
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12770-3
10.1016/j.heliyon.2024.e36739
e36739
Research Article
Gray matter structural alterations of cortico-striato-thalamo-cortical loop in familial Paroxysmal Kinesigenic Dyskinesia
Wang Dongcui ab1
Jin Hong ab1
Xie Fangfang ab
Wang Ziyun ab
Xing Wu xingwu@csu.edu.cn
ab⁎
a Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
b National Clinical Research Center for Geriatric Disorders, XiangYa Hospital, Central South University, Changsha, China
⁎ Corresponding author. Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan Province, 410008, China. xingwu@csu.edu.cn
1 Equal contribution.

22 8 2024
15 9 2024
22 8 2024
10 17 e3673924 3 2024
20 8 2024
21 8 2024
© 2024 The Authors. Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Background

Previous studies have indicated that patients with Paroxysmal Kinesigenic Dyskinesia (PKD) exhibit reduced gray matter volume in certain brain regions within the cortico-striato-thalamo-cortical (CSTC) loop. However, a comprehensive investigation specifically targeting the CSTC loop in PKD has never been conducted.

Objectives

To provide evidence for the involvement of the CSTC loop in the pathogenesis of PKD from the perspective of structural alterations, this study carried out a surface-based morphometry (SBM), voxel-based morphometry (VBM), and structural covariance networks (SCN) combined analysis in familial PKD patients.

Methods

A total of 8 familial PKD patients and 10 healthy family members were included in the study and underwent Brain MRI examinations. Based on 3D T1 MPRAGE data, neuroimaging metrics of cortical thickness from SBM, subcortical nuclei volume from VBM, and covariance coefficient from SCN were used to systematically investigate the brain structural alterations along the CSTC loop of PKD patients.

Results

A significant decrease in the average cortical thickness of the left S1 region in the PKD group was observed. The volumes of subcortical nuclei, including the thalamus, putamen, and globus pallidus were reduced, with a pronounced effect observed in the bilateral putamen. And the structural covariance connection between the left putamen and the left globus pallidus was significantly strengthened.

Conclusions

The study confirms the involvement of the CSTC loop in the pathogenesis of PKD from the perspective of structural alterations, and the findings may provide potential targets for objective diagnosis and therapeutic monitoring of PKD.

Keywords

Paroxysmal Kinesigenic Dyskinesia
Cortico-striato-thalamo-cortical loop
Cortical thickness
Subcortical nuclei volumes
Structural covariance network
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pmc1 Introduction

Paroxysmal Kinesigenic Dyskinesia (PKD), of which the prevalence is estimated to be 1/150,000 worldwide, is the most common form of paroxysmal movement disorder, characterized by sudden and transient episodes of movement disturbance triggered by movement itself. This condition can lead to temporary loss of motor abilities and interfere with daily activities such as walking and working, significantly impairing quality of life. While the clinical features of this disorder have been well-documented, the understanding of its pathophysiological mechanisms from the perspective of brain structure and function remains limited. As structure serves as the material basis for functional activity, a comprehensive understanding of its alterations in PKD is crucial. It not only aids in revealing the pathophysiological mechanisms of PKD but also holds the potential to uncover promising targets for the prevention and treatment of PKD.

The cortico-striato-thalamo-cortical (CSTC) loop is a neural network that forms a continuous loop connecting the prefrontal cortex, basal ganglia, and thalamus. It functions as a crucial neuronal loop involved in controlling movement selection, initiation, reinforcement, and reward processing. Not only a few studies have reported reduced gray matter volume in certain brain regions within the CSTC loop of individuals with PKD, such as the thalamus [1] and supplementary motor area [2], researches but also have revealed alterations in brain iron levels within the CSTC loop [3]. However, there has been no systematic investigation specifically focused on the CSTC loop. Therefore, the aim of this study is to analyze various data on the structure of the CSTC loop, with the goal of observing the relationship between PKD and structural changes within this loop.

Recent studies have revealed that the coordinated interactions between different regions of the brain give rise to distinct brain subnetworks [4]. The Structural Covariance Network (SCN) [5] utilizes the structural covariance of various morphological properties (such as gray matter volume, cortical thickness, sulcal depth, surface area, etc.) across different brain regions to construct a network that reflects the collaborative and interconnected patterns, highlighting the topological structure of the brain. This method has been widely applied to investigate the changes of structural connectivity in various neurological disorders, including those without obvious radiological alterations [[6], [7], [8], [9], [10], [11], [12], [13], [14]]. In a study related to epilepsy, an SCN was constructed using gray matter volume to analyze structural abnormalities within the CSTC loop, revealing enhanced structural connectivity within the striatum and thalamus [11]. Considering that PKD shares similar or overlapping pathogenic mechanisms with epilepsy [17], and the disease-causing gene PRRT2 is considered a gene involved in brain network stability [18], it is reasonable to presume that studying the abnormality of PKD at the network level using SCN is a promising approach.

Building upon existing literature [1], it is hypothesized that PKD patients had changed structural imaging profiles in brain regions along the CSTC loop. Consequently, we employed surface-based morphometry (SBM) analysis to extract cortical thickness, voxel-based morphometry (VBM) analysis to extract subcortical nuclei volumes, and structural covariance networks (SCN) to analyze structural connectivity characteristics of brain regions inside the CSTC loop of PKD patients, aiming to systematically investigate the brain structural alterations in PKD patients.

2 Materials and methods

2.1 Study participants

The present study included 8 PKD outpatients and 10 healthy controls from the same pedigree. All PKD patients have the same c.324_334del (p. Val109Argfs*21) mutation in the second exon of the PRRT2 gene. The inclusion criteria for the PKD group in this study adhere to the diagnostic criteria established by Cao et al. [4]. Participants with a history of traumatic brain injury, brain tumors, cerebral infarction, cerebral hemorrhage, epilepsy, or other neurological disorders were excluded. Healthy and closely related family members from the PKD families were recruited as normal controls (NC). This study has obtained approval from the Ethics Review Committee of Xiangya Hospital (approval number 202307146), Central South University. Informed consent forms have been obtained from all PKD patients and healthy controls.

2.2 Data acquisition

The high-resolution 3D T1-weighted sagittal images were acquired using the Siemens 3 T (Prisma, Siemens Healthcare, Erlangen, Germany) magnetic resonance imaging system, combined with a 64-channel head-neck coil. The imaging parameters for the Magnetization Prepared Rapid Gradient Echo Imaging (MPRAGE) sequence were as follows: a repetition time (TR) of 2110 ms, an echo time (TE) of 3.18 ms, an inversion time (TI) of 1030 ms, a flip angle (FA) of 9°, a slice thickness of 0.73 mm, a field of view (FOV) size of 232 mm, a matrix size of 320 × 320, a single excitation (NEX) acquisition, and a total scan time of 4 min and 36 s (TA).

2.3 Data preprocessing

Data preprocessing involved cortical reconstruction and volume segmentation, performed using the latest stable version (version 7.1.1) of FreeSurfer. The primary steps are as follows:(a) Format conversion (b) Motion correction (c) Talairach coordinate system transformation (d) Signal intensity normalization (e) Tissue extraction: Non-brain tissues such as the skull and dura were removed, leaving only the brain tissues for further analysis. (f) Automated gray matter volume segmentation and white matter segmentation with topological correction. (g) Boundary delineation of brain tissues: The boundaries between gray matter, white matter, and the pial surface (soft brain tissue) were delineated. (h) Cortical surface reconstruction and computation of cortical thickness. After the automated processing by FreeSurfer, a manual quality check was performed on the results of each subject by a senior radiologist (F.X) who was blind to the diagnosis. Any reconstruction or segmentation errors were corrected manually if necessary.

2.4 Extraction of neuroimaging metrics

The standard space Human Motor Area Template (HMAT) (Fig. 1A) [5] was transformed into the FreeSurfer space. Based on this template, the average cortical thickness was extracted for specific regions of interest, including the pre-supplementary motor area (preSMA), supplementary motor area proper (SMA), dorsal aspects of the lateral premotor cortex (PMd), ventral aspects of the lateral premotor cortex (PMv), primary motor cortex (M1), and primary somatosensory cortices (S1). Cortical thickness is defined as the shortest linear distance between the gray/white matter boundary and the pial surface. Then, using the Aseg segmentation template [6], volumes of subcortical nuclei such as the thalamus (THAL), putamen (PUT), and globus pallidus (GP) were extracted from the reconstructed images. Finally, the Brain Covariance Connectivity Toolkit (BCCT) [8], a MATLAB toolbox, was utilized to construct a structural covariance network based on morphological measurements of the aforementioned subcortical nuclei. Pearson correlation coefficients were computed to capture the structural covariation of the subcortical nuclei mentioned above, and all correlation coefficients were subjected to Fisher Z transformation.Fig. 1 A: brain region distribution map of the HMAT Template. PreSMA: pre-supplementary motor area, SMA: supplementary motor area proper, PMd: dorsal aspects of the lateral premotor cortex, PMv: ventral aspects of the lateral premotor cortex, M1: primary motor cortex, S1: primary somatosensory cortices. B: subcortical nuclei distribution Purple: Putamen (PUT), blue: Globus Pallidus (GP), green: Thalamus (THAL). C–D: presents the standardized coefficients of the structural co-variation network (SCN) (Fig. 1C) and the intergroup differences (Fig. 1D). The lower triangle and upper triangle in Fig. 1C represent respectively the SCN standardized coefficients of the PKD group and the NC group.

Fig. 1

2.5 Statistical analysis

Statistical analysis of clinical data, cortical thickness, and subcortical nuclei volumes was performed using the SPSS 23 software. For group comparisons, independent samples t-tests were conducted for normally distributed variables, while the Mann-Whitney U test was used for variables that did not follow a normal distribution. Chi-square tests were employed for analyzing gender distribution. The statistical analysis of the structural covariance network was conducted using the BCCT toolbox, employing 250 permutation tests during the analysis. Furthermore, partial correlation analysis was performed, with age as a controlling factor, to examine the associations between each neuroimaging metric and clinical manifestation. Imaging metrics included cortical thickness, subcortical nuclei volumes, and structural covariance coefficients. Clinical manifestations contained onset age, attack frequency, attack duration, and the scores obtained from self-rating scales of psychological well-being, namely SAS, SCL-90, and SDS. Additionally, age was utilized as a covariate for intergroup comparisons of neuroimaging metrics, and the Bonferroni correction was applied to correct for multiple comparisons. For all statistical tests, significance level was set at p < 0.05.

3 Results

3.1 Clinical features

Statistical analysis was conducted to examine the intergroup differences in general clinical data. The results are presented in Table 1, which shows that there were no statistically significant differences in age distribution, gender proportion, and most of the psychological assessment scale scores between the two groups.Table 1 Statistical analysis results for general clinical data.

Table 1Feature	PKD	HC	Test Power	p-value	
Age (years)	41.380 ± 19.777	48.600 ± 18.887	0.790	0.441	
Gender (M/F)	4/4	5/5	Crosstab	1	
SAS	42.750 ± 10.740	37.700 ± 6.977	1.207	0.245	
SDS	51.000 ± 12.189	44.600 ± 11.501	1.143	0.270	
SCL-90 Total	18.803 ± 5.703	14.544 ± 2.142	2.002	0.078a	
Notes: Continuous variables are presented as mean ± standard deviation.

a Levene's test reveals unequal variances.

3.2 Cortical thickness

Based on the HMAT template [5], we extracted the average cortical thickness of six brain regions: preSMA, SMA, PMd, PMv, M1, and S1, as shown in Table 2. A multivariate regression analysis, adjusting for age and gender as covariates, revealed a significant reduction in the average cortical thickness of the left S1 region in the PKD group compared to the NC group (p = 0.048, uncorrected).Table 2 Comparison of average cortical thickness in HMAT cortical regions.

Table 2Hemisphere	Cortical Region	PKD (mm)	NC (mm)	Test Power	p-value	
Right	M1	2.039 ± 0.128	2.086 ± 0.111	−1.628	0.126	
S1	2.192 ± 0.190	2.171 ± 0.142	−0.192	0.851	
SMA	2.552 ± 0.425	2.580 ± 0.366	−0.444	0.664	
preSMA	2.845 ± 0.281	2.893 ± 0.240	−1.191	0.254	
PMd	2.441 ± 0.335	2.466 ± 0.286	−0.477	0.640	
PMv	2.509 ± 0.241	2.447 ± 0.226	0.314	0.758	
Left	M1	2.121 ± 0.134	2.165 ± 0.124	−1.665	0.118	
S1	1.991 ± 0.091	2.125 ± 0.193	−2.171	0.048a	
SMA	2.698 ± 0.227	2.652 ± 0.348	0.048	0.962	
preSMA	2.883 ± 0.189	2.836 ± 0.226	0.002	0.998	
PMd	2.590 ± 0.228	2.463 ± 0.225	0.866	0.401	
PMv	2.590 ± 0.158	2.494 ± 0.214	0.724	0.481	
Notes: Continuous variables presented as mean ± standard deviation. Abbreviations: preSMA: pre-supplementary motor area, SMA: supplementary motor area proper, PMd: dorsal aspects of the lateral premotor cortex, PMv: ventral aspects of the lateral premotor cortex, M1: primary motor cortex, S1: primary somatosensory cortex.

a p < 0.05.

3.3 Subcortical nuclei volumes

As mentioned earlier, we extracted the volumes of subcortical nuclei, including the putamen (PUT), globus pallidus (GP), and thalamus (THAL) (Fig. 1B). Additionally, to account for inter-individual differences in head size, the volumes were standardized using the estimated total intracranial volume (eTIV) computed by FreeSurfer. A group comparison analysis was performed, with age and gender included as regression variables. The results revealed a decrease in the volumes of the thalamus, putamen, and globus pallidus in the PKD group compared to the NC group (Table 3). However, statistically significant differences were only observed bilaterally in the putamen (right PUT: p = 0.032, left PUT: p = 0.033, both uncorrected).Table 3 Comparison of relative volumes of subcortical nuclei.

Table 3Hemisphere	Subcortical Nuclei	PKD (10−3)	NC (10−3)	Test Power	p-value	
Right	PUT	3.037 ± 0.314	3.328 ± 0.467	5.624	0.032a	
GP	1.240 ± 0.118	1.276 ± 0.243	1.285	0.275	
THAL	4.822 ± 0.249	4.998 ± 0.586	2.782	0.116	
Left	PUT	3.083 ± 0.422	3.316 ± 0.383	5.539	0.033a	
GP	1.256 ± 0.079	1.326 ± 0.231	1.844	0.195	
THAL	4.918 ± 0.412	5.046 ± 0.614	1.160	0.298	
Notes: Continuous variables presented as mean ± standard deviation. Abbreviations: PUT: Putamen, GP: globus pallidus, THAL: thalamus.

a p < 0.05.

3.4 Structural covariance network

The standardized correlation coefficients of structural covariance networks constructed using subcortical nuclei volume indices are depicted in Fig. 1C and the intergroup differences are illustrated in Fig. 1D. Compared to the NC group, the PKD group exhibited significantly enhanced structural covariance connections between the left putamen and the left globus pallidus (p < 0.05, Bonferroni-corrected).

3.5 Correlation analysis

No significant correlations were found between the various imaging metrics and clinical manifestations examined in this study (p > 0.05).

4 Discussion

PKD primarily arises from a functional deficiency mutation in the proline-rich transmembrane protein 2 (PRRT2) gene, which is located in the pericentromeric region of chromosome 16. However, the specific neurobiological mechanisms through which this gene dysfunction mediates the occurrence, development, and termination of involuntary movement disorders in PKD remain unclear. The prevailing consensus suggests that the pathogenesis of PKD is associated with aberrant functioning of the basal ganglia in the brain. In this study, an analysis was conducted on a Chinese family with PKD to investigate the structural alterations within the CSTC loop. The aim of this analysis was to further unravel the structural basis of the pathological mechanisms of PKD.

We analyzed the average cortical thickness of six motor and premotor brain regions: preSMA, SMA, PMd, PMv, M1, and S1. Different from vertex-based comparisons, comparing the average cortical thickness of a specific brain region reflects overall changes. In case of no detectable differences in cortical thickness at the vertex level, the cumulative effect of subtle changes can lead to significant differences in average cortical thickness, making the average cortical thickness more sensitive as an imaging indicator. Our results revealed a significant decrease in the average cortical thickness of the left S1 region in the PKD group compared to the NC group. To the best of our knowledge, this is the first report on the morphological changes of the somatosensory cortex in PKD patients. S1 serves as an indispensable component of the somatosensory system, responsible for processing sensory information such as tactile recognition, proprioception, and motor control. It also receives cortical projections from the thalamus, which is the sensory input domain [7,8]. In PKD patients, prior to or concurrent with sudden movement-induced involuntary movements, sensory prodromes are frequently observed. These prodromes commonly manifest as sensory abnormalities, including numbness and tingling sensations in the affected limbs. Typically, these sensations originate from distal limbs and then spread to proximal limbs. Some scholars have hypothesized that the origin of these sensory prodromes may be associated with excitatory-inhibitory imbalances in the primary somatosensory area [9,10]. Motor abnormalities, characterized primarily by movement disturbances, are increasingly recognized as network disorders. They also involve the somatosensory system, where sensory processing impairments may serve as a contributing factor to the pathogenesis [11,12]. PKD, as a type of movement disorder, have been reported to involve the motor cortex and somatosensory cortex in terms of their neurophysiology. It has been suggested that an imbalance between cortical excitation and inhibition may underlie the potential neurophysiological mechanism of this disorder [13,14]. PKD is considered to be an ion channel disorder. Mutations in the PRRT2 gene have been found in 91 % of familial PKD cases and 35 % of sporadic PKD cases [15]. PRRT2 negatively regulates Na+ channels, regulates the distribution and function of Na+/K+ ATPase pumps on the cell membrane, and inhibits neuronal excitability. A reduction in inhibitory neurotransmitter release may be one of the reasons for the occurrence of involuntary movements in PKD patients [16]. Carbamazepine and phenytoin sodium, among other antiepileptic drugs, have shown favorable therapeutic effects on PKD. They can significantly reduce or even terminate the occurrence of movement disorders. The main mechanism of these drugs is to inhibit the excitability of cell membranes by lowering the activity of Na+ channels. The cortical thickness in patients with focal dystonia shows no significant difference compared to healthy individuals [17]. However, patients with oromandibular dystonia exhibit increased gray matter volume in the left facial S1 region [18]. It could be concluded that PKD and dystonia do not share similar pathophysiological mechanisms involving S1. Research findings have revealed that the cortical thickness of the little brown bat is thinner during the winter compared to the summer, and there is a reduced presence of inhibitory neurons. This leads to lower threshold for detecting sensory stimuli and more easy activation of somatosensory cortex neurons [19]. In individuals with schizophrenia, gray matter damage is observed in the right sensorimotor cortex-striatum network, accompanied by a decrease in sensorimotor cortex thickness. A replicated deficit in sensorimotor gating has been observed in a mouse model of schizophrenia, and this structural alteration is associated with GABA neurons, which are inhibitory neurons [20]. In summary, the aforementioned aspects, symptomatology, neurophysiology, pharmacology, and neuroimaging included, all suggest that the excitatory-inhibitory imbalance of S1 neurons may play a role in the pathophysiological processes of PKD.

Regarding to the striatum, we primarily analyzed the volume of its nuclei. It was found that the nucleus volume of the bilateral putamen in PKD patients is reduced compared to that in healthy individuals. Additionally, there was a slight decrease in the volume of the globus pallidus. The striatum, consisting of the putamen and caudate nucleus, receives its three primary sources of input from dopaminergic cells in the cerebral cortex, thalamus, and brainstem. Through a complex cortico-basal ganglia network, the striatum collaborates with the cortex to execute and generate complex behaviors [21]. The putamen is involved in various functional activities [22] and is associated with learning and motor control [23]. Impairment of its function can lead to various motor and cognitive dysfunctions [24], such as Parkinson's disease, Huntington's disease. The globus pallidus is an important component of the basal ganglia, which serves as a relay nucleus of the extrapyramidal system. Lesions in the globus pallidus can result in movement disorders like dystonia. Previous neuroimaging studies have indicated that alterations in the basal ganglia-thalamo-cortical loop play a role in the pathophysiological mechanisms of PKD. There is relatively limited literature focusing on the morphometrical alterations of these brain structures in PKD. Only a few studies have been conducted on volume changes of putamen in dyskinesia diseases, and the results are not consistent. Studies on patients with primary dystonia have reported conclusions of decreased [25,26], unchanged [27,28], or increased putamen volume [29,30]. Our research found that PKD patients had reduced bilateral putamen volume, suggesting a potential association between decreased putamen volume and PKD pathogenesis. However, further in-depth research is needed to clarify the relationship between putamen volume and PKD. In recent years, the globus pallidus has become one of the therapeutic targets for dystonia, including targeted deep brain stimulation of the medial globus pallidus and pallidotomy [31,32]. The negative results of GP volumetric changes in PKD patients suggested there were different pathological mechanisms between PKD and other dyskinesia diseases like dystonia.

Regarding to the thalamus, there was a decreasing trend in bilateral thalamic volume among PKD patients. The thalamus is an intermediate brain structure located between the cortex and midbrain and is involved in various functions, including motor control [31]. Kim et al. [1] reported a reduced volume of the bilateral thalamus in PKD patients compared to the control group, as well as abnormal morphology in the anterior and medial regions of the thalamus. Our findings align with this study, suggesting that thalamic dysfunction may also contribute to the involuntary movement abnormalities observed in PKD patients.

Regarding to subcortical nuclei, including the thalamus, putamen, and globus pallidus, we further conducted a structural covariance network analysis based on the volume of these nuclei. The study revealed a significantly increased structural covariance connection between the left putamen and the left globus pallidus in the PKD group. Previous analyses of the gray matter morphological network topology in PKD patients have shown altered nodal properties in the basal ganglia [32]. Our findings align with this study, confirming the involvement of the cortico-basal ganglia-thalamo-cortical pathway in the pathophysiological mechanisms of PKD. Furthermore, these changes were predominantly observed in the left hemisphere, which may be related to handedness or hemispheric asymmetry alterations. More in-depth investigations are needed to validate these hypotheses.

5 Limitations

This study also has certain limitations. First, the sample size was small, only one family line was included, and thus the generalizability of the results needs to be verified by studies with larger sample sizes. Second, the PKD patients participated were at different stages of the disease. Although they had the same type of PRRT2 mutations, the effect of disease stage could not be completely excluded from interfering with the results of the study. Finally, the current study returned no significant correlations between behavioral and brain imaging features. That's what future studies should make improvement in order to clarify the clinical significance of imaging features.

6 Conclusions

In conclusion, we systematically investigated structural alterations of brain regions within the CSTC loop in patients with PKD by a surface-based morphology, voxel-based morphology and structural covariance network combined method. The results revealed a significant thinning of the left S1, pronounced reduction in volume of bilateral putamen and significantly increased covariant connections between the left putamen and the left globus pallidus in PKD patients. These findings provide additional evidence for the involvement of the cortico-striato-thalamo-cortical loop in the pathogenesis of PKD and may offer potential targets for objective diagnosis, treatment monitoring, and therapeutic interventions in PKD.

Funding sources

This research was partially sponsored by the 10.13039/501100004735 Natural Science Foundation of Hunan Province (2023JJ30954 ).

Ethical statement

This study has obtained approval from the Ethics Review Committee of Xiangya Hospital (approval number 202307146), Central South University. Informed consent forms have been obtained from all PKD patients and healthy controls.

Data availability statement

All the relevant data are included in the manuscript and the supplementary document. No separate repository is attached.

CRediT authorship contribution statement

Dongcui Wang: Writing – original draft, Methodology. Hong Jin: Writing – original draft. Fangfang Xie: Formal analysis. Ziyun Wang: Data curation. Wu Xing: Writing – review & editing, Supervision.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Wu Xing reports financial support was provided by 10.13039/501100004735 Natural Science Foundation of Hunan Province . If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following are the Supplementary data to this article:Multimedia component 1

Multimedia component 1

Multimedia component 2

Multimedia component 2

Acknowledgments

We would like to express our sincere gratitude to colleagues in the department of neurology for their tremendous support throughout the patient recruitment process.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e36739.
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References

1 Kim J.H. Kim D.W. Kim J.B. Thalamic involvement in paroxysmal kinesigenic dyskinesia: a combined structural and diffusion tensor MRI analysis [J] Hum. Brain Mapp. 36 4 2015 1429 1441 25504906
2 Li H.F. Yang L. Yin D. Associations between neuroanatomical abnormality and motor symptoms in paroxysmal kinesigenic dyskinesia [J] Parkinsonism Relat Disord 62 2019 134 140 30635245
3 Xie F. Mao T. Tang J. Evaluation of iron deposition in the motor CSTC loop of a Chinese family with paroxysmal kinesigenic dyskinesia using quantitative susceptibility mapping [J] Front. Neurol. 14 2023 1164600
4 Cao L. Huang X. Wang N. Recommendations for the diagnosis and treatment of paroxysmal kinesigenic dyskinesia: an expert consensus in China [J] Transl. Neurodegener. 10 1 2021 7 33588936
5 Mayka M.A. Corcos D.M. Leurgans S.E. Three-dimensional locations and boundaries of motor and premotor cortices as defined by functional brain imaging: a meta-analysis [J] Neuroimage 31 4 2006 1453 1474 16571375
6 Fischl B. Salat D.H. Busa E. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain [J] Neuron 33 3 2002 341 355 11832223
7 Viaene A.N. Petrof I. Sherman S.M. Synaptic properties of thalamic input to layers 2/3 and 4 of primary somatosensory and auditory cortices [J] J. Neurophysiol. 105 1 2011 279 292 21047937
8 Ngo G.N. Haak K.V. Beckmann C.F. Mesoscale hierarchical organization of primary somatosensory cortex captured by resting-state-fMRI in humans [J] Neuroimage 235 2021 118031
9 Huang X.J. Wang T. Wang J.L. Paroxysmal kinesigenic dyskinesia: clinical and genetic analyses of 110 patients [J] Neurology 85 18 2015 1546 1553 26446061
10 Huang X.J. Wang S.G. Guo X.N. The phenotypic and genetic spectrum of paroxysmal kinesigenic dyskinesia in China [J] Mov. Disord. 35 8 2020 1428 1437 32392383
11 Erro R. Rocchi L. Antelmi E. High frequency somatosensory stimulation in dystonia: evidence fordefective inhibitory plasticity [J] Mov. Disord. 33 12 2018 1902 1909 30376603
12 Erro R. Antelmi E. Bhatia K.P. Reversal of temporal discrimination in cervical dystonia after low-frequency sensory stimulation [J] Mov. Disord. 36 3 2021 761 766 33159823
13 Hsu W.Y. Kwan S.Y. Liao K.K. Altered inhibitory modulation of somatosensory cortices in paroxysmal kinesigenic dyskinesia [J] Mov. Disord. 28 12 2013 1728 1731 24114929
14 Hsiao F.J. Hsu W.Y. Chen W.T. Abnormal somatosensory synchronization in patients with paroxysmal kinesigenic dyskinesia: a magnetoencephalographic study [J] Clin. EEG Neurosci. 48 4 2017 288 294 27515699
15 Meneret A. Gaudebout C. Riant F. PRRT2 mutations and paroxysmal disorders [J] Eur. J. Neurol. 20 6 2013 872 878 23398397
16 Sterlini B. Romei A. Parodi C. An interaction between PRRT2 and Na(+)/K(+) ATPase contributes to the control of neuronal excitability [J] Cell Death Dis. 12 4 2021 292 33731672
17 Gianni C. Pasqua G. Ferrazzano G. Focal dystonia: functional connectivity changes in cerebellar-basal ganglia-cortical circuit and preserved global functional architecture [J] Neurology 98 14 2022 e1499 e1509 35169015
18 Mantel T. Altenmuller E. Li Y. Structure-function abnormalities in cortical sensory projections in embouchure dystonia [J] Neuroimage Clin 28 2020 102410
19 Ray S. Li M. Koch S.P. Seasonal plasticity in the adult somatosensory cortex [J] Proc Natl Acad Sci U S A 117 50 2020 32136 32144 33257560
20 Zhang C. Ni P. Liu Y. GABAergic abnormalities associated with sensorimotor cortico-striatal community structural deficits in ErbB4 knockout mice and first-episode treatment-naive patients with schizophrenia [J] Neurosci. Bull. 36 2 2020 97 109 31388929
21 Haber S.N. Corticostriatal circuitry [J] Dialogues Clin. Neurosci. 18 1 2016 7 21 27069376
22 Starr C.J. Sawaki L. Wittenberg G.F. The contribution of the putamen to sensory aspects of pain: insights from structural connectivity and brain lesions [J] Brain 134 Pt 7 2011 1987 2004 21616963
23 Cohen Y. Schneidman E. Paz R. The geometry of neuronal representations during rule learning reveals complementary roles of cingulate cortex and putamen [J] Neuron 109 5 2021 839 851 e9 33484641
24 Ghandili M. Munakomi S. Neuroanatomy, Putamen [M] StatPearls 2022 Treasure Island (FL)
25 Sigirli D. Ozdemir S.T. Erer S. Statistical shape analysis of putamen in early-onset Parkinson's disease [J] Clin. Neurol. Neurosurg. 209 2021 106936
26 Pantano P. Totaro P. Fabbrini G. A transverse and longitudinal MR imaging voxel-based morphometry study in patients with primary cervical dystonia [J] AJNR Am J Neuroradiol 32 1 2011 81 84 20947646
27 Bai X. Vajkoczy P. Faust K. Morphological abnormalities in the basal ganglia of dystonia patients [J] Stereotact. Funct. Neurosurg. 99 4 2021 351 362 33472209
28 Gracien R.M. Petrov F. Hok P. Multimodal quantitative MRI reveals No evidence for tissue pathology in idiopathic cervical dystonia [J] Front. Neurol. 10 2019
29 Vilany L. DE Rezende T.J.R. Piovesana L.G. Exploratory structural assessment in craniocervical dystonia: global and differential analyses [J] PLoS One 12 8 2017 e0182735
30 Bradley D. Whelan R. Walsh R. Temporal discrimination threshold: VBM evidence for an endophenotype in adult onset primary torsion dystonia [J] Brain 132 Pt 9 2009 2327 2335 19525326
31 Sherman S.M. Guillery R.W. Exploring the Thalamus 2001 [M]
32 Li L. Lei D. Suo X. Brain structural connectome in relation to PRRT2 mutations in paroxysmal kinesigenic dyskinesia [J] Hum. Brain Mapp. 41 14 2020 3855 3866 32592228
