEG analysis in mild hepatic encephalopathy. Clin. Neurophysiol. 1999b;110:1334–44. Amodio P, Marchetti P, Del Piccolo F, Rizzo C, Iemmolo RM, Caregaro L, et al. Study on the sternberg paradigm in cirrhotic patients without overt hepatic encephalopathy. Metab. Brain Dis. 1998;13:59–72. Aston-Jones G, Cohen JD. An integrative theory of locus coeruleus-norepinephrine function: adaptive gain and optimal performance. Annu. Rev. Neurosci. 2005;28:403–50.

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