Metabolic correlates of cognitive impairment in mesial temporal lobe epilepsy

Metabolic correlates of cognitive impairment in mesial temporal lobe epilepsy

Epilepsy & Behavior 105 (2020) 106948 Contents lists available at ScienceDirect Epilepsy & Behavior journal homepage: www.elsevier.com/locate/yebeh ...

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Epilepsy & Behavior 105 (2020) 106948

Contents lists available at ScienceDirect

Epilepsy & Behavior journal homepage: www.elsevier.com/locate/yebeh

Metabolic correlates of cognitive impairment in mesial temporal lobe epilepsy Agathe Laurent a, Eric Artiges b, Charles Mellerio c, Magali Boutin-Watine a, Elisabeth Landré a, Franck Semah d, Francine Chassoux a,e,f,⁎ a

Epilepsy Unit, Department of Neurosurgery, GHU Paris Sainte-Anne, 75014 Paris, France INSERM U1000 “Neuroimaging and Psychiatry,”, Paris Sud University–Paris Saclay University, Psychiatry Department, 91G16 Orsay, France Department of Neuroradiology, GHU Paris Sainte-Anne, 75014 Paris, France d Department of Nuclear Medicine and INSERM U1171, CHU Lille, F-59000 Lille, France e Nuclear Medicine Department, SHFJ, Orsay, France f University Paris-Saclay, CEA, CNRS, Inserm, BioMaps, Orsay, 91401, France b c

a r t i c l e

i n f o

Article history: Received 11 November 2019 Revised 7 January 2020 Accepted 24 January 2020 Available online xxxx Keywords: Cognition Memory Mesial temporal lobe epilepsy Hippocampal sclerosis FDG-PET Default mode network

a b s t r a c t Purpose: The purpose of the study was to determine the correlations between brain metabolism and cognitive impairment in patients with drug-resistant mesial temporal lobe epilepsy (MTLE). Methods: [18F]-FluoroDeoxyGlucose positron emission tomography ([18F]-FDG-PET) and neuropsychological assessment were performed in 97 patients with MTLE (53 females, 15–56 years old, mean: 31.6 years, standard deviation (SD) = 10.4) with unilateral hippocampal sclerosis (HS, 49 left). We compared brain metabolism and gray matter volume (GMV) between patients with cognitive impairment (intelligence quotient (IQ) and memory index b 80) and patients with normal cognition, using statistical parametric mapping (SPM), in the whole population then in right and left HS (RHS, LHS) separately. Results: Intelligence quotient (40–121, mean: 83.7 ± 16.9) and memory index (45–133, mean: 80.7 ± 19.3) were impaired in 43% and 51% of the patients, respectively, similarly in RHS and LHS. We did not find any correlations between IQ and clinical factors related to epilepsy; however, there was a significant correlation between low memory index and early age of onset in LHS (p = 0.021), and widespread epileptogenic zone in the whole population (p = 0.033). Impaired IQ correlated with extratemporal hypometabolism, involving frontoparietal networks implicated in the default mode network (DMN), predominantly in the midline cortices. Metabolic asymmetry regarding HS lateralization included the precuneus (pC) in LHS and the anterior cingulate cortex (ACC) in RHS, both areas corresponding to key nodes of the DMN. Memory index correlated with the same frontoparietal networks as for IQ, with an additional involvement of the temporal lobes, which was ipsilateral in RHS and contralateral in LHS. A diffuse decrease of GMV including the ipsilateral hippocampus correlated with cognitive impairment; however, the structural alterations did not match with the hypometabolic areas. Conclusions: Cognitive impairment in MTLE correlates with extratemporal hypometabolism, involving the mesial frontoparietal networks implicated in the DMN and suggesting a disconnection with the affected hippocampus. Asymmetric alterations of connectivity may sustain the predominant ACC and pC metabolic decrease in patients with cognitive impairment. © 2020 Elsevier Inc. All rights reserved.

1. Introduction Cognitive impairment is a major comorbidity in mesial temporal lobe epilepsy (MTLE), especially in patients with drug-resistant epilepsy. Expected deficits involve episodic memory due to hippocampal sclerosis (HS) and language disturbances characteristic of dominant temporal lobe dysfunction. However, formal neuropsychological ⁎ Corresponding author at: Epilepsy Unit, Department of Neurosurgery, GHU Paris Sainte-Anne, 1, rue Cabanis, 75014 Paris, France. E-mail address: [email protected] (F. Chassoux).

https://doi.org/10.1016/j.yebeh.2020.106948 1525-5050/© 2020 Elsevier Inc. All rights reserved.

assessment in patients with MTLE reveals a wide spectrum of cognitive alterations encompassing mesial temporal functions, including intelligence quotient (IQ), attention, processing speed, and visuospatial and executive processes [1–4]. These observations raised questions about the mechanisms underlying these diffuse alterations, comprising the role of initial lesions, the age at epilepsy onset, and the impact of epilepsy on developing brain, the consequences of uncontrolled seizures, the effects of medications, and the duration of the disease [5–6]. All these factors may have a confounding effect, and none can alone explain the variability of the clinical picture. Beyond specific deficits that can be related to a locoregional dysfunction, differences between patients with

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normal or subnormal functioning and those who have a moderate to severe intellectual disability remain unexplained. Recent imaging studies have paved the way for new perspectives, showing that structural abnormalities may affect extratempor.al regions, including subcortical structures [7–8]. From the first description of the MTLE syndrome, it became increasingly evident that HS was a regional disease with remote structural and functional alterations rather than a focal pathology [9–10]. Accordingly, diffuse cognitive impairment has been related to a distributed decrease of gray matter volumes (GMVs) and an alteration of white matter tract integrity [11–13]. Extratemporal hypometabolism is a common feature in MTLE [14– 16]. Previous reports have shown that hypometabolism of the prefrontal cortex correlated with low IQ and executive dysfunction in unilateral temporal lobe epilepsy (TLE) [17–19]. However, further studies failed to demonstrate a relationship between frontal metabolism and neuropsychological tests including IQ [20]. Contradictory findings may be related to the methods of positron emission tomography (PET) analysis using predefined regions of interest (ROIs) [17,18] or to the limited sample of the studied population (21–32 patients) in studies using statistical parametric mapping (SPM) [19,20]. We aimed to examine the correlations between metabolic changes and cognitive performance in a large population of patients with MTLE, considering the hemispheric lateralization. Our primary goal was to determine factors contributing to diffuse cognitive impairment in MTLE and the metabolic substrates underlying the intellectual functioning in the patients with the most severe impairment. We also investigated the role of structural alterations in the neuropsychological performance and the relationship between metabolic decrease and gray matter atrophy. 2. Materials and methods

2.3. Neuropsychological assessment Neuropsychological tests are listed in supplemental data. Intellectual efficiency was assessed by the Full-Scale Intelligence Quotient (FSIQ) with the Wechsler Adult Intelligence Scale (WAIS, version R or III) and the Wechsler Intelligence Scale for Children (WISC, version III). For correlations with the brain metabolism, we compared two groups classified as impaired (“low IQ”) and unimpaired (“normal IQ”). We used a categorical division of the FSIQ scores corresponding to the qualitative descriptions provided by the Wechsler manual. Impaired IQ was defined as b80 (low and exceptionally low categories), and unimpaired was defined as ≥ 80 (low average, average, and high categories). This dichotomous indicator was chosen to distinguish patients with normal or subnormal functioning from those with cognitive impairment, considering that measures of IQ may underestimate their intellectual performance [23]. Memory efficiency was assessed by the delayed memory index with the Wechsler Memory Scale for adults (WMS, version R or III) and the Cohen Memory Scale for Children (CMS). Significantly dissociated scores were defined by a difference of at least 12 points between verbal IQ (VIQ) and performance IQ (PIQ) and auditory–visual memory scores. As for IQ, we compared two groups classified as impaired (memory index b80) and unimpaired (≥ 80). Auditory and visual memory scores were also used for correlation analyses. 2.4. Image acquisition and processing The [18F]-FDG-PET, 3D-MRI, and image processing were performed interictally as previously described [21] (for more details, see the additional note in Supplementary material). For patients, the mean delay between neuropsychological assessment and imaging was 70 days (range: 1–261 ± 67).

2.1. Subjects We selected all consecutive patients who had undergone a [18F]FluoroDeoxyGlucose positron emission tomography ([18F]-FDG-PET) scan and a neuropsychological assessment for drug-resistant MTLE due to unilateral HS in our institution between 2002 and 2012. The selection criteria were as follows: (1) formal neuropsychological testing including IQ and memory function assessment, (2) [18F]-FDG-PET performed during the interictal state on the same PET camera, and (3) MTLE diagnosis established by electroclinical data and magnetic resonance imaging (MRI) findings. Patients aged under 15 years or having another associated lesion were excluded from the study. All patients except those considered as poor candidates for surgery (i.e., older than 60 years or having severe psychiatric disturbances) were referred for PET investigation. Among a previously reported cohort of 114 patients with MTLE-HS [21,22], 97 patients met the selection criteria and were included in the present study. Hippocampal sclerosis was on the right side (RHS) in 48 patients and on the left (LHS) in 49. Control population consisted a total of 30 paid healthy comparison subjects (16 males, 21–54 years old, median: 35) with no personal or family history of psychiatric or neurologic disorder, right-handed, and were scanned during the same time period using the same imaging protocol. 2.2. Presurgical workup Electroencephalography (EEG)-video monitoring, MRI, Wada test, and/or functional MRI (fMRI) were performed in all patients and stereo-EEG in 12. Electroclinical patterns were defined as previously described [15], classified into four subtypes according to the propagation of ictal discharges: mesial, anterior mesial–lateral (AML), widespread mesial–lateral (WML), and bitemporal (BT); (for a description, see the additional note in Supplementary material).

2.5. Statistical analysis Clinical data analyses were performed using SPSS statistics software. The analysis of variance (ANOVA)/(analysis of covariance) ANCOVA analysis and Pearson's Chi2 test were used for categorical and continuous variables in between-group comparisons. The following clinical variables (gender, mean age at evaluation and epilepsy onset, disease duration, febrile seizures, early brain injury, familial history of epilepsy, seizure frequency, electroclinical seizure pattern, antiepileptic drugs (AED), educational years, index of manual laterality, and hemispheric dominance (HD) for language) were compared between RHS and LHS on the one hand and between patients with cognitive impairment and patients with normal cognition on the other hand. Statistical significance was defined as p b 0.05. For PET image analysis, we first compared baseline glucose uptake values between each IQ and memory index group (unimpaired versus impaired) and control subjects in the whole group then in RHS and LHS separately, using an ANCOVA with group as between-subject factor and age–gender as confounding covariates. Group comparisons of structural images were performed on GMVs using the same method. A t-test was used to compare the different groups. The significance threshold was set at p b 0.001 at the individual voxel level; cluster significance thresholds were set at 25 contiguous voxels (voxel size = 8 mm3) and p b 0.05. The spatial coordinates of the maximal hypometabolic areas were used to identify brain areas that significantly differed from controls, based on Talairach atlas [24]. We then performed comparisons between the groups of patients who had unimpaired and impaired scores on each HS side and analyses of correlations according to individual scores (FSIQ, auditory, and visual memory). Comparisons between patients and correlations were examined with a significance threshold set at p b 0.001 and 0.005 at individual voxel level and p b 0.05 at cluster level.

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2.6. Standard protocol approvals, registrations, and patient consents Informed consent was obtained from all subjects. The study protocol was approved by the local ethics committee and conformed to the Declaration of Helsinki on human investigation.

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LHS, including VIQ and PIQ scores (Table e1). However, in RHS, there were significantly dissociated scores in 28 (58%) patients, with an unexpected ratio (PIQ N VIQ) in 19 patients (40%). In LHS, significantly dissociated scores were found in 22 (45%) patients, with an unexpected ratio (VIQ N PIQ) in seven (14%) cases. These results did not differ significantly after exclusion of patients with atypical HD.

3. Results 3.1. Demographic data and cognitive profile of the patients Characteristics of the studied population are presented Table 1. Except for HD, none of these clinical data differed significantly between RHS and LHS. Anterior-mesial temporal corticectomy was performed in 88 patients, with histological confirmation of HS. Favorable outcome (Engel's class I) was obtained in 76 cases (86%) including class IA in 40 of them (45%). Among the nine patients who were not operated on, five declined surgery, two became free of disabling seizures during the presurgical work-up, one developed psychiatric disturbance, and the latter one was considered to have a high risk of postoperative cognitive deterioration (LHS, verbal memory score = 138). Except for this patient with exceptionally high memory performance, none of them were different from the others. 3.1.1. Intellectual functioning In the whole population, IQ ranged from 40 to 121 (mean: 83.7 ± 16.9), 42 (43%) of the patients obtained scores below the normal range (b80) including 19 (20%) in the very low range (b 70) (Fig. 1). The mean IQ was similar in the group with RHS and the group with

3.1.2. Memory performance Memory index ranged from 45 to 133 (mean: 80.7 ± 19.3) in the whole population, 49 (51%) patients having memory impairment. Intelligence quotient and memory index were correlated in both RHS and LHS (R = 0.6, p b 0.0001). As expected, auditory memory scores were higher in RHS than in LHS, and the inverse relationship was found for visual memory scores, with a significant interaction between visual memory scores and the side of HS (p = 0.014). Dissociated scores were less frequent than for IQ. They were found in 14 (29%) patients with RHS, with an unexpected ratio (visual N auditory memory) in three (6%) patients. In LHS, significantly dissociated scores were found in nine (18%) patients, with an unexpected ratio (auditory N visual memory) in three (6%) patients. Of note, the classical material-specific memory deficits (visual in right and auditory in left TLE) were found in only 23% of patients with RHS and 12% of patients with LHS. As for IQ, these results did not differ after exclusion of atypical HD. 3.1.3. Correlations with clinical data We did not find any correlation between IQ and age at onset of epilepsy, epilepsy duration, nor other factors related to epilepsy (Table e1). The only significant findings were a higher education level in patients

Table 1 Clinical and neuropsychological data in the whole population. Clinical data

All patients n = 97

RHS n = 48

LHS n = 49

p-Value

Age (years) mean ± SD (min-max)

31.63 ± 10.38 (15–56) 53/44 10.99 ± 7.32 (1–35) 20.65 ± 11.27 (4–53)

31.06 ± 9.95 (15–56) 22/26 11.33 ± 7.6 (1–30) 19.73 ± 10.2 (4–41)

NS

67 (71) 19 (20) 17 (18) 22 (23) 55/12/5 (59/13/5) 83/14 (86/14) 84/9 (90/10) 2.51 ± 0.83 (1–4) 7.6 ± 6.9 (1–45) 44/19/18/16 83.7 ± 16.9 (40–121) 82.7 ± 17.4 (45–129) 87.6 ± 17.6 (45–135) 80.7 ± 19.3 (45–133) 84.4 ± 19.1 (50–140) 81.6 ± 19.6 (45–126)

31 (67) 12 (25) 10 (21) 10 (21) 24/8/5 (50/17/10) 44/4 (92/8) 43/3 (94/6) 2.48 ± 0.82 (1–4) 7.4 ± 7.5 (2–45) 22/10/9/7 83.1 ± 17.5 (40–117) 81.7 ± 17.8 (45–121) 88.2 ± 20.4 (45–135) 80.7 ± 20.6 (45–133) 85.4 ± 18.8 (50–140) 78.3 ± 19.2 (45–118)

32.18 ± 10.84 (15–56) 31/18 10.63 ± 7 (2–35) 21.55 ± 12.25 (4–53) 36 (73) 7 (14) 7 (14) 12 (25) 31/4/0 (63/8/0) 39/10 (80/20) 41/6 (87/13) 2.53 ± 0.84 (1–4) 7.9 ± 6.4 (1–30) 22/9/9/9 84.3 ± 16.4 (53–121) 83.8 ± 17.2 (51–129) 87 ± 16.8 (50–126) 80.8 ± 18.5 (50–133) 83.3 ± 19.4 (50–138) 84.4 ± 19.5 (50–126)

Gender F/M Epilepsy onset (years) mean ± SD (min-max) Epilepsy duration (years) mean ± SD (min-max) Febrile seizures (%) Early brain injury (%) Familial epilepsy (%) Mesial seizures/ Other seizure typesa (%) Manual laterality R/L (%) Hemispheric dominance L/R-Bb (%) AED n mean ± SD (min-max) Seizure frequency/month mean ± SD (min-max) Education level (years) ≤4/5/6/7 FSIQ mean ± SD (min-max) VIQ mean ± SD (min-max) PIQ mean ± SD (min-max) Memory index mean ± SD (min-max) Auditory memory mean ± SD (min-max) Visual memory mean ± SD (min-max)

NS NS NS NS NS NS NS 0.079 0.025 NS NS NS NS NS NS NS NS 0.014

AED = antiepileptic drugs; RHS = right hippocampal sclerosis; LHS = left hippocampal sclerosis; F = female; M = male; L/R-B = left/right-bilateral; FSIQ = full scale intelligence quotient; VIQ = verbal IQ; PIQ = performance IQ. a Anterior mesiolateral, widespread mesiolateral, bitemporal (undetermined in 3 cases). b Hemispheric dominance undetermined in 4 cases; out of 14 left-handed patients, 4 had an atypical language hemispheric dominance (HD) (right in 3 and bilateral in 1). Atypical HD was also found in 5 right-handed patients (right in 2, bilateral in 3). Left-handedness and atypical HD were more frequent in LHS than in RHS; however, the threshold of significance was reached for HD only.

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3.2. Correlations between brain metabolism and cognitive performance 3.2.1. Results in the whole population Comparison of brain metabolism between patients with low and normal IQ in the whole population is presented in Table 2 and Fig. 2. The major difference consisted of a decrease of FluoroDeoxyGlucose (FDG) uptake in the precuneus (pC) (p = 0.005). Positive correlations between IQ and metabolism were found in the right precentral gyrus, and in the anterior cingulate cortex (ACC), the superior frontal gyrus, and the pC bilaterally.

3.2.2. Results in RHS and LHS In RHS, patients with low IQ had larger extratemporal hypometabolism when compared with patients with normal IQ, but the main difference between the two groups was found in the ACC. In LHS, the main metabolic difference between low and normal IQ groups was found in the pC bilaterally (Fig. 3). In addition, in RHS, we found a positive correlation between IQ and metabolism in the ACC (p = 0.001), whereas in LHS, the threshold of significance was approached (p = 0.07) in the pC. Similar results were found after exclusion of patients with atypical HD. According to these results, the ACC involvement appeared mainly related to RHS and the pC involvement to LHS. Overall, contralateral hemispheric involvement was predominant in LHS. Notably, most of these areas corresponded to the major nodes implicated in the default mode network (DMN). Fig. 1. Distribution of IQ and memory index scores in the population with MTLE and comparison with general population (from Wechsler D, Manual for the Wechsler Adults Intelligence Scale-Third Edition. The Psychological Corporation, 1997). Note the shift of the peak to the left compared with healthy subjects due to the relatively high proportion of patients with borderline intellectual impairment and patients with severe impairment (IQ and memory scores b80 and 70, respectively).

with normal IQ and a more frequent atypical HD in patients with LHS with low IQ. In contrast, we found a significant relationship between earlier age at epilepsy onset and lower memory index in LHS only (r = 0.332, p = 0.021). In addition, there was a significant correlation between memory index and electroclinical patterns, which was higher in “mesial” than in more widespread (AML, WML, BT) subtypes in the whole population (mean: 87.5, SD: 18.6 in mesial group versus 77.9, SD: 18.5 in other groups (p = 0.033)).

3.2.3. Metabolism and memory Correlations between metabolism and memory performance also showed predominant extratemporal involvement, including a higher number of brain regions and a greater volume (9.9 versus 2.75 cm3) in LHS than in RHS (Fig. e1, Table e2). In RHS, metabolism in the group with memory impairment was lower in the right inferior parietal lobule and the inferior temporal gyrus. The only significant positive correlation was found in the right pC for visual memory. In LHS, metabolism in the group with impairment was lower in the right middle temporal and angular gyri, and the pC bilaterally. Positive correlations were found in both hemispheres but predominantly on the right side, including the pC and frontoparietal regions for verbal memory, the right precentral gyrus, and the cuneus for visual memory. Of note, we did not find any correlations with the mesial temporal structures.

Table 2 Correlations between brain metabolism and IQ in the whole population, RHS and LHS patients. Population contrast Whole population Low IQ b Normal IQ Positive correlation

RHS Positive correlation

LHS Low IQ b Normal IQ Positive correlation

Brain region

p-Value (cluster level)

Z score

Cluster size

Spatial coordinates

L precuneus R precentral gyrus R-L precuneus R sup. frontal gyrus L ant. cingulate cortex R ant. cingulate cortex L sup. frontal gyrus

0.005 0.010 0.002 0.049 0.024

4.23 4.75 4.10 4.00 3.93

319 470 292 244 337

0.008

3.89

503

−4 −62 34 64 −2 36 0 −62 32 20 12 58 −6 −2 36 8 18 30 −26 44 38

L ant. cingulate cortex R ant. cingulate cortex

0.001 0.001

4.34 3.75

812

−4 −4 34 4 12 26

L-R precuneus

0.006

4.00

293

R precentral gyrus R sup. frontal gyrus

0.011 0.027

4.44 3.84

376 265

−12 −54 30 4 −64 30 64 −2 32 18 12 54

RHS = right hippocampal sclerosis; LHS = left hippocampal sclerosis; R = right; L = left; Ant. = anterior; Inf. = inferior; Mid. = middle; Sup. = superior. Voxel size = 8 mm3. For categorical comparisons, height threshold is set at p b 0.001; for correlations, height threshold is set at p b 0.005.

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Fig. 2. Correlations between IQ and brain metabolism in the whole population. (In neurological convention, the right side of the images is the right side of the subjects). A: Group comparison between patients with impairment versus patients without impairment at the higher threshold (p b 0.001, uncorrected, gender and age as covariates) showing a bilateral precuneus (pC) metabolism decrease as the only difference between the groups; B: Analysis of correlations (p b 0.005) including IQ as a continuous variable of interest, positive correlations between IQ and metabolism in pC, ACC bilaterally, and frontoparietal areas predominating on the right hemisphere. Note the correspondence between the hypometabolic networks and the key nodes of the default mode network (DMN).

3.3. Relationship with structural abnormalities Comparisons between GMVs on structural MRI and cognitive performance revealed several areas of GMV decrease in patients with low IQ compared with those with normal IQ, including temporal and extratemporal areas on both sides (Fig. 4, Table e3). The most significant results in RHS included the right hippocampal gyrus, the right middle temporal gyrus, the inferior temporal, the fusiform gyri bilaterally, and the left occipital gyrus. In LHS, GMV decrease involved the left hippocampus, the right inferior and medial frontal gyri, the right inferior parietal lobule, the ACC bilaterally, the brainstem, and the cerebellum. Overall, we found dissociation between areas of GMV decrease and those with hypometabolism (Fig. 4).

4. Discussion The main result of this study is that diffuse cognitive impairment assessed by IQ in patients with MTLE correlates with extratemporal hypometabolism involving frontoparietal networks, predominantly in the midline cortices and partly overlapping with the DMN. These results are in line with new concepts linking the efficiency of functional brain networks (including the frontoparietal regions) to intellectual abilities [25,26]. We found a metabolic asymmetry regarding the HS lateralization, including bilateral pC involvement on the left side and ACC

involvement on the right. Memory index correlated with the same frontoparietal networks as for IQ, with an additional involvement of the temporal lobe, which was ipsilateral in RHS and contralateral in LHS. A diffuse decrease of GMV including the ipsilateral hippocampus was found in both RHS and LHS; however, the structural alterations did not match with the hypometabolic areas. Overall, our results support the role of the DMN in cognitive functions and suggest that a disconnection between the affected hippocampus and the DMN may be related to the cognitive impairment in patients with MTLE. 4.1. Cognitive characteristics of the population We found that 43% of our population had an IQ below 80, a proportion that may appear high compared with previous cohorts with MTLE reporting a mean IQ around 90–100; however, in these studies, patients with IQ b 70 were excluded [12,17,27]. In contrast, our findings are in line with other series including unselected patients, reporting IQ b80 in 45% of the cases and b60 in 23% [28,29]. We included all patients referred for surgery in our center, even those with a very low IQ, who represented 20% of our study cohort. We also included left-handed patients and those with atypical dominance for language, considering that they reflected the whole spectrum of MTLE-HS. The cutoff of 80 between patients with cognitive impairment and patients with normal cognition may appear arbitrary but is commonly used to define the cognitive impairment in patients with epilepsy [5,28,30]. In addition, we performed

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analyses of correlation using IQ scores as continuous variables to limit the cutoff effect and found confirmatory results. On another side, it may be argued that IQ is not a reliable indicator of intellectual abilities in patients with epilepsy, being influenced by the number of AED and

epilepsy duration [23]. However, we believe that these factors did not impact our results significantly, as they were similar in patients with or without impairment. We did not find any relationship between IQ and age at epilepsy onset, in contradiction with pediatric series

Fig. 3. Correlations between IQ and brain metabolism in RHS and LHS. (In neurological convention, the right side of the images is the right of the subjects). Group comparisons between patients with RHS with normal and low IQ and healthy controls (HC), p b 0.001, gender and age as covariates: (A) contrast normal IQ b HC, classical hypometabolism in the right temporal lobe (hippocampus, pole, anterior lateral cortex) and mild extratemporal involvement (inferior frontal gyrus, thalamus); (B) contrast low IQ b HC, the same temporal involvement, larger extratemporal involvement (right frontal and perisylvian regions); (C) contrast low IQ b normal IQ, metabolic decrease on the anterior cingulate cortex (ACC) in patients with low IQ. Comparisons between patients with LHS with normal and low IQ and HC, p b 0.001: (D) contrast normal IQ b HC, more limited involvement on the left temporal lobe compared with RHS (hippocampus, pole) and extratemporal involvement limited to the ipsilateral thalamus; (E) contrast low IQ b HC, additional involvement of the posterior cingulate cortex (PCC) and the precuneus (pC); (F) contrast low IQ b normal IQ, metabolic decrease on the pC bilaterally in patients with low IQ.

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[5,30–32], but the heterogeneity of underlying pathologies in these cohorts may account for this discrepancy. Noteworthy, the mean epilepsy onset age of 11 years in our series was similar to that reported in large surgical cohorts (11.3 years in 3463 patients with MTLE-HS) [33], but late compared with pediatric series (3.7 to 6.8 years) [5,30–32]. In contrast, we found a significant correlation between low memory performance and an early onset age in LHS, and better memory performance in the more focal electroclinical patterns, indicating that early onset of epilepsy and extent of epileptic networks may interfere with the more vulnerable cognitive functions in MTLE. 4.2. Cognitive impairment correlates with frontoparietal hypometabolism To our knowledge, this is the first study to have established a relationship between cognitive impairment and mesial frontoparietal hypometabolism in MTLE. Similar findings were recently reported in patients with hypothalamic hamartomas, in whom reduced glucose metabolism was found in frontal, parietal, and posterior midline cortices, reflecting cognitive impairment in remote but connected network nodes [34]. A few studies have investigated whether extratemporal hypometabolism in MTLE was correlated with cognitive functioning assessed by intelligence measures, all focusing on the frontal lobe. Metabolic asymmetry of the prefrontal cortex has been related to IQ in two studies based on predefined ROIs [17,18]; however, the ROIs did not include the mesial posterior cortices. Another study, using SPM methodology, described a correlation between prefrontal hypometabolism and neuropsychological subtests reflecting executive functions [19] but without reference to IQ. The last of these studies failed to find any relationship between frontal lobe measures, including IQ, and hypometabolism despite the use of SPM methodology [20]. These authors concluded that frontal lobe neuropsychology tests may not be good measures of frontal lobe functioning. However, potential interactions with other brain areas were not tested in their analysis. All these “a priori” hypotheses may have limited the strength of the conclusions and missed the role of networks involving posterior areas. 4.3. Memory functions and metabolism The memory profile of our patients was in line with previous reports, showing lower scores for visual tasks in RHS and verbal tasks in LHS. However, significant material-specific memory deficits related to HS lateralization appeared relatively less marked than previously reported, with a high variability at the individual level, possibly due to atypical HD and functional reorganization in some patients. Interestingly, the “typical” auditory memory deficits in LHS and visual memory deficits in RHS were found in only 25% in a large cohort of right-handed patients with MTLE-HS [29], challenging the material-specific model of memory function [35]. On the other hand, the nature of the memory task may be a critical variable in the identification of a memory deficit. Contradictory findings have been previously reported [20,36,37], depending on the choice of a neuropsychological test (specific-material task versus composite score), the technique of analysis (“a priori” defined ROIs versus SPM) and the selection of patients (excluding low IQ and left-handed patients). Using a composite memory score (delayed memory index from adult WMS or Children CMS) and a non a priori hypothesis in unselected patients, we found that impaired memory functions correlated with hypometabolic patterns like for IQ, except for temporal involvement that was only found for memory. Maintenance of memory functions may be the result of effective, intellectual compensatory strategies, a higher intelligence level predicting better cognitive outcome [38,39]. Our results support this view in demonstrating the significant correlations between IQ and memory index, and a similar involvement of extratemporal networks for both scales. Worthy of note, we did not find any involvement of the mesial temporal structures in patients with memory impairment, an unexpected result that may indicate that the scales we used did not strictly reflect the episodic

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memory. An alternative explanation is that hypometabolism may be difficult to detect in the mesial structures with SPM. However, using the same methodology, hippocampal hypometabolism was always detected at the group level when involved in the epileptogenic zone (EZ) [21]. This suggests that the present findings are rather due to the weak specificity of the memory tasks than to a limitation of SPM methodology. 4.4. Is structural atrophy correlated with hypometabolic networks? We found that ipsilateral hippocampal atrophy and several bilateral temporal and extratemporal areas were significantly related to cognitive deficits, both in LHS and RHS. These results are in line with most previous studies showing a lower total volume and a smaller cortical thickness of temporal, frontal, and parietal regions in patients with lower IQ [13,28]. We confirm the large distribution of structural alterations, including the ipsilateral hippocampus, in the patients with cognitive impairment; however, we emphasize that GMV decrease areas did not match with the hypometabolic areas. We already demonstrated the dissociation between structural and metabolic alterations when searching for the determinants of hypometabolism in the whole cohort (114 patients), regardless of the cognitive impairment [21]. Dissociation between hippocampal atrophy and pC/PPC hypometabolism has been also reported in patients with mild cognitive impairment [40]. These findings were interpreted as reflecting the direct effect on hippocampal functioning and the indirect effect on remote areas with functional disruption, resulting from a decreased connectivity with the atrophied hippocampus. We believe that the same mechanisms may partly sustain the cognitive deficits in MTLE. 4.5. DMN alterations underlying intellectual and memory impairment Based on modern neuroimaging, a relationship between the efficiency of functional brain networks, especially the medial prefrontal and precuneus/posterior cingulate cortex (pC/PCC) and intellectual performance has been established [26]. Moreover, current concepts based on brain connectome have brought to light that highly efficient hub nodes showed more connections, but were also more biologically expensive, with higher glucose metabolic rates [41]. Spontaneous synchronized activity at rest constituting the DMN, including the medial prefrontal and medial, lateral, and inferior parietal cortices, pC and cerebellum, supports the idea that resting state networks are involved in controlling higher order brain functions such as consciousness, cognition, affective behavior, or attention [42]. In patients with MTLE, the DMN proved to be altered with decreased connectivity between the affected hippocampus (and to a lesser degree, the contralateral nonaffected hippocampus) and the different areas of the DMN [42–46]. Based on these findings, the concept of TLE as a “resting state network disease” has opened new perspectives for understanding the psychiatric and cognitive complications [42]. The main studies were based on fMRI, but 18FDG-PET proved to be a reliable tool in capturing neuronal activity in steady state with the advantage of being independent of vascular coupling [47]. Despite recording different aspects of neural activity, similar resting state networks were recently detected in fMRI and 18FDG data [48–50]. Our results support these assumptions regarding the cognitive impairment in MTLE, showing that the main hypometabolic areas correspond to the key structures implicated in the DMN and that 18FDG-PET may be used to explore the cognitive damages in these areas. 4.6. Hemispheric asymmetry and the DMN key nodes As a key node of the DMN, the pC/PCC has proved to be the only node that exhibited interactions with virtually all other network nodes and therefore represents a key region that sustains a range of cognitive tasks [51]. In addition, metabolic activity is higher in the pC/PCC than

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in all other regions during rest [52]. Thus, the major involvement of pC found in our study makes sense regarding intellectual efficiency. However, we found that areas correlating with the cognitive impairment were asymmetric, predominantly in ACC in RHS and in pC in LHS, both regions being connected with the hippocampal formations.

Interestingly, the most characteristic alteration of DMN in both left and right TLE is the loss of functional connectivity between anterior and posterior regions, including the ACC and the pC, with more extensive alterations and functional reorganization on the left side and posterior areas [53–56]. Accordingly, we postulate that asymmetric

Fig. 4. Group comparisons between 18FDG uptake and GMV decrease in RHS and LHS. (In neurological convention, the right side of the images is the right side of the subjects) p b 0.005, contrast low IQ b normal IQ, gender and age as covariates. FluoroDeoxyGlucose uptake decrease in patients with low IQ predominant in the ACC in RHS (A) and pC in LHS (B). Gray matter volume decrease (VBM) in patients with low IQ in right parahippocampal and bilateral parietofrontal regions in RHS (C), and in left hippocampal, bilateral parietofrontal regions, midbrain, and cerebellar in LHS (D). Note that there was no overlap between metabolic and structural alterations.

A. Laurent et al. / Epilepsy & Behavior 105 (2020) 106948

alterations of connectivity related to the side of the damaged hippocampus may support the asymmetric metabolic findings.

4.7. Strengths and limitations This study was based on a large cohort allowing the distinct analysis of patients with right and left MTLE with a homogeneous underlying lesion (isolated unilateral HS) and well-characterized electroclinical patterns, without bias of selection within the recruitment of a tertiary center. In addition, the confrontation of metabolic and structural data provided an opportunity to compare the relationship between hypometabolism and cortical atrophy, a condition that was not available in previous PET studies focusing on cognition. Finally, using SPM analysis, we avoided a priori hypotheses that might have limited the strength of the results. The retrospective analysis represents a first limitation, considering the variation in the delay between neuropsychological assessment and PET acquisition among the cohort (mean: 70 days), even if IQ and memory index are unlikely to have changed greatly during this time. Secondly, the neuropsychological tests that we used to measure the intellectual efficiency and memory performance do not necessarily reflect the functioning of patients and may underestimate their cognitive abilities or miss some specific deficits more relevant for characterizing their difficulties. However, as our primary goal was to distinguish patients with normal or subnormal intellectual functioning from those with more severe intellectual disability, we believe that these scores were suitable to investigate the metabolic substrates underlying the diffuse cognitive impairment in patients with MTLE. Finally, more advanced imaging data such as diffusion tensor imaging (DTI) or fMRI were not available to support our hypotheses on the concomitant involvement of the DMN and decreased connectivity between the damaged hippocampus and the DMN. However, it is difficult to obtain all this information simultaneously in large cohorts. This represents a major issue when studying the functional and structural substrates of cognitive impairment in patients with epilepsy as the results may be influenced by the clinical status, including the delay from the last seizure, fatigue, motivation, emotional or mood disorders, and antiepileptic treatment. Further studies are necessary to control these different factors and would ideally benefit from new hybrid PET-MR machines allowing simultaneous multimodal assessment.

5. Conclusions In this series of patients investigated for drug-resistant MTLE, cognitive impairment was found in about half of them. Bilateral frontoparietal hypometabolism, especially in the pC and ACC, correlated with intellectual deficits. These hypometabolic networks partly coincided with the DMN, including more posterior and bilateral areas on the left side. Despite correlations between cognitive impairment and GMV decrease, hypometabolism and cortical atrophy did not overlap. Overall, our results support the view of MTLE as a DMN disease, in which cognitive impairment may be related to a disconnection with the damaged hippocampus. Supplementary data to this article can be found online at https://doi. org/10.1016/j.yebeh.2020.106948.

Funding Agathe Laurent received funding from INSERM U1129, a unit directed by Dr. Catherine Chiron. This research did not receive any other specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Author contribution We affirm that (1) all coauthors have been substantially involved in the study and/or the preparation of the manuscript; (2) no undisclosed groups or persons have had a primary role in the study and/or in manuscript preparation; and (3) all coauthors have seen and approved the submitted version of the paper and accept responsibility for its content. A.L. analyzed the data, performed the statistical analyses, and drafted the manuscript for intellectual content. E.A., C.M., E.L., M.B.W., and F.S. played a major role in the acquisition of data and drafted the manuscript for intellectual content. F.C. designed and conceptualized the study, analyzed the data, and drafted the manuscript for intellectual content. Ethical publication statement 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. Data availability statement Anonymized data not published within this article will be made available by request through the DRCI of GHU Paris-Sainte-Anne from any qualified investigator. Declaration of competing interest None of the authors has any conflict of interest to disclose. Acknowledgments We thank the team of SHFJ and especially Dr. Philippe Gervais for performing the FDG-PET studies. We also thank Drs. Sonia BaudoinChial and Aimée de Vanssay-Maigne, who contributed to the neuropsychological testing of patient. References [1] Hermann BP, Seidenberg M, Schoenfeld J, Davies K. Neuropsychological characteristics of the syndrome of mesial temporal lobe epilepsy. Arch Neurol 1997;54:369–76. [2] Elger CE, Helmstaedter C, Kurthen M. Chronic epilepsy and cognition. Lancet Neurol 2004;3:663–72. [3] Oyegbile TO, Dow C, Jones J, Bell B, Rutecki P, Sheth R, et al. The nature and course of neuropsychological morbidity in chronic temporal lobe epilepsy. Neurology 2004; 62:1736–42. [4] Helmstaedter C, Kockelmann E. Cognitive outcomes in patients with chronic temporal lobe epilepsy. Epilepsia 2006;47(Suppl. 2):96–8. [5] Cormack F, Cross JH, Isaacs E, Harkness W, Wright I, Vargha-Khadem F, et al. The development of intellectual abilities in pediatric temporal lobe epilepsy. Epilepsia 2007;48:201–4. [6] Helmstaedter C, Elger CE. Chronic temporal lobe epilepsy: a neurodevelopmental or progressively dementing disease? Brain 2009;132:2822–30. [7] Bernasconi N, Duchesne S, Janke A, Lerch J, Collins DL, Bernasconi A. Whole-brain voxel-based statistical analysis of gray matter and white matter in temporal lobe epilepsy. Neuroimage 2004;23:717–23. [8] Bernhardt BC, Kim H, Bernasconi N. Patterns of subregional mesiotemporal disease progression in temporal lobe epilepsy. Neurology 2013;81:1840–7. [9] Hermann B, Seidenberg M, Jones J. The neurobehavioural comorbidities of epilepsy: can a natural history be developed? Lancet Neurol 2008;7:151–60. [10] Bell B, Lin JJ, Seidenberg M, Hermann B. The neurobiology of cognitive disorders in temporal lobe epilepsy. Nat Rev Neurol 2011;7:154–64. [11] Oyegbile TO, Bhattacharya A, Seidenberg M, Hermann BP. Quantitative MRI biomarkers of cognitive morbidity in temporal lobe epilepsy. Epilepsia 2006;47: 143–52. [12] Hermann BP, Seidenberg M, Dow C, Jones J, Rutecki P, Bhattacharya A, et al. Cognitive prognosis in chronic temporal lobe epilepsy. Ann Neurol 2006;60:80–7. [13] Dabbs K, Jones J, Seidenberg M, Hermann B. Neuroanatomical correlates of cognitive phenotypes in temporal lobe epilepsy. Epilepsy Behav 2009;15:445–51. [14] Savic I, Altshuler L, Baxter L, Engel Jr J. Pattern of interictal hypometabolism in PET scans with fludeoxyglucose F18 reflects prior seizure types in patients with mesial temporal lobe seizures. Arch Neurol 1997;54:129–36. [15] Chassoux F, Semah F, Bouilleret V, Landre E, Devaux B, Turak B, et al. Metabolic changes and electro-clinical patterns in mesiotemporal lobe epilepsy: a correlative study. Brain 2004;127:164–74.

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Glossary ACC: anterior cingulate cortex AML: anterior-mesial-lateral BT: bitemporal CMS: Children's Memory Scale DMN: default mode network DTI: diffusion tensor imaging EZ: epileptogenic zone FSIQ: full scale intelligence quotient [18F]-FDG-PET: [18F]-FluoroDeoxyGlucose positron emission tomography GMV: gray matter volume HC: healthy controls HD: hemispheric dominance HS: hippocampal sclerosis LHS: left hippocampal sclerosis MTLE: mesial temporal lobe epilepsy pC: precuneus PCC: posterior cingulate cortex RHS: right hippocampal sclerosis ROI: region of interest SEEG: stereo-electroencephalography SPM: statistical parametric mapping TLE: temporal lobe epilepsy WAIS: Wechsler Adult Intelligence Scale WISC: Wechsler Intelligence Scale for Children WMS: Wechsler Memory Scale WM: white matter WML: widespread mesial–lateral