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Research Article: Study Protocol Systematic Review

Cortical thickness in Parkinson disease

A coordinate-based meta-analysis

Sheng, LiQin MDa; Zhao, PanWen MDb; Ma, HaiRong MDa; Radua, Joaquim MD, PhDc,d,e; Yi, ZhongQuan MDb; Shi, YuanYuan MDb; Zhong, JianGuo MDf; Dai, ZhenYu MDg; Pan, PingLei MDb,f,∗

Author Information
doi: 10.1097/MD.0000000000021403
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Abstract

1 Introduction

Parkinson disease (PD) is a common neurodegenerative disease[1] that affected 6.1 million individuals worldwide.[2,3] PD is a highly clinically heterogeneous condition traditionally characterized by cardinal motor symptoms, such as resting tremor, rigidity, and bradykinesia; however, PD also manifests various non-motor symptoms throughout the course of disease, such as cognitive impairment, apathy, and depression.[4,5] The neurophysiology of PD is complex. Modern neuroimaging techniques have featured prominently in attempts to understand the pathophysiology in vivo.[6–9]

Cortical thickness (CTh) analysis is popular surface-based technique for cortical gray matter (GM) assessment. A growing number of studies have used CTh analysis to investigated brain morphology in PD relating to demographic and clinical characteristics, such as age of onset, age, disease duration, motor deficits, disease stages, and divergent non-motor symptoms.[10–38] Some studies suggested that CTh alterations may be indicators of neural degeneration occurring in PD,[39,40] while some other CTh studies argued against that view as they failed to identify morphological features in patients with PD relative to healthy controls (HCs).[31,41–44] Despite the many advances in our understanding of the neurobiological underpinnings of PD, the results of the CTh analysis in PD across studies are inconsistent and have not been systematically reviewed.

Coordinate-based meta-analysis (CBMA) is powerful tool to quantitatively integrate the results of individual neuroimaging studies.[45,46] Recently, CBMA has been developed for surface-based morphometric (SBM) studies to identify consistent CTh abnormalities in major depressive disorder.[47] In the present study, we conducted a CBMA of SBM studies that investigate CTh alterations in PD using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI).[48,49]

2 Methods

This protocol will follow the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMA-P).[50] The protocol of this CBMA was registered at PROSPERO (http://www.crd.york.ac.uk/PROSPERO) (registration number: CRD42020148775). No ethical approval is required because the data used in this paper are from published studies without the involvement of animals or individual experiments.

2.1 Data sources and study selection

The online databases PubMed, Embase, and Web of Science will be searched, using the keywords

((Parkinson disease) or Parkinson∗) and ((cortical Thickness) OR (cortical thinning) OR (surface-based morphometry)) without language limitations from the database inception to the July 1, 2019 and updated on Feb 2, 2020. The following databases China National Knowledge Infrastructure (CNKI), WanFang, and SinoMed will be also searched for studies published in Chinese. Additionally, the reference lists of relevant reviews and the articles selected for inclusion were further manually searched.

2.2 Eligibility criteria

2.2.1 Inclusion criteria

The articles included in the CBMA should meet the following inclusion criteria: included patients with idiopathic PD diagnosed according to the accepted criteria; compared regional CTh differences between patients with idiopathic PD and HC subjects at the whole-brain cortical level; reported peak coordinates of significant clusters in standard Montreal Neurological Institute (MNI) or Talairach space; was a case-control original article published in a peer-reviewed journal without the limit of language.

2.2.2 Exclusion criteria

Publications will be excluded if they met the following exclusion criteria: the sample size was fewer than 7 either in the PD group or the HC group[45]; the studies included PD patients with dementia; the studies reported significant results without listing three-dimensional coordinates; the studies only employed regions of interest (ROI) analysis; the studies only conducted the global CTh analysis; the studies lacked an HC group; the studies with the patient samples were overlapped with the another one with the largest sample size. The studies were longitudinal without performing baseline comparisons. The publications were not an original type, such as conference abstracts, research protocols, letters, reviews, and editorials.

Figure 1 presents the process of study selection in accordance with the PRISMA flowchart.

Figure 1
Figure 1:
Study selection process in accordance with the PRISMA flowchart. CTh = cortical thickness; PD = Parkinson's disease; PRISMA = Preferred Reporting Items of Systematic Review and Meta-Analysis, ROI = regions of interest; SBM = surface-based morphometry.

2.3 Quality assessment

Currently, there was no objective tool to perform quality assessment for CTh studies. Referring to the previous work,[47] we used a 12-point checklist to assess the quality of each included study in the CBMA (Table 1). This checklist integrated the items regarding the sample characteristics and imaging-specific methodology employed in the studies.

Table 1
Table 1:
The checklist of quality assessment for the included cortical thickness studies.

2.4 Data extraction

The following specific data will be extracted from the included studies: the first author's family name, publication year, sample size, male number, age, education (years), disease duration, United Parkinson disease rating scale, part III (UPDRS-III), Hoehn and Yahr (HY) stage, mini-mental state examination (MMSE), levodopa equivalent daily dose (LEDD), MRI scanner manufacturer and platform, field strength, head coil, MRI sequence, voxel size, imaging processing software package, smooth kernel, statistical model, covariate, statistical threshold, peak coordinates, height of the peaks (t value or z-value, but a P-value is also useful), and their stereotactic reference space.

Two of the authors (LQS and PWZ) will independently perform the literature search, study selection, quality assessment, and data extraction. Any inconsistencies will be discussed by consensus.

2.5 Data analysis

2.5.1 Main CBMA

The main CBMA will be carried out using the SDM-PSI software package (version 6.21, https://www.sdmproject.com/). Its standard procedures include: calculation of the maps of the lower and upper bounds of possible effect sizes for each study separately based on the peak information using a specific FreeSurfer GM mask, full anisotropy = 1, isotropic full width half maximum = 20 mm, and voxel = 2 mm; the mean analysis: estimation of the map of most likely effect size and its standard error based on the MetaNSUE algorithms,[51,52] conducting multiple imputations of the maps of effect size of the individual studies, meta-analysis of these maps using a standard random-effects model, and Rubin rules to pool the different meta-analyses resulting from the multiple imputations[52]; family-wise error (FWE) correction for multiple comparisons using common permutation tests; and finally use of threshold-free cluster enhancement (TFCE) in the statistical thresholding (P < .05, voxel extent ≥10). The details of these procedures have been extensively described in prior publications[48,49] and the SDM-PSI reference manual (https://www.sdmproject.com/manual/).

2.5.2 Subgroup CBMA

Subgroup CBMA will be conducted when the number of the datasets is sufficient (n ≥ 10). Subgroup CBMA would be performed in clinical subtypes (such as PD patients without mild cognitive impairment and patients with mild cognitive impairment) and imaging methodology variables (including the datasets using 3.0 Tesla MRI scanners, slice thickness lower than 1 mm or voxel size lower than 1 × 1 × 1 mm3, FreeSurfer software packages, FWHM of the smoothing kernel size of ≤15 mm, at least 1 covariates included in the statistical model, and thresholds corrected for multiple comparisons as well as those datasets performing quality control for imaging data and those not specifying it).

2.5.3 Jackknife sensitivity, heterogeneity, and publication bias analyses

To test the influence of each dataset on the pooled CBMA results, Jackknife sensitivity analysis will be performed by iteratively repeating the same analysis K – 1 times (K = the number of datasets included), discarding one dataset each time.[53,54]

Where there is a significant cluster with a peak MNI coordinate reported in the CBMA, we will extract the information to derive standard heterogeneity statistics I2, with I2 < 50% indicating low heterogeneity.

Publication bias of the significant cluster will be assessed with a test analogue to the Egger test (P < .05).

2.5.4 Meta-regression analysis

Meta-regression analyses will be carried out to examine the potential effects of age, male sex, disease duration, UPDRS-III, HY stage, MMSE, and LEDD on the CBMA results. Statistical significance will be determined using the TFCE-based FWE corrected threshold (P < .05, voxel extent ≥10).[48,49]

3 Discussion

To our best knowledge, this is the first CBMA of SBM studies to quantitatively identify consistent CTh alterations in PD using the latest algorisms of SDM-PSI[48,49] and following the recent guidelines and recommendations.[45,46] In addition to the main CBMA, we will conduct several supplementary analyses to test the robustness of the results, such as jackknife analyses, subgroup CBMA analyses, heterogeneity analyses, publication bias analyses, and meta-regression analyses. This CBMA will provide the latest evidence of CTh alterations in PD.

PD is a progressive neurodegenerative disorder. It has been suggested that cortical neurodegeneration emerged at stage 4 (associated with early phase motor dysfunction) and later stages according to the well-established brain pathologic staging scheme for PD proposed by Braak et al.[55] All the datasets included in the current CBMA enrolled patients with PD at their symptomatic stages, which indicate that cortical neurodegeneration should exist probably manifesting CTh alterations. If there was statistically significant consistence of CTh alterations with robustness in PD, we would discuss the importance of such findings involved in the pathophysiological mechanisms. If not, we would discuss the potential confounding factors the lead to this lack of consistence, such as small sample sizes, sample heterogeneity, and imaging methodological variations. We would further propose some recommendations to design robust CTh studies.

Author contributions

Conceptualization: JianGuo Zhong, ZhenYu Dai, PingLei Pan.

Data curation: PanWen Zhao, ZhongQuan Yi.

Formal analysis: HaiRong Ma, LiQin Sheng.

Funding acquisition: PingLei Pan.

Investigation: LiQin Sheng, PanWen Zhao, ZhongQuan Yi.

Methodology: Joaquim Radua, LiQin Sheng, PingLei Pan.

Project administration: LiQin Sheng, PingLei Pan.

Resources: PanWen Zhao, ZhongQuan Yi.

Software: Joaquim Radua, LiQin Sheng.

Supervision: JianGuo Zhong, ZhenYu Dai, PingLei Pan.

Validation: PingLei Pan.

Visualization: HaiRong Ma, LiQin Sheng.

Writing – original draft: LiQin Sheng, PanWen Zhao, HaiRong Ma.

Writing – review & editing: Joaquim Radua, JianGuo Zhong, ZhenYu Dai, PingLei Pan.

References

[1]. Ascherio A, Schwarzschild MA. The epidemiology of Parkinson's disease: risk factors and prevention. Lancet Neurol 2016;15:1257–72.
[2]. Group GBDNDC. Global, regional, and national burden of neurological disorders during 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet Neurol 2017;16:877–97.
[3]. Collaborators GBDPsD. Global, regional, and national burden of Parkinson's disease, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol 2018;17:939–53.
[4]. Marras C, Chaudhuri KR. Nonmotor features of Parkinson's disease subtypes. Mov Disord 2016;31:1095–102.
[5]. Sauerbier A, Jenner P, Todorova A, et al. Non motor subtypes and Parkinson's disease. Parkinsonism Relat Disord 2016;22: (suppl): S41–6.
[6]. Helmich RC, Vaillancourt DE, Brooks DJ. The future of brain imaging in Parkinson's Disease. J Parkinsons Dis 2018;8:S47–51.
[7]. Strafella AP, Bohnen NI, Pavese N, et al. Imaging markers of progression in Parkinson's disease. Mov Disord Clin Pract 2018;5:586–96.
[8]. Prange S, Metereau E, Thobois S. Structural imaging in Parkinson's disease: new developments. Curr Neurol Neurosci Rep 2019;19:1–3.
[9]. Weingarten CP, Sundman MH, Hickey P, et al. Neuroimaging of Parkinson's disease: expanding views. Neurosci Biobehav Rev 2015;59:16–52.
[10]. Cerasa A, Salsone M, Morelli M, et al. Age at onset influences neurodegenerative processes underlying PD with levodopa-induced dyskinesias. Parkinsonism Relat Disord 2013;19:883–8.
[11]. Claassen DO, Dobolyi DG, Isaacs DA, et al. Linear and curvilinear trajectories of cortical loss with advancing age and disease duration in Parkinson's disease. Aging Dis 2016;7:220–9.
[12]. Wilson H, Niccolini F, Pellicano C, et al. Cortical thinning across Parkinson's disease stages and clinical correlates. J Neurol Sci 2019;398:31–8.
[13]. Lyoo CH, Ryu YH, Lee MS. Cerebral cortical areas in which thickness correlates with severity of motor deficits of Parkinson's disease. J Neurol 2011;258:1871–6.
[14]. Jubault T, Gagnon JF, Karama S, et al. Patterns of cortical thickness and surface area in early Parkinson's disease. Neuroimage 2011;55:462–7.
[15]. Hanganu A, Bedetti C, Jubault T, et al. Mild cognitive impairment in patients with Parkinson's disease is associated with increased cortical degeneration. Mov Disord 2013;28:1360–9.
[16]. Yadav SK, Kathiresan N, Mohan S, et al. Gender-based analysis of cortical thickness and structural connectivity in Parkinson's disease. J Neurol 2016;263:2308–18.
[17]. Gao Y, Nie K, Mei M, et al. Changes in cortical thickness in patients with early Parkinson's disease at different Hoehn and Yahr stages. Front Hum Neurosci 2018;12:469.
[18]. Kim JS, Yang JJ, Lee JM, et al. Topographic pattern of cortical thinning with consideration of motor laterality in Parkinson disease. Parkinsonism Relat Disord 2014;20:1186–90.
[19]. Zarei M, Ibarretxe-Bilbao N, Compta Y, et al. Cortical thinning is associated with disease stages and dementia in Parkinson's disease. J Neurol Neurosurg Psychiatry 2013;84:875–81.
[20]. Biundo R, Calabrese M, Weis L, et al. Anatomical correlates of cognitive functions in early Parkinson's disease patients. PLoS One 2013;8:e64222.
[21]. Pellicano C, Assogna F, Piras F, et al. Regional cortical thickness and cognitive functions in non-demented Parkinson's disease patients: a pilot study. Eur J Neurol 2012;19:172–5.
[22]. Segura B, Baggio HC, Marti MJ, et al. Cortical thinning associated with mild cognitive impairment in Parkinson's disease. Mov Disord 2014;29:1495–503.
[23]. Pagonabarraga J, Corcuera-Solano I, Vives-Gilabert Y, et al. Pattern of regional cortical thinning associated with cognitive deterioration in Parkinson's disease. PLoS One 2013;8:e54980.
[24]. Pereira JB, Svenningsson P, Weintraub D, et al. Initial cognitive decline is associated with cortical thinning in early Parkinson disease. Neurology 2014;82:2017–25.
[25]. Zhang L, Wang M, Sterling NW, et al. Cortical thinning and cognitive impairment in Parkinson's disease without dementia. IEEE/ACM Trans Comput Biol Bioinform 2018;15:570–80.
[26]. Gasca-Salas C, Garcia-Lorenzo D, Garcia-Garcia D, et al. Parkinson's disease with mild cognitive impairment: severe cortical thinning antedates dementia. Brain Imaging Behav 2019;13:180–8.
[27]. Kunst J, Marecek R, Klobusiakova P, et al. Patterns of grey matter atrophy at different stages of Parkinson's and Alzheimer's diseases and relation to cognition. Brain Topogr 2019;32:142–60.
[28]. Biundo R, Weis L, Facchini S, et al. Patterns of cortical thickness associated with impulse control disorders in Parkinson's disease. Mov Disord 2015;30:688–95.
[29]. Tessitore A, Santangelo G, De Micco R, et al. Cortical thickness changes in patients with Parkinson's disease and impulse control disorders. Parkinsonism Relat Disord 2016;24:119–25.
[30]. Huang P, Lou Y, Xuan M, et al. Cortical abnormalities in Parkinson's disease patients and relationship to depression: a surface-based morphometry study. Psychiatry Res Neuroimaging 2016;250:24–8.
[31]. Luo C, Song W, Chen Q, et al. Cortical thinning in drug-naive Parkinson's disease patients with depression. J Neurol 2016;263:2114–9.
[32]. Zanigni S, Sambati L, Evangelisti S, et al. Precuneal thickness and depression in parkinson disease. Neurodegener Dis 2017;17:97–102.
[33]. Campabadal A, Uribe C, Segura B, et al. Brain correlates of progressive olfactory loss in Parkinson's disease. Parkinsonism Relat Disord 2017;41:44–50.
[34]. Rahayel S, Gaubert M, Postuma RB, et al. Brain atrophy in Parkinson's disease with polysomnography-confirmed REM sleep behavior disorder. Sleep 2019;42:1–2.
[35]. Polli A, Weis L, Biundo R, et al. Anatomical and functional correlates of persistent pain in Parkinson's disease. Mov Disord 2016;31:1854–64.
[36]. Yoon EJ, Ismail Z, Hanganu A, et al. Mild behavioral impairment is linked to worse cognition and brain atrophy in Parkinson disease. Neurology 2019;93:e766–77.
[37]. Chung SJ, Park YH, Yoo HS, et al. Mild cognitive impairment reverters have a favorable cognitive prognosis and cortical integrity in Parkinson's disease. Neurobiol Aging 2019;78:168–77.
[38]. Chung SJ, Park YH, Yun HJ, et al. Clinical relevance of amnestic versus non-amnestic mild cognitive impairment subtyping in Parkinson's disease. Eur J Neurol 2019;26:766–73.
[39]. Pereira JB, Ibarretxe-Bilbao N, Marti MJ, et al. Assessment of cortical degeneration in patients with Parkinson's disease by voxel-based morphometry, cortical folding, and cortical thickness. Hum Brain Mapp 2012;33:2521–34.
[40]. Ibarretxe-Bilbao N, Junque C, Segura B, et al. Progression of cortical thinning in early Parkinson's disease. Mov Disord 2012;27:1746–53.
[41]. Worker A, Blain C, Jarosz J, et al. Cortical thickness, surface area and volume measures in Parkinson's disease, multiple system atrophy and progressive supranuclear palsy. PLoS One 2014;9:e114167.
[42]. Acosta-Cabronero J, Cardenas-Blanco A, Betts MJ, et al. The whole-brain pattern of magnetic susceptibility perturbations in Parkinson's disease. Brain 2017;140:118–31.
[43]. Yao N, Shek-Kwan Chang R, Cheung C, et al. The default mode network is disrupted in Parkinson's disease with visual hallucinations. Hum Brain Mapp 2014;35:5658–66.
[44]. Kamagata K, Zalesky A, Hatano T, et al. Gray matter abnormalities in idiopathic Parkinson's disease: evaluation by diffusional Kurtosis imaging and neurite orientation dispersion and density imaging. Hum Brain Mapp 2017;38:3704–22.
[45]. Tahmasian M, Sepehry AA, Samea F, et al. Practical recommendations to conduct a neuroimaging meta-analysis for neuropsychiatric disorders. Hum Brain Mapp 2019;40:5142–54.
[46]. Muller VI, Cieslik EC, Laird AR, et al. Ten simple rules for neuroimaging meta-analysis. Neurosci Biobehav Rev 2018;84:151–61.
[47]. Li Q, Zhao Y, Chen Z, et al. Meta-analysis of cortical thickness abnormalities in medication-free patients with major depressive disorder. Neuropsychopharmacology 2020;45:703–12.
[48]. Albajes-Eizagirre A, Solanes A, Vieta E, et al. Voxel-based meta-analysis via permutation of subject images (PSI): theory and implementation for SDM. Neuroimage 2019;186:174–84.
[49]. Albajes-Eizagirre A, Solanes A, Fullana MA, et al. Meta-analysis of voxel-based neuroimaging studies using seed-based d mapping with permutation of subject images (SDM-PSI). J Vis Exp 2019;153:e59841.
[50]. Shamseer L, Moher D, Clarke M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ 2016;350:g7647.
[51]. Radua J, Schmidt A, Borgwardt S, et al. Ventral striatal activation during reward processing in psychosis: a neurofunctional meta-analysis. JAMA Psychiatry 2015;72:1243–51.
[52]. Albajes-Eizagirre A, Solanes A, Radua J. Meta-analysis of non-statistically significant unreported effects. Stat Methods Med Res 2019;28:3741–54.
[53]. Radua J, Mataix-Cols D. Voxel-wise meta-analysis of grey matter changes in obsessive-compulsive disorder. Br J Psychiatry 2009;195:393–402.
[54]. Radua J, Rubia K, Canales-Rodriguez EJ, et al. Anisotropic kernels for coordinate-based meta-analyses of neuroimaging studies. Front Psychiatry 2014;5:13.
[55]. Braak H, Del Tredici K, Rub U, et al. Staging of brain pathology related to sporadic Parkinson's disease. Neurobiol Aging 2003;24:197–211.
Keywords:

coordinate-based meta-analysis; cortical thickness; gray matter; Parkinson disease; Seed-based d Mapping; surface-based morphometry

Copyright © 2020 the Author(s). Published by Wolters Kluwer Health, Inc.