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TYPE Original Research
PUBLISHED 02 February 2023
DOI 10.3389/fneur.2023.1062149
OPEN ACCESS
EDITED BY
Clarissa Lin Yasuda,
State University of Campinas, Brazil
REVIEWED BY
Panpan Hu,
Anhui Medical University, China
Rui Feng,
Fudan University, China
Rebecca Kerestes,
Monash University, Australia
*CORRESPONDENCE
Lili Long
longlili1982@126.com
Fangfang Xie
76335922@qq.com
†These authors have contributed equally to this
work and share first authorship
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This article was submitted to
Epilepsy,
a section of the journal
Frontiers in Neurology
RECEIVED 05 October 2022
ACCEPTED 06 January 2023
PUBLISHED 02 February 2023
CITATION
Peng Y, Wang K, Liu C, Tan L, Zhang M, He J,
Dai Y, Wang G, Liu X, Xiao B, Xie F and Long L
(2023) Cerebellar functional disruption and
compensation in mesial temporal lobe epilepsy.
Front. Neurol. 14:1062149.
doi: 10.3389/fneur.2023.1062149
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©2023 Peng, Wang, Liu, Tan, Zhang, He, Dai,
Wang, Liu, Xiao, Xie and Long. This is an
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does not comply with these terms.
Cerebellar functional disruption
and compensation in mesial
temporal lobe epilepsy
Yiqian Peng1†, Kangrun Wang2,3† , Chaorong Liu2, Langzi Tan2,
Min Zhang2, Jialinzi He2, Yuwei Dai2, Ge Wang2, Xianghe Liu2,
Bo Xiao2, Fangfang Xie4*and Lili Long2,5,6*
1Department of Radiology, The First Aliated Hospital of Wenzhou Medical University, Wenzhou, China,
2Department of Neurology, Xiangya Hospital, Central South University, Changsha, China, 3Department of
Neurosurgery, Xiangya Hospital, Central South University, Changsha, China, 4Department of Radiology,
Xiangya Hospital, Central South University, Changsha, China, 5Clinical Research Center for Epileptic Disease
of Hunan Province, Xiangya Hospital, Central South University, Changsha, China, 6National Clinical Research
Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
Background: Cerebellar functional alterations are common in patients with mesial
temporal lobe epilepsy (MTLE), which contribute to cognitive decline. This study
aimed to deepen our knowledge of cerebellar functional alterations in patients
with MTLE.
Methods: In this study, participants were recruited from an ongoing prospective
cohort of 13 patients with left TLE (LTLE), 17 patients with right TLE (RTLE), and 30
healthy controls (HCs). Functional magnetic resonance imaging data were collected
during a Chinese verbal fluency task. Group independent component (IC) analysis
(group ICA) was applied to segment the cerebellum into six functionally separated
networks. Functional connectivity was compared among cerebellar networks,
cerebellar activation maps, and the centrality parameters of cerebellar regions.
For cerebellar functional profiles with significant dierences, we calculated their
correlation with clinical features and neuropsychological scores.
Result: Compared to HCs and patients with LTLE, patients with RTLE had higher
cerebellar functional connectivity between the default mode network (DMN) and
the oculomotor network and lower cerebellar functional connectivity from the
frontoparietal network (FPN) to the dorsal attention network (DAN) (p<0.05, false
discovery rate- (FDR-) corrected). Cerebellar degree centrality (DC) of the right lobule
III was significantly higher in patients with LTLE compared to HC and patients with
RTLE (p<0.05, FDR-corrected). Higher cerebellar functional connectivity between
the DMN and the oculomotor network, as well as lower cerebellar degree centrality
of the right lobule III, was correlated with worse information test performance.
Conclusion: Cerebellar functional profiles were altered in MTLE and correlated with
long-term memory in patients.
KEYWORDS
temporal lobe epilepsy, cerebellum, fMRI, verbal fluency task, independent component
analysis (ICA), graph theory
1. Introduction
Cerebellar alterations are common in mesial temporal lobe epilepsy (MTLE), one of the
most prevalent forms of focal epilepsy in adults. The cerebellum is a potential target for seizure
control in patients with drug-resistant MTLE because it contributes to cognitive deficiency in
MTLE (1–4). Previous studies focused primarily on cerebellar structural abnormalities in MTLE.
Cerebellar volume decreased by 4–6.6% in patients with chronic MTLE (5), but not in those
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Peng et al. 10.3389/fneur.2023.1062149
with newly diagnosed temporal lobe epilepsy (TLE) (6). Cerebellar
volume decreased more with longer disease duration (6–9), higher
seizure frequency (6,10), and a higher total seizure burden (5,6,11).
Three independent studies reported cerebellar hyperfusion during
temporal lobe seizures (12–14). In addition, the cerebellar damage
in patients with epilepsy was similar to that in patients with cerebral
hypoxia (11). Therefore, cerebellar structural damage was considered
to be an acquired abnormality caused by hypoxia, seizure discharges,
and hyperfusion during uncontrolled seizures.
The cerebellum is essential for language and long-term memory
retrieval, except for motor control (15). Activation in the cerebellum
rose with increasing memory load (16,17). Meanwhile, sequence
processing, one of the language functions of the cerebellum, affects
the word retrieval strategy during verbal fluency tasks (18–20).
Phonemic and semantic verbal fluency tasks are important scales
clinically applied for routine and presurgical evaluation of TLE
to predict prognosis and postsurgical language outcomes (21–
23). According to lesion studies, phonemic verbal fluency was
largely attributed to the left frontal cortex and anterior temporal
lobe, and semantic verbal fluency was correlated with the left
posterior temporal cortex (24). Phonemic verbal fluency requires
category switching, causes greater cognitive load, and is, therefore,
more dependent on cerebellar function (19,20). Same as the
cerebrum, language function in the cerebellum is lateralized. In
right-handed participants, the right posterolateral cerebellum, which
is functionally connected to the left prefrontal cortex, supports
phonemic processing and linguistic prediction (25,26). During a
phonemic verbal fluency task, the left cerebellum was activated in
left-handed participants (25).
Previous studies detected a deviation in cerebellar function in
patients with TLE using voxel- and seed-based approaches. During
the attentional network test, activation in the cerebellum was reduced
in patients with MTLE compared to healthy controls (HC) (27).
Impaired functional connectivity between the right dentate nuclei
and the left cerebral hemisphere was related to cognitive impairment
in MTLE (2,3). Graph theory analyses provided additional
knowledge regarding the role of cerebellar regions in TLE. Centrality
statistics represent the importance of a given node in the entire
network. In MTLE, cerebellar nodes with higher functional centrality
were reported, indicating an attempted compensatory process (28).
While the abovementioned approaches unfold cerebellar function at
the regional level, the cerebellum is organized as a functional network
(29,30). Group independent component (IC) analysis (group ICA)
provides an ideal and robust approach for separating the cerebellum
into functionally segregated networks. Phonemic verbal fluency tasks
effectively mobilized the cerebellum and provided an ideal tool for
studying cerebellar malfunction at the network level. In addition,
the clinical application of the Chinese version of the phonemic
verbal fluency tasks was delayed due to the linguistic difference
between Chinese and Indo-European languages. The involvement of
the cerebellum in Chinese verbal fluency remained unclear. Further
studies on cerebellar abnormalities of functional networks would
deepen our understanding of the pathogenesis of cognitive decline
in MTLE.
Based on the Chinese character version of a verbal fluency
task, a cross-sectional study aimed to investigate alterations in
functional connectivity between cerebellar networks parcellated
using group ICA. A comprehensive view of cerebellar functional
alterations in MTLE in China was provided using voxel- and seed-
based approaches.
2. Methods
2.1. Participants
A total of 30 consecutive participants with temporal lobe epilepsy
and hippocampal sclerosis (HS) were selected from an ongoing
prospective cohort (31,32). All participants visited the outpatient
department of Xiangya Hospital between 9 November 2018 and
9 January 2021. A total of 30 HC matched for sex, age, and
educational level participated in this study. Sex, age, years of
education, age of onset, disease duration, number of antiseizure
medications (ASMs), and seizure frequency were extracted from
the database. 3DT1, T2WI, and T2WI fluid-attenuated inversion
recovery sequences were applied to all participants to identify HS
and potential lesions. The diagnosis of HS follows a robust protocol
(33): (1) Neuroimagers diagnose HS based on visible changes,
including decreased hippocampal volume, increased temporal
horn volume, gray–white matter boundary blurring, asymmetric
hippocampus, loss of internal structure, and increased T2 signals
(34); (2) Reduced hippocampal volume calculated with the online
automatic segmentation tool Hipposeg (35); and (3) A disagreement
between procedures one and two may be reconciled with a
blind rater.
Mesial temporal lobe epilepsy was diagnosed and lateralized
based on a comprehensive evaluation of semiology, clinical
history, electroencephalography, and magnetic resonance imaging
(MRI). All participants in our cohort are right-handed (31,36).
Exclusion criteria included: (1) Those who had a psychiatric
or neurological disorder other than MTLE, (2) Those who had
a cerebral or cerebellar lesion other than HS, (3) Those who
were below 16 or over 65 years of age, (4) Those who were
unable to endure or comprehend the procedure, (5) Those
who had poor imaging quality, including excessive head motion
or poor task effect, and (6) Those who received phenytoin
treatment (37).
This study was approved by the Ethics Committee of the Xiangya
Hospital of Central South University. Written informed consent was
obtained from all participants.
2.2. Neuropsychological tests
All participants underwent (1) Montreal Cognitive Assessment
(MoCA) (38) to test the overall cognitive function, (2) the
Information subtest of the Chinese version of the Wechsler Adult
Intelligence Scale (WAIS) as an evaluation for long-term memory
(32), (3) Digit Span subsets of WAIS-RC (revised by China) for
working memory (39), (4) the Digit Symbol Substitution subset of
WAIS-RC to exam the processing speed (32), (5) the Block Design
subset of WAIS-RC for perceptual organization, and (6) Verbal
Fluency-Chinese Character, Verbal Fluency-Chinese Pinyin (31), and
the Boston Naming Test (40) to test the language function.
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2.3. MRI data acquisition and preprocessing
Magnetic resonance imaging data were collected at the MRI
center of the Xiangya Hospital using a Siemens MAGNETOM
Prisma 3.0T MR scanner and standard head coils. Structural images
were collected using magnetization-prepared rapid acquisition and
a gradient echo sequence (field of view, 233 mm; repetition time,
1 s; echo time, 37 ms; flip angle, 9◦; 320 ×320 matrix). When
participants performed a Chinese character verbal fluency task (31,
41), functional images were obtained with a gradient echo-planar T2-
weighted sequence (field of view, 225 mm; repetition time, 1 s; echo
time, 37 ms; flip angle, 52◦; 90 ×90 matrix) when conducting the
Chinese character version of a phonemic verbal fluency task (41).
The task was divided into five blocks, each containing a 30-s
rest module and a 30-s task module. In the rest module, a crosshair
fixation would be projected on a white background. Participants were
instructed to rest while looking at the screen. In each task module,
one Chinese character would be displayed on a white background.
Participants were instructed to covertly generate words beginning
with the given Chinese character.
Raw images were realigned, co-registered, segmented,
normalized, and spatially smoothed (6 mm) with the default
preprocessing pipeline (31) of Statistical Parametric Mapping
12 (https://www.fil.ion.ucl.ac.uk/spm/). Next, image data were
processed using Toolbox CONN v.20.b (42) (http://www.nitrc.
org/projects/conn) for further denoising. Head motion, outlying
scan detection using an embedded functional outlier, the effect of
modules, and signals in the white matter and cerebrospinal fluid
were removed as confounders. A bandpass filter [0.009–0.10 Hz] was
applied to remove the noise.
2.4. Group ICA and network connectivity
analysis
Toolbox CONN v.20.b was used for group ICA and subsequent
network connectivity analysis. According to a previous study, the
cerebellum was segmented into six ICs at the group level (43).
Then, group cerebellum ICs were reconstructed back to individual
ICs. For each participant, the average blood oxygen level-dependent
signal time series in each individual IC was extracted for network
connectivity analysis. A generalized linear model (GLM) based on
semi-partial correlation was applied to calculate individual-level
functional connectivity (42). Individual connectivity values were
processed using Fisher transformation (inverse hyperbolic tangent
functions) and then compared between HC, left TLE (LTLE), and
right TLE (RTLE) using analysis of covariance (ANCOVA) and
post hoc pairwise comparisons, with sex, age, years of education,
and MoCA as covariates of no interest. The significant threshold
for network connectivity was p<0.05, false discovery rate-
(FDR-) corrected.
2.5. Voxel-based analysis
SPM 12 was used to perform a two-level voxel-based analysis of
cerebellar activation maps. At the individual level, a task-dependent
contrast map was calculated for each participant. At the second level,
contrast maps were compared between groups via ANCOVA and
post- hoc pairwise comparison, with sex, age, years of education,
and MoCA as covariates of no interest. Clusters were considered
significant at p<0.05, FDR-corrected with an additional threshold
of 10-voxel minimum cluster size.
2.6. Seed-based analysis
The whole brain was parcellated into 210 cortical regions (44),
36 subcortical regions (44), and 26 cerebellar regions (45). For each
participant, a GLM based on a bivariate correlation was used to
construct a 272 ×272 weighted matrix, which was then transformed
into 36 binary matrices with connection density ranging from 5 to
40%, in steps of 1% (46). Under each density, betweenness centrality
(BC) and degree centrality (DC) for 26 cerebellar regions were
calculated. BC is the frequency that a given node is on the shortest
path between all node pairs. DC is the number of suprathreshold
connections linked to a particular node. Centralities were compared
among the three groups by (1) area under the curve (AUC) across all
densities and (2) a subsequent comparison at each density. AUC was
calculated using R studio.
The bilateral cerebellar lobule and vermis were defined as seeds in
a seed-to-voxel analysis. Functional connectivity between the three
regions and whole-brain voxels was computed with CONN v.20.b,
based on a GLM and semi-partial correlation. Seed-based functional
connectivity was compared among the three groups with ANCOVA
and post-hoc pairwise comparison, with sex, age, years of education,
and MoCA as covariates. Clusters were considered significant at
p<0.05, FDR-corrected with an additional minimum cluster size
threshold of 10 voxels.
2.7. Statistical analysis
IBM SPSS Statistics 23 (https://www.ibm.com/products/spss-
statistics) was used for statistical analysis. The distribution of
qualitative variables was assessed using the Shapiro–Wilko test.
Variables without a normal distribution or homogeneity of
variance were compared between the groups with nonparametric
approaches and reported as median and interquartile ranges. Sex,
age, years of education, and MoCA were compared among the
three groups using the chi-square test or the Kruskal–Wallis H-
test. ANCOVA or Quade nonparametric ANCOVA and post- hoc
pairwise comparisons were used to compare neuropsychological
test scores among the three groups, with sex, age, years of
education, and MoCA controlled for confounders. Qualitative
and categorical variables were compared between the two patient
groups using the two-tailed two-sample t-test and Fisher’s exact
test, respectively.
For functional network connectivity and centrality
metrics with a significant group difference, we calculated
their partial correlation with age of onset, disease duration,
seizure frequency, disease burden =disease duration∗seizure
frequency, number of ASMs, and neuropsychological
scores, adjusting for sex, age, years of education,
and MoCA.
Ap-value of <0.05, FDR-corrected, was considered significant.
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3. Results
3.1. Demographic and clinical data
The three groups did not differ in age, sex, years of education,
and MoCA scores. HC outperformed both patient groups in verbal
fluency Pinyin (VFP) scores (p=0.004 and 0.02, FDR-corrected for
HC vs. LTLE and HC vs. RTLE, respectively). Patients with LTLE also
had worse Boston Naming (BN) scores than HC and patients with
RTLE (p=0.004 and 0.03, FDR-corrected for LTLE vs. HC and LTLE
vs. RTLE, respectively).
Patients with LTLE and RTLE did not differ in their clinical
features (see the details in Table 1).
3.2. Group-ICA and network connectivity
analysis
Cerebellar ICs are shown in Figure 1. Cerebellar IC 1 contained
bilateral crus I and II, the lobules III–VI, and the vermis III–V. IC
2 and IC 6 had relatively symmetric regions because they included
the left crus I and II and the right crus I and II, respectively. IC 6
also contained the left crus I, the bilateral lobules VII and VIII. IC 3
comprised the bilateral crus I and II, the lobules IX, and the vermis
IX–X. IC 4 encompassed bilateral crus I and II, and the lobules VI–
VIII. IC 5 consisted of the vermis IV–IX and the bilateral lobules VIII
and IX.
Independent component (IC) 1 had a cerebral parcellation, which
contained the visual region and frontoparietal network (FPN). IC
2 and IC 6 were connected to the left and right FPN, respectively.
IC 6 had a cerebral parcellation, which contained regions similar
to the default mode network (DMN). IC 3 had connectivity to the
DMN, and IC 4 was connected to the dorsal attention network
(DAN). IC 5 had a cerebral parcellation consisting of auditory and
sensorimotor regions.
Functional network connectivity from IC 3 to IC 5 (p=0.04,
FDR-corrected), IC 5 to IC 3 (p=0.04, FDR-corrected), and IC 6 to
IC4 (p=0.04, FDR-corrected) differed significantly among the three
groups. Post-hoc comparisons showed that connectivity between IC
3 and IC 5 (Figures 2A,B) was enhanced and connectivity from IC 6
to IC 4 (Figure 2C) was impaired in patients with RTLE compared to
HC and patients with LTLE.
3.3. Voxel-based analysis
Cerebellar activation maps did not differ among the three groups.
3.4. Seed-based analysis
On the overall scale (Figure 3, boxplot), DC of the right lobule
III differed significantly among the three groups (p=0.05, FDR-
corrected), as it was elevated in LTLE compared to HCs (p=0.002,
FDR-corrected) and RLTE (p=0.03, FDR-corrected). DC of the right
lobule III was higher in LTLE than in HC at all densities (p<0.05,
FDR-corrected). When the connectivity density was <32%, LTLE
also had a higher DC in the right lobule III than RTLE (p<0.05,
FDR-corrected; Figure 3, line chart). There was a trend (p<0.05,
FDR uncorrected) for the group difference of DC of bilateral lobule
X, vermis X, and the BC of the right lobule III, the right lobule X, and
vermis X.
Regarding seed-based functional connectivity, we did not observe
any significant differences among the three groups.
3.5. Correlation analysis
In all participants, weaker connectivity from IC 3 to IC 5 (r=
−0.33, p=0.02), from IC 5 to IC 3 (r= −0.29, p=0.04), and from
higher DC of the right lobule III (r=0.31, p=0.02) was related to
higher scores on Information Tests (Figure 4).
3.6. Sensitivity analysis
One patient with RTLE (female, 29 years old) received topiramate
(25 mg, bid). Considering that topiramate was associated with
language and cognitive network dysfunction (47,48), we conducted
a sensitivity analysis excluding this patient. The exclusion of this
patient did not affect the overall results. In patients with RTLE,
functional connectivity between IC 3 and IC 5 and from IC 6 to IC
4 was altered compared to HCs and patients with LTLE. DC of the
right lobule III was increased in LTLE compared to HCs (p=0.002,
FDR-corrected) and patients with RTLE (p=0.03, FDR-corrected).
4. Discussion
Cerebellar abnormality is a common phenotype of MTLE. The
cerebellar abnormality contributed to cognitive impairment in MTLE
(2,3,49). In this study, we applied group ICA and graph theoretical
approaches to verbal fluency task-based functional MRI (fMRI) data.
We divided the cerebellum into six functionally separated ICs and
found disruption in functional connectivity between ICs in patients
with RTLE. In LTLE, cerebellar centrality of the right lobule III was
significantly increased compared to HC and patients with RTLE.
These functional alterations were correlated with a decline in long-
term memory in MTLE.
We separated the cerebellum into six functionally discrete
components to identify cerebellar networks. Our cerebellar ICs and
cerebral parcellations were similar to previously reported atlases. IC
3 and IC 6 contained cerebellar lobule IX, part of the DMN (50,51),
and were connected to cerebral DMN as expected. In our parcellation,
IC 1 consists of the primary sensorimotor zone of the cerebellum, and
IC 5 is the oculomotor network of the cerebellum (52). Similar to ICs
generated with the MELODIC software (29) or the MICA toolbox
(43), IC 2 and IC 6 in this study encompassed roughly symmetrical
cerebellar regions (crus I and II) and were connected to the right and
left FPN, respectively (53). Meanwhile, we noted some differences
between our observations and those found in previous studies. In
contrast to previous reports in the resting-state data, we noted greater
activation in IC 6 than in IC 2 because the Chinese character fluency
task was left-hemisphere dominant for right-handed participants.
In addition, Alsady et al. observed a cerebellar IC connected to
the cerebral DAN and the sensorimotor network. In this study,
we separated the cerebellar oculomotor network IC 5, which was
connected to the cerebral sensorimotor network, while cerebral DAN
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TABLE 1 Participant demographic and clinical data.
HC LTLE RTLE Statistic q
N 30 13 17 - -
Age, y, median (IQR) 26.0 (18.0) 30.0 (10.0) 26.0 (10.0) 2.31a0.60
Sex, Male/Female 14/16 6/7 8/9 0.00b1.00
Education, median (IQR) 12.3 (3.6) 11.4 (3.6) 11.6 (3.4) 0.98a0.78
MoCA, median (IQR) 27.4 (4.2) 26.7 (2.6) 24.0 (5.5) 9.00a0.07
Information, mean (SD) 15.8 (6.5) 14.5 (6.4) 11.9 (5.3) 1.62c0.49
DSF, median (IQR) 8.2 (1.6) 7.6 (1.1) 7.2 (1.3) 1.92d0.49
DSB, median (IQR) 5.1 (1.8) 4.8 (0.7) 4.9 (1.8) 0.31d0.82
DSST, median (IQR) 60.0 (24.0) 55.0 (18.0) 60.0 (16.0) 0.71d0.68
Block design, mean (SD) 37.6 (8.4) 31.7 (6.1) 34.1 (10.5) 2.21c0.46
VFC, median (IQR) 25.6 (14.7) 16.9 (7.0) 20.4 (5.4) 3.47d0.18
VFP, median (IQR) 47.3 (18.1) 32.9 (11.7) 38.4 (10.7) 7.35d0.03
BN, median (IQR) 27.0 (5.0) 21.0 (10.0) 25.0 (3.0) 5.81d0.05
AOO, y, mean (SD) 20.0 (8.4) 16.2 (6.8) 0.37e0.49
duration, y, mean (SD) 11.5 (8.8) 10.4 (8.4) 1.39e0.82
Disease burden, mean (SD) 22.1 (18.4) 22.5 (20.7) −0.05e1.00
Febrile convulsion 2 (15.4%) 7 (41.2%) -f0.64
SGS history 1 (7%) 0 (0%) -f0.49
Number of ASM -f0.60
1 8 8
2 4 9
3 1 0
Seizure frequency -f0.60
Every year 5 2
Every month 4 9
Every week 2 4
Every day 2 2
aH-value of the Kruskal–Wallis H-test.
bχ2value of the chi-squared test.
cF-value of analysis of covariates.
dF-value of Quade nonparametric analysis of covariates.
et-value of the two-tailed two-sample t-test.
fFisher’sexact test.
AOO, age of onset; ASM, antiseizure medication; BN, Boston Naming Test; DSB, Digit Span-Backward; DSF, Digit Span-Forward; DSST, Digit Symbol Substitution Test; HC, healthy controls; IQR,
interquartile range; LTLE, left temporal lobe epilepsy; MoCA, Montreal Cognitive Assessment; RTLE, right temporal lobe epilepsy; SD, standard deviation; SGS, secondary generalized seizures; VFC,
verbal fluency character test; VFP, verbal fluency Pinyin test.
was connected to IC 4. Despite the minor discrepancy in group ICA,
this study supported other studies regarding cerebellar segregation
and cerebro-cerebellar connectivity.
During the task, functional connectivity between IC 3 and
IC 5 was enhanced and functional connectivity from IC 6 to IC
4 was impaired in patients with RTLE compared to HCs and
patients with LTLE. A mutual connection between IC 3 and IC
5 represented a bidirectional communication between DMN and
the sensorimotor system in the cerebellum. Connectivity between
the cerebral sensorimotor system and DMN was enhanced in
drug-resistant epilepsy (54), frontal lobe epilepsy (55), juvenile
myoclonic epilepsy (56), and generalized tonic–clonic seizures
(57), and it was thought to lead to epileptic susceptibility (56,
57). Connectivity between the DMN and sensory regions caused
lapses in certain ways (58). In addition, hypoconnectivity from
IC 6 to IC 4 indicated disconnection between the left FPN,
DMN, and DAN. In MTLE, connectivity between DAN and the
executive control network was decreased in patients with cognitive
impairment (59).
Degree centrality measures the number of suprathreshold
connections linked to a certain node. In this study, the right lobule
III in LTLE had significantly higher DC than that in RTLE and
HCs. The cerebellum became a functional hub for the whole-brain
network in MTLE. Garcia-Ramos et al. hypothesized that this is
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FIGURE 1
Cerebellar independent components and their cerebral parcellations. (A) ICA-based cerebellar parcellation. (B) Whole-brain back-reconstruction of
cerebellar ICs.
a compensatory reaction as the cerebellum was more integrated
into the cerebral network and tried to compensate for the impaired
cerebral function (28).
The Chinese version of the Information Test covered basic
questions on geography, literature, history, and general knowledge.
At the same educational level, the Information Test estimates long-
term semantic memory (32). In RTLE, hyperconnectivity between IC
3 and IC 5 contributed to worse performance on the Information
Test. In LTLE, higher compensatory DC of the right lobule III was
correlated with higher Information Test scores. Though semantic
memory was impaired at the same level for LTLE and RTLE
in alphabetic languages (60,61), our previous studies involving
more participants reported worse Information Test scores in RTLE
compared to HC and LTLE (32,62), indicating that the Information
Test might be a right-hemisphere dominant test in Chinese.
Our results indicated that cerebellar disruption and compensation
contributed to long-term memory in MTLE and were potential
intervention targets for cognitive deficiency in MTLE.
The etiology of cerebellar alterations in TLE remains a
controversial topic. Traditionally, cerebellar alterations were
considered to be secondary due to seizures or disruption of cerebral
networks. Studies focusing on anatomical damage of the cerebellum
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FIGURE 2
Network functional connectivity with a significant group dierence among the three groups. *p<0.05, FDR-corrected; **p<0.005, FDR-corrected; ***p
<0.001, FDR-corrected. (A) Network functional connectivity from IC 3 to IC 5; (B) Network functional connectivity from IC 5 to IC 3; and (C) Network
functional connectivity from IC 6 to IC 4. HC, healthy controls; IC, independent component; LTLE, left temporal lobe epilepsy; RTLE, right temporal lobe
epilepsy.
FIGURE 3
DC of the right cerebellum lobule III was dierent across the three groups. Red ***, p<0.001, FDR-corrected; Red - & *, p < 0.05, FDR-corrected; black -,
p<0.05, uncorrected. The boxplot presents the group comparison of AUC, and the line chart shows the group comparison across all connectivity
densities. ANCOVA, analysis of covariates; AUC, area under the curve; DC, degree centrality; HC, healthy controls; IC, independent component; LTLE, left
temporal lobe epilepsy; RTLE, right temporal lobe epilepsy.
demonstrated a relationship between more severe cerebellar damage
and a higher disease burden. In addition, the volume alteration of the
vermis is correlated with the temporal lobe volume (63). However,
functional studies of the cerebellum failed to confirm a relationship
between disease burden and functional alterations (2,3). Notably,
cerebellar functional abnormalities also did not correlate with the
clinical features in this study. Several points might explain the lack of
correlation. First, a composite destructive and compensatory process
might weaken the correlation between functional abnormalities and
clinical features. Second, the cerebellar function was susceptible
Frontiers in Neurology 07 frontiersin.org
Peng et al. 10.3389/fneur.2023.1062149
FIGURE 4
Correlation analysis. HC, healthy controls; IC, independent component; LTLE, left temporal lobe epilepsy; RTLE, right temporal lobe epilepsy. (A)
Functional connectivity from IC 3 to IC5. (B) Functional connectivity from IC 5 to IC 3. (C) Degree centrality of right lobule III.
to ASM. The combinations and types of ASM could have biased
the results.
Etiological and functional differences between LTLE and RTLE
had been noted in previous studies. The language network (64)
and the episodic memory network (65) were contralaterally shifted
in LTLE but not in RTLE. Meanwhile, the fiber bundles of the
alertness network were impaired, specifically in RTLE (66). The
graph theoretical study also revealed different electrophysiological
reorganizations in LTLE and RTLE, as functional connectivity was
altered in the alpha band in LTLE and in the theta, beta, and gamma
bands in RTLE (67). A multivariate pattern classification model
constructed with cerebellar and cerebral structural connectivity
achieved 93% accuracy in differentiating LTLE from RTLE (68).
Consistent with our findings, Zanão et al. observed that the functional
connectivity of DMN was enhanced in RTLE, compared to that in
LTLE (69). Our result generalized previous findings to the cerebellum
and strengthened the idea that LTLE and RTLE might be different
epilepsy entities.
This study has limitations. First, ASM affects the results of
fMRI (70). Correlation analysis demonstrated that cerebellar function
was not biased by the number of ASMs. We also excluded
patients who received phenytoin treatment, and we performed a
sensitivity analysis excluding a patient who received topiramate.
However, different types and combinations of ASM would still
influence functional connectivity and correlation analysis. Second,
the cerebellar alterations were explored only in fMRI data. The
sample sizes of each patient group are also small. Nevertheless, we
obtained detailed clinical information and applied robust methods,
allowing us to detect cerebellar functional alterations under a
stringent threshold. Correlation analysis also excluded potential
clinical confounders that might bias the result. Future studies on the
combination of structural and functional images in a larger cohort
would provide further evidence of cerebellar involvement in MTLE.
5. Conclusion
We noted functional disruption and compensation in the
cerebellum of patients with MTLE. Functional connectivity between
cerebellar networks was modulated in RTLE, while the centrality of
the right lobule III was increased in LTLE. These alterations were
correlated with long-term memory in patients. Our results further
support the cerebellar involvement in cognitive decline in MTLE and
provided potential intervention targets for MTLE.
Data availability statement
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
Ethics statement
The studies involving human participants were reviewed and
approved by the Ethics Committee of the Xiangya Hospital of Central
South University. Written informed consent to participate in this
study was provided by the participants’ legal guardian/next of kin.
Author contributions
YP, FX, LL, and BX contributed to the conception and design
of the study. YP and KW undertook the analysis and drafted the
manuscript. FX, KW, CL, LT, and XL organized the database. FX,
KW, CL, LT, MZ, JH, YD, and GW collected the data. All authors
contributed to the article and approved the submitted version.
Funding
This study was supported by the National Key Research and
Development Program of China (2021YFC1005305), the National
Natural Science Foundation of China (82171454), the Key Research
and Development Program of Hunan Province (2022SK2042),
the Natural Science Foundation of Hunan Province (2020JJ5914),
the Innovative Construction Foundation of Hunan Province
(2021SK4001), and the National Multidisciplinary Cooperative
Diagnosis and Treatment Capacity Project for Major Diseases of
Xiangya Hospital, Central South University (z027001).
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Frontiers in Neurology 08 frontiersin.org
Peng et al. 10.3389/fneur.2023.1062149
Publisher’s note
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authors and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the reviewers.
Any product that may be evaluated in this article, or claim that may
be made by its manufacturer, is not guaranteed or endorsed by the
publisher.
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