Altered white matter microarchitecture in Parkinson’s disease: a voxel-based meta-analysis of diffusion tensor imaging studies

Abstract

This study aimed to define the most consistent white matter microarchitecture pattern in Parkinson’s disease (PD) reflected by fractional anisotropy (FA), addressing clinical profiles and methodology-related heterogeneity. Web-based publication databases were searched to conduct a meta-analysis of whole-brain diffusion tensor imaging studies comparing PD with healthy controls (HC) using the anisotropic effect size–signed differential mapping. A total of 808 patients with PD and 760 HC coming from 27 databases were finally included. Subgroup analyses were conducted considering heterogeneity with respect to medication status, disease stage, analysis methods, and the number of diffusion directions in acquisition. Compared with HC, patients with PD had decreased FA in the left middle cerebellar peduncle, corpus callosum (CC), left inferior fronto-occipital fasciculus, and right inferior longitudinal fasciculus. Most of the main results remained unchanged in subgroup meta-analyses of medicated patients, early stage patients, voxel-based analysis, and acquisition with ˂30 diffusion directions. The subgroup meta-analysis of medication-free patients showed FA decrease in the right olfactory cortex. The cerebellum and CC, associated with typical motor impairment, showed the most consistent FA decreases in PD. Medication status, analysis approaches, and the number of diffusion directions have an important impact on the findings, needing careful evaluation in future meta-analyses.

Keywords

Parkinson’s disease / diffusion tensor imaging / fractional anisotropy / meta-analysis / anisotropic effect size–signed differential mapping

Introduction

Parkinson’s disease (PD) is the second most common neurodegenerative disease [¹]. Clinically, PD is characterized by progressive motor symptoms, such as bradykinesia, rigidity, resting tremor, and postural instability, and various non-motor symptoms [²]. The pathophysiology of PD has yet to be fully elucidated, and this disease has no preventative or curative treatments. Recently, psychoradiology is emerging as the study of brain diseases through a variety of non-invasive imaging techniques. Diffusion tensor imaging (DTI), a widely used magnetic resonance imaging (MRI) technique that visualizes and measures water diffusion, is particularly useful because of its high sensitivity to changes in white matter (WM) integrity [³]. Fractional anisotropy (FA), the most commonly used DTI parameter, is positively associated with WM anisotropy and can detect microstructural abnormalities of WM at very early stages [⁴,⁵].

Many DTI studies of PD have identified decreased FA in various brain regions, including corpus callosum (CC) [⁶–⁹], cerebellum [⁸,¹⁰–¹²], and superior and inferior longitudinal fasciculus (ILF) [⁹,¹⁰], but others have found no significant FA differences between patients with PD and healthy controls (HC) [¹³–¹⁵]. These discrepancies may be attributable to factors such as small or heterogeneous study samples and methodological differences. To gain insights into the pathophysiology, a meta-analysis has a good chance of detecting consistent regions of FA alterations in PD.

Earlier reviews and meta-analyses confirmed that symptoms of PD are a result of the degeneration of the substantia nigra [¹⁶–¹⁸]. Although the pathologic process is mainly attributed to disruptions in the nigrostriatal dopamine system, previous neuroimaging studies in PD have focused on the substantia nigra using a seed-point method. By examining pre-defined regions of interest (ROI), some important cerebral alterations may have been missed. To overcome region placement preference bias, we chose a widely used whole-brain method to analyze the WM changes in PD. Previous DTI meta-analyses [¹⁹] did not directly test the hypothesis of WM changes in patients with PD compared with HC, and confounding factors such as medication status, disease stage, and methodological differences were not fully considered. A comprehensive meta-analysis is needed to explore the WM microstructure abnormalities and to investigate the effects of symptom severity and other clinical characteristics on regional WM alterations.

The aim of this meta-analysis was to define the most consistent WM microstructural changes, reflected by FA, in patients with PD using published whole brain DTI studies. To reduce the effects of heterogeneity, several subgroup analyses were conducted. Meta-regression analyses were used to evaluate the influence of clinical and demographic characteristics.

Materials and methods

Data sources, study selection, and quality assessment

This retrospective study was approved by the local institutional review committee. In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [²⁰], a comprehensive search of studies published before May 14, 2018 was conducted in the PubMed, Web of Science, and Embase databases using the keywords “Parkinson” OR “Parkinson’s disease” OR “PD”; AND “diffusion tensor” OR “diffusion tensor imaging” OR “DTI.” The references of these studies and relevant review articles were checked for additional relevant studies. Studies that satisfied the following conditions were included in the meta-analysis: (1) patients were diagnosed with idiopathic PD; (2) FA was compared between patients and HC; (3) three-dimensional coordinates (Montreal Neurological Institute (MNI) or Talairach) were reported for the whole-brain FA analysis; (4) significant results were reported using thresholds for significance corrected for multiple comparisons or uncorrected with spatial extent thresholds; and (5) the study was published as an original article (not as a letter or abstract) in a peer-reviewed English-language journal. Data sets were excluded if they explicitly included patients diagnosed with comorbid neurological or psychiatric diseases (e.g., cognitive impairment or depression) [²¹]. To avoid sample overlaps: (1) for longitudinal studies, only baseline data were included [²¹]; (2) for studies reporting both on- and off-state results, only off-state data sets were included [²¹]; (3) for multiple studies using the same patient group, only the largest sample was included. Finally, the meta-analysis included 27 data sets from 24 studies with 808 patients with PD (429 men and 379 women) and 760 HC (393 men and 367 women) (Fig. 1).

Two of the authors independently rated each included study for quality and completeness using a 12-point checklist adapted from previous published meta-analyses [²²] (Table S1). Any discrepancies were resolved by a third investigator.

Voxel-wise meta-analysis

We analyzed FA differences in WM between patients with PD and HC using anisotropic effect size–signed differential mapping (AES-SDM), a voxel-based meta-analytic approach (see Supplementary Material). As detailed elsewhere [²³–²⁵], we extracted peak coordinates and effect sizes (e.g., t-values) of FA differences between patients with PD and HC from each data set. For each data set, we recreated a standard MNI map of FA differences using an anisotropic Gaussian kernel, from which the mean map was generated by voxel-wise calculation, weighted by sample size, intra-data set variability, and between-data set heterogeneity. To optimally balance false positive and negative findings, we used the default SDM kernel size and thresholds (full width at half maximum (FWHM) = 20 mm, uncorrected P = 0.005 was used as the main threshold, peak height Z = 1, cluster extent= 10 voxels) [²³,²⁴]. This FWHM kernel is intended to assign indicators of proximity to reported coordinates but not to smooth any image that is different in nature [²⁵]. We adopted the tract-based spatial statistics (TBSS) template included in AES-SDM to allow the combination of voxel-based analysis (VBA) and TBSS studies [²⁶]. We used MRIcron software to visualize AES-SDM maps overlaid onto a high-resolution brain image template created by the International Consortium for Brain Mapping.

Jackknife sensitivity analysis

Following preprocessing, we performed a whole-brain voxel-based jackknife sensitivity analysis to test the robustness of the findings by iteratively repeating the analysis, excluding one data set each time. If a brain region remains significant in all or most of the combinations of studies, the finding is considered highly replicable [²³].

Analyses of heterogeneity and publication bias

We conducted a heterogeneity analysis using a random effects model with Q statistics to explore unexplained between-study variability in the results; heterogeneous brain regions were obtained using the default SDM kernel size and thresholds [²⁵]. We also performed Egger’s test for publication bias by extracting the values from statistically significant relevant peaks between patients with PD and HC [²⁷].

Subgroup analyses

Several subgroup meta-analyses were conducted to analyze clinical and methodological differences between these studies. This subgroup analysis included only studies with defined clinical and methodological homogeneity and was conducted for studies that investigated medicated and medication-free (including both medication-naïve and off-state) patients, studies that investigated early stage (Hoehn and Yahr (H&Y) stage 1–2.5) patients [²⁸], studies where the number of acquisition diffusion directions was≥30 and ＜30 [²⁹], and studies that used VBA or TBSS approaches.

Meta-regression analyses

We used meta-regression analyses to examine the effects of age, percentage of female patients, illness duration, H&Y stage, Unified Parkinson’s Disease Rating Scale (UPDRS) Part III score, and levodopa equivalent daily dose (LEDD) of patients with PD. We decreased the probability threshold to 0.0005 and cluster extent= 10 voxels, as described previously [²⁶]. Regression plots were visually inspected to discard the fits driven by too few studies [²³,²⁴].

Results

Included studies and sample characteristics

The meta-analysis included 24 studies [⁶–¹⁵,³⁰–⁴³], and 3 of them included 2 independent databases [⁸,¹³,¹⁵], resulting in 27 data sets in all. Table 1 summarizes the demographic, clinical, and imaging information of these studies, which provided a total of 41 coordinates of altered FA in patients with PD versus HC. Ten data sets from 8 studies and 13 data sets from 12 studies explicitly indicated patients in the medication off-state and on-state, and 1 study included medication-naïve patients. Three studies did not provide any information about the medication status (on- or off-state) at DTI acquisition. The patients recruited in 6 data sets from 5 studies were at their early stage. The number of acquisition diffusion directions was≥30 in 11 data sets from 10 studies and ＜30 in 16 data sets from 14 studies. Seventeen studies used the TBSS approach, 5 used VBA, 1 used whole-brain-based spatial statistics, and 1 used gray-matter-based spatial statistics. Five of the 24 studies were performed on 1.5T scanners and 19 on 3T scanners. The quality scores, ranging from 9.5 to 12 (mean 10.7), demonstrated that the included studies were of high quality (Table 1).

FA differences in the main meta-analysis

Meta-analysis revealed decreased FA in patients with PD relative to HC, in the left middle cerebellar peduncles (MCP), CC, left inferior fronto-occipital fasciculus (IFOF), and right ILF (Fig. 2, Table 2).

Jackknife sensitivity analysis

Whole-brain jackknife sensitivity analysis showed that the most robust results were obtained for FA decreases in the WM regions of the left MCP and left CC, which were preserved throughout all combinations; right ILF and right CC were relatively replicable, being preserved throughout all but one combination; FA decreases in the left IFOF remained significant in all but two combinations (Table 3).

Analyses of heterogeneity and publication bias

The left CC with altered FA had significant statistical heterogeneity among studies (P<0.005; Table 4). Egger’s tests for publication bias were not significant with respect to FA differences in the left MCP (P = 0.107), left CC (P = 0.122), left IFOF (P = 0.126), right CC (P = 0.059), or right inferior longitudinal fasciculus (P = 0.057) (Table 2).

Subgroup analyses

The medication-free subgroup (comprising off-state (n = 10 data sets) and medication-naïve patients (n = 1 data set)) included 11 data sets comprising 374 patients with PD and 328 HC. The analysis revealed FA decreases in the right olfactory cortex. The medicated group (n = 13 data sets) comprised 340 patients with PD and 347 HC. The main results remained unchanged. We then used a threshold of LEDD= 400 mg/day to define two medicated subgroups. In the LEDD < 400 mg/day subgroup analysis (n = 5 data sets), comprising 205 patients with PD and 144 HC, the main results remained unchanged; by contrast, the LEDD≥400 mg/day subgroup (n = 7 data sets), comprising 122 patients with PD and 157 HC, revealed FA increases in the bilateral CC.

In the early stage disease (n = 6 data sets) and VBA (n = 5 data sets) subgroups, the main results remained unchanged, but the TBSS (n = 20 data sets) subgroup analysis failed to detect between-group differences. In the ＜ 30 diffusion directions subgroup analysis (n = 16 data sets), four of the main results remain unchanged, whereas the≥30 diffusion directions subgroup (n = 11 data sets) only revealed one main result in the left IFOF. Detailed results of subgroup analyses are shown in Table 5 and Figs. S1–S6.

Meta-regression analyses

The mean age of patients, percentage of women with PD (in the whole PD group in each study), mean illness duration, mean H&Y stage, mean UPDRS-III score, and mean LEDD of patients with PD were not significantly associated with PD-related WM FA changes, at least linearly.

Discussion

The main abnormalities and their potential significance

Our pooled meta-analysis of DTI studies revealed significant WM microarchitecture alterations in patients with PD compared with HC as indicated by the decreased FA values. A consistent decrease in FA was observed in the left MCP, CC, left IFOF, and right ILF. Jackknife sensitivity analysis, heterogeneity analysis, and Egger’s tests confirmed the robustness of these findings. Although the subgroup analysis of medication-free patients did not reveal decreased FA as in the main analysis, it detected an additional cluster in the right olfactory cortex. Meta-regression analyses revealed no significant association between WM alterations and relevant demographic and clinical variables.

Given the role of the MCP as the main afferent pathway from the cortex to the cerebellum and additional findings of diffusion changes in brainstem and pons [⁴⁴], these data emphasize the role of the cortico-ponto-cerebellar loop in PD pathology. This loop contains afferent axons from the pontine nuclei [⁴⁵] and mediates complex brain functions concerning movement and cognition. PD features motor dysfunction such as tremor, rigidity, bradykinesia, gait disturbance, and postural instability [⁴⁶]. Studies of patients with freezing of gait found WM abnormalities in the pontine-cerebellar tracts [⁸,⁴⁷,⁴⁸], which might explain their reduced ability to recruit the cerebellum. Functional studies in PD found hypoactivation in the cerebellum during a motor task [⁴⁹]. This neurodegeneration in MCP is consistent with the critical role of the cerebellum for motor performance [⁴⁶,⁵⁰]. Moreover, the subgroup analysis of PD patients with early stage disease detected decreased FA in MCP, an area with a significant decline in diffusivity in early PD, correlating with motor progression [⁵¹].

Being the largest WM bundle in the human brain, the CC connects the bilateral cerebral hemispheres [⁵²]; therefore, this finding, suggesting hemispheric involvement [⁵³], is consistent with resting-state studies reporting decreased interhemispheric functional connectivity in PD [⁵⁴–⁵⁶]. In previous DTI studies in PD, altered FA was reported in different subdivisions of the CC, such as the genu, which is usually associated with cognitive impairment [³⁷,³⁸,⁵⁷]. Microstructural impairment of CC has been reported in cognitively normal patients with PD, which points toward an affected interhemispheric connectivity that possibly shapes the onset of cognitive deficits [⁵⁸]. In our meta-analysis, the abnormal microstructure of the posterior CC was detected in the pooled analysis and the subgroups of patients with early stage disease, medicated patients, VBA method, and ＜30 diffusion directions; this finding is consistent with reports of a smaller posterior CC volume in patients with PD [⁵⁹]. Transcallosal motor fiber bundles cross the CC in the posterior part [⁶⁰]. The anatomic projections found in this study are compatible with previous observations that CC abnormalities are related to postural instability and freezing of gait in PD [⁶⁰], which becomes more severe with motor worsening [⁶¹]. Thus, the importance of neurodegeneration of CC in PD is also reflected through its role in motor features of the disease.

The ILF is an associative bundle connecting the occipital and temporal lobes. Its long fibers connect visual areas to the amygdala and hippocampus, the main components of the limbic system related to emotional behavior [⁶²]. It is involved in face recognition [⁶³], visual perception [⁶⁴], and visual memory [⁶⁵]. The IFOF is a ventral associative bundle connecting the ventral occipital lobe and the orbitofrontal cortex [⁶⁶]. Its occipital component runs parallel to the ILF. On approaching the anterior temporal lobe, its fibers gather together and enter the external capsule dorsally to the fasciculus. The IFOF is involved in reading [⁶⁷] and visual processing [⁶⁸]. The functions of these two bundles are not yet completely understood in PD. Guan and colleagues found that the ILF is associated with motor impairment and global disease severity in PD [³⁰], which has also been reported in relation to the tremor-dominant (TD) subtype of PD, indicating probably different underlying pathologies between TD and other motor subtypes [⁶⁹]. FA values in the IFOF were negatively correlated with bradykinesia of PD [⁷⁰], and disruption of this structure might contribute to freezing of gait due to impaired visuospatial processing [⁷¹]. However, a recent DTI study showed significant differences in these two bundles between patients with postural instability gait disorder and HC [⁷²]. The role of these two bundles in differentiating WM changes in PD motor subtypes requires further investigation. Notably, previous studies also found relationships between these two bundles and non-motor symptoms of PD, such as depression [⁷³] and cognitive dysfunction [⁷⁴]. Although studies with comorbid depression and cognitive dysfunction were excluded in the current meta-analysis, the potentially confounding effects could not be completely ruled out. Moreover, although detailed depression scales were not used in these included studies, depression is a frequent non-motor symptom in patients with PD, and its prevalence can be as high as 90% [⁷⁵].

The quantitative statistical meta-analyses have been carried out on DTI studies in PD [¹⁷]. Although these earlier meta-analyses provide valuable insights into the underlying WM pathology, differences in the ROI placement likely result in significant heterogeneity between studies. Relative to ROI-based analyses, coordinate-based analyses are less susceptible to errors relating to the lack of spatial distinction [⁷⁶]. There is relatively poor consistency of results between previous ROI meta-analyses and our current coordinate-based meta-analysis, which has implications for neuroimaging meta-analyses of other disorders, such as amnestic mild cognitive impairment [⁷⁶] and post-traumatic stress disorder [⁷⁷]. Several factors might contribute to these differences. First, ROI studies typically report on a subset of brain regions and are likely to suffer from publication bias. Second, where seed-based d mapping uses coordinate data, the effect size is biased toward zero in brain regions where there are no significant clusters.

Subgroup analyses

The subgroup analysis of medication-free patients detected a cluster in the right olfactory cortex, which is considered an early affected site in the Braak model of temporal degeneration in PD [⁷⁸]. A recent DTI study suggested that olfactory regions were particularly efficient at distinguishing drug-naïve patients with PD from HC [⁷⁹]. There is evidence of a strong link between olfactory discrimination and orbitofrontal cortex [⁸⁰]. Decreased FA in the olfactory cortex may be related to the olfactory dysfunction, which is present in ~90% of early stage PD [⁸¹]. However, we did not observe this alteration in the olfactory cortex in the pooled whole-group results. The reason for this discrepancy is unclear, although changes associated with medication may normalize, or at least obscure, the intrinsic changes in these regions. Medication exposure is an important potential confounder, and understanding the effect of medications on white-matter abnormalities in patients with PD is critical for the interpretation of results. In functional MRI studies, levodopa can normalize altered functional connectivity in PD [⁸²]. Medications have also been shown to affect cerebral WM; for example, benztropine used in the treatment of tremor in PD promotes re-myelination [⁸³]. In the current meta-analysis, the main results of the LEDD ＜ 400 mg/day subgroup analysis remain unchanged, whereas the results of the LEDD≥400 mg/day subgroup show increased FA in the bilateral CC, sharing these two clusters with the results of the pooled meta-analysis. This observation seems to fit a “normalization” effect. Although other diffusion MRI studies have shown that medication per se has little influence on WM integrity or does not alter diffusion [³⁸,⁸⁴], the effect of dopaminergic medication on WM is still unclear. The differential influence of dopamine medication is likely determined by additional factors. Inter-individual differences and disease severity should also be considered. Further studies that are specifically designed to detect the effect of medication exposure on WM changes in patients with PD are needed.

Scan acquisition parameters also affect FA measurements individually and in group comparisons. The minimum requirement for a qualitative DTI calculation is 6 non-collinear diffusion directions, whereas robust estimation of FA and tensor orientation requires at least 30 unique and evenly distributed sampling orientations [²⁹]. DTI studies with less than 30 diffusion directions tend to overestimate FA values, possibly affecting group-wise differences. However, our subgroup analysis of≥30 diffusion directions showed the same robust decrease in FA in the left IFOF as in the main analysis. Nevertheless, future DTI meta-analyses should consider the number of diffusion directions.

Subgroup analysis of the VBA studies revealed clusters that were identified in the pooled analysis; however, the subgroup analysis of the TBSS studies did not. This difference may be a methodological issue. TBSS was developed to analyze diffusion data, and it isolates the central core of WM tracts with the highest FA and reports significant clusters within that WM skeleton [⁸⁵]; however, it may miss abnormalities in the peripheral WM [⁸⁶]. VBA investigates the integrity of all WM voxels, including peripheral regions of WM and fiber crossings [⁸⁷]. Differences in findings between the two methods are thus to be expected. Moreover, given the small number of VBA studies (n = 5), we cannot completely rule out the possibility of chance findings.

Limitations of this study

First, methodological heterogeneity cannot be completely excluded: it was impossible to perform analyses or subgroup analyses of all relevant variables. Second, the medication-free patients of subgroup meta-analysis were mostly medicated patients in the off-state at scanning. Clearly, the best way to minimize the effects of treatment is to study medication-naïve patients; however, this subgroup was small. The results of the subgroup meta-analysis for medication-free data sets should therefore be interpreted with caution, considering the possible methodological differences between the studies, such as diffusion directions and analysis methods. Third, as is inherent in all coordinate-based methods, our meta-analysis used the coordinates from the included studies rather than the original t-statistic maps, which somewhat limits the accuracy of the results. Fourth, although we performed several subgroup meta-analyses, including VBA and different LEDD medicated data sets, for the early stage, the results should be interpreted with caution. The small sample size of these subgroup meta-analyses limits the generalizability of the results.

In conclusion, this meta-analysis demonstrates a consistent pattern of decreased FA in the left MCP, CC, IFOF, and ILF. In particular, FA decreases in the MCP and CC in PD were robustly present in the sensitivity analyses. These results suggest that the altered WM microstructures in the MCP and CC may serve as a potential neuroimaging biomarker of PD. Furthermore, the subgroup analyses show that medication status, analysis approaches, and the number of diffusion directions have an important impact on the findings, which should be treated with caution in future meta-analysis studies.

References

[1] de Lau LM, Breteler MM. Epidemiology of Parkinson’s disease. Lancet Neurol 2006; 5(6): 525–535

[2] Reichmann H, Brandt MD, Klingelhoefer L. The nonmotor features of Parkinson’s disease: pathophysiology and management advances. Curr Opin Neurol 2016; 29(4): 467–473

[3] Kubicki M, Westin CF, Maier SE, Mamata H, Frumin M, Ersner-Hershfield H, Kikinis R, Jolesz FA, McCarley R, Shenton ME. Diffusion tensor imaging and its application to neuropsychiatric disorders. Harv Rev Psychiatry 2002; 10(6): 324–336

[4] Taylor WD, Hsu E, Krishnan KR, MacFall JR. Diffusion tensor imaging: background, potential, and utility in psychiatric research. Biol Psychiatry 2004; 55(3): 201–207

[5] Le Bihan D, Mangin JF, Poupon C, Clark CA, Pappata S, Molko N, Chabriat H. Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging 2001; 13(4): 534–546

[6] Chen B, Fan G, Sun W, Shang X, Shi S, Wang S, Lv G, Wu C. 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 2017; 72(7): 610.e9–610.e15

[7] Ji L, Wang Y, Zhu D, Liu W, Shi J. White matter differences between multiple system atrophy (parkinsonian type) and Parkinson’s disease: a diffusion tensor image study. Neuroscience 2015; 305: 109–116

[8] Vercruysse S, Leunissen I, Vervoort G, Vandenberghe W, Swinnen S, Nieuwboer A. Microstructural changes in white matter associated with freezing of gait in Parkinson’s disease. Mov Disord 2015; 30(4): 567–576

[9] Karagulle Kendi AT, Lehericy S, Luciana M, Ugurbil K, Tuite P. Altered diffusion in the frontal lobe in Parkinson disease. AJNR Am J Neuroradiol 2008; 29(3): 501–505

[10] Chiang PL, Chen HL, Lu CH, Chen PC, Chen MH, Yang IH, Tsai NW, Lin WC. White matter damage and systemic inflammation in Parkinson’s disease. BMC Neurosci 2017; 18(1): 48

[11] Chen MH, Chen PC, Lu CH, Chen HL, Chao YP, Li SH, Chen YW, Lin WC. Plasma DNA mediate autonomic dysfunctions and white matter injuries in patients with Parkinson’s disease. Oxid Med Cell Longev 2017; 2017: 7371403

[12] Zhang K, Yu C, Zhang Y, Wu X, Zhu C, Chan P, Li K. Voxel-based analysis of diffusion tensor indices in the brain in patients with Parkinson’s disease. Eur J Radiol 2011; 77(2): 269–273

[13] Luo C, Song W, Chen Q, Yang J, Gong Q, Shang HF. White matter microstructure damage in tremor-dominant Parkinson’s disease patients. Neuroradiology 2017; 59(7): 691–698

[14] Worker A, Blain C, Jarosz J, Chaudhuri KR, Barker GJ, Williams SC, Brown RG, Leigh PN, Dell’Acqua F, Simmons A. Diffusion tensor imaging of Parkinson’s disease, multiple system atrophy and progressive supranuclear palsy: a tract-based spatial statistics study. PLoS One 2014; 9(11): e112638

[15] Agosta F, Canu E, Stojković T, Pievani M, Tomić A, Sarro L, Dragašević N, Copetti M, Comi G, Kostić VS, Filippi M. The topography of brain damage at different stages of Parkinson’s disease. Hum Brain Mapp 2013; 34(11): 2798–2807

[16] Schwarz ST, Abaei M, Gontu V, Morgan PS, Bajaj N, Auer DP. Diffusion tensor imaging of nigral degeneration in Parkinson’s disease: a region-of-interest and voxel-based study at 3 T and systematic review with meta-analysis. Neuroimage Clin 2013; 3: 481–488

[17] Atkinson-Clement C, Pinto S, Eusebio A, Coulon O. Diffusion tensor imaging in Parkinson’s disease: review and meta-analysis. Neuroimage Clin 2017; 16: 98–110

[18] Cochrane CJ, Ebmeier KP. Diffusion tensor imaging in parkinsonian syndromes: a systematic review and meta-analysis. Neurology 2013; 80(9): 857–864

[19] Albrecht F, Ballarini T, Neumann J, Schroeter ML. FDG-PET hypometabolism is more sensitive than MRI atrophy in Parkinson’s disease: a whole-brain multimodal imaging meta-analysis. Neuroimage Clin 2019; 21: 101594

[20] Moher D, Liberati A, Tetzlaff J, Altman DG; PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 2009; 6(7): e1000097

[21] Pan P, Zhan H, Xia M, Zhang Y, Guan D, Xu Y. Aberrant regional homogeneity in Parkinson’s disease: a voxel-wise meta-analysis of resting-state functional magnetic resonance imaging studies. Neurosci Biobehav Rev 2017; 72: 223–231

[22] Shepherd AM, Matheson SL, Laurens KR, Carr VJ, Green MJ. Systematic meta-analysis of insula volume in schizophrenia. Biol Psychiatry 2012; 72(9): 775–784

[23] Radua J, Mataix-Cols D. Voxel-wise meta-analysis of grey matter changes in obsessive-compulsive disorder. Br J Psychiatry 2009; 195(5): 393–402

[24] Radua J, Mataix-Cols D, Phillips ML, El-Hage W, Kronhaus DM, Cardoner N, Surguladze S. A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps. Eur Psychiatry 2012; 27(8): 605–611

[25] Radua J, Rubia K, Canales-Rodríguez EJ, Pomarol-Clotet E, Fusar-Poli P, Mataix-Cols D. Anisotropic kernels for coordinate-based meta-analyses of neuroimaging studies. Front Psychiatry 2014; 5: 13

[26] Wise T, Radua J, Nortje G, Cleare AJ, Young AH, Arnone D. Voxel-based meta-analytical evidence of structural disconnectivity in major depression and bipolar disorder. Biol Psychiatry 2016; 79(4): 293–302

[27] Radua J, Grau M, van den Heuvel OA, Thiebaut de Schotten M, Stein DJ, Canales-Rodríguez EJ, Catani M, Mataix-Cols D. Multimodal voxel-based meta-analysis of white matter abnormalities in obsessive-compulsive disorder. Neuropsychopharmacology 2014; 39(7): 1547–1557

[28] Chen S, Chan P, Sun S, Chen H, Zhang B, Le W, Liu C, Peng G, Tang B, Wang L, Cheng Y, Shao M, Liu Z, Wang Z, Chen X, Wang M, Wan X, Shang H, Liu Y, Xu P, Wang J, Feng T, Chen X, Hu X, Xie A, Xiao Q. The recommendations of Chinese Parkinson’s disease and movement disorder society consensus on therapeutic management of Parkinson’s disease. Transl Neurodegener 2016; 5(1): 12

[29] Jones DK. The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: a Monte Carlo study. Magn Reson Med 2004; 51(4): 807–815

[30] Guan X, Huang P, Zeng Q, Liu C, Wei H, Xuan M, Gu Q, Xu X, Wang N, Yu X, Luo X, Zhang M. Quantitative susceptibility mapping as a biomarker for evaluating white matter alterations in Parkinson’s disease. Brain Imaging Behav 2019; 13(1): 220–231

[31] Wen MC, Heng HSE, Lu Z, Xu Z, Chan LL, Tan EK, Tan LCS. Differential white matter regional alterations in motor subtypes of early drug-naive Parkinson’s disease patients. Neurorehabil Neural Repair 2018; 32(2): 129–141

[32] Péran P, Barbagallo G, Nemmi F, Sierra M, Galitzky M, Traon AP, Payoux P, Meissner WG, Rascol O. MRI supervised and unsupervised classification of Parkinson’s disease and multiple system atrophy. Mov Disord 2018; 33(4): 600–608

[33] Rektor I, Svátková A, Vojtíšek L, Zikmundová I, Vaníček J, Király A, Szabó N. White matter alterations in Parkinson’s disease with normal cognition precede grey matter atrophy. PLoS One 2018; 13(1): e0187939

[34] Acosta-Cabronero J, Cardenas-Blanco A, Betts MJ, Butryn M, Valdes-Herrera JP, Galazky I, Nestor PJ. The whole-brain pattern of magnetic susceptibility perturbations in Parkinson’s disease. Brain 2017; 140(1): 118–131

[35] Zanigni S, Evangelisti S, Testa C, Manners DN, Calandra-Buonaura G, Guarino M, Gabellini A, Gramegna LL, Giannini G, Sambati L, Cortelli P, Lodi R, Tonon C. White matter and cortical changes in atypical parkinsonisms: a multimodal quantitative MR study. Parkinsonism Relat Disord 2017; 39: 44–51

[36] Vervoort G, Leunissen I, Firbank M, Heremans E, Nackaerts E, Vandenberghe W, Nieuwboer A. Structural brain alterations in motor subtypes of Parkinson’s disease: evidence from probabilistic tractography and shape analysis. PLoS One 2016; 11(6): e0157743

[37] Agosta F, Canu E, Stefanova E, Sarro L, Tomić A, Špica V, Comi G, Kostić VS, Filippi M. Mild cognitive impairment in Parkinson’s disease is associated with a distributed pattern of brain white matter damage. Hum Brain Mapp 2014; 35(5): 1921–1929

[38] Kamagata K, Motoi Y, Tomiyama H, Abe O, Ito K, Shimoji K, Suzuki M, Hori M, Nakanishi A, Sano T, Kuwatsuru R, Sasai K, Aoki S, Hattori N. Relationship between cognitive impairment and white-matter alteration in Parkinson’s disease with dementia: tract-based spatial statistics and tract-specific analysis. Eur Radiol 2013; 23(7): 1946–1955

[39] Kim HJ, Kim SJ, Kim HS, Choi CG, Kim N, Han S, Jang EH, Chung SJ, Lee CS. Alterations of mean diffusivity in brain white matter and deep gray matter in Parkinson’s disease. Neurosci Lett 2013; 550: 64–68

[40] Melzer TR, Watts R, MacAskill MR, Pitcher TL, Livingston L, Keenan RJ, Dalrymple-Alford JC, Anderson TJ. White matter microstructure deteriorates across cognitive stages in Parkinson disease. Neurology 2013; 80(20): 1841–1849

[41] Hattori T, Orimo S, Aoki S, Ito K, Abe O, Amano A, Sato R, Sakai K, Mizusawa H. Cognitive status correlates with white matter alteration in Parkinson’s disease. Hum Brain Mapp 2012; 33(3): 727–739

[42] Kamagata K, Zalesky A, Hatano T, Ueda R, Di Biase MA, Okuzumi A, Shimoji K, Hori M, Caeyenberghs K, Pantelis C, Hattori N, Aoki S. 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(7): 3704–3722

[43] Rosskopf J, Müller HP, Huppertz HJ, Ludolph AC, Pinkhardt EH, Kassubek J. Frontal corpus callosum alterations in progressive supranuclear palsy but not in Parkinson’s disease. Neurodegener Dis 2014; 14(4): 184–193

[44] Ziegler E, Rouillard M, André E, Coolen T, Stender J, Balteau E, Phillips C, Garraux G. Mapping track density changes in nigrostriatal and extranigral pathways in Parkinson’s disease. Neuroimage 2014; 99: 498–508

[45] Stoodley CJ, Schmahmann JD. Evidence for topographic organization in the cerebellum of motor control versus cognitive and affective processing. Cortex 2010; 46(7): 831–844

[46] Wu T, Hallett M. The cerebellum in Parkinson’s disease. Brain 2013; 136(3): 696–709

[47] Schweder PM, Hansen PC, Green AL, Quaghebeur G, Stein J, Aziz TZ. Connectivity of the pedunculopontine nucleus in parkinsonian freezing of gait. Neuroreport 2010; 21(14): 914–916

[48] Canu E, Agosta F, Sarasso E, Volontè MA, Basaia S, Stojkovic T, Stefanova E, Comi G, Falini A, Kostic VS, Gatti R, Filippi M. Brain structural and functional connectivity in Parkinson’s disease with freezing of gait. Hum Brain Mapp 2015; 36(12): 5064–5078

[49] Tessa C, Lucetti C, Diciotti S, Paoli L, Cecchi P, Giannelli M, Baldacci F, Ginestroni A, Vignali C, Mascalchi M, Bonuccelli U. Hypoactivation of the primary sensorimotor cortex in de novo Parkinson’s disease: a motor fMRI study under controlled conditions. Neuroradiology 2012; 54(3): 261–268

[50] Hall JM, Ehgoetz Martens KA, Walton CC, O’Callaghan C, Keller PE, Lewis SJ, Moustafa AA. Diffusion alterations associated with Parkinson’s disease symptomatology: a review of the literature. Parkinsonism Relat Disord 2016; 33: 12–26

[51] Sobhani S, Rahmani F, Aarabi MH, Sadr AV. Exploring white matter microstructure and olfaction dysfunction in early Parkinson disease: diffusion MRI reveals new insight. Brain Imaging Behav 2019; 13(1): 210–219

[52] Catani M, Howard RJ, Pajevic S, Jones DK. Virtual in vivo interactive dissection of white matter fasciculi in the human brain. Neuroimage 2002; 17(1): 77–94

[53] Cronin-Golomb A. Parkinson’s disease as a disconnection syndrome. Neuropsychol Rev 2010; 20(2): 191–208

[54] Hu X, Zhang J, Jiang X, Zhou C, Wei L, Yin X, Wu Y, Li J, Zhang Y, Wang J. Decreased interhemispheric functional connectivity in subtypes of Parkinson’s disease. J Neurol 2015; 262(3): 760–767

[55] Luo C, Guo X, Song W, Zhao B, Cao B, Yang J, Gong Q, Shang HF. Decreased resting-state interhemispheric functional connectivity in Parkinson’s disease. BioMed Res Int 2015; 2015: 692684

[56] Li J, Yuan Y, Wang M, Zhang J, Zhang L, Jiang S, Wang X, Ding J, Zhang K. Decreased interhemispheric homotopic connectivity in Parkinson’s disease patients with freezing of gait: a resting state fMRI study. Parkinsonism Relat Disord 2018; 52: 30–36

[57] Zheng Z, Shemmassian S, Wijekoon C, Kim W, Bookheimer SY, Pouratian N. DTI correlates of distinct cognitive impairments in Parkinson’s disease. Hum Brain Mapp 2014; 35(4): 1325–1333

[58] Gorges M, Müller HP, Liepelt-Scarfone I, Storch A, Dodel R; LANDSCAPE Consortium, Hilker-Roggendorf R, Berg D, Kunz MS, Kalbe E, Baudrexel S, Kassubek J, Kassubek J. Structural brain signature of cognitive decline in Parkinson’s disease: DTI-based evidence from the LANDSCAPE study. Ther Adv Neurol Disorder 2019; 12: 1756286419843447

[59] Vasconcellos LF, Pereira JS, Adachi M, Greca D, Cruz M, Malak AL, Charchat-Fichman H. Volumetric brain analysis as a predictor of a worse cognitive outcome in Parkinson’s disease. J Psychiatr Res 2018; 102: 254–260

[60] Chan LL, Ng KM, Rumpel H, Fook-Chong S, Li HH, Tan EK. Transcallosal diffusion tensor abnormalities in predominant gait disorder parkinsonism. Parkinsonism Relat Disord 2014; 20(1): 53–59

[61] Galantucci S, Agosta F, Stankovic I, Petrovic I, Stojkovic T, Kostic V, Filippi M. Corpus callosum damage and motor function in Parkinson’s disease (P2.006). Neurology 2014; 82(10 Supplement):P2.006

[62] Catani M, Jones DK, Donato R, Ffytche DH. Occipito-temporal connections in the human brain. Brain 2003; 126(9): 2093–2107

[63] Fox CJ, Iaria G, Barton JJ. Disconnection in prosopagnosia and face processing. Cortex 2008; 44(8): 996–1009

[64] Ffytche DH. The hodology of hallucinations. Cortex 2008; 44(8): 1067–1083

[65] Ross ED. Sensory-specific amnesia and hypoemotionality in humans and monkeys: gateway for developing a hodology of memory. Cortex 2008; 44(8): 1010–1022

[66] Catani M. From hodology to function. Brain 2007; 130(3): 602–605

[67] Catani M, Mesulam M. The arcuate fasciculus and the disconnection theme in language and aphasia: history and current state. Cortex 2008; 44(8): 953–961

[68] Rudrauf D, Mehta S, Grabowski TJ. Disconnection’s renaissance takes shape: formal incorporation in group-level lesion studies. Cortex 2008; 44(8): 1084–1096

[69] Haghshomar M, Dolatshahi M, Ghazi Sherbaf F, Sanjari Moghaddam H, Shirin Shandiz M, Aarabi MH. Disruption of inferior longitudinal fasciculus microstructure in Parkinson’s disease: a systematic review of diffusion tensor imaging studies. Front Neurol 2018; 9: 598

[70] Lee E, Lee JE, Yoo K, Hong JY, Oh J, Sunwoo MK, Kim JS, Jeong Y, Lee PH, Sohn YH, Kang SY. Neural correlates of progressive reduction of bradykinesia in de novo Parkinson’s disease. Parkinsonism Relat Disord 2014; 20(12): 1376–1381

[71] Wang M, Jiang S, Yuan Y, Zhang L, Ding J, Wang J, Zhang J, Zhang K, Wang J. Alterations of functional and structural connectivity of freezing of gait in Parkinson’s disease. J Neurol 2016; 263(8): 1583–1592

[72] Tan SYZ, Keong NCH, Selvan RMP, Li H, Ooi LQR, Tan EK, Chan LL. Periventricular white matter abnormalities on diffusion tensor imaging of postural instability gait disorder parkinsonism. AJNR Am J Neuroradiol 2019; 40(4): 609–613

[73] Wu JY, Zhang Y, Wu WB, Hu G, Xu Y. Impaired long contact white matter fibers integrity is related to depression in Parkinson’s disease. CNS Neurosci Ther 2018; 24(2): 108–114

[74] Duncan GW, Firbank MJ, Yarnall AJ, Khoo TK, Brooks DJ, Barker RA, Burn DJ, O’Brien JT. Gray and white matter imaging: a biomarker for cognitive impairment in early Parkinson’s disease? Mov Disord 2016; 31(1): 103–110

[75] Reijnders JS, Ehrt U, Weber WE, Aarsland D, Leentjens AF. A systematic review of prevalence studies of depression in Parkinson’s disease. Mov Disord 2008; 23(2): 183–189, quiz 313

[76] Yu J, Lam CLM, Lee TMC. White matter microstructural abnormalities in amnestic mild cognitive impairment: a meta-analysis of whole-brain and ROI-based studies. Neurosci Biobehav Rev 2017; 83: 405–416

[77] Bromis K, Calem M, Reinders AATS, Williams SCR, Kempton MJ. Meta-analysis of 89 structural MRI studies in posttraumatic stress disorder and comparison with major depressive disorder. Am J Psychiatry 2018; 175(10): 989–998

[78] Hawkes CH, Del Tredici K, Braak H. Parkinson’s disease: a dual-hit hypothesis. Neuropathol Appl Neurobiol 2007; 33(6): 599–614

[79] Nigro S, Riccelli R, Passamonti L, Arabia G, Morelli M, Nisticò R, Novellino F, Salsone M, Barbagallo G, Quattrone A. Characterizing structural neural networks in de novo Parkinson disease patients using diffusion tensor imaging. Hum Brain Mapp 2016; 37(12): 4500–4510

[80] Zatorre RJ, Jones-Gotman M. Human olfactory discrimination after unilateral frontal or temporal lobectomy. Brain 1991; 114(Pt 1A): 71–84

[81] Doty RL. Olfactory dysfunction in Parkinson disease. Nat Rev Neurol 2012; 8(6): 329–339

[82] Berman BD, Smucny J, Wylie KP, Shelton E, Kronberg E, Leehey M, Tregellas JR. Levodopa modulates small-world architecture of functional brain networks in Parkinson’s disease. Mov Disord 2016; 31(11): 1676–1684

[83] Dean DC 3rd, Sojkova J, Hurley S, Kecskemeti S, Okonkwo O, Bendlin BB, Theisen F, Johnson SC, Alexander AL, Gallagher CL. Alterations of myelin content in Parkinson’s disease: a cross-sectional neuroimaging study. PLoS One 2016; 11(10): e0163774

[84] Degirmenci B, Yaman M, Haktanir A, Albayrak R, Acar M, Caliskan G. The effects of levodopa use on diffusion coefficients in various brain regions in Parkinson’s disease. Neurosci Lett 2007; 416(3): 294–298

[85] Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE, Watkins KE, Ciccarelli O, Cader MZ, Matthews PM, Behrens TE. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 2006; 31(4): 1487–1505

[86] Zalesky A. Moderating registration misalignment in voxelwise comparisons of DTI data: a performance evaluation of skeleton projection. Magn Reson Imaging 2011; 29(1): 111–125

[87] Nortje G, Stein DJ, Radua J, Mataix-Cols D, Horn N. Systematic review and voxel-based meta-analysis of diffusion tensor imaging studies in bipolar disorder. J Affect Disord 2013; 150(2): 192–200

RIGHTS & PERMISSIONS

Higher Education Press