Epilepsy Research (2015) 112, 84—91

journal homepage: www.elsevier.com/locate/epilepsyres

Cortical thickness, surface area and folding in patients with psychogenic nonepileptic seizures c a,∗, Marko Dakovi´ c b, Michael Kerr c, Aleksandar J. Risti´ cevi´ c a, Aleksandra Parojˇ ci´ c a, Dragoslav Soki´ ca Maˇ sa Kovaˇ a

Epilepsy Center, Neurology Clinic, Clinical Center of Serbia, University of Belgrade, Belgrade, Serbia Faculty of Physical Chemistry, University of Belgrade, Belgrade, Serbia c Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom b

Received 10 November 2014; received in revised form 12 February 2015; accepted 27 February 2015 Available online 7 March 2015

KEYWORDS Psychogenic non-epileptic seizures; Cortical thickness; Sulcal depth; Insula; Freesurfer

Summary Objective: To determine cortical thickness (CTh), cortical surface area (CSA), curvature and sulcal depth (SD) in patients with psychogenic nonepileptic seizures (PNES). Methods: Freesurfer software was used to identify differences between active and control group in Cth, CSA, curvature, and SD. Neuropsychological tests intending to document possible frontal lobe deficit were applied. Results: We included 37 patients with PNES (age 37.3 ± 13.8; female/male 31/6; age of disease onset 26.1 ± 10.6; age of disease duration 11.1 ± 11.1), and 37 healthy controls (age 38.4; ±12.7; female/male 26/11). No difference in CSA and curvature was detected between groups. Patients with PNES had increased CTh in the left insula, left and right medial-orbitofrontal, and left lateral-orbitofrontal, and decreased CTh in the left and right precentral, right enthorinal, and right lateral-occipital region than healthy controls. SD was increased at the level of the left and right insula, right rostral anterior cingulate, right posterior cingulate, and left cuneus, and reduced at the level of the right and left medial-orbitofrontal sulci in patients with PNES compared to healthy controls. Conclusion: Individuals with PNES display a distinct profile of changes in CTh, in association with increase in SD in both insula as compared to controls. Our results may contribute to the understanding of the neurobiological background of PNES. Further research, to include replication of the findings and directed to understand the role of insula is needed. © 2015 Elsevier B.V. All rights reserved.



Corresponding author. Tel.: +381 112685596; fax: +381 112684577. E-mail addresses: [email protected] (A.J. Risti´ c), [email protected] (M. Dakovi´ c), mp [email protected] (M. Kerr), [email protected] (M. Kovaˇ cevi´ c), [email protected] (A. Parojˇ ci´ c), [email protected] (D. Soki´ c). http://dx.doi.org/10.1016/j.eplepsyres.2015.02.015 0920-1211/© 2015 Elsevier B.V. All rights reserved.

Cortical thickness, surface area and folding in patients with psychogenic nonepileptic seizures

Introduction The International League Against Epilepsy (ILAE) has identified psychogenic nonepileptic seizures (PNES) as one of the 10 key neuropsychiatric issues associated with epilepsy (Kerr et al., 2011). The accurate diagnosis of PNES is essential, as misdiagnosis of PNES leads to inappropriate treatment of presumed epilepsy with significant consequences. The most reliable diagosis of PNES, in addition to good clinical data, relies on the habitual event being recorded on video EEG (vEEG) (LaFrance et al., 2013). Although classical psychiatric theory explains PNES in the context of psychological and psychodynamic models, preliminary data from recent neuroimaging studies argue for the presence of impairment in motor conceptualization or abnormal limbic-motor interaction (Voon et al., 2010). The results of resting-state functional MRI results in PNES patients show abnormal, strong functional connectivity between the insula and precentral sulcus. This provides a possible neurophysiological correlate where emotions can influence executive control, resulting in altered motor function (van der Krujis et al., 2012). Disease-related cortical volume and cortical thickness measures have been employed to better understand the underlying pathophysiology in temporal lobe epilepsy (Keller and Roberts, 2008). In addition, these data were used in explore the role of frequently used antiepileptic drugs. The authors showed that the use of sodium valproate is associated with parietal lobe thinning, reduced total brain volume, and reduced white matter volume (Pardoe et al., 2013). Both morphologic whole-brain MRI measurements, voxel-based morphometry (VBM) (Ashburner and Friston, 2000), and Freesurfer analysis (Fischl and Dale, 2000), have been used in epilepsy patients. VBM and cortical thickness on Freesurfer were analysed in a single study of subjects with PNES. The authors showed abnormal cortical atrophy of the motor and premotor regions in the right hemisphere and the cerebellum bilaterally, in addition to significant association between increasing depression scores and atrophy involving the premotor regions. Nevertheless, the authors acknowledged the sample size (20 patients) as the major weakness of the study (Labate et al., 2012). Several other brain morphometric measurement methods are currently available. Cortical volume is the composite of cortical surface area and thickness. Recent work has illustrated that both are highly heritable but genetically unrelated (Panizzon et al., 2009). Some authors have found distinct results when analysing patterns of cortical surface area reduction and neocortical thinning in temporal lobe epilepsy patients (Alhusaini et al., 2012). Studies of the morphology of the cortical surface (variability of folding patterns) have provided sufficient results to deserve the interest of scientific audience (Mangin et al., 2010). Studying the morphologic patterns of the inferior surface of the temporal lobe in healthy controls and patients with temporal lobe epilepsy, authors have described ‘‘simplified’’ and unbroken collateral sulcus as the predominant sulcal pattern. They suggest that these may be an indicator of neurodevelpomental abnormality associated with this condition (Kim et al., 2008). The single published morphometric study on PNES studied to date did not publish data on cortical surface area, and morphology of the cortical surface (Labate et al., 2012).

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There is a strong case for further exploration of the morphometric abnormalities in patients with PNES. Our aim was to examine, by comparing PNES patients to healthy control subjects, several parameters of cortical anatomy in patients with PNES; including cortical thickness, cortical surface area, and cortical folding (curvature and sulcal depth) that may contribute to PNES pathophysiology. In addition, we investigated the association between clinical parameters and neuropsychological assessment measures and data from morphometric analysis.

Materials and methods Patients and controls Patients were recruited from a cohort of 564 subjects who underwent video-EEG telemetry at the Epilepsy Center, Neurology Clinic, Clinical Center of Serbia in the period between June 2010 and December 2013. Indications for telemetry included: differential diagnosis, epileptic syndrome definition and presurgical evaluation. The diagnosis of definite PNES was made when: (a) patients with indicative clinical history had spontaneous seizures recorded during vEEG or habitual attacks provoked by a subcutaneous administration of saline solution while under vEEG; (b) all recorded seizures were considered habitual by seizure witnesses, and (c) epileptiform interictal discharges and ictal EEG that correlates with clinical event were not registered. All PNES patients underwent the protocol routinely used for patients with epilepsy (brain MRI and neuropsychological assessment). Only patients with diagnosis of definitive PNES were included in the analysis. PNES were classified according to their resemblance to epileptic seizures during telemetry: (1) dialeptic-like-loss of consciousness without motor phenomena; (2) astaticlike-loss of consciousness and muscle tone with fall; (3) motor—different motor phenomena; and (4) multiple. A control group (n = 37) was identified among staff of the Neurology Clinic, Clinical Center of Serbia, and students from the Medical School, University of Belgrade with no previous history of neurological and psychiatric diseases. Neurological examination were normal in all patients and control subjects. The research was performed in accordance with the Declaration of Helsinki of the World Medical Association and was approved by the Ethics Committee on Human Research of the Clinical Centre of Serbia. Written informed consents were obtained from every patient and control subject.

Image acquisition All control and PNES subjects MR examinations were performed using Philips Achieva 1.5 T using an 8-channeled head coil. T1W-3D-FFE sequences (TR = 25 ms, TE = 5 ms, FA = 30◦ , matrix 256 × 256, slice thickness 1 mm, slice gap 1 mm, sagital plane) were used for analysis. The imaging protocol also contained T1 W and T2 W and FLAIR sequences in the axial plane in order to exclude the presence of gross pathology. 3D-T1 W images were transferred to a MacBookPro workstation and converted to NifTI (Neuroimaging Informatics Technology Initiative) format using MRIcron

86 (http://www.mricro.com) (Rorden and Brett, 2000). The orientation of the images was checked/corrected using fslreorient2std script (Jenkinson et al., 2012; Smith et al., 2004).

A.J. Risti´ c et al. two-sided and conducted using the Statistical Package for Social Sciences (Version 16 for Windows; SPSS Inc., Chicago, IL, U.S.A.).

Results Freesurfer analysis Freesurfer software (version 5.3.0) (http://www.surfer. nmr.mgh.harvard.edu) analysis was performed on a MacBookPro (OS X version 10.9.3; 8 GB RAM, Intel Core i7; 4 × 2 GHz) using the recon-all script which performs automatic cortical reconstruction and brain segmentation. The detailed description of the algorithm can be found elsewhere (Dale et al., 1999; Fischl, 2012; Fischl et al., 2002). Average run time per subject was 6.3 h. After completion of script, segmentation of tissues and structures, as well as structure labelling were inspected for accuracy. Errors in segmentation of cortical grey matter (such as assignation of parts of dura to grey matter) were corrected manually using Freesurfer’s integrated tkmedit software. After correction, the recon-all script was re-run in order to recreate final surfaces. In order to prepare obtained surfaces for statistical analysis smoothening was applied using recon-all with qcache option added. QDEC (Query Design Estimate Contrast, version 1.4), a tool within Freesurfer, was used to identify differences between PNES patients and healthy subjects in cortical thickness, cortical surface area, and cortical folding (curvature and sulcal depth). Age and estimates for total intracranial volume were used as nuisance factors. Statistical significance levels were set at p < 0.01, uncorrected. To control for multiple comparison, statistical significance levels were cluster corrected for both hemispheres using False Discovery Rate (FDR).

Neuropsychological assessment In a subsection of the PNES patients, a battery of neuropsychological tests intending to document possible frontal lobe deficit were applied. Neuropsychological assessment included the following: Full-Scale IQ, Verbal IQ, Performance IQ (WAIS-III), Similarities test (WAIS-III), Matrix Reasoning test (WAIS-III), Coding test (WAIS-III), Verbal Fluency test (total score S/K/L), Trial Making Test B, ReyOsterrieth Complex Figure copy, Wisconsin Card Sorting Test categories achieved, and perseverative errors. Neuropsychological testing was not applied when Serbian was not a native language, and in subjects with low educational levels.

Statistical analysis Skewness, kurtosis, and Kolmogorov—Smirnov test were used to test the normality. Categorical variables were analysed using the chi-square analysis or Fisher’s exact test, whereas continuous variables were analysed using the Mann—Whitney test or the Kruskal—Wallis test. Correlations were tested using bivariate two-tailed parametric or nonparametric correlation procedures (Pearson or Spearman’s coefficient). The alpha error was set at 0.05. All statistical analyses were

A total of 49 patients with PNES were documented following video-EEG telemetry. Nine patients were excluded due to comorbid epilepsy, 2 patients with PNES were excluded due to lack of volumetric MRI data, 1 patient with PNES was excluded due to brain MRI pathology (periventricular greymatter heterotopy in the right occipital lobe). A final cohort of 37 patients were analysed.

Patients and control subject demographics In total 94.5% (n = 35) of patients with PNES were righthanded (mean age 37.3 ± 13.8; range 17—69; female/male 31/6; mean age of disease onset 26.1 ± 10.6; range 7—53; mean age of disease duration 11.1 ± 11.1; range 1—56). In the control groups, 97.2% (n = 36) were right-handed (mean age 38.4; ±12.7; range 18—63; female/male 26/11) There were no significant differences on these parameters between the active and control group.

PNES description A positive family history of epilepsy was seen in 16.2% (n = 6); a family history of psychiatric diseases in 13.5% (n = 5); history of head trauma in 24.3% (n = 9). Previous history of abuse was classified as: no history 70.3% (n = 26), psychological abuse 10.8% (n = 4), physical abuse 13.5% (n = 5) and sexual abuse 5.4% (n = 2). A stressful trigger event was seen in 27% (n = 10). Non epileptic attack frequency in the last 6 months was: 5.4% 1/week. Median number of overall AED used was 3 (range 0—10). Data was available from a mean of 81 h of recording in the video EEG monitoring unit (range 1—96). The median number of PNES recorded was 2 (range 1—78); Event types seen in telemetry were: dialeptic-like 13.5%, astatic-like 5.4%, motor 67.6%, and multiple 13.5%.

Freesurfer analysis No statistically significant difference in area surface and curvature analysis was detected between PNES patients and healthy controls. Cortical thickness Fig. 1 illustrates the results of the cortical thickness analysis using QDEC with significance threshold set at p < 0.01, uncorrected for multiple comparison. Patients with PNES had significantly thicker cortices than healthy controls in the left insula, left and right medial orbitofrontal, left lateral orbitofrontal, left rostral anterior cingulate, right postcentral region, and left middle temporal, inferior temporal and left and right inferior parietal region. Patients with PNES had significantly thinner cortices than healthy control in the left

Cortical thickness, surface area and folding in patients with psychogenic nonepileptic seizures

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Fig. 1 Freesurfer whole-brain vertex-wise analysis of cortical thickness. Mean difference maps were generated by aligning and averaging brain MRIs across participants in spherical space to demonstrate the main cortical thickness differences between the two groups at each point on the cortex. Statistical maps controlling for the effect of age showing cortical thinning and thickening patterns in PNES patients relative to controls for the (a and b) lateral, (c and d) superior, (e and f) medial, and (g and h) inferior surfaces of the left and right hemisphere presented on the inflated cortical surface (dark grey = sulci; light gray = gyri). Red and yellow represent areas where patients with PNES had significantly thicker cortices than healthy controls. Dark and pale blue represent areas where patients with PNES had significantly thinner cortices than healthy control. In order to visually demonstrate the widespread changes, significant thresholds were set at p < 0.01, uncorrected. Cortical maps were smoothed with full-width at half-maximum (FWHM) Gaussian kernel set at 10 mm. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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A.J. Risti´ c et al.

Table 1 Clusters of differences in cortical thickness between PNES patients and healthy subjects for each hemisphere controlling for the effects of age (p-value threshold set at 0.05; clusters of cortical thickness difference that survived FDR for multiple comparison at p < 0.01 are presented). Cluster no.

Cluster region

1 2 3 4 5 6 7 8

Left insula Right medial orbitofrontal Left medial orbitofrontal Right precentral Right entorinal Left precentral Right lateral occipital Left lateral orbitofrontal

Maximum -log10 (p-value) in the cluster 7.71 7.10 6.35 −6.26 −5.70 −−5.26 −5.43 4.85

precentral, right precentral, left precuneus, right enthorinal, right lateral occipital, and right pericalcarine region. Table 1. presents the position and surface of clusters of differences in cortical thickness at each vertex between PNES patients and healthy control tested for each hemisphere by QDEC controlling for the effects of age (p-value threshold set at 0.01; clusters of cortical thickness difference survived FDR set at p < 0.01). We further correlated eight clusters that emerged following FDR correction for multiple comparison. Left insula thickness positively correlated with left medial-orbitofrontal thickness (Pearson Coefficient 0.407 p = 0.013), right medial-orbitofrontal thickness (Pearson Coefficient 0.398 p = 0.015), left precentral thickness (Pearson Coefficient 0.435 p = 0.007) and right precentral thickness (Pearson Coefficient 0.463 p = 0.004). Left medialorbitofrontal thickness positively correlated with right medial-orbitofrontal thickness (Pearson Coefficient 0.587 p < 0.001), left precentral thickness (Pearson Coefficient 0.446 p = 0.006), and right precentral thickness (Pearson Coefficient 0.426 p = 0.009). Right medial-orbitofrontal thickness positively correlated with left precentral thickness (Pearson Coefficient 0.404 p = 0.013). Left precentral thickness positively correlated with right precentral thickness (Pearson Coefficient 0.801 p < 0.001), right lateral occipital thickness (Pearson Coefficient 0.732 p < 0.001), and left lateral orbitofrontal thickness (Pearson Coefficient 0.386 p = 0.022). Right precentral thickness positively correlated with right lateral occipital thickness (Pearson Coefficient 0.630 p < 0.001). Left insula thickness negatively correlated with age (Correlation Coefficient −0.452 p = 0.005) and disease onset (Correlation Coefficient −0.372 p = 0.023). Left precentral thickness negatively correlated with disease duration (Correlation Coefficient −0.348 p = 0.035), and right enthorinal thickness positively correlated with disease onset (Correlation Coefficient 0.375 p = 0.022). A significant relation between the eight clusters of different cortical thickness and the clinical parameters was not found (family history of epilepsy; family history of psychiatric diseases; history of head trauma; history of abuse; stressful event as identified trigger; seizure frequency in last 6 months; number of AED used; seizure types) (Mann—Whitney, and Kruskal—Wallis).

MNIx

MNIy

MNIz

Vertex number at the maximum

Surface area of cluster (mm2 )

−37.4 5.7 −6.6 24.9 51.6 −34.0 20.6 −24.3

−4.5 37.6 14.3 −21.1 26.2 −14.9 −93.1 38.0

−13.5 −23.6 −14.7 66.3 1.3 65.4 −8.6 −10.0

122,634 27,329 35,709 56,213 13,147 128,777 62,799 145,230

126.4 47.31 41.89 168.15 51.64 65.01 66.99 27.1

Sulcal depth Sulcal depth was increased at the level of the left and right insular sulci, right rostral anterior cingulate, right posterior cingulate, and left cuneus, and reduced at the level of the right and left medial orbito-frontal sulci in patients with PNES compared to healthy controls (Fig. 2.).

Neuropsychological assessment Ten patients with PNES were not tested due to low educational level in 7, and Serbian not being their native language in 3. Thus, in total 27/37 (72.9%) patients underwent neuropsychological testing: average full score IQ 92.7 ± 13.9 (range 67—121); verbal IQ 92.8 ± 15.1 (range 66—119); performance IQ 92.7 ± 14.2 (range 68—125); Similarities scores 8.8 ± 3 (range 3—15); Matrix Reasoning 7.3 ± 2.2 (range 1—11); Coding 7.8 ± 2.6 (range 2—15); verbal fluency 26 ± 6.5 (range 14—41); Trial Making Test—B 137.4 ± 70.2 (range 53—372); Rey—Osterrieth Complex Figure copy 2.6 ± 1.2 (range 68—125), Wisconsin Card Sorting Test categories achieved (4.3 ± 2.1 (range 0—6), and perseverative errors (30.2 ± 24 (range 3—118). Right medialorbitofrontal thickness negatively correlated with total IQ (Pearson Coefficient −0.421 p = 0.026), verbal IQ (Pearson Coefficient −0.384 p = 0.044), performance IQ (Pearson Coefficient −0.436 p = 0.020), and similarities (Pearson Coefficient −0.393 p = 0.038). Right medial-orbitofrontal thickness positively correlated with TMT-B (Pearson Coefficient 0.411 p = 0.03), and Rey—Osterrieth Complex Figure copy (Pearson Coefficient 0.419 p = 0.027).

Discussion In attempt to assess different cortical morphometry measurements in a large sample of patients with PNES, we found increased cortical thickness in the left insula, left and right medial orbitofrontal, and left lateral orbitofrontal region, and decreased thickness in the left and right precentral, right enthorinal, and right lateral occipital region of patients with PNES as compared to age matched healthy controls. Cortical surface area did not differ between groups. Moreover, whereas robust sulcal deepening was found in the PNES

Cortical thickness, surface area and folding in patients with psychogenic nonepileptic seizures

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Fig. 2 Freesurfer whole-brain vertex-wise analysis of sulcal depth. Sulcation conveys information on how far a particular surface vertex point is from a hypothetical ‘‘midsurface,’’ which exists between the gyri and sulci. The sulcal depth measure in Freesurfer is the integrated dot product between the movement vector and the surface normal, so objects that move consistently outwards (like sulci) will be positive. Maps showing clusters of significant sulcal depth difference of lateral, (c and d) superior, (e and f) medial, and (g and h) inferior surfaces of the left and right hemisphere are presented on the inflated cortical surface (dark grey = sulci; light gray = gyri). Sulcal depth was increased at the level of the left and right insular sulci, right rostral anterior cingulate, right posterior cingulated, and left cuneus, and reduced at the level of the right and left medial orbital-frontal sulci in patients with PNES compared to healthy controls. All labeled clusters survived False Discovery Rate for multiple comparison at p < 0.001. Cortical maps were smoothed with full-width at half-maximum (FWHM) Gaussian kernel set at 10 mm.

90 group compared to control, mainly in the left and right insular cortex, no difference was found in the gyral curvature. The current findings dynamically corroborate recent study examining alteration in resting-state networks that underlie PNES (van der Kruijs et al., 2014). Namely, van der Kruijs et al. (2014) have shown that PNES patients have an increased coactivation of several regions in the resting-state networks associated with fronto-parietal activation (orbito-frontal, insular and subcallosal region), executive control (cingulate and insular cortex), sensorimotor functioning (cingulate gyrus, superior parietal lobe, pre- and postcentral gyri, and suplementar motor area), and the default mode (precuneus, and (para-) cingulate region). The importance of their study is that it provides a potential explanation of the specific alteration of consciousness seen in people with PNES. In fact, loss of consciousness is the common feature in different clinical expressions of PNES, in addition to abnormal coping styles, and lack of motor control (van der Krujis et al., 2012). Hence, through structural MRI data our results support observations and conclusions coming out of the previous functional connectivity study (van der Kruijs et al., 2014). It is of interest to speculate that our findings of differential insula thickness may be of relevance in the emotional processes driving PNES. Researchers have for example highlighted an important role of the insula in behavioural and emotional dysregulation (Bebko et al., 2015; Pagliaccio et al., 2014). This may be a central functional abnormality in people with PNES. Genetic factors that influence functional connectivity and grey-matter density seem to be distinct in the healthy population (Glahn et al., 2010). In the clinical settings, studies found no correlation between functional connectivity and grey-matter volumes (Guo et al., 2014), explained by compensatory changes that occurred in functional connectivity following progressive grey matter volume reduction. However, it was suggested that in some brain disorders regional cortical morphometric alterations could be an important consideration in functional imagining studies (He et al., 2007; Whitwell et al., 2011). It has also been noted that reduction in grey-matter volume correlates with drug treatment in epilepsy (Pardoe et al., 2013) and schizophrenia (Ho et al., 2011). We haven’t systematically investigated the effect of antiepileptic drugs on different cortical measures in our patients. Though, in the present study more than half of cortical thickness regions of interest are thicker in PNES compared to normal controls, increase cortical thickness has not been described as a feature of medical treatment to date. To our knowledge, only one study to date has examined cortical thickness in PNES patients (Labate et al., 2012). Since the demography and disease duration are comparable between this study and ours, we propose that some of the findings in our study could be different due to the larger sample size. Indeed, apart from the right precentral cortical thinning our results confer much wider structural evidence potentially illuminating the pathophysiology of PNES. Another potential, but less likely reason for such discrepancy may lie in the difference in neuropsychological features. Our cohort is in fact more similar to the neuropsychological profile seen in studies in PNES (Dodrill, 2008).

A.J. Risti´ c et al. A novel element in the present study is a difference in local gyrification especially in the insula region. The precise mechanisms underlying cortex folding are still largely unknown (Mangin et al., 2010). Nevertheless, interest in gyrogenesis has undergone a renaissance, which has been augmented by progress in the characterization of different types of cortical progenitor cells (Sun and Hevner, 2014). For instance, fibroblast growth factor 2 has shown a selective effect on expansion and gyrification of the insula in mice (Rash et al., 2013). A number of observations and hypotheses support the idea that the cortical folding geometry is a macroscopic probe for hidden architectural organization or developmental events (Mangin et al., 2010). Advances in neuroimaging analysis methodologies have allowed the examination of local gyrification at ever increasing resolution: deviations of cortex gyrification are reported in schizophrenia (Plaze et al., 2011) and bipolar disorders (McIntosh et al., 2009). A speculative theory would be that an element of the pathophysiology of PNES can be attributed to variation in sulcal morphology in the insula, but further research is needed. Some limitations in our findings need to be addressed. In a recent study in PNES patients, altered long-range functional connectivity density in the occipital cortex that correlated with disease duration was described, and this was justified through adaptation for long-term hypervigilance and increased response to external stimuli (Ding et al., 2014). Although we determined cortical thinning in the right lateral occipital region in PNES patients, the rationale explanation for this observation is not evident. Further, while one might perhaps anticipate that thicker and thinner cortices may negatively correlate to some extent, proving direct association between these regions, we have not found such relations in our sample. The same applies to the lack of correlations between cortical morphometry measures and clinical parameters. Only right medial orbitofrontal thickness correlated with neuropsychological variables mainly demonstrating executive dysfunction. Although recent study provide compelling evidence for the correlation between structural asymmetry and cognitive function (Plessen et al., 2014), the absence of more abundant correlations most likely reflect small sample size. Additionally, many models of gyrification posit that thinner cortex should be associated with increased cortical folding which is consistent with polymicrogyria—–a condition associated with thinning of cortex (Fornito et al., 2008). Why the insula in our sample specifically breaks this pattern is unclear. In conclusion, we have demonstrated that individuals with PNES display a distinct profile of bilateral changes in cortical thickness, in association with increased in sulcal depth in both insular cortices as compared to controls. Our results may contribute to the understanding of the neurobiological background of PNES. Further research, to include replication of the findings and directed to understand the role of insula is needed.

Conflict of interest statement All authors report no conflicts of interest.

Cortical thickness, surface area and folding in patients with psychogenic nonepileptic seizures

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Cortical thickness, surface area and folding in patients with psychogenic nonepileptic seizures.

To determine cortical thickness (CTh), cortical surface area (CSA), curvature and sulcal depth (SD) in patients with psychogenic nonepileptic seizures...
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