Quantifying the effects of practicing a semantic task according to subclinical schizotypy

doi:10.21203/rs.3.rs-3088061/v1

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Quantifying the effects of practicing a semantic task according to subclinical schizotypy

https://doi.org/10.21203/rs.3.rs-3088061/v1

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published 05 Feb, 2024

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Greater adaptability of patients should go with easier psychosocial rehabilitation. Medications should thus also be chosen according to their impact on practice effects, as they measure adaptability. We are thus developing a pre-treatment test aimed, in fine, at assessing the impact of medications on these effects.

Here, we report the practice effects observed across the two sessions of a semantic categorization task. Participants (n = 47) completed the Schizotypal Personality Questionnaire (SPQ) and performed this task twice, 1.5 hours apart. Practice was found to reduce reaction times in both low- and high-SPQ scorers. It was also found to decrease the amplitudes of the N400 event-related brain potentials elicited by semantically matching words in low SPQ scorers only, which showed the sensitivity of the task to schizotypy. Both RTs and N400 amplitudes were also found to have a good test-retest reliability across the two sessions.

This task could thus be a valuable tool. On-going studies are assessing the impacts of fully deceptive placebos and of real antipsychotic medications on these effects of practice. This should, later, help psychiatrists to choose the best medication for the psychosocial rehabilitation of a patient.

Biological sciences/Neuroscience/Cognitive neuroscience

Biological sciences/Neuroscience/Learning and memory

Biological sciences/Biological techniques/Electrophysiology

Recent literature and clinical practice show that patients with schizophrenia have important difficulties in learning new tasks and getting used to new social environments^1–3. These learning deficits have a huge negative impact on their vocational and psychosocial rehabilitation^4–6. It thus appears necessary to assess the ability at learning new tasks, and thus, to measure the effects of practicing new tasks and to detect and quantify rapid effects of medication on these measures. Indeed, they might predict the improvement of clinical symptoms and/or of social rehabilitation on the long term.

Effects of practice refer to the spontaneous learnings that occur, without a training, between two testing sessions. These effects reflect various abilities, such as procedural memory and developments of test-taking strategies, to name but a few. Such abilities appear essential for optimizing performance in many daily activities^7–9. Such practice effects differ from what is often called learning potential (LP)⁵, which consists of the improvement induced by training interventions that occur between a test and a retest session (i.e., test-train-test approaches)^10,11.

In a meta-analysis¹², scores on executive functions and attention tasks were found to improve over time in healthy subjects while no significant improvement was found in schizophrenia patients. Another meta-analysis¹³ also report that, after onset, such patients did not progress across the repetitions of cognitive tests compared to the control group. This lack of practice effect reflects abnormal learning of new skills in patients with schizophrenia. Investigating the effects of practice in a laboratory setting using experimental psychology methods, such as the systematic measure of reaction times, may be valuable to better specify and understand the cognitive changes and the learning deficits in people with schizophrenia. As mentioned by the American Academy of Clinical Neuropsychology (AACN), “there is an obvious need for more data on normal change trajectories for all types of measures with all types of demographic variables and patient groups”¹⁴.

The effects of practice mentioned above were observed between testing sessions that were several days, weeks, or even months apart. However, today, we know that antipsychotic medications can improve some of the clinical symptoms of patients much faster. For instance, previous studies reported that the early psychosis factor and the scores at a positive symptom subscale significantly decreased 4 hours after the intake of ziprasidone¹⁵. Patients with schizophrenia taking 10 mg of olanzapine showed overall relief of symptoms 2 hours after¹⁶ and clinical scores decreased significantly in both olanzapine and haloperidol plus lorazepam groups 2 hours after the first injection¹⁷. Moreover, in that study, some particular clinical scores were reported to decrease after only one hour of taking olanzapine. As a matter of fact, such early clinical effects are not surprising. They match neurochemical observations. A single dose of quetiapine gives rise to transiently high (58%-64%) striatal dopamine type 2 (D2) occupancy 2 to 3 hours after its intake¹⁸.

Nevertheless, whether antipsychotic medications also have an acute effect on cognition and on practice effects remain unknown. Yet, given the extent to which the social rehabilitation of patient depends on cognition¹⁹, it could be useful to know whether medications induce fast improvements also in these domains. Together with fast decrease of clinical symptoms, such improvements might predict the efficacy of the medication for the psychosocial rehabilitation of the patient on the long run.

However, in a clinical setting, it would be impractical to measure practice effects on a patient across all the tests of an entire cognitive battery. A particular task has to be selected. One is then placed in front of a difficult choice. However, as mentioned, getting better at any cognitive task is dependent on many different types of learning. Quite a few of these learning types may thus be assessed by only one task, provided that it is complex enough, but, of course, not overly complex. Most patients should make less than 10% of errors, as generally recommended in experimental psychology. Indeed, uncertainty about the correctness of the responses can induce changes in the cognitive strategy used during the task and/or disengagement. On the other hand, it seems that the task to be selected should use language stimuli, as those are the stimuli patients commonly encounter when interacting with others or when receiving instructions in the workplace. Moreover, the task should focus on the meaning of these stimuli, given that understanding such meanings is of critical importance for rehabilitation. Finally, the task should allow not only the recording of behavioral responses but also that of brain activities so as to be able to pinpoint the stages of processing that change and those that do not change with practice in a patient. These reasons may be why quite a few studies on schizophrenia used words as stimuli and included the recording of the event-related brain potentials (ERPs) that they elicit. Some of these studies comprised multiple test sessions and thus measured practice effects. They were the focus of a large-scale meta-analysis²⁰.

Some of these studies used a lexical decision task (LDT), that is, a task where strings of letters are presented and participants have to decide whether or not each of these strings, such as “toble” or “table”, is a real English word^21–24. In these studies, stimulus meaning was manipulated by presenting, before each of these targets (e.g., table), a word that could be semantically related to them (e.g., chair). This was done because thousands of studies have confirmed, after Collins and Loftus²⁵, that the time taken to make the lexical decision is shorter for these targets words than for those that are preceded by an unrelated item (e.g., car), an effect called the semantic priming effect on reaction times (RTs)²⁶. Generally, when there are long time delays (i.e., more than 500 ms) between the onset of the prime and the onset of the target word, schizophrenia patients and individuals with high schizotypal traits show smaller RT priming effect than healthy controls with low schizotypal traits²⁷.

At the electrophysiological level, one event-related brain potential (ERP) has been found to depend on semantic priming. This ERP is named the N400 because of its negative-going (N) electrical polarity and because its amplitude, that is, its voltage, is maximal around 400 ms after the onset of the target stimulus^28–30. Like RTs, N400 amplitudes are smaller for targets that are preceded by a stimulus that primes their meaning than for targets that are not preceded by such a priming stimulus. This phenomenon is known as the N400 semantic priming effect. It is usually interpreted as indexing an easier processing of semantic information²⁸. Researchers have investigated N400 priming impairments in schizophrenia patients. The amplitude of their N400s to unprimed targets has generally been found a bit smaller, and that to primed targets, a bit larger than those of healthy controls. This double difference results in reduced N400 effects^31–35. N400 semantic priming deficits have been shown to predict worse symptomatic and functional outcomes after one year³⁶ and two years³⁷. While abnormalities in other electrophysiological indexes, such as Mismatch negativity, P3a, and Auditory steady-state response have been observed in schizophrenia patients³⁸, their applicability in studying practice effects is limited because they only reflect automatic pre-attentive function.

Both RTs and N400 amplitudes have been used to measure practice effects. In healthy participants, a significant effect of practice and priming on both measures was found over a 3-month test to retest delay²⁴. Interestingly, at least two other studies also report the effects of practice on the effects of priming. According to Besche-Richard et al.'s results²¹, in healthy participants, practice does not change priming effects. In contrast, in schizophrenia patients, the behavioral semantic priming remained impaired whereas their smaller N400 priming effect and their clinical symptoms were found to be significantly improved at their one-year retest session²¹. In contrast to Besche-Richard et al.'s results in healthy participants, Kiang et al.²³ reported that in such participants, the amplitude of the N400 semantic priming effect decreased by about 1.22 µV from the test to the retest session one week apart. However, the effects of practice on priming effects on RTs were not significant, which may be due to a small sample size.

One way to improve part of these discrepancies observed across the results of those studies might be to use tasks other than lexical decision tasks (LDTs). Indeed, tasks that focus directly on the meaning of target words and that produce more robust N400 effects³⁹ might generate more replicable results. Moreover, these other tasks may be designed to circumvent one potential problem of previous LDTs, namely, the maintaining instructions in working memory for a long time. In prior studies, participants were given the instructions defining the lexical decision task only once, just before the experiment, together with other instructions (e.g., not to move and not to blink at inappropriate times). The deficiency of working memory in schizophrenia may then be partly responsible for the priming and practice differences found between patients and healthy controls^40–42. To address this issue and to increase the focus on the meaning of the words used as stimuli, Debruille et al.⁴³ used a particular semantic task. Word stimuli were presented, and a question-word reminding the task instruction was systematically presented at the beginning of each trial, in order to refresh participants' working memory^42,44. These authors showed that this task circumvents the deficiency in maintaining context in the working memory⁴³.

However, before using this particular semantic task to assess practice effects in patients, it is first necessary to understand these effects in healthy participants. Nevertheless, the degree of schizotypy of such participants may be measured to have a preliminary idea of the sensitivity of the test to this variable and the impact of the position of each participant on the normality to schizophrenia continuum^45–47. Schizotypy, a recognized psychological construct in schizophrenia research, shows similarities in neurocognitive performance between high schizotypy individuals and schizophrenia patients⁴⁸. Antipsychotics also have comparable effects on neurocognition in both groups⁴⁹. Finally, from the perspective of antipsychotics development, schizotypy research has many advantages, such as the availability of reliable and objective psychometric questionnaires⁵⁰; the absence of effects of disease chronicity, and of previous antipsychotic exposure⁵¹.

Our end goal is to investigate whether the effects of practicing the particular semantic categorization task could be used to predict the efficacy of an antipsychotic medication in a patient in a delay short enough to be usable in clinical practice, namely, over a 90-minute interval, which is close to the maximum plasma concentration reached after the intake of one pill of most antipsychotics50. To achieve this end goal, we first needed to evaluate the reliability of the behavioral and electrophysiological measures and sensitivity of these measures to schizotypy in that task.

Questionnaires

The SPQ scores of our participants covered a relatively wide range of the continuum between low and high schizotypy, namely, from 0 to 38 (out of 74, the maximal score). The mean of the total SPQ scores of all participants was 17.4 (SD = 11.0). High-and low-schizotypy subgroups did not significantly differ in terms of sex, age, and level of education (see Table 1). There was a SPQ x session interaction on the anxiety level (F (1, 45) = 10.42, p = 0.002, ηp2 = 0.19). It was explored by pairwise comparisons, which revealed that, before the experiment, the mean anxiety level of the high SPQ subgroup (mean = 28.4) was significantly higher than that of the low SPQ subgroup (mean = 15.0). STAI scores of two groups significantly increased along with the experiment. This increase was larger for the low- than for the high-SPQ subgroup so that there was no significant difference between the mean anxiety level of the low SPQ subgroup (mean = 59.4) and that of the high SPQ subgroup (mean = 56.6) after the experiment. On the contrary, fatigue did not significantly increase along the experiment. Fatigue of the high SPQ subgroup (mean = 38.2, SD = 17.0) was significantly higher than that of the low SPQ group (mean = 21.9, SD = 17.6) both before and after the experiment (F (1, 45) = 11.24, p = 0.002, ηp2 = 0.20).

Table 1

Demographic characteristics of participants.
	Low schizotypy	High schizotypy
	(N = 23)	(N = 24)
Sex: male, % (N)	43.5% (10.0)	45.8% (11.0)
Age, M (SD, RANGE)	23.7 (3.1, 19–30)	22.5 (3.0, 18–30)
Years of Education, M (SD, RANGE)	15.4 (1.6, 13–18)	14.8 (1.7, 11–18)
Total SPQ, M (SD, RANGE)	7.5 (4.9, 0–15)	26.9 (5.2, 18–38)
Anxiety (pre-experiment), (SD, RANGE)	15.0 (14.0, 0–50)	28.4 (17.3, 0–64)
Anxiety (post-experiment), (SD, RANGE)	59.4 (4.5, 52–67)	56.6 (5.7, 48–67)
Fatigue (pre-experiment), (SD, RANGE)	20.4 (17.0, 0–63)	40.8 (16.0, 0–75)
Fatigue (post-experiment), M (SD, RANGE)	23.4 (18.4, 0–69)	36.6 (18.1, 6–75)

Practice effect of behavioral data (RTs and RAs) and N400 amplitudes

Behavioral data

The mean of response accuracy (RA) was 93.0% (SD = 7.3, range = 54–100). Participants' RAs in the non-exemplar condition (mean = 95.8%, SD = 4.1) were significantly higher than in the exemplar condition (mean = 90.1%, SD = 8.6) (F (1, 45) = 32.12, p = 9.72 x 10^− 7, ηp2 = 0.42). The debriefing session of prior studies⁵² showed that those few errors in the exemplar condition were, for a large part, due to insects, which were not considered to be animals by some participants. Participants' reaction times (RTs) in session 2 (mean = 774 ms, SD = 82) were significantly faster than in session 1 (mean = 824 ms, SD = 86) (F (1, 45) = 37.72, p = 1.92 x 10^− 7, ηp2 = 0.46). RTs of the non-exemplar condition (mean = 817 ms, SD = 90) were significantly slower than in the exemplar condition (mean = 780 ms, SD = 81) (F (1, 45) = 39.26, p = 1.25 x 10^− 7, ηp2 = 0.47). There was no category x session interaction on RTs (Fig. 1).

……………….……………………………….Insert Fig. 1here…………….………………………………….

N400 amplitudes

The omnibus ANOVA performed on N400 mean amplitudes revealed several statistically significant interactions: session x SPQ (F (1, 45) = 5.3, p = 0.026, ηp² = 0.1), electrode x category (F (10, 450) = 3.6, p = 0.002, ηp² = 0.1), and session x category (F (1, 45) = 4.1, p = 0.05, ηp² = 0.08). We then focused on each category condition to look for the source of the Session x SPQ x electrode interaction. There was a SPQ x session interaction on the exemplar condition (F (1, 45) = 6.38, p = 0.015, ηp2 = 0.12). The post-hoc ANOVAs run there revealed that N400 amplitudes in session 2 (-0.3 µV) were significantly smaller than those of session 1 (-1.5 µV) only in the exemplar condition and only for participants with low SPQ scores (Fig. 2). This was not the case for participants in the high SPQ subgroup.

……………….……………………………….Insert Fig. 2here…………….………………………………….

There was a marginally significant effect of session on the N400 effect, (F (1, 45) = 4.1, p = 0.05, ηp2 = 0.08). The N400 effect of session 2 (-1.6 µV) was a bit larger than that of session 1 (-1.0 µV) (see Fig. 3). There was neither an effect of SPQ on the N400 effect nor any interaction including this factor. ERP figures for Raw N400 amplitudes for the exemplar and non-exemplar conditions were added to the supplementary materials.

……………….……………………………….Insert Fig. 3here…………….………………………………….

Test-retest and internal consistency reliability of behavioral data and N400 amplitudes

Behavioral data

To see whether behavioral data obtained in this particular semantic task were reliable, we examined whether participants who have, for instance, fast mean reaction times at session 1 relative to the other participants also have fast mean reaction times at session 2, still relative to other participants. As illustrated in Fig. 4, these correlations were significantly positive for the non-exemplar- (ICCs = 0.55, p = 7.0 x 10^− 7, 95% confidence interval (CI) [0.19, 0.75]; Pearson's r = 0.63, p = 9.1 x 10^− 7, 95% CI [0.38, 0.86]) and for the exemplar-condition (ICCs = 0.66, p = 1.6 x 10^− 11, 95% CI [0.088, 0.86]; Pearson's r = 0.79, p = 2.0 x 10^− 11, 95% CI [0.67, 0.88]). No significant reliability was found in the STAI-Y questionnaire whereas there was a high test-retest reliability of the fatigue questionnaire (ICCs = 0.84, p = 1.4 x 10^− 13, 95% confidence interval (CI) [0.73, 0.91]; Pearson's r = 0.84, p = 2.6 x 10^− 13, 95% CI [0.70, 0.92]).

……………….……………………………….Insert Fig. 4here…………….………………………………….

Reliability of N400 amplitudes were assessed at Cz and the central cluster of 11 electrodes (FC3/4, C3/4, CP3/4, P3/4, Pz, Cz, and Fcz) where N400 effects obtained when using written words as stimuli are usually found to be maximal. We also examined whether participants have similar scores from different subsets of trials (internal consistency reliability). Table 2 displays the Test-retest Reliability (TRR) and Internal consistency Reliability (ICR) found. It shows that, in general, mean N400 amplitudes of both non-exemplar and exemplar conditions had good reliability and that the mean reliability of N400 amplitudes of the exemplar condition was better than that of the non-exemplar condition at Cz and the central cluster. Figure 5 shows scatterplots of correlations between session 1 and session 2 for N400 amplitudes at Cz.

Table 2

Test-retest Reliability (TRR) and Internal consistency Reliability (ICR) of N400 mean amplitudes for non-exemplar-and exemplar-condition in the two sessions at Cz and central cluster. CI is 95% confidence interval. Central cluster includes: FC3/4, C3/4, CP3/4, P3/4, Pz, Cz, and Fcz.
	Mean amplitude
	Non-exemplar				Exemplar
	ICR	CI	TRR	CI	ICR	CI	TRR	CI
Cz	0.88	[0.84, 0.92]	0.69	[0.51, 0.82]	0.88	[0.82, 0.92]	0.80	[0.67, 0.89]
Central Cluster	0.87	[0.82, 0.91]	0.55	[0.31, 0.73]	0.86	[0.81, 0.91]	0.73	[0.55, 0.85]

……………….……………………………….Insert Fig. 5here…………….………………………………….

Correlations between N400 amplitudes and SPQ

We do not find a strong correlation between SPQ scores and N400 (most Pearson’s r were smaller than 0.3). Only one significant correlation was found between SPQ Interpersonal scores and N400 effects of session 2 at Pz (Pearson's r = 0.314, p = 0.016) (Fig. 6). One possible reason for this is that healthy participants were recruited in our study. The highest SPQ scores were thus not very high compared to those of schizophrenia patients⁵³.

……………….……………………………….Insert Fig. 6here…………….………………………………….

The present study was a first step towards an end goal which is to evaluate if the effects of practicing a particular semantic categorization task could be used to fast predict the efficacy of an antipsychotic medication for the psychosocial rehabilitation of a patient. Forty-seven healthy individuals were tested twice in a particular semantic categorization task. One of our aims was to examine whether practice effects can be observed over the 90 minutes separating the two sessions of this task. This was the case. Reaction times significantly decreased from session one to session two. Another aim was reached. A good test-retest reliability was observed and the test was found sensitive to the mild schizotypal traits of our subclinical participants.

A high response accuracy was observed, indicating that participants were attending to the stimuli and performing the task. In addition, as mentioned, practice effects were observed. Participants responded 50 ms faster in session 2 than in session 1, consistently with the results found in three previous studies, namely, in Kiang et al.²³, who reported a 30 ms decrease over a one-week interval; in Besche-Richard et al.²¹, who found a 48 ms decrease over a one-year interval and in Yu et al.²⁴, who found a 45 ms RT reduction in a lexical decision task at the retest session, 3 months later.

RTs and N400 measures had a good test-retest reliability, consistent with previous studies^22–24. The test-retest reliability for the N400 at Cz is 0.69 for non-exemplar- and 0.80 for the exemplar condition and the internal consistency reliability is 0.88 for two conditions. In other studies, the reliability of N400 amplitudes at Cz is reported as 0.69 for unrelated- and 0.74 for related targets with a one-week interval²³; 0.75 for unrelated- and 0.55 for related targets with a three-month interval²⁴. The high test-retest reliability of N400 amplitude found in healthy controls in the present study is comparable to or better than other putative ERP biomarkers, like the amplitude of P300 and that of the mismatch negativity^54,55. The present results also further support N400 as a potential bioindicator in longitudinal treatment studies of schizophrenia. However, we did not find retest reliability on the N400 priming effect. One possible reason is the relatively small proportion of related prime-target pairs (RP) in this study, at approximately 33.3%. Stolz et al.⁵⁶ reported that RP and reliability are inversely proportional.

The N400 semantic priming effect of session 2 was 0.5 µV larger than that of Session 1. This effect of practice on the N400 semantic priming effect did not interact with the SPQ. This finding is inconsistent with previous studies. For example, Besche-Richard et al.²¹ reported that, in healthy participants, practice does not change semantic priming effects at their one-year retest session. Kiang et al.²³ found that the N400 semantic priming effect decreased by about 1.22 µV from the test to the retest session one week apart. One possible explanation of these discrepancies is that, in the present study, the interval between two sessions was 90 minutes, which is much shorter than in other studies, that is, one year and one week, respectively. Barnett et al.⁵⁷ proposed that the size of the practice effect is inversely proportional to the interval between the test- and the retest-session.

Secondly, we found that N400 amplitudes for the exemplar condition decrease significantly with practice in participants with low SPQ traits, but not in participants with high SPQ scores. Interestingly, this difference between SPQ subgroups was not reflected in the RTs, suggesting that N400s may index processes other than those indexed by RTs. As a matter of fact, in tasks like the present one, where participants have to answer as fast as possible (in addition to being as accurate as they can), RTs mainly depend on activations, as repeatedly found when testing the so-called race models of RTs⁵⁸. In divided-attention tasks, when two target stimuli are presented simultaneously, participants respond faster than when only one stimulus is presented. To account for these facts, Raab⁵⁹ proposed the “race model” in which the two target stimuli are processed independently. RTs are then determined by which stimulus triggers or activates the response first: the winner of the race. This should also be the case for one stimulus that activates more than one characteristic corresponding to a response. The characteristic that is processed the fastest will be the one activating the response first. In contrast to RTs, N400s might index inhibitory mechanisms^60–62. The decrease of N400 amplitudes observed with practice in the low SPQ participants could be the consequence of a decrease in the activation of inappropriate representations by the question-word 'ANIMAL?'. Less inhibition would then be necessary during the processing of exemplar target words, hence, the reduced N400 amplitudes found in these conditions. If this were the case, it would mean that one of the effects of practice is the development of a more focused processing and that such improvements are compromised by a higher degree of schizotypal traits. It would also mean that a notable inhibition remains to be performed at the occurrence of the target word in participants with higher SPQ scores.

Besche-Richard et al.'s results²¹ can be used to further support a difference between RT and N400. They found, at a retest session one year later, that symptom-relieved schizophrenia patients still have deficits in priming effects on RTs, whereas their priming effects on N400 were significantly improved. These findings also suggest that the effect of practice on N400s might be a better predictor of clinical improvement than the effects of practice on RTs, a claim that can be reinforced by the overall slower RTs usually exhibited by patients with schizophrenia, which can sometimes inflate relative differences in RTs between non-exemplar and exemplar conditions, leading to spurious behavioral results⁶³.

One of the potential limitations of this study could be the repetition effect. Indeed, the task used included identical stimuli across the two consecutive sessions. The practice effects found could thus be partly due to repetition effects. This repetition is very well known to cause a robust decrease in the amplitude of the raw N400 and of the N400 effects that are obtained in lexical decisions^64–66. Surprisingly, such a robust N400 decrease was neither observed for non-exemplar words nor for both types of words in the high-SPQ group. What was observed here was a moderate N400 decrease only for exemplar words and only in the low-SPQ subgroup. This absence of robust and systematic effect of repetition on N400 could be due to the other task participants had to performe between the semantic task of session 1 and the semantic task of session 2. This task used words other than those used in the semantic task. It also pertained to the meaning of these words but in a very different way. This intervening task could have prevented the classical, robust and systematic effects of repetition on N400s.

Classically, stimulus repetition also induces a decrease of reaction times^30,67,68. The faster RTs observed in session 2 than in session 1 here could thus be due to such effects. Nevertheless, this possibility is unlikely because the three previous studies mentioned above found RT reductions of a size similar to the one reported here whereas they used much longer times between the two sessions. Repetition effects on RTs are much bigger when using shorter delays^69–71. Thus, if the RT decrease observed here were actually due to stimulus repetition itself, it would have been (much) bigger than the 50.4 ms found. This RT decrease is thus more likely to be due to pure effects of the practice of the task itself. This claim can also be supported by the very long duration of procedural memory, which remains almost unchanged over years^72,73. This could account for the fact that the RT decrease remains similar to those of the three previous studies.

The interpretation of the results found here should take into account the participants' anxiety levels. High SPQ subgroup exhibited significantly higher anxiety levels compared to the low SPQ subgroup before the experiment. This is consistent with previous research showing that the SPQ score is positively correlated with trait anxiety⁷⁴. This may have contributed to the lack of practice effect on the N400 amplitudes of this subgroup in the exemplar condition. Indeed, this high level of pre-experiment anxiety may partially impede flexibility and the ability to maintain and develop focus during practice⁷⁵. A meta-analysis of 177 studies proved that self-reported measures of anxiety are reliably associated with poorer performance on measures of working memory capacity⁷⁶. Anxiety-related behavioral phenotypes have been related to disrupted prefrontal cortex neural activity in healthy individuals^77,78. Anxiety levels of both groups increased along the experiment. Some other EEG studies also reported increased levels of anxiety at the end of the experiment^79,80. This may be due to the novelty of the experience, concerns about the expected outcome, the discomfort of wearing electrode caps, the need to control blinks and eye movements etc.

Next, a normative sample of schizophrenia patients should be tested longitudinally to know whether the initial change of practice effects induced by the chosen medication predicts the long term (e.g., a year) functional outcome of a patient. In addition, the study of such effects maybe critical not only in clinical trials, but also in all studies using placebo controls. In effect, improvements in patients receiving placebos are likely to be at least partly due to practice effects and not only to the expectation bias inherent to placebo-controlled designs⁹. Future studies should thus compare practice effects in patients receiving a placebo to those of individuals who are not receiving anything. Other studies investigating the impacts of antipsychotic medications (e.g., olanzapine and risperidone) on practice effects, could then do it controlling for the placebo effect of the medication studied. This would be a more rigorous way to see whether impacts of medication on practice effects predict patients' psychosocial rehabilitation and whether such effects can thus be used to select what would be the best medication for a particular patient.

Participants were selected among candidates who answered our English or French online advertisements in a variety of social media (e.g., Kijiji, Facebook, and the McGill Classified Ads website). Participants had to be native English or French speakers with at least ten years of education in either language. We end up having 45 Anglophones and 2 Francophones. They reported having no previous history of a neurological condition, no medical condition that compromises brain functioning, and no head injury with a loss of consciousness longer than 5 minutes. We also excluded those with a personal history of DSM-IV Axis I psychiatric disorder, a family history of schizophrenia or bipolar disorder, alcohol or substance abuse disorder, or current use of a medication related to a psychiatric disorder. Forty-seven healthy right-handed participants between the ages of 18 and 30 (mean = 23.1, SD = 3.1, 21 females) were retained. All participants gave written informed consent form prior to participation. This study was approved by the Douglas Ethics Review Board (project number: IUSMD-06-42). All research was performed in accordance with relevant guidelines/regulation and in accordance with the Declaration of Helsinki.

Psychometric scales Before the EEG recording session, participants also completed a set of questionnaires in their preferred language (English or French). This set included the State-Trait Anxiety Inventory Form Y (the STAI-Y State)⁸¹ and a self-created questionnaire evaluating fatigue (see Supplementary Materials). The STAI-Y has high reliability coefficients, 0.92 for the State and 0.90 for Trait scales (Form Y) respectively⁸². The questionnaire set also included the Schizotypal Personality Questionnaire (the SPQ), which has high internal reliability (coefficient alpha > 0.90) and test-retest reliability (r = 0.82)⁸³. This questionnaire was initially designed to measure the severity of schizotypal personality traits in the general population. It has been widely used as a psychometric tool in research⁸⁴. Nevertheless, it is based on the DSM-III-R criteria used to diagnose schizophrenia and is also used with schizophrenia patients^53,85,86. The STAI-Y and the fatigue questionnaires had to be completed twice, that is, once before and once after the experiment, in order to evaluate the effects of sessions on fatigue and anxiety as potential confounding factors. In contrast, the SPQ was administered only once before the EEG session since SPQ total score remained relatively stable over time⁸⁷.

Procedure Upon arrival, participants completed a demographics questionnaire where they provided information regarding their sex, age, and level of education. Immediately after completing the set of questionnaires, the EEG recording session began, during which participants provided responses in the semantic categorization task (session 1) for about 15 minutes. Right after, for approximately 15 minutes, participants had to perform another task using word stimuli and focusing on their meanings. However, this task was unconnected to the present study. A one-hour lunch break was then given to all participants. It was followed by session 2, where the target words of the semantic task remained the same as those used in session 1.

Stimuli The semantic task was identical to the one used in Debruille et al.⁵², which has both an English and a French version. Participants completed tasks in their preferred language, English or French. It included 180 trials, each made of two serially presented words. In two-thirds of the trials, the first word was the question word “ANIMAL?”, followed by an exemplar (e.g., dog) or a non-exemplar (e.g., table) of the animal category. Participants had to decide if the target word belonged to the animal category as accurately and rapidly as possible by pressing a ‘Yes’ for matching targets or ‘No’ key for mismatching targets with their right index finger. Exemplar and non-exemplar words were matched for the number of letters and frequency of usage using the Content et al.⁸⁸ database for the French words and the Kucera and Francis⁸⁹ counts for the English words. In one-third of the trials, the first word was “INACTION”, which meant that participants should not respond to the second stimulus of the trial.

The priming word 'ANIMAL?' (or 'INACTION') appeared in the center of the screen for 500 ms and was replaced by a fixation cross for 500 ms. This cross was followed by the target word for 1000 ms or until a valid key press occurred (Fig. 7). Such a long stimulus onset asynchrony (SOA), that is, such a long time between the onset of the prime and the onset of the target word, was chosen because it allows observing robust N400 priming deficits in schizophrenia patients^90–92. Moreover, long SOAs have been proven to boost test-retest reliability^56,93. Each target word was then replaced, 1.5 to 2 s later, by the word “Blink”, which lasted for 500 ms.

……………….……………………………….Insert Fig. 7here…………….………………………………….

Data acquisition The time taken to make the exemplar/non-exemplar decision (the RT) was recorded at each trial. The electroencephalogram (EEG) was recorded from 28 tin electrodes mounted on an Electro-Cap International (ECI) cap. The following sites of the international 10–20 system were used: Fp1/2, F3/4, Fc3/4, C3/4, Cp3/4, P3/4, O1/2, Fz, Fcz, Cz, Pz, F7/8, Ft7/8, T3/4, Tp7/8, T5/6. The right earlobe was used as the reference and the ground was placed 2 cm anterior to Fz. The impedance was measured before the experiment using a 30 Hz current and was kept below 5 KΩ. An electronic notch filter was used to reduce the 50 Hz EM noise coming from power lines. The high- and low-pass filters had their half amplitude cut-off set at 0.01 Hz and 100 Hz, respectively. EEG signals were digitized at a 248 Hz sampling rate.

2.5 Data processing and measures The data were processed in MATLAB using the EEGLAB toolbox with the ERPLAB extension. An independent component analysis (ICA) was first used to identify and remove artifactual components such as blinks, eye movements, and myograms. The infomax algorithm ICA was performed on a copy of the continuous EEG that was high-pass filtered at 1 Hz and low-pass filtered at 30 Hz. The resulting ICA weight matrix and sphering matrix were then applied to the continuous 0.1–30 Hz filtered EEG by using the ICLabel EEGLAB extension to signal artifactual independent components (ICs) that had > 20% of chance of being muscle activity or > 8% of chance of being eye activity⁹⁴. These ICs were then systematically subtracted from this continuous 0.1–30 Hz filtered EEG, as in Finke et al.⁹⁵, Goregliad et al.⁹⁶ and Markey et al.⁹⁷.

Only trials including behavioral responses performed between 300 ms and 2500 ms post-onset were retained. This was done to eliminate trials where participants did not pay enough attention, were too hesitant, or, on the contrary, provided responses that were too prompt, revealing an absence of real stimulus evaluation. The EEG epochs of those trials were taken from 200 ms pre-stimulus to 1000 ms post-stimulus. Their baselines were set by computing the mean voltage in the − 200 to 0 ms time window for each electrode and by subtracting this mean value from each point of the − 200 to 1000 ms epoch. Trials with voltages exceeding the amplitude range of -/+100 µV for the 4 frontal electrodes (Fp1/2, F7/8) and of -/+75 µV for the remaining 24 electrodes were rejected, as well as epochs containing flat lines persisting for more than 100 ms. Participants were included in the analyses only if a minimum of 30 trials in each condition survived these artifact rejection criteria. Although the ERP Reliability Analysis (ERA) Toolbox identifies the minimum number of trials needed to be 16 in the non-exemplar condition and 15 in the exemplar condition for adequate reliability, we were not only interested in grand average ERPs, but also in individual participant study using Monte Carlo methods. For this purpose, we kept a cut-off of 30 trials (50% of all trials) for each condition. Tables 3 and 4 provide the average number of accepted trials and the standardized measurement error (SME) for the N400 amplitude on each condition across participants. An ERP was computed by averaging all the remaining EEG epochs corresponding to one condition (e.g., exemplar target words). The measures of N400 amplitude used were the mean voltage of the signal of each electrode in the 300–500 ms time window. The N400 priming effect was computed by subtracting the ERPs elicited by exemplars (i.e., names of animals) from the ERPs elicited by non-exemplars (i.e., names of objects).

Table 3

Average numbers of accepted trials, N400 mean amplitude at Cz/central cluster, and SME (standardized measurement error) by condition across participants in session 1. Central cluster includes: FC3/4, C3/4, CP3/4, P3/4, Pz, Cz, and Fcz.
		Session 1
		Low SPQ		High SPQ
		Non-exemplar	Exemplar	Non-exemplar	Exemplar
Numbers of Trials accepted	M (SD)	48.0 (6.2)	44.7 (7.4)	46.6 (7.5)	43.5 (11.8)
Numbers of Trials accepted	range	34.0–56.0	25.0–56.0	32.0–57.0	18.0–59.0
N400 at Cz (µV)	M (SD)	-2.2(4.3)	-3.1 (4.1)	-2.2 (4.2)	-1.3 (4.0)
N400 at Cz (µV)	range	-10.4-2.9	-9.9-5.8	-11.2-4.2	-9.6-4.2
N400 at central cluster (µV)	M (SD)	-2.6 (3.4)	-1.5 (3.2)	-1.3 (3.3)	-0.5 (3.1)
N400 at central cluster (µV)	range	-10.4-4.6	-9.9-5.9	-11.2-5.8	-9.6-7.9
SME	M (SD)	1.0 (0.2)	1.0 (0.2)	1.1 (0.3)	1.2 (0.4)
SME	range	0.7–1.3	0.7–1.7	0.3–1.7	0.3–2.5

Table 4

Average numbers of accepted trials, N400 mean amplitude at Cz/central cluster, and SME (standardized measurement error) by condition across participants in session 2. Central cluster includes: FC3/4, C3/4, CP3/4, P3/4, Pz, Cz, and Fcz.
		Session 2
		Low SPQ		High SPQ
		Non-exemplar	Exemplar	Non-exemplar	Exemplar
Numbers of Trials accepted	M (SD)	49.9 (6.9)	44.3 (9.0)	49.2 (6.1)	46.0 (7.5)
Numbers of Trials accepted	range	31.0–57.0	30.0–58.0	31.0–57.0	29.0–57.0
N400 at Cz (µV)	M (SD)	-4.2 (4.2)	-1.8 (4.5)	-3.5 (3.3)	-1.9 (3.8)
N400 at Cz (µV)	range	-13.0-4.6	-11.8-7.7	-10.1-3.1	-9.0-6.3
N400 at central cluster (µV)	M (SD)	-2.2 (3.0)	-0.3 (3.2)	-2.2 (2.8)	-0.9 (3.3)
N400 at central cluster (µV)	range	-13.0-4.6	-11.8-7.7	-10.5-5.5	-11.2-7.6
SME	M (SD)	0.9 (0.2)	0.9 (0.2)	1.0 (0.3)	1.1 (0.3)
SME	range	0.5–1.3	0.5–1.3	0.4–1.5	0.3–1.7

Statistical analyses In the current study, the total SPQ score was also used to median split participants into high- and low-schizotypy subgroups. Questionnaire scores, mean reaction times (RTs), and mean accuracies (RAs) were analyzed with mixed-model repeated-measures analysis of variance (ANOVA) using a multivariate approach. SPQ subgroup was the between-subjects factor whereas category (animal vs. object names) and session (S1 vs. S2) were the within-subject factors.

Mean N400 amplitudes and N400 effects (subtracting the ERP elicited by exemplars from the ERP elicited by unrelated non-exemplars) were also analyzed with mixed-model repeated-measures ANOVA having the same between- and within-subject factors, to which electrode was added as a within-subject factor (11 levels: FC3/4, C3/4, CP3/4, P3/4, Pz, Cz, and Fcz). As previous literature has shown that the N400 effect is usually most prominent at central and parietal sites²⁸, we further focused on those electrodes. Nevertheless, to further reduce the possibility of errors, a mean of all these electrodes was also computed and these simpler statistics were added to the supplementary materials. These latter statistics revealed no such problem.

For the reliability analysis, test-retest and internal consistency reliability of N400 amplitudes for non-exemplar- and exemplar-condition between session 1 and session 2 were processed in the ERP Reliability Analysis (ERA) Toolbox⁹⁸. The acceptable reliability cutoff was set as 0.7 due to the novelty of assessing the reliability of this particular semantic task⁹⁸. For the test-retest reliability of behavioral data and questionnaires, intraclass correlation coefficient (ICCs) and Pearson’s r correlation coefficients between session 1 and session 2 were calculated. A “Two-Way mixed effects model” and “absolute agreement” were used in the ICCs calculation^24,99.

All statistical analyses were performed with IBM SPSS Statistics (version 27). The Greenhouse and Geisser’s adjustment of the degrees of freedom was used to compensate for the heterogeneity of variances across electrodes. In those cases, the original F values and degrees of freedom are provided together with the corrected p values. The Benjamini-Hochberg false discovery rate (B-H FDR) procedure was then used to evaluate the significance of each p-value of each series of tests. P-values were thus first ranked from the most to the least significant. One B-H FDR threshold for each of these p-values was then computed by dividing its rank by the total number of tests and by multiplying the result by the false discovery rate chosen (i.e., 10%). The p-value was declared significant if it was smaller than that threshold.

Acknowledgments

Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results”. We thank Ola Mohammed Ali and Timothy Hadjis for testing the participants.

Author Contributions

MD reprocessed all the data collected, performed new statistical analyses and wrote the current manuscript. ID and GA wrote preliminary documents (i.e., Master's theses) on the part of the results presented here. YC contributed to the statistical analyses and the interpretation of the results. JBD wrote the research project, got the funding, designed the experiment, supervised MD, ID, GA, YC, and corrected earlier versions of this manuscript.

Funding

This study was supported by the 2014-PR-171935 grant from the Fonds de la Recherche du Québec—Nature et technologies—allocated to the last author. This funding organization did not participate in any aspect of the research being conducted and had no role in the scientific outputs.

Data availability

All analysis scripts and datasets (behavioral and EEG) can be obtained from the corresponding author upon request.

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Quantifying the effects of practicing a semantic task according to subclinical schizotypy

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Abstract

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Introduction

Results

Questionnaires

Practice effect of behavioral data (RTs and RAs) and N400 amplitudes

Behavioral data

N400 amplitudes

Test-retest and internal consistency reliability of behavioral data and N400 amplitudes

Behavioral data

Correlations between N400 amplitudes and SPQ

Discussion

Methodology

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