Cognitive impairments and neurofunctional alterations in children with electrical status epilepticus during sleep: insights from WISC-IV and resting-state fMRI

Abstract

Background

Electrical Status Epilepticus During Sleep (ESES) is a rare pediatric epilepsy syndrome characterized by sleep-induced epileptiform discharges, leading to cognitive and behavioral impairments. We aimed to investigate the underlying neural mechanisms that are critical for advancing early diagnosis and targeted interventions.

Methods

Twenty-five children with ESES and 30 age- and sex-matched healthy controls were enrolled. Cognitive function was assessed via the Wechsler Intelligence Scale for Children, Fourth Edition (WISC-IV), focusing on Full-Scale IQ (FSIQ), Verbal Comprehension Index (VCI), Perceptual Reasoning Index (PRI), Working Memory Index (WMI), and Processing Speed Index (PSI). Resting-state functional MRI (rs-fMRI) was conducted to evaluate neural activity via regional homogeneity (ReHo) analysis. Correlations between ReHo values and cognitive scores were analyzed in the ESES group.

Results

Compared with healthy controls, children with ESES presented significantly lower FSIQ and PRI scores (P = 0.017, P = 0.016), indicating cognitive impairments in global intellectual abilities and perceptual reasoning. rs-fMRI revealed decreased ReHo in the left Superior Frontal Gyrus (premotor area), bilateral postcentral gyri (primary somatosensory cortex), right cerebellum posterior, and rolandic operculum. In contrast, increased ReHo was observed in the left precuneus and left middle frontal gyrus. ReHo in the right postcentral gyrus showed a positive correlation with FSIQ and VCI scores (r = 0.56, P = 0.01; r = 0.67, P < 0.001).

Conclusions

ESES is associated with significant cognitive deficits, particularly in perceptual reasoning and verbal comprehension, alongside altered neural activity patterns. ReHo analysis revealed changes in local neural activity within key brain regions linked to cognitive function. These findings highlight the potential of rs-fMRI metrics as biomarkers for assessing cognitive impairments and guiding therapeutic interventions in ESES.

Background

Electrical Status Epilepticus during Sleep (ESES) is an age-dependent epileptic encephalopathy characterized by significant sleep-induced epileptiform discharges (spike-wave index ≥ 85%) and acquired cognitive or behavioral deficits [1–3]. As a rare but impactful syndrome, ESES poses a major threat to neurodevelopment, with prolonged activity often leading to enduring impairments in language, memory, and executive functions, even after seizure remission [4–7]. Consequently, elucidating the neural mechanisms underlying cognitive dysfunction in ESES is critical for advancing early diagnosis and targeted interventions.

While traditional neuropsychological scales, such as the Wechsler Intelligence Scale for Children (WISC), provide valuable assessments of cognitive domains and are recognized for their comprehensiveness and reliability [8, 9], they are limited by subjectivity and an inherent inability to detect deficits before they become clinically overt [10]. There is, therefore, an urgent need for objective biomarkers that can precisely quantify neural alterations associated with ESES-related cognitive impairment.

Resting-state functional magnetic resonance imaging (rs-fMRI) offers a powerful, non-invasive window into brain function, enabling the investigation of neural circuits and cognitive processes [11–13]. Among its analytical methods, Regional Homogeneity (ReHo) is particularly suited for investigating local neural synchrony and has been widely used to identify localized brain dysfunction in various neurological and psychiatric disorders [14–16]. By measuring the temporal synchronization of neural activity within a given brain region, ReHo can reveal subtle alterations in local brain function that may serve as sensitive biomarkers.

Guided by this rationale, the present study integrated the WISC-IV and rs-fMRI-based ReHo analysis to investigate the relationship between cognitive deficits and localized neural dysfunction in children with ESES. We aimed to (1) identify specific brain regions with altered ReHo in ESES patients compared to matched healthy controls, and (2) explore the correlations between these neurofunctional alterations and deficits in specific cognitive domains as measured by the WISC-IV. We hypothesize that ESES is associated with distinct patterns of abnormal local neural activity, which are linked to the characteristic cognitive profile of the syndrome.

Methods

Participants

ESES group

A cohort of twenty-five children diagnosed with ESES was recruited from Shenzhen Children’s Hospital. The sample comprised 13 males and 12 females, aged between 6.75 and 12.33 years (mean age = 8.57 ± 1.99 years; refer to Table 1). All patients had a history of epileptic seizures before meeting the ESES diagnostic criteria, with seizure types including focal seizures (e.g., motor seizures, asymmetric tonic/clonic seizure), generalized seizures (e.g., absence, myoclonic, atonic), and characteristic negative seizures like aphasia. The EEG findings include: scattered focal spike-and-wave discharges during wakefulness, replacement of the background by diffuse and continuous spike-and-wave activity during non-rapid eye movement (NREM) sleep, and significant reduction of discharges during rapid eye movement (REM) sleep.

Table 1.

Demographic and clinical data of the ESES patients and HCs

Characteristics	ESES	HC	Z	P
Number	25	30
Gender				1.000a
Male	13(52%)	17(57%)
Female	12(48%)	13(43%)
Age (m ± std, year)	8.57 ± 1.99	9.50 ± 1.80	-1.139b	0.266
Parents’ Education(year)
≤12	50%	43.7%
≥13	50%	56.3%
Right Hand	25(100%)	30(100%)
Han Nationality	25(100%)	30(100%)
Onset Age (year)	5.26 ± 2.70	/
Duration (year)	3.31 ± 2.02	/
Numbers of Asm c		/
0	4(16%)
1	9(36%)
2	6(24%)
≥3	6(24%)

a: P value of Fisher accurate test

b: Z value of Nonparametric rank sum test. There was no significant difference in gender and age between the two groups

c: anti-seizure medications

The duration of illness was defined as the time from the definitive diagnosis of ESES to the study assessment. The mean duration from diagnosis to the study assessments (WISC-IV and fMRI) was 3.31 ± 2.02 years.

The inclusion criteria for this study were as follows: (a) Age between 6 and 16 years, of Han nationality, with no specifications regarding gender. (b) Established right-handedness. (c) The diagnosis of Electrical Status Epilepticus during Sleep (ESES) was established based on the consensus criteria proposed by the International League Against Epilepsy (ILAE). The inclusion criteria for the ESES group were as follows: ①EEG Criterion: The presence of a marked increase in epileptiform activity during non-rapid eye movement (NREM) sleep, characterized by a Spike-Wave Index (SWI) of ≥ 85%. The SWI is defined as the percentage of NREM sleep time occupied by near-continuous spike-wave discharges. ②Clinical Criterion: The presence of cognitive regression/decline, behavioral disturbances, and/or motor deficits (e.g., ataxia, dyspraxia). ③Epilepsy Criterion: The presence of seizures, which are typically mild and infrequent, and may include focal motor, generalized tonic-clonic, or atypical absence seizures. (d) Negative findings on MRI.

Conversely, the exclusion criteria included the following: (a) the presence of systemic diseases or other neurological disorders. (b) contraindications for undergoing MRI. (c) Inability to comply with examination protocols.

The demographic and clinical characteristics of the participants are summarized in Table 1.

Healthy controls (HCs)

Thirty volunteers were recruited for this study and were matched in terms of age, sex, and handedness. The cohort comprised 17 boys and 13 girls, with ages ranging from 6.42 to 12 years, and a mean age of years (mean age = 9.50 ± 0.80 years; refer to Table 1).

Data acquisition

Intelligence assessment

All the subjects enrolled in the study were tested for cognitive function via the WISC-IV, by professionals with more than 10 years of work experience in the Psychology Department of Shenzhen Children’s Hospital. The test content includes 10 core subtests and 5 supplementary subtests: Similarities, Vocabulary, Comprehension, Block Design, Picture Concepts, Matrix Reasoning, Digit Span, Letter-Number Sequencing, Coding, Symbol Search, Information, Word Reasoning, etc., each with a population mean of 10 and a standard deviation of 3. The core subtests are used to create four index scores: VCI, PRI, WMI, and PSI. The FSIQ is computed from all 10 core subtests. The factor indices and FSIQ each have a population mean of 100 and a standard deviation of 15. The standard level or percentage level is used to indicate the cognitive level of each subject.

MR data acquisition

Conventional structural images and eye-open resting-state functional MRI data were collected via the Siemens MAGNETOM Skyra 3.0T superconducting magnetic resonance imaging system (Siemens Healthcare, Germany) and standard head coils. The subjects were placed in the supine position, their ears were stuffed with ear pads to reduce noise effects, and their heads were fixed with spongy pads to reduce head movement. A “+” on the large screen on the side of the head was observed through a mirror attached to the head coil. Before the examination, the subjects were informed of relevant matters needing attention and told that they would stay still.

Conventional structural image acquisition

Conventional structural images include axial T1WI (t1_tirm_dark-fluid, TR = 200 ms, TE = 2.49 ms), T2WI (t2_tse_tra, TR = 3800 ms, TE = 112 ms), and FLAIR (t2_tirm_tra_dark-fluid, TR = 9000 ms, TE = 81ms, TI = 2500 ms).

rs-fMRI data acquisition

(a) rs-fMRI data were acquired by using an echo planar imaging sequence with the following parameters: 32 axial slices, 130 Volumes, Slice Thickness = 3.0 mm, Repetition Time = 2000 ms, Echo Time = 30 ms, voxel size = 3.0 × 3.0 × 3.0 mm³, and Flip angle = 90. (b) Anatomical data: Three-dimensional magnetization prepared rapid acquisition gradient echo sequences with the following parameters: 176 sagittal slices, Slice Thickness = 1.0 mm, Spacing Between Slices = 0.5, Repetition Time = 2300 ms, Echo Time = 2.26 ms, voxel size = 1.0 × 1.0 × 1.0 mm³.

Data processing and analyses

WISC-Ⅳ data analysis

All the data were analyzed by using GraphPad Prism (version 9) software. Before data analysis, the normal distribution test (Shapiro-Wilk test) and homogeneity of variance test were performed on all the data. (a) A nonparametric rank sum test was used to compare the scale scores between groups. (b) Spearman rank correlation analysis was used to analyze the correlation between the score and variables such as examination age and course of disease. The significance level threshold was set as 0.05. If P < 0.05, the difference or correlation between the groups was considered statistically significant.

fMRI data processing

The data were preprocessed and analyzed using REST based on SPM12 (http://restfmri.net/forum/index.php, http://www.fil.ion.ucl.ac.uk/spm/). The first 10 time points of each subject’s rs-fMRI data were discarded because of the instability of the initial MRI signal. The remaining 120 volumes were first realigned to correct for head motion before being corrected by the acquisition time delay among different slices.

Data preprocessing includes: (1) Slice timing, (2) Realignment, (3) Spatial smoothing, (4) Coregistration, (5) Normalization (voxel size 3 × 3 × 3 mm³), and (6) Filtering. Any subjects with head motion greater than 2 mm or 2° in any of the six parameters (x, y, z, pitch, roll, yaw) were excluded. A bandpass (0.01–0.08 Hz) filter was set up to reduce the effect of very low-frequency and high-frequency physiological noise. Individual ReHo maps were generated by calculating Kendall’s coefficient concordance (KCC, also called the ReHo value) of the time series of a given voxel with those of its nearest neighbors (26 voxels), on a voxelwise basis. The data were subsequently smoothed with a Gaussian filter of 6 mm full width at half-maximum (FWHM) to reduce noise and residual differences in gyral anatomy. The ReHo maps were generated for each subject in each group.

Second-level fmri data analysis

One-sample t-tests were performed in each group to identify significant brain areas that were greater than the whole-brain average. The significance threshold was set at P < 0.05, and the results were corrected for multiple comparisons via FDR correction. The second-level random effects two-sample t-tests were subsequently performed to compare the ReHo results between the ESES group and the HC group within a conjunction mask. A conjunction mask was generated from one-sample t-test results. The t-map was set at a threshold of P < 0.05 (combined height threshold P < 0.01 and a minimum cluster size of 15 voxels), using the FDR correction in the REST software.

Results

WISC-Ⅳ results

The analysis revealed statistically significant differences in the FSIQ and PRI among the groups, with P values of 0.017 and 0.016, respectively. However, there were no statistically significant differences in the VCI, WMI, or PRI scores between the two groups, with P values of 0.111, 0.108, and 0.335, respectively (see Table 2; Fig. 1).

Table 2.

Intergroup comparison of scale scores

Groups	Parameters	FSIQ	VCI	PRI	WMI	PSI
ESES	Median	90	90.5	98	85	96.5
	Interquartile Range	14.25	13	14.5	13.5	12.25
	Maximum	106	103	125	109	113
	Minimum	55	65	56	67	56
HC	Median	98.5	94	110	95.5	100
	Interquartile Range	8	9	8.5	16.5	15.75
	Maximum	121	106	132	119	138
	Minimum	90	81	92	75	74
	P value	0.017*	0.111	0.016*	0.108	0.335

P > 0.05 indicated no significant difference between the two groups. P ≤ 0.05 indicated a statistically significant difference between groups

FSIQ Full-Scale Intelligence Quotient, VCI Verbal Comprehension Index, PRI Perceptual Reasoning Index, WMI Working Memory Index, PSI Processing Speed Index

Fig. 1.

Differences in WISC scores between ESES and HC groups. The analysis revealed statistically significant differences in FSIQ and PRI among the groups, with P values of 0.017 and 0.016

fMRI results

The findings from the two-sample t-test are detailed in Table 3 and illustrated in Fig. 2. Compared with the HC group, the ESES group demonstrated significantly lower ReHo values in several brain regions, specifically the right superior frontal gyrus, left postcentral gyrus, right postcentral gyrus, right posterior cerebellum, and right rolandic operculum. In contrast, the ESES group exhibited significantly elevated ReHo values in the left precuneus and left mid frontal gyrus.

Table 3.

Differences in regional homogeneity (ReHo) between patients with ESES and controls

Brain Region	Hemisphere	Peak MNI coordinate			Peak t value	Cluster voxel
	(L/R)	X	Y	Z
ESES<HC
Superior Frontal Gyrus	R	9	0	63	-4.68	124
Postcentral	L	-42	-12	36	-4.38	49
Postcentral Gyrus	R	51	-18	21	-3.41	43
Cerebellum Posterior	R	27	-57	-27	-4.66	28
Rolandic_Oper	R	48	0	9	-3.60	18
ESES>HC
Precuneus	L	-6	-87	-39	5.78	1606
Frontal_Mid	L	-33	54	0	4.04	31

t value, statistical value of peak voxel showing ReHo differences between the groups (positive t value means increased ReHo in ESES group compared to healthy controls; negative t value means decreased ReHo in ESES group compared to healthy controls). L: left; R: right

Fig. 2.

Two sample t-test results. ReHo Changes shown as a comparison of KCC maps between patients with ESES and controls (P < 0.01, minimum cluster > 15, with FDR correction). T score bars are shown on the right. Hot (red) and cold (blue) colors indicate ESES group ReHo increases and decreases, respectively. A Right Superior Frontal Gyrus B Left Postcentral Gyrus C Right Postcentral Gyrus D Right Cerebellum Posterior E Right Rolandic Operculum F Left Precuneus G Left Frontal Middle Gyrus.

Correlations between brain regions and WISC

The right postcentral gyrus demonstrated a moderate positive correlation with FSIQ and VCI scores (r = 0.56, P = 0.01; r = 0.67, P < 0.001). In contrast, the correlations observed between other brain regions and the index dimensions fail to reach statistical significance. For details, please see Table 4 and Fig. 3.

Table 4.

Correlation analysis results between brain regions and WISC scores

Brain Regions	FSIQ	VCI	PRI	WMI	PSI
Cerebellum Posterior Lobe_R	0.07r/0.77P	0.22/0.33	0.00/0.99	-0.09/0.70	0.32/0.15
Precuneus_L	0.28/0.21	0.01/0.95	0.26/0.25	0.29/0.19	0.17/0.45
Frontal_Mid_L	-0.20/0.38	-0.06/0.80	-0.30/0.18	-0.09/0.70	-0.06/0.78
Rolandic_Oper_R	-0.07/0.77	0.03/0.90	-0.06/0.80	-0.04/0.87	-0.08/0.71
Postcentral Gyrus_R	0.56/0.01*	0.67/0.00*	0.24/0.28	0.46/0.32	0.37/0.09
Postcentral_L	-0.13/0.55	0.13/0.57	-0.09/0.68	-0.30/0.17	0.15/0.49
Superior Frontal Gyrus_R	-0.13/0.58	0.23/0.30	-0.06/0.78	-0.31/0.16	0.26/0.25

r represents the correlation coefficient; P represents the significance level of the hypothesis test. When P ≤ 0.05, the correlation was statistically significant. 0.00* mean P < 0.001

Fig. 3.

A Heatmap showing correlations between brain regions and WISC. B, C The ReHo value of the right postcentral gyrus was positively correlated with FSIQ and VCI, reaching statistical significance. D The correlation between the ReHo value of the right postcentral gyrus and PRI did not reach statistical significance.

The right Postcentral Gyrus correlates moderately positively with FSIQ and VCI dimensions. In contrast, the correlations observed between other brain regions and the index dimensions fail to attain statistical significance.

Discussion

This study investigated the neural basis of cognitive dysfunction in children with ESES by integrating WISC-IV cognitive assessment and rs-fMRI ReHo analysis. Our main findings can be summarized as follows:

First, children with ESES exhibited significant deficits in global intellectual ability (FSIQ) and perceptual reasoning (PRI). Second, compared to healthy controls, children with ESES showed decreased ReHo in several brain regions, including the left superior frontal gyrus (premotor area), bilateral postcentral gyri (primary somatosensory cortex), and right posterior cerebellum, while increased ReHo was observed in the left precuneus and left middle frontal gyrus. Third, and most importantly, ReHo in the right postcentral gyrus showed a significant positive correlation with both FSIQ and VCI scores.

These findings not only confirm the broad impact of ESES on children’s cognitive function but, more importantly, reveal specific local brain functional alterations associated with this impairment, providing new insights into the neural mechanisms of ESES.

Reduced ReHo in the right postcentral gyrus and its cognitive implications in ESES

The correlation between reduced ReHo in the right postcentral gyrus and lower FSIQ/VCI scores represents the most significant finding warranting in-depth discussion. The postcentral gyrus, serving as the primary somatosensory cortex (S1), has traditionally been associated with processing basic somatosensory information such as touch, pressure, temperature, and pain [17, 18].

However, a growing body of evidence suggests that S1 is not an isolated functional area; through its extensive connections with higher-order association cortices, it participates in more complex cognitive processes, including sensory integration, working memory, and even language comprehension [19, 20].

Our findings align well with this modern perspective. The decreased local synchrony (ReHo) in the right postcentral gyrus may reflect that ESES-related abnormal discharges disrupt the normal synergistic activity within the somatosensory cortex itself and between it and cognitive-related networks [21, 22]. This desynchronization of local neural activity could impair the brain’s ability to effectively integrate and process sensory information, consequently leading to the observed decline in global intelligence and verbal comprehension measured by the WISC-IV [23].

Placing our findings in a broader context, while studies specifically examining ReHo alterations in children with ESES are scarce, research on other types of epilepsy or brain injury has also reported functional abnormalities in the sensorimotor cortex correlated with cognitive deficits [24]. Furthermore, in typical development and certain neurodevelopmental disorders, the functional connectivity properties of the postcentral gyrus have been shown to be closely related to cognitive abilities [25]. Therefore, our study links the cognitive dysfunction in ESES to a specific, measurable neuroimaging biomarker, laying the groundwork for future research.

Based on the above analysis, we propose a potential pathophysiological model: the persistent epileptiform discharges during sleep in ESES interfere with normal functional reorganization and synaptic pruning processes in the brain. This interference particularly affects brain regions with clear topographic organization like the primary somatosensory cortex, leading to impaired synchronization of local neuronal activity (manifested as decreased ReHo). As S1 is a key node within multiple distributed brain networks, its local functional disruption may propagate through connected networks, thereby affecting higher-order cognitive processing and ultimately manifesting as widespread cognitive dysfunction. The right postcentral gyrus might play a critical “bottleneck” role in this pathological process.

Of course, this hypothesis requires validation through future longitudinal studies incorporating task-based fMRI, diffusion tensor imaging (DTI), and other techniques. For instance, designing sensory integration cognitive tasks while observing the brain activation patterns and functional connectivity states of children with ESES during task performance could provide further insights.

Limitations and future directions

This study has several limitations that should be acknowledged. First, the small sample size, though common in rare disease studies, may affect the statistical power and generalizability of our findings. Future multi-center collaborations are needed to validate these results with larger cohorts. Although the sample size is limited, we employed non-parametric statistical methods (such as Spearman correlation analysis and Mann-Whitney U test) for analysis. These methods are more robust for small sample data and can, to some extent, mitigate the statistical power issues caused by the limited sample size.

Second, the cross-sectional design limits causal inference between ESES, neural changes, and cognitive deficits. Longitudinal studies tracking patients over time are essential to establish temporal relationships.

Third, while ReHo effectively identifies local neural disturbances, it does not capture inter-regional functional connectivity. Combining ReHo with functional connectivity analyses in future work could reveal network-level dysfunctions in ESES.

Despite these limitations, our findings provide a foundation for understanding ESES-related neural alterations and highlight the need for more comprehensive research approaches.

Conclusions

In summary, our study demonstrates that cognitive impairments in children with ESES are linked to aberrant local neural activity, particularly evidenced by reduced Regional Homogeneity (ReHo) in the right postcentral gyrus. This association not only underscores the role of the somatosensory cortex in higher-order cognitive processes but also provides a quantifiable neuroimaging biomarker for ESES-related cognitive dysfunction.

The convergence of neuropsychological assessment and rs-fMRI metrics in this study offers a robust framework for understanding the neural substrates of ESES. The identified biomarker paves the way for future research aimed at developing targeted interventions to normalize neural synchrony and improve cognitive outcomes in this vulnerable population.

Abbreviations

ESES

Electrical Status Epilepticus During Sleep

WISC-IV

Wechsler Intelligence Scale for Children, Fourth Edition

FSIQ

Full-Scale Intelligence Quotient

VCI

Verbal Comprehension Index

PRI

Perceptual Reasoning Index

WMI

Working Memory Inde

PSI

Processing Speed Index

rs-fMRI

Resting-state functional Magnetic Resonance Imaging

ReHo

Regional Homogeneity

SWI

Spike-Wave Index

ILAE

International League Against Epilepsy

ICA

Independent Component Analysis

ALFF

Amplitude of Low-Frequency Fluctuations

HC

Healthy Controls

FWHM

Width at half-Maximum

DCML

Dorsal Column Medial Lemniscus

Author contributions

TM contributed to the study design and drafted the manuscript. XYW, JJL and JQL performed the data analysis and interpretation. XLM assisted in collecting the fMRI data. ZQH, LC, YC, and DZC supported patient recruitment and clinical data management. LLZ assisted in completing the WISC assessments. JXL and HWZ supervised the study and provided overall guidance. All the authors read and approved the final manuscript.

Funding

This project was supported by the Shenzhen Clinical Research Center (No. 20220819113341005), the Sciences and Technology Project of Shenzhen (No. JCYJ20250604180058075), the Natural Science Foundation of Guangdong Province (No. 2022A1515011427), and the Guangdong High-level Hospital Construction Fund (ynkt2021-zz47).

Data availability

The datasets analyzed during the current study are not publicly available due to patient privacy and ethical restrictions but are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

All participants agreed to participate, and informed consent was obtained from their parents or legal guardians. The study protocol received approval from the Medical Ethics Committee of Shenzhen Children’s Hospital (No. 201904102).

Consent for publication

Written informed consent for publication was obtained from the parents or legal guardians of all child participants included in this study.

Competing interests

The authors declare no competing interests.