Neurometabolite abnormalities and associations with epilepsy duration and cognition in patients with idiopathic generalized epilepsies

Yun Lien; Christina Bankston; Ayushe A Sharma; Brandon S Mitchell; Rodolphe Nenert; Anna Moyana; Jennifer Pilkington; Roy C Martin; Charity J Morgan; Lawrence Ver Hoef; Jerzy P Szaflarski; Jane B Allendorfer

doi:10.1002/epi4.70366

. 2026 Oct 8:10.1002/epi4.70366. Online ahead of print. doi: 10.1002/epi4.70366

Neurometabolite abnormalities and associations with epilepsy duration and cognition in patients with idiopathic generalized epilepsies

Yun Lien ^1,^2,^✉, Christina Bankston ³, Ayushe A Sharma ^2,⁹, Brandon S Mitchell ^1,², Rodolphe Nenert ², Anna Moyana ², Jennifer Pilkington ², Roy C Martin ², Charity J Morgan ⁴, Lawrence Ver Hoef ^2,^5,⁶, Jerzy P Szaflarski ^2,^5,^7,⁸, Jane B Allendorfer ^2,^5,^7,^✉

PMCID: PMC13648258 PMID: 42847905

Abstract

Objective

This study investigated whether idiopathic generalized epilepsies (IGEs) are associated with neuroinflammation by measuring neurometabolite levels and brain temperature using whole‐brain magnetic resonance spectroscopy imaging (MRSI). We compared patients with IGEs to healthy controls (HCs), and in IGEs examined relationships with seizure control, MRSI markers, epilepsy duration, and cognitive performance.

Methods

Seventeen patients with IGEs and 15 HCs underwent 3T‐MRI scans. IGEs were also administered the Montreal Cognitive Assessment (MoCA) and Flanker test. Whole‐brain ¹H‐MRSI provided metabolite and water maps. Levels for myo‐inositol (MI; glial marker), glutamate‐glutamine complex (Glx), and N‐acetylaspartate (NAA) were normalized to creatine (Cr) levels. Brain temperature was obtained by frequency difference between water and Cr. IGEs were stratified into seizure‐free (IGE−) and treatment‐resistant (IGE+) groups. Group analyses were performed using permutation‐based threshold‐free cluster enhancement (TFCE) within bilateral regions of interest: anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), hippocampus, parietal cortex, and thalami.

Results

Compared with HCs, IGEs showed higher MI/Cr in the parietal cortex, PCC, and thalamus; higher hippocampus Glx/Cr; and lower parietal cortex NAA/Cr. Brain temperature did not differ between groups. IGE− exhibited higher hippocampal NAA/Cr and better MoCA scores than IGE+. Longer epilepsy duration was associated with lower NAA/Cr in the ACC and PCC, and parietal cortex, as well as lower Glx/Cr in the ACC, PCC, parietal cortex, and hippocampus. Lower NAA/Cr in the ACC, PCC, hippocampus, and thalamus, together with higher thalamus MI/Cr, was associated with lower MoCA scores in IGEs. Lower NAA/Cr in the ACC, thalamus, and hippocampus was associated with poorer Flanker performance in IGEs.

Significance

We provide novel evidence of neurometabolic abnormalities in IGEs. The associations between metabolite levels and clinical and cognitive variables suggest potential biomarkers for disease progression and treatment response. Furthermore, these results highlight the roles of abnormal glial activation, neuronal loss, and hyperexcitability in IGE pathophysiology.

Plain Language Summary

We compared patients with idiopathic generalized epilepsies (IGEs) to healthy controls (HCs) to investigate brain chemistry and temperature differences and their relationships to seizure control, epilepsy duration, and cognitive performance. We found group differences in brain N‐acetylaspartate (NAA; neuronal marker), myo‐inositol (MI; glial cell marker), and glutamate/glutamine (Glx; excitatory neurotransmission) levels, but not in brain temperature. Longer epilepsy duration was associated with brain chemistry levels. In IGEs, lower NAA levels and higher MI levels were associated with worse cognitive performance. This study highlights the roles of abnormal glial activation, neuronal loss, and hyperexcitability in epilepsy duration and cognition in IGEs.

Keywords: brain metabolites, brain temperature, cognition, idiopathic generalized epilepsies, magnetic resonance spectroscopy imaging

Key points.

Compared to HCs, IGEs showed lower N‐acetylaspartate and higher glutamate‐glutamine and myo‐inositol in brain regions of interest.
HCs and IGEs did not differ in overall whole‐brain temperature or in regions of interest.
IGEs who were, compared to those not seizure‐free in past 6 months, exhibited higher hippocampal N‐acetylaspartate and better MoCA scores.
Longer epilepsy duration was associated with lower N‐acetylaspartate and glutamate‐glutamine levels in brain regions of interest.
Higher myo‐inositol and lower N‐acetylaspartate levels were associated with poorer cognitive performance in IGEs.

1. INTRODUCTION

As a chronic neurological disorder, epilepsy affects approximately 50–65 million people worldwide and imposes substantial personal and societal burdens, including increased healthcare utilization, reduced productivity, and diminished quality of life for patients and caregivers. ¹ , ² , ³ Idiopathic generalized epilepsies (IGEs) account for nearly 20% of all patients with epilepsy, yet represent less than 1% of the research in epilepsy. ⁴ , ⁵ IGEs are characterized by generalized seizure types—myoclonic, absence, and generalized tonic–clonic seizures—and are believed to have a genetic cause. ⁶ Increasing evidence indicates that IGEs are commonly associated with cognitive impairment, particularly in executive function and attention. ⁷ , ⁸ Correspondingly, in other neurological disorders, executive function and attention deficits have been associated with alterations in brain metabolism and neuroinflammation. ⁹ , ¹⁰ , ¹¹

Neuroinflammation is gaining recognition as a key mechanism in seizure initiation and maintenance. ¹² , ¹³ , ¹⁴ , ¹⁵ This process is primarily facilitated by microglia, ¹⁶ the brain's resident immune cells, which respond to insults through proliferation, activation, recruitment of other immune cells, phagocytosis, and release of inflammatory cytokines and chemokines. ¹⁶ , ¹⁷ , ¹⁸ , ¹⁹ , ²⁰ While neuroinflammation is typically a transient response to acute central nervous system (CNS) insults, ²¹ , ²² chronic and self‐perpetuating neuroinflammation may develop if the insult persists or if microglia enter a “primed” state—becoming hypersensitive to minor changes in the microenvironment. ²⁰ , ²³ , ²⁴ , ²⁵ , ²⁶ In IGEs, this primed state and chronic neuroinflammation may contribute to neuronal hyperexcitability and epileptogenesis. ¹⁵ , ²⁷ , ²⁸ , ²⁹ , ³⁰

Assessing neuroinflammation in vivo typically involves invasive procedures such as cerebrospinal fluid sampling, brain tissue investigation (usually in the context of resective surgery), or positron emission tomography (PET) with radioactive ligands. However, resective surgery is not indicated for IGEs, making noninvasive imaging techniques the only practical option for assessing neuroinflammation in vivo in these patients. ³¹ Magnetic resonance spectroscopic imaging (MRSI) offers a noninvasive alternative to PET and can estimate brain temperature (MRSI‐t) as a proxy for neuroinflammation. ³² , ³³ , ³⁴ Higher brain temperature reflects increased metabolic activity in inflamed regions ³⁵ , ³⁶ , ³⁷ and is calculated from the chemical shift between the water peak and a temperature‐invariant reference peak, typically creatine (Cr). ³⁸ , ³⁹ Because the water peak resonance frequency is temperature‐dependent, its displacement from the Cr peak can be used to infer internal temperature. ³² , ³⁹ MRSI also quantifies several metabolites associated with neuroinflammation, including myo‐inositol (MI), glutamate‐glutamine complex (Glx), and N‐acetylaspartate (NAA). MI is a glial marker that increases with glial activation and proliferation. ⁴⁰ Glx is a marker of excitatory neurotransmission that is elevated with neuronal hyperexcitability and excitotoxic damage. ⁴¹ NAA is a neuronal marker that decreases in conditions involving neurodegeneration or apoptosis. ⁴² Unlike PET, MRSI does not require radioligand or contrast (e.g., gadolinium) administration. As a whole‐brain spectroscopic imaging technique, MRSI allows for the comprehensive assessment of global cortical and subcortical involvement in IGEs. ⁴³ , ⁴⁴ , ⁴⁵ , ⁴⁶ , ⁴⁷ , ⁴⁸

This study used MRSI and MRSI‐t to assess neuroinflammation in patients with IGEs. We compared whole‐brain temperature and metabolite levels (MI, Glx, and NAA) between patients with IGE and healthy controls (HCs). Specific regions of interest (ROIs) were selected based on prior studies in IGEs which identified increased functional connectivity in the cingulate cortex, parietal cortex, hippocampus, and thalamus. ⁴³ , ⁴⁹ , ⁵⁰ , ⁵¹ , ⁵² Moreover, we examined differences between patients with IGEs who had been seizure‐free for 6 months (IGE−) and who were treatment‐resistant (IGE+; i.e., had experienced a seizure in the past 6 months) to explore the relationship between neuroinflammation and seizure control. Finally, we investigated relationships between epilepsy duration, cognitive performance, and MRSI neuroinflammatory markers to examine the potential impact of neuroinflammation on cognition and its ability to reflect disease progression.

2. MATERIALS AND METHODS

2.1. Participants

The Institutional Review Board of the University of Alabama at Birmingham (UAB) approved all study procedures. Participants provided written informed consent prior to any study procedures. Epilepsy participant inclusion criteria were aged 18–55 with a clinical diagnosis compatible with IGE, which was confirmed by a study epileptologist (JPS or LVH). Participants with IGE were recruited via referral from treating providers at the UAB outpatient neurology clinics, the UAB Epilepsy Center, and the Epilepsy Monitoring Unit. The IGE dataset was acquired as part of a larger randomized controlled trial (NCT04959019) investigating the effects of a 6‐week exercise intervention that was ongoing during the analysis of the current study; therefore, only the baseline data (prior to any intervention) are presented here. HCs without epilepsy were recruited via study flyers and word‐of‐mouth. Some of the data from HCs have been published previously, ⁵³ and only those aged 18–55 were included to age‐match the group of IGEs. The following exclusion criteria were applied for all participants: (1) pregnancy; (2) MRI contraindications (e.g., MRI‐incompatible implants or severe claustrophobia); and (3) inability to provide written informed consent. HCs were required to have no history of neurological disorders, and patients with IGEs were required to have no neurological conditions other than epilepsy and must have had the ability to participate in the exercise intervention (data from intervention are not reported in this study).

To examine the effect of seizure control status on neurometabolites, the group of IGEs was further stratified into two subgroups—seizure‐free if they reported not experiencing a seizure in the past 6 months (IGE−) and treatment‐resistant if they reported experiencing at least one seizure in the past 6 months (IGE+; Tables 1 and S1).

TABLE 1.

Demographic, clinical, and behavioral variables for eight patients who were seizure‐free for at least the past 6 months (IGE−) and nine patients who experienced at least one seizure in the past 6 months (IGE+).

	IGE–	IGE+	d, 95% CI
N	8	9
Age, years	29.4 (8.0)	31.8 (5.2)	−0.4 [−1.3, 0.6]
Sex, female ^a	5 (62.5%)	4 (44.4%)
Clinical variables
Age of epilepsy onset, years	14.4 (2.5)	11.5 (4.6)	0.8 [−0.2, 1.8]
Duration of epilepsy, years	14.9 (7.4)	20.4 (6.8)	−0.8 [−1.7, 0.2]
Number of anti‐seizure medications	1.5 (0.8)	2.3 (1.8)	−0.6 [−1.6, 0.4]
Number of seizures in past 6 months		3.7 (5.4)
Behavioral variables
MoCA score^*	27.5 (2.3)	23.3 (3.7)	1.3 [−2.4, 0.3]
Flanker score	106.4 (4.6)	99.1 (11.8)	−0.8 [−1.8, 0.2]
Flanker Congruent reaction time, seconds	0.6 (0.1)	0.7 (0.3)	−0.6 [−1.6, 0.4]
Flanker Incongruent reaction time, seconds	0.6 (0.2)	1.0 (0.8)	−0.6 [−1.6, 0.4]

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Note: Data presented as mean (standard deviation) except sex, which was reported by the number of female participants (percentage).

^{^a}

X ² = 0.55, p = 0.457.

p < 0.05.

2.2. Cognitive assessments in patients with IGEs

Patients with IGEs were examined for general cognitive performance with the Montreal Cognitive Assessment (MoCA), where higher scores indicate better cognitive function. ⁵⁴ Moreover, the Flanker test, which is part of the NIH Toolbox cognition battery, was administered to assess attention. ⁵⁵ During this task, participants were presented with five arrows (pointing left or right) in a horizontal line, with the middle one being the target stimulus. Participants are asked to identify the direction of the target stimulus (left or right), which points in either the same (congruent condition) or opposite (incongruent condition) direction as the surrounding stimuli. Performance was represented by the Flanker standard score, provided by the NIH toolbox and derived from a standardized formula that incorporates both accuracy and reaction time on the Flanker test; higher scores indicate better performance. To further characterize behavior, we also present reaction times to congruent and incongruent conditions, with shorter reaction times indicating better performance.

2.3. MRI acquisitions

Neuroimaging took place on a 3‐Tesla Siemens Magnetom PRISMA (Siemens Healthineers, Erlangen, Germany) using a 20‐channel head coil. An anatomical T1‐weighted Magnetization Prepared RApid Gradient Echo (MP‐RAGE) sequence was acquired for reference with the following parameters: TR = 2400 ms, TE = 2.22 ms, flip angle = 8°, field‐of‐view (FoV) = 256 × 240 × 167 mm, slice thickness = 0.8 mm. A 3D echoplanar spectroscopic imaging (EPSI) sequence was acquired with the following parameters: TR1 = 1500 ms; TR2 (water reference) = 511 ms; TE = 17.6 ms; lipid inversion‐nulling with TI = 198 ms; flip angle = 71°; FoV = 280 × 280 × 180 mm; matrix = 50 × 50 × 18; GRAPPA acceleration = 1.3. The FoV was oriented in‐plane without alignment to anatomical landmarks. Prior to the EPSI acquisition, adjustments for off‐resonance frequency were performed to reduce frequency deviation effects. Interactive shimming with a target full‐width half‐maximum (FWHM) of ≤30 Hz was performed to minimize magnetic field inhomogeneities and was achieved in all participants that completed EPSI scans. ⁵⁶

2.4. MRI data processing

Preprocessing of the spectroscopy data was performed with MIDAS (Metabolite Imaging and Data Analysis System, version 8.7.1) software. ³² , ⁵⁷ , ⁵⁸ The automated preprocessing pipeline included image reconstruction, magnetic field inhomogeneity correction, interpolation to a 64 × 64 × 32 grid, and spatial smoothing using a 6 mm FWHM Gaussian kernel. The FITT2 module of MIDAS was used to perform automatic fitting of the metabolite peaks (Figure S1). The SINorm module was used to normalize metabolite maps to the participant's own Cr levels, facilitating comparison with existing literature reporting metabolite ratios. The MSREG module was then used to register the T1‐weighted image to the metabolite maps. Images were then further warped into the Montreal Neurological Institute (MNI) stereotactic space using a nonlinear spatial transform within MIDAS. Prior to spectral integration, voxels met the following quality criteria or were discarded: (1) a fitted metabolite linewidth between 2 and 12 Hz; (2) value is not more than 2.5 times the standard deviation of all valid voxels over the image; and (3) cerebrospinal fluid content is not more than 30% of the voxel volume. ³⁸ , ³⁹ , ⁵⁹ , ⁶⁰ All the images were inspected visually to ensure the results of registration and included voxels for the following analysis. For each participant, brain temperature was also calculated with the TMap module ³² using the following equation: T_Cr = −102.61 × Δ_Water‐Cr + 206.1°C, where Δ_Water‐Cr represents the frequency difference between the water and Cr resonance on the spectral plot.

2.5. Statistical analysis

Nonparametric independent t‐tests and regression analyses were performed using permutation‐based statistical tests (5000 permutations) with Threshold‐Free Cluster Enhancement (TFCE) via FSL's randomise program (FMRIB Software Library, version 6.0.7.4). Specifically, clinical and behavioral measures (i.e., epilepsy duration, MoCA scores, and Flanker scores) were demeaned and included as a single EV in the corresponding analysis separately, and no additional EVs (e.g., group) were included. Prior to statistical analysis, all metabolite images were aligned to each participant's T1‐weighted image which has been registered to the MNI152 template brain via AFNI's @Align_Centers program (Analysis of Functional NeuroImages, version 23.0.07). TFCE analyses for each metabolite image were conducted independently within the five ROIs based on brain regions previously found to be involved during generalized seizures and in cognition and the default mode network: bilateral hippocampus, parietal cortex (PC), thalamus, anterior cingulate cortex (ACC), and posterior cingulate cortex (PCC). ⁴³ , ⁵⁰ , ⁵¹ , ⁵² , ⁶¹ The ROI brain masks were generated using the Harvard‐Oxford standard atlas and refined to include only nonzero voxels shared across the groups being compared (Table S2).

All results were considered significant at p < 0.05, following family‐wise error (FWE) correction for multiple comparisons within each ROI mask. Within ROIs showing significant group differences, significant TFCE clusters were binarized to create masks. These binary masks were used to extract mean metabolite values from the metabolite maps in MNI space. The extracted mean metabolite values were visualized using box plots.

3. RESULTS

3.1. Demographic, clinical, and behavioral measures

Thirty‐nine participants aged 18–55 completed a T1‐weighted anatomical scan and a 3D EPSI scan. Of these, seven participants (4 HCs and 3 IGEs) were excluded from all the analysis due to incomplete data or failure to complete the preprocessing with MIDAS. Therefore, 32 participants were included in statistical analyses in the current study; 15 (6 females; average age = 29.6 ± 7.7) were HCs, and 17 (9 females; average age = 30.6 ± 6.6) were patients with IGEs (IGE−: n = 8; IGE+: n = 9). There were no significant group differences in age or sex distribution between the HCs and IGEs. In the IGEs, the mean clinical variables were as follows: seizure frequency 1.9 ± 4.3 seizures per month over the past 6 months; number of anti‐seizure medications (ASMs) 1.9 ± 1.4; age at onset 12.9 ± 3.9 years; and epilepsy duration, 17.9 ± 7.4 years. The means for cognitive measures were as follows: MoCA score 25.3 ± 3.7; Flanker score 102.5 ± 9.7.

There were no significant differences in sex or age distributions between IGE− and IGE+ groups (Table 1). Similarly, clinical characteristics did not differ significantly between groups, including seizure frequency within the current month, number of ASMs, age at onset, or duration of epilepsy. On cognitive assessments, no significant group difference was observed in Flanker scores. However, MoCA scores were significantly lower in the IGE+ group compared to the IGE− group (t(15) = −2.75, p = 0.015, d = −1.3, 95% CI [−2.4, −0.3]; Table 1).

3.2. MRSI differences between HCs and IGEs

Voxel‐wise TFCE analysis identified significant clusters of group differences in metabolite ratios (Figure 1A,E,F; Table S3). Mean values of metabolite ratios inside the clusters were extracted from the maps by applying binary masks of the clusters. We found significantly higher MI/Cr in the IGE group within the parietal cortex (Figure 1A,B), PCC (Figure 1A,C), and thalamus (Figure 1A,D). Within the hippocampus, Glx/Cr was significantly higher in the IGE group compared to HCs (Figure 1E,G). NAA/Cr ratios were significantly lower in the IGE group within the parietal cortex (Figure 1F,H) compared to HCs. No significant group differences were found in the ACC. No significant differences in brain temperature (MRSI‐t) were found in either the whole brain or in any ROI.

Threshold‐free cluster enhancement (TFCE) analysis comparing patients with idiopathic generalized epilepsies (IGEs) and healthy controls (HCs). Brain maps show t‐values from TFCE analysis. (A) Higher MI/Cr in IGEs in parietal cortex, PCC, thalamus. (B–D) Boxplots for group differences in MI/Cr within significant clusters. (E) Higher Glx/Cr in IGEs in the hippocampus. (F) Lower NAA/Cr in parietal cortex in IGEs. (G) Boxplots for group differences in Glx/Cr within significant clusters. (H) Boxplots for group differences in NAA/Cr within significant clusters.

3.3. MRSI differences between IGE− and IGE+

Clusters within the hippocampus had significantly lower NAA/Cr ratios in the IGE+ group than the IGE− group (Figure 2A,B). No other group differences in NAA/Cr were found in the remaining ROIs or in any other metabolites or brain temperature (MRSI‐t).

Threshold‐free cluster enhancement (TFCE) analysis comparing seizure‐free patients (IGE−) with treatment‐resistant patients (IGE+). (A) t‐values from TFCE analysis overlaid on standard anatomical reference show higher NAA/Cr in the hippocampus in the IGE− group compared to the IGE+ group. (B) NAA/Cr values within the cluster in IGE− and IGE+.

3.4. Relationships between neurometabolites and clinical outcomes in IGE

To investigate the key neurometabolites and their associations with duration of epilepsy, general cognitive performance, and executive functioning, individual TFCE analyses were conducted for each ROI and brain metabolite. Significant clusters within each ROI were identified at p < 0.05 after FWE correction (Figures 3, 4, 5).

TFCE analysis showing significant associations between metabolite ratios and epilepsy duration in patients with IGEs. Brain maps show t‐values from TFCE analysis overlaid on standard anatomical reference. (A) Negative associations between epilepsy duration and NAA/Cr were significant in the ACC, PCC, and parietal cortex. (E) Negative associations between epilepsy duration and Glx/Cr ratios were significant in the ACC, PCC, parietal cortex, and hippocampus. To visualize these relationships, average metabolite ratio values were extracted from each brain region for each participant (y‐axis) and graphed against epilepsy duration (x‐axis) with a trendline (B–D, F–I).

TFCE analysis showing significant associations between metabolite ratios and MoCA scores in patients with IGEs. Brain maps show t‐values from TFCE analysis overlaid on standard anatomical reference. (A) Positive associations between MoCA scores and NAA/Cr were significant in the ACC, PCC, hippocampus, and thalamus. (F) The negative association between MoCA scores and MI/Cr was significant in the thalamus. To visualize these relationships, average metabolite ratio values were extracted from each brain region for each participant (y‐axis) and graphed against MoCA scores (x‐axis) with a trendline (B–E, G).

TFCE analysis showing significant associations between metabolite ratios and Flanker scores in patients with IGEs. Brain maps show t‐values from TFCE analysis overlaid on standard anatomical reference. (A) Positive associations between Flanker scores and NAA/Cr were significant in the ACC, thalamus, and hippocampus. To visualize these relationships, average metabolite ratio values were extracted from each brain region for each participant (y‐axis) and graphed against Flanker scores (x‐axis) with a trendline (B–D).

3.4.1. Duration of epilepsy

Voxel‐wise TFCE analyses demonstrated that epilepsy duration in IGEs was significantly associated with NAA/Cr within the ACC, PCC, and parietal cortex (Figure 3A; Table S4) and Glx/Cr within the ACC, PCC, parietal cortex, and hippocampus (Figure 3E; Table S4). To more clearly visualize these relationships, average metabolite ratio values were extracted from each brain region for each participant and graphed against epilepsy duration (Figure 3B–D,F–I). No significant associations were observed in the thalamus for the voxel‐wise TFCE analysis.

3.4.2. MoCA scores

Voxel‐wise TFCE analyses revealed clusters where MoCA scores in IGEs were significantly associated with NAA/Cr within the ACC, PCC, thalamus, and hippocampus (Figure 4A; Table S4) and with MI/Cr in the thalamus (Figure 4F; Table S4). To more clearly visualize these relationships, average metabolite ratio values were extracted from each brain region for each participant and graphed against MoCA scores (Figure 4B–E,G).

3.4.3. Flanker scores

Voxel‐wise TFCE analyses revealed that Flanker scores were significantly positively associated with NAA/Cr in the ACC, thalamus, and hippocampus (Figure 5A; Table S4). To more clearly visualize these relationships, average metabolite ratio values were extracted from each brain region for each participant and graphed against Flanker scores (Figure 5B–D).

4. DISCUSSION

We examined differences in brain metabolites and brain temperature between IGEs and HCs. We also investigated the impact of seizure control on metabolites and relationships between metabolites and epilepsy duration, MoCA scores, and Flanker scores. We found several metabolite differences between patients with IGEs and HCs in ROIs, primarily higher MI/Cr and Glx/Cr and lower NAA/Cr. We also found seizure control to be a factor in the hippocampal NAA/Cr levels of IGEs. Furthermore, IGEs showed negative associations between epilepsy duration and NAA/Cr and Glx/Cr in specific ROIs, along with significant associations between cognitive measures and ROI metabolite ratios. We did not observe significant group differences or associations related to brain temperature.

4.1. NAA levels and disease progression in IGEs

NAA is a neuroimaging biomarker of neuronal integrity in the CNS, with lower levels indicating neuronal loss as commonly observed in aging and neurological disorders. ⁴² , ⁶² In epilepsy, reduced NAA levels correlate with neuronal loss and damage, thereby assisting in the identification of the epileptogenic zone and in monitoring disease processes. ⁶³ , ⁶⁴ Our study revealed significant reductions in NAA in the parietal cortex of IGEs compared to HCs. Interestingly, an EEG/fMRI study by Szaflarski et al. found clear parietal involvement ahead of other brain regions in the generation of generalized spike and wave discharges in patients with IGEs. ⁶⁵ These findings suggest that regional NAA/Cr reductions—particularly in the parietal cortex—might serve as a potential biomarker of disease progression in IGE and might be associated with seizure control status, although future longitudinal studies are still required to confirm this.

We did not observe significant differences in thalamic NAA/Cr ratios in patients with IGEs compared to HCs. Prior studies have reported varying findings with regard to NAA in IGE, ⁶⁶ , ⁶⁷ , ⁶⁸ , ⁶⁹ , ⁷⁰ , ⁷¹ , ⁷² , ⁷³ with some studies showing lower NAA/Cr in the bilateral thalamus of IGEs when compared to HCs. ⁶⁶ , ⁶⁸ , ⁷¹ , ⁷³ However, one study observed significantly lower thalamic NAA/Cr in the left but not the right thalamus of IGEs, ⁷⁰ while another showed lower NAA/Cr in the right thalamus but had not acquired data in the left thalamus. ⁶⁹ Another study observed differing effects in subtypes of IGEs, finding lower right thalamic NAA/Cr in those with generalized tonic–clonic epilepsy compared to HCs, but not in those with juvenile myoclonic epilepsy. ⁷² Thus, it is possible we did not observe thalamic NAA/Cr differences in our study due to including mixed IGE subtypes in our sample. The lack of significant associations between thalamic NAA levels and epilepsy duration in our sample is also inconsistent with a previous study, ⁶⁶ although we found that lower NAA/Cr in the ACC, PCC, and parietal cortex was associated with longer epilepsy duration, suggesting progressive neuronal loss in these regions. Furthermore, NAA/Cr in the ACC, PCC, hippocampus, and thalamus was positively associated with MoCA scores, indicating that neuronal loss within seizure and memory networks, potentially due to ongoing seizure activity, medication effects, and/or interictal epileptiform discharges, ⁵² may contribute to general cognitive decline. ⁷⁴ Lower NAA/Cr in the ACC, thalamus, and hippocampus was also associated with lower Flanker scores, suggesting that neuronal loss underlies impairments in executive function and attention in IGE. These findings align with previous research linking reduced NAA to cognitive deficits in IGE. ⁶⁷ , ⁷² In summary, our results support the presence of neuronal loss within the key regions of seizure‐related brain network in IGE.

4.2. Glx levels may serve as imaging biomarker of epilepsy progression

Our study found significantly higher Glx/Cr in the hippocampus in IGE compared to HCs. This finding aligns with prior evidence reporting higher Glx levels in IGEs, particularly in the thalamus and frontal brain regions. ⁶⁹ , ⁷⁵ , ⁷⁶ However, we could not find any previous studies that specifically examined Glx levels in the hippocampus of patients with IGEs. The observed higher Glx in our sample may be the result of astrocyte dysfunction in epilepsy. ⁷⁷ Impaired astrocytes exhibit reductions in glutamate uptake and suppression of glutamine synthetase, leading to persistently elevated extracellular glutamate and neuronal hyperexcitability in affected brain regions. ⁷⁷ These observed abnormalities suggest the presence of neuroinflammation in seizure‐related networks. Importantly, we found that Glx/Cr in multiple brain regions, including the ACC, PCC, parietal cortex, and hippocampus, was negatively associated with epilepsy duration. This novel finding suggests that neuronal hyperexcitability may be more prominent during the early stages of epilepsy. Studies have found hyperexcitability in newly diagnosed epilepsy patients, including those with IGEs or focal epilepsies, as well as in preclinical models during early stages of epilepsy. ⁷⁸ , ⁷⁹ In summary, our results suggest that incorporating Glx measurement in future studies of IGE may yield greater insight into disease progression in this condition.

4.3. Higher MI may reflect neuroinflammation and be associated with cognitive deficits in IGEs

Our study revealed significantly higher MI/Cr in the PCC, thalami, and parietal cortex in IGE compared to HCs. These findings are notable given that previous studies have reported lower thalamic MI levels in patients with IGEs compared to HCs. ⁶⁹ , ⁷² , ⁷⁶ Valproate is a commonly prescribed ASM that has been shown to reduce brain MI levels in both animal models of epilepsy ⁸⁰ and patients with IGEs receiving sodium valproate treatment. ⁸¹ , ⁸² Notably, these studies reporting reduced MI levels included a high proportion (>50%) of participants on valproate, ⁶⁹ , ⁷⁶ in contrast to our sample, which included only one participant taking valproate. Therefore, the widespread higher MI/Cr we observed may be due to the lack of valproate exposure in our sample and suggest that MI alterations are strongly influenced by treatment. Furthermore, MI is a surrogate neuroimaging biomarker of neuroinflammation, ¹⁵ , ⁸³ often linked to astrocyte activation in response to CNS injury. ¹² , ⁸⁴ , ⁸⁵ Higher MI levels have been documented in patients with traumatic brain injury ⁸⁴ , ⁸⁶ and TLE, ³⁷ , ⁸⁷ where gliosis is thought to underlie higher MI levels. Moreover, animal models have identified MI as a biomarker of early epileptogenesis. ⁸⁸ , ⁸⁹ Therefore, increased MI/Cr in brain regions involved in generalized seizures and cognitive functions could reflect ongoing neuroinflammation in IGE. Additionally, higher thalamic MI/Cr was associated with lower MoCA scores, highlighting the thalamus as particularly relevant to cognitive impairment in IGE. While higher MI levels have been associated with neuroinflammation in previous research, the observed alterations of higher MI/Cr in IGEs in the present study could not be fully attributed to neuroinflammation as MI lacks specificity. Future research should integrate well‐established neuroinflammatory biomarkers (e.g., blood biomarkers) to better characterize neuroinflammation in patients with IGEs.

4.4. No significant differences in brain temperature in IGE compared to HCs

Unlike previous studies in focal epilepsies, ³⁷ , ³⁸ , ⁵³ we did not observe significant differences in brain temperature between patients with IGEs and HCs. This was not altogether unexpected given that people with IGEs lack a localized seizure onset zone. In contrast, studies of TLE and extra‐temporal lobe focal epilepsies have reported higher brain temperature in the seizure focus area, presumably reflecting neuroinflammation localized to the seizure onset zone. ⁹⁰ In IGEs, seizures propagate through distributed brain networks rather than originating from a focal region. Thus, it is possible that brain temperature is not a sensitive marker of low‐level neuroinflammation in IGEs, though this cannot be determined until our findings are replicated, ideally with larger sample sizes, and validated with established neuroinflammatory markers. Additionally, approximately half of the patients with IGEs in our study were seizure‐free and may have had lower levels of neuroinflammation than treatment‐resistant patients who have ongoing seizure activity. However, we note that the IGE− and IGE+ groups in our study did not significantly differ in brain temperature, so the possibility remains that neuroinflammation is too diffuse in IGE to be a sensitive biomarker for the condition. While MRSI‐t remains a promising tool for focal epilepsies, our findings suggest that MI may be a more promising indicator of neuroinflammation in IGEs.

5. CONCLUSION AND LIMITATIONS

This study is the first to provide evidence of widespread metabolite alterations suggesting neuroinflammation in patients with IGEs using whole‐brain MRSI. We observed higher MI/Cr and Glx/Cr and lower NAA/Cr across multiple brain regions, indicating concurrent glial activation, neuronal hyperexcitability, and neuronal loss. Hippocampal NAA/Cr was significantly lower in IGE+ than IGE− patients, suggesting that hippocampal NAA could be developed into a potential biomarker for seizure control status. Such a measure would be of great clinical value given the current reliance on self‐report when evaluating ASM response in a condition that frequently involves memory deficits and the possibility of comorbid functional seizures masking epileptic seizure freedom. Furthermore, reductions in NAA/Cr and Glx/Cr were associated with longer epilepsy duration, highlighting their relevance to disease progression. Alterations in NAA/Cr and MI/Cr were linked to general cognitive performance, and NAA/Cr was additionally associated with attention. Overall, these findings support the notion that chronic neuroinflammation may underlie progressive neural and cognitive impairments in IGEs.

Limitations of this study should be considered. The relatively small sample size did not allow us to explore differences between IGE subtypes, precluding the identification of syndrome‐specific biomarkers. This also did not allow for examination of the effect of ASMs on metabolites; thus, we could not exclude the possibility that some metabolite differences (or lack of differences) were influenced by medication use. Further, we did not consider the correspondence between the severity of abnormal epileptic discharge and neurometabolite levels, which would be interesting to pursue with future research utilizing simultaneous EEG and MRSI. In addition, the signal‐to‐noise ratio across brain regions should be taken into consideration. The percentage of signal loss was higher in some ROIs, particularly the ACC, than in others. Finally, a previous study in patients with TLE assessed blood biomarkers of inflammation in addition to brain temperature and brain metabolites, which should be considered for future studies for a more comprehensive characterization of inflammation in IGEs. ³⁷ Altogether, future research is warranted to further explore the utility of MRSI‐derived biomarkers in tracking epilepsy progression and evaluating therapeutic response in IGEs, with the potential to inform personalized treatment strategies.

AUTHOR CONTRIBUTIONS

Yun Lien: Data analysis, writing, and editing initial draft and final versions of the manuscript. Christina Bankston: Data collection, writing and editing initial draft, guidance on data analysis, and review and editing final versions of the manuscript. Ayushe A. Sharma: Data collection, guidance on data analysis, review and editing final versions of the manuscript. Brandon S. Mitchell: Data collection, data analysis, review and editing final versions of the manuscript. Rodolphe Nenert: Guidance on data analysis, review and editing final versions of the manuscript. Anna Moyana: Data collection, review and editing final versions of the manuscript. Jennifer Pilkington: Data collection, review and editing final versions of the manuscript. Roy C. Martin: Review and editing final versions of the manuscript. Charity J. Morgan: Review and editing final versions of the manuscript. Lawrence Ver Hoef: Data collection, review and editing final versions of the manuscript. Jerzy P. Szaflarski: Funding, project administration and supervision, guidance on data collection and analysis, data collection, review and editing final versions of the manuscript. Jane B. Allendorfer: Conceptualization, funding, project administration and supervision, guidance on data collection and analysis, review and editing the initial draft and final versions of the manuscript.

FUNDING INFORMATION

Research reported in this publication was supported, in part, by the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health (NIH) under Award Number R01HD102723 to JBA, in part by the State of AL General Fund (Carly's Law) to JPS, and by the UAB Epilepsy Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or the State of Alabama.

CONFLICT OF INTEREST STATEMENT

C. Bankston is supported by a career development award from the National Institute of Neurological Disorders and Stroke (1K23NS144635–01). L. Ver Hoef receives support from Cognizance Biomarkers. J. P. Szaflarski, Funding: NIH, NSF, DoD, State of Alabama, Shor Foundation for Epilepsy Research, UCB Pharma Inc., NeuroPace Inc., Greenwich Biosciences Inc., Biogen Inc., Xenon Pharmaceuticals, Serina Therapeutics Inc., and Eisai, Inc. Consulting/Advisory Boards: Greenwich Biosciences Inc., Pure Tech Health Inc., NeuroPace, Inc., Serina Therapeutics Inc., AdCel Biopharma Inc., iFovea Inc., LivaNova Inc., UCB Pharma Inc., SK Lifesciences Inc., and medico‐legal services. Paid Editorial Work: Editor‐in‐Chief—Epilepsy & Behavior Reports. He has served as a member on the Alabama State Medical Cannabis Study Commission (nominated by Gov. Ivey). He has also served on the Alabama Medical Cannabis Commission (2021–2025; nominated by Dr. Scott Harris, State Health Officer). J. B. Allendorfer has received funding from the NIH, the U.S. Department of Defense (W81XWH‐17‐1‐0619), the state of Alabama Carlys Law, LivaNova Inc., and the Evelyn F. McKnight Brain Institute, has received honoraria from the Cleveland Clinic, the Texas Neurological Society, Medscape Education Global, and University of Auckland and travel funds from the International League Against Epilepsy, and has served as a consultant for LivaNova Inc., and serves as an editorial board member for Epilepsy & Behavior Reports. Y. Lien, A. A. Sharma, B. S. Mitchell, R. Nenert, A. Moyana, J. Pilkington, R. C. Martin, and C. J. Morgan report no conflicts of interest.

ETHICS 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.

Supporting information

Table S1. Current anti‐seizure medications used by patients with idiopathic generalized epilepsies (IGEs). Patients with IGEs were categorized into two groups based on seizure control in the past 6 months: seizure‐free (IGE−) and active‐seizure (IGE+).

Table S2. The number and percentage of quality‐accepted nonzero voxels for each ROI brain mask utilized for each statistical test. The percentage of accepted voxels retained within each ROI is derived relative to the original number of voxels in the atlas‐defined mask, averaged across all participants.

Table S3. Results of threshold‐free cluster enhancement (TFCE) analysis for group comparisons. The peak t‐value and its corresponding MNI coordinates, the number of voxels per cluster, and the mean ratio values in a cluster for metabolites showing significant group differences are specified per brain region of interest.

Table S4. Results of threshold‐free cluster enhancement (TFCE) analysis examining relationships between neurometabolites and clinical outcomes in IGEs. The peak t‐value and its corresponding MNI coordinates and the number of voxels per cluster are specified per brain region of interest.

Figure S1. Representative MR spectra from the posterior cingulate cortex. The left image displays the spectrum for a patient with idiopathic generalized epilepsy (IGE), and the right image displays the spectrum for a healthy control (HC). Peaks for neurometabolites of interest are labeled: MI = myo‐inositol; Cr = creatine; Glx = glutamate‐glutamine complex; NAA = N‐acetylaspartate.

EPI4-9999-0-s001.docx^{(148.8KB, docx)}

ACKNOWLEDGMENTS

This work was presented in part at the 2022 American Epilepsy Society Annual Meeting. Research reported in this publication was supported in part by the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Number R01HD102723 to Dr. Jane B. Allendorfer. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Research reported in this publication was also supported in part by the State of AL General Fund (Carly's Law) to Dr. Jerzy P. Szaflarski, and by the UAB Epilepsy Center.

Contributor Information

Yun Lien, Email: ylien@uab.edu.

Jane B. Allendorfer, Email: jallendorfer@uabmc.edu.

DATA AVAILABILITY STATEMENT

Data used in the current study may be made available after completion of the final data analysis for NCT04959019 and upon reasonable request from the authors.

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Associated Data

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

Supplementary Materials

EPI4-9999-0-s001.docx^{(148.8KB, docx)}

Data Availability Statement

Data used in the current study may be made available after completion of the final data analysis for NCT04959019 and upon reasonable request from the authors.

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PERMALINK

Neurometabolite abnormalities and associations with epilepsy duration and cognition in patients with idiopathic generalized epilepsies

Yun Lien

Christina Bankston

Ayushe A Sharma

Brandon S Mitchell

Rodolphe Nenert

Anna Moyana

Jennifer Pilkington

Roy C Martin

Charity J Morgan

Lawrence Ver Hoef

Jerzy P Szaflarski

Jane B Allendorfer

Abstract

Objective

Methods

Results

Significance

Plain Language Summary

Key points.

1. INTRODUCTION

2. MATERIALS AND METHODS

2.1. Participants

TABLE 1.

2.2. Cognitive assessments in patients with IGEs

2.3. MRI acquisitions

2.4. MRI data processing

2.5. Statistical analysis

3. RESULTS

3.1. Demographic, clinical, and behavioral measures

3.2. MRSI differences between HCs and IGEs

FIGURE 1.

3.3. MRSI differences between IGE− and IGE+

FIGURE 2.

3.4. Relationships between neurometabolites and clinical outcomes in IGE

FIGURE 3.

FIGURE 4.

FIGURE 5.

3.4.1. Duration of epilepsy

3.4.2. MoCA scores

3.4.3. Flanker scores

4. DISCUSSION

4.1. NAA levels and disease progression in IGEs

4.2. Glx levels may serve as imaging biomarker of epilepsy progression

4.3. Higher MI may reflect neuroinflammation and be associated with cognitive deficits in IGEs

4.4. No significant differences in brain temperature in IGE compared to HCs

5. CONCLUSION AND LIMITATIONS

AUTHOR CONTRIBUTIONS

FUNDING INFORMATION

CONFLICT OF INTEREST STATEMENT

ETHICS STATEMENT

Supporting information

ACKNOWLEDGMENTS

Contributor Information

DATA AVAILABILITY STATEMENT

REFERENCES

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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