
==== Front
Hum Brain Mapp
Hum Brain Mapp
10.1002/(ISSN)1097-0193
HBM
Human Brain Mapping
1065-9471
1097-0193
John Wiley & Sons, Inc. Hoboken, USA

10.1002/hbm.70020
HBM70020
Research Article
Research Article
Behavioral and brain morphological changes before and after hemispherotomy
Yu et al.
Yu Hao 1
Chen Yijun 2
Bao Ziyu 2
Luo Junhao 2
Liu Qingzhu 1
Qin Peipei 2
Wang Changtong 2
Qu Jingli 2
Wang Wei 2
Cai Lixin 1 pufhpec_clx@163.com

Gong Gaolang https://orcid.org/0000-0001-5788-022X
2 3 4 gaolang.gong@bnu.edu.cn

1 Pediatric Epilepsy Center Peking University First Hospital Beijing China
2 State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research Beijing Normal University Beijing China
3 Beijing Key Laboratory of Brain Imaging and Connectomics Beijing Normal University Beijing China
4 Chinese Institute for Brain Research Beijing China
* Correspondence
Gaolang Gong, State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
Email: gaolang.gong@bnu.edu.cn
Lixin Cai, Pediatric Epilepsy Center, Peking University First Hospital, Beijing 100034, China.
Email: pufhpec_clx@163.com

03 9 2024
9 2024
45 13 10.1002/hbm.v45.13 e7002012 8 2024
29 4 2024
21 8 2024
© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

Abstract

Hemispherotomy is an effective surgery for treating refractory epilepsy from diffuse unihemispheric lesions. To date, postsurgery neuroplastic changes supporting behavioral recovery after left or right hemispherotomy remain unclear. In the present study, we systematically investigated changes in gray matter volume (GMV) before and after surgery and further analyzed their relationships with behavioral scores in two large groups of pediatric patients with left and right hemispherotomy (29 left and 28 right). To control for the dramatic developmental effect during this stage, age‐adjusted GMV within unaffected brain regions was derived voxel by voxel using a normative modeling approach with an age‐matched reference cohort of 2115 healthy children. Widespread GMV increases in the contralateral cerebrum and ipsilateral cerebellum and GMV decreases in the contralateral cerebellum were consistently observed in both patient groups, but only the left hemispherotomy patients showed GMV decreases in the contralateral cingulate gyrus. Intriguingly, the GMV decrease in the contralateral cerebellum was significantly correlated with improvement in behavioral scores in the right but not the left hemispherotomy patients. Importantly, the preoperative voxelwise GMV features can be used to significantly predict postoperative behavioral scores in both patient groups. These findings indicate an important role of the contralateral cerebellum in the behavioral recovery following right hemispherotomy and highlight the predictive potential of preoperative imaging features in postoperative behavioral performance.

Hemispherotomy disconnects the entire affected cerebral hemisphere for treating refractory epilepsy, but postsurgery neuroplastic changes supporting behavioral recovery remain unclear. This study indicates an important role of the contralateral cerebellum in the behavioral recovery following right hemispherotomy and highlight the predictive potential of preoperative imaging features in postoperative behavioral performance.

epilepsy
gray matter volume
hemispherotomy
magnetic resonance imaging
plasticity
National Natural Science Foundation of China 10.13039/501100001809 T2325006 82172016 Fundamental Research Funds for the Central Universities 10.13039/501100012226 2233200020 source-schema-version-number2.0
cover-dateSeptember 2024
details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.4.8 mode:remove_FC converted:03.09.2024
Yu, H. , Chen, Y. , Bao, Z. , Luo, J. , Liu, Q. , Qin, P. , Wang, C. , Qu, J. , Wang, W. , Cai, L. , & Gong, G. (2024). Behavioral and brain morphological changes before and after hemispherotomy. Human Brain Mapping, 45 (13 ), e70020. 10.1002/hbm.70020

Hao Yu, Yijun Chen, and Ziyu Bao contributed equally to this work.
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pmcAbbreviations

GM gray matter

GMV gray matter volume

LASSO least absolute shrinkage and selection operator

RR ridge regression

SVR support vector regression

WM white matter

1 INTRODUCTION

Hemispherotomy can successfully stop seizures in carefully selected pediatric epilepsy patients by disconnecting and disabling the entire affected cerebral hemisphere (Jonas et al., 2004; Kossoff et al., 2003; Moosa, Gupta, et al., 2013; Moosa, Jehi, et al., 2013). Due to complications, such as hydrocephalus and superficial cerebral hemosiderosis, hemispherotomy has been more widely used for refractory epilepsy than anatomical hemispherectomy while achieving the same seizure‐free rate (Yates et al., 2023). In addition to seizure freedom, many patients following such neurosurgery showed recovery to some degree in certain behavioral domains (Jonas et al., 2004; Kossoff et al., 2003; Moosa, Gupta, et al., 2013; Moosa, Jehi, et al., 2013), and such recovery should be largely underpinned by neuroplastic reorganization in the unaffected brain regions.

There has been evidence showing neuroplastic changes following hemispherotomy. For example, animal studies revealed that the cortex of the unaffected hemisphere following hemispherotomy may project to multiple nuclei and limbs that were normally targeted by the removed cortex (Gómez‐Pinilla et al., 1986; Machado et al., 2003). Increased cortical thickness and metabolism in the unaffected hemisphere were also reported in animals after surgery (Hovda et al., 1996; Schmanke & Villablanca, 1999). In humans, functional neuroimaging studies of post‐hemispherotomy patients suggestted that cortical representation originally localized in the disconnected hemisphere can be shifted to the contralateral hemisphere, accompanied by a certain degree of behavioral recovery (Hertz‐Pannier et al., 2002; Pilato et al., 2009; Zhang et al., 2013). This shift, however, has been observed in both the preoperative and postoperative periods (Rutten et al., 2002; Zhang et al., 2013), highlighting the necessity of pre‐ and postoperative data in ascertaining the nature of such changes. To date, due to the difficulty of imaging acquisition for such rare patient cases, there have been very few studies on hemispherotomy or hemispherotomy with very small sample sizes (Govindan et al., 2013; Hertz‐Pannier et al., 2002; Rosazza et al., 2018; Zhang et al., 2013). In particular, these studies were confined to neuroplasticity in functional activation or white matter (WM) integrity, leaving morphological changes in gray matter (GM) unexplored.

As is well documented, damage to the left and right hemispheres of the brain likely cause functional deficits, for example, language deficits from left hemisphere damage and hemispatial neglect from right hemisphere damage (Corbetta & Shulman, 2011; Price, 2000). Moreover, recent studies showed differences in structural neuroplastic patterns underlying postdamage behavioral recovery between patients with left and right hemisphere damage (Chen et al., 2021). Along this line, left and right hemispherotomy may be accompanied by differential postsurgical neuroplastic changes or similar changes but distinct relationships with behavioral recovery. Consequently, the postsurgery neuroplasticity of left and right hemispherotomy should be evaluated separately.

The present study examined the pre‐ and postsurgery MRI scans of two large groups of patients with left and right hemispherotomy, aiming to investigate (1) changes in GM volume (GMV) following surgery in the two patient groups; (2) whether and how GMV changes in the unaffected regions relate to behavioral recovery; and (3) whether preoperative GMV features can be used to predict postoperative behavioral scores.

2 MATERIALS AND METHODS

2.1 Patients

Pediatric patients with refractory epilepsy undergoing peri‐insular hemispherotomy were recruited in our present study. Peri‐insular hemispherotomy is a surgical variant of functional hemispherotomy in which the affected hemisphere is completely disconnected while resecting a minimal amount of cortical structures to avoid long‐term complications (Marras et al., 2010). The patient inclusion criteria were as follows: (1) underwent peri‐insular hemispherotomy between 2016 and 2021 at the Pediatric Epilepsy Center, Peking University First Hospital; (2) were at least half a year old when the surgery was performed; (3) had available pre‐ and postoperative MRI images; and (4) no other brain surgery except hemispherotomy. Initially, 74 patients met these criteria, but the image processing procedures failed for 17 of them due to the poor quality of either pre‐ or postoperative MRI images (e.g., severe head motion, bad intensity contrast, abnormalities and severe distortion of the unaffected regions). Finally, 29 pediatric patients with left hemispherotomy and 28 pediatric patients with right hemispherotomy were included in our analyses. Demographic and clinical information is listed in Table 1. The study was approved by the local institutional review board. Written informed consent was obtained from the legal guardians of the patients.

TABLE 1 Demographic and clinical information of the pediatric hemispherotomy patients.

	Left hemispherotomy	Right hemispherotomy	Group difference	
Number of patients	29	28	‐	
Sex (M/F)	16/13	16/12	p = .88	
Age of surgery (years)	3.5 ± 2.5 (0.7–10.6)	4.6 ± 2.5 (1.1–11.0)	p = .11	
Epilepsy onset (years)	1.2 ± 2.2 (0.0–9.6)	1.3 ± 1.6 (0.0–5.7)	p = .88	
Etiology (D/A/P)	16/10/3	22/5/1	p = .17	
Prescan to surgery (months)	5.7 ± 4.9 (0.1–19.3)	3.8 ± 4.3 (0.2–15.7)	p = .13	
Surgery to postscan (months)	5.0 ± 4.1 (0.1–22.7)	4.9 ± 4.6 (3.0–24.4)	p = .92	
Seizure outcomes (Engel I/II/IV)	26/2/1	28/0/0	p = .21	
Note: M, male; F, female; D: developmental; A: acquired; P: progressive. Age of surgery, epilepsy onset, prescan to surgery, and surgery to postscan are presented as the mean ± standard deviation (minimum, maximum).

2.2 MRI acquisition

High‐quality MRI was acquired before and after the surgery using a Philips Achieva 3T scanner at the Peking University First Hospital. All T1‐weighted (T1w) scans used the same MPRAGE sequence with the following parameters: axial acquisition; time of repetition = 8 ms; time of echo = 3.7 ms; flip angle = 8°; acquisition matrix = 220 × 220, slice thickness = 1 mm; voxel size = 0.86 × 0.86 × 1.0 mm.

2.3 Behavioral evaluation

The Griffiths Scales of Child Development (3rd edition, Griffiths III), a widely used tool for measuring the developmental rate of infants and young children, was applied to assess the patient's behavioral abilities (Luiz et al., 2006). The tool includes five domains: foundations of learning, language and communication, eye‐hand coordination, personal–social–emotional ability, and gross motor function. The evaluation was usually performed on the same day as the MRI. Among the included patients, only 19 left and 19 right hemispherotomy patients underwent this evaluation both before and after the surgery. Notably, the scores of the five domains are highly correlated with each other (mean r = .9); therefore, we performed a factorial analysis on the preoperative scores, yielding one main factor that accounts for 92.4% of the total variance. We used this factor as an overall behavioral score in our behavioral analyses. To ensure comparability, we obtained the factor score for the postoperative evaluation using the weights from the factor analysis of the preoperative evaluation.

2.4 Voxel‐based morphometry processing

For each patient, we manually outlined the mask of the cerebral hemisphere that underwent surgery on both the pre‐ and postoperative T1‐weighted images (Figure 1a). We then carried out voxel‐based morphometry (VBM) analyses using SPM12. To ensure unbiased comparisons between the two hemispheres, a symmetric T1 template in MNI space was constructed using 2115 healthy children (see below for the details of these children) (Kurth et al., 2015). Longitudinal VBM processing included the following. (1) For each patient, an average image across the longitudinal images was generated using the Serial Longitudinal Registration toolbox (Ashburner & Ridgway, 2013). (2) The average images were bias‐corrected and segmented into GM, WM, and cerebrospinal fluid probability maps. These tissue‐segmented images were then registered to the symmetric template, resulting in participant‐specific GM probability maps in MNI space. (3) The GM probability maps were then modulated with twofold Jacobian determinants: from the native space of each time point to the participant‐average space and from the participant‐average space to the MNI space. For each patient, this resulted in a raw GMV map in MNI space for each time point, in which each voxel's value represents its corresponding raw GMV in the native space. (4) Finally, all raw GMV images were smoothed with an 8‐mm FWHM Gaussian kernel. During these procedures, all cerebral hemispheres that underwent surgery were masked out. The resultant images of each step were carefully checked by visual inspection.

FIGURE 1 Schematic of MRI image processing. (a) T1‐weighted (T1w) and gray matter volume (GMV) images of left and right hemispherotomy patients. The cerebral hemisphere was manually masked out (indicated by red) in the T1w native space and excluded in the GMV‐based voxel‐based morphometry (VBM) analyses in the MNI space. Pre: Preoperative; Post: Postoperative. (b) Normative model of GMV, for one voxel example, as estimated by the Gaussian process regression in 2115 healthy children. The selected voxel is indicated by yellow in the left panel. Each circle in the middle panel represents one healthy child. On the right panel, the mean of the normative model is represented by a solid blue line, while the ±1 standard deviation is indicated by dotted light blue lines. The light blue shaded area represents the 95% confidence interval. The preoperative and postoperative GMV values of each patient are indicated by two connected gray dots.

Notably, distinguishing between GM and WM boundaries on T1w images of babies is difficult (Boshuisen et al., 2010). We carefully examined the T1 images of our patients, particularly at younger ages, and excluded those with poor contrast.

2.5 Age‐adjusted GMV from a normative model

To control for the confounding effect of normal development on the changes in raw GMV in pediatric patients, we applied normative modeling to transform the raw GMV of each individual image to its age‐adjusted value, that is, its derivation from the developmental norm (Figure 1b). Recently, normative modeling has been increasingly used for individual‐specific inferences in heterogeneous clinical groups (e.g., Alzheimer's disease (Verdi et al., 2023), attention‐deficit/hyperactivity disorder (Wolfers et al., 2019), schizophrenia (Wolfers et al., 2018)). This approach provides a statistical framework to investigate individual deviations from normative ranges with reference to the healthy cohort. Specifically, it first applies a large healthy cohort to estimate the normative range of variation for a brain phenotype, such as GMV, given individuals' age, sex, or other variables of interest. Next, fitting the normative model to the data yields an individualized deviation value that quantifies the extent to which each person deviates from population norms.

Here, the healthy subjects used for normative model estimation were from 11 MRI datasets, including the Autism Brain Imaging Data Exchange (Di Martino et al., 2014; Di Martino et al., 2017), the UNC/UMN Baby Connectome Project (Howell et al., 2019), the Calgary Preschool MRI Dataset (Reynolds et al., 2020), the Children School Functions and Brain Development Project (Lei et al., 2022), the Chinese Color Nest Project (Liu et al., 2021), the Healthy Brain Network (Alexander et al., 2017), the Human Connectome Project‐Development (Harms et al., 2018), the Infant Brain Imaging Study (Lewis et al., 2017), the Multicenter Database on Perinatal Factors in Child Brain‐Mind Development (Wang et al., 2024), the Pediatric Imaging, Neurocognition, and Genetics (Jernigan et al., 2016), and the Pixar Dataset (Richardson et al., 2018; see supplementary materials for more details). Given the age range of the hemispherotomy patients (0.6–11.6 years old), we included T1‐weighted images from all healthy children aged between 0.3 and 13.0 years across these datasets, with a maximum of 100 subjects for a half‐year interval (in total 2343 children). For children with multiple T1‐weighted images, only the image of the highest quality was used. This ensured independency of all included images. Among these images, 228 were excluded due to poor image quality or image processing failure. VBM processing was then applied to the remaining 2115 children using the same symmetric T1 template, which yielded a GMV image for each child.

The voxelwise GMV normative modeling procedure was then applied using Python 3.8.13 and PCNtoolkit (0.26). The GMV images of the 2115 healthy children and our pediatric hemispherotomy patients were used as the training and testing data, respectively. Age and sex were included as covariates in the model. For each voxel, Gaussian process regression was applied to predict each patient's GMV, yielding a Z score representing the patient's derivation from the age‐ and sex‐matched healthy norm. The individual‐specific deviation would ensure the comparability of GMV before and after the surgery as well as between pediatric patients by removing the age and sex effects on raw GMV values. The age‐adjusted GMV value (i.e., Z score) was then used in subsequent analysis.

2.6 Identifying changes in each patient group

We used a linear mixed‐effects model (LMEM) to handle the hierarchical nature of the data. LMEM was applied to evaluate the pre‐ and postoperative changes in behavioral scores and voxelwise GMV. Specifically, the “fitlme” function in MATLAB was used. In the model, “MRI to surgery” (i.e., the days between surgery and the pre/post‐MRI scan) and other covariates were modeled as fixed effects, and “individual identities” were modeled as random effects. The intercept and slope were allowed to vary across individuals. The LMEMs were formulated as follows: (1) Yij=intercept+β1MRIto surgery+β…covariates+dij+eij

where the intercept and β terms are fixed effects, d ij is the random effect, and e ij represents the residual error.

For each group, to determine the pre‐ and postoperative changes in behavioral scores, we applied the LMEM and evaluated the β1 term. The onset of epilepsy, age at surgery, sex, and etiology were included as covariates. To evaluate whether and how GMV changes, we performed a voxelwise LMEM analysis within the GM mask of the unaffected regions, with the onset of epilepsy, age at surgery, and etiology as covariates. Multiple comparisons were corrected using the random field theory method (uncorrected p < .001), and clusters with a corrected p < .05/2 (2 patient groups) were considered significant.

2.7 Relating GMV change to behavioral improvement

For all identified clusters above, we evaluated whether the GMV change related to the change in the overall behavioral score in each patient group using the Pearson correlation.

2.8 Predicting postoperative behavioral scores with preoperative GMV

To evaluate whether preoperative GMV can be used to predict postoperative outcomes, we applied three machine learning regression algorithms: support vector regression (SVR), least absolute shrinkage and selection operator (LASSO), and ridge regression (RR). The presurgery GMV values within the GM mask of the unaffected regions were inputted as predicting features. For each algorithm, nested leave‐one‐out‐cross‐validation (LOOCV) was applied, with an outer LOOCV loop estimating the generalizability of the model and an inner LOOCV loop determining the optimal parameter if any. Specifically, N‐1 patients were used as the training set for each outer LOOCV loop, where N was the total number of patients, and the only remaining patient was the testing sample. This procedure was repeated N times, with each patient being the testing sample once. Within each loop of the outer LOOCV, the inner LOOCVs were applied to determine the optimal parameter for the outer LOOCV fold to construct a prediction model, which was then used to predict the score of the testing sample.

To evaluate the performance of each model/algorithm, the Pearson correlation (r) and normalized root mean square error (NRMSE) were calculated. A permutation test (1000 times) was applied to determine whether the r and NRMSE were significantly better than expected by chance. To evaluate whether prediction performance differed significantly between the two patient groups, we compared the two groups in the squared residuals between the actual and predicted scores using the Wilcoxon signed‐rank test (Salvalaggio et al., 2020).

To identify the significantly contributing features for the prediction, we assessed the weight of each feature using the premutation procedure, and p < .05 after false discovery rate was considered a significantly contributing feature.

3 RESULTS

As shown in Table 1, there was no significant difference in age, sex, onset or etiology of epilepsy between the left and right hemispherotomy groups. According to neuroimaging and histopathology results, the classification of etiology followed the categorization method applied by children with refractory epilepsy in the Chinese Pediatric Epileptic Center (Ji et al., 2019). Seizure outcome was assessed using the Engel scale (Engel Jr., 1993), and 90% of left and 100% of right hemispherotomy patients reached Engel 1 at the follow‐up of 3 months (Chi‐square test, p = .21).

3.1 Changes in behavioral ability

For each patient, an overall behavioral score was obtained using the Griffiths III. Neither the preoperative nor postoperative behavioral scores significantly differed between the two groups (two‐sample t tests, p > .05). For both groups, the LMEM showed a significant increase in the postoperative score compared with the preoperative score (left hemispherotomy: t = 2.10, p = .04; right hemispherotomy: t = 3.97, p < .001).

3.2 Overall GM shrinkage both before and after the surgery

The age‐adjusted GMV values (i.e., Z scores) represent the patient's deviation from the age‐ and sex‐matched norm: negative and positive values indicate a trend of GM shrinkage and expansion, respectively. In most patients in the two groups, both preoperative and postoperative age‐adjusted GMV values were negative across the vast majority of the unaffected regions, suggesting an overall shrinking pattern and underdevelopment of GM compared with the healthy children (Figure 2). Specifically, 79 and 59% of left hemispherotomy patients had more than 80% voxels showing a negative value before and after the surgery, respectively. In the right hemispherotomy group, 71 and 54% of patients had more than 80% of voxels showing a negative value before and after the surgery, respectively.

FIGURE 2 Overall GM shrinkage both before and after surgery for the two patient groups. The color denotes the percentage of the patients with a negative value of age‐adjusted GMV. A negative age‐adjusted GMV value indicates a trend of GM shrinkage relative to the age‐matched norm. As shown, the majority of patients within each group showed a negative value across most of the GM mask of unaffected regions, both before and after hemispherotomy.

3.3 Changes in GMV within the unaffected regions

To identify local age‐adjusted GMV changes before and after the surgery, we applied a voxelwise LMEM search within the GM mask of the unaffected regions. For the two groups, the resultant T maps of age‐adjusted GMV change largely mirrored each other (after flipping one map, r = .86), suggesting overall similar spatial patterns of ipsilateral and contralateral GMV changes between the two groups (Figure 3).

FIGURE 3 Change in age‐adjusted GMV before and after surgery for the two patient groups. The results for the left and right hemispherotomy are shown in (a) and (b), respectively. The first row represents the T maps of the age‐adjusted GMV change from the linear mixed model for the two groups. The identified significant clusters are denoted by arrows. Within the panel of each cluster, the fitted change in age‐adjusted GMV is represented as a black thick line. The change in each patient is indicated by a gray line. MRI time (years) indicates the MRI to surgery time, the negative time represents the prescan to surgery time and the passive time represents the surgery to postscan time.

After correcting for multiple comparisons, we observed four clusters showing significant age‐adjusted GMV changes for the left hemispherotomy group and three significant clusters for the right hemispherotomy group (Figure 3). In both groups, the largest cluster covered almost the entire contralateral cerebrum and exhibited significantly increased GMV (left hemispherotomy: t = 6.92, p < .001; right hemispherotomy: t = 7.44, p < .001). At the individual level, all left and 96.4% of right hemispherotomy patients showed an increase in age‐adjusted GMV in this cluster. Given the normative increasing pattern of raw GMV during this developmental stage, the observed increase in age‐adjusted GMV should be accompanied by increased raw GMV. The additional LMEM analysis to the total raw GMV value within the cluster confirmed such an increase after the surgery (left hemispherotomy: t = 7.21, p < .001; right hemispherotomy: t = 7.69, p < .001). The observed increase in both age‐adjusted and raw GMV indicates a greater GM growth/development of the patients during the scanning interval compared with the age‐ and sex‐matched healthy children.

The second largest cluster was located around the entire ipsilateral cerebellum and exhibited significantly increased age‐adjusted GMV (left hemispherotomy: t = 6.95, p < .001; right hemispherotomy: t = 8.53, p < .001). At the individual level, all left and right hemispherotomy patients had an increase in age‐adjusted GMV in this cluster. Similarly, the additional LMEM analysis of the total raw GMV value of the cluster showed an increase in raw GMV after the surgery (left hemispherotomy: t = 8.59, p < .001; right hemispherotomy: t = 9.16, p < .001), suggesting greater GM growth/development.

In contrast to the two clusters, the cluster in the contralateral cerebellum consistently showed significantly decreased age‐adjusted GMV in both groups (left hemispherotomy: t = −8.32, p < .001; right hemispherotomy: t = −7.73, p < .001). At the individual level, 82.8% of the left and 96.4% of right hemispherotomy patients showed a decrease in age‐adjusted GMV. With the normative increasing pattern of raw GMV during this developmental range, the observed decrease in age‐adjusted GMV may be accompanied by increased, decreased, or unchanged raw GMV. The additional LMEM analysis of the total raw GMV value within the cluster showed a significant decrease in raw GMV in the left hemispherotomy patients (t = −6.93, p < .001) and no significant change in raw GMV in the right hemispherotomy patients (t = −1.60, p = .11). The age‐adjusted GMV increase together with no change or even a decrease in raw GMV indicates a lack or delay of normal GM growth/development or even GM shrinkage during the scanning interval compared with the age‐ and sex‐matched healthy children.

Finally, there was one significantly decreased age‐adjusted GMV cluster around the contralateral cingulate gyrus in the left hemispherotomy group (t = −5.23, p < .001) but not in the right hemispherotomy group. At the individual level, 79.3% of the left hemispherotomy patients showed a decrease in age‐adjusted GMV. The additional LMEM analysis to the total raw GMV value of the cluster showed no significant change in raw GMV after the surgery (t = −0.33, p = .74), suggesting a lack of normal GM growth/development.

3.4 Association of GMV change with behavioral recovery

For each patient group, we evaluated whether GMV changes in each cluster correlated with behavioral changes before and after the surgery. As shown in Figure 4, the change in age‐adjusted GMV in the contralateral cerebellum was negatively correlated with the change in overall behavioral score in the right hemispherotomy patients (r = −.64, p = .007) but not in the left hemispherotomy patients (r = −.10, p = .73). Given the negative direction of the GMV change within the contralateral cerebellum and the positive direction of the change in behavioral score, such a correlation indicates that a greater GMV decrease was associated with greater behavioral improvement in right hemispherotomy patients.

FIGURE 4 Correlation between age‐adjusted GMV changes in the contralateral cerebellum and behavioral improvements before and after surgery. A significant correlation is observed only in the right but not in the left hemispherotomy group. Each circle represents a patient.

No significant correlation was observed between the age‐adjusted GMV change in the other identified clusters above and behavioral recovery before and after the surgery. Particularly, given the extensive coverage of the largest cluster on the contralateral cerebrum, we performed a voxelwise analysis searching for such correlations within both groups and found no significant results.

3.5 Predictability of behavioral ability after surgery

To predict individualized postoperative behavioral scores, we applied three machine learning regression algorithms using preoperative GMV features of the unaffected region. As shown in Table 2 and Figure 5, all three algorithms can significantly predict the postoperative behavioral score at the individual level. The Wilcoxon signed‐rank test further revealed a better prediction performance of all the algorithms in the left hemispherotomy patients than in the right hemispherotomy patients (RR: p = 002; SVR: p = .007; LASSO: p = .02). For each group, the prediction feature weight maps across the unaffected regions (i.e., feature importance map) are highly similar between the three algorithms (the minimum r > .93). For each algorithm, the feature importance maps of the two groups, however, are quite dissimilar (correlation after left–right flipping one map: RR, r = .14; SVR, r = .16; LASSO, r = .15).

TABLE 2 Prediction of postoperative behavioral scores with preoperative gray matter volume features.

Algorithm	Left hemispherotomy group	Right hemispherotomy group	
R (p value)	NRMSE (p value)	R (p value)	NRMSE (p value)	
RR	0.52 (.006 a )	0.20 (.005 a )	0.48 (.021 a )	0.25 (.016 a )	
SVR	0.45 (.012 a )	0.21 (.001 a )	0.46 (.021 a )	0.26 (.018 a )	
LASSO	0.48 (.008 a )	0.21 (.006 a )	0.47 (.024 a )	0.25 (.019 a )	
Note: R: Pearson correlation coefficient between the actual postoperative behavioral score and its predicted value.

Abbreviations: LASSO, least absolute shrinkage and selection operator; NRMSE, normalized root means square error; RR, ridge regression; SVR, support vector regression.

a Significantly better than chance.

FIGURE 5 Prediction of postoperative behavioral score using ridge regression (RR) with preoperative age‐adjusted GMV features for both patient groups. (a) The correlation between the actual and predicted postoperative behavioral scores. (b) GMV feature weight maps for the two groups. The top and bottom rows indicate unthresholded and thresholded (p < .05 after FDR correction) maps, respectively. Color represents the estimated weight of the GMV feature.

As shown in Figure 5b, the significantly contributing GMV features for the left hemispherotomy patients were concentrated around the bilateral cerebellum and contralateral occipital lobe. In contrast, the significantly contributing GMV features for the right hemispherotomy patients mainly involved the bilateral cerebellum and contralateral temporal lobe.

4 DISCUSSION

Using pre‐ and postsurgery MRI data from two large groups of pediatric patients with hemispherotomy, the present study revealed GMV changes and their relationship with behavioral improvements before and after hemispherotomy. Both left and right hemispherotomy patients consistently showed widespread GMV increases in the contralateral cerebrum and ipsilateral cerebellum but GMV decreases in the contralateral cerebellum. In addition, GMV decease around the contralateral cingulate gyrus was observed only in the left hemispherotomy patients. Moreover, the GMV decrease in the contralateral cerebellum significantly correlated with the improvement in behavioral scores in the right hemispherotomy patients but not in the left hemispherotomy patients. Importantly, the presurgery GMV features can be used to significantly predict the postsurgery behavioral scores in both left and right hemispherotomy patients. These results provide novel insight into the neuroplastic changes underlying the behavioral recovery of hemispherotomy patients and highlight the clinical potential of presurgery GMV in predicting postsurgery behavioral ability.

Several mechanisms may account for the observed GMV changes before and after the surgery: neurodevelopment, neurodegeneration, or neuroplasticity. With normative modeling, the neurodevelopment‐induced and hemispherotomy‐induced increases in GMV can be well differentiated. The currently observed increase in both age‐adjusted and raw GMV in the contralateral cerebrum and ipsilateral cerebellum indicate a greater GM growth/development of these regions than the sex‐ and age‐matched healthy children, indicating a hemispherotomy‐induced positive effect on GM development across the majority of the unaffected regions for these pediatric patients. This widespread greater GM development could be considered a part of neuroplastic changes induced by the hemispherotomy. Notably, hemispherotomy leads to a disconnection between the two hemispheres. In terms of the interhemispheric inhibition hypothesis (Van Der Knaap & Van Der Ham, 2011), such a disconnection will result a loss of inhibition from the contralateral hemisphere, which may account for the widespread greater GM development in the intact hemisphere.

Importantly, there exist multiple regions (i.e., contralateral cingulate gyrus and cerebellum) showing an age‐adjusted GMV decrease with unchanged or decreased raw GMV before and after the surgery. In these regions, a lack of normal GM growth/development and even GM atrophy can be inferred. Neurodegeneration and neuroplasticity induced by hemispherotomy are likely responsible for these particular GM change patterns. Interestingly, atrophy of the contralateral cingulate gyrus has also been observed following traumatic brain injury (TBI) (Yount et al., 2002), suggesting a similar neurodegenerative or neuroplastic mechanism underlying TBI and hemispherotomy. Regarding the contralateral cerebellum, previous studies have well demonstrated its critical role in postcerebral hemispherectomy recovery: contralateral hemicerebellectomy 2 weeks after cerebral hemispherectomy did not impair functional recovery in rats (Marino Jr. et al., 2001), but contralateral hemicerebellectomy before or simultaneous with cerebral hemispherectomy led to significantly worse functional recovery. In line with this, the currently observed contralateral cerebellar atrophy also shows a positive effect: a greater GMV decrease was associated with better behavioral improvement in the right hemispherotomy patients, supporting a positive neuroplastic nature for such atrophy. Compatibly, a previous study of stroke patients also revealed a positive effect of GM atrophy on functional recovery, for example, a greater decrease in GMV around the middle cingulate cortex and greater improvement in poststroke motor recovery (Chen et al., 2021). However, it remains possible that crossed cerebellar diaschisis and progressive neurodegeneration may also contribute to the currently observed GMV decrease, at least to some degree.

Achieving the presurgery prediction of postsurgery functional or behavioral recovery is of great clinical importance. A few univariate preoperative MRI‐based markers have been used to predict behavioral outcomes after hemispherotomy, including contralateral MRI abnormalities (Boshuisen et al., 2010; Hallbook et al., 2010), asymmetries in the brain stem or corticospinal tracts within the brain stem (Chan et al., 2019; Du et al., 2018; Küpper et al., 2016; Mullin et al., 2016; Nelles et al., 2015; Wakamoto et al., 2006; Wang et al., 2018), and sensorimotor fMRI results (Wang et al., 2018). In contrast to these univariate‐based predictions, the present study proposed a multivariate machine learning‐based framework, resulting in quite robust prediction performance. More importantly, the framework took preoperative GMV values across the unaffected regions as predicting features, and these features can be easily derived from routine structural MRI for hemispherotomy patients. Therefore, these proposed prediction models have excellent clinical applicability. However, it should be noted that the absolute sample size of patients is small, although the relative sample size for hemispherotomy patients was quite large for an investigation in hemispherotomy. Particularly, the number of training subjects for the machine learning algorithm is small, limiting the confidence in the validity and generalizability of the prediction. A larger dataset of pediatric hemispherotomy patients (e.g., from different hospitals or sources) are warranted to further validate our currently proposed machine learning‐based prediction.

Given the well‐documented hemispheric specialization and asymmetries, the present study divided the left and right hemispherotomy patients into two separate groups. As expected, both common and unique GMV changes in the ipsilateral and contralateral hemispheres were observed between the two groups, for example, the common GMV increase in the contralateral cerebrum in both groups but the unique GMV decrease in the contralateral cingulate gyrus only in the left hemispherotomy patients. This is compatible with previously observed common and unique GMV changes following unilateral stroke (Chen et al., 2021). Regarding the contralateral cingulate gyrus, previous studies have also shown the association of its activation with improved recovery for basal ganglia stroke patients (Li et al., 2016). On the other hand, both healthy (Gong et al., 2005) and diseased (Albanese et al., 1995) populations have consistently shown structural asymmetries of this structure. Taken together, the observed GMV decrease of the contralateral cingulate gyrus in the left but not the right hemispherotomy patients implies biological distinction of how the right and left hemispheres are reorganized following hemispherotomy. Moreover, similar changes of the same region of the two hemispheres are linked to distinct behavioral consequences, for example, GMV change of contralateral cerebellum correlated with behavior improvement only in the right but not in the left hemispherotomy patients. Finally, the spatial importance distribution of preoperative GMV features in postoperative behavioral prediction also differed substantially between the two patient groups, further suggesting a differential brain‐behavior relationship between the two groups. Taken together, it is likely that the left and right hemispherotomy are accompanied by differential postsurgical neuroplastic changes and distinct mechanisms underlying behavioral performance/recovery; therefore, they should be evaluated or investigated separately. To elucidate the underlying mechanisms for such hemisphere‐dependent phenomena, further investigation is warranted.

AUTHOR CONTRIBUTIONS

Conceptualization: Gaolang Gong and Lixin Cai. Methodology and formal analysis: Yijun Chen, Ziyu Bao, Junhao Luo, Peipei Qin, Changtong Wang, Jingli Qu, Wei Wang, and Gaolang Gong. Resources, Hao Yu, Qingzhu Liu, and Lixin Cai. Writing and editing: Yijun Chen, Ziyu Bao, Hao Yu, Lixin Cai, and Gaolang Gong. Visualization, Yijun Chen and Ziyu Bao. Supervision and funding acquisition: Gaolang Gong and Lixin Cai.

CONFLICT OF INTEREST STATEMENT

The authors declare no competing interests.

Supporting information

DATA S1: Supporting Information.

ACKNOWLEDGMENTS

The authors thank Qian Zhang from Peking University First Hospital for her technical assistance. This work is supported by the National Natural Science Foundation of China (Nos. T2325006 and 82172016, G.G.) and Fundamental Research Funds for the Central Universities (No. 2233200020, G.G.).

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.
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