Metacognition of visuomotor decisions in conversion

Neuropsychologia 114 (2018) 251–265

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Neuropsychologia journal homepage: www.elsevier.com/locate/neuropsychologia

Metacognition of visuomotor decisions in conversion disorder a,b,c,d,⁎

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Indrit Bègue , Rebekah Blakemore , Julian Klug , Yann Cojan , Silvio Galli , Alexandre Berneye, Selma Aybeka,d,f, Patrik Vuilleumiera,b,c,d a

Laboratory of Neurology and Imaging of Cognition, Department of Fundamental Neurosciences, University of Geneva, Switzerland Swiss Center for Affective Studies, University of Geneva, Switzerland Department of Mental Health and Psychiatry, University Hospitals of Geneva, Switzerland d Geneva Neuroscience Center, University of Geneva, Switzerland e Service of Consultation-Liaison Psychiatry, Lausanne University Hospital, Lausanne, Switzerland f Neurology Department, Inselspital, Bern University Hospital, Bern, Switzerland b c

A R T I C LE I N FO

A B S T R A C T

Keywords: Conversion disorder Functional neurological symptoms Metacognition Confidence Motor action

Motor conversion disorder (CD) entails genuine disturbances in the subjective experience of patients who maintain they are unable to perform a motor function, despite lack of apparent neurological damage. Abilities by which individuals assess their own capacities during performance in a task are called metacognitive, and distinctive impairment of such abilities is observed in several disorders of self-awareness such as blindsight and anosognosia. In CD, previous research has focused on the recruitment of motor and emotional brain systems, generally linking symptoms to altered limbic-motor interactions; however, metacognitive function has not been studied to our knowledge. Here we tested ten CD patients and ten age-gender matched controls during a visuallyguided motor paradigm, previously employed in healthy controls (HC), allowing us to probe for motor awareness and metacognition. Participants had to draw straight trajectories towards a visual target while, unbeknownst to them, deviations were occasionally introduced in the reaching trajectory seen on the screen. Participants then reported both awareness of deviations and confidence in their response. Activity in premotor and cingulate cortex distinguished between conscious and unconscious movement corrections in controls better than patients. Critically, whereas controls engaged the left superior precuneus and middle temporal region during confidence judgments, CD patients recruited bilateral parahippocampal and amygdalo-hippocampal regions instead. These results reveal that distinct brain regions subserve metacognitive monitoring for HC and CD, pointing to different mechanisms and sources of information used to monitor and form confidence judgments of motor performance. While brain systems involved in sensory-motor integration and vision are more engaged in controls, CD patients may preferentially rely on memory and contextual associative processing, possibly accounting for how affect and memories can imbue current motor experience in these patients.

1. Introduction Conversion disorder (CD), a fascinating and controversial condition, has attracted the attention of physicians, scientists and philosophers for several centuries. It is defined by functional neurological symptoms (FND), such as paralysis, anesthesia, or blindness, without organic damage to the nervous system (American Psychiatric Association, 2013). Still today, CD is a common and clinically important nosography, representing up to 16% of referrals to neurology clinics (Sharpe et al., 2010), between 30% and 60% of neurological outpatients (Carson et al., 2000; Nimnuan et al., 2001) and, on average, 20% of neurologists’ workload (Kanaan et al., 2009). The prevalence of CD in general hospital inpatients ranges between 5% and 16% (Lazare, 1981).

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Moreover, it causes important disability in everyday life (Stone et al., 2003), comparable to organic neurological disease (Stone et al., 2010), and frequently associated with poor prognosis (Stone et al., 2003). Patients with motor CD present with symptoms of altered voluntary motor function such as weakness or paralysis, abnormal movement (e.g., tremor, dystonia, myoclonus), and/or gait disorder. According to the DSM-5 criteria (American Psychiatric Association, 2013), clinical findings are incompatible with lesions in central or peripheral nervous system. These patients do not feign symptoms in order to obtain material gain, but their impairment seems to reflect a genuine distortion in subjective experience of motor function despite intact anatomical pathways (Vuilleumier, 2014). Since the influential work of Freud (1910) and others (Babinski and Froment, 1918; Charcot, 1886), CD has

Corresponding author at: Laboratory of Neurology and Imaging of Cognition, Department of Fundamental Neurosciences, University of Geneva, Switzerland. E-mail address: [email protected] (I. Bègue).

https://doi.org/10.1016/j.neuropsychologia.2018.04.018 Received 6 November 2017; Received in revised form 19 April 2018; Accepted 20 April 2018 Available online 23 April 2018 0028-3932/ © 2018 Elsevier Ltd. All rights reserved.


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that allowed us to independently probe (i) the ability of participants to make corrective movements during their action (reaching to a visual target with either veridical or altered visual feedback), with or without awareness of such correction; and (ii) their explicit confidence in motor performance, thus yielding distinct but complementary measures of motor awareness and monitoring.

been linked to psychodynamic conflict between the conscious and unconscious mind, putatively triggered by stressful events and/or personal memories. Freud stated that these patients “suffer from reminiscences” (Freud, 1910, p. 187), however, whether (and how) memory-related processes may influence motor function and motor awareness still remains unresolved. Recent research using functional neuroimaging has shown abnormal brain activation patterns during motor tasks, suggesting that symptoms during motor conversion disorder may be produced by top-down modulation from higher-order regions that ultimately interfere with motor control (Blakemore et al., 2016; de Lange et al., 2010; Marshall et al., 1997; Tiihonen et al., 1995; Voon et al., 2011). Such modulation might reflect heightened inhibition by emotional processes (Marshall et al., 1997), exaggerated self-monitoring by prefrontal executive systems (Cojan et al., 2009; de Lange et al., 2007), abnormal integration of sensory and motor signal generating subjective experience of volition (Voon et al., 2011), or pathological recruitment of ventromedial prefrontal areas involved in self-referential representations, emotional regulation, and autobiographical memory (Cojan et al., 2009), which could imbue motor action with particular affective significance and thus ultimately interfere motor experience (Vuilleumier, 2014). Other studies reported that CD patients fail to accurately monitor internallygenerated interoceptive signals (Ricciardi et al., 2016) and show impaired sense of agency subserved by fronto-parietal areas (Spence et al., 2000; Voon et al., 2010a, 2010b), further pointing to disturbances in self-awareness and self-monitoring functions. More generally, self-monitoring abilities are related to metacognitive mechanisms that allow evaluating and adjusting one's current performance based on both external and internal signals generated during goal-oriented behavior (Fleming et al., 2010). Metacognitive abilities are impaired in different disorders of self-awareness such as blindsight (Ko and Lau, 2012) or anosognosia for hemiplegia (Vocat et al., 2013). However, whether metacognitive abnormalities are present in CD patients and lead to abnormal self-evaluation of motor performance remains unclear. Here, we specifically tested for metacognitive function in motor CD patients using a visuomotor paradigm, previously used in healthy controls (HC) to assess motor awareness and motor monitoring (Fourneret and Jeannerod, 1998; Sinanaj et al., 2015). Neuroscience research has pointed to specific brain circuits for metacognitive judgment of confidence during various tasks, partly distinct from those directly mediating task performance itself (Chua et al., 2005; Chua et al., 2006, 2009). In this previous research, metacognition has mainly been studied in perceptual decision and memory tasks (Fleming and Dolan, 2012), but recent work also pointed to specific mechanisms underlying confidence of motor actions (Sinanaj et al., 2015). Using the same paradigm as employed here (Sinanaj et al., 2015), in healthy volunteers, we showed that metacognitive sensitivity for motor performance and performance accuracy itself are associated with different behavioral parameters and different neural correlates. We therefore hypothesized that, in motor CD patients, abnormal metacognitive function may be manifested in significant changes in confidence judgments, despite intact sensorimotor performance. Alternatively, patients might possess intact visuomotor metacognition but engage different neurocognitive processes to compute confidence judgments. In particular, their self-monitoring evaluation might rely more heavily on affect- or memory-based signals, in line with recent theories highlighting the role of integrative processes for metacognition (Shadlen and Shohamy, 2016) and accounts of CD pointing to the influence of self-relevant autobiographical information retrieved from memory or imagery (Vuilleumier, 2014). Furthermore, although metacognitive processes allow for explicit evaluation of one's actions and conscious adaptive behavior, motor performance can also be monitored and adjusted without direct conscious access to the underlying sensorimotor parameters (Fourneret and Jeannerod, 1998). In our study, we therefore used a visuomotor task

2. Methods 2.1. Populations Ten patients (CD; 2 males, mean age = 33 years old, min = 19, max = 49, SD = 9.07 years) were recruited from an ambulatory setting in the Service of Neurology, University Hospitals of Geneva (HUG). Inclusion was based on a diagnosis of motor CD without any other neurological disorder, according to DSM-5 criteria, by a board certified neurologist (SA). The healthy controls’ group (HC) consisted of ten agematched healthy volunteers (3 males, mean age = 28.8 years old, min = 20, max = 41, SD = 6.32 years), recruited from posted advertisements at the University of Geneva and online announcements. A summary of the clinical presentation and severity of CD, evaluated at the time of recruitment is presented in Tables 1 and 2. Participants had no history of any other psychiatric or neurological disorders and had normal or corrected-to-normal vision. They all completed the Hospital Anxiety and Depression Scale (HADS; Zigmond and Snaith, 1983), which showed no difference in scores between patients and controls (HC: mean = 5.7, SD = 1.7, CD = 7.8, SD = 3.4, p = 0.1). Patients were evaluated with the National Institute of Health Stroke Scale (Schlegel et al., 2003) to measure the severity of paralysis and with the Hinson scale to measure the degree of psychogenic movements (Hinson et al., 2005). Severity ratings were based on clinical evaluation, with the following scores: none (NIH and Hinson Scale), minimal/mild (Hinson 1–10 and NIHSS 1–2), moderate (Hinson 11–19 and NIHSS 3–4), severe (Hinson > 20 and NIHSS 5–6), very severe (Hinson > 60 and NIHSS 7–8). Patients had recovered normal motor ability when tested in the fMRI task. Inability to draw straight trajectories at the time of experiment was considered a strict exclusion criterion. The study was approved by the Ethics Committee of the University of Geneva and University Hospitals of Geneva, Switzerland. All participants read and signed an informed consent form, and were screened for contraindications to MRI with a standard safety questionnaire. 2.2. Procedure and experimental material 2.2.1. Task Our task was adapted from a paradigm initially designed to test for motor awareness through a perceptual conflict between the visual and proprioceptive feedback of voluntary movements (Fourneret and Jeannerod, 1998). This task was recently used to investigate motor metacognition in healthy people (Sinanaj et al., 2015). Participants were instructed to produce a hand movement with an Table 1 Summary of main demographic characteristics.

Age (years) (range) Gender Handedness Most affected side

HC mean ± sd

CD mean ± sd

Group comparisona p - value

28.8 ± 6.3 20–41 3M:7F 10R:0L −

33 ± 9.07 19–49 2M:8F 8R:2L 6L/3R/1(L+R)

p = 0.26 p = 0.65 p = 0.16

CD, conversion disorder patients; L, left; R, right; M, male; F, female; sd, standard deviation. a Unpaired t-test. 252


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Table 2 Summary of clinical characteristics of CD patients. Patient

Handedness

Clinical symptoms

Affected side

Severitya

Durationb

Medicationc

1 2 3 4 5 6 7 8 9 10

R L R R R R R L R R

Motor weakness and paresthesia in both feet Tremor with right predominance Motor - sensitive deficit Hand dystonia; Motor weakness and hypoesthesia Left weakness (but remitted) and hypoesthesia/paresthesia Motor weakness in left upper limb and hypoesthesia Gait disorder, paraparesis Gait disorder and mild left sided hemiparesis Gait disorder and mild right sided hemiparesis Gait disorder and mild left sided hemiparesis

L and R R R L L L L L R L

1 2 2 2 1 1 2 2 1 2

4 144 24 24 11 120 0.2 3 18 3

NA NA 4, 2 4 NA 2, 3, 4 NA NA NA 2

CD, conversion disorder patients; L, left; R, right. a Based on clinical evaluation, NIH and Hinson Scale 0 = none, 1 = minimal/mild (Hinson 1–10 et NIHSS 1–2) 2 = moderate (Hinson 11–19 et NIHSS 3–4) 3 = severe (Hinson > 20 et NIHSS 5–6) 4 = very severe (Hinson > 60 et NIHSS 7–8). b Duration = months from symptom presentation. c Medication: N/A = no treatment for CD symptoms; 1 = neuroleptic, 2 = antidepressant, 3 = benzodiazepine, 4 = antiepileptic 5 = opioid.

Fig. 1. Experimental task. Participants had to draw a straight line between a starting point (at the bottom of the screen) and a target (at the top of the screen), by using a joystick. The starting position was displayed as a white triangle, while the target was yellow but varied in shape to make the task more engaging. When the bottom triangle turned red (jittered delay of 1–2 s after onset of the white triangle), the participant was instructed to start moving the joystick in order to direct the triangle toward the target. They were asked to draw a line as straight as possible and informed that deviations may occur (or not) randomly during the trajectory, in which case they had to correct their movement and then report such deviations at the trial end (deviation detection: yes/no). Finally, they were prompted to provide a confidence level concerning their accuracy in detecting the latter deviation (on a scale from 1 = not certain at all to 5 = very certain). This was followed by a blank screen with a jittered duration of 3–6 s until the next trial. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

the trials, the visual trajectory presented on the screen was altered by a systematic deviation towards either the right of left side (see below for online adjustment). Thus, to reach the visual target with a straight trajectory, the participants needed to correct/compensate for this deviation (Fourneret and Jeannerod, 1998). They were told that such deviations did not occur all the time, and that when they did, they never occurred in the beginning or the end of the trajectory, but always at some point around the middle of the movement. The joystick was fixed to the participant's body via Velcro strips, to allow for a stable position and comfortable manipulation while looking at the screen. On

MRI-compatible joystick (HHSC-JOY-1 by Current Designs Inc., http:// www.curdes.com/index.php/hhsc-joy-1.html) in order to reach a visual target presented at the top of the screen (Fig. 1). Patients and controls always used their dominant hand to draw straight lines. The joystick controlled the position of a cursor that first appeared in the lower part of screen and had to be moved following a straight line towards the target. Pushing the joystick forward made the trajectory line move upward, while right/left pushes moved it right/leftward respectively. Participants could not see their hand but observed the direct consequence of their movement on the screen. Critically, on a proportion of 253


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of shorter duration for patients who may be prone to fatigue; however, this implied reducing the number of trials and prevented a reliable calculation of the Mratio (relating confidence level to task performance level), a measure used to quantify metacognitive sensitivity in some studies (Maniscalco and Lau, 2012). For this reason, we calculated the over/underconfidence score (O/UC), another metacognitive measure that also links confidence to task accuracy and is computed as the difference between mean confidence level and mean accuracy of responses (Fischhoff et al., 1977). Perfect calibration is achieved when O/ UC = 0 (Fischhoff et al., 1977) meaning that confidence tracks accuracy perfectly. A positive O/UC score means overconfidence, whereas a negative O/UC means underconfidence in task performance. To derive this measure, we transformed the raw confidence ratings into a normalized scale from 0 to 1, with 0 corresponding to the lowest confidence level (1) and 1 corresponding to the highest confidence level (5). Likewise, accuracy was derived from percent correct using the same 0−1 scale. The difference between the latter (mean confidence level) and the former (mean accuracy) was computed to obtain a O/UC score for each participant reflecting the quality of their metacognitive judgments, and calculated using the following formula (Fischhoff et al., 1977):

each trial, after reaching the target, participants had to report whether they detected a deviation in the trajectory caused by the computer (yes/ no), and then rated their confidence in this response using a Likert-like scale (1 – not certain at all, 2 – a little uncertain 3 – neither certain nor uncertain, 4 – a little certain, 5- very certain). They were encouraged to use the whole scale to best reflect their subjective confidence level. Subjects performed one training run and two experimental runs that each lasted approximatively 10 min. During training, they drew trajectories in which no deviation was introduced, such that they could become familiar with the task and the MRI environment. There was a total of 110 trials for the two experimental runs (21% without deviation, see below), during which fMRI was performed. Between these two runs, a high resolution structural scan was also recorded. Trials lasted 9.5 s in the training run and 11.5 s in the experimental run (due the addition of the confidence response periods). Each trial was followed by a blank screen with a jittered duration (from 3 s to 6 s). The magnitude of the deviation was determined for each subject by starting with a deviation angle of 30 degrees, and then adjusting the angle online through a staircase procedure, so as to obtain a balanced proportion of detected and undetected deviations overall. The staircase procedure made the task more difficult after two consecutive correct responses by increasing the next deviation by 2.64 degrees, but made it easier after an incorrect response by reducing the next deviation by 1°. The task and procedure are explained in detail in our previous study in healthy controls (Sinanaj et al., 2015).

over /underconfidence score = x − c where x is the mean confidence score and c is the mean proportion correct (for a detailed description see discussion (Pulford, 1996)). Next, we calculated the area under the ROC (AROC) type 2 curves (Fleming and Lau, 2014) a well-established non-parametric measure of metacognitive ability (see (Fleming and Lau, 2014) for detailed review). In addition, we calculated the metacognitive bias (BROC) to determine how participants generally issued confidence judgments, i.e., their propensity to employ certain confidence levels (over- or underconfidence). Computations were performed with scripts used in previous studies and made publicly available to quantify these parameters (see https://github.com/metacoglab).

2.3. Behavioral analysis Accuracy as well as confidence ratings in this response, were computed as behavioral variables of interest, in addition to the area under the reaching trajectory curve that was drawn on every trial. Accuracy was defined as the percentage of correct responses. Confidence was rated on a scale from 1 to 5, where the initial value display was centered on 3.5. The magnitude of deviation during reaching, relative to the ideal straight trajectory, was defined as the sensorimotor adjustment to the bias (SMAB) and quantified by calculating the area between trajectory and the straight line (defined as the line from the initial position of the cursor to the target position). The SMAB thus provided an objective measure of the subject's ability to successfully monitor their motor performance (Slachevsky et al., 2001). It was computed using the Riemann sum that consists of dividing the area into rectangles for each increment along the y-axis of the screen, and then summing these rectangles (Slachevsky et al., 2001).

Sensori − motor adjustment to thebias =

n=1

∑i =0

2.5. Functional magnetic resonance imaging analyses 2.5.1. Data acquisition Functional MRI images were acquired using a 3 T whole body MRI scanner (Trio TIM, Siemens, Germany) with the product 12 channel head-coil. Functional images were acquired with a susceptibility weighted EPI sequence (TR/TE = 2100/30 ms, flip angle = 80 degrees, PAT factor = 2, 64 × 64 voxel, 3.2 × 3.2 mm, 36 slices, 3.2 mm slice thickness, 20% slice gap). Structural images were acquired with a T1 weighted 3D sequence (MPRAGE, TR/TI/TE = 1900/900/2.32 ms, flip angle = 9°, voxel dimensions: 0.9 mm isotropic, 256 × 256 × 192 voxels). Visual stimuli were presented on a back projection screen inside the scanner bore using an LCD projector (CP-SX1350, Hitachi, Japan). Responses were recorded with buttons placed on the joystick used for the visuomotor reaching task (HH-JOY-4, Current Designs Inc., USA).

f (xi) △x

We also registered the average magnitude of the deviation threshold as determined by the staircase procedure (see above) to adjust the ratio of detected and undetected perturbations for each individual. Statistical analyses were performed using the R software (R Core Team, 2016) using mixed general linear models. We first performed a 2 × 2 × 2 ANOVA to assess the main effects of group (controls vs patients), deviation of trajectory (present vs absent), and conscious detection response (yes vs no), with subject as random factor, for each behavioral measure (accuracy [percent correct], SMAB, and confidence rating). As there were very few trials with “yes” responses in the undeviated condition in both groups (HC: mean = 3.9 trials, SD = 2.9; CD: mean = 3.3 trials, SD = 2.4), we also performed an additional analysis using a single factor for trial type with three levels (YES DEV, NO DEV, NO UNDEV) according to deviation presence (deviated or undeviated) and deviation detection (yes vs no) in a 3 Condition x 2 Group ANOVA.

2.5.2. Preprocessing Statistical analysis was performed using the SPM8 software (http:// www.fil.ion.ucl.ac.uk/spm/). Functional images were corrected for head movement between scans by an affine registration (Friston et al., 1995), and then realigned to the mean of all images. The anatomical image was spatially normalized on the EPI template. The functional images were also normalized to the EPI template, which were thereby transformed into standard stereotaxic space and resampled with a 3 × 3 × 3 mm voxel size. The normalized images were spatially smoothed using an 8 mm full-width at half-maximum (FWHM) Gaussian kernel.

2.4. Quantification of metacognitive ability 2.5.3. First level analyses Data were analyzed using the general linear model (GLM)

We adapted our original paradigm (Sinanaj et al., 2015) to make it 254


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framework implemented in SPM. In a first analysis, we modeled the convolved standard hemodynamic response function with a delta (or “stick”) function at the onset of the preparatory phase (appearance of white triangle – PREP regressor), at the onset of the joystick movement (appearance of the red triangle – MOV regressor), and at the onset of the confidence screen (CONF regressor). These event regressors were modulated by additional parametric factors representing the trial-bytrial values of trajectory area (SMAB) and confidence level. To account for head motion-related variance, we included the six differential parameters derived from the realignment process [x, y, and z translations (in millimeters) plus pitch, roll, and yaw rotations] as regressors of no interest. Low-frequency signal drifts were filtered using a cut-off period of 128 s. Global scaling was applied, with each fMRI value rescaled to a percentage value of the average whole-brain signal for that scan. In addition to the parametric model, to examine the brain regions differentially activated with movement correction and conscious detection of deviation, we built a categorical GLM where neural activity was modeled with a delta function at the onset of joystick movement for the four different trial types according to a 2 × 2 design (Deviation occurrence x Detection response). These 4 conditions included deviated trials consciously detected (YES DEV) and undetected (NO DEV), plus undeviated trials correctly detected (NO UNDEV) and incorrectly detected trials (YES UNDEV). The latter trials were very rare in our design, but modeled separately and excluded from our group analysis later on. This led to a GLM design with 3 conditions of interest (YES DEV, NO DEV, NO UNDEV), for each subject in each group. Thus, our main contrasts in this analysis focused on the comparison of brain activity related to conscious detection (YES DEV > NO DEV) and unconscious monitoring (NO DEV vs NO UNDEV). We included the SMAB values as parametric modulators in this analysis to account for purely motor differences between conditions. Our design matrix also included motion parameters as regressors of no interests. Functional contrasts were weighted for number of trials for number of condition across each run, for every subject to account for any inequality in the number of trials (Huettel and McCarthy, 2001).

statistical inference about the size of differences between groups. In our study, contrasts of interest (e.g. PREP, MOV, CONF) from one group were thresholded at p < 0.001 uncorrected, and then masked by excluding voxels activated even at a trend level (p < 0.05 uncorrected) for the same exact contrast in the other group. Note that the liberal thresholds for the exclusive masks (i.e., p < 0.05 uncorrected) correspond to more conservative masking (i.e. more voxels are excluded) and greater selectivity of residual activations (Hayama et al., 2012; Schwartz et al., 2008). Similar exclusive masking approach has been employed in various neurological and psychiatric diseases (including CD) to examine differential neural activation compared to healthy controls (Blakemore et al., 2016; Desseilles et al., 2009; Piguet et al., 2015; Rudner et al., 2013; Schwartz et al., 2008; van der Stouwe et al., 2015; Vaudano et al., 2017). Finally, we also performed a conjunction analysis testing for a logical “AND” to identify common activations in both groups. Finally, to identify brain areas whose activity was modulated by individual confidence levels the time of confidence judgment, we explored the positive and negative parametric effects of confidence values on the CONF contrast for CD > HC and HC > CD as defined above. For the categorical model, we used one sample t-tests corresponding to each condition (YES DEV, NO DEV, NO UNDEV) from the first level analysis for each group (HC, CD). To compare brain activity in each group for these parametric effects, we employed the same exclusive masking and interaction contrast procedure as above. Voxels were identified as significant only if they passed a threshold corresponding to p < 0.05 corrected at the cluster level, with an underlying height threshold of at least t(27) = 3.42, corresponding to p < 0.001 (uncorrected). 3. Results 3.1. Behavior As expected with our staircase procedure, we found no group differences (HC relative to CD) in accuracy for the detection of deviation in movement trajectory (HC = 67%, SEM = 0.03; CD = 64%, SEM = 0.05) (Fig. 2, C). Likewise, there was no difference in mean confidence levels (HC = 3.93, SEM = 0.13, CD = 3.87, SEM = 0.14) (Fig. 2, D) and overall calibration scores (HC = 0.12, SEM = 0.04, CD = 0.13, SEM = 0.05; all p-values > 0.05) (Fig. 2, F). We also found similar metacognitive sensitivity as measured by AROC values between HC (mean = 0.56, SEM = 0.03) and CD (mean = 0.59, SEM = 0.02), p = 0.34, and similar metacognitive bias measured by BROC values (HC: mean = 2.56, SEM = 0.61, CD: mean = 2.60, SEM = 0.70; p = 0.96) (Fig. S1). Motor monitoring performance was assessed by computing the area under the trajectory curve (relative to a straight line between the starting point and the target), which reflects the degree of sensorimotor adjustment to the bias (SMAB) introduced during reaching. A mixed GLM analysis of these data revealed a significant effect of deviation occurrence (perturbed vs unperturbed trials, F = 4.59, p = 0.36) and deviation detection (detected vs undetected reports, F = 13.67, p < 0.001). There was also a significant effect of group (HC = 0.01 pixels square, SEM = 0.001, CD = 0.03, SEM = 0.003, F = 8.66, pvalue = .005) (Fig. 2, B), with a larger SMAB area indicating that patients made a more curved trajectory and therefore less precise corrections than controls (Fig. 2, E). In keeping with the above, the average threshold angle of deviations leading to correct detection (derived from the staircase procedure) showed slightly lower values in HC compared to CD, just reaching significance (mean = 13.34, SEM = 2.79 vs 21.44, SEM = 2.79, respectively; p = 0.055) (Fig. 2, A). Thus, while patients had a tendency towards requiring larger perturbations to detect deviations in their reaching trajectory, their ability to detect these deviations during the task and their confidence in their ability was well matched with the performance of controls who were sensitive to smaller deviations. Further analyses showed a trending

2.5.4. Second-level analyses Contrast images from one-sample t-tests corresponding to each event (PREP, MOV, and CONF) and their parametric modulators (trajectory area and confidence levels), averaged across the two runs for each participant, were then fed into a second-level random-effect analysis. Conditions were compared using linear contrasts between parameter estimates from each condition. Activations were considered as significant only for voxels that passed a threshold of p < 0.05 corrected at the cluster level, with an underlying height threshold of at least t(27) = 3.42, corresponding to p < 0.001 (uncorrected), k = 10 voxels. Group differences were examined separately and independently by formal interaction contrasts at the whole-brain level and selective masking analysis. Interaction contrasts probe for activation differences that are statistically larger in one condition for one group relative to the other, by subtracting one contrast from CD patients from the same contrast in HC (or vice versa). In addition, we used an exclusive masking analysis to verify the direction of differences observed in interaction analyses, by testing for significant effects in one group that did not reach (or even approach) significance in the other group (exclusive mask p < 0.05). Exclusive masking is particularly useful when comparing clinical with healthy populations, in order to identify changes specific to a given condition, whether activity is present in one group only, or whether it similar but weaker in one group (Abe et al., 2014; Bennion et al., 2015; Blakemore et al., 2016; Danelli et al., 2015; Desseilles et al., 2009; Fliessbach et al., 2007; Gottlieb et al., 2010; Piguet et al., 2016, 2015; Pochon et al., 2002; Rudner et al., 2013; Schwartz et al., 2008; Uncapher et al., 2006; van der Stouwe et al., 2015; Vaudano et al., 2017). However, masking does not make 255


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Fig. 2. Main behavioral results. Threshold angle for detected vs undetected deviation (A), sensorimotor adjustment to bias (B), accuracy expressed as percentage of correct deviation detection responses (C), overall mean confidence ratings (D), mean trajectories (E), and overall over/underconfidence (O/UC) scores (F) for both the healthy controls (HC) and conversion disorder (CD) patients. Bar plots of accuracy (% correct) (G), mean confidence ratings (H), and sensorimotor adjustment to bias (I) according to the YES DEV, NO DEV, and NO UNDEV conditions for HC and CD. Green square is the target to be reached. Error bars represent standard error of the mean.* p-value = 0.05, ** p-value = 0.005, n.s. not significant.

strong activation in the VMPFC (MNI coordinates [−12, 50, −2], T = 5.27, p < 0.05 FWE corrected, Fig. 3, A), whereas HC did not reveal any significant brain activity even in liberal thresholds (Fig. 3, B). We then examined brain activity differences by comparing patients versus controls (PREP: CD > HC) and controls versus patients (PREP: HC > CD). The first contrast replicated a selective activation of VMPFC (MNI coordinates [−12, 50, −2], T = 5.27, pFWE = 0.03), Fig. 3, A and B. Both the masking and a formal whole-brain interaction analysis between CD versus HC, revealed a selective activation of VMPFC during motor preparation (group x condition, MNI peak coordinates [6, 47, 11], T = 3.94, p < 0.001). The overlap between statistical maps of the two groups tested via a conjunction analysis (logical “AND”) did not show any common region engaged during movement preparation for both CD and HC. Next we examined brain activity related to movement execution (from onset of trajectory until reaching the target) resulting from the one-sample t-test of MOV contrast. In both groups, significant activations were found in bilateral (left predominant) motor and premotor cortex, SMA, posterior parietal areas, and basal ganglia (see Table 3 for a summary). We formally tested the overlap between statistical maps of the two groups through a conjunction analysis (testing for a logical “AND”), which demonstrated highly similar networks engaged during the movement phase in both CD and HC (Fig. 4). A whole-brain interaction contrast comparing the two groups did not show significant differences. However, a comparison between groups with exclusive masking revealed differential increase during the

correlation between SMAB and accuracy (p = 0.07) across groups, reflecting more accurate detection of deviations with more curved trajectories. There was no correlation between confidence and accuracy (p = 0.47) or between confidence and SMAB (p = 0.57). A summary of the 3 Condition × 2 Group ANOVA for mean accuracy, confidence ratings, and SMAB, split according to the three conditions of interest (YES DEV, NO DEV, and NO UNDEV) for each group, is also presented in Fig. 2 (G, I, J) There were main effects of Condition for mean accuracy and confidence ratings (F = 15.39, p < 0.001 and F = 11.7, p < 0.001), showing that participants in both groups monitored their task performance adequately. Results for the SMAB showed a main effect of both Condition (F = 17.75 and p = 0.004) and Group (F = 17.75, p < 0.001). Importantly, no Condition x Group interaction was observed for any of these analyses. Further, we analyzed confidence ratings separately for correct and incorrect detection trials for each group. A 2 × 2 Group (HC vs CD) x Accuracy (Correct vs. Incorrect) ANOVA showed a main effect of response accuracy (F = 4.13, p = 0.04), no effect of group (F = 0.31, p = 0.58), and no significant interaction (F = 0.95, p = 0.33), indicating that all participants made confidence ratings sensibly according to their overall performance.

3.2. Brain Imaging 3.2.1. Motor performance We first examined brain activity at the moment of the preparation for movement (onset of new target display). CD patients revealed a 256


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Fig. 3. fMRI results showing main and differential effects of preparation in CD and HC. Statistical activation maps at the moment of preparation for the reaching movement, for conversion disorder (CD) patients (A) and HC (B), in addition to CD > HC (C). Mean beta coefficients from the VMPFC illustrate the differential effects of motor preparation between groups (D). Statistical thresholds are shown in the maps. Coordinates correspond to the MNI template (x, y, z; mm). Error bars represent standard error. VMPFC, ventral medial prefrontal cortex.

CD). A full summary of whole brain activations across these conditions is provided in Table 4. During conscious control (detected vs undetected deviations), HC activated a widespread brain network (Table 4) with significantly greater effects in motor, visual, and cerebellar regions as compared with CD (Fig. 5, top pannel, right, and Table 4). A whole brain interaction analysis yielded similar results, notably in the middle occipital gyrus ([−18, −103 4], T = 3.78), inferior temporal gyrus ([51, −55, −20], T = 3.60), and preSMA/ACC ([−9, 20, 70], T = 3.91) (all p < 0.001). CD activated very few areas in this contrast, with greater effect in the right superior frontal gyrus as compared with HC (Table 4), which however was not found in the interaction analysis. Conversely, during unconscious control (adjustment to undetected deviations vs no deviation, NO DEV > NO UNDEV), HC showed no significant activation above our statistical threshold. By contrast, in CD, this comparison showed activations of several areas in motor and attentional networks, with greater increases in the left precentral gyrus, left pre-supplementary area/dorsal anterior cingulate cortex, right inferior frontal gyrus, and right precuneus (see Table 4 and Fig. 5, bottom pannel). These group differences were replicated in the interaction analysis, notably in the inferior frontal gyrus ([39, 35, −11], T=4.26), precentral gyrus ([−18, −16, 49], T=3.73), precuneus ([9, −64, 37], T=4), and SMA ([−18, −16, 49], T=3.73), all p < 0.001. In order to exclude any potential motor confounds arising from the fact that CD patients made more curved trajectories (larger deviation area), we also repeated these analysis with SMAB values as a parametric modulator. Results again showed greater activation for patients in the precentral gyrus ([−24, −16, 46], T = 4.32), right inferior frontal gyrus ([42, 32, −11], T = 3.82), and precuneus/middle cingulate cortex ([−3, −22, 40], T = 4.50), but activity in the left pre-SMA and ACC did not pass threshold under this condition, suggesting that their engagement was dependent on the magnitude of the motor correction. Altogether, these data point to a differential sensitivity of motor control processes between controls, whose brain activity responded mainly to consciously detected deviations, and patients, who responded mainly to unconsciously detected/adjusted deviations.

Table 3 Whole brain common voxel-wise activations during movement execution. Significant clusters and their MNI coordinates (centre of mass), voxels, and Zscore of peak activity. MNI coordinates (mm)

Conjunction: HC AND CD R Middle Temporal Gyrusa L Middle Occipital Gyrusa L Precentral Gyrusa R Thalamus L superior colliculus R Putamen R Inferior Frontal Gyrus, Pars Opercularis L SMA R Inferior Parietal Lobule R Precentral Gyrus L Thalamus L SMA R Insula L Middle Occipital Gyrus

Voxels

Z-score

X

y

z

48 − 48 − 36 9 −3 18 51

− 61 − 73 − 10 − 16 − 25 14 11

1 1 52 4 −8 −5 19

131 149 192 72 15 26 30

4.64 4.63 4.13 4.04 4 3.99 3.99

−6 36 30 −9 −6 33 − 30

− 16 − 43 −7 − 19 −1 23 − 94

52 49 52 4 55 1 1

25 37 54 37 40 30 29

3.87 3.85 3.74 3.74 3.65 3.64 3.4

a) Clusters listed at p < 0.001; uncorrected, minimum 10 voxels. a pFWE corrected. HC, healthy control participants; CD, Conversion disorder patients; L, left; R, right; SMA, Supplementary Motor Area.

movement phase in upper brainstem for CD relative to controls, [3; − 16; − 20], t = 4.64), but in right thalamus ( [9, −25, 4], t = 6.02), left cerebellum ([−18, −58, −32], t = 3.95), and left postcentral gyrus ([−27, −58, 64], t = 4.61) for controls relative to CD (all p < 0.001).

3.2.2. Motor monitoring performance We also examined brain activity associated with conscious control (DEV YES > NO DEV) and unconscious control (NO DEV > NO UNDEV) of movement trajectory, for each of the two groups (HC vs. 257


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Fig. 4. fMRI results showing conjunction effects in CD and HC during movement. A-B. Statistical activation maps for movement execution (contrast MOV) in conversion disorder (CD) patients AND healthy controls (HC). Results are displayed at p-value < 0.001 uncorrected, with a minimum of 10 voxels using an exclusive masking procedure. C. Overlap of activations seen in both groups. Coordinates correspond to the MNI template (x, y,z; mm). Error bars represent standard error. M1, motor cortex, SMA, supplementary motor area, BG, basal ganglia (putamen), see Table 3 for whole brain activations.

p < 0.001; whereas HC again showed greater activity in the left anterior precuneus ([−6, −46, 73], T=6.06) and middle temporal gyrus (MT: [−63, −49, −5], T=4.35) all p < 0.001 . We also tested for common regions engaged during confidence judgements in both groups using a conjunction analysis (contrasts CONF in CD AND HC), but found no regions that passed the significance threshold. Only at lower statistical thresholds (p-value < 0.005, uncorrected), this conjunction activated the left cerebellum (crus 2, [−36, −67, −44], T=3.77), bilateral rostral orbito-frontal areas ([33, 44, −14], T=4.17 and [−36, 35, −17], T=3.50), in addition to the right parahippocampal gyrus ([24, −25, −23], T=4.15) and right amygdala ([21, 5, −20], T=3.75).

Most critically for the purpose of our study, we then examined the main effect of metacognitive confidence judgments (CONF). Despite the fact that both groups exhibited similar motor activation and detection performance overall (but with more ample trajectories in CD), we found that distinct brain regions were recruited when rating confidence of their deviation detection accuracy in patients versus controls (Fig. 6). CD patients showed strong activations in bilateral medial temporal lobes over the hippocampal complex and amygdala (right peak [24, −19, −23,], T = 7.10, p = 0.02 FWE corrected and left peak [−30, −25, −17], T = 5.15), as well as bilateral orbitofrontal cortex ([−36, 38,−14], T = 5.81 and [33, 44, −14], T = 4.28) and left lingual gyrus ([−12, −67, −8], T = 4.06) (p < 0.001) (Fig. 6, A). Conversely, HC mainly activated strongly the visual cortex ([−6, −91, 37], T = 6.20, p < 0.05 FWE corrected) the left anterior precuneus ([−6, −46, 73], T = 6.06), left cerebellum ([−33, −67, −41], T = 5.48), middle temporal gyrus (MT, [−63, −49, −5], T = 4.35) right anterior lingual gyrus ([15, −43, −2], T = 4.33), and right orbitofrontal cortex ([36, 41, −14], T = 4.83) – all p < 0.001, Fig. 6, B). The whole-brain interaction analysis comparing groups confirmed differential effects for CD > HC in bilateral hippocampal and parahippocampal regions (right: [21 −13 −26], T= 3.03; left: [−30 −10 −14], T= 2.97), as well as in the left cerebellum ([−21 −31 −35], T=4.23), but greater effects for HC > CD in left anterior precuneus ([−6, −46, 73], T=4.19) and left SMA ([−6 14 70], T=3.68). Likewise, comparing groups with masking contrasts confirmed higher activation in CD over the parahippocampal gyrus (PHG) bilaterally (peak [27, −10, −17], T =5.67; [−27, −25, −17], T = 5), extending to the left hippocampus and amygdala region on the left side ([−21, −7, −17], T=3.92) all

4. Discussion This study investigated neural processes associated with motor awareness and metacognition in CD patients, using a visuomotor paradigm (Fourneret and Jeannerod, 1998; Sinanaj et al., 2015) that required subjects to monitor their action and confidence of performance. After moving their hand to reach a visual target, participants reported whether they had detected an externally induced deviation in their trajectory (i.e. due to the computer), and how confident they were about this response. Behaviorally, CD patients needed larger deviations to detect them as compared with controls, but exhibited similar accuracy (percent correct), confidence ratings and overall metacognitive sensitivity (indexed by AROC type II) under these conditions, allowing us to compare monitoring abilities across groups unconfounded by performance. In 258


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Table 4 Whole-brain voxel-wise activations during conscious (YES DEV > NO DEV) and unconscious (NO DEV > NO UNDEV) control. Significant clustersa are listed with their MNI coordinates (centre of mass), voxels, and Z-score. Yes DEV > No DEV MNI coordinates (mm) x

y

z

Voxels

Z score

HC L R L R L L R R R

Middle occipital gyrus Temporal inferior gyrus Middle temporal gyrus Middle occipital gyrus Pre-Supplementary motor area/dorsal Anterior Cingulate Cortex Superior orbito-frontal gyrus Caudate Fusiform gyrus Cerebellum crus 2

− 18 54 − 54 33 −6 − 21 9 42 39

− 103 − 55 − 37 − 79 14 14 5 − 76 − 67

4 − 20 −8 10 70 − 14 −5 − 14 − 41

246 37 22 70 31 33 10 15 23

4.94 4.22 4.21 4.13 3.86 3.84 3.61 3.61 3.41

CD R L

superior frontal gyrus Pre-Supplementary motor area/dorsal Anterior Cingulate Cortex

21 −3

2 − 13

67 67

39 20

4.34 3.79

HC > CD L L R L R L L R

Middle occipital gyrus Pre-Supplementary motor area/dorsal Anterior Cingulate Cortex Inferior temporal gyrus Inferior temporal gyrus Calcarine gyrus Supplementary motor area Gyrus recti Cerebellum crus 2

− 15 −9 54 − 54 30 −6 − 18 39

− 103 20 − 55 − 37 − 76 14 14 − 67

1 46 − 20 −8 10 70 − 14 − 41

129 20 37 22 20 22 30 23

4.84 4.31 4.22 4.21 4.11 3.86 3.79 3.41

CD > HC R

Frontal superior gyrus

18

−1

64

16

4.16

No DEV > No UNDEV HC

No activation above statistical threshold

CD R R R L HC > CD

Inferior frontal gyrus Pre-Supplementary motor area/dorsal Anterior Cingulate Cortex Precentral gyrus Precuneus

42 −3 36 3 No activation above

32 29 − 16 − 64 statistical threshold

− 11 37 31 43

12 16 12 58

3.78 3.77 3.72 3.46

Inferior frontal gyrus Pre-Supplementary motor area/dorsal Anterior Cingulate Cortex Precentral gyrus Precuneus

42 −3 − 24 3

32 29 − 16 − 64

− 11 37 46 43

12 16 15 58

3.78 3.77 3.57 3.47

CD > HC R L L R a

Results at p-value < 0.001 uncorrected, with a minimum of 10 voxels using an exclusive masking procedure.

linked to self-monitoring functions (de Lange et al., 2007, 2010), but also self-referential affective representations (Chavez et al., 2016; D'Argembeau, 2013) and autobiographical memory retrieval (Bonnici et al., 2012). More broadly, the VMPFC is thought to code for subjective affective meaning (Roy et al., 2012), defined as “a constellation of environmental and internal cues” with self-relevant value (p. 2). In this framework, VMPFC may construct meaning by recalling previous similar settings and representing self-relevant affective features from them in order to guide actions. Accordingly, previous findings of increased VMPFC activity in CD patients were taken to suggest an important role of this region in the modulation of motor awareness and the subjective experience of willed action (Vuilleumier, 2014). Heightened recruitment of VMPFC (and other interconnected limbic regions including the amygdala; see (Voon et al., 2010a, 2010b; Voon et al., 2011)) may influence self-awareness by tagging behavior with emotionally relevant associations and autobiographical memory information through integration of sensorimotor activity with affective memory or imagery representations encoded in this region (Vuilleumier, 2014). These processes might also directly influence motor selection programs through projections to motor pathways (such as subcortical relays in basal ganglia) and thus determine different motor behaviors in response to emotionally

neither group did confidence correlate with sensorimotor adjustment (SMAB) or accuracy (percent correct), but all participants were generally overconfident (as indexed by positively signed O/UC scores, Fig. 2, F). This points to a dissociation between what participants actually do and how they subjectively evaluate their motor action, in line with previous behavioral findings (Fourneret and Jeannerod, 1998). The larger area under the trajectory curve (SMAB index) in CD patients may however reflect reduced sensorimotor integration abilities and/or lower motor correction precision in motor tasks, relative to controls. However, as discussed below, brain activation patterns were globally similar in both groups during motor execution, in agreement with the fact that elementary motor functions are intact in CD (Cojan et al., 2009; de Lange et al., 2007, 2008; Luaute et al., 2010). In contrast, brain activation patterns strikingly differed during both the preparation and the confidence rating phases. Notably, we found selective activity in the ventromedial prefrontal cortex (VMPFC) for CD patients when they prepared for the upcoming movement (Fig. 3). The VMPFC was also previously found to uniquely activate in CD during motor preparation with the affected hand in a go/ no-go paradigm (Cojan et al., 2009; Luaute et al., 2010), as well as during motor imagery (de Lange et al., 2007, 2010) and attempts to move a paralyzed limb (Marshall et al., 1997). This region has been 259


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Fig. 5. fMRI results of brain activity associated with conscious and unconscious control of movement deviations using an exclusive masking approach. Statistical activation maps for conscious control (YES DEV > NO DEV) in healthy controls (HC) vs. conversion disorder (CD) (top pannel, left) and in CD vs. HC (top pannel, right). Statistical activation maps for unconscious control (NO DEV > NO UNDEV) in CD vs. HC (bottom pannel). Results are displayed at p-value < 0.001 uncorrected, with a minimum of 10 voxels using an exclusive masking procedure. MOG, middle occipital gyrus; IT, inferior temporal sulcus, preSMA/dACC, preSupplementary motor area/ dorsal Anterior Cingulate Cortex, see Table 4 for whole brain activations.

visuomotor decision performance in both groups and preserved motor pathways in CD for movement made with either the unaffected (Cojan et al., 2009; Luaute et al., 2010) or affected hand (de Lange et al., 2007, 2008). However, both groups differed according to the brain regions engaged during conscious monitoring of movement (detected vs. undetected deviations), with significantly greater effects in motor, visual, and cerebellar regions for HC as compared with CD (Fig. 5, top pannel, left, and Table 4). Conversely, during unconscious monitoring (adjustment to undetected deviations vs. no deviation), CD showed greater activations of several areas in motor, attention and error monitoring networks, including ACC, right IFG, and precuneus, relative to HC; whereas the latter showed no differential increase. Together, these data may suggest differences in the access of action monitoring signals to awareness between controls and patients, such that brain activity elicited by motor deviations was closely associated with conscious detection in HC, but not in CD. Higher activation of ACC and premotor areas during unconsciously detected/corrected deviations in CD may contribute to a deficient feeling of error during movement execution (Boeckle et al., 2016; van Beilen et al., 2010). Most critically in our study, patients and controls recruited distinct brain systems during confidence judgment following the executed movement (Fig. 6). HC activated the left anterior precuneus and left posterior middle temporal gyrus, whereas CD patients activated the bilateral parahippocampal and amygdala regions. Remarkably, no common brain activation was found between the two groups during confidence judgments (as tested by conjunction analysis). The anterior superior region of precuneus implicated in confidence in our study corresponds to the sensorimotor sector of this area, which is functionally coupled to the superior parietal cortex, paracentral lobule, and

significant events (Vuilleumier et al., 2001), for example by biasing or arbitrating between potential motor schemata. Alternatively, activity in the VMPFC may reflect higher attention in patients due to their motor illness. It would be valuable in future studies to include control patients with organic (i.e., stroke) motor impairment in order to disentangle whether the observed VMPFC activation is specific to CD or reflect a more general effect of concomitant motor symptoms. In line with a role in self-reflective functions and affective evaluation, the VMPFC is also implicated in prospective metacognition processes such as judgments of learning (JOL; i.e., judgments whether one would remember the stimulus in a later recognition-memory test) (Kao et al., 2005) or feeling-of-knowing (FOK) (Schnyer et al., 2005), see for a review (Hebscher and Gilboa, 2016). VMPFC lesions produce metacognitive deficits such as decreased accuracy of FOK judgments (Schnyer et al., 2004) and decreased recall confidence (Modirrousta and Fellows, 2008). Better metacognitive ability is associated with higher functional connectivity between the VMPFC and rostrolateral prefrontal cortex (De Martino et al., 2013), a region also linked to metacognition (Fleming et al., 2010). Moreover, VMPFC activity has been linked to an “early confidence readout” about decisions, even prior to the decision itself or the confidence report (Benedetto De Martino et al., 2013; Gherman and Philiastides, 2017), and even when confidence judgments are not explicitly required (Lebreton et al., 2015). Altogether, these studies indicate a key role for the VMPFC in metacognitive appraisals and self-reflective awareness for outcome of actions, which appears to be differentially engaged by patients with motor CD in the context of prospective movements. During movement execution itself, both HC and CD activated similar brain networks (Fig. 4). This accords with globally similar 260


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Fig. 6. fMRI results showing main and differential effects between CD and HC during confidence judgment. Main effect of confidence in CD (A) and HC (B). Statistical activation map for the contrast CD relative to HC (C) with mean beta coefficients extracted from the lPHG/AMY (left panel) and rPHG (right pannel) clusters, illustrating the differential effects of confidence judgment between groups. Statistical map for the contrast HC relative to CD with mean beta coefficients from the PEc (D) and MT (E). Statistical thresholds are shown in the maps. Coordinates correspond to the MNI template (x, y,z; mm). Error bars represent standard error. rPHG, right parahippocampal gyrus; lPHG/lAmy, left parahippocampal gyrus/left amygdala; PEc, superior anterior portion of the precuneus. MT - middle temporal gyrus.

how one is doing in visuomotor decisions might abnormally be tagged with affective valence in CD patients, and at least partly influenced by memory associations rather than by sensorimotor signals only. Intriguingly, Voon et al. (2011) also found bilateral parahippocampal gyrus activation in CD patients, during both internally and externally generated action selection, consistent with a distinctive recruitment of memory systems during motor action processes in these patients. Moreover, we have recently observed higher hippocampal activity in CD patients than controls during voluntary force production in negative vs positive emotional conditions (Blakemore et al., 2016). Such recruitment of affective memory systems during movement preparation (VMPFC) and metacognitive evaluation (PHG) could possibly subserve aberrant beliefs about motor function underlying the patients' subjective symptom experience (e.g. that the limb is weak or paralyzed), despite the lack of neurological damage and intact recruitment of motor

(pre)motor areas (see Margulies et al., 2009). Such activation suggests that healthy controls primarily relied on sensorimotor information to evaluate their performance, unlike CD patients. This is further corroborated by concomitant activation of the posterior middle temporal gyrus, a region implicated in tracking moving objects (Pinto et al., 2012) and smooth visual pursuit (Ilg, 2008). Instead, CD patients recruited the parahippocampal and amygdala regions when rating their confidence. The PHG is intimately linked to autobiographical memories (Cabeza et al., 2004; Maguire, 2001; Rekkas and Constable, 2005). In particular it is involved in the retrieval of neutral stimuli that have been associated to emotional context (Smith et al., 2004; Sterpenich et al., 2006), thus playing an important role in contextual associative processing (Aminoff et al., 2013). Altogether, PHG activity findings converge with our VMPFC results during motor preparation to suggest that judging 261


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4.1. Strength and limitations

areas during movement execution. Furthermore, PHG activation was reported to predict retrospective confidence evaluation in a face-name associative memory task (Chua et al., 2006; Moritz et al., 2006), and increase the likelihood of realitymonitoring errors (Kensinger and Schacter, 2005), putatively by promoting more vivid imagery (Ishai et al., 2002; Kensinger and Schacter, 2005; Kosslyn et al., 2000; Roland and Gulyas, 1995; Wise et al., 2000). Hence, in CD patients, their impaired experience of movement control may entail exaggerated reliance on internal cues retrieved from memory, prior beliefs, and/or affective associations, acquired through past and/or recent experiences, imagination, or suggestions (Babinski and Froment, 1918; Charcot, 1886; Freud, 1910, p. 187) Alternatively, these findings may be interpreted as differences in task approach during confidence judgments, with CD patients rehearsing their previous trajectory in order to evaluate confidence. However, this seems unlikely given the lack of activity in prefrontal working memory areas in the same condition (Funahashi, 2017). To clarify the link of such activity with current symptoms, future studies should employ a longitudinal approach to investigate whether PHG activity changes with clinical evolution. We also note that increased activity in PHG extended to the left amygdala in CD patients but not controls. While the amygdala is known to be crucially implicated in emotional appraisal and learning, several recent studies have highlighted its involvement in performance monitoring (Garavan et al., 2002; Li et al., 2008; Polli et al., 2008, 2009), consistent with a broader role in personal relevance detection and behavioral adaptation (N'Diaye et al., 2009; Sagaspe et al., 2011). Using a Go/No go task in two patients with intracranial electrodes in the amygdala and dorsal cingulate cortex, Pourtois et al. (2010) reported amygdala responses to errors and suggested that the amygdala might register the behavioral relevance of action outcomes in accordance with behavioral goals (Ousdal et al., 2012; Sander et al., 2003). Other studies found hyperactive amygdala in CD patients (Voon et al., 2010a, 2010b), which may impact on motor performance in various ways (Sagaspe et al., 2011). Functional connectivity between amygdala and motor control regions was found to be increased in a group of patients with psychogenic movement disorders (Voon et al., 2011), while amygdala increase was coupled with reduced motor cortex activity in a single case study of conversion paralysis (Kanaan et al., 2007). Taken together, these findings suggest that the amygdala may also contribute to confidence reports in CD through its role in affective relevance processing and action outcome monitoring, with direct modulatory impact on both motor and memory systems. Overall, our data show that patients, like controls, compute their confidence adequately with respect to their actual performance, in that they rate higher their correct decisions and lower their incorrect decisions (Pleskac and Busemeyer, 2010) in a manner that scales with actual variations in reaching performance itself. However, patients required larger deviations hence larger sensorimotor signals than controls (by task design) in order to achieve this performance. With respect to metacognitive abilities, one might hypothesize that patients either have lower metacognition than controls, or have intact metacognition but use different information to make their confidence judgments despite similar conditions. Our results do not support the first hypothesis, but instead corroborate the second hypothesis, since correct metacognition sensitivity in CD patients was associated with higher recruitment of memory-related brain areas rather than visuo-motor processing (as seen in controls). One possible explanation for the absence of significant differences in overall metacognitive sensitivity, despite differences in brain patterns and underlying cognitive mechanisms, may come from the emerging view (Shadlen and Shohamy, 2016) that metacognitive behavior is a multi-componential process relying on several subsystems which can jointly, but variably, contribute to confidence monitoring (Shadlen and Shohamy, 2016).

Our study has some strengths and limitations. First it is the first study that bridges research on metacognition with conversion disorder, and thus informs both fields. Notably, neuroscience research on metacognition has mainly focused on prefrontal contributions. Our findings point to an important contribution of limbic (memory and affective) systems to metacognition in neuropsychiatric disorders such as CD. These effects should be further studied to determine whether our results are specific to CD or generalize to other populations with motor impairment, an question also concerning many previous studies of CD (Baek et al., 2017; Blakemore et al., 2016; Burgmer et al., 2006; de Lange et al., 2007, 2010; Demartini et al., 2016; Macerollo et al., 2015; Marotta et al., 2017; Parees et al., 2012, 2013; Ricciardi et al., 2016; Stone et al., 2007; To et al., 2017; Voon et al., 2010a, 2010b; Voon et al., 2011). Moreover, whilst our results point to a significant role of memory processes in metacognitive function in CD patients, we advise caution in interpretations given our relatively small sample size, low trial number and power, in addition to high patient heterogeneity. Our cross-sectional design with remitted patients is another potential limitation, and longitudinal studies will be required to more fully explore the role of these areas in distinct aspects of the disorder. Larger cohorts allowing for comparison of different motor symptoms are needed. Although CD is common in clinical practice in primary care settings, these patients are still rarely referred to more specialized centers and often suffer from delays for correct diagnosis and management (Nicholson et al., 2011; Stone, 2011). Therefore, these patients remain difficult to recruit for neuroimaging studies that tend to either recruit small samples with well-defined symptoms (Burgmer et al., 2006; Cojan et al., 2009; Marshall et al., 1997) or larger groups with mixture of motor symptoms (e.g. paresis and abnormal movement such as tremor or dystonia) and/ or psychogenic seizures (Baek et al., 2017; To et al., 2017). In addition, psychiatric comorbidities are frequent (Stone et al., 2010) and pose further constraints and limits on studies comparing these patients with other groups. 5. Conclusion Overall, our work bridges for the first time research in metacognition with research in conversion disorder. Our results reveal that distinct brain regions subserve metacognitive confidence in HC and CD, whereas motor execution itself recruited similar brain networks in both groups. We provide novel evidence for a recruitment of systems involved in affective and memory associative processing during the subjective evaluation of visuomotor decisions in CD patients, rather than areas integrating multimodal sensorimotor information with agency representations and executive control as observed in controls. Specifically, confidence judgements in CD patients engaged bilateral parahippocampal regions and amygdala, pointing to differential reliance on internally generated information and memory systems mediating the retrieval of affective autobiographical associations. Moreover, only CD patients showed a selective activation of VMPFC during motor preparation, an area intimately connected to the limbic system and implicated in self-referential processing. These results do not only point to fundamental mechanistic differences for subjective evaluation of visuomotor decisions in CD, but also highlight the role of memory systems in metacognition processes as proposed by recent theoretical accounts in other domains (Shadlen and Shohamy, 2016), which may be pathologically exaggerated in some neuropsychiatric conditions, such as motor CD. Funding This work was supported by a grant from the Swiss National Science Foundation (No 320030-143764) and an award of the Academic 262


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Society of Geneva to PV (Foremane Fund).

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