The role of set-shifting in auditory verbal hallucinations

    The role of set-shifting in auditory verbal hallucinations Sara Siddi, Donatella Rita Petretto, Caterina Burrai, Rosanna Scanu, Antonella Baita, Pierfranco Trincas, Emanuela Trogu, Liliana Campus, Augusto Contu, Antonio Preti PII: DOI: Reference:

S0010-440X(16)30450-3 doi: 10.1016/j.comppsych.2017.01.011 YCOMP 51797

To appear in:

Comprehensive Psychiatry

Please cite this article as: Siddi Sara, Petretto Donatella Rita, Burrai Caterina, Scanu Rosanna, Baita Antonella, Trincas Pierfranco, Trogu Emanuela, Campus Liliana, Contu Augusto, Preti Antonio, The role of set-shifting in auditory verbal hallucinations, Comprehensive Psychiatry (2017), doi: 10.1016/j.comppsych.2017.01.011

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ACCEPTED MANUSCRIPT Running head: Set-shifting and auditory verbal hallucinations

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The role of set-shifting in auditory verbal hallucinations

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Sara Siddia,b,c,d, Donatella Rita Petrettoa, Caterina Burraie, Rosanna Scanua, Antonella Baitae,

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Pierfranco Trincasf, Emanuela Troguf, Liliana Campuse, Augusto Contug, Antonio Pretia,h

Section of Clinical Psychology, Department of Education, Psychology, and Philosophy, University of Cagliari, Italy

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Research and Development Unit, Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Spain, CIBERSAM

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Faculty of Medicine, University of Barcelona, Spain

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Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.

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Psychiatric Diagnosis and Treatment Service I, Department of Mental Health, ASL Cagliari, Cagliari, Italy f Psychiatric Diagnosis and Treatment Service II, Department of Mental Health, ASL Cagliari, Cagliary, Italy g Head, Department of Mental Health, ASL Cagliari, Cagliari, Italy Medical Center, Cagliari, Italy

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h Genneruxi

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*Requests for reprints should be addressed to:

Sara Siddi, PsyD

Unitat de Recerca i Desenvolupament - Parc Sanitari Sant Joan de Déu Dr. Antoni Pujadas, 42, 08830 - Sant Boi de Llobregat, Barcelona, Spain Tel. +34 93 640-6350 Fax: +34 93 600-9771 E-mail address: [email protected]

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ACCEPTED MANUSCRIPT Highlights Auditory verbal hallucinations are related to a dysfunction of specific cognitive domain.

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Lower ability in set-shifting and semantic fluency distinguished patients with auditory

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verbal hallucinations from those without.

Poorer semantic fluency could be a secondary deficit of set-shifting failure.

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Patients experiencing auditory verbal hallucinations could fail to shift the attention

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away from the voices.

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ACCEPTED MANUSCRIPT The role of set-shifting in auditory verbal hallucinations Abstract

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Background: Auditory verbal hallucinations (AVHs) are a cardinal characteristic of psychosis.

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Recent research on the neuropsychological mechanism of AVHs has focused on source monitoring failure, but a few studies have suggested the involvement of attention, working

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memory, processing speed, verbal learning, memory, and executive functions. In this study we examined the neuropsychological profile of patients with AVHs, assuming that the mechanism

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underlying this symptom could be a dysfunction of specific cognitive domains. Methods: A large neuropsychological battery including set-shifting, working memory, processing speed, attention, fluency, verbal learning and memory, and executive functions was administered to 90 patients with psychotic disorders and 44 healthy controls. The group of

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patients was divided into two groups: 46 patients with AVHs in the current episode and 44 who

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denied auditory hallucinations or other modalities in the current episode. AVHs were assessed with the Psychotic Symptom Rating Scales (PSYRATS); the Launay-Slade Hallucination Scale

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was used to measure long-term propensity to auditory verbal hallucination-like experiences

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(HLEs) in the sample.

Results: Patients showed poorer performances on all neuropsychological measures compared to the healthy controls‘ group. In the original dataset without missing data (n = 58), patients with AVHs (n = 29) presented poorer set shifting and verbal learning, higher levels of visual attention, and marginally significant poorer semantic fluency compared to patients without AVHs (n = 29). In the logistic model on the multiple imputed dataset (n = 90, 100 imputed datasets), lower capacity of set shifting and semantic fluency distinguished patients with AVHs from those without them. Conclusions: Patients experiencing persistent AVHs might fail to shift their attention away from the voices; poorer semantic fluency could be a secondary deficit of set-shifting failure.

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Key words: neuropsychology, auditory verbal hallucinations, transdiagnostic model

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The role of set-shifting in auditory verbal hallucinations

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1. Introduction

Auditory hallucinatory phenomena are auditory perceptions that occur in the absence of corresponding external stimuli. They are often experienced as voices, although they may also take the form of non-verbal sounds (e.g., ringing, whistling, or animal sounds). Traditionally

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associated with psychiatric and neurological diagnoses, they may also be observed in healthy

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individuals [1,2].

Cognitive models have related hallucinations to a specific dysfunction, namely, source

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monitoring failure [3–6]. More specifically, auditory verbal hallucinations (AVHs) have been linked to a deficit in the self-monitoring of language [7–12]. Only a few studies have explored

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the role of specific neuropsychological functions such as attention and set shifting, working memory, verbal learning, processing speed, fluency and other executive functions in the genesis of AVHs. The use of a global hallucination score in previous research might have masked potential specific associations with AVHs. Some studies have shown that auditory hallucinations are associated with impaired attention in patients with psychosis [13,14], and with impaired auditory perception in firstdegree relatives of patients with AVHs [15]. Other studies have shown that auditory hallucinations were positively related to increased attention in patients with psychotic disorders [16–18], and in healthy individuals with auditory hallucinations [19]. Other studies proposed that these patients have a difficulty in shifting attention focus [20]. Hughdahl et al. [14]

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ACCEPTED MANUSCRIPT observed that patients with schizophrenia reported difficulty in the modulation of attention and in the cognitive control of the voices. This may depend upon involuntary and voluntary attention process. Voluntary attention is a top-down process where the individual controls

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his/her attention to external stimuli. When the attention is involuntarily captured by stimuli, for example an inner voice, this is identified as an automatic, bottom-up process [21]. In patients with AVHs, attention was reported to be involuntarily attracted by stimuli with emotional

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prosody, especially the negative tone of voices [22]. Alba-Ferrara et al. [22] suggested a

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neuropsychological model of AVHs, in which the precursor of the AVH originates at the primary auditory cortex. According to their model, deficits in perception and integration of stimuli may lead to emotional prosody comprehension deficits, which would contribute to the formation of AVHs via voice misidentification. These alterations in patients with AVHs are

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linked to other deficits that might occur at the behavioral level. Working memory deficit was observed in patients with psychosis [23,24], in first

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episode of psychosis with AHs [25] and in otherwise healthy individuals who experienced AVHs [26]. Other cognitive functions were found to be compromised in schizophrenia spectrum

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disorders such as processing speed, verbal learning and clinical severity, but the association

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with specific symptoms such as AVHs was not specified [27]; semantic fluency, a proxy of processing speed, was found to be associated with auditory hallucinations [28,29]. So far no clear evidence was found on the links between specific neuropsychological functions and the experience of AVHs. Our objective was to delineate a neuropsychological profile of patients with psychotic disorders and AVHs. We investigated the association between the cognitive functions and AVH across psychotic-spectrum disorder according to the transdiagnostic model of AVH of Waters et al. [20]. This model maintains that cognitive mechanisms could underlie hallucinations regardless of the diagnostic category, or independently of other symptoms associated to schizophrenia-spectrum disorders. We expected the neuropsychological profile of

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ACCEPTED MANUSCRIPT patients with psychotic disorders to be specific of the experience of AVHs. The main predictors of AVHs were selected according to a multivariable logistic regression model, a standard

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procedure for binary classification in machine learning [see 28].

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2. Materials and methods

The competent institutional review boards of the involved institutions approved the

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study protocol in accordance with the guidelines of the 1995 Declaration of Helsinki (as revised in Tokyo in 2004, and further revised in Fortaleza, Brazil, in 2013) [31]. The study was approved on March 28, 2013 by the Department of Psychology of the University of Cagliari,

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Ethics Committee.

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2.1. Participants

One hundred and thirty-four participants were recruited within the study eNCROAcH-

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Neuropsychological CoRrelates Of Auditory Hallucination, aimed at advancing expert

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knowledge about hallucination-like experiences. All participants provided written informed consent after receiving a description of the study. Participation was voluntary and no fee or other compensation was offered. Participants were recruited from 2013 to 2015 and were assessed with the psychosis section of the Composite International Diagnostic Interview Version 3.0 CIDI 3.0)[32].

2.2. Patients Ninety patients with psychosis were in-patients from two psychiatric services of the Department of Mental Health of Cagliari (Italy) and were diagnosed according to DSM-IV-TR criteria. The catchment area of the Psychiatric Services of Diagnosis and Treatment (I and II)

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ACCEPTED MANUSCRIPT caters for about 650,000 people, mainly in urban and suburban areas. Ninety-nine consecutive in-patients attending these psychiatric services over a non-consecutive six-month period were selected and were asked to take part in the study. Five patients refused to participate (2 of them

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after reading the informed consent), and four voluntarily interrupted the evaluation. The best estimate diagnosis at discharge was used. In Italy, clinicians are expected to

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provide a diagnosis when the patient is discharged into the community. This diagnosis is based on longitudinal assessment gathered during the hospital stay, and on all the data available about

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the subject as collected by an expert clinician from key informants (usually, a relative), a review of the medical records, and clinical staff observations as reported in the patient‘s file. This procedure approximates the LEAD (Longitudinal observation by Experts using All Data) proposed by Spitzer [33].

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Inclusion criteria were: being aged between 18 and 65 years, diagnosis of psychosis according to DSM-IV, fluent Italian language, being able to give informed consent. Exclusion

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criteria were: a history of alcohol or substance abuse, neurologic illness, pregnancy during the

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assessment, intellectual disability.

Most participants were right handed (n=85). Patients with psychosis were grouped

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based on their experience of hallucinations in the acute phase as ascertained by direct clinical interview. The first group included 46 patients with AVHs in the current episode (Pat+): 29 with schizophrenia spectrum, 8 with bipolar disorder, and 9 with schizoaffective disorder. Patients in the second group (n=44) denied auditory hallucinations or other modalities in the current episode (Pat-): 27 patients diagnosed with schizophrenia spectrum, 12 with bipolar disorder, and 5 with schizoaffective disorder. Patients were also assessed with the verbal hallucination subscale of the PSYRATS (Psychotic Symptom Rating Scales) [34] to confirm they had not experienced auditory hallucinations in the current episode. Patients in the Pat- group were confirmed to score 0 on the PSYRATS.

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2.3. Healthy controls

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The healthy control group consisted of 44 adults from the Cagliari metropolitan and

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suburban areas. The inclusion criteria were age between 18 and 60 years and fluency in Italian. Controls were enrolled via a snowball procedure [35], a method that avoids the bias of self-

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selection that occurs when recruiters only tap their personal social network [36]. Briefly, the first three participants known to the examiner were asked to invite an acquaintance in their

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social network to take part in the study. The invited participants were asked, in their turn, to invite an acquaintance in their social network to take part in the study, and so on. Overall, 54 individuals were contacted, 44 agreed to be interviewed and tested, 10 individuals declined the

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invitation or withdrew consent after seeing the amount of tests. They were interviewed using the CIDI 3.0 to exclude subjects with current or past

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psychopathology. The additional exclusion criteria were the same as those for patients. Forty of

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2.4. Procedure.

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the healthy participants were right-handed.

Participants were evaluated individually on the clinical rating scales and neuropsychological battery, on the same day or within a couple of days. Interviews and neuropsychological assessment were conducted by a trained neuropsychologist in a private area, away from the distraction of ongoing activities in the unit. Prescribed medications were reported for both groups of patients.

2.4.1. Measure of auditory verbal hallucinations and delusions.

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ACCEPTED MANUSCRIPT The PSYRATS is an instrument designed to quantify positive symptoms in people with psychosis in the preceding two weeks [34]. It has two subscales: auditory verbal hallucinations and delusions. The auditory verbal hallucination subscale consists of 11 items: Frequency,

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Duration, Location, Loudness, Origin, Negativity (Amount/Degree), Distress

(Amount/Intensity), Disruption, and Controllability. Each item of this scale is evaluated on a 5point Likert Scale ranging from 0 to 4. The total score was used to measure the severity of

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verbal hallucinations. Delusions were measured by 4 items of the CIDI, 2 items related to

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bizarre delusions and 2 to paranoid delusions. We converted these delusions types into a dichotomous variable (yes/no) for the purpose of data analysis.

LSHS- Launay Slade Hallucination Scales. The 16-item LSHS-E is a self-report scale

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investigating the multidimensionality of hallucinatory-like experiences (HLEs) in the general population [37–39]. Respondents have to rate each item on a five-point scale: (0) ―certainly

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does not apply to me‖; (1) ―possibly does not apply to me‖; (2) ―unsure‖; (3) ―possibly applies

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to me‖; and (4) ―certainly applies to me‖. The time frame for the answers was the latest five years, and the score measures propensity to experience Hallucination-like experiences. To

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assess long-term propensity to verbal hallucinations we constructed a subscale including the items 4 (―In the past I have had the experience of hearing a person‘s voice and then found that there was no-one there‖), 8 (―I often hear a voice speaking my thoughts aloud‖), and 9 (―I have been troubled by hearing voices in my head‖) of the validated Italian translation of the Larøi et al. revised and extended version of the LSHS [37–39]. The reliability of the LSHS, measured by Cronbach‘s alpha, was 0.93 in the patients‘ sample and 0.79 in the healthy control sample, which is acceptable for group comparisons [40]. The reliability of the subscale assessing longterm propensity to experience verbal hallucinations was 0.75 in the patients‘ sample; all controls scored ‗zero‘ on this subscale, so no reliability could be measured.

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ACCEPTED MANUSCRIPT 2.4.2. Neuropsychological assessment The patients were administered a broad neuropsychological battery. This study focused

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executive functions. We therefore selected the followings tasks:

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on attention, processing speed, working memory, verbal learning and memory, fluency, and

Premorbid IQ: The Italian version of the NART (National Adult Reading Test) [41].

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Participants were asked to read aloud a list of 54 words with regular and irregular

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pronunciation. The sum of pronunciation and accent errors was recorded. Verbal Attention and working memory: The forward and backward digit spans from the Wechsler Adult Intelligence Subscale, 3rd edition (WAIS-III) [42] were used. Digit sequences were presented, beginning with a length of two digits, and two trials were made at each increase

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of list length. The test stopped when the participant failed both trials of a sequence length, or when the maximum list length was reached (9 for digit forward, 8 for digit backward). Digit

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forward was used to measure attention, and digit backward to measure working memory span.

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Visual attention and set-shifting: Trail Making test (TMT). Subjects were required to connect a series of 25 encircled numbers in numerical order as quickly as possible (TMT-A). In the

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second part, they were required to connect 25 encircled numbers and letters in numerical and alphabetical order, alternating the numbers and the letters, as quickly as possible (TMT-B). The time needed to complete these tasks was recorded. TMT-A was used as a measure of visual attention, and TMT-B as a measure of set-shifting. Processing speed: Digit Symbol Substitution subtest (DSST) [42]. This consists of 9 symbols with their corresponding numerical digits. Participants had 90 seconds to associate the numbers with the symbols. The number of digits correctly matched with their symbols was the measure of perceptual-motor speed. Verbal learning and Memory: Rey Auditory Verbal Learning Test (RAVLT)[43]. Participants had to read aloud a list of 15 unrelated words and were asked to recall as many words as

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ACCEPTED MANUSCRIPT possible in any order. This procedure was carried out 5 times consecutively (trials 1 – 5 or acquisition trials), and the score on each trial was the number of correctly recalled words. After an interval of 15 minutes, participants had to recall the words again without listening to the list.

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The tallied measures were:

Short memory: the number of words recalled on the first trial (Rey 1st trial).

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Learning: the total number of words recalled over the five trials (Rey 1-5).

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Delayed free recall: the number of words recalled after an interval of 15 minutes (Rey

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-

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recall).

Fluency: Subjects were required to produce as many words as possible starting with the letter F within 1 min (phonological fluency), and as many names of animals as possible within 1 min (semantic fluency).

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Cognitive flexibility: (Stroop Test) Subjects were instructed to name the ink colour of the printed words as quickly and accurately as possible. The ink color was incongruent with the

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printed name (e.g., the word "red" printed in blue ink instead of red ink) (third trial, CW). The Stroop interference effect was calculated according to the following formula: [(CW-

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(W×C)/(W+C)].

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Frontal assessment battery (FAB): This is a short cognitive and behavioral battery assessing frontal lobe functions [44,45]. Four measures were selected to evaluate non-verbal cognitive functions (the maximum score on each sub-item was 3): o Motor programming (Motor series ‗Luria‘): organization, maintenance, and execution of successive actions (Luria‘s motor series such as ‗fist–palm–edge‘). o Sensitivity to interference (conflicting instructions): subjects must provide an opposite response to the examiner‘s alternating signal, e.g. tapping once when the examiner taps twice. o Inhibitory control (go-no go): the subjects must inhibit a response that was previously

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ACCEPTED MANUSCRIPT given to the same stimulus, e.g. not tapping when the examiner taps twice.

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2.5. Statistics

Some data were missing because subjects were sometimes unable to complete the whole

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battery (see below, multiple imputations).

All data were coded and analyzed using the Statistical Package for Social Sciences (SPSS)

tailed, with alpha set at p <0.05.

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version 20 and dedicated packages available in the R environment [46]. All tests were two-

2.5.1 Descriptive and exploratory analyses

Mean with standard deviation and median was reported for continuous variables.

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Counts and percentage were reported for categorical variables. Differences by groups in

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continuous variables were explored with ANOVA or ANCOVA whenever appropriate. Bonferroni-adjusted significance test was conducted for multiple comparisons. Categorical

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analyses were carried out with the Chi-square, with Yates correction whenever necessary, or with Fisher exact test when n < 5 in any cell. Pearson correlation analysis was performed to

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assess the association among the PSYRATS and LSHS measures. At the first step, we compared patients with (Pat+) or without (Pat-) hallucinations in the current episode and healthy controls on sociodemographic and clinical data in order to adjust potential differences on these measures, and then we compared them on neuropsychological measures, controlling for the resulting potential socio-demographic confounding factor. This first round of analyses was aimed at detecting imbalance between the groups on variables of interest and to confirm that the patients had a worse neuropsychological functioning than the healthy control participants, as expected. A power analysis using the G*Power 3.1 [47,48] suggested that a total sample of 66 subjects would be needed to detect large effects

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ACCEPTED MANUSCRIPT (Cohen‘s f=.40) with 80% power using an F test between means in three groups with alpha at .05. A total sample of 159 people would be needed to detect medium effects (Cohen‘s f=.25)

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with 80% power in the same test.

2.5.2 Inferential analyses

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After proving that the neuropsychological variables discriminated patients from controls, thus resulting to be correlates of the psychotic state, we applied a multivariable logistic

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regression model to the sample of patients with psychosis with completed data on all variables. By taking into account the variables of interest, we had 58 patients out of 90 with complete data across all variables.

The dependent variable in the logistic regression model was group belonging according

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to the experience of auditory verbal hallucinations in the current episode (Pat+ = 1; Pat- = 0). The independent variables (predictors) were the neuropsychological measures, which were

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entered as continuous variables. Since sex, age and educational attainment did not differ

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between Pat+ and Pat-, they were not entered in the regression model. This multivariable logistic regression model was aimed at identifying the variable(s) that contributed to classifying

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the sample by the experience of auditory verbal hallucinations in the current episode. Adjusted odds ratio (OR), with 95% confidence interval (95%CI), and estimated Wald test‘s p were reported for each predictor. The variance explained by the model was estimated on the basis of Tjur‘ s coefficient of discrimination (D), which is the difference in the average of the event probabilities between the groups of observations with observed events and non-events [49]. We also used more traditional pseudo-R2 measures of explained variance in logistic regression, such as the McFadden, the Cox-Snell and the Nagelkerke pseudo-R2 [50]. All these indicators are on a similar scale, ranging from 0 to 1, with higher values reflecting better fit of the model.

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ACCEPTED MANUSCRIPT Fit of the models was also assessed with the le Cessie - van Houwelingen - Copas Hosmer unweighted sum of squares test [51], and the Tukey-Pregibon test [52]. In these tests, the null assumes that the model has a good fit, so p<0.05 (refutation of the null) indicates

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misspecification of the model.

The area under the receiver operating characteristic (ROC) curve (AUC) was used as a

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measure of accuracy of the model in discriminating cases with auditory hallucinations from cases without auditory hallucinations in the current episode. AUC between .80 and .90 are

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considered good; AUC between .70 and .80 are considered fair; AUC between .60 and .70 are considered poor.

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2.5.3. Sensitivity analysis: Multiple imputation using chained equations (MICE) As specified above, sometimes patients were not able to complete the whole battery.

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Missing data were a few by row, and varied from patient to patient (See Table 4).

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To maximize power, we proceeded to a multiple imputation using chained equations (MICE), a very flexible method that can handle variables of varying types (e.g. continuous or

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binary) and complexities such as bounds [53]. The main requirement to apply the MICE procedure is that the missing data are missing completely at random (MCAR), i.e., the probability that a value is missing should depend only on observed values and not on unobserved values [54]. We therefore tested the dataset with Little‘s missing completely at random (MCAR) test. In this test, the null assumes that the missing data in the dataset are MCAR, so p<0.05 (refutation of the null) indicates that data are not MCAR and the MICE procedure should not be applied. The results of Little‘s MCAR test were: 252.40; df = 236; p = 0.221. The result indicates that the null hypothesis of data missing completely at random cannot be

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ACCEPTED MANUSCRIPT rejected, so the missing data can be interpreted as MCAR. In the original sample, age was positively related to the occurrence of missing data, with

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older people‘s being more likely to fail to complete one or more tasks. All other potential

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correlates of missingness, such as sex, education, illness duration, and verbal QI, were not related to the occurrence of missing data.

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We therefore proceeded by creating 100 imputed datasets. In the imputation we included all the variables involved in the logistic model to be applied to the imputed datasets plus age, which

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was related to the occurrence of missing data.

Multiple imputation was carried out with the package ‗mice‘ [55] running in the R statistical environment [46].

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We applied our multiple logistic model, the same that was applied to the original dataset with complete data (n = 58), to the pooled MICE imputed datasets, deriving averaged

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estimations of our model from the 100 independent datasets. Rubin‘s method was used to derive

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pool averaging across all imputed datasets [56]. It should be noted that pool averaging does not allow additional analyses (such as estimation of the explained variance, goodness of fit tests,

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and ROC analysis). To estimate the fit of the model as applied to the MICE imputed datasets we compared several nested models. The first model was the null model (the model without predictors); the second model included all non-statistically significant variables; the third model included all variables of the complete model. The calculation was based on the article by Meng and Rubin [57].

3. Results 3.1. Descriptive measures and comparisons between patients and healthy controls

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ACCEPTED MANUSCRIPT There were no statistically significant differences between the three groups on sociodemographic factors except for education level, with the control group showing higher educational attainment than the two patient groups. Patients with and without AVHs were often

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found to be under the influence of delusions, according to their treating physicians, with no difference. Patients and controls differed on LSHS score, as expected. As we expected, the healthy controls presented better performance on all neuropsychological measures compared to

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the patients‘ groups in the univariate analyses (Table 1).

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A post-hoc analysis (Bonferroni) demonstrated that the patients with current AVHs (Pat+) had higher scores on the LSHS than patients without current AVHs (Pat-) or controls. In particular, the effect size for the difference on the LSHS between the Pat+ and Pat- groups was large according to conventional criteria: Hedges‘ g = 1.06 (95%CI: 0.62 to 1.50). As written

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above, all patients groups differed significantly from the healthy controls on all neuropsychological measures (post-hoc Bonferroni p < 0.01), but no difference among the

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patients groups, except for interference measured by the FAB, was found in the univariate analyses. AVHs measured by PSYRATS and propensity to experience hallucination-like

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experiences (LSHS) were positively interrelated (r = 490; p < 0.0001) in the Pat+ group.

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Pat+ did not differ from Pat- by sex (2 = 0.68, df = 1, p = 0.41), age (t-test = -0.36, df = 88, p = 0.72), or education (t-test = -0.11, df = 88, p = 0.91). Pat+ were not more likely than Pat- to have been prescribed antipsychotics (30 [65%] versus 35 [79%]; 2=1.64,df=1,p=0.20), antidepressants (1 [2%] versus 1 [2%]; Fisher exact test: p = 1.00), or anxiolytics (23 [50%] versus 27 [61%]; 2=0.76,df=1,p=0.38); however, they were more likely to have received the prescription of a mood stabilizer (6 [13%] versus 3 [6%]; Fisher exact test: p = 0.02) (Table 2).

3.2. Regression analyses of the links between neuropsychological performance and auditory verbal hallucinations

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ACCEPTED MANUSCRIPT The results of the logistic model on the original dataset were summarized in Table 3. In the dataset without missing data (n = 58), Pat+ (n = 29) were distinguished by Pat- (n =

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29) by poorer verbal learning (Rey-learning test), poorer set shifting (TMT-B) and higher visual

being Pat+, with adjusted OR = 0.79 (95%CI: 0.60 – 0.99).

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attention (TMT-A). Semantic fluency, too, was marginally (p = 0.064), negatively related to

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The model had a good fit: both the le Cessie et al. test and the Tukey-Pregibon test were congruent with the null, which assumes that the model has a good fit. The explained variance of

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the model varied from 31.8% to 47%, according to the method. Accuracy was fair (0.79; 95%CI: 0.66 – 0.88).

Since a fraction of patients had missing items (>25%), a multiple imputation was applied to

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the variables to get more accurate regressions estimates. The results of the logistic model on the multiple imputed dataset (n = 90, 100 imputed datasets) were summarized in Table 4.

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In this analysis, lower capacity of set shifting and of semantic fluency distinguished patients

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with AVHs (n = 46 in each imputed dataset) from patients without AVHs (n = 44 in each imputed dataset). The results of pooled likelihood ratio test of the model with set shifting and

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semantic fluency, as against a model without them, suggested that the addition of these variables was warranted in explaining the distribution of the target (dependent) variable (being a Pat+ or a Pat-).

4. Discussion In this study we investigated the neuropsychological correlates of AVHs, comparing patients with AVHs and patients without AVHs during the acute phase.

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ACCEPTED MANUSCRIPT We observed that AVHs in the current episode was positively related to long-term propensity to experience hallucinations. Patients without AVHs reported hallucinations less

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frequently in the past five years.

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We found that both groups of patients reported worse performance on neuropsychological measures when compared to healthy controls. In the first analysis without missing data, patients

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with AVHs presented higher levels of visual attention, poorer set shifting, verbal learning, and semantic fluency - although only marginally significant compared to patients without AVHs;

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but we did not find any significant result for working memory, memory and other executive functions. The sensitivity analysis confirms that the lower ability in set-shifting and semantic

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fluency distinguished patients with AVHs from patients without AVHs.

4.1. Comparison with previous literature

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Cognitive deficits represent significant characteristics of schizophrenia spectrum disorders,

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and they persisted also in the absence of medication [58]. Previous studies confirmed that there is a generalized impairment of various neuropsychological functions also in patients with

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affective psychosis and bipolar disorder [59,60]. However, a few studies have explored the role of specific neuropsychological functions in the genesis of AVHs. Our patients with AVHs showed better performance in visual attention compared to other patients without AVHs when they had to pay attention to one kind of stimuli (numbers only), while presented a worse performance when they had to shift their attention to two types of stimuli (numbers and letters). Other studies reported that patients with schizophrenia used an inefficient strategy in the set-shifting task; the analysis of eye tracking and hand movement revealed that they processed numbers and letters sequentially and not in parallel like the healthy subject or patients with unipolar depression [61,62]. This task could also depend on the visuomotor component [63], but both groups of our patients performed equally in other tasks

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ACCEPTED MANUSCRIPT requiring visuo-motor coordination. Deficit in set-shifting has been previously documented in psychotic disorders [64–66] but a few studies investigated this deficit when related to AVHs.

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Hugdahl et al. [14] demonstrated that a high level of AVHs in patients with schizophrenia

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interfered with performance on a dichotic listening task, the ability to keep attention on the stimuli originating from either the right or the left ear. They argued that this could depend on

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involuntary perception of the voices originating in the left temporal plane in the peri-Sylvian region, which is maintained through an interior attention focus, and on failure to suppress these

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internal voices [16]. The involuntary perception could depend on the emotional valence of the voices that captures the attention of patients with AVHs. Indeed, one study observed that the automatic shift of emotional salience of prosodic stimuli attracted the involuntary attention of the patients with AVHs, regardless of their focusing on other stimuli [67].

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Other studies found similar results. A prospective study found that psychotic patients with frequent AVHs had a deficit in the dichotic listening task that persisted after 3 months, while

PT

this did not happen in patients with non-frequent AVHs [68]. Another study found

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abnormalities in the same task in patients with schizophrenia and with ongoing auditory hallucinations or only a history of auditory hallucinations [69]. Løberg et al. [69] suggested that

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the difficulty in attention modulation could be caused by the abnormal functional brain asymmetry of patients with auditory hallucinations. Similar findings were observed in other studies in patients with positive symptoms [70]. Contrarily, Gisselgård et al. [25] did not find any relationship between AVHs and set-shifting in patients with the first episode of psychosis. Another study observed that poor performance in set-shifting was related to negative symptoms in patients with first episode of psychosis [71]. We observed that our patients with AVHs presented difficulty in verbal learning compared to other patients without AVHs. The deficit observed in verbal learning may be due to inability in retaining new information caused by a set-shifting deficit [72]. Verbal learning was found to

19

ACCEPTED MANUSCRIPT be affected in other studies with schizophrenia patients [73,74], and people at risk of psychosis [75,76], although these studies did not investigate the association with AVHs.

T

We observed deficits in semantic and phonetic fluency in both patients groups, but patients

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with AVHs showed poorer performance in semantic fluency compared to patients without AVHs. The semantic fluency task required the production of words from within a particular

SC

semantic category and the ability to shift attention efficiently to a new subcategory once the previous category is exhausted. For example several subcategories can be formed within the

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category ―animals‖: ―domestic animals‖, ―sea animals‖, ―wild animals‖, etc. A switch would be identified as they move from the ―domestic animals‖ to ―wild animals‖ once the first subcategory has been completed. Phonetic fluency requires generating as many words as possible beginning with a specific letter regardless of the semantic connection. Both kinds of

ED

fluency depend on executive functions [77,78], and semantic fluency needs semantic memory as well [79].

PT

Semantic fluency deficits might be caused by set-shifting deficit in our patients with AVHs,

CE

the inability to switch attention to different subcategories. Consistently with our findings, a previous study [28] found that semantic fluency impairments were related to hallucinations in

AC

75 patients with psychotic disorders; the kind of hallucinations was not specified. De Freitas et al. suggested that a breakdown in the semantic network leads to a more loosely associated inner speech. Other studies found that increased semantic fluency was related to hallucinations in patients with schizophrenia [29], and no deficit in semantic fluency was observed in healthy individuals with AVHs [26] compared to their controls. We did not observe deficit in working memory and other executive functions. One possible interpretation could be that these deficits were a consequence of set-shifting deficit, or other clinical symptoms might interfere in their functioning. Contrary to our finding, other studies observed deficits in verbal working memory in patients [25] with AVHs. Problems with verbal working memory were also described in otherwise healthy individuals [26] reporting AVHs.

20

ACCEPTED MANUSCRIPT However, the sensitivity analysis confirmed that only the set-shifting and semantic fluency

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deficits were impaired in patients with AVHs.

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4.2. Conclusion and limitations of the study

SC

In conclusion, our findings showed that a specific set-shifting mechanism might underlie the AVHs [20,80]. The semantic fluency deficit observed in this study could be secondary to the

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set-shifting deficit.

There are several limitations in this study; the main limitation is the small size of the sample. In addition, the clinical symptoms other than auditory verbal hallucinations (e.g., thought disorganization) were not assessed, and therefore we were not able to demonstrate the

ED

specificity of the observed associations with verbal hallucinations.

PT

Despite these limitations, the relationship between AVHs and set shifting deficit may be a mechanism underlying the AVHs. Cognitive behavior therapy [81] and mindfulness-based

CE

interventions [82] could be a benefit for patients with AVHs. These therapies might help focus on changing the way the patient attends to and controls their voices. Future studies should

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determine the suitability of this task for use in neuroimaging studies by examining the stability of these deficits across time, and the specificity of lower performance in patients with AVHs.

Acknowledgments The authors wish to thank all the specialists of the eNCROAcH team for their time and effort: Antonella Baita, Tommaso Brundu, Caterina Burrai, Liliana Campus, Monica Caula, Marisa Coni, Maria Luisa La Croce, Carlo Lanzano, Sergio Massa, Domenico Mazzella, Marco Murtas, Andrea Pibiri, Paolo Pili, Gloria Piras, Serafino Pusceddu, Luciana Scamonatti, Ambra Secchi, Alfonso Spagnesi, Pierfranco Trincas, Emanuela Trogu and Antonio Tronci.

21

ACCEPTED MANUSCRIPT SS and DRP designed the study and wrote the protocol. SS managed the literature search and undertook the statistical analyses. She wrote the first draft

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and the final manuscript.

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AP was the guarantor of the work: he had full responsibility for the work and the conduction of the study, had access to the data, and controlled the decision to publish. All authors contributed

SC

to the organization of the study, to data collection and analysis, and they all approved the

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submission.

Role of the funding source

Dr S. Siddi was the recipient of a grant from the Regione Sardegna, Italy (Grant no. PRRMAB-

ED

A2011-19251). The Regione Sardegna had no further role in study design; in the collection,

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paper for publication.

PT

analysis and interpretation of data; in the writing of the report; and in the decision to submit the

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Conflict of interest

The authors report no conflicts of interest.

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ACCEPTED MANUSCRIPT

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Table 1. Baseline characteristics of patients and healthy controls and comparison by neuropsychological measure. All data are mean and SD. Pat- (N=44)

Age (Mean/SD)

37.24

36.34

Sex (Female, N)

12

Education (years Mean/SD)

11.15

3.39

11.11

3.76

15.05

Illness duration (years M/SD)

9.15

9.9

7.16

8.63

----

Delusions (yes)

39 (85%)

36 (82%)

PSYRATS (total)

26.63

9.04

0.0

LSHS (tot)

33.20

17.94

16.7

Neuropsychological measures

Mean

SD

Median

Premorbid IQ

101

8.29

102

Verbal Attention (Digit Forward)

5.13

.86

5

Working memory (Digit Backward)

3.13

.84

Short-memory (Rey 1trial)

3.30

Verbal Learning (Rey 1-5 trials) Delayed Recall (Rey recall after

11.04

Healthy subjects (N=44)

CR I

Pat + (N=46)

12.42

33.77

F=1.18,

p=.103

3.99

F=17.71, df=2,

p<.001

----

t=1.015, df=88

p=.313

MA

x2=0.01,df=1,p=0.924

TE

D

0.0

12.24

---

----

10.6

8.06

F=33,66, df=2, p<0.0001

SD

Median Mean

SD

Median

99

9.17

100

107

4.32

108

F= 3.54, df=2; 133

p<0.05

5.35

1.08

5

6.34

.834

6

F=11.29 df=2; 133

p<0.001

3

3.44

.88

3

4.70

1.15

5

F=21.24 df=2;131

p<0.001

1.71

3

3.84

1.73

4

5.61

1.67

6

F=12.70 df=2;131

p<.001

27.3

11.46

26

31.43

11.76

32

47.70

8.95

48

F=26.95 df=2;133

p<.001

4.6

3.05

5

5.55

3.64

5

10.66

2.74

10

F=29.67 df=2;133

p<.001

AC

CE P

Mean

df= 2, p=.311

X2=4.54, df=2,

21

NU S

16

9.55

Statistics

29

ACCEPTED MANUSCRIPT

5.41

15

F=23.79 df=2;129

p<.001

23.81

5.46

22

F=35.99 df=2;127

p<.001

79.34

22.52 75

F=24.82 df=2;111

p<.001

46

31.36

11.5

32

F=9.37

df=2;111

p<.001

126.5

119

70.23

54.02 58

F=5.49

df=2; 125

p<.005

MA

9.27

3.76

9

9.29

3.680

9

Semantic fluency (Animals)

13.65

4.19

14

15.46

4.38

15

Cognitive flexibility (Stroop)

166.8

56.8

155

170.52

61.09

166

Visual attention (TMT-A)

70.7

40.3

59

56.9

34.14

Set shifting (TMT-B)

147.23 79.2

140

164.4

Processing speed (DSST)

20.5

10.4

19

23.3

23

51.50

11.9

53

65.62

df=2; 114

p<.001

Motor programming

1.70

0.978

1

2.10

.88

2

2.64

.75

3

9.59

df=2;128

p<.001

Sensitivity to interference

2.05

1.03

2

2.50

.90

3

2.98

.15

3

11.04

df=2;127

p<.001

Inhibitory to control

1.95

1.011

2

2.28

.96

3

2.95

.21

3

13.93

df=2;127

p<.001

NU S

11.5

AC

D

Fab subtest

CE P

CR I

16.3

TE

Phonological fluency

PT

15 min)

30

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35

Antidepressants

1

1

Anxiolytics

23

27

Mood stabilizer

6

3

CR I

30

NU S

Antipsychotics

MA

N = 29

D

N = 29

TE

Pat-

CE P

Pat+

AC

Kind of Drug

PT

Table 2. Number of drugs divided by medication type.

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ACCEPTED MANUSCRIPT

Wald’s p

4.14 0.04

0.99 (0.90 – 1.07)

0.82

-0.06

0.07

0.94 (0.80 – 1.08)

0.41

0.71

0.39

2.04 (0.99 – 4.84)

0.068

-0.17

0.08

0.83 (0.69 – 0.97)

0.036

0.28

0.21

1.32 (0.90 – 2.06)

0.17

0.19

0.15

1.21 (0.92 – 1.67)

0.19

-0.22

0.12

0.79 (0.60 – 0.99)

0.064

-0.0005

0.007

0.99 (0.98 – 1.01)

0.94

0.04

0.02

1.04 (1.01– 1.11)

0.045

Set shifting (TMT-B)

-0.02

0.009

0.97 (0.95 – 0.99)

0.023

Processing speed (DSST)

0.02

0.04

1.02 (0.94 – 1.11)

0.62

Fab subtest - Motor programming

-0.10

0.42

0.90 (0.38 – 2.14)

0.81

Fab subtest - Sensitivity to interference

-1.34

0.74

0.26 (0.04 – 0.94)

0.07

Beta

Constant

7.92

Verbal Attention (Digit Forward)

-0.01

Working memory (Digit Backward)

MA

Predictors

D

Short-memory (Rey 1trial)

CE P

Delayed Recall (Rey recall after 15 min) Phonetic fluency

AC

Semantic fluency (Animals)

Cognitive flexibility (Interference Stroop) Visual attention (TMT-A)

TE

Verbal Learning (Rey 1-5 trials)

CR I

Adj. OR (95%)

NU S

PT

Table 3. Predictors of Auditory Verbal Hallucinations (AVHs) group membership in the original dataset without missing data (n = 58)

SE Beta

32

ACCEPTED MANUSCRIPT

-0.15

0.44

Likelihood ratio test

25.58

df = 14

0.79 (0.66 – 0.88)

0.86 (0.35 – 2.14)

0.73

p = 0.029

CR I

Accuracy

PT

Fab subtest - Inhibitory control

NU S

Explained variance McFadden’s pseudo-R2

31.8%

Nagelkerke pseudo-R2

47.5%

MA

Cox-Snell’s pseudo-R2

35.6%

Tjur’s D

TE

D

36.9%

CE P

Global fit

Le Cessie-Van Houwelingen-Copas-Hosmer test

z = 0.16, p = 0.86

AC

Tukey-Pregibon test Hat2

z = -1.84, p = 0.065

Target: Patients with AVHs

N = 29

50%

Patients without AVHs

N = 29

50%

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SE Beta

Adj. OR (95%)

d.f.

p

0.95 (0.89 – 1.02)

13563.74

0.15

2

8.5%

0.94 (0.84 – 1.05)

23930.43

0.28

2

6.4%

1.01 (0.63 – 1.62)

11606.06

0.95

0

9.2%

CR I

Beta

N° missing

f.m.i.

5.66

2.81

Verbal Attention (Digit Forward)

-0.04

0.03

Working memory (Digit Backward)

-0.06

0.05

Short memory (Rey 1trial)

0.011

0.24

Verbal Learning (Rey 1-5 trials)

-0.06

0.05

0.93 (0.84 – 1.04)

17081.23

0.22

0

7.6%

Delayed Recall (Rey recall after 15 min)

0.25

0.16

1.29 (0.93 – 1.78)

24338.64

0.13

0

6.4%

Phonetic fluency

0.17

0.10

1.18 (0.95 – 1.45)

7584.75

0.11

4

11.4%

-0.19

0.09

0.82 (0.69 – 0.98)

9593.03

0.036

6

10.1%

-0.006

0.007

0.99 (0.98 – 1.01)

790.18

0.42

21

35.5%

0.02

0.01

1.02 (0.99– 1.05)

1931.22

0.08

8

22.7%

Set shifting (TMT-B)

-0.01

0.005

0.98 (0.97 – 0.99)

2274.21

0.040

19

20.9%

Processing speed (DSST)

-0.01

0.03

0.99 (0.92 – 1.05)

20557.71

0.68

3

6.9%

Fab subtest - Motor programming

-0.11

0.34

0.89 (0.46 – 1.73)

15604.73

0.74

6

7.9%

Fab subtest - Sensitivity to interference

-0.02

0.37

0.93 (0.47 – 2.03)

3111.34

0.96

6

17.9%

Visual attention (TMT-A)

MA

D

CE P

Interference Stroop

AC

Semantic fluency (Animals)

NU S

Constant

TE

Predictors

PT

Table 4. Predictors of Auditory Verbal Hallucinations (AVHs) group membership in the multiple imputed dataset (n = 90, 100 imputed datasets)

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ACCEPTED MANUSCRIPT

-0.02

0.35

0.97 (0.49 – 1.93)

Pooled Likelihood ratio test of the model without Set shifting and Semantic fluency

0.87

d.f.1=12

d.f.2=11924.48

Pooled Likelihood ratio test of the model with Set shifting and Semantic fluency

3.94

d.f.1=2

d.f.2=2317.89

3325.76

0.94

8

17.3%

p = 0.575

p = 0.020

NU S

CR I

PT

Fab subtest - Inhibitory control

N = 46

51%

Patients without AVHs

N = 44

49%

AC

f.m.i. = Fraction of missing information due to nonresponse

CE P

d.f. = degrees of freedom in the pooled summary of the model

TE

D

Patients with AVHs

MA

Target:

35