Abstract
Background: Tract-based spatial statistics (TBSS) studies based on diffusion tensor imaging (DTI) have revealed extensive abnormalities in white matter (WM) fibers of Parkinson's disease (PD); however, the results were inconsistent. Therefore, a meta-analytical approach was used in this study to find the most prominent and replicable WM abnormalities of PD.
Methods: Online databases were systematically searched for all TBSS studies comparing fractional anisotropy (FA) between patients with PD and controls. Subsequently, we performed the meta-analysis using a coordinate-based meta-analytic software called seed-based d mapping. Meanwhile, meta-regression was performed to explore the potential correlation between the alteration of FA and the clinical characteristics of PD.
Results: Out of a total of 1,701 studies that were identified, 23 studies were included. Thirty datasets, including 915 patients (543 men) with PD and 836 healthy controls (449 men), were included in the current study. FA reduction was identified in the body of the corpus callosum (CC; 245 voxels; z = −1.739; p < 0.001) and the left inferior fronto-occipital fasciculus (IFOF) 118 voxels; z = −1.182; p < 0.001). Both CC and IFOF maintained significance in the sensitivity analysis. No increase in FA was identified, but the percentage of male patients with PD was positively associated with the value of FA in the body of the CC.
Conclusions: Although some limitations exist, DTI is regarded as a valid way to identify the pathophysiology of PD. It could be more beneficial to integrate DTI parameters with other MRI techniques to explore brain degeneration in PD.
Introduction
Parkinson's disease (PD) is a neurodegenerative disorder characterized by cardinal symptoms, including tremor, rigidity, bradykinesia, and postural instability (Tolosa et al., 2006). Although PD is defined by its motor symptoms, non-motor symptoms such as depression, sleep disturbance, autonomic dysfunction, and olfactory dysfunction are also present in patients with PD, and may even predate the emergence of the classic motor features (Berg et al., ). Over the past generations, the incidence and prevalence rates of PD have increased because of the general aging of the population and other factors (, ). The heterogeneous symptoms and the huge burden of PD have prompted scientists to explore the neural basis of this disease to allow for both better diagnosis and intervention.
Numerous studies have sought to explain the mechanism behind PD via several different factors, e.g., genetic high risk (Chung et al., ; Wile et al., 2017), pathological findings of α-synuclein (Kalia, ), and PET with the dopamine tracer (Wile et al., 2017; Kalia, ). Nevertheless, these studies mostly focused on the dopamine pathway; however, patients with PD did not present with the symptoms resulting from a dopamine deficit. The development of MRI techniques offers scientists more ways, which are generally less invasive and more cost-effective, to gain insight into the neural basis of PD, bridge the cerebral changes to the clinical symptoms, and predict the progression of disease in the early stage. In numerous MRI studies, patients with PD showed extensive morphological abnormalities. A recent review summarized these abnormalities as (Sarasso et al., 2020) cortical atrophy (e.g., the caudate, the temporal/hippocampal, the frontal areas, and so on) that had accumulated with the progression of the disease and finally reached a plateau. Subsequently, a new question arose: whether these abnormalities were developed independently or resulted from the degeneration of white matter (WM) fibers. There is a hypothesis that the degeneration of WM tracts is the cause of cerebral atrophy and connectivity changes. Some studies (Lee et al., 2011; Duncan et al., ; Rektor et al., 2018) have suggested that changes in the WM precede changes in the gray matter (GM) in patients with PD.
Diffusion tensor imaging (DTI) is a newly developed MRI technique that can examine WM properties at a microstructural level (Basser, ) by encoding information about the directionality and magnitude of water molecules. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) are four parameters derived from DTI data to assess the WM properties (Andica et al., ). In the past, increasing interest in axonal analyses using DTI has deepened our understanding of the WM damage in the pathology of PD. For example, a study demonstrated that neurodegenerative microstructural alterations in patients with PD were different from normal physiological aging (Koirala et al., ). However, there is a great deal of inconsistency between the findings of various studies. While some studies (Kim et al., ; Carriere et al., ; Rossi et al., 2014; Duncan et al., ; Acosta-Cabronero et al., ; Georgiopoulos et al., ; Luo et al., 2017; Mishra et al., 2019) observed no significant differences in FA between patients with PD and healthy controls (HCs), other studies reported decreased FA in various regions, including the corpus callosum (CC; Chen Y. S. et al., ); left inferior fronto-occipital fasciculus (IFOF; Chen Y. S. et al., ); cingulum (Karagulle Kendi et al., ); insular (Chiang et al., ); thalamus (Youn et al., 2015); and the parietal, occipital, and cerebellar WM regions (Zhang et al., 2011). There have been studies reporting increased FA in the WM regions of PD, for example, the brainstem (Youn et al., 2015; Lorio et al., 2019), cerebellar, anterior corpus callosal, and inferior fronto-occipital WM (Taylor et al., 2018).
To eliminate the inconsistencies between these different studies, coordinate-based meta-analyses (Albrecht et al., ; Suo et al., 2020) on DTI studies were carried out, and decreases in FA were found in the CC, the left middle cerebellar peduncles (MCPs), the left IFOF, and the right inferior longitudinal fasciculus of patients with PD compared with that of HCs. These analyses combined whole-brain tract-based spatial statistics (TBSS; Smith et al., 2006) and voxel-based analyses (VBA) studies, thus attenuating the bias of region-of-interest (ROI) selection. However, VBA is inevitable with partial volume effects and misregistration errors. Compared to VBA, TBSS restricts the analysis to the center of major WM tracts and can more accurately identify WM abnormalities (Rae et al., 2012). To determine the inherent methodical differences between TBSS and VBA, a meta-analysis solely based on TBSS studies was needed.
Therefore, this study carried out a meta-analysis of only TBSS studies to identify the most robust WM microstructural abnormalities in patients with PD. Seed-based d mapping (SDM) software, a voxel-based quantitative meta-analysis approach, was performed in this study. Compared with activation likelihood estimation (ALE), another frequently used meta-analysis software, the advantage of SDM is that it considers null results. In addition, the potential relationship between clinical parameters [age, sex, the Unified Parkinson Disease Rating Scale-III (UPDRS-III) score, disease duration, the Mini-Mental State Examination (MMSE), and the levodopa equivalent daily dose (LEED)] and WM microstructural abnormalities in patients with PD was explored by regression analysis.
Materials and Methods
Study Selection
We searched for pertinent literature in PubMed, Embase, and Web of Science. The following keywords were used to identify relevant articles published up to December 2020: “Parkinson” or “Parkinson's disease”; “tract-based spatial statistical;” or “diffusion tensor” or “fractional anisotropy.” The flow diagram of the literature search is presented in Figure 1, and the inclusion/exclusion criteria are explained in the Supplementary Methods. In summary, only peer-reviewed whole-brain TBSS studies comparing WM abnormalities in patients with PD to HCs were included in this analysis, even those that report null results. The corresponding authors of studies that met all of the above inclusion criteria, but lacked global brain coordinates, were contacted to obtain additional information. Moreover, studies that used multiple independent patient samples were compared separately with the same HC groups, and the appropriate results were regarded as separate datasets. There were no overlapping samples in all included studies. All included studies were assessed and scored for quality using a 15-point checklist adapted from a previously published meta-analysis (Chen et al., ; Supplementary Table 1). A similar search strategy was used to explore underlying consistent MD changes in patients with PD compared to HCs. Details are presented in the Supplementary Methods.
Figure 1
Data Extraction
One author (CYL) searched the literature, and another author (XW) extracted demographic characteristics (sample size, age, and sex), clinical data (disease duration, UPDRS-III, MMSE, medication status, and LEED), imaging parameters, statistical thresholds, and major findings from the included studies. For studies reported with interquartile range and median, we calculated the mean and SD (Wan et al., 2014; Luo et al., 2018; http://www.math.hkbu.edu.hk/tongt/papers/median2mean.html). The three-dimensional coordinates were extracted according to the anisotropic effect-size-based algorithms AES-SDM methods (Radua et al., 2014). All information was double-checked, and decisions were reached after discussing the disagreements.
Seed-Based d Mapping Meta-Analysis and Fiber Tracking
We used AES-SDM software (version 5.15) to detect consistent FA abnormalities in patients with PD compared with HCs and used DTI-Query software (Stanford, USA) to exhibit the fibers crossing peak coordinates of FA alteration.
Following the tutorial of SDM (Radua and Mataix-Cols, 2009; Radua et al., 2014), we first recreated statistical maps and effect-sized maps of the coordinates extracted from each study (“FA,” anisotropy = 1, isotropic full width at half maximum FWHM = 20 mm, mask = “TBSS,” 10 randomizations). Then, individual study maps were entered into the meta-analysis, and the outcome was further tested in terms of sensitivity and heterogeneity, including jackknife sensitivity, heterogeneity, and publication bias analysis. The analytical parameters applied in the present study were as follows: voxel threshold p = 0.005, peak height threshold Z = 1.00, and cluster size threshold = 100 voxels, and the outcome maps were visualized using MRIcron software (University of South Carolina, USA). Subgroup analysis of studies reported with corrected results, studies with 3T scanner, and studies with a b-value of 1,000 s/mm2 were carried out. Finally, meta-regressions using a linear model were performed to detect the relationship between WM abnormalities and clinical parameters (age, sex, disease duration, UPDRS-III, MMSE, and LEED). We selected a more conservative threshold of p = 0.0005, as used in previous studies (Chen et al., ; Barona et al., ; Li et al., 2020) with peak height threshold Z = 1.00, and cluster size threshold = 10 voxels. With DTI-Query software used in previous studies (Li et al., 2012; Zhang et al., 2018), we tracked the probable fibers across the peak coordinates detected by SDM. More details of the SDM method and DTI-Query are presented in the Supplementary Methods.
Results
Included Studies and Sample Characteristics
A total of 23 whole-brain TBSS studies (Hattori et al., ; Kamagata et al., ; Kim et al., ; Melzer et al., 2013; Agosta et al., ; Carriere et al., ; Worker et al., 2014; Diez-Cirarda et al., ; Ji et al., ; Vercruysse et al., 2015; Vervoort et al., 2016; Wen et al., 2016; Acosta-Cabronero et al., ; Chen B. et al., ; Georgiopoulos et al., ; Luo et al., 2017; Firbank et al., ; Li et al., 2018; Minett et al., 2018; Guan et al., ; Quattrone et al., 2019; Inguanzo et al., ; Pelizzari et al., 2020) were identified using our search protocol. Six of them compared separate independent patient subgroups with the same HC groups, with details present in Table 1. Thus, a total of 30 datasets were included in our meta-analysis. Eight datasets revealed decreased FA in patients with PD in distributed regions, involving commissural fibers (e.g., CC), association fibers [e.g., IFOF and uncinate fasciculi (UF)], and projection fibers [e.g., middle cerebellar peduncle (MCP), interior capsule (IC), and anterior corona radiata (ACR)]. In addition, 22 datasets found no FA alteration in patients with PD compared to HCs. A total of 915 patients with PD (543 men, mean age 64.89 years, mean disease duration 5.65 years, mean UPDRS-III 26.83 years) and 836 matched HCs (mean age 65.19 years, males 449) were analyzed. All patients were diagnosed with PD, excluding any other Parkinsonism syndromes. The field strength of most of these studies was 3T (26/30 datasets), and the b-value was mostly 1,000 s/mm2 (21/30 datasets). The details of the demographic and clinical characteristics and study information of all included FA studies are presented in Table 1.
Table 1
| Study (subgroup) | PD | HC | Study information | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Participants (male) | Age, yrs | Disease duration | UPDRS-III | MMSE | Medication status | LEDD, mg/day | Participants (male) | Age, yrs | Scanner | Direction | b-value | Software | Threshold | Major findings | |
| (Pelizzari et al., 2020) (LPD) | 9 (4) | 65.99 | 3.81 | 17 | NA | On-state | 158.85 | 17 (9) | 63.16 | 1.5 | 64 | 1,500 | FSL | P < 0.05 (FWE) | No significant FA alteration |
| (Pelizzari et al., 2020) (RPD) | 12 (7) | 68.15 | 2.18 | 18.17 | NA | On-state | 269.06 | 17 (9) | 63.16 | 1.5 | 64 | 1,500 | FSL | P < 0.05 (FWE) | No significant FA alteration |
| (Inguanzo et al., ; Pelizzari et al., 2020) (PD1) | 15 (13) | NA | NA | NA | NA | On-state | NA | 33 (18) | NA | 3 | 30 | 1,000 | FSL | P < 0.05 (FWE) | left IFOF |
| (Inguanzo et al., ) (PD2) | 21 (14) | NA | NA | NA | NA | On-state | NA | 33 (18) | NA | 3 | 30 | 1,000 | FSL | P < 0.05 (FWE) | No significant FA alteration |
| (Inguanzo et al., ) (PD3) | 26 (19) | NA | NA | NA | NA | On-state | NA | 33 (18) | NA | 3 | 30 | 1,000 | FSL | P < 0.05 (FWE) | No significant FA alteration |
| (Quattrone et al., 2019) | 37 (26) | 72 | 7.8 | 33.26 | 21.87 | Off-state | NA | 38 (15) | 72.6 | 3 | 27 | 1,000 | FSL | P < 0.05 (FWE) | No significant FA alteration |
| (Guan et al., ) | 65 (32) | 55.5 | 4.7 | 27.1 | 27.8 | Off-state | NA | 46 (21) | 57.8 | 3 | 15 | 1,000 | FSL | p < 0.001, cluster-based Corr | right UF |
| (Li et al., 2018) | 31 (16) | 60.5 | NA | 26.4 | 25.26 | NA | NA | 22 (12) | 59.7 | 3 | 32 | 1,200 | FSL | P < 0.05 (FWE) | bilateral CI, CE, ACR, SL, genu, body, and pad of CC |
| (Firbank et al., ) (PD-nonVH) | 19 (17) | 72.3 | NA | 34.7 | 25.6 | NA | 673.5 | 20 (14) | 75.4 | 3 | 64 | 1,000 | FSL | P < 0.05 (TFCE) | No significant FA alteration |
| (Firbank et al., ) (PD-VH) | 17 (13) | 75.5 | NA | 55.9 | 23.1 | NA | 717.3 | 20 (14) | 75.4 | 3 | 64 | 1,000 | FSL | P < 0.05 (TFCE) | wide spread FA reduction |
| (Minett et al., 2018) | 93 (61) | 64.3 | 0.53 | 25.9 | NA | NA | 161 | 48 (28) | 66 | 3 | 64 | 1,000 | FSL | P < 0.05 (TFCE_FWE) | No significant FA alteration |
| (Luo et al., 2017) (TD PD) | 30 (16) | 53.42 | 2 | 25.37 | NA | Off-state | 262 | 26 (13) | 54.46 | 3 | 25 | 1,000 | FSL | P < 0.05 (FWE) | No significant FA alteration |
| (Luo et al., 2017) (NoTD PD) | 30 (15) | 52.55 | 2.35 | 22.27 | NA | Off-state | 305 | 26 (13) | 54.46 | 3 | 25 | 1,000 | FSL | P < 0.05 (FWE) | No significant FA alteration |
| (Chen B. et al., ) | 18 (7) | 62.28 | 3.06 | 17.39 | NA | Off-state | NA | 24 (11) | 62.88 | 3 | 25 | 1,000 | FSL | P < 0.017, TFCE & Bonferroni Corr | left hippocampus, body of CC |
| (Acosta-Cabronero et al., ) | 25 (20) | 63.6 | 6 | 16.3 | 26.7 | On-state | 748 | 50 (28) | 63.6 | 3 | 30 | 1,000 | FSL | P < 0.001, Uncorr | No significant FA alteration |
| (Georgiopoulos et al., ) | 22 (12) | 68.35 | 7 | 21.08 | 29.36 | On-state | NA | 13 (5) | 67.64 | 3 | 32 | 800 | FSL | P < 0.05 (TFCE) | No significant FA alteration |
| (Vervoort et al., 2016) (TD PD) | 16 (9) | 55.1 | 4.8 | 28.9 | 28.9 | Off-state | 249.2 | 19 (14) | 58.1 | 3 | 61 | 1,300 | FSL | P < 0.05 (TFCE) | No significant FA alteration |
| (Wen et al., 2016) | 87 (55) | 62.01 | 0.63 | 25.14 | NA | Drug-naïve | 0 | 60 (40) | 60.33 | 3 | 64 | 1,000 | FSL | P < 0.01 (TFCE) | No significant FA alteration |
| (Vercruysse et al., 2015) (PD with FOG) | 11 (8) | 68.6 | 9.5 | 36.6 | 27.2 | On-state | 703.8 | 15 (11) | 68.1 | 3 | 25/40/75 | 700/1,000/2,800 | FSL | P < 0.05 (FDR) | right MCP |
| (Vercruysse et al., 2015) (PD without FOG) | 15 (11) | 67.6 | 7.6 | 32.5 | 28.3 | On-state | 461.3 | 15 (11) | 68.1 | 3 | 25/40/75 | 700/1,000/2,800 | FSL | P < 0.05 (FDR) | No significant FA alteration |
| (Ji et al., ) | 20 (11) | 64.2 | 4.64 | 28.76 | NA | Off-state | NA | 20 (10) | 59.95 | 3 | 30 | 1,000 | FSL | P < 0.05 (TFCE_FWE) | Body of CC |
| (Diez-Cirarda et al., ) | 37 (22) | 67.97 | 6.96 | 21.72 | NA | On-state | 808.59 | 15 (11) | 65.07 | 3 | 32 | 1,000 | FSL | p < 0.001 uncorrected. | right UF |
| (Worker et al., 2014) | 17 (9) | 63.9 | 6.6 | 21.8 | 29.5 | On-state | NA | 17 (9) | 63.9 | 3 | 64 | 1,300 | FSL | P < 0.017, TFCE & Bonferroni Corr | No significant FA alteration |
| (Carriere et al., ) (Apathetic PD) | 10 (6) | 67.2 | 11.9 | 28.1 | NA | On-state | 857.2 | 10 (4) | 66.8 | 3 | 15 | 1,000 | FSL | P < 0.05 (TFCE) | No significant FA alteration |
| (Carriere et al., ) (Non-apathetic PD) | 10 (6) | 60.7 | 11.9 | 28.7 | NA | On-state | 1,072.10 | 10 (4) | 66.8 | 3 | 15 | 1,000 | FSL | P < 0.05 (TFCE) | No significant FA alteration |
| (Agosta et al., ) | 43 (29) | 65.8 | 9.1 | 32.6 | 27.6 | On-state | 607.1 | 33 (17) | 64 | 1.5 | 12 | 1,000 | FSL | P < 0.05 (FWE) | No significant FA alteration |
| (Kamagata et al., ) | 20 (8) | 71.6 | 7.83 | NA | 25.7 | On-state | 464.2 | 20 (10) | 72.7 | 3 | 32 | 1,000 | FSL | P < 0.05 (TFCE_FWE) | No significant FA alteration |
| (Kim et al., ) | 64 (22) | 62.9 | 5.3 | NA | NA | Off-state | NA | 64 (22) | 63 | 3 | 15 | 800 | FSL | P < 0.05 (TFCE_FWE) | No significant FA alteration |
| (Melzer et al., 2013) | 63 (43) | 64 | 3.7 | 25.3 | 29 | On-state | 208 | 32 (22) | 70.1 | 3 | 28 | 1,000 | FSL | P < 0.05 (TFCE) | No significant FA alteration |
| (Hattori et al., ) | 32 (12) | 75.9 | 5.8 | 20 | 27.8 | NA | NA | 40 (18) | 76.9 | 1.5 | 12 | 1,000 | FSL | P < 0.05 (TFCE) | No significant FA alteration |
| Total | 915 (543) | 64.89 | 5.66 | 26.84 | 26.92 | 484.79 | 836 (449) | 65.19 | |||||||
Demographic and clinical characteristics of participants in the 23 Parkinson's disease studies (30 datasets) included in the meta-analysis.
PD, Parkinson's Disease; HC, healthy controls; UPDRS-III, Unified Parkinson Disease Rating Scale-III; RPD, right-dominant symptom PD patients; LPD, left-dominant symptom PD patients; PD1= PD1 had lower gray matter (GM) volumes than HCs mainly in occipital and medial temporal; PD2, PD2 had GM atrophy compared with HC mainly in bilateral orbital and prefrontal cortical regions; PD3, PD3 did not show significant GM volume differences compared with HC; VH, visual hallucination; TD, tremor dominated; FOG, freezing of gait; PIGD, postural instability and gait difficulty; NC, normal cognition; H&Y, Hoehn & Yahr staging; NA, not available; MRI, Magnetic Resonance Image; Uncorr= uncorrected; Corr, corrected; FEW, family-wise error; TFCE, threshold-free cluster enhancement; FA, fractional anisotropy; MCP, middle cerebellar peduncle; UF, uncinate fasciculus; CC, corpus callosum; CI, capsula interna; CE, capsula externa; ACR, anterior corona radiata; SL, sagittal layer.
Pooled Voxel-Based Meta-Analysis
The pooled meta-analysis of 30 datasets identified a decrease of FA in two clusters of patients with PD compared to HCs (Table 2 and Figure 2). The first cluster (245 voxels; z = −1.739; p < 0.001) peaked at the body of the CC (Montreal Neurological Institute (MNI) space: 12, 14, and 24), extending to the genu of CC. This is because the CC is interhemispheric, even if it is represented in the right hemisphere. The second cluster (118 voxels; z = −1.182; p < 0.001) included the sagittal stratum, the fornix, and the stria terminalis of the left hemisphere, peaked at the left IFOF (Montreal Neurological Institute (MNI) Space: −42, −18, −12). The main fibers likely to pass through the above peak coordinates are shown in Figure 3. No increase in FA was identified.
Table 2
| WM Tract | Voxels | MNI Coordinates | SDM Z score | P, uncorrected | Cluster breakdown (voxels) | Jackknife | ||
|---|---|---|---|---|---|---|---|---|
| X | Y | Z | ||||||
| Corpus callosum | 245 | 12 | 14 | 24 | −1.739 | 0.000004888 | Corpus callosum (242) | 30/30 |
| Left inferior network, inferior fronto-occipital fasciculus | 118 | −42 | −18 | −12 | −1.182 | 0.000615716 | Left inferior network, inferior fronto-occipital fasciculus (44) Corpus callosum (19) Left cortico-spinal projections (12) Left optic radiations (12) Left pons (12) Left inferior network, inferior longitudinal fasciculus (10) | 27/30 |
Regional differences in FA between patients with PD and healthy controls.
< 10 voxels are not represented in the breakdown of voxels.
PD, Parkinson's Disease; FA, fractional anisotropy; WM, white matter; MNI, Montreal Neurological Institute Space; SDM, Seed-based d Mapping; Jackknife: The jackknife sensitivity analysis column gives the number of studies whose omission does not affect the finding.
Figure 2
Figure 3
Reliability Analysis
None of the clusters with altered FA showed statistically significant between-group heterogeneity or publication biases (Supplementary Figure 1). All clusters are highly reliable in jackknife sensitivity analysis (Supplementary Table 2), especially the body of CC, by remaining significant in all iterations. This indicates that all clusters are highly reliable.
Subgroup analysis findings of studies with corrected results and studies with 3T scanner reproduced pooled results. However, subgroup analysis of studies with a b-value of 1,000 s/mm2 failed to find any FA difference between patients with PD and HCs (Supplementary Table 3).
Result of Meta-Regression Analysis
The percentage of male patients with PD was positively associated with the FA value in the body of the CC (Figure 4). Other information on the clinical variables, including the mean age of the patients, illness duration, UPDRS-III, MMSE, and LEED is statistically non-significantly associated with FA alterations in patients with PD.
Figure 4
Discussion
As far as we know, this is the first coordinate-based meta-analysis focusing on TBSS studies comparing FA in patients with PD to HCs. Our study identified lower FA mainly in the body of the CC and the left IFOF in patients with PD using the AES-SDM meta-analytical approach. Remarkably, these findings remained reproducible in jackknife sensitivity analysis and some subgroup meta-analysis, especially the body of the CC, suggesting robust disruption of the WM microstructure in those regions.
Using SDM software, Albrecht et al. () identified the reduction of FA in the CC and the MCPs, and Suo et al. (2020) revealed a decrease in FA in supplementary regions including the left IFOF and the right inferior longitudinal fasciculus when merging both VBA and TBSS studies comparing FA in patients with PD to HCs. We reproduced the FA reduction of the CC and the left IFOF in patients with PD. However, some discrepancies emerged between our meta-analysis and Albrecht's findings (Albrecht et al., ). For instance, we failed to confirm a decrease in FA in the MCPs. There may be several reasons for such discrepancies. First, TBSS has a relative strength in the precise estimation of WM compared to the traditional VBA analysis (Smith et al., 2006), and we only included TBSS studies to avoid the inconsistency of results originating from methodological differences. Second, different samples with diverse clinical characteristics were included in our meta-analysis. The UPDRS-III score was lower in patients with PD in our sample (mean score: 26.84) than Albrecht's sample (mean score: 32.5), which might explain why we did not find WM lesions in the MCPs—a structure that mainly participates in motor coordination (Hasegawa et al., ).
The first cluster we identified decreased FA with a peak in the body of CC, which is consistent with many previous studies (Auning et al., ; Kamagata et al., ; Canu et al., ; Jiang et al., ; Lucas-Jiménez et al., 2016; Vervoort et al., 2016; Wang et al., 2016; Galantucci et al., ; Guimarães et al., ; Ji et al., ) comparing the WM diffusional properties of patients with PD to HCs. The CC plays a major role in communicating sensory, motor, and cognitive information between two cerebral hemispheres, and different parts of the CC participate in diverse cortical information communication revealed by DTI or functional MRI studies (Hofer and Frahm, ; Fabri et al., ; Courtemanche et al., ). Our study found decreased FA in the body and the genu of the CC, suggesting an impairment in the interhemispheric information transformation of the prefrontal lobe, motor, and supplementary motor areas (SMAs). The prefrontal cortex is a part of the limbic system and is related to higher cognitive function; apathy or reduced activation of the prefrontal lobe has been reported in patients with PD with cognitive impairment (Brück et al., ; Nagano-Saito et al., 2005; Trujillo et al., 2015; Gao et al., ). Damage to the prefrontal WM and the genu of the CC was revealed to be mainly associated with poorer intellectual or executive functions in patients with PD (Baggio et al., ; Hattori et al., ; Kamagata et al., ; Randver, 2018; Chondrogiorgi et al., ). The motor cortex generates motorial neural impulses, and functional alterations in this region are related to many motor signs in patients with PD (Burciu and Vaillancourt, ). The SMAs are associated with motor imagery (Iseki et al., ; La Fougère et al., 2010) and participates in the coupling of movement and posture during tasks (Viallet et al., 1992; Jacobs et al., ). Dysfunction or atrophy of the SMAs has been consistently reported in studies of patients with PD (Rosenberg-Katz et al., 2013; Herz et al., ; Kübel et al., ). In addition, transcranial magnetic stimulation (TMS) treatment (Shirota et al., 2013; Eggers et al., ) targeting the SMAs has been demonstrated to improve the motor function (e. g., UPSRS-III scores) of PD patients. In line with these findings, decreased FA in the body of the CC has been demonstrated to be related to poorer motor function in patients with PD (Galantucci et al., ), implicating the damaged interhemispheric connectivity of motor areas and SMAs in patients with PD.
However, our finding of decreased FA in the CC body in patients with PD relative to HCs was contradictory to the findings from a previous study by Wen et al. (2016) that reported increased FA in the CC. This discrepancy may be due to the disease stage of PD since, in Wen's study, these WM changes were mainly presented in the patients with PD with Hoehn & Yahr stage I, which may reflect neural compensations (Sanjari Moghaddam et al., 2020) for dopaminergic deficiency in the very early disease stage.
Furthermore, the higher percentage of male patients with PD included in our meta-analysis was associated with increased FA values in the body of the CC. It has been reported that women with PD have a higher mortality rate and faster disease progression (Bjornestad et al., ; Heinzel et al., ). Our results indicate that male patients are likely to present with a lower rate of WM degeneration. Additionally, men's WM accounted for a higher percentage of the whole-brain volume (Cosgrove et al., ) in a healthy population, which might contribute to this result as well.
In addition to the robust finding of decreased FA in the body of the CC, we also identified lower FA in the long fibers crossing the left temporal lobe, especially the left IFOF, in agreement with the results of previous studies (Rae et al., 2012; Gallagher et al., ; Theilmann et al., 2013; Lucas-Jiménez et al., 2016; Wang et al., 2016; Guimarães et al., ). The IFOF, a direct pathway connecting the occipital, posterior temporal, and orbitofrontal areas (Wu et al., 2016), plays an important role in visual-perceptual performance (Ashtari, ) and cognitive (Duncan et al., ) performance. Visual elaboration (Chen et al., ; Ekker et al., ) resulting from IFOF impairment might account for symptoms such as postural instability and gait difficulties (Wang et al., 2016; Tan et al., 2019) in patients with PD, and patients with predominant postural instability and gait difficulties were more likely to develop dementia in the late stage of disease (Lee et al., 2015). The current study included samples with the freezing of gait and samples with visual hallucinations, both of which have been associated with cognitive impairment in PD (Vercruysse et al., 2015; Firbank et al., ; Quattrone et al., 2019). The reduction of FA of WM located in the left temporal region in the present study is consistent with the lateral principle (Colosimo et al., ; Riederer et al., 2018) in patients with PD. Another meta-analysis (Xu et al., 2016) revealed that PD patients with mild cognitive impairment (PD-MCI) presented prominent gray matter volume (GMV) reduction in the left temporal-frontal regions, while PD patients with dementia (PDD) had bilateral reductions in those areas. The unilateral-to-bilateral development of GMV reduction in the frontal-limbic-temporal regions is a possible indicator of PD-MCI to PDD progression (Xu et al., 2016). Thus, the decreased FA in the left temporal lobe might underlie the early WM pathology of PD.
Limitations
Although this meta-analysis avoids some methodological differences by only including TBSS studies and indicates possible stable WM microstructure alterations in PD, it still has some limitations. First, the differences in scanning parameters, field strength, and MR scanner vendors between studies were impossible to eliminate by statistical means. Second, FA is a dimensional value which cannot illustrate the axonal or myelin pathology of WM (Rae et al., 2012). It might be more helpful to combine other parameters (MD, AD, and RD) with FA in a future analysis. Third, publication bias could not be avoided, although we attempted to include as many studies as possible (even if they were negative). Finally, our study uses the coordinates from the included studies rather than their original t-statistic maps, which limits the accuracy of our results. Image-based meta-analysis should be considered as an improved alternative in future studies.
Conclusion
The current quantitative meta-analysis of TBSS studies provides convergent evidence that WM disorganization exists in patients with PD and that the most robust WM abnormalities are in the body of the CC and the left IFOF, suggesting that abnormalities of interhemispheric connections and the disconnection of the left hemispheric cortical lobes might be the basis of the pathogenesis of PD. Future MRI studies could further investigate the diagnostic and prognostic value of WM microstructural abnormalities in patients with PD.
Statements
Data availability statement
The original contributions generated for the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.
Author contributions
CL conceived the project and designed the protocol. XW obtained the data and analyzed the results. XW and CL wrote the main manuscript. CL and SL revised the manuscript. All authors critically reviewed the manuscript.
Funding
This study was supported by National Natural Science Foundation of China (grants 81901702 and 81761128023); grants from the Humboldt Foundation (Ref 3.5-1206715-CHN-BES); Science-Technology Support Plan Projects in Sichuan province (No. 2020YFS0117); 1.3.5 project for disciplines of excellence, Post-Doctor Research Project, West China Hospital, Sichuan University (Grant: 2018HXBH05, ZYJC18020, and ZYYC08001).
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.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnagi.2020.610962/full#supplementary-material
References
1
Acosta-CabroneroJ.Cardenas-BlancoA.BettsM. J.ButrynM.Valdes-HerreraJ. P.GalazkyI.et al. (2017). The whole-brain pattern of magnetic susceptibility perturbations in Parkinson's disease. Brain140, 118–131. 10.1093/brain/aww278
2
AgostaF.CanuE.StefanovaE.SarroL.TomićA.ŠpicaV.et al. (2014). Mild cognitive impairment in Parkinson's disease is associated with a distributed pattern of brain white matter damage. Hum. Brain Mapp.35, 1921–1929. 10.1002/hbm.22302
3
AlbrechtF.BallariniT.NeumannJ.SchroeterM. L. (2019). FDG-PET hypometabolism is more sensitive than MRI atrophy in Parkinson's disease: A whole-brain multimodal imaging meta-analysis. Neuroimage. Clin.21:101594. 10.1016/j.nicl.2018.11.004
4
AndicaC.KamagataK.HatanoT.SaitoY.OgakiK.HattoriN.et al. (2020). MR biomarkers of degenerative brain disorders derived from diffusion imaging. J. Magnet. Reson. Imag.52, 1620–1636. 10.1002/jmri.27019
5
AshtariM. (2012). Anatomy and functional role of the inferior longitudinal fasciculus: a search that has just begun. Dev. Med. Child Neurol.54, 6–7. 10.1111/j.1469-8749.2011.04122.x
6
AuningE.KjærvikV. K.SelnesP.AarslandD.HaramA.BjørnerudA.et al. (2014). White matter integrity and cognition in Parkinson's disease: A cross-sectional study. BMJ Open4:e003976. 10.1136/bmjopen-2013-003976
7
BaggioH. C.SeguraB.Ibarretxe-BilbaoN.ValldeoriolaF.MartiM. J.ComptaY.et al. (2012). Structural correlates of facial emotion recognition deficits in Parkinson's disease patients. Neuropsychologia50, 2121–2128. 10.1016/j.neuropsychologia.2012.05.020
8
BaronaM.BrownM.ClarkC.FrangouS.WhiteT.MicaliN. (2019). White matter alterations in anorexia nervosa: evidence from a voxel-based meta-analysis. Neurosci. Biobehav. Rev.100, 285–295. 10.1016/j.neubiorev.2019.03.002
9
BasserP. J. (1995). Inferring microstructural features and the physiological state of tissues from diffusion-weighted images. NMR Biomed8, 333–344. 10.1002/nbm.1940080707
10
BergD.LangA. E.PostumaR. B.MaetzlerW.DeuschlG.GasserT.et al. (2013). Changing the research criteria for the diagnosis of Parkinson's disease: obstacles and opportunities. Lancet Neurol.12, 514–524. 10.1016/S1474-4422(13)70047-4
11
BjornestadA.ForsaaE. B.PedersenK. F.TysnesO.-B.LarsenJ. P.AlvesG. (2016). Risk and course of motor complications in a population-based incident Parkinson's disease cohort. Parkinsonism Relat. Disord.22, 48–53. 10.1016/j.parkreldis.2015.11.007
12
BrückA.KurkiT.KaasinenV.VahlbergT.RinneJ. O. (2004). Hippocampal and prefrontal atrophy in patients with early non-demented Parkinson's disease is related to cognitive impairment. J. Neurol. Neurosurg. Psychiatr.75, 1467–1469. 10.1136/jnnp.2003.031237
13
BurciuR. G.VaillancourtD. E. (2018). Imaging of motor cortex physiology in Parkinson's disease. Movement Disord.33, 1688–1699. 10.1002/mds.102
14
CanuE.AgostaF.SarassoE.VolontèM. A.BasaiaS.StojkovicT.et al. (2015). Brain structural and functional connectivity in Parkinson's disease with freezing of gait. Hum. Brain Mapp.36, 5064–5078. 10.1002/hbm.22994
15
CarriereN.BessonP.DujardinK.DuhamelA.DefebvreL.DelmaireC.et al. (2014). Apathy in Parkinson's disease is associated with nucleus accumbens atrophy: a magnetic resonance imaging shape analysis. Movement Disord.29, 897–903. 10.1002/mds.25904
16
ChenB.FanG.SunW.ShangX.ShiS.WangS.et al. (2017). Usefulness of diffusion-tensor MRI in the diagnosis of Parkinson variant of multiple system atrophy and Parkinson's disease: a valuable tool to differentiate between them?Clin. Radiol.72, 610.e619–610.e615. 10.1016/j.crad.2017.02.005
17
ChenL.HuX.OuyangL.HeN.LiaoY.LiuQ.et al. (2016). A systematic review and meta-analysis of tract-based spatial statistics studies regarding attention-deficit/hyperactivity disorder. Neurosci. Biobehav. Rev.68, 838–847. 10.1016/j.neubiorev.2016.07.022
18
ChenS. T.GellerA. C.TsaoH. (2013). Update on the epidemiology of melanoma. Curr. Dermatol. Rep.2, 24–34. 10.1007/s13671-012-0035-5
19
ChenY. S.ChenM. H.LuC. H.ChenP. C.ChenH. L.YangI. H.et al. (2017). Associations among cognitive functions, plasma DNA, and white matter integrity in patients with early-onset Parkinson's disease. Front. Neurosci.11:9. 10.3389/fnins.2017.00009
20
ChiangP. L.ChenH. L.LuC. H.ChenP. C.ChenM. H.YangI. H.et al. (2017). White matter damage and systemic inflammation in Parkinson's disease. BMC Neurosci18:48. 10.1186/s12868-017-0367-y
21
ChondrogiorgiM.AstrakasL. G.ZikouA. K.WeisL.XydisV. G.AntoniniA.et al. (2019). Multifocal alterations of white matter accompany the transition from normal cognition to dementia in Parkinson's disease patients. Brain Imaging Behav.13, 232–240. 10.1007/s11682-018-9863-7
22
ChungS. J.ArmasuS. M.BiernackaJ. M.LesnickT. G.RiderD. N.LincolnS. J.et al. (2011). Common variants in PARK loci and related genes and Parkinson's disease. Movement Disord.26, 280–288. 10.1002/mds.23376
23
ColosimoC.Martínez-MartínP.FabbriniG.HauserR. A.MerelloM.MiyasakiJ.et al. (2010). Task force report on scales to assess dyskinesia in Parkinson's disease: critique and recommendations. Movement Disord.25, 1131–1142. 10.1002/mds.23072
24
CosgroveK. P.MazureC. M.StaleyJ. K. (2007). Evolving knowledge of sex differences in brain structure, function, and chemistry. Biol. Psychiatr.62, 847–855. 10.1016/j.biopsych.2007.03.001
25
CourtemancheM. J.SparreyC. J.SongX.MackayA.D'arcyR. C. N. (2018). Detecting white matter activity using conventional 3 Tesla fMRI: An evaluation of standard field strength and hemodynamic response function. Neuroimage169, 145–150. 10.1016/j.neuroimage.2017.12.008
26
Diez-CirardaM.OjedaN.PenaJ.Cabrera-ZubizarretaA.Gomez-BeldarrainM. A.Gomez-EstebanJ. C.et al. (2015). Neuroanatomical correlates of theory of mind deficit in Parkinson's disease: a multimodal imaging study. PLoS ONE10:e0142234. 10.1371/journal.pone.0142234
27
DuncanG. W.FirbankM. J.YarnallA. J.KhooT. K.BrooksD. J.BarkerR. A.et al. (2016). Gray and white matter imaging: A biomarker for cognitive impairment in early Parkinson's disease?Movement Disord.31, 103–110. 10.1002/mds.26312
28
EggersC.GüntherM.RothwellJ.TimmermannL.RugeD. (2015). Theta burst stimulation over the supplementary motor area in Parkinson's disease. J. Neurol.262, 357–364. 10.1007/s00415-014-7572-8
29
EkkerM. S.JanssenS.SeppiK.PoeweW.De VriesN. M.TheelenT.et al. (2017). Ocular and visual disorders in Parkinson's disease: common but frequently overlooked. Parkinsonism Relat. Disord.40, 1–10. 10.1016/j.parkreldis.2017.02.014
30
FabriM.PierpaoliC.BarbaresiP.PolonaraG. (2014). Functional topography of the corpus callosum investigated by DTI and fMRI. World J. Radiol.6, 895–906. 10.4329/wjr.v6.i12.895
31
FirbankM. J.ParikhJ.MurphyN.KillenA.AllanC. L.CollertonD.et al. (2018). Reduced occipital GABA in Parkinson disease with visual hallucinations. Neurology91, e675–e685. 10.1212/WNL.0000000000006007
32
GalantucciS.AgostaF.StankovicI.PetrovicI.StojkovicT.ComiG.et al. (2014). Corpus callosum damage and motor function in Parkinson's disease (P2.006). Neurology82:P2.006.
33
GalantucciS.AgostaF.StefanovaE.BasaiaS.Van Den HeuvelM. P.StojkovićT.et al. (2017). Structural brain connectome and cognitive impairment in Parkinson disease. Radiology283, 515–525. 10.1148/radiol.2016160274
34
GallagherC.BellB.BendlinB.PalottiM.OkonkwoO.SodhiA.et al. (2013). White matter microstructural integrity and executive function in parkinson's disease. J. Int. Neuropsychol. Soc.19, 349–354. 10.1017/S1355617712001373
35
GaoY.NieK.HuangB.MeiM.GuoM.XieS.et al. (2017). Changes of brain structure in Parkinson's disease patients with mild cognitive impairment analyzed via VBM technology. Neurosci. Lett.658, 121–132. 10.1016/j.neulet.2017.08.028
36
GBD 2016 Parkinson's Disease Collaborators (2018). Global, regional, and national burden of Parkinson's disease, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol.17, 939–953. 10.1016/s1474-4422(18)30295-3
37
GeorgiopoulosC.WarntjesM.DizdarN.ZachrissonH.EngströmM.HallerS.et al. (2017). Olfactory impairment in Parkinson's disease studied with diffusion tensor and magnetization transfer imaging. J. Parkinson Dis.7, 301–311. 10.3233/JPD-161060
38
GuanX.HuangP.ZengQ.LiuC.WeiH.XuanM.et al. (2019). Quantitative susceptibility mapping as a biomarker for evaluating white matter alterations in Parkinson's disease. Brain Imaging Behav.13, 220–231. 10.1007/s11682-018-9842-z
39
GuimarãesR. P.CamposB. M.De RezendeT. J.PiovesanaL.AzevedoP. C.Amato-FilhoA. C.et al. (2018). Is diffusion tensor imaging a good biomarker for early Parkinson's disease?Front. Neurol.9:626. 10.3389/fneur.2018.00626
40
HasegawaT.YamadaK.TozawaT.ChiyonobuT.TokudaS.NishimuraA.et al. (2018). Cerebellar peduncle injury predicts motor impairments in preterm infants: A quantitative tractography study at term-equivalent age. Brain Dev.40, 743–752. 10.1016/j.braindev.2018.04.013
41
HattoriT.OrimoS.AokiS.ItoK.AbeO.AmanoA.et al. (2012). Cognitive status correlates with white matter alteration in Parkinson's disease. Hum. Brain Mapp.33, 727–739. 10.1002/hbm.21245
42
HeinzelS.KastenM.BehnkeS.VollstedtE.-J.KleinC.HagenahJ.et al. (2018). Age- and sex-related heterogeneity in prodromal Parkinson's disease. Movement Disord.33, 1025–1027. 10.1002/mds.27349
43
HerzD. M.EickhoffS. B.LøkkegaardA.SiebnerH. R. (2014). Functional neuroimaging of motor control in Parkinson's disease: a meta-analysis. Hum. Brain Mapp.35, 3227–3237. 10.1002/hbm.22397
44
HoferS.FrahmJ. (2006). Topography of the human corpus callosum revisited–comprehensive fiber tractography using diffusion tensor magnetic resonance imaging. Neuroimage32, 989–994. 10.1016/j.neuroimage.2006.05.044
45
InguanzoA.Sala-LlonchR.SeguraB.ErostarbeH.AbosA.CampabadalA.et al. (2020). Hierarchical cluster analysis of multimodal imaging data identifies brain atrophy and cognitive patterns in Parkinson's disease. Parkinsonism Relat. Disord.82, 16–23. 10.1016/j.parkreldis.2020.11.010
46
IsekiK.HanakawaT.ShinozakiJ.NankakuM.FukuyamaH. (2008). Neural mechanisms involved in mental imagery and observation of gait. Neuroimage.41, 1021–1031. 10.1016/j.neuroimage.2008.03.010
47
JacobsJ. V.LouJ. S.KraakevikJ. A.HorakF. B. (2009). The supplementary motor area contributes to the timing of the anticipatory postural adjustment during step initiation in participants with and without Parkinson's disease. Neuroscience.164, 877–885. 10.1016/j.neuroscience.2009.08.002
48
JiG. J.RenC.LiY.SunJ.LiuT.GaoY.et al. (2019). Regional and network properties of white matter function in Parkinson's disease. Hum. Brain Mapp.40, 1253–1263. 10.1002/hbm.24444
49
JiL.WangY.ZhuD.LiuW.ShiJ. (2015). White matter differences between multiple system atrophy (parkinsonian type) and parkinson's disease: a diffusion tensor image study. Neuroscience305, 109–116. 10.1016/j.neuroscience.2015.07.060
50
JiangM. F.ShiF.NiuG. M.XieS. H.YuS. Y. (2015). A novel method for evaluating brain function and microstructural changes in parkinson's disease. Neural Regene. Res.10, 2025–2032. 10.4103/1673-5374.172322
51
KaliaL. V. (2019). Diagnostic biomarkers for Parkinson's disease: focus on α-synuclein in cerebrospinal fluid. Parkinsonism Relat. Disord.59, 21–25. 10.1016/j.parkreldis.2018.11.016
52
KamagataK.MotoiY.TomiyamaH.AbeO.ItoK.ShimojiK.et al. (2013). Relationship between cognitive impairment and white-matter alteration in Parkinson's disease with dementia: tract-based spatial statistics and tract-specific analysis. Eur. Radiol.23, 1946–1955. 10.1007/s00330-013-2775-4
53
KamagataK.TomiyamaH.HatanoT.MotoiY.AbeO.ShimojiK.et al. (2014). A preliminary diffusional kurtosis imaging study of Parkinson disease: comparison with conventional diffusion tensor imaging. Neuroradiology56, 251–258. 10.1007/s00234-014-1327-1
54
Karagulle KendiA. T.LehericyS.LucianaM.UgurbilK.TuiteP. (2008). Altered diffusion in the frontal lobe in Parkinson disease. Am. J. Neuroradiol.29, 501–505. 10.3174/ajnr.A0850
55
KimH. J.KimS. J.KimH. S.ChoiC. G.KimN.HanS.et al. (2013). Alterations of mean diffusivity in brain white matter and deep gray matter in Parkinson's disease. Neurosci. Lett.550, 64–68. 10.1016/j.neulet.2013.06.050
56
KoiralaN.AnwarA. R.CiolacD.GlaserM.PinteaB.DeuschlG.et al. (2019). Alterations in white matter network and microstructural integrity differentiate Parkinson's disease patients and healthy subjects. Front. Aging Neurosci.11:191. 10.3389/fnagi.2019.00191
57
KübelS.StegmayerK.VanbellingenT.WaltherS.BohlhalterS. (2018). Deficient supplementary motor area at rest: Neural basis of limb kinetic deficits in Parkinson's disease. Hum. Brain Mapp.39, 3691–3700. 10.1002/hbm.24204
58
La FougèreC.ZwergalA.RomingerA.FörsterS.FeslG.DieterichM.et al. (2010). Real versus imagined locomotion: a [18F]-FDG PET-fMRI comparison. Neuroimage50, 1589–1598. 10.1016/j.neuroimage.2009.12.060
59
LeeE.-Y.SenS.EslingerP. J.WagnerD.KongL.LewisM. M.et al. (2015). Side of motor onset is associated with hemisphere-specific memory decline and lateralized gray matter loss in Parkinson's disease. Parkinsonism Relat. Disord.21, 465–470. 10.1016/j.parkreldis.2015.02.008
60
LeeS. H.KimS. S.TaeW. S.LeeS. Y.ChoiJ. W.KohS. B.et al. (2011). Regional volume analysis of the Parkinson disease brain in early disease stage: gray matter, white matter, striatum, and thalamus. AJNR32, 682–687. 10.3174/ajnr.A2372
61
LiJ.PanP.HuangR.ShangH. (2012). A meta-analysis of voxel-based morphometry studies of white matter volume alterations in Alzheimer's disease. Neurosci. Biobehav. Rev.36, 757–763. 10.1016/j.neubiorev.2011.12.001
62
LiQ.ZhaoY.ChenZ.LongJ.DaiJ.HuangX.et al. (2020). Meta-analysis of cortical thickness abnormalities in medication-free patients with major depressive disorder. Neuropsychopharmacology45, 703–712. 10.1038/s41386-019-0563-9
63
LiX. R.RenY. D.CaoB.HuangX. L. (2018). Analysis of white matter characteristics with tract-based spatial statistics according to diffusion tensor imaging in early Parkinson's disease. Neurosci. Lett.675, 127–132. 10.1016/j.neulet.2017.11.064
64
LorioS.SambataroF.BertolinoA.DraganskiB.DukartJ. (2019). The combination of DAT-SPECT, structural and diffusion MRI predicts clinical progression in Parkinson's disease. Front. Aging Neurosci.11:57. 10.3389/fnagi.2019.00057
65
Lucas-JiménezO.OjedaN.PeñaJ.Díez-CirardaM.Cabrera-ZubizarretaA.Gómez-EstebanJ. C.et al. (2016). Altered functional connectivity in the default mode network is associated with cognitive impairment and brain anatomical changes in Parkinson's disease. Parkinsonism Relat. Disord.33, 58–64. 10.1016/j.parkreldis.2016.09.012
66
LuoC. Y.SongW.ChenQ.YangJ.GongQ. Y.ShangH. F. (2017). White matter microstructure damage in tremor-dominant Parkinson's disease patients. Neuroradiology59, 691–698. 10.1007/s00234-017-1846-7
67
LuoD.WanX.LiuJ.TongT. (2018). Optimally estimating the sample mean from the sample size, median, mid-range, and/or mid-quartile range. Stat. Methods Med. Res.27, 1785–1805. 10.1177/0962280216669183
68
MelzerT. R.WattsR.MacaskillM. R.PitcherT. L.LivingstonL.KeenanR. J.et al. (2013). White matter microstructure deteriorates across cognitive stages in Parkinson disease. Neurology80, 1841–1849. 10.1212/WNL.0b013e3182929f62
69
MinettT.SuL.MakE.WilliamsG.FirbankM.LawsonR. A.et al. (2018). Longitudinal diffusion tensor imaging changes in early Parkinson's disease: ICICLE-PD study. J. Neurol.265, 1528–1539. 10.1007/s00415-018-8873-0
70
MishraV. R.SreenivasanK. R.ZhuangX.YangZ.CordesD.WalshR. R. (2019). Influence of analytic techniques on comparing DTI-derived measurements in early stage Parkinson's disease. Heliyon5:e01481. 10.1016/j.heliyon.2019.e01481
71
Nagano-SaitoA.WashimiY.ArahataY.KachiT.LerchJ. P.EvansA. C.et al. (2005). Cerebral atrophy and its relation to cognitive impairment in Parkinson disease. Neurology64, 224–229. 10.1212/01.WNL.0000149510.41793.50
72
PelizzariL.Di TellaS.LaganàM. M.BergslandN.RossettoF.NemniR.et al. (2020). White matter alterations in early Parkinson's disease: role of motor symptom lateralization. Neurol. Sci.41, 357–364. 10.1007/s10072-019-04084-y
73
QuattroneA.CaligiuriM. E.MorelliM.NigroS.VescioB.ArabiaG.et al. (2019). Imaging counterpart of postural instability and vertical ocular dysfunction in patients with PSP: A multimodal MRI study. Parkinsonism Relat. Disord. 63, 124–130. 10.1016/j.parkreldis.2019.02.022
74
RaduaJ.GrauM.Van Den HeuvelO. A.Thiebaut De SchottenM.SteinD. J.Canales-RodriguezE. J.et al. (2014). Multimodal voxel-based meta-analysis of white matter abnormalities in obsessive-compulsive disorder. Neuropsychopharmacology39, 1547–1557. 10.1038/npp.2014.5
75
RaduaJ.Mataix-ColsD. (2009). Voxel-wise meta-analysis of grey matter changes in obsessive-compulsive disorder. Br. J. Psychiatr.195, 393–402. 10.1192/bjp.bp.108.055046
76
RaeC. L.CorreiaM. M.AltenaE.HughesL. E.BarkerR. A.RoweJ. B. (2012). White matter pathology in Parkinson's disease: the effect of imaging protocol differences and relevance to executive function. Neuroimage62, 1675–1684. 10.1016/j.neuroimage.2012.06.012
77
RandverR. (2018). Repetitive transcranial magnetic stimulation of the dorsolateral prefrontal cortex to alleviate depression and cognitive impairment associated with Parkinson's disease: A review and clinical implications. J. Neurol. Sci.393, 88–99. 10.1016/j.jns.2018.08.014
78
RektorI.SvátkováA.VojtíšekL.ZikmundováI.VaníčekJ.KirályA.et al. (2018). White matter alterations in Parkinson's disease with normal cognition precede grey matter atrophy. PLoS ONE13:e0187939. 10.1371/journal.pone.0187939
79
RiedererP.JellingerK. A.KolberP.HippG.Sian-HülsmannJ.KrügerR. (2018). Lateralisation in Parkinson disease. Cell Tissue Res.373, 297–312. 10.1007/s00441-018-2832-z
80
Rosenberg-KatzK.HermanT.JacobY.GiladiN.HendlerT.HausdorffJ. M. (2013). Gray matter atrophy distinguishes between Parkinson disease motor subtypes. Neurology80, 1476–1484. 10.1212/WNL.0b013e31828cfaa4
81
RossiM. E.RuottinenH.SaunamäkiT.ElovaaraI.DastidarP. (2014). Imaging brain iron and diffusion patterns. a follow-up study of Parkinson's disease in the initial stages. Acad. Radiol.21, 64–71. 10.1016/j.acra.2013.09.018
82
Sanjari MoghaddamH.DolatshahiM.MohebiF.AarabiM. H. (2020). Structural white matter alterations as compensatory mechanisms in Parkinson's disease: a systematic review of diffusion tensor imaging studies. J. Neurosci. Res.98, 1398–1416. 10.1002/jnr.24617
83
SarassoE.AgostaF.PiramideN.FilippiM. (2020). Progression of grey and white matter brain damage in Parkinson's disease: a critical review of structural MRI literature. J. Neurol. 10.1007/s00415-020-09863-8. [Epub ahead of print].
84
ShirotaY.OhtsuH.HamadaM.EnomotoH.UgawaY. (2013). Supplementary motor area stimulation for Parkinson disease: a randomized controlled study. Neurology80, 1400–1405. 10.1212/WNL.0b013e31828c2f66
85
SmithS. M.JenkinsonM.Johansen-BergH.RueckertD.NicholsT. E.MackayC. E.et al. (2006). Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage31, 1487–1505. 10.1016/j.neuroimage.2006.02.024
86
SuoX.LeiD.LiW.LiL.DaiJ.WangS.et al. (2020). Altered white matter microarchitecture in Parkinson's disease: a voxel-based meta-analysis of diffusion tensor imaging studies. Front. Med. 10.1007/s11684-019-0725-5. [Epub ahead of print].
87
TanS. Y. Z.KeongN. C. H.SelvanR. M. P.LiH.OoiL. Q. R.TanE. K.et al. (2019). Periventricular white matter abnormalities on diffusion tensor imaging of postural instability gait disorder Parkinsonism. AJNR Am. J. Neuroradiol.40, 609–613. 10.3174/ajnr.A5993
88
TaylorK. I.SambataroF.BoessF.BertolinoA.DukartJ. (2018). Progressive decline in gray and white matter integrity in de novo Parkinson's disease: an analysis of longitudinal Parkinson progression markers initiative diffusion tensor imaging data. Front. Aging Neurosci.10:318. 10.3389/fnagi.2018.00318
89
TheilmannR. J.ReedJ. D.SongD. D.HuangM. X.LeeR. R.LitvanI.et al. (2013). White-matter changes correlate with cognitive functioning in Parkinson's disease. Front. Neurol.4:37. 10.3389/fneur.2013.00037
90
TolosaE.WenningG.PoeweW. (2006). The diagnosis of Parkinson's disease. Lancet Neurol.5, 75–86. 10.1016/S1474-4422(05)70285-4
91
TrujilloJ. P.GerritsN. J. H. M.VriendC.BerendseH. W.Van Den HeuvelO. A.Van Der WerfY. D. (2015). Impaired planning in Parkinson's disease is reflected by reduced brain activation and connectivity. Hum. Brain Mapp.36, 3703–3715. 10.1002/hbm.22873
92
VercruysseS.LeunissenI.VervoortG.VandenbergheW.SwinnenS.NieuwboerA. (2015). Microstructural changes in white matter associated with freezing of gait in Parkinson's disease. Movement Disord.30, 567–576. 10.1002/mds.26130
93
VervoortG.LeunissenI.FirbankM.HeremansE.NackaertsE.VandenbergheW.et al. (2016). Structural brain alterations in motor subtypes of Parkinson's disease: evidence from probabilistic tractography and shape analysis. PLoS ONE11:e0157743. 10.1371/journal.pone.0157743
94
VialletF.MassionJ.MassarinoR.KhalilR. (1992). Coordination between posture and movement in a bimanual load lifting task: putative role of a medial frontal region including the supplementary motor area. Experi. Brain Res.88, 674–684. 10.1007/BF00228197
95
WanX.WangW.LiuJ.TongT. (2014). Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med. Res. Methodol.14:135. 10.1186/1471-2288-14-135
96
WangM.JiangS.YuanY.ZhangL.DingJ.WangJ.et al. (2016). Alterations of functional and structural connectivity of freezing of gait in Parkinson's disease. J. Neurol.263, 1583–1592. 10.1007/s00415-016-8174-4
97
WenM. C.HengH. S.NgS. Y.TanL. C.ChanL. L.TanE. K. (2016). White matter microstructural characteristics in newly diagnosed Parkinson's disease: an unbiased whole-brain study. Sci. Rep.6:35601. 10.1038/srep35601
98
WileD. J.AgarwalP. A.SchulzerM.MakE.DinelleK.ShahinfardE.et al. (2017). Serotonin and dopamine transporter PET changes in the premotor phase of LRRK2 parkinsonism: cross-sectional studies. Lancet Neurol.16, 351–359. 10.1016/S1474-4422(17)30056-X
99
WorkerA.BlainC.JaroszJ.ChaudhuriK. R.BarkerG. J.WilliamsS. C. R.et al. (2014). Diffusion tensor imaging of Parkinson's disease, multiple system atrophy and progressive supranuclear palsy: a tract-based spatial statistics study. PLoS ONE9:e112638. 10.1371/journal.pone.0112638
100
WuY.SunD.WangY.WangY. (2016). Subcomponents and connectivity of the inferior fronto-occipital fasciculus revealed by diffusion spectrum imaging fiber tracking. Front. Neuroanat.10:88. 10.3389/fnana.2016.00088
101
XuY.YangJ.HuX.ShangH. (2016). Voxel-based meta-analysis of gray matter volume reductions associated with cognitive impairment in Parkinson's disease. J. Neurol.263, 1178–1187. 10.1007/s00415-016-8122-3
102
YounJ.LeeJ. M.KwonH.KimJ. S.SonT. O.ChoJ. W. (2015). Alterations of mean diffusivity of pedunculopontine nucleus pathway in Parkinson's disease patients with freezing of gait. Parkinsonism Relat. Disord.21, 12–17. 10.1016/j.parkreldis.2014.10.003
103
ZhangF.ChenG.HeM.DaiJ.ShangH.GongQ.et al. (2018). Altered white matter microarchitecture in amyotrophic lateral sclerosis: a voxel-based meta-analysis of diffusion tensor imaging. Neuroimage Clin.19, 122–129. 10.1016/j.nicl.2018.04.005
104
ZhangK.YuC.ZhangY.WuX.ZhuC.ChanP.et al. (2011). Voxel-based analysis of diffusion tensor indices in the brain in patients with Parkinson's disease. Eur. J. Radiol.77, 269–273. 10.1016/j.ejrad.2009.07.032
Summary
Keywords
meta-analysis, Parkinson disease, diffusion tensor imaging, tract-based spatial statistics, fractional anisotropy, seed-based d mapping
Citation
Wei X, Luo C, Li Q, Hu N, Xiao Y, Liu N, Lui S and Gong Q (2021) White Matter Abnormalities in Patients With Parkinson's Disease: A Meta-Analysis of Diffusion Tensor Imaging Using Tract-Based Spatial Statistics. Front. Aging Neurosci. 12:610962. doi: 10.3389/fnagi.2020.610962
Received
28 September 2020
Accepted
28 December 2020
Published
28 January 2021
Volume
12 - 2020
Edited by
Panteleimon Giannakopoulos, Université de Genève, Switzerland
Reviewed by
Sachchida Nand Rai, University of Allahabad, India; Thomas Welton, National Neuroscience Institute (NNI), Singapore
Updates
Copyright
© 2021 Wei, Luo, Li, Hu, Xiao, Liu, Lui and Gong.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Su Lui lusuwcums@hotmail.com
†These authors have contributed equally to this work
Disclaimer
All claims expressed in this article are solely those of the 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.