SEARCH

SEARCH BY CITATION

Keywords:

  • Cortical area and thickness;
  • Temporal lobe epilepsy;
  • Interictal psychosis;
  • Surface-based morphometry;
  • Magnetic resonance imaging;
  • Cognitive impairment

Summary

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. Funding
  8. Disclosure
  9. References
  10. Supporting Information

Purpose:  To determine whether cortical abnormalities are more severe and widespread in patients with temporal lobe epilepsy (TLE) and interictal psychosis (IP) compared to those with TLE only (NIP) and healthy controls (HC), and to explore the associations between cortical parameters (area, thickness and volume), psychotic symptoms, and cognitive performance.

Methods:  Twenty-two patients with IP (9 male; 10 hippocampal sclerosis, HS), 23 TLE nonpsychotic (NIP) patients (11 male; 13 HS) matched for duration of epilepsy and 20 HC participated. Surface-based morphometry (SBM) was used to measure cortical parameters. Cognition was examined in IP and NIP patients. Associations between cortical parameters and cognition were examined using linear mixed models adjusted by age, gender, and brain volume.

Key Findings:  IP patients had an earlier onset of epilepsy, more status epilepticus, and worse cognitive performance than NIP patients. In IP patients, cortical thickness was reduced in the inferior frontal gyrus (IFG), and their current IQ was associated with decreases in area, but not thickness, in regions of the frontotemporal cortex.

Significance:  IP likely reflects the interplay of psychosis-related genetic factors and the cumulative effects of seizure activity on the brain. Cortical thinning in the IFG, a region implicated in schizophrenia, is likely to be related to seizure activity, whereas changes in IQ, associated with reductions in area of frontotemporal cortex, may be related to the presence of psychosis.

Chronic interictal psychosis (IP) appears years after the onset of epilepsy and clinically resembles schizophrenia (Perez & Trimble, 1980; Toone et al., 1982). The link between epilepsy and IP is complex. Both conditions may share common etiologic factors, and brain damage caused by recurrent seizures may also increase the risk of IP. A longitudinal study (Qin et al., 2005) has established that the risk for acquiring the diagnosis of schizophrenia is age-related and two to three times higher in patients with epilepsy than in the general population. A family history of psychosis is a risk factor for IP as is the cumulative effect of epilepsy. IP may be more common in those with partial complex seizures, that is, temporal lobe epilepsy (TLE) and particularly in those with hippocampal sclerosis (HS) (Sachdev, 1998; Kanemoto et al., 2001).

Imaging studies have searched for differences in hippocampal and amygdalar abnormalities in patients with TLE, with or without IP, with variable results. Some have reported greater loss of volume in the hippocampus and amygdala (Marsh et al., 2001) and greater reduction of the neuronal marker N-acetyl-aspartate (Maier et al., 2000), whereas others (Tebartz Van Elst et al., 2002) have reported bilateral increases in amygdalar volume in patients with TLE and IP compared to those without IP.

Neuropathologic abnormalities predominantly involving the frontotemporal cortex (i.e., mesiotemporal sclerosis, neuronal loss, and gliosis) have been described in HS patients without (Cavanagh & Meyer, 1956; Falconer et al., 1964; Margerison & Corsellis, 1966; Kuzniecky et al., 1987; Nishio et al., 2000) and with psychosis (Taylor, 1975; Roberts et al., 1990; Bruton et al., 1994; Suckling et al., 2000). Microdysgenesis, local excitotoxic effects of seizures, and deafferentation of synaptically connected areas are possible mechanisms for these abnormalities that may be progressive in some patients (Bernhardt et al., 2009; Dabbs et al., 2009).

Magnetic resonance imaging (MRI) studies have demonstrated extratemporal cortical abnormalities (i.e., away from the epileptogenic focus) in TLE patients with or without IP, especially in the frontal cortex, and some (Marsh et al., 2001; Cormack et al., 2005) but not others (Rusch et al., 2004; Guarnieri et al., 2005) have reported these abnormalities to be more prominent in patients with IP.

The pattern of cognitive impairment in TLE (Hermann et al., 2009) also points to the presence of extratemporal pathology. In addition to memory impairment, attributable to hippocampal abnormalities, decline in IQ and deficits in language, executive function, and motor speed have also been reported (Oyegbile et al., 2004; Keller et al., 2009; Tuchscherer et al., 2010). These deficits have been found to be more severe in TLE patients with IP than in nonpsychotic patients (Kristensen & Sindrup, 1979; Adachi et al., 2000; Nathaniel-James et al., 2004; Flugel et al., 2006c) and associated with cortical abnormalities (Oyegbile et al., 2004).

Surface-based morphometry (SBM) allows the independent measurement of cortical area and thickness—indexes that share a high heritability but are determined by different genetic mechanisms (Panizzon et al., 2009) and that respond differently to developmental or acquired disease (Gutierrez-Galve et al., 2010). FreeSurfer (Fischl & Dale, 2000) is a software tool for SBM with realistic cortical reconstruction that allows for manual correction of topologic errors (Lee et al., 2006).

In patients with TLE, SBM has shown increased complexity of temporal and frontal cortical folding distant from the epileptic focus (Voets et al., 2011) and widespread cortical thinning in regions connected with the hippocampus (Bernhardt et al., 2008; Mueller et al., 2009; Raj et al., 2010). In patients with HS, cortical thinning is associated with loss of volume in the hippocampus and anterior thalamus (Moran et al., 2001; Mueller et al., 2010), and similar changes are also present in TLE without HS (Mueller et al., 2009). Increased prefrontal and temporal cortical thickness has been reported in patients with postictal psychosis (Dubois et al., 2011), but SBM has not been applied to the study of IP.

Using magnetization transfer ratio (MTR) we have previously reported (Flugel et al., 2006a) abnormalities in the left superior and middle temporal gyri undetected by conventional MRI in patients with TLE and IP compared to those without. Using SBM (FreeSurfer) in the same patient cohort, we report herein an exploratory study of cortical abnormalities and their associations with clinical symptoms and cognition. We hypothesized that cortical thinning would be present in patients with TLE compared to HC and that it would be more severe in those with IP. We also hypothesized that impaired cognitive performance will be associated with the severity of cortical abnormalities.

Methods

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. Funding
  8. Disclosure
  9. References
  10. Supporting Information

Subjects

We identified 54 patients with TLE and chronic IP from among those referred to the neuropsychiatric services of the National Hospital for Neurology and Neurosurgery, London, and the National Society for Epilepsy, Buckinghamshire (United Kingdom) between 1995 and 2005. All patients had a diagnosis of TLE and were under the care of consultant epileptologists at the National Hospital for Neurology and Neurosurgery. They previously had either routine or prolonged electroencephalographic evaluation, with and without video recording. The classification of TLE was based on semiology, MRI, and electroencephalography (EEG) findings. Unilateral HS was concordant with semiology and EEG in all cases. In patients with no focal lesions on MRI, the semiology and interictal and ictal EEGs were highly suggestive of temporal lobe origin. All patients had a prospective psychiatric assessment and all fulfilled criteria for schizophrenia from the Diagnostic and Statistical Manual, Fourth Edition (DSM-IV-TR). Of the 54 patients, 22 patients (9 male and 13 female) with either HS or no focal lesions on conventional MRI were included in our study. Subjects were excluded if they had lesions other than HS on conventional MRI (acquired strokes, n = 2; traumatic brain damage, n = 2; cortical malformations, n = 3; white matter lesions, n = 2; tuberous sclerosis, n = 1) and for other reasons, such as nonresponse or refusal (n = 16), inability to give consent owing to psychosis (n = 4), and if they were older than 65 years (n = 2). The excluded patients (21 male and 11 female) did not differ significantly from the patients in the study in the age of onset or duration for either epilepsy or psychosis. The mean age of the 22 patients with IP was 38.9 years (standard deviation, SD 9.7). Ten IP patients had no MRI focal lesions, six had left HS, four had right HS, and two had bilateral HS. All patients with IP were prescribed antiepileptic drugs and 18 were also taking antipsychotic medication: 15 were prescribed atypical antipsychotic drugs (5 risperidone, 5 sulpiride, 3 olanzapine, 1 clozapine, and 1 amisulpride); one was prescribed zuclopenthixol and 2 haloperidol).

Twenty-three patients with TLE without psychosis (NIP) (11 male and 12 female) with a mean age of 38.7 years (SD 12.1) were selected from the same hospital population to match the IP group as closely as possible. IP and NIP patients were matched for age, premorbid IQ, duration of epilepsy, findings on conventional MRI (10 without focal lesions, 6 left HS, 4 right HS, and 3 bilateral HS) and EEG findings. All NIP patients were taking antiepileptic drugs, but none had been prescribed antipsychotic medication. Patients were excluded if they had extrahippocampal lesions on conventional MRI.

Twenty-one healthy controls (HCs) who had been recruited for another MRI study using the same imaging protocol were used as MRI controls. This group comprised 11 male and 10 female controls with a mean age of 36.0 years (SD 11.3). None of the HCs had a history of psychiatric disorder. A history of drug abuse or alcohol intake of more than 21 units per week was exclusion criteria for all groups. There was no family history of psychosis in either the IP or NIP groups. Written informed consent was obtained from all subjects. Ethical approval was obtained from the ethics committee of the National Hospital for Neurology and Neurosurgery, University College London Hospital, Queen Square, London, United Kingdom.

Studies describing MTR, diffusion imaging, and cognitive findings in this cohort have already been published (Flugel et al., 2006a,b,c).

Clinical and neuropsychological assessment and MRI were performed on the same day. Demographic details are given in Table 1.

Table 1.   Demographic and cognitive measures in patients and controls
Mean (SD) [range] n testedIP (n = 22)NIP (n = 23)Healthy controls (n = 21)Comparison
  1. aThe differences between the IP and NIP groups failed to reach significance (t (43) = −0.69; p = 0.491).

Age (years)38.9 (9.7) [22–58] 2238.7 (12.1) [18–63] 2336.0 (11.3) [19–55] 21F2,63 = 0.47; p = 0.626
Male gender, N (%) 9 (40.9) 2211 (47.8) 2311 (52.4) 21χ2 (2) = 0.58; p = 0.749
Total brain volume (ml)1365.0a (1741.3) 221396.8 (1308.6) 231531.6 (1261.4) 21F2,63 = 7.92; p < 0.001
Left handed, N (%)4 (18.2) 221 (4.6) 22NAFisher’s exact = 0.345
Age at onset of epilepsy (years)7.0 (7.0) [1–26] 2212.5 (7.2) [1–25] 22NAt (42) = −2.62; p = 0.012
Duration of epilepsy (years)31.9 (11.1) [3–51] 2225.7 (14.2) [8–54] 22NAt (42) = 1.61; p = 0.114
History of status epilepticus13 (59.1) 223 (13.6) 22NAFisher’s exact = 0.004
Seizure frequency
Minimum/month6.4 (14.7) [0–70] 224.9 (9.3) [0–33] 21NAt (41) = 0.39; p = 0.701
Maximum/month15.6 (17.6) [0–70] 2211.9 (18.5) [0–70] 21NAt (41) = 0.67; p = 0.507
Age at onset of psychosis (years)26.7 (6.3) [17–38] 22NANANA
Duration of psychosis (years)12.1 (9.1) [1–34] 22NANANA
PANSS positive score16.4 (3.5) [9–24] 21NANANA
PANSS negative score24 (7.1) [14–39] 21NANANA
Antipsychotic drugs (chlorpromazine equivalent dose in mg/day)230NANANA
Premorbid IQ90.6 (14.9) [69–111] 1994.8 (14.2) [64–117] 20NAt (38) = −1.06; p = 0.295
Current IQ76.1 (10.7) [53–93] 2185.6 (14.3) [58–103] 21NAt (41) = −2.44; p = 0.019
IQ change11.6 (9.0) [−2 to 32] 189.1 (12.3) [−8 to 45] 20NAt (37) = 0.61; p = 0.545
Working memory span4.2 (1.2) [2–7] 225.3 (1.4) [3–9] 22NAt (43) = −2.83; p = 0.007
Working memory manipulation60.6 (24.8) [9–116] 2243.4 (23.7) [0–73] 22NAt (43) = 2.84; p = 0.007
Story recall (immediate)16.5 (11.1) [0–43] 1921.9 (11.8) [0–51] 20NAt (38) = −1.56; p = 0.128
Story recall (delayed)13.6 (10.6) [0–36] 1916.4 (12.2) [0–49] 20NAt (38) = −0.78; p = 0.443

Clinical assessment

The Positive and Negative Syndrome Scale (PANSS) (Kay et al., 1987) was used to assess psychotic symptoms. Positive and negative symptoms were ranked 1–7 depending on severity. Ages of onset of epilepsy and psychosis, and history of status epilepticus and seizure frequency were recorded from the clinical notes and corroborated by an informant. Handedness was assessed using the Annett scale (Annett, 1970).

Details of clinical variables are given in Table 1.

Neuropsychological assessment

Neuropsychological data were available for IP and NIP patients, but not for HCs. The battery has been described previously (Flugel et al., 2006c):

Premorbid IQ using the Revised National Adult Reading Test (NART) (Nelson & Willson, 1991), a validated surrogate for premorbid IQ in schizophrenia (Crawford et al., 1992; O’Carroll et al., 1992). Current IQ was measured using a short form of the Wechsler Adult Intelligence Scale-III (WAIS III) (Wechsler, 1997) also validated in schizophrenia (Blyler et al., 2000). IQ change was the difference between premorbid and current IQ.

The following measures of executive function were derived from the computerized Cambridge Neuropsychological Test Battery (CANTAB) (Sahakian & Owen, 1992): (1) Working memory span, from the Spatial Span Task; (2) working memory manipulation, from the Spatial Working Memory Task (between errors).

Verbal episodic memory using the Story Recall subtest from the Adult Memory and Information Processing Battery (Coughlan & Hollows, 1984).

Cognitive scores are given in Table 1.

MRI data acquisition

MRI was performed on a GE SIGNA 1.5 Tesla scanner (General Electric, Milwaukee, WI, U.S.A.), using a standard quadrature head coil. T1-weighted volumetric images were obtained using an inversion recovery spoiled gradient-recalled (IR-SPGR) echo sequence with an isotropic voxel size of 1.2 × 1.2 × 1.2 mm3. One hundred twenty-four axial contiguous slices were acquired. Other parameters were: echo time (TE) = 5.4 msec, repetition time (TR) = 15 msec, inversion time (TI) = 450 msec, field of view = 31 × 16 cm2, acquisition matrix 256 × 128, number of averages = 1, excitation flip angle = 15 degrees, receive bandwidth = 15.63 kHz.

Image processing

The SBM programme FreeSurfer 4.3.0 (http://surfer.nmr.mgh.harvard.edu/) was used to generate maps of surface area and cortical thickness in standard Montreal Neurological Institute (MNI) space (Dale et al., 1999; Fischl et al., 1999). After skull stripping and white matter segmentation, the cortical surface of each hemisphere is inflated to an average spherical surface to locate the pial surface and the gray–white matter boundary (Dale et al., 1999). The distance between the two at each vertex (i.e., surface point) across the cortex is considered to measure cortical thickness. Cortical maps are smoothed with a 10-mm full-width half-maximum Gaussian kernel and aligned to a common surface template using a high-resolution surface-based averaging technique, and 32 cortical parcellations are automatically generated (Desikan et al., 2006). The only manual step was the correction of topologic errors when the above steps had been completed. Total brain volume was estimated using FreeSurfer (Buckner et al., 2004). Measurement of cortical parameters was performed by one of us (LGG) blind to participant status.

Analysis of cortical parameters

Regional thickness, surface area, and volume in frontal, temporal, parietal, occipital, and cingulate cortex were compared between the three groups (IP, NIP, and HC). For this analysis, 22 cortical parcellations in each hemisphere were selected, from the Desikan template (Desikan et al., 2006): six in the frontal cortex (superior, rostral middle, caudal middle, pars opercularis, pars triangularis, and pars orbitalis), six in the temporal (transverse, superior, middle, inferior, temporal pole, and fusiform), three in the parietal (superior, inferior, and precuneus), three occipital (lateral, cuneus, and lingual), and four in the cingulate (rostral anterior, caudal anterior, posterior, and isthmus). The average cortical thickness, total surface area, and total cortical volume for the frontal, temporal, parietal, occipital, and cingulate regions in each hemisphere were calculated from the individual parcellations. The cortical parameters of each parcellation within these five regions were individually compared across groups.

Statistical analysis

Demographic and cognitive variables

Age, total brain volume, and gender across IP, NIP, and HC groups were compared using analysis of variance (ANOVA) and chi-square tests. For comparisons between IP and NIP groups, Fisher’s exact tests were used to compare handedness and history of status epilepticus; t-tests to compare age of onset, duration of epilepsy, and seizure frequency; and linear regression models adjusted for age and gender to compare the cognitive scores and their associations with clinical variables.

Regional cortical parameters

Regional cortical parameters across the three groups and between groups (IP vs. NIP; IP vs. HC; and NIP vs. HC) were compared using STATA 10 (StataCorp, College Station, TX, U.S.A.). Age, gender, and total brain volume were covariates in all models.

Linear mixed models, which allow repeated measurements within subjects, were used to assess separately average cortical thickness, total surface area, and total volume of the parcellations for each of the five brain regions (frontal, temporal, parietal, occipital, and cingulate). Differences in cortical parameters due to diagnosis (side entered as a within-subject effect and diagnosis as a between-subject effect) and side, gender, and age with two-way interactions (diagnosis by side, diagnosis by gender, diagnosis by age) were estimated. When significant differences were present for one region, the model was repeated for the parcellations within each brain region (side and parcellation entered as a within-subject effect) with two-way interaction (diagnosis by parcellation).

Cortical parameters and clinical variables

Linear mixed models were used to explore associations between clinical variables and cortical parameters (side entered as a within-subject effect) and when significant associations were present for one region, the model was repeated for the parcellations within each brain region (side and parcellation entered as a within-subject effect) with two-way interactions (clinical variable by parcellation).

Cortical parameters and cognition

Linear mixed models with two-way interaction (diagnosis by cognitive score) were used to explore associations between cortical parameters and cognitive scores (side entered as a within-subject effect), and when significant associations were present for one region, the model was repeated for the parcellations within each brain region (side and parcellation entered as a within-subject effect) with two-way (diagnosis by parcellation; cognitive score by parcellation) and three-way interactions (diagnosis by cognitive score by parcellation).

Adjustment for multiple comparisons was performed for each model using false discovery rate (FDR) correction, and the level of significance was set at 0.05.

Results

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. Funding
  8. Disclosure
  9. References
  10. Supporting Information

Demographic and cognitive results are given in Table 1.

Cortical parameters are given in Table 2.

Table 2.   Cortical parameters in frontal, temporal, parietal, occipital, and cingulate regions in IP, NIP, and healthy controls
Region of cortex Mean SD p-valueThickness (mm)aSurface area (mm2)bVolume (mm3)b
IPcNIPdHCIPcNIPdHCIPcNIPdHC
  1. Bold indicates p < 0.05.

  2. aValues are means (SD) of six parcellations each.

  3. bValues are means (SD) of the sums of six parcellations each.

  4. Level of significance (p-value) comparing cIP versus HC and dIP versus NIP adjusted by age, gender, and total brain volume; *p < 0.05.

Frontal
 Left2.46*2.542.5917284.61738218466.648494.349995.653603.5
0.180.140.102685.41756.31736.78280.75920.54389.2
p = 0.02p = 0.06 p = 0.54p = 0.67 p = 0.63p = 0.68 
 Right2.472.532.5416985.617018.518219.748005.949618.953141.1
0.160.140.102698.91654.51880.47642.46011.74809.7
p = 0.07p = 0.08 p = 0.62p = 0.59 p = 0.59p = 0.62 
Temporal
 Left2.572.642.6813281.613526.614638.338541.640636.744094.1
0.200.120.121605.61805.91303.35846.35495.74280.9
p = 0.09p = 0.12 p = 0.42p = 0.99 p = 0.90p = 0.66 
 Right2.602.662.6812485.012923.514233.937178.539648.743446.6
0.220.120.111763.81260.51510.06219.14793.35011.0
p = 0.15p = 0.25 p = 0.42p = 0.51 p = 0.48p = 0.40 
Parietal
 Left2.202.252.2812158.912528.313153.128997.130741.532832.1
0.170.070.081617.91431.11439.84856.23695.73554.3
p = 0.15p = 0.20 p = 0.95p = 0.66 p = 0.38p = 0.24 
 Right2.202.262.3213276.813168.214073.832012.132426.335585.1
0.180.100.091694.61223.01588.25037.93518.13453.3
p = 0.07p = 0.10 p = 0.33p = 0.37 p = 0.76p = 0.93 
Occipital
 Left1.941.961.988946.59218.89665.319322.520023.921068.7
0.140.120.081332.71142.21216.43599.42832.12919.9
p = 0.60p = 0.71 p = 0.76p = 0.64 p = 0.59p = 0.62 
 Right1.992.022.028993.29159.69769.319677.520360.421517.8
0.140.110.091338.81053.21274.23272.02873.53007.1
p = 0.95p = 0.51 p = 0.92p = 0.99 p = 0.99p = 0.69 
Cingulate
 Left2.702.762.702949.13034.93218.88406.28898.19137.1
0.220.150.13412.9391.7482.61298.51235.81372.8
p = 0.17p = 0.19 p = 0.58p = 0.71 p = 0.94p = 0.28 
 Right2.722.802.762784.32891.63057.38061.98705.59007.1
0.150.120.15483.4375.8430.71459.01176.41291.9
p = 0.32p = 0.06 p = 0.33p = 0.64 p = 0.66p = 0.14 

Cortical thickness

Cortical thickness for the parcellations within each region are shown in supporting Table S1.

Frontal cortical thickness differed between the three groups (F2,127 = 3.79; p = 0.050). These differences were accounted for by differences in the cortical thickness of the pars opercularis (part of the inferior frontal gyrus or IFG) (F4,771 = 2.58; p = 0.040). Frontal cortical thickness was different between the IP group and HC (IP–HC mean = −0.08539 mm; 95% confidence interval [CI; −0.15868 to −0.01210]; p = 0.022) and this difference survived FDR correction in the pars opercularis (IP–HC mean = −0.22104 mm; 95% CI [−0.31177 to −0.08092]; p = 0.048) (Fig. 1). Frontal cortical thickness did not differ between NIP patients and HCs (NIP–HC mean = −0.01695 mm, p = 0.645) and showed a trend level difference between IP and NIP groups (IP–NIP mean = −0.06844 mm; 95% CI [−0.13942 to −0.00253]; p = 0.059). Differences in frontal cortical thickness between IP patients and HCs were also observed in the superior frontal (IP–HC mean = −0.11450 mm; 95% CI [−0.20483 to −0.02418]; p = 0.013) and pars triangularis (part of the IFG) (IP–HC mean = −0.11081 mm; 95% CI [−0.20113 to −0.02048]; p = 0.016) parcellations, but these differences did not survive FDR correction.

image

Figure 1.   Parcellation where cortical thickness was significantly different between IP patients and HCs: pars opercularis.

Download figure to PowerPoint

Age, gender, and side were not significantly associated with cortical thickness. The small sample size precluded the exploration of cortical thickness in relation of the side of epileptic foci, but there were no differences in the cortical thickness of the pars opercularis between patients with and without HS.

Cortical thickness and clinical variables

Age at onset and duration of epilepsy, history of status epilepticus, age at onset, duration of psychosis, and dose of antipsychotic drugs were not significantly associated with cortical thickness in the IP or NIP groups.

Cortical thickness and cognition

Cognitive scores were not significantly associated with cortical thickness.

Cortical area

There were no significant differences in cortical area between the IP, NIP, and HC groups in the five cortical regions examined (frontal, temporal, parietal, occipital, and cingulate), but a post hoc multiple linear regression comparing the cortical area of the pars opercularis across the groups demonstrated a left-sided area reduction in IP patients compared to HCs (IP–HC mean = −108.90 mm, p = 0.015).

Cortical area and clinical variables

In the IP group, the severity of negative symptoms was associated with the area of the temporal cortex with a reduction of 83.38 mm2 per score point (95% CI [−146.48 to −20.29]; p = 0.010). Reductions in the area of the superior (95% CI [−45.12 to −5.86]; p = 0.011) and inferior temporal (95% CI [−42.81 to −3.54]; p = 0.021) parcellations accounted for this association. This association did not survive FDR correction. There was a similar association for the area of the frontal cortex, with a reduction of 148.57 mm2 per score point (95% CI [−297.14 to −0.01]; p = 0.050). Reductions in the area of the superior frontal (95% CI [−72.69 to −8.24]; p = 0.014) parcellation accounted for this association. This association did not survive FDR correction.

Cortical area and cognition

Current IQ was more closely associated in the IP than NIP group with the area of the frontal and temporal cortex, with an increase of 121.31 mm2 (95% CI [46.66–195.97]; p = 0.001) for frontal and 43.14 mm2 (95% CI [1.04–85.24]; p = 0.045) for temporal cortex per IQ point. The area of the superior frontal (95% CI [19.36–52.94]; p = 0.006), rostral middle frontal (95% CI [22.90–56.48]; p = 0.012), and superior temporal (95% CI [7.10–38.05]; p = 0.048) parcellations accounted for these associations. These associations survived FDR correction (Figs 2–4).

image

Figure 2.   Scatter plots of the associations between current IQ with the average cortical area for the right and left hemispheres in IP and NIP patients: superior frontal.

Download figure to PowerPoint

image

Figure 3.   Scatter plots of the associations between current IQ with the average cortical area for the right and left hemispheres in IP and NIP patients: rostral middle frontal.

Download figure to PowerPoint

image

Figure 4.   Scatter plots of the associations between current IQ with the average cortical area for the right and left hemispheres in IP and NIP patients: superior temporal.

Download figure to PowerPoint

Cortical volume

There were no significant differences in cortical volume between the IP, NIP, and HC groups in the five cortical regions examined.

Cortical volume and clinical variables

Clinical variables were not significantly associated with cortical volume.

Cortical volume and cognition

Current IQ was more closely associated in IP than NIP patients, with cortical volume in frontal (with an increase of 360.49 mm3 per IQ point; 95% CI [141.70–579.28]; p = 0.001), temporal (with an increase of 218.38 mm3; 95% CI [71.69–365.07]; p = 0.004), and parietal (with an increase of 204.47 mm3; 95% CI [75.33–333.61]; p = 0.002) regions. The superior frontal (95% CI [95.34–195.29]; p = 0.006), rostral middle frontal (95% CI [40.38–140.33]; p = 0.012), superior temporal (95% CI [33.48–125.68]; p = 0.012), fusiform (95% CI [21.38–113.58]; p = 0.048), and inferior parietal (95% CI [71.71–178.06]; p = 0.012) parcellations accounted for these associations. These associations survived FDR correction.

Discussion

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. Funding
  8. Disclosure
  9. References
  10. Supporting Information

Frontal cortical thinning was present in IP patients compared to HCs, and this difference reached statistical significance for the pars opercularis of the IFG, although trend level differences were present in other extratemporal areas (i.e., superior frontal, pars triangularis). No such differences were observed between NIP patients and HCs.

Our findings are in keeping with those of previous studies in TLE patients (Bernhardt et al., 2008; Dabbs et al., 2009; Mueller et al., 2009; Raj et al., 2010; Voets et al., 2011) that have reported bilateral cortical abnormalities distant from the epileptic focus. However, in our study, reductions of cortical thickness were detected only in the frontal lobe, unlike the previous studies that found reductions in frontal and temporal regions. The small number of patients with HS made it impossible to explore whether cortical abnormalities differed between those with or without HS (Mueller et al., 2010; Raj et al., 2010). For the same reason, it was not possible to explore whether cortical thinning was associated with a family history of psychosis.

Our findings of more severe frontal cortical thinning in patients with IP are consistent with previous reports of extratemporal cortical abnormalities in these patients (Marsh et al., 2001; Sundram et al., 2010). However, these studies found more extensive cortical and white matter abnormalities involving frontotemporal regions using different methodologies (e.g., voxel-based morphometry, VBM) albeit in smaller samples. These methodologic differences may have accounted for our findings, as SBM and VBM may detect different aspects of structural abnormalities (Scanlon et al., 2011). Our IP and NIP groups were well matched for seizure frequency at the time of the study, although epilepsy had started earlier in those with IP who also had experienced status epilepticus more often. Epilepsy-related variables (i.e., age of onset, duration, and history of status epilepticus) were not significantly associated with cortical parameters in either group, but differences in the history of epilepsy between the groups may explain, at least in part, the cortical thinning in the IFG in patients with IP. It remains uncertain whether genetic or environmental risk factors for schizophrenia also contribute to the observed IFG abnormalities. Some support for this possibility accrues from studies that have reported abnormalities in the IFG in association with impaired cognition and loss of frontotemporal connections in schizophrenia (Kuperberg et al., 2003; Goldman et al., 2009; Minatogawa-Chang et al., 2009; Kubicki et al., 2011), in unaffected siblings of schizophrenic patients (Harms et al., 2010) and in those at ultra risk for the disease (Witthaus et al., 2009). The IFG is part of the “mirror neuron system” that links the observation and imitation of actions necessary for communication between individuals and that may be defective in schizophrenia (Rizzolatti & Arbib, 1998; Molnar-Szakacs et al., 2005).

In our previous studies using diffusion tensor imaging and MTR frontotemporal cortical abnormalities were reported in IP patients, but not in NIP. Temporal and frontal fractional anisotropy reductions, distant from the epileptic focus, were seen in patients with IP and were correlated to poorer performance in Verbal Fluency and Spatial Span (Flugel et al., 2006b), and using MTR, left superior and middle temporal gyri abnormalities were reported in a small subgroup of IP patients with no HS compared to NIP patients with no HS (Flugel et al., 2006a). Cortical abnormalities in extratemporal areas probably reflect the spread of seizure activity through the thalamus, in addition to the limbic pathways (Bernhardt et al., 2008). This mechanism, common to IP and NIP patients, may explain the lack of clear cortical differences between the two groups. Cortical thinning without change in cortical area has often been described in progressive neurologic diseases including Huntington’s disease and Alzheimer’s disease (Rosas et al., 2002), and in traumatic brain injury (Thompson & Apostolova, 2007; Turken et al., 2009).

Atypical and typical antipsychotics may cause focal gray matter changes (Navari & Dazzan, 2009), but this is unlikely to explain our findings, as we found no significant associations between cortical parameters, dose of antipsychotic medication, or duration of psychosis.

Our IP patients had lower current IQs than those in the NIP group, although their premorbid IQs were similar. This suggests that IQ may have declined over time in the IP group, although our sample is too small to detect significant differences in IQ decline between the two groups, and the cross-sectional design of our study was not well suited to detect IQ decline over time. Our findings add some support to those of others (Adachi et al., 2000) who found low IQ to be a risk factor for IP in patients with epilepsy. We did not find associations between IQ and cortical thickness in our IP and NIP groups. Such associations may have been detected in a larger sample, but it is also possible that different factors may explain the observed changes in cortical thickness and IQ. Therefore, although seizure activity may be more closely linked to cortical thinning, decline in IQ may be more closely associated with the development of psychosis. It is now well established that cognitive impairment is integral to schizophrenia (Joyce & Huddy, 2004) and that those with schizophrenia have lower IQs than their childhood peers (Woodberry et al., 2008), and furthermore that low current IQ predicts poor clinical outcome (Leeson et al., 2011). Further support for the link between psychosis and IQ accrues from the similar associations between IQ and frontotemporal cortical area and volume, but not thickness, present in our IP patients and in patients with first-episode psychosis using SBM (Gutierrez-Galve et al., 2010) and in those with bipolar disorder (Gutierrez-Galve et al., 2012).

The association between severity of negative symptoms and area reduction in the frontotemporal cortex in IP patients did not reach statistical significance after FDR correction. This finding is in keeping with our previous negative results using SBM in first-episode psychosis (Gutierrez-Galve et al., 2010). In contrast with this, we have previously reported an association of negative symptoms with reduced fractional anisotropy in frontal white matter in IP patients (Flugel et al., 2006b).

The findings reported here have to be considered as exploratory given our small sample size and will need to be replicated in larger cohorts using a longitudinal design to determine whether cortical changes are progressive. Within these limitations, we have documented cortical thinning distant from the epileptic focus more marked in IP patients likely to be related to seizure activity that involves regions, such as the IFG, known to be relevant to schizophrenia. Our results also suggest that IQ changes in IP patients may be more closely related to psychosis than to seizure activity.

Acknowledgments

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. Funding
  8. Disclosure
  9. References
  10. Supporting Information

We wish to thank members of the National Society of Epilepsy MRI Unit for their assistance, and all the subjects who participated in this study. We are also grateful to Professors M.R. Trimble and J.S. Duncan for assisting with patient recruitment.

We thank Professor J.S. Duncan for the use of normal control data.

Funding

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. Funding
  8. Disclosure
  9. References
  10. Supporting Information

This study was supported by the Big Lottery Fund (RG/1/010026026). Dr Gutiérrez-Galve was supported by grants from the Instituto de Salud Carlos III (FIS, CM07/00048).

Disclosure

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. Funding
  8. Disclosure
  9. References
  10. Supporting Information

None of the authors has any conflict of interest to disclose.

We confirm that we have read the Journal’s position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.

References

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. Funding
  8. Disclosure
  9. References
  10. Supporting Information
  • Adachi N, Matsuura M, Okubo Y, Oana Y, Takei N, Kato M, Hara T, Onuma T. (2000) Predictive variables of interictal psychosis in epilepsy. Neurology 55:13101314.
  • Annett M. (1970) A classification of hand preference by association analysis. Br J Psychol 61:303321.
  • Bernhardt BC, Worsley KJ, Besson P, Concha L, Lerch JP, Evans AC, Bernasconi N. (2008) Mapping limbic network organization in temporal lobe epilepsy using morphometric correlations: insights on the relation between mesiotemporal connectivity and cortical atrophy. Neuroimage 42:515524.
  • Bernhardt BC, Worsley KJ, Kim H, Evans AC, Bernasconi A, Bernasconi N. (2009) Longitudinal and cross-sectional analysis of atrophy in pharmacoresistant temporal lobe epilepsy. Neurology 72:17471754.
  • Blyler CR, Gold JM, Iannone VN, Buchanan RW. (2000) Short form of the WAIS-III for use with patients with schizophrenia. Schizophr Res 46:209215.
  • Bruton CJ, Stevens JR, Frith CD. (1994) Epilepsy, psychosis, and schizophrenia: clinical and neuropathologic correlations. Neurology 44:3442.
  • Buckner RL, Head D, Parker J, Fotenos AF, Marcus D, Morris JC, Snyder AZ. (2004) A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume. Neuroimage 23:724738.
  • Cavanagh JB, Meyer A. (1956) Aetiological aspects of Ammon’s horn sclerosis associated with temporal lobe epilepsy. Br Med J 2:14031407.
  • Cormack F, Gadian DG, Vargha-Khadem F, Cross JH, Connelly A, Baldeweg T. (2005) Extra-hippocampal grey matter density abnormalities in paediatric mesial temporal sclerosis. Neuroimage 27:635643.
  • Coughlan AK, Hollows SE. (1984) Use of memory tests in differentiating organic disorder from depression. Br J Psychiatry 145:164167.
  • Crawford JR, Besson JA, Bremner M, Ebmeier KP, Cochrane RH, Kirkwood K. (1992) Estimation of premorbid intelligence in schizophrenia. Br J Psychiatry 161:6974.
  • Dabbs K, Jones J, Seidenberg M, Hermann B. (2009) Neuroanatomical correlates of cognitive phenotypes in temporal lobe epilepsy. Epilepsy Behav 15:445451.
  • Dale AM, Fischl B, Sereno MI. (1999) Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9:179194.
  • Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ. (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31:968980.
  • Dubois JM, Devinsky O, Carlson C, Kuzniecky R, Quinn BT, Alper K, Butler T, Starner K, Halgren E, Thesen T. (2011) Abnormalities of cortical thickness in postictal psychosis. Epilepsy Behav 21:132136.
  • Falconer MA, Serafetinides EA, Corsellis JA. (1964) Etiology and pathogenesis of temporal lobe epilepsy. Arch Neurol 10:233248.
  • Fischl B, Dale AM. (2000) Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci U S A 97:1105011055.
  • Fischl B, Sereno MI, Dale AM. (1999) Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage 9:195207.
  • Flugel D, Cercignani M, Symms MR, Koepp MJ, Foong J. (2006a) A magnetization transfer imaging study in patients with temporal lobe epilepsy and interictal psychosis. Biol Psychiatry 59:560567.
  • Flugel D, Cercignani M, Symms MR, O’Toole A, Thompson PJ, Koepp MJ, Foong J. (2006b) Diffusion tensor imaging findings and their correlation with neuropsychological deficits in patients with temporal lobe epilepsy and interictal psychosis. Epilepsia 47:941944.
  • Flugel D, O’Toole A, Thompson PJ, Koepp MJ, Cercignani M, Symms MR, Foong J. (2006c) A neuropsychological study of patients with temporal lobe epilepsy and chronic interictal psychosis. Epilepsy Res 71:117128.
  • Goldman AL, Pezawas L, Mattay VS, Fischl B, Verchinski BA, Chen Q, Weinberger DR, Meyer-Lindenberg A. (2009) Widespread reductions of cortical thickness in schizophrenia and spectrum disorders and evidence of heritability. Arch Gen Psychiatry 66:467477.
  • Guarnieri R, Wichert-Ana L, Hallak JE, Velasco TR, Walz R, Kato M, Alexandre V Jr, Terra-Bustamante VC, Bianchin MM, Zuardi AW, Deakin JF, Sakamoto AC. (2005) Interictal SPECT in patients with mesial temporal lobe epilepsy and psychosis: a case–control study. Psychiatry Res 138:7584.
  • Gutierrez-Galve L, Wheeler-Kingshott CA, Altmann DR, Price G, Chu EM, Leeson VC, Lobo A, Barker GJ, Barnes TR, Joyce EM, Ron MA. (2010) Changes in the frontotemporal cortex and cognitive correlates in first-episode psychosis. Biol Psychiatry 68:5160.
  • Gutierrez-Galve L, Bruno S, Wheeler-Kingshott C, Summers M, Cipolotti L, Ron M. (2012) IQ and the fronto-temporal cortex in bipolar disorder. J Int Neuropsychol Soc 18:370374.
  • Harms MP, Wang L, Campanella C, Aldridge K, Moffitt AJ, Kuelper J, Ratnanather JT, Miller MI, Barch DM, Csernansky JG. (2010) Structural abnormalities in gyri of the prefrontal cortex in individuals with schizophrenia and their unaffected siblings. Br J Psychiatry 196:150157.
  • Hermann BP, Lin JJ, Jones JE, Seidenberg M. (2009) The emerging architecture of neuropsychological impairment in epilepsy. Neurol Clin 27:881907.
  • Joyce E, Huddy V. (2004) Defining the cognitive impairment in schizophrenia. Psychol Med 34:11511155.
  • Kanemoto K, Tsuji T, Kawasaki J. (2001) Reexamination of interictal psychoses based on DSM IV psychosis classification and international epilepsy classification. Epilepsia 42:98103.
  • Kay SR, Fiszbein A, Opler LA. (1987) The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr Bull 13:261276.
  • Keller SS, Baker G, Downes JJ, Roberts N. (2009) Quantitative MRI of the prefrontal cortex and executive function in patients with temporal lobe epilepsy. Epilepsy Behav 15:186195.
  • Kristensen O, Sindrup EH. (1979) Psychomotor epilepsy and psychosis. III. Social and psychological correlates. Acta Neurol Scand 59:19.
  • Kubicki M, Alvarado JL, Westin CF, Tate DF, Markant D, Terry DP, Whitford TJ, De SJ, Bouix S, McCarley RW, Kikinis R, Shenton ME. (2011) Stochastic tractography study of Inferior Frontal Gyrus anatomical connectivity in schizophrenia. Neuroimage 55:16571664.
  • Kuperberg GR, Broome MR, McGuire PK, David AS, Eddy M, Ozawa F, Goff D, West WC, Williams SC, van der Kouwe AJ, Salat DH, Dale AM, Fischl B. (2003) Regionally localized thinning of the cerebral cortex in schizophrenia. Arch Gen Psychiatry 60:878888.
  • Kuzniecky R, de lS V, Ethier R, Melanson D, Andermann F, Berkovic S, Robitaille Y, Olivier A, Peters T, Feindel W. (1987) Magnetic resonance imaging in temporal lobe epilepsy: pathological correlations. Ann Neurol 22:341347.
  • Lee JK, Lee JM, Kim JS, Kim IY, Evans AC, Kim SI. (2006) A novel quantitative cross-validation of different cortical surface reconstruction algorithms using MRI phantom. Neuroimage 31:572584.
  • Leeson VC, Sharma P, Harrison M, Ron MA, Barnes TR, Joyce EM. (2011) IQ trajectory, cognitive reserve, and clinical outcome following a first episode of psychosis: a 3-year longitudinal study. Schizophr Bull 37:768777.
  • Maier M, Mellers J, Toone B, Trimble M, Ron MA. (2000) Schizophrenia, temporal lobe epilepsy and psychosis: an in vivo magnetic resonance spectroscopy and imaging study of the hippocampus/amygdala complex. Psychol Med 30:571581.
  • Margerison JH, Corsellis JA. (1966) Epilepsy and the temporal lobes. A clinical, electroencephalographic and neuropathological study of the brain in epilepsy, with particular reference to the temporal lobes. Brain 89:499530.
  • Marsh L, Sullivan EV, Morrell M, Lim KO, Pfefferbaum A. (2001) Structural brain abnormalities in patients with schizophrenia, epilepsy, and epilepsy with chronic interictal psychosis. Psychiatry Res 108:115.
  • Minatogawa-Chang TM, Schaufelberger MS, Ayres AM, Duran FL, Gutt EK, Murray RM, Rushe TM, McGuire PK, Menezes PR, Scazufca M, Busatto GF. (2009) Cognitive performance is related to cortical grey matter volumes in early stages of schizophrenia: a population-based study of first-episode psychosis. Schizophr Res 113:200209.
  • Molnar-Szakacs I, Iacoboni M, Koski L, Mazziotta JC. (2005) Functional segregation within pars opercularis of the inferior frontal gyrus: evidence from fMRI studies of imitation and action observation. Cereb Cortex 15:986994.
  • Moran NF, Lemieux L, Kitchen ND, Fish DR, Shorvon SD. (2001) Extrahippocampal temporal lobe atrophy in temporal lobe epilepsy and mesial temporal sclerosis. Brain 124:167175.
  • Mueller SG, Laxer KD, Barakos J, Cheong I, Garcia P, Weiner MW. (2009) Widespread neocortical abnormalities in temporal lobe epilepsy with and without mesial sclerosis. Neuroimage 46:353359.
  • Mueller SG, Laxer KD, Barakos J, Cheong I, Finlay D, Garcia P, Cardenas-Nicolson V, Weiner MW. (2010) Involvement of the thalamocortical network in TLE with and without mesiotemporal sclerosis. Epilepsia 51:14361445.
  • Nathaniel-James DA, Brown RG, Maier M, Mellers J, Toone B, Ron MA. (2004) Cognitive abnormalities in schizophrenia and schizophrenia-like psychosis of epilepsy. J Neuropsychiatry Clin Neurosci 16:472479.
  • Navari S, Dazzan P. (2009) Do antipsychotic drugs affect brain structure? A systematic and critical review of MRI findings. Psychol Med 39:17631777.
  • Nelson H, Willson J. (1991) The Revised National Adult Reading Test (NART)-Test Manual, 2nd edn. NFER-Nelson, Windsor, Berks.
  • Nishio S, Morioka T, Hisada K, Fukui M. (2000) Temporal lobe epilepsy: a clinicopathological study with special reference to temporal neocortical changes. Neurosurg Rev 23:8489.
  • O’Carroll R, Walker M, Dunan J, Murray C, Blackwood D, Ebmeier KP, Goodwin GM. (1992) Selecting controls for schizophrenia research studies: the use of the National Adult Reading Test (NART) is a measure of premorbid ability. Schizophr Res 8:137141.
  • Oyegbile T, Hansen R, Magnotta V, O’leary D, Bell B, Seidenberg M, Hermann BP. (2004) Quantitative measurement of cortical surface features in localization-related temporal lobe epilepsy. Neuropsychology 18:729737.
  • Panizzon MS, Fennema-Notestine C, Eyler LT, Jernigan TL, Prom-Wormley E, Neale M, Jacobson K, Lyons MJ, Grant MD, Franz CE, Xian H, Tsuang M, Fischl B, Seidman L, Dale A, Kremen WS. (2009) Distinct genetic influences on cortical surface area and cortical thickness. Cereb Cortex 19:27282735.
  • Perez MM, Trimble MR. (1980) Epileptic psychosis – diagnostic comparison with process schizophrenia. Br J Psychiatry 137:245249.
  • Qin P, Xu H, Laursen TM, Vestergaard M, Mortensen PB. (2005) Risk for schizophrenia and schizophrenia-like psychosis among patients with epilepsy: population based cohort study. BMJ 331:23.
  • Raj A, Mueller SG, Young K, Laxer KD, Weiner M. (2010) Network-level analysis of cortical thickness of the epileptic brain. Neuroimage 52:13021313.
  • Rizzolatti G, Arbib MA. (1998) Language within our grasp. Trends Neurosci 21:188194.
  • Roberts GW, Done DJ, Bruton C, Crow TJ. (1990) A “mock up” of schizophrenia: temporal lobe epilepsy and schizophrenia-like psychosis. Biol Psychiatry 28:127143.
  • Rosas HD, Liu AK, Hersch S, Glessner M, Ferrante RJ, Salat DH, van der KA, Jenkins BG, Dale AM, Fischl B. (2002) Regional and progressive thinning of the cortical ribbon in Huntington’s disease. Neurology 58:695701.
  • Rusch N, Tebartz Van Elst L, Baeumer D, Ebert D, Trimble MR. (2004) Absence of cortical gray matter abnormalities in psychosis of epilepsy: a voxel-based MRI study in patients with temporal lobe epilepsy. J Neuropsychiatry Clin Neurosci 16:148155.
  • Sachdev P. (1998) Schizophrenia-like psychosis and epilepsy: the status of the association. Am J Psychiatry 155:325336.
  • Sahakian BJ, Owen AM. (1992) Computerized assessment in neuropsychiatry using CANTAB: discussion paper. J R Soc Med 85: 399402.
  • Scanlon C, Mueller SG, Tosun D, Cheong I, Garcia P, Barakos J, Weiner MW, Laxer KD. (2011) Impact of methodologic choice for automatic detection of different aspects of brain atrophy by using temporal lobe epilepsy as a model. AJNR Am J Neuroradiol 32:16691676.
  • Suckling J, Roberts H, Walker M, Highley JR, Fenwick P, Oxbury J, Esiri MM. (2000) Temporal lobe epilepsy with and without psychosis: exploration of hippocampal pathology including that in subpopulations of neurons defined by their content of immunoreactive calcium-binding proteins. Acta Neuropathol 99:547554.
  • Sundram F, Cannon M, Doherty CP, Barker GJ, Fitzsimons M, Delanty N, Cotter D. (2010) Neuroanatomical correlates of psychosis in temporal lobe epilepsy: voxel-based morphometry study. Br J Psychiatry 197:482492.
  • Taylor DC. (1975) Factors influencing the occurrence of schizophrenia-like psychosis in patients with temporal lobe epilepsy. Psychol Med 5:249254.
  • Tebartz Van Elst L, Baeumer D, Lemieux L, Woermann FG, Koepp M, Krishnamoorthy S, Thompson PJ, Ebert D, Trimble MR. (2002) Amygdala pathology in psychosis of epilepsy: a magnetic resonance imaging study in patients with temporal lobe epilepsy. Brain 125:140149.
  • Thompson PM, Apostolova LG. (2007) Computational anatomical methods as applied to ageing and dementia. Br J Radiol 80(Spec No 2):S78S91.
  • Toone BK, Garralda ME, Ron MA. (1982) The psychoses of epilepsy and the functional psychoses: a clinical and phenomenological comparison. Br J Psychiatry 141:256261.
  • Tuchscherer V, Seidenberg M, Pulsipher D, Lancaster M, Guidotti L, Hermann B. (2010) Extrahippocampal integrity in temporal lobe epilepsy and cognition: thalamus and executive functioning. Epilepsy Behav 17:478482.
  • Turken AU, Herron TJ, Kang X, O’Connor LE, Sorenson DJ, Baldo JV, Woods DL. (2009) Multimodal surface-based morphometry reveals diffuse cortical atrophy in traumatic brain injury. BMC Med Imaging 9:20.
  • Voets NL, Bernhardt BC, Kim H, Yoon U, Bernasconi N. (2011) Increased temporolimbic cortical folding complexity in temporal lobe epilepsy. Neurology 76:138144.
  • Wechsler D. (1997) Wechsler Adult Intelligence Scale-3rd edition (WAIS-3). The Psychological Corporation, San Antonio, TX.
  • Witthaus H, Kaufmann C, Bohner G, Ozgurdal S, Gudlowski Y, Gallinat J, Ruhrmann S, Brune M, Heinz A, Klingebiel R, Juckel G. (2009) Gray matter abnormalities in subjects at ultra-high risk for schizophrenia and first-episode schizophrenic patients compared to healthy controls. Psychiatry Res 173:163169.
  • Woodberry KA, Giuliano AJ, Seidman LJ. (2008) Premorbid IQ in schizophrenia: a meta-analytic review. Am J Psychiatry 165:579587.

Supporting Information

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. Funding
  8. Disclosure
  9. References
  10. Supporting Information

Table 1. Mean (95% CI) thickness of healthy control, TLE non-psychotic control and interictal psychosis group in individual parcellations in frontal, temporal, parietal, occipital lobes and cingulate gyrus. All values are in mm and results are adjusted by age, gender and total brain volume after FDR correction.

FilenameFormatSizeDescription
EPI_3504_sm_TableS1.doc136KSupporting info item

Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.