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. 2025 Aug 26;2025(1):niaf029. doi: 10.1093/nc/niaf029

Disruption of consciousness depends on insight in obsessive-compulsive disorder and on positive symptoms in schizophrenia

Selim Tumkaya 1,2, Bengü Yücens 3, Muhammet Gündüz 4, Maxime Maheu 5,6, Lucie Berkovitch 7,8,9,✉
PMCID: PMC12378575  PMID: 40873443

Abstract

Disruption of conscious access contributes to the advent of psychotic symptoms in schizophrenia and could also explain lack of insight in other psychiatric disorders. In this study, we explored how insight and psychotic symptoms related to disruption of consciousness in obsessive-compulsive disorder (OCD) and schizophrenia, respectively. Patients with schizophrenia, and patients with OCD with good versus poor insight and matched controls underwent clinical assessments and performed a visual masking task. We used a principal component analysis to reduce symptom dimensionality. We found that clinical dimensions could be isolated by principal components that correlated with consciousness measures. More specifically, positive symptoms were associated with impaired conscious access in patients with schizophrenia, whereas the level of insight delineated two subtypes of OCD patients, those with poor insight who had consciousness impairments similar to patients with schizophrenia, and those with good insight who resemble healthy controls. Our study provides new insights about consciousness disruption in psychiatric disorders, showing that it relates to positive symptoms in schizophrenia and with insight in OCD. In OCD, it revealed a distinct subgroup sharing neuropathological features with schizophrenia. Our findings refine the mapping between symptoms and cognition and confirm that consciousness disruption can be observed in various psychiatric disorders.

Keywords: consciousness, obsessive-compulsive disorder, schizophrenia, insight, visual masking, cognitive neuroscience

Introduction

Consciousness can be defined as the ability to generate and maintain mental representations (Baars 2002). In psychiatry, patients often suffer from the content of their thoughts, which can be painful, out of control, or inappropriately updated and adjusted to the context and the external world (Wang and Krystal 2014; Gilbert et al. 2022). In this perspective, understanding how people with psychiatric disorders consciously access information is a crucial topic to apprehend their psychopathology.

Across various experimental paradigms, patients with schizophrenia were shown to have an impaired conscious access (for a review, see Berkovitch et al. 2017). They need, for instance, a longer delay to consciously perceive an external stimulus compared with healthy participants (Del Cul et al. 2006). Importantly, a disruption of conscious access may be associated with psychotic symptoms not only in schizophrenia, but also in other nosographical categories. For example, we showed in a recent study (Berkovitch et al. 2021) that bipolar patients with a history of psychotic symptoms had an elevated consciousness threshold, similar to that observed in schizophrenia. Interestingly, in this study, a mediation analysis suggested that conscious access disruption may be a cause rather than a consequence of psychotic features. However, the relationship between psychotic symptoms and consciousness disruption remains underexplored and most studies used global clinical scores that do not distinguish between clinical dimensions (Del Cul et al. 2006; Charles et al. 2017; Berkovitch et al. 2018, 2021).

Strikingly, other disorders, such as multiple sclerosis or frontal dysfunction, are also associated with an elevated consciousness threshold (Reuter et al. 2007, 2009; Del Cul et al. 2009), while they are not characterized by psychotic symptoms. Instead, they are associated with cognitive impairments that are also present in schizophrenia, so that conscious access impairments could in fact be linked to a broader range of symptoms and not specific to psychosis.

To determine whether conscious disruption is associated with psychotic symptoms or with other, possibly broader symptomatic dimensions, we investigated conscious access in patients with obsessive-compulsive disorder (OCD). Indeed, OCD is characterized by obsessive thoughts and compulsions, which cannot be controlled by the subject. These symptoms can be triggered by external stimuli, suggesting inability to filter information entering consciousness (Stern et al. 2017). Furthermore, pervasive doubt has been conceptualized as a difficulty in integrating external information susceptible to dampen obsessions (Amir and Kozak 2002; Rotge et al. 2015; Fradkin et al. 2020). Although OCD is classically characterized by ‘egodystonia,’ i.e. the ability to identify obsessional thoughts as pathological, some OCD patients instead exhibit poor insight. Interestingly, lack of insight, also termed in extreme cases anosognosia, has been related to impaired conscious representations and impaired self-awareness, notably in hemiplegia and schizophrenia (Schacter 1990; Amador and David 2004; Vuilleumier 2004; Fotopoulou et al. 2010; Prigatano 2014). It could therefore be associated with conscious access deficits. In OCD, lack of insight has been related to specific cognitive dysfunctions, some of which are shared with schizophrenia (Tumkaya et al. 2009). However, whether patients with OCD present an impaired conscious access similar to patients with schizophrenia has never been tested, and it remains unknown whether such an impairment could relate to poor insight.

In the current study, we explore how conscious access and processing vary according to psychotic symptoms in patients with schizophrenia on the one hand, and with various symptoms including the level of insight in patients with OCD on the other hand. Clinical scales, such as the Over Valued Ideas Scale (OVIS), can be used to measure insight in patients with OCD (Neziroglu et al. 1999, 2004; Kitis et al. 2007; Tumkaya et al. 2009, 2012; Karadag et al. 2011). We expected that patients with OCD and poor insight (i.e. high OVIS scores) would have an impaired conscious access close to that of patients with schizophrenia, whereas conscious access in OCD patients with good insight (i.e. low OVIS scores) would resemble that of healthy controls. To test this hypothesis, we recruited patients with OCD, with various levels of insight (as measured by the OVIS), and patients with schizophrenia and healthy controls, who all performed a visual masking task that allows quantification of conscious and nonconscious processing in controlled lab settings. We defined conscious thresholds as the sensory evidence level associated with conscious perception in 50% of cases; and we measured thresholds separately for objective (i.e. correct identification) and subjective (i.e. visibility rating) reports. We also explored the relationships between these two measures. Indeed, visibility ratings and discrimination performances are strongly correlated in healthy volunteers (Sergent and Dehaene 2004; Del Cul et al. 2006; Sergent et al. 2021). Nevertheless, dissociation between objective performances and subjective reports have been observed, notably in ‘blindsight’ patients who deny seeing a stimulus that they can still accurately discriminate in a force-choice task (Stoerig and Cowey 2007). Conversely, reporting a stimulus as being visible without being able to categorize it could reflect a lack of insight about one’s cognitive processing and be associated with specific psychiatric symptoms.

In addition to consciousness measures, patients were submitted to relevant clinical scales (e.g. Yale-Brown Obsession Compulsion Scale, Y-BOCS, the Dimensional Obsession Compulsion Scale, DOCS, on top of OVIS for OCD and Scale for the Assessment for Positive Symptoms, SAPS, Scale for the Assessment for Negative Symptoms, SANS, for schizophrenia) and we used a principal component analysis (PCA) to reduce symptom dimensionality. By doing so, we could explore how symptomatology axes related to consciousness measures as derived from the visual masking task.

We found that meaningful clinical dimensions identified by PCA-decomposition of clinical scales predicted impairments of consciousness in patients. More specifically, positive symptoms were associated with impaired conscious access in patients with schizophrenia, whereas the level of insight delineated two different subtypes of patients with OCD, those with poor insight who had disruption of consciousness similar to patients with schizophrenia and those with good insight who were comparable with healthy controls.

Methods and material

Participants

We recruited 60 people: 20 patients with OCD, 20 patients with psychosis (schizophrenia or schizoaffective disorder) according to DSM-5 criteria from Pamukkale University Faculty of Medicine, Psychiatry Hospital Polyclinics, and 20 healthy volunteers who were free from any psychiatric or neurological disease and matched to patients in terms of age, gender, and education. OCD symptom was an exclusion criterion for the psychosis group and psychotic symptom was an exclusion criterion for the OCD group (see Supplementary Methods for the detail of inclusion and exclusion criteria). For statistical analyses, the OCD group was split into two subgroups with good and poor insight according to their OVIS scores (good insight if OVIS was <6 and poor insight if OVIS equaled or exceeded 6) (Neziroglu et al. 1999, 2004; Kitis et al. 2007; Tumkaya et al. 2009, 2012; Karadag et al. 2011). Descriptive statistics confirmed that the four groups were comparable in terms of gender, age, and education levels (see Table 1 for participants’ characteristics) and the two OCD subgroups did not show any statistical difference apart from the OVIS scores (see Supplementary Table 1). All participants gave informed written consent. The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008 and were approved by Pamukkale University Clinical Research Ethics Committee (decision n°60 116 787-020/49020). Some participants had to be fully or partly excluded from the analysis because they were outliers (see Supplementary Methods).

Table 1.

Participants’ characteristics

Characteristic Controls,
N = 19a 		OCD with good insight,
N = 10a 		OCD with poor insight,
N = 9a 		Schizophrenia,
N = 17a 		P-value b
Gender (F/M) 11/8 5/5 7/2 8/9 0.5
Age (years) 34.05 (1.86) 28.80 (2.82) 35.78 (3.17) 38.00 (2.69) 0.12
Smoking 7 (37%) 1 (10%) 0 (0%) 5 (29%) 0.11
Education (years) 14.00 (0.56) 14.80 (0.94) 12.22 (0.97) 12.82 (0.70) 0.14
Number of hospitalizations – 0.00 (0.00) 0.44 (0.34) 3.35 (0.84) 0.002c,d
Duration of the disease (years) – 12.10 (2.76) 16.00 (2.96) 14.12 (2.58) 0.7c,d
Fluoxetine equivalent (mg/day) – 25.29 (4.99) 45.08 (9.12) 12.73 (6.67) 0.013c,d
Olanzapine equivalent (mg/day) – 0.00 (0.00) 0.00 (0.00) 23.30 (3.52) <0.001c
Y-BOCS (total score) – 17.50 (2.45) 22.56 (1.83) – 0.12
DOCS (total score) – 22.20 (4.27) 29.22 (4.13) – 0.3
HAM-D (total score) – 4.10 (0.86) 9.00 (3.02) – 0.12
HAM-A (total score) – 7.50 (1.73) 9.56 (3.60) – 0.6
OVIS (total score) – 4.18 (0.27) 6.47 (0.13) – <0.001
SAPS (total score) – – – 9.00 (2.58) –
SANS (total score) – – – 29.71 (5.03) –
Calgary Depression Scale (total score) – – – 3.29 (0.62) –
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a

n (%); mean (sem)

b

Pearson’s Chi-squared test; one-way ANOVA

c

P-values calculated only between the three groups of patients

d

P-values did not significantly differ between the two OCD subgroups (see Supplementary Table 1)

Clinical assessments

In the OCD group, we used the Y-BOCS (Goodman et al. 1989), the DOCS (Abramowitz et al. 2010), the Hamilton Depression and Anxiety Rating Scales (HAM-D and HAM-A) (Hamilton 1959, 1960), and the OVIS (Neziroglu et al. 1999), which was used post hoc to split the OCD group into two subgroups for the analysis: OCD with good insight (OVIS < 6) and OCD with poor insight (OVIS ≥6) (Neziroglu et al. 1999, 2004; Kitis et al. 2007; Tumkaya et al. 2009, 2012; Karadag et al. 2011). In the psychosis group, we used the SAPS, the SANS (Andreasen 1990), and the Calgary Depression Scale (CDS) (Addington et al. 1990). Treatment doses were converted to olanzapine (Leucht et al. 2016) and fluoxetine equivalents (Hayasaka et al. 2015). To assess the clinical profile of our sample of patients, we run a PCA on all items from all clinical scales, separately for the schizophrenia and OCD group using the same approach as previous studies (Ji et al. 2021; Moujaes et al. 2022), which capture data-driven symptomatic dimensions (see Supplementary Methods).

Visual backward masking paradigm

Participants performed a visual masking task similar to previous studies (Del Cul et al. 2006; Berkovitch et al. 2018, 2021), where a target digit masked by a group of letters had to be compared with 5. Target digit visibility was parametrically manipulated with eight target–mask delays (from 16.7 to 166.7 ms) (Fig. 1). The longer the delay, the easier the digit is to perceive. For each participant, we measured the accuracy in comparing the target against 5 (discrimination) and the rate of seen trials among non-catch trials (visibility). An objective measure of conscious access was computed by confronting subjective visibility (seen versus not seen) against the presence or absence of a target (target versus catch trials where no target was presented), resulting in a d-prime measure (detection).

Figure 1.

Figure 1

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Visual backward masking paradigm: a fixation cross was displayed in the center of the screen during ~1 s (randomly jittered across trials). A target digit (2, 3, 7, or 8) was then presented for a fixed duration of 16.7 ms at a random position among four (1.4 degrees above or below and 1.4 degrees right or left of the fixation cross). In 20% of the trials, the target digit was replaced by a blank of the same duration (catch trials) to measure participants’ ability to detect the presence of the digit. After a variable delay (stimulus onset asynchrony), a metacontrast mask appeared at the target location for 200 ms. The mask was composed of four letters (two horizontally aligned M and two vertically aligned E) surrounding the target stimulus location without superimposing or touching it. Target digit visibility was parametrically manipulated by using eight possible target–mask delays (16.7, 33.3, 50, 66.7, 83.3, 100, 116.7, or 166.7 ms) that were randomly intermixed across trials. Participants had at most 10 s to determine whether the number was smaller or greater than 5, by pressing ‘S’ or ‘L’, respectively, on the keyboard. Then, they had to rate the subjective visibility of the digit. The response words ‘seen’ and ‘unseen’ randomly appeared on the right and the left sides of the fixation cross and participants responded by pressing the button (‘S’ or ‘L’) corresponding to the side of the response they wanted to select. The two alternatives remained on screen until a response was made. Each participant performed a training block of 20 trials, followed by two blocks of 160 trials each, separated by a break. Our experiment was coded with MATLAB and Psychtoolbox (Brainard 1997; Kleiner et al. 2007).

Conscious trials were defined as trials where digits were both rated as ‘seen’ and correctly categorized. Discrimination and visibility usually increase nonlinearly with the target–mask delay following a sigmoid curve (Del Cul et al. 2006; Sergent et al. 2021), which we fitted using a logistic model, allowing to measure thresholds for consciousness measures (target–mask delays for which half of the trials were perceived above chance) and the integrity of conscious and unconscious processing (upper and lower asymptotes) (see Supplementary Methods).

To compute a corrected consciousness threshold, independent from alterations in conscious processing, we run a second analysis after scaling each participant’s data according to their upper asymptote. To assess the alignment between visibility verbal report and objective performances, we measured the difference in discrimination accuracy between seen and unseen trials. Finally, we analyzed reaction times to compare speed/accuracy trade-off between groups.

Statistical analyses

Statistical analyses were performed using the R statistical software (https://www.r-project.org) and the BayesFactor package (Morey et al. 2015) and are detailed in the Supplementary Methods. Bootstrapping and the creation of null distributions by random permutations were performed using custom-written MATLAB scripts.

Results

Impaired discrimination and visibility in schizophrenia and OCD with poor insight

In line with previous studies, we found that patients with schizophrenia had lower discrimination accuracy (F1,32 = 14.75, P < .001, Cohen f = 0.68) and visibility (F1,34 = 11.88, P = .002, Cohen f = 0.59) than healthy controls (see Fig. 2A). Similarly, and consistently with our main working hypothesis, patients with OCD and poor insight had a lower discrimination accuracy than healthy controls (F1,24 = 11.51, P = .002, Cohen f = 0.69), whereas patients with OCD and good insight had a discrimination accuracy similar to that of healthy controls (F1,24 = 0.27, P = .61, 1/BF = 6.1). This result was also observed for visibility and detection d-primes (Table 2). Crucially, there was no significant difference in all consciousness-related measures considered between patients with schizophrenia and patients with OCD and poor insight (all 1/BF > 4).

Figure 2.

Figure 2

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Impairments of conscious perception across groups: A. Left, discrimination accuracy (i.e. comparing the masked digit with 5); middle, visibility (i.e. proportion of seen trials) for target (thick lines) and catch trials (thin lines) separately; right, proportion of conscious trials (i.e. trials seen and correctly compared with 5); all as a function of target–mask delays for patients with OCD and good insight, patients with OCD and poor insight, patients with schizophrenia and healthy controls. The inset in the middle plane depicts detection d-prime, which is the ability of visibility ratings to distinguish between digit and catch trials. The thick lines correspond to fit of the logistic model. The error bars represent standard errors of the mean. B. Left, schematic of the logistic model and its different free parameters. The consciousness threshold is determined by finding the target–mask delay for which 50% of the trials are consciously perceived (i.e. accurate and seen). Middle, model parameters (i.e. upper asymptote, inflection point, lower asymptote, and slope) for each participant (dots) in each group. The horizontal lines represent the group averages. Overall, patients with OCD and good insight have similar parameter values as healthy controls, whereas patients with OCD and poor insight have similar parameter values as patients with schizophrenia. Right, consciousness threshold measured on fitted logistic models in the different groups. C. Left, the logistic model was simulated with different upper asymptote values (between 50 and 100%, with 5% increments), all other parameters being held constant. Measured consciousness threshold (i.e. duration, in milliseconds, for which the logistic model equals 50%) decreases with increasing values of the upper asymptote. Middle, proportion of conscious trials (as in panel A) before (dotted thin line) and after (thick line) correcting for non-one upper asymptotes on a participant basis. Right, consciousness threshold measured before (dotted lines) and after (thick lines) setting the upper asymptote to 1 in all groups. Group differences survive the correction for non-one upper asymptotes.

Table 2.

Consciousness measures

Measures F -value P -value Bayes factor
Discrimination
Target–mask delay F 7,336  = 361.91 P < .001 –
Group F 3,48  = 8.66 P < .001 –
Group × target–mask delay F 21,336  = 5.11 P < .001 –
Schizophrenia versus healthy controls
 Group F 1,32  = 14.75 P < .001 –
 Group × target–mask delay F 7,224  = 5.68 P < .001 –
OCD poor insight versus healthy controls
 Group F 1,24  = 11.51 P = .002 –
 Group × target–mask delay F 7,168  = 6.22 P < .001 –
OCD good insight versus healthy controls
 Group F 1,24 = 0.27 P = .61 1/BF = 6.1
 Group × target–mask delay F 7,168 = 1.55 P = .16 1/BF = 5.8
OCD poor insight versus schizophrenia
 Group F 1,24 = 0.10 P = .76 1/BF = 5.9
 Group × target–mask delay F 7,168 = 0.66 P = .71 1/BF = 26.5
OCD good versus poor insight
 Group F 1,16  = 12.50 P = .003 –
 Group × target–mask delay F 7,112  = 10.28 P < .001 –
Visibility for non-catch trials a
Target–mask delay F 7,350  = 580.07 P < .001 –
Group F 3,50  = 5.56 P = .002 –
Group × target–mask delay F 21,350  = 3.91 P < .001 –
Schizophrenia versus healthy controls
 Group F 1,34  = 11.88 P = .002 –
 Group × target–mask delay F 7,238  = 5.2 P < .001 –
OCD poor insight versus healthy controls
 Group F 1,26  = 7.75 P = .010 –
 Group × target–mask delay F 7,182  = 3.59 P = .001 –
OCD good insight versus healthy controls
 Group F 1,26 = 0.16 P = .69 1/BF = 6.5
 Group × target–mask delay F 7,182  = 2.06 P = .049 –
OCD poor insight versus schizophrenia
 Group F 1,24 = 0.076 P = .79 1/BF = 6.1
 Group × target–mask delay F 7,168 = 0.22 P = .98 1/BF = 31.9
OCD good versus poor insight
 Group F 1,16  = 4.80 P = .044 –
 Group × target–mask delay F 7,112  = 5.65 P < .001 –
Detection d − primes
Target–mask delay F 7,350  = 190.15 P < .001 –
Group F 3,50  = 5.66 P = .002 –
Group × target–mask delay F 21,350 = 1.04 P = .41 1/BF = 116.2
Schizophrenia versus healthy controls
 Group F 1,34  = 7.11 P = .012 –
 Group × target–mask delay F 7,238 = 1.16 P = .33 1/BF = 18.9
OCD poor insight versus healthy controls
 Group F 1,26  = 11.04 P = .003 –
 Group × target–mask delay F 7,182 = 0.28 P = .96 1/BF = 31.6
OCD good insight versus healthy controls
 Group F 1,26 = 0.41 P = .53 1/BF = 5.4
 Group × target–mask delay F 7,182 = 0.56 P = .79 1/BF = 23.5
OCD poor insight versus schizophrenia
 Group F 1,24 = 0.42 P = .52 1/BF = 4.9
 Group × target–mask delay F 7,168 = 0.95 P = .47 1/BF = 14.5
OCD good versus poor insight
 Group F 1,16  = 10.86 P = .005 –
 Group × target–mask delay F 7,112 = 0.83 P = .56 1/BF = 12.6
Proportion of conscious trials
Target–mask delay F 7,329  = 607.64 P < .001 –
Group F 3,47  = 9.64 P < .001 –
Group × target–mask delay F 21,329  = 5.43 P < .001 –
Schizophrenia versus healthy controls
 Group F 1,32  = 16.48 P < .001 –
 Group × target–mask delay F 7,224  = 6.77 P < .001 –
OCD poor insight versus healthy controls
 Group F 1,24  = 11.32 P = .003 –
 Group × target–mask delay F 7,168  = 5.27 P < .001 –
OCD good insight versus healthy controls
 Group F 1,23 = 2.51 P = .13 1/BF = 4.8
 Group × target–mask delay F 7,161  = 2.61 P = .014 –
OCD poor insight versus schizophrenia
 Group F 1,24 = 0.22 P = .64 1/BF = 5.6
 Group × target–mask delay F 7,168 = 0.24 P = .98 1/BF = 35.0
OCD good versus poor insight
 Group F 1,15  = 12.27 P = .003 –
 Group × target–mask delay F 7,105  = 10.06 P < .001 –
Difference of discrimination accuracy between seen and unseen trials
Group F 3,47  = 3.78 P = .017 –
Schizophrenia versus healthy controls t31 = −1.65 P = .11 BF = 1.1
OCD poor insight versus healthy controls t 18 = 0.66 P = .52 1/BF = 2.7
OCD good insight versus healthy controls t13 = 1.85 P = .088 BF = 1.4
Schizophrenia versus OCD good insight t 15  = −3.05 P = .008 –
OCD poor insight versus schizophrenia t 20  = 2.10 P = .049 –
OCD good versus poor insight t14 = 1.20 P = .25 1/BF = 1.5
OCD poor insight versus healthy controls t 18 = 0.66 P = .52 1/BF = 2.7
Discrimination reaction times
Target–mask delay F 7,336  = 10.03 P < .001 –
Group F 3,48  = 10.65 P < .001 –
Group × target–mask delay F 21,336  = 1.73 P = .026 –
Schizophrenia versus healthy controls
 Group F 1,32  = 26.17 P < .001 –
 Group × target–mask delay F 7,224 = 1.93 P = .066 1/BF = 23.1
OCD poor insight versus healthy controls
 Group F 1,24 = 2.93 P = .10 BF = 1.1
 Group × target–mask delay F 7,168  = 3.85 P < .001 –
OCD good insight versus healthy controls
 Group F 1,24 = 0.08 P = .78 1/BF = 5.2
 Group × target–mask delay F 7,168 = 0.98 P = .45 1/BF = 31.1
OCD poor insight versus schizophrenia
 Group F 1,24  = 5.92 P = .023 –
 Group × target–mask delay F 7,168 = 0.29 P = .96 1/BF = 34.3
Schizophrenia versus OCD good insight
 Group F 1,24  = 13.56 P = .001 –
 Group × target–mask delay F 7,168 = 1.67 P = .11 1/BF = 35.9
OCD good versus poor insight
 Group F 1,16 = 1.32 P = .27 1/BF = 1.6
 Group × target–mask delay F 7,112  = 3.94 P < .001 –
Visibility reaction times
Target–mask delay F 7,350 = 1.62 P = .13 1/BF = 496.4
Group F 3,50  = 4.70 P = .006 –
Group × target–mask delay F 21,350 = 1.02 P = .43 1/BF = 843.2
Schizophrenia versus healthy controls t 33  = −3.94 P < .001 –
OCD poor insight versus healthy controls t 11 = −0.81 P = .43 BF = 1.9
OCD good insight versus healthy controls t17 = 0.32 P = .76 1/BF = 2.6
Schizophrenia versus OCD good insight t 14  = −3.59 P = .003 –
OCD poor insight versus schizophrenia t10 = 1.23 P = .25 1/BF = 1.2
OCD good versus poor insight t13 = 0.97 P = .35 1/BF = 1.8
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aNo significant difference was observed in the number of false alarms among the catch trials (all P > .1 and 1/BF > 5).

Disruption of conscious access and conscious processing in schizophrenia and obsessive-compulsive disorder with poor insight

The proportion of conscious trials (‘seen and correct’) confirmed results obtained with discrimination and visibility separately, with significant—and similar—impairments in patients with schizophrenia and patients with OCD and poor insight, but not in patients with OCD and good insight (Table 2).

The consciousness threshold obtained from the logistic model (Fig. 2B), i.e. the target–mask delay for which half of the trials were seen and correctly compared with 5 by the participant, reproduced the same pattern of differences between groups (controls: 52.8 ms, OCD with good insight: 47.5 ms, OCD with poor insight: 67.3 ms, schizophrenia: 73.2 ms; F3,47 = 7.61, P < .001, Cohen f = 0.70, Table 3). Both patients with schizophrenia and patients with OCD and poor insight had a significantly elevated consciousness threshold compared with controls (schizophrenia: t21 = 3.65, P = .002, Cohen’s d = 1.25; OCD and poor insight: t10 = 2.27, P = .048, Cohen’s d = 1.18). By contrast, patients with OCD and good insight had a consciousness threshold significantly shorter to that of healthy controls (t22 = −2.15, P = .043, Cohen’s d = −0.76). Moreover, there was no significant difference in consciousness thresholds between patients with OCD and poor insight, and patients with schizophrenia (t18 = −0.64, P = .53, 1/BF = 2.3), whereas they significantly differ from patients with OCD and good insight (t9 = −3.15, P = .012, Cohen’s d = −1.5). In line with our main working hypothesis, these results demonstrate that the threshold for conscious access in OCD patients with poor insight more closely resembles that of patients with schizophrenia rather than that of OCD patients with good insight.

Table 3.

Parameters of logistic model

Measures F -value P -value Bayes factor
Discrimination threshold
Group F 3,48  = 6.98 P < .001 –
Schizophrenia versus healthy controls t 23  = 3.42 P = .002 –
OCD poor insight versus healthy controls t 11  = 2.98 P = .012 –
OCD good versus poor insight t 10  = −3.32 P = .008 –
OCD good insight versus healthy controls t24 = −0.45 P = .66 1/BF = 2.5
OCD poor insight versus schizophrenia t20 = −0.21 P = .84 1/BF = 2.6
Visibility threshold
Group F 3,50  = 5.13 P = .004 –
Schizophrenia versus healthy controls t 24  = 3.17 P = .005 –
OCD poor insight versus healthy controls t10 = 1.82 P = .097 BF = 2.1
OCD good versus poor insight t14 = −2.03 P = .062 BF = 1.6
OCD good insight versus healthy controls t14 = −0.63 P = .54 1/BF = 2.3
OCD poor insight versus schizophrenia t17 = 0.59 P = .56 1/BF = 2.4
Consciousness threshold (seen and correct)
Group F 3,47  = 7.61 P < .001 –
Schizophrenia versus healthy controls t 21  = 3.65 P = .002 –
OCD poor insight versus healthy controls t 10  = 2.27 P = .048 –
OCD good versus poor insight t 9  = −3.15 P = .012 –
OCD good insight versus healthy controls t 22  = −2.15 P = .043 –
OCD poor insight versus schizophrenia t18 = −0.64 P = .53 1/BF = 2.3
Inflection point
Group F 3,47  = 5.70 P = .002 –
Schizophrenia versus healthy controls t 23  = 3.30 P = .003 –
OCD poor insight versus healthy controls t10 = 1.61 P = .14 BF = 1.51
OCD good insight versus healthy controls t23 = −1.81 P = .08 1/BF = 1.25
OCD poor insight versus schizophrenia t 15 = −0.66 P = .52 1/BF = 2.3
OCD good versus poor insight t 9  = −2.39 P = .041 –
Lower asymptote
Group F 3,47  = 0.49 P = .69 1/BF = 6.0
Upper asymptote
Group F 3,47  = 5.33 P = .003 –
Schizophrenia versus healthy controls t 22  = −3.00 P = .006 –
OCD poor insight versus healthy controls t 12  = −2.89 P = .013 –
OCD good insight versus healthy controls t21 = 0.46 P = .65 1/BF = 2.5
OCD poor insight versus schizophrenia t23 = 0.41 P = .69 1/BF = 2.6
OCD good versus poor insight t 11  = 3.23 P = .008 –
Slope
Group F 3,47  = 3.87 P = .015 –
Schizophrenia versus healthy controls t32 = 1.64 P = .11 1/BF = 1.1
OCD poor insight versus healthy controls t 21  = 2.21 P = .038 –
OCD good insight versus healthy controls t22 = −1.50 P = .15 1/BF = 1.5
OCD poor insight versus schizophrenia t21 = −0.64 P = .53 1/BF = 2.4
OCD good versus poor insight t 15  = −3.89 P = .002 –
R 2
Group F 3,47  = 7.87 P < .001 –
Schizophrenia versus healthy controls t 29  = −2.98 P = .006 –
OCD poor insight versus healthy controls t 17  = −3.51 P = .003 –
OCD good insight versus healthy controls t17 = 1.51 P = .15 BF = 1.3
OCD poor insight versus schizophrenia t22 = 0.36 P = .72 1/BF = 2.6
OCD good versus poor insight t 15  = 4.60 P < .001 –
Corrected consciousness threshold (seen and correct)
Group F 3,47  = 6.64 P < .001 –
Schizophrenia versus healthy controls t 23  = 3.37 P = .003 –
OCD poor insight versus healthy controls t10 = 1.94 P = .081 BF = 2.5
OCD good insight versus healthy controls t 22  = −2.10 P = .047 –
OCD poor insight versus schizophrenia t17 = −0.58 P = .57 1/BF = 2.4
OCD good versus poor insight t 9  = −2.91 P = .017 –
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To study whether patients exhibit differences in conscious processing on top of elevated consciousness threshold, we compared the upper asymptotes of the fitted logistic model. We found a significant difference across groups with the same pattern of impairments in patients with schizophrenia or with OCD and poor insight compared with healthy controls (controls: 95.4%, OCD with good insight: 96.3%, OCD with poor insight: 85.9%, schizophrenia: 84.1%; F3,47 = 5.33, P = .003, Cohen f = 0.58, Table 3). By contrast, there was no group difference for the lower asymptotes (F3,47 = 0.49, P = .69, 1/BF = 6.0), which reflect unconscious processing. Altogether, this suggests that poor insight OCD and schizophrenia patients not only have a higher threshold for conscious access but also have impaired conscious processing.

Elevated consciousness threshold exists on top of conscious processing impairments and is not due to shift in speed-accuracy tradeoff

To check whether the elevated consciousness threshold in patients with schizophrenia and OCD with poor insight was due to a decrease of upper asymptote (Fig. 2C), we corrected the data for non-one upper asymptotes on a participant-basis before re-fitting the logistic model. We obtained the same kind of results (controls: 52.1 ms, OCD with good insight: 47.0 ms, OCD with poor insight: 62.8 ms, schizophrenia: 66.5 ms; F3,47 = 6.64, P < .001, Cohen f = 0.65). Patients with schizophrenia still had an elevated consciousness threshold compared with healthy controls (t23 = −3.37, P = .003, Cohen d = 1.15). By contrast, comparison between controls and patients with OCD and poor insight did not reach significance (t10 = 1.94, P = .081, Cohen d = 0.97, BF = 2.52), suggesting that altered conscious processing and elevated consciousness threshold co-exist in schizophrenia and, to a lesser extent, in patients with OCD with poor insight.

The analysis of reaction times confirmed that differences between groups could not be accounted for by shifted speed-accuracy tradeoff. Indeed, patients with schizophrenia and patients with OCD and poor insight were not faster in performing the discrimination and the visibility task than the other groups (patients with schizophrenia were on the contrary significantly slower), whereas OCD patients with good insight did not exhibit longer reaction times that could have explained a lower consciousness threshold as an accuracy-oriented tradeoff (Fig. 3A and Table 2). Interestingly, discrimination reaction times were significantly modulated by the target–mask delay (F7,336 = 10.03, P < .001) with reaction-time variations that were strikingly similar between poor insight OCD and schizophrenia patients, on the one hand, and good insight OCD patients and healthy controls, on the other hand (Fig. 3A).

Figure 3.

Figure 3

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Reaction times and relationships between visibility and discrimination across groups: A. Top, reaction time distributions corresponding to the discrimination and visibility questions. Insets depict the cumulative distributions. The vertical dotted line shows the lower cutoff for exclusion of abnormal reaction times. Bottom, average reaction times corresponding to the discrimination and visibility questions; and discrimination reaction times as a function of target–mask delay. Patients with schizophrenia are significantly slower than any other group for both the discrimination and visibility tasks, thus precluding that differences in performance between groups are due to a shifted speed-accuracy tradeoff. B. Left, discrimination accuracy is shown separately in target trials rated as seen versus unseen. All participants have a discrimination accuracy at chance level for unseen trials and a near-ceiling accuracy for seen trials. Right, sensitivity of visibility ratings to discrimination accuracy is measured by the difference in accuracy in unseen versus seen trials. Patients with schizophrenia have seen versus unseen difference in discrimination accuracy compared with the other groups (healthy controls, patients with OCD and good insight, and with OCD and poor insight). Each dot represents a participant and the horizontal lines represent the group average.

Patients with schizophrenia have more discrepancy between visibility and discrimination than other groups

We then explored to what extent participants’ visibility ratings were predictive of discrimination accuracy in our task as a dissociation between the two has been observed in clinical population and could be associated with a specific range of symptoms (Fig. 3B). To do so, we computed the difference in discrimination accuracy between seen and unseen trials. This measure significantly differed across groups (controls: 44.0%, OCD with good insight: 49.2%, OCD with poor insight: 45.6%, schizophrenia: 40.3%, F3,47 = 3.78, P = .016, Cohen f = 0.49, Table 2). In particular, seen versus unseen difference in discrimination accuracy was lower for patients with schizophrenia compared with patients with OCD (good insight: t15 = −3.05, P = .008, Cohen d = −1.28; poor insight: t20 = −2.10, P = .049, Cohen d = −0.80), suggesting that patients with schizophrenia were specifically impaired for this measure.

Principal component analysis decomposition of clinical scales reveals meaningful clinical dimensions

To reveal clinical dimensions beyond classical nosography as well as to reduce symptom dimensionality, we applied PCA on all items from relevant clinical scales in patient groups (Y-BOCS, OVIS, HAM-A, HAM-D, and DOCS scales in patients with OCD and SANS, SAPS, and CDS in patients with schizophrenia).

In the group of patients with OCD, PCA decomposition of clinical scales yielded three significant principal components (PCs) (Fig. 4A). The first PC, (PC1), represented global psychopathology. PC2 was dominated by OVIS items. PC3 revealed an inverse relationship between contamination and responsibility concerns.

Figure 4.

Figure 4

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Clinical dimensions yielded by PCA: A. In the group of patients with OCD, PCA yielded nine significant PCs accounting for the 55% of the symptoms. In PC1, accounting for 31.4% of the variance, almost all items have positive loadings except one item of the DOCS (avoidance related to unpleasant thoughts), some physical items of the HAM-D (hypochondriasis, loss of weight) and of the HAM-A (cardiovascular and respiratory symptoms). PC2 accounting for 12.9% of the variance was dominated by the OVIS items and also shows high loadings for Y-BOCS, the contamination items of the DOCS and suicide, but negative loadings for the thought items of the DOCS and many items of HAM-A and HAM-D (HAM-A: somatic symptoms, cardiovascular, genitourinary, autonomic symptoms, HAM-D: retardation, insomnia, feeling of guilt). PC3, accounting for 10.9% of the variance, shows an inverse relationship between contamination concerns, on the one hand, and the responsibility concerns (and to a lesser extent, symmetry concerns) on the other hand, as measured by the DOCS. B. In the group of patients with schizophrenia, PCA yielded four significant PC accounting for 66% of the symptoms. In PC1, accounting for 32.4% of the variance, all SANS items have high loadings, as well as several items of the CDS (‘depression,’ ‘morning depression,’ ‘observed depression’). PC2, accounting for 20.2% of the variance, is highly dominated by positive symptoms (all items of hallucinations, all items of thought disorder, except circumstantiality, many delusional items apart from influence syndrome) and few depressive symptoms (pathological guilt and suicide). Regarding negative symptoms, there was a negative loading for several alogia symptoms. PC3, accounting for 11.6% of the variance, has positive and negative loadings both in the positive symptoms and the depression dimension. More specifically, it shows an anticorrelation between, on the one hand, delusion of reference, most of the items related to thought disorder in the SAPS and guilty ideas of reference and self-depreciation in the CDS and, on the other hand, hallucinations (visual, olfactory and voices), delusions (religious, guilt, grandiose), and specific symptoms of depression (suicide, pathological guilt and morning depression). PC4, accounting for 10.0% of the variance, captures mostly bizarre behavior, and a mixture of positive and negative symptoms (religious and grandiose delusions, avolition including hygiene and apathy).

In the group of patients with schizophrenia, PCA yielded four significant PCs (Fig. 4B). PC1 mostly reflected negative symptoms, as well as specific depressive symptoms. PC2 was highly dominated by positive symptoms and few depressive symptoms. PC3 captured the most complex pattern, characterized by positive and negative loadings, both in the positive symptoms and the depression dimensions. PC4 reflected mostly bizarre behavior, and a mixture of delusional and avolition symptoms.

Insight correlates with consciousness threshold in obsessive-compulsive disorder patients

We then run an analysis of variance (ANOVA) to measure how consciousness measures were influenced by clinical variables. We first confirmed the significant interaction between OVIS and consciousness threshold in the OCD group (F1,14 = 9.22, P = .009, Cohen f = 0.81, Fig. 5A). We did not find any other significant interaction (all P > .1), notably no significant interaction between consciousness threshold and fluoxetine equivalent (P = .86, 1/BF = 2.4). Regarding PCs, PC2 significantly interacted with the consciousness threshold (F1,14 = 9.78, P = .007, Cohen f = 0.84; for other PC: P > .8), the upper asymptote, and all other measures of consciousness (all P < .05).

Figure 5.

Figure 5

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Correlation between consciousness threshold and clinical measures: A. In the group of patients with OCD, consciousness thresholds (in ms) significantly correlate with OVIS and PC2 scores (which included insight items). The vertical gray line represents the OVIS threshold used to distinguish patients with good (OVIS < 6) and poor insight (OVIS ≥6). B. In the group of patients with schizophrenia, consciousness thresholds significantly correlate with SAPS, PC2 scores (which included hallucinations and delusions items), and PC3 scores (which includes both positive and depressive symptoms, including delusion of reference, thought disorder, guilty ideas of reference, and self-depreciation). Each dot represents a participant, the lines represent linear regressions, and the shaded areas represent 95% confidence intervals. The bar plots represent null distributions of the correlation coefficient between each clinical measure and consciousness threshold (10 000 permutations), significance thresholds (vertical lines), and the observed correlation value (arrow).

The seen versus unseen difference in discrimination accuracy and discrimination reaction times significantly interacted with Y-BOCS with different patterns between patients with good and poor insight: relationships between visibility ratings and discrimination were significantly modulated by the Y-BOCS in the poor insight group, whereas reaction times were modulated by Y-BOCS in the good insight group (see Supplementary Results and Supplementary Fig. 2A).

Positive symptoms correlate with consciousness threshold in schizophrenia

Running the same ANOVAs in the group of patients with schizophrenia, we found that SAPS scores significantly interacted with consciousness threshold (F1,14 = 6.38, P = .024, Cohen f = 0.68, Fig. 5B) without any interaction with the SANS, the CDS scores (all P > .1), or with olanzapine equivalents (P = .71, 1/BF = 2.3). Regarding PCs, we found that PC2 and PC3 significantly interacted with the consciousness threshold (PC2: F1,14 = 5.35, P = .037, Cohen f = 0.62; PC3: F1,14 = 4.63, P = .049, Cohen f = 0.58; other PCs: P > .4).

Discrimination accuracy significantly interacted with SAPS (F1,14 = 8.00, P = .013, Cohen f = 0.76) and PC2 (F1,14 = 6.18, P = .026, Cohen f = 0.65), whereas discrimination threshold only correlated with SAPS (F1,14 = 5.12, P = .040, Cohen f = 0.61). Discrimination reaction times overall did not have a main significant interaction with clinical measures but this interaction was strongly modulated by target–mask delays. More specifically reaction times were influenced by SANS and PC1 at the shortest target–mask delays, whereas they were modulated by the SAPS and the PC2 at the longest target–mask delays (see Supplementary Results and Supplementary Fig. 2B). Other consciousness measures did not significantly interact with clinical measures (all P > .05).

Discussion

Summary of the results

In this study, we found that conscious access and conscious processing were impaired both in patients with schizophrenia and in patients with OCD who had a poor insight. By contrast, patients with OCD and good insight had a lower consciousness threshold than healthy controls. In patients with schizophrenia, impairments of consciousness were associated with positive symptoms as assessed by the SAPS and relevant PCs. In the OCD group, impairments of consciousness were related to the OVIS scores and the corresponding clinical dimension computed with PCA.

Disruption of conscious access in schizophrenia is linked to positive symptoms

In the schizophrenia group, our results are in line with previous findings, showing that patients with schizophrenia have a disruption of conscious access and a preserved subliminal processing (Berkovitch et al. 2017). Specifically, in the current study, disruption of consciousness was associated with different aspects of positive symptoms. All positive symptoms including PC2 (dominated by delusional items) and PC3 (capturing positive and depressive dimensions) correlated with consciousness thresholds. By contrast, discrimination measures mostly depended on delusional symptoms and thought disorder independently from depressive symptoms (SAPS and PC2 but not PC3).

We also found that patients with schizophrenia had longer reaction times when performing the discrimination task. In particular, negative symptoms tended to slow down responses for short target–mask delays (subliminal processing), whereas positive symptoms were associated with delayed responses for longer target–mask delays. Thus, participants with many positive symptoms may need more time to process conscious information, as an indirect sign of conscious processing impairment. Consistent with this hypothesis, patients with OCD and poor insight—who also exhibit consciousness disruption in our study—appeared to be specifically slowed down for long target–mask delays.

We previously proposed that conscious access disruption enhanced adherence to beliefs disconnected from the reality, which may culminate in delusions (Berkovitch et al. 2017), notably through an abnormal attentional amplification of random stimuli, which leads to a feeling of strangeness regarding consciousness content (Berkovitch et al. 2018). In the current study, we found that impaired conscious access was associated with the advent of hallucinations, which have high loadings in PC2. In line with previous proposals (Powers et al. 2016; Berkovitch et al. 2017; Corlett et al. 2019), this result emphasizes that hallucinations are internal constructions likely favored by a reduced sensitivity to the external world.

Consciousness threshold, obsessions, and insight

Importantly, our results confirm that an impairment of consciousness does not necessarily lead to psychotic symptoms (Reuter et al. 2007, 2009; Del Cul et al. 2009). Indeed, we demonstrate for the first time that consciousness is disrupted in a specific subgroup of patients with OCD, characterized by poor insight, whereas patients with OCD and good insight have normal or even enhanced conscious access. One interesting possibility for patients with OCD and poor insight is that a limited access to external stimulation could leave more room for internal representations that may turn into obsessions. Under these circumstances, patients might have difficulty to perceive the pathological nature of their symptoms as their thought content would only be shifted from external stimulation toward internal representations, which have no reason to be considered as abnormal by them.

We also observed that obsession levels had a negative impact on discrimination reaction times and decreased the seen versus unseen difference in discrimination accuracy. Interestingly, those effects differed in patients with good versus poor insight. A plausible explanation is that patients with good insight and high level of obsession may take more time in order to feel more confident (Roth et al. 2004; Abramovitch et al. 2011; Riesel et al. 2019), and may more easily rate a trial as ‘unseen’ whenever unsure, resulting in a preserved (or improved) accuracy. By contrast, patients with poor insight and high level of obsession may not specifically compensate their level of uncertainty, so that the interferences due to obsessions lead to a decrease in discrimination accuracy for ‘seen’ trials, and ultimately a smaller seen versus unseen difference in discrimination accuracy (Tumkaya et al. 2009). Overall, those results highlight that the pathophysiology of obsessions may rely on distinct mechanisms in patients with OCD with good and poor insight and that these subgroups may adapt differently to their obsessive-compulsive symptoms.

From lack of insight to delusions

One naturally arising question is, if consciousness is disrupted in both patients with schizophrenia and patients with poor insight OCD, why this would participate to the advent of psychotic symptoms in schizophrenia but not in OCD. One of the only differences that we observed between patients with OCD and poor insight and patients with schizophrenia (on top of slower reaction times for the discrimination task) relates to the difference in discrimination accuracy between seen and unseen trials, which was weaker in schizophrenia. In other words, patients with schizophrenia perceive less information—similarly to patients with OCD and poor insight—but on top of this deficit, they tend to make more mistakes for information rated as ‘seen’. This can be interpreted in three different and nonexclusive ways. First, patients with schizophrenia may be overconfident in their ability to see and rate as ‘seen’ information that they in fact did not consciously perceive. Alternatively, they may be less impaired in detection than in discrimination, resulting in situations where they have indeed seen something but are not able to identify it. Finally, they may sometimes ‘hallucinate’ rather than truly see the target stimulus, resulting in a misclassification (but note, however, that they do not exhibit more false alarms for catch trials). Irrespective of its etiology, such a discrepancy between mental experience and external events may have an impact regarding social cognition (Timmermans et al. 2012) and destabilize trust in others. Indeed, a rational explanation of being contradicted by others when overly confident is that others’ reports are wrong or deliberate lies. Similarly, having the impression of perceiving information that is difficult to discriminate likely increases proneness to interpretations and to a feeling of conspiracy. All in all, these abnormalities may fuel delusional constructions with persecution content (which have high loadings in PC2).

Patients with obsessive-compulsive disorder and poor insight: closer to schizophrenia than to obsessive-compulsive disorder?

Patients with OCD and poor insight have been shown to exhibit more severe illness (Türksoy et al. 2002; Kishore et al. 2004; Jakubovski et al. 2011). Moreover, they share neuropsychological deficits and neural overlaps with patients with schizophrenia (Matsunaga et al. 2002; Gross-Isseroff et al. 2003; Kitis et al. 2007; Tumkaya et al. 2009, 2012; Karadag et al. 2011; Kashyap et al. 2012). In our study, they strikingly resemble patients with schizophrenia in terms of consciousness disruption, while they strongly differ from OCD patients with good insight. These results support the hypothesis of a continuum between OCD and schizophrenia, including patients having an OCD with poor insight, or with psychotic features, and a schizo-obsessive subtype of schizophrenia (Insel and Akiskal 1986; Eisen and Rasmussen 1993; Catapano et al. 2001; Poyurovsky et al. 2004; Bottas et al. 2005). Their consciousness deficit could be underpinned by neuronal features common with schizophrenia, such as a decrease of the event-related potential P300 (Jeon and Polich 2003; Raggi et al. 2021) or large-scale brain dysconnectivity (Pettersson-Yeo et al. 2011; Anticevic et al. 2014; Gürsel et al. 2018). Interestingly, some patients with refractory OCD benefit from an augmentation of their treatment by an antipsychotic (Sareen et al. 2004; Bloch et al. 2006). In our cohort, no OCD patient was treated with antipsychotics precluding any analysis of their potential impact on consciousness disruption. Further explorations could determine whether OCD patients with impairments of consciousness and poor insight are more likely to respond to antipsychotics, allowing a better adjustment of therapeutic strategies to patients’ cognitive profile.

Limitations

The main limitation of our study is that we did not include other patient groups exhibiting a lack of insight, or use a specific scale to measure insight in the schizophrenia group, so we could not explore whether the relationship between consciousness impairment and insight was restricted to patients with OCD. Our sample size is small, in particular for the two OCD subgroups. Therefore, some analyses may lack power and our results should be interpreted with cautious (Button et al. 2013).

Conclusions

Our study provides new insights about consciousness disruption in psychiatric disorders. It confirms that impairment of consciousness is observed in patients with schizophrenia and associated with positive symptoms, but also demonstrates that it can manifest as a lack of insight in patients with OCD, delineating a distinct subgroup in terms of cognitive deficits and clinical features. Using PCA, we were able to obtain data-driven clinical descriptions, beyond classical clinical scales, and show that specific symptoms correlate to consciousness disruption, thereby refining the mapping between semiology and cognition. Further studies are needed to confirm these results in bigger sample sizes and extend them to other clinical populations with good and poor insight, in order to better characterize the neurocognitive mechanisms underlying disruption of consciousness in psychiatric disorders and assess whether it should benefit from specific therapeutics.

Supplementary Material

Supplementary_OCD_NeuroscCons_clean_niaf029
supplementary_ocd_neurosccons_clean_niaf029.docx (584.9KB, docx)
SuppFig1new_niaf029
suppfig1new_niaf029.pdf (185.3KB, pdf)
CorrelationFigsupp_MM_niaf029
correlationfigsupp_mm_niaf029.pdf (294.7KB, pdf)

Acknowledgements

We thank all the participants.

Contributor Information

Selim Tumkaya, Department of Psychiatry, Pamukkale University School of Medicine, 20070 Denizli, Turkey; Department of Neuroscience, Pamukkale University School of Medicine, 20070 Denizli, Turkey.

Bengü Yücens, Department of Psychiatry, Pamukkale University School of Medicine, 20070 Denizli, Turkey.

Muhammet Gündüz, Department of Psychiatry, Government Hospital of Bolvadin, 03300 Bolvadin, Turkey.

Maxime Maheu, Department of Neurophysiology and Pathophysiology, Center for Experimental Medicine, University Medical Centre Hamburg-Eppendorf, Martinistraße 52 20246 Hamburg, Germany; Department of Synaptic Physiology, Centre for Molecular Neurobiology, University Medical Centre Hamburg-Eppendorf, Falkenried 94 / Martinistraße 85 20251 Hamburg, Germany.

Lucie Berkovitch, Institut de Neuromodulation, GHU Paris, Psychiatrie et Neurosciences, Centre Hospitalier Sainte-Anne, Pôle Hospitalo-universitaire 15, Université Paris Cité, 12 rue Cabanis, 75014 Paris, France; Cognitive Neuroimaging Unit, NeuroSpin (INSERM-CEA), University of Paris-Saclay, Bât 145 - Point Courrier 156 91191 Gif-sur-Yvette, France; Université Paris Cité, 15 Rue de l’École de Médecine, 75006 Paris, France.

Author contributions

Selim Tumkaya (Conceptualization [equal], Data curation [equal], Investigation [equal], Project administration [equal], Resources [equal], Writing—review & editing [equal]), Bengü Yücens (Conceptualization [equal], Data curation [equal], Investigation [equal]), Muhammet Gündüz (Conceptualization [equal], Data curation [equal], Investigation [equal]), Maxime Maheu (Methodology [equal], Validation [equal], Visualization [lead], Writing—review & editing [lead]), and Lucie Berkovitch (Conceptualization [equal], Data curation [equal], Formal analysis [lead], Methodology [equal], Software [lead], Visualization [supporting], Writing—original draft [lead])

Conflict of interest

L.B. received honoraria from Janssen and S.T. received honoraria from Lundbeck with no financial or other relationship relevant to the subject of this article. M.M., B.Y., and M.G. have no disclosure to declare.

Funding

L.B. thanks the Fondation Bettencourt-Schueller, the Philippe Foundation, the Foundation L’Oréal-Unesco, and the National Institute of Mental Health (R01MH116038 and U01MH121766) for their support. M.M. thanks the Alexander von Humboldt Stiftung and the Fondation Bettencourt-Schueller for their support.

Data availability

Custom codes written in R and MATLAB are available from the corresponding author upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary_OCD_NeuroscCons_clean_niaf029
supplementary_ocd_neurosccons_clean_niaf029.docx (584.9KB, docx)
SuppFig1new_niaf029
suppfig1new_niaf029.pdf (185.3KB, pdf)
CorrelationFigsupp_MM_niaf029
correlationfigsupp_mm_niaf029.pdf (294.7KB, pdf)

Data Availability Statement

Custom codes written in R and MATLAB are available from the corresponding author upon reasonable request.

Articles from Neuroscience of Consciousness are provided here courtesy of Oxford University Press

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