Skip to main content

The clinical and neuroimaging differences between vascular parkinsonism and Parkinson’s disease: a case-control study



Parkinson’s disease (PD) and vascular parkinsonism (VaP) have highly overlapping phenotypes, and different prognosis. This study comprehensively investigated the clinical, brain MRI and transcranial sonography differences between VaP and PD.


Forty-eight patients with PD, 27 patients with VaP, and 29 healthy controls were compared. All patients were assessed using the MDS-UPDRS, Berg Balance Scale (BBS), Ten-Meter Walking Test (10-MWT), Time Up and Go Test, and Non-Motor Symptoms Scale. Beck Depression Inventory, PD questionnaire- 39, international urine incontinence scale, cognitive assessment scales, MRI brain and transcranial colour-coded doppler. The study was registered on (NCT04308135) on 03/12/2020.


VaP patients showed significantly older age of onset, shorter disease duration, lower drug doses and levodopa responsiveness, higher On and Off axial scores, On and Off BBS, higher On scores for PIGD, rigidity, bradykinesia and total motor MDS-UPDRS, lower On and Off tremor, lower-half predominance, lower asymmetrical presentation and symmetric index than PD patients. VaP patients had worse non-motor symptoms Scale (NMSS) than controls except for perceptual problems/hallucinations but better symptoms than PD patients except for urinary dysfunction. Quality of life (QoL) was impaired in VaP patients and was correlated with motor function and NMSs. The VaP group had significantly higher white matter lesions and brain atrophy, with lower hyperechogenicity of the substantia nigra and more impaired cerebral vascular resistance and vasoreactivity than the PD group.


VaP has a characteristic motor and non-motor profile, with impaired QoL, white matter, and transcranial sonography abnormalities that differentiate it from PD. Further studies are warranted to explore the role of vascular lesions in the pathogenesis of VaP.

Trial registration

The registered identifier NCT04308135 on Registered on 03/12/2020.

Peer Review reports


Parkinson’s disease (PD) and vascular parkinsonism (VaP) have highly overlapping phenotypes, with mixed pathologies, making distinguishing between these two parkinsonian diseases challenging [1]. VaP is a form of acquired parkinsonism in which the parkinsonian features are of vascular origin in contrast to PD, which is neurodegenerative in etiology [2]. It accounts for 4.4–12% of all cases of parkinsonism and is associated with vascular changes in the globus pallidus, white matter, and to a lesser extent, in the substantia nigra (SN) [3]. Consequently, common risk factors for VaP are the same as those for cerebrovascular disease, and their prevention and treatment are of utmost importance [1].

Few studies have identified the characteristic clinical features of VaP compared to PD [4]. Additionally, investigations such as magnetic resonance imaging (MRI) and transcranial color-coded Doppler (TCCD) could aid in differentiating VaP from PD. TCCD was found to display increased hyperechogenicity at the SN in patients with PD compared to normal controls or participants with other Parkinsonian syndromes [5].

However, VaP, particularly the insidious form, is still a debatable concept due to the lack of correlation between vascular risk factors, white matter lesions (WMLs) and parkinsonian features and its overlap with high-order gait disorder, PD or atypical parkinsonism [6]. Therefore, differentiating PD and VaP is clinically important due to the overlapping clinical characteristics as well as different responses to dopaminergic drugs and prognoses [7]. Moreover, identifying clinical and neuroimaging differences between the two diseases is essential to characterize and justify the concept of VaP.

The aim of the current study was to comprehensively investigate the differences between VaP and PD, including clinical profile (motor, non-motor symptoms (NMSs), and gait), radiological and transcranial sonographic characteristics (MRI brain, carotid duplex and TCCD) and laboratory tests.


This case–control study compared age and gender matched patients with PD, patients with VaP and healthy controls. Consecutive patients were recruited from movement disorders and stroke outpatient clinics at Ain Shams University Hospitals during the period from March 2020 to December 2021. Age and gender healthy controls were recruited from other patients’ companions and relatives visiting the hospital. The study was approved by the ethical committee of the Faculty of Medicine, Ain Shams University, and was registered on (NCT04308135) on March 12, 2020. Written informed consent was obtained from all participants. In a one-way ANOVA study, sample sizes of at least 40 PD patients, 24 VaP patients, and 24 controls were obtained representing the 3 groups whose means are to be compared. The total sample of 88 subjects achieves 80% power to detect differences among the means versus the alternative of equal means using an F test with a 0.05 significance level.

Motor and non-motor assessments

The diagnosis of PD was based on the Movement Disorders Society (MDS) diagnostic criteria [8], while the diagnosis of VaP was based on the Zijlmans et al. criteria for probable VaP [9]. Patients were excluded if they had any alternative cause that significantly impairs gait, had any contraindication for neuroimaging, had a poor transtemporal window in TCCD or could not perform the tests. Patients with atypical and other causes of secondary parkinsonism were also excluded.

All patients were subjected to a comprehensive clinical evaluation and laboratory investigations and were assessed in OFF and ON states using the MDS-Unified Parkinson disease rating scale (MDS-UPDRS), modified Hoehn and Yahr scale (H&Y), Schwab and England activities of daily living scale (S&E-ADL) [10, 11], new freezing of gait questionnaire (NFOG-Q) [12], 10-metre walk test [13], time up and go (TUG) test [14], and Berg balance scale [15]. The lower body predominance was determined by a two-point difference between the upper and lower limb scores of bradykinesia, rigidity or postural instability of the MDS-UPDRS-III [4]. Clinical asymmetry was defined as the difference between the summed MDS-UPDRS scores of the left and right extremities (items 3.3–3.8 and 3.15–3.17) [16]. The symmetric index parameter was calculated for asymmetrical subjects. Higher values indicate higher degrees of asymmetry [17]. The motor subtypes were determined for the PD and VaP groups [18]. Using the dopa challenge test, the proper response to levodopa was considered if the MDS-UPDRS-III improved by more than 24.5% [19]. For patients with VaP who are mostly drug naïve, we used a morning dosage of Levodopa/carbidopa 250/25 mg preceded by domperidone (10 mg). Patients with PD, who were under treatment, were received 120% of the morning levodopa dose [20].

All patients and controls were evaluated by the non-motor symptoms scale (NMSS) for NMSs [21], the Beck depression inventory (BDI) [22], Parkinson’s Disease Questionnaire (PDQ-39) for quality of life (QoL) [23], the Arabic version of Montreal Cognitive Assessment (MoCA) [24], the Wechsler memory scale-III (WMS) [25], Frontal Assessment Battery (FAB) for executive functions [26], the verbal fluency test, and the clock drawing test (for visuospatial skills) of Addenbrooke’s cognitive examination-III (ACE-III) [27]. Patients’ urinary symptoms were assessed by the Arabic version of the International Consultation on Incontinence Questionnaire-Short Form (ICIQ-SF) [28].

MRI brain assessment

Brain MRI was performed for all patients using a 1.5-T Siemens Magnetom Symphony scanner machine for assessing the WMLs by the Fazekas scale [29] and the Scheltens scale [30] and assessing brain atrophy using the Scheltens-Graz visual rating scale [31]. MRI brain included T1-weighted imaging, T2-weighted imaging, diffusion-weighted imaging, and fluid-attenuated inversion recovery.

Vascular and substania nigra ultrasound assessments

TCCD (Esaote My Lab Five, Italy) was performed using a color-coded ultrasound system with a phase array 2 Hz probe through the transtemporal bone window. The mean flow velocity (MFV) and pulsatility index (PI) of the middle and posterior cerebral arteries (MCA, PCA) were recorded bilaterally [32]. Cerebral vasomotor reactivity (CVR) was evaluated by measuring the breath holding index (BHI) [33]. The hyperechogenic area at the SN was measured automatically by encircling the outer circumference of the area of ​​hyperechogenicity, and the highest value was recorded [34]. Patients with at least one echogenic size ≥ 18 mm2 were classified as hyperechogenic [35]. The ultrasonographic examination of extracranial vessels was performed using a 12 Hz linear probe (Esaote My Lab Five, Italy), measuring the intimal medial thickness (IMT) of the common carotid artery (CCA) in B-mode. The carotid arteries were evaluated for the presence of atherosclerotic plaques, the degree of stenosis, and the peak systolic velocity (PSV) [36]. Controls underwent TCCD and ultrasonographic examination of extracranial vessels.

Statistical analysis

Data analysis was performed using IBM SPSS software package version 25.0 (Armonk, NY: IBM Corp). The Mann‒Whitney test was used for nonnormally distributed quantitative variables, and Chi-square test was applied to assess the statistical significance between categorical variables. The Kruskal‒Wallis test was used to assess the statistical significance of the difference between more than two study groups. Correlation analyses between the variables were performed by Spearman correlation coefficients. A p value of 0.05 or less was considered statistically significant, except in the case of correlation; after using Bonferroni correction, it became < 0.004.


A total of 104 participants, 48 PD patients, 27 VaP patients and 29 healthy controls, were included in the study. Eight patients were excluded, including a patient with frontal meningioma, one with exposure to antipsychotic drugs, one with progressive supranuclear palsy, two patients with normal pressure hydrocephalus (NPH) and three patients with severe knee osteoarthritis causing gait impairment.

The mean age of PD group was 58.6 ± 6.2 years, while the mean age of VaP group was 63.1 ± 10.3 years. The three groups were matched regarding age, gender, and years of education. Vascular risk factors were significantly more frequent among VaP group, compared to PD group (hypertension, hyperlipidaemia, smoking (p < 0.001), diabetes mellitus (p = 0.018), ischemic heart disease (p = 0.003)), while consanguinity and family history of PD were significantly more frequent among patients with PD (p < 0.001 and 0.025, respectively) (Table 1).

Table 1 Demographics and clinical characteristics of patients with vascular parkinsonism, Parkinson’s disease, and controls

Motor characteristics of PD and VaP

Compared to PD group, VaP group showed significantly older age of onset (p < 0.001), shorter disease duration (the time from the onset of motor symptoms to the time of evaluation) (p = 0.007), lower-half predominance (p = 0.005), worse MDS-UPDRS-III On (p = 0.002), rigidity On (p < 0.001), bradykinesia On (p = 0.035), worse On and Off axial scores (p < 0.001 and 0.028, respectively), PIGD On scores (p < 0.001), On and Off BBS (p < 0.001), TUG-On (p = 0.003), and ICIQ-SF (p = 0.014), while lower tremor On and Off (p < 0.001 and 0.007, respectively), asymmetrical presentation and symmetric index (p < 0.001), NFOG-Q-OFF (p < 0.010), levodopa equivalent daily dose (LEDD) (p < 0.001) and percentage of levodopa responsiveness (p < 0.001). Seven patients with VaP (25.9%) showed response to levodopa, with mean LEDD was 467.86 mg (range 425 to 525 mg). Seventeen (63%) patients with VaP had insidious presentation, while 10 had acute presentation (37%). Patients with VaP were more frequently of the PIGD type (16 (59.3%)) than patients with PD (6 (12.5%)), who more frequently had the TD type (37 (77.1%)) (p > 0.001) (Table 2). Twenty-four (88.9%) patients with VaP had pyramidal signs. There were no significant differences in MDS-UPDRS-I, II, IV and total scores, dyskinesia, part III-Off, rigidity, bradykinesia, PIGD-Off and TUG-Off between the groups (Table 2).

Table 2 comparison of motor characteristics between Parkinson’s disease and vascular parkinsonism

Non-motor symptoms, quality of life and cognitive functions of PD and VaP

Compared to controls, patients with PD and VaP had significantly worse BDI, total NMSS and subscores of NMSS, except for perceptual problems/hallucinations of VaP group. Compared to VaP group, PD group had significantly worse NMSS total score (p = 0.030), sleep/fatigue (p = 0.031), mood/cognition (p = 0.006), and miscellaneous domains (p < 0.001), while VaP patients had significantly worse urinary domain than PD patients (p = 0.037). There were no significant differences in other domains of NMSS and BDI between PD and VaP groups. 33 (68.7%) patients with PD and 21 (77.8%) patients with VaP had depression with comparable frequency and severity types (p = 0.302).

Compared to controls, patients with PD and VaP showed significantly worse total PDQ-39 and its domains (p < 0.001), except for bodily discomfort for VaP group. Compared to patients with VaP, patients with PD had significantly worse total PDQ-39 (p = 0.047), emotional wellbeing (p = 0.044), stigma (p < 0.001), social support (p < 0.001) and bodily discomfort (p = 0.003), with no significant difference in other domains (Table 3).

Table 3 Non-motor functions and quality of life among people with Parkinson’s disease and vascular parkinsonism

PD and VaP groups were significantly worse on the MoCA, WMS and clock drawing test than controls. Only VaP group had significantly worse FAB and verbal fluency than controls (p < 0.001). Compared to PD group, VaP group had significantly worse MOCA (p = 0.015), FAB (p < 0.001), verbal fluency (p < 0.001) and WMS (p = 0.006) scores and similar clock drawing test scores (Table 3).

Acute versus insidious VaP

The insidious group had significantly worse rigidity OFF, PIGD OFF (p = 0.02), total NMSS (p = 0.02), cardiovascular (p = 0.03), gastrointestinal (p = 0.02), and sexual functions (p = 0.03) domains of the NMSS, BDI (p = 0.02), and stigma (p = 0.04) and better body discomfort (p = 0.04). Both types showed similar MRI vascular and atrophy scores (Supplementary Table 1).

Laboratory differences between PD and VaP

VaP group showed significantly lower serum haemoglobin (p = 0.005), albumin (p < 0.001), and HbA1c (p = 0.014) and higher uric acid (p = 0.015), cholesterol (p = 0.002) and low-density lipoprotein (LDL) (p = 0.012) than PD group (Supplementary Table 2).

Neuroimaging differences between PD and VaP

The VaP group had a significantly higher Fazekas scale (p < 0.001), Scheltens’ scale (p < 0.001) and visual rating scale for atrophy (p < 0.001) than the PD group, implying more severe white matter ischemic changes and brain atrophy (Supplementary Table 3). Seventeen patients with VaP (62.96%) had Fazekas grade 2, eight patients (29.63%) had grade 3, two patients (7.4%) had grade 1, and no patient had grade 0, while 18 patients with PD (37.5%) had Fazekas grade 0, 24 patients (50%) had grade 1, 6 patients (12.5%) had grade 2, and no patient had grade 3. Three patterns of brain white matter hyperintensities were identified including: bilateral cerebral periventricular hyperintense foci and confluent patches (7 patients, 25.9%), bilateral cerebral periventricular and basal ganglia hyperintensities (8 patients, 29.6%), and bilateral cerebral periventricular, basal ganglia and pontine hyperintensities (12 patients, 44.4%) (Fig. 1). 20 patients with VaP (74.07%) had basal ganglionic ischemic changes, while only 3 patients with PD (6.25%) had these changes.

Fig. 1
figure 1

Examples of three patterns of white matter hyperintensities demonstrated in Brain MRI FLAIR: (A) bilateral cerebral periventricular bright signal foci and confluent patches, (B) bilateral cerebral periventricular and basal ganglia signal abnormalities, (C) bilateral cerebral periventricular, basal ganglia and pontine hyperintensities

TCCD could be performed for 47 patients with PD, 24 patients with VaP, and all controls, while 3 patients with VaP and one patient with PD had poor transtemporal window. The VaP group had significantly higher IMT of the CCA than the PD and control groups (p = 0.011 and < 0.001, respectively) but similar carotid plaques. The VaP group had a significantly lower PSV than the controls but was comparable to the PD group. The average and PCA MFV were significantly lower in the VaP group than in the PD and control groups. Additionally, the VaP group had a significantly higher PI of the MCA than the PD group (p = 0.031) and controls (p = 0.004) and a higher PI of the PCA than controls (Table 4). Compared to controls, the PD group had significantly impaired BHI of both sides MCA (p = 0.012 and p < 0.001) and PCA (p < 0.001 and p = 0.004), and the VaP group had impaired BHI of both PCA (p < 0.001 and p = 0.019). However, there were no significant differences between the PD and VaP groups regarding BHI. Hyperechogenicity of the SN was more significantly detected in PD group (43 patients (91.5%)) than in VaP group (5 patients (20.8%)) (p < 0.001) and controls (3 (10.3%)) (p < 0.001) (Table 4).

Table 4 Carotid duplex and transcranial colour-coded doppler findings among all study groups

Correlations of motor, non-motor symptoms and quality of life in PD and VaP

Among VaP group, MDS-UPDRS-III -Off was significantly correlated with age, duration, and number of vascular risk factors (r = 0.541, p = 0.006). MoCA was correlated with FAB and depression. Total NMSS was significantly correlated with age, BDI, MDS-UPDR-III Off, and FAB. The PDQ scores were significantly correlated with the MDS-UPDRS-Off, H&Y Off, NMSS and BDI scores (r = 0.429, p = 0.025; r = 0.477, p = 0.012; r = 0.621, p = 0.001; and r = 0.529, p = 0.005, respectively) (Table 5). Average MFV was correlated with PIGD Off and FAB scores (r = 0.473, p = 0.013, and r = -0.390, p = 0.045, respectively).

Table 5 Correlations of clinical characteristics and quality of life of Parkinson’s disease and vascular parkinsonism

Among PD group, MDS-UPDRS-III Off was significantly correlated with duration, H&Y Off, BDI (p < 0.001) and FAB (p = 0.015). MoCA scores were significantly correlated with BDI, MDS-UPDRS-III Off, H&Y Off, and FAB scores. Total NMSS was significantly correlated with duration, BDI, MDS-UPDRS III Off and H&Y Off. The PDQ was significantly correlated with duration (p = 0.001), BDI, MDS-UPDRS-III Off, H&Y Off (p < 0.001), total NMSS (r = 0.790, p > 0.001) and FAB scores (p = 0.034). Correlations with p < 0.004 are significant after Bonferroni correction (Table 5).


The current study comprehensively characterized the clinical and radiological differences between VaP and PD. In addition to confirming the motor and non-motor characteristics of VaP that differentiate it from PD, the study described its motor subtypes, impaired QoL and factors associated with motor, NMSs and impaired QoL. Moreover, it demonstrated the cerebral hemodynamic changes associated with VaP. This study included matched ages, gender and education years for proper comparison and to avoid previous studies’ limitations [4].

The present study demonstrated that VaP was associated with more vascular risk factors, similar to previous studies [4, 37,38,39]. Moreover, vascular risk factors were correlated with motor severity. Furthermore, laboratory tests showed higher vascular risk factors, such as serum uric acid and LDL, among patients with VaP. Similarly, one study showed higher serum uric acid in VaP than in PD, which could also be explained by the association between low serum uric acid and developing PD [40].

On the other hand, family history and consanguinity were significantly more frequent among PD patients, in contrast to a previous study that showed no significant difference, [4] implying the acquired nature of VaP. Most of the studies reported more male predominance and older age among VaP than PD [37, 38]. This may be explained by the higher stroke incidence in males and the protective role of estrogen among females [41]. Moreover, it confirmed the older age of onset and shorter duration of VaP compared to age-matched PD, similar to previous studies of matched [42] and unmatched ages [37,38,39].

The characteristic motor features of VaP have been confirmed, including lower body predominance, more symmetrical symptoms, worse motor scores, wide-based gait, worse balance, more postural instability, less tremor, less satisfactory response to levodopa and associated pyramidal signs, in agreement with previous studies. Moreover, the PIGD type is more frequent among VaP (about 60%), but TD might also be present in 25.9%, denoting the overlap with PD. TD with VaP could be related to the prevalence of postural upper limbs and jaw tremor [37].

Significant differences between VaP and PD were more pronounced during the On state, implying different responses to levodopa [4, 37, 39, 43, 44]. Additionally, the current study showed worse balance and physical activity among VaP. Off scores of total and motor MDS-UPDRS, bradykinesia and rigidity were comparable in both groups in contrast to other studies, which could be explained by the lower severity, duration, and age of PD group in this study. Meanwhile, other studies found no significant difference in rigidity and bradykinesia between PD and VaP [37, 44]. Consequently, motor features such as tremor, balance disturbance, axial symptoms and PIGD are more consistent differentiating features between VaP and PD in On and Off states.

Freezing of gait is one of the common features of VaP. However, this study showed higher gait freezing in PD group than in VaP group, in contrast to previous studies [7, 37, 39]. Meanwhile, other studies reported no difference in gait dysfunction and freezing between VaP and PD [44, 45]. This discrepancy could be attributed to the greater cognitive impairment among patients with VaP that might affect the recognition and reporting of freezing and the short duration of illness of VaP in our study. Additionally, gait freezing is more closely related to the localization of lesions that might differ among groups [46, 47].

Cognitive impairment was more prominent in the VaP of different domains, especially frontal lobe dysfunction, with less impairment of visuospatial functions than PD and healthy individuals, while patients with PD showed preserved frontal functions. Similarly, previous studies reported more global cognitive impairment in VaP [7, 38, 48]. Benítez-Rivero et al. described the same findings after adjustment for age, in addition to a greater effect on visuospatial functions of patients with PD [42]. However, the current study found more impaired memory among patients with VaP in contrast to previous studies that showed comparable memory tasks [42]. This could be attributed to ischemia-related changes in the functional connectivity of the caudate nucleus with the cingulate cortex, inducing severe executive/frontal lobe dysfunction [49, 50].

Few studies have described the non-motor aspects of VaP. VaP had worse total and domains of NMSS and urinary symptoms but better sleep/fatigue, mood/cognition, and miscellaneous domains than PD. Benítez-Rivero et al. reported less frequent NMSs in the VaP than in the PD but non-significantly worse NMSs in the VaP than in the controls [42]. On the other hand, Raimundo et al. reported a non-significantly higher prevalence of NMSs, particularly sleep/fatigue and mood/cognition, in VaP than in PD, but VaP patients were older and of small number [51]. Additionally, the PRIAMO study reported a high prevalence of different NMSs in the VaP among patients with PD and other atypical parkinsonism but with unmatched ages [52]. Urinary dysfunction and urinary incontinence are characteristic features of VaP in concordance with previous studies [4, 38, 39, 43, 48]. Remarkably, total NMSS was related to motor severity, age and frontal cognitive dysfunction but not disease duration or stage such as PD, implying different underlying pathogenesis. In contrast to NMSS, the total score of MDS-UPDRS part I did not show a significant difference between both groups. This could be explained by the different structures and contents of both instruments and the variable association between them according to the severity of NMSs [53].

Patients with VaP showed worse QoL than normal individuals and better total and domains of PDQ-39 than PD patients, except for mobility and cognition, which was related to motor severity, NMS and depression similar to PD patients, but not to duration [23]. Similarly, the PRIAMO study showed impaired QoL in VaP that was also related to disease severity and motor scores [52]. Consequently, management of QoL determinants is essential for better care of patients with VaP. Interestingly, patients with VaP had better stigma and social support domains than patients with PD.

Vascular lesions in neuroimaging are essential for diagnosing VaP. Previous studies confirmed more vascular lesions and atrophy in neuroimaging in VaP than in PD, but few studies used visual rating scales for WMLs and correlated abnormalities with clinical characteristics [4, 37,38,39, 42]. Fazekas scores were higher in VaP than in PD, in accordance with other studies [4]. We also used the Schelten scale with its regional parts, which confirmed higher WMLs in the VaP. The periventricular ischemic changes represent the most brain MRI changes in VaP, followed by deep white matter, basal ganglionic and, to a lesser extent, infratentorial lesions. Similar findings were reported by a clinicopathological study [44]. Demirkiran et al. reported that all patients with VaP had ischemic lesions, mainly in subcortical white matter and, to a lesser extent, basal ganglia and brainstem in brain MRI, while 70% of patients with PD had normal MRIs [37]. Rath and colleagues reported that periventricular ischemic change, generalized brain atrophy, and multiple lacunar infarcts were the most common radiological abnormalities found significantly more frequently in VaP [38]. The prevalence of ischemic brain MRI changes in PD must be considered to prevent incorrect diagnosis of VaP [54].

However, there was no significant correlation between WMLs and motor severity. In contrast, Chen et al. reported a significant correlation with motor severity, daily activity and UPDRS-I, suggesting that disruption of cortical subcortical circuits by WMLs is the underlying cause of these symptoms [55]. In the current study, patients with VaP had a younger age and shorter duration, which may explain this variability in addition to the small number of patients in different studies. Moreover, using more sensitive neuroimaging techniques and rating scales for white matter is required to investigate this association.

Transcranial sonography has been suggested as a supplementary tool to differentiate VaP from PD [56]. TCCD has evident advantages, including non-invasiveness, speed of examination, and widespread availability [56]. Moreover, hyperechogenicity of the SN is a sensitive tool in the differentiation between PD and other parkinsonian syndromes [57]. Our study showed that patient with PD had significantly higher SN hyperechogenicity than patients with VaP and controls. SN hyperechogenicity was detected in only 20.8% of patients with VaP and in 90.1% of patients with PD, similar to the results of a previous study [58]. Another study reported its presence in 42% of patients with VaP [59]. Therefore, SN hyperechogenicity can be used as a simple cost-effective method to differentiate between VaP and PD.

Detecting cerebral hemodynamic vascular changes is another tool to differentiate VaP from PD and understand the underlying pathogenesis. Remarkably, low flow velocity, high PI and impaired BHI were detected in the VaP, especially the PCA, implying increased cerebral vascular resistance and decreased vasoreactivity distally as a marker of small vessel disease. Similarly, Tsai et al. reported a higher PI with VaP than with PD and controls but with similar intracranial flow velocities [58]. Another study reported a higher PI with VaP than with PD, supporting its use to confirm the diagnosis of VaP [60]. Remarkably, average flow velocity showed a correlation with motor (PIGD Off) and cognitive dysfunction (FAB). On the other hand, lower intracranial flow velocity has been detected in PD patients than in controls [61, 62].

There was no significant difference between the PD and VaP groups regarding BHI, while both the PD and VaP groups had significantly impaired BHI compared with the control group, suggesting the presence of impaired vasomotor reactivity in both groups. Previous studies reported impaired BHI and cerebrovascular reactivity compared with controls [63]. The impairment of vasomotor reactivity in VaP may be due to associated vessel wall disease. These associated vascular changes may disrupt the basal ganglia-thalamocortical circuits, resulting in the clinical features of VaP [58].

The small number of participants among different groups is one of the study limitations, despite including larger numbers than other previous studies. Expectedly, the patients with VaP were older, but this was not statistically difference. Specific NMSs were not included such as olfactory dysfunction and rapid eye movement sleep behavior disorder. Additional limitations include the presence of a poor transtemporal window for TCCD among some patients and the lack of magnetic resonance angiography. The use of more advanced functional neuroimaging and DAT scan is required for assessing patients with PD and VaP, which were not available in our country. The strengths of this study include comprehensive clinical and neuroimaging characteristics of VaP compared to age- and gender-matched PD and controls, identifying the correlations of motor, nonmotor and QoL in VaP, comprehensive assessment of cerebral vascular hemodynamics and the use of visual rating scales of WML.

The diagnosis of insidious VaP remains challenging and debatable due to overlapping features with other diseases e.g., NPH and higher-level gait disorders, lack of specific clinical features or diagnostic tests, inadequate diagnostic criteria, lack of pathologically confirmed angiopathy, and the possibility of underlying specific genetic syndromes [6, 64]. Additionally, several caveats were reported for the commonly used criteria by Zjilmans et al., particularly the poorly defined clinical and neuroimaging criteria and its reliance on a cohort of mixed pathology [6, 65].

Despite the reported cases of parkinsonism-related genetic leukoencephalopathy, these cases represent a small percentage of patients with cerebral small vessel disease, with overlapped clinical features and neuroimaging [66, 67], indicating the need for genetic testing of patients’ cohorts to confirm its frequency among patients diagnosed currently with VaP. Most cerebral small vessel disease cases are attributed to interaction between environmental factors and multiple genetic variants, while monogenic variants represent a minor percentage (up to 5%) [67, 68]. Furthermore, arteriopathy might have a variable role in these genetic leukoencephalopathies, ranging from a primary role to minor or no causal evidence [66].

Therefore, VaP is considered a heterogenous syndrome, with different underlying pathogeneses, and with no or minor role of vascular changes, implying the need for reconstructing this syndrome, proper dissection from other diagnoses or underlying genetic leukoencephalopathies and using different description instead of VaP [6]. “Adult leukoencephalopathy-associated parkinsonism” might be suggested for those patients, if clinical criteria of parkinsonism exist (e.g., bradykinesia plus rigidity or tremor), that might involve heterogeneous conditions. Until resolving these challenges, further comprehensive studies might help identify this clinical syndrome using the existing criteria as a starting point with considering its caveats or identifying new criteria [64, 67].


The current study confirmed the clinical motor, non-motor, brain MRI and transcranial sonography characteristics of VaP that might differentiate it from PD. It also identified the impaired QoL in VaP that was correlated with motor and nonmotor features. Addressing and managing motor and NMSs are essential for better QoL and care of patients with VaP. Furthermore, this study demonstrated impaired distal cerebral vascular changes in VaP, which were correlated with motor and cognitive dysfunction, suggesting a role of vascular dysfunction in its pathogenesis. However, there was a lack of correlation of WMLs with disease characteristics, implying the need for further studies to explore the role of vascular lesions and to reconstruct properly this clinical syndrome.

Data availability

The data and materials used along the current study are available from the corresponding author on reasonable request.



Addenbrooke’s cognitive examination III


Berg Balance Scale


Beck Depression Inventory


Breath holding index


Common carotid artery


Cerebral vasomotor reactivity

H & Y:

Hoehn and Yahr


International Consultation on Incontinence Questionnaire-Short Form


Intimal medial thickness


Levodopa equivalent daily dose


Low-denisty lipoprotein


Middle cerebral artery


Movement disorders society – unified Parkinson’s disease rating scale


Movement disorders society


Mean flow velocity


Montreal cognitive assessment


Magnetic resonance imaging


New freezing of gait questionnaire


Non motor symptoms scale


Normal pressure hydrocephalus


Posterior cerebral artery


Parkinson’s Disease


Pulsatility index


Postural instability and Gait disorder


Peak systolic velocity


Quality of life


Schwab and England activities of daily living scale


Substantia nigra


Transcranial color-coded Doppler


time up and go


Vascular Parkinsonism


White matter lesion


Wechsler memory scale


  1. Mostile G, Nicoletti A, Zappia M. Vascular parkinsonism: still looking for a diagnosis. Front Neurol. 2018;9:411.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Udagedara TB, Dhananjalee Alahakoon AM, Goonaratna IK. Vascular parkinsonism: a review on management updates. Ann Indian Acad Neurol. 2019;22 1:17–20.

    Article  Google Scholar 

  3. Gupta D, Kuruvilla A. Vascular parkinsonism: what makes it different? Postgrad Med J. 2011;87 1034:829–36.

    Article  Google Scholar 

  4. Vale TC, Caramelli P, Cardoso F. Vascular parkinsonism: a case series of 17 patients. Arq Neuropsiquiatr. 2013;71:757–62.

    Article  PubMed  Google Scholar 

  5. Li D-H, He Y-C, Liu J, Chen S-D. Diagnostic accuracy of transcranial sonography of the substantia nigra in Parkinson’s disease: a systematic review and meta-analysis. Sci Rep. 2016;6 1:1–9.

    Google Scholar 

  6. Vizcarra JA, Lang AE, Sethi KD, Espay AJ. Vascular parkinsonism: deconstructing a syndrome. Mov Disord. 2015;30 7:886–94.

    Article  CAS  Google Scholar 

  7. Vale TC, Caramelli P, Cardoso F. Clinicoradiological comparison between vascular parkinsonism and Parkinson’s disease. J Neurol Neurosurg Psychiatry. 2015;86 5:547–53.

    Article  Google Scholar 

  8. Postuma RB, Berg D, Stern M, Poewe W, Olanow CW, Oertel W, et al. MDS clinical diagnostic criteria for Parkinson’s disease. Mov Disord. 2015;30 12:1591–601.

    Article  Google Scholar 

  9. Zijlmans JC, Daniel SE, Hughes AJ, Révész T, Lees AJ. Clinicopathological investigation of vascular parkinsonism, including clinical criteria for diagnosis. Mov Disorders: Official J Mov Disorder Soc. 2004;19 6:630–40.

    Article  Google Scholar 

  10. Goetz CG, Tilley BC, Shaftman SR, Stebbins GT, Fahn S, Martinez-Martin P, et al. Movement Disorder Society‐sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS‐UPDRS): scale presentation and clinimetric testing results. Mov Disorders: Official J Mov Disorder Soc. 2008;23 15:2129–70.

    Article  Google Scholar 

  11. Hoehn MM, Yahr MD. Parkinsonism: onset, progression, and mortality. 1967. Neurology. 2001;57 10 Suppl 3:S11-26.

  12. Giladi N, Tal J, Azulay T, Rascol O, Brooks DJ, Melamed E, et al. Validation of the freezing of gait questionnaire in patients with Parkinson’s disease. Mov Disorders: Official J Mov Disorder Soc. 2009;24 5:655–61.

    Article  Google Scholar 

  13. Wolf SL, Catlin PA, Gage K, Gurucharri K, Robertson R, Stephen K. Establishing the reliability and validity of measurements of walking time using the Emory Functional Ambulation Profile. Phys Ther. 1999;79 12:1122–33.

    Article  Google Scholar 

  14. Bohannon RW. Reference values for the timed up and go test: a descriptive meta-analysis. J Geriatr Phys Ther. 2006;29(2):64–8.

    Article  PubMed  Google Scholar 

  15. Berg K. Measuring balance in the elderly: Development and validation of an instrument. 1992.

  16. Boonstra TA, van Vugt JP, van der Kooij H, Bloem BR. Balance asymmetry in Parkinson’s disease and its contribution to freezing of gait. PLoS ONE. 2014;9 7:e102493.

    Article  ADS  Google Scholar 

  17. Baek SU, Kang SY, Kwon S, Park IW, Suh W. Motor asymmetry and interocular retinal thickness in Parkinson’s Disease. J Korean Med Sci. 2021;36:6.

    Article  Google Scholar 

  18. Stebbins GT, Goetz CG, Burn DJ, Jankovic J, Khoo TK, Tilley BC. How to identify tremor dominant and postural instability/gait difficulty groups with the movement disorder society unified Parkinson’s disease rating scale: comparison with the unified Parkinson’s disease rating scale. Mov Disord. 2013;28 5:668–70.

    Article  Google Scholar 

  19. Merello M, Gerschcovich ER, Ballesteros D, Cerquetti D. Correlation between the Movement Disorders Society Unified Parkinson’s Disease rating scale (MDS-UPDRS) and the Unified Parkinson’s Disease rating scale (UPDRS) during L-dopa acute challenge. Parkinsonism Relat Disord. 2011;17 9:705–7.

    Article  Google Scholar 

  20. Saranza G, Lang AE. Levodopa challenge test: indications, protocol, and guide. J Neurol. 2021;268 9:3135–43.

    Article  CAS  Google Scholar 

  21. Chaudhuri KR, Martinez-Martin P, Brown RG, Sethi K, Stocchi F, Odin P, et al. The metric properties of a novel non‐motor symptoms scale for Parkinson’s disease: results from an international pilot study. Mov Disord. 2007;22 13:1901–11.

    Article  Google Scholar 

  22. Fawzi M, Fawzi M, Abu Hindi W. Arabic version of the Major Depression Inventory as a diagnostic tool: reliability and concurrent and discriminant validity. EMHJ-Eastern Mediterranean Health Journal, 18 (4), 304–310, 2012. 2012.

  23. Shalash AS, Hamid E, Elrassas HH, Bedair AS, Abushouk AI, Khamis M, et al. Non-motor symptoms as predictors of quality of life in Egyptian patients with Parkinson’s disease: a cross-sectional study using a culturally adapted 39-item Parkinson’s disease questionnaire. Front Neurol. 2018;9:357.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Saleh AA, Alkholy RSAEHA, Khalaf OO, Sabry NA, Amer H, El-Jaafary S, et al. Validation of Montreal Cognitive Assessment-Basic in a sample of elderly egyptians with neurocognitive disorders. Aging Ment Health. 2019;23 5:551–7.

    Article  Google Scholar 

  25. Wechsler D. Wechsler Memory Scale. San Antonio, Texas; 1997.

  26. Dubois B, Slachevsky A, Litvan I, Pillon B. The FAB: a frontal assessment battery at bedside. Neurology. 2000;55 11:1621–6.

    Article  Google Scholar 

  27. Qassem T, Khater MS, Emara T, Rasheedy D, Tawfik HM, Mohammedin AS, et al. Validation of the egyptian-arabic version of the Addenbrooke’s cognitive examination III (ACE-III) in diagnosing dementia. Dement Geriatr Cogn Disord. 2020;49:2179–84.

    Google Scholar 

  28. Al-Shaikh G, Al-Badr A, Al Maarik A, Cotterill N, Al-Mandeel HM. Reliability of Arabic ICIQ-UI short form in Saudi Arabia. Urol Annals. 2013;5(1):34.

    Article  Google Scholar 

  29. Fazekas F, Chawluk JB, Alavi A, Hurtig HI, Zimmerman RA. MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging. Am J Neuroradiol. 1987;8 3:421–6.

    Google Scholar 

  30. Scheltens P, Barkhof F, Leys D, Pruvo JP, Nauta J, Vermersch P, et al. A semiquantative rating scale for the assessment of signal hyperintensities on magnetic resonance imaging. J Neurol Sci. 1993;114 1:7–12.

    Article  Google Scholar 

  31. Scheltens P, Launer LJ, Barkhof F, Weinstein HC, van Gool WA. Visual assessment of medial temporal lobe atrophy on magnetic resonance imaging: interobserver reliability. J Neurol. 1995;242 9:557–60.

    Article  Google Scholar 

  32. Walter U, Behnke S, Eyding J, Niehaus L, Postert T, Seidel G, et al. Transcranial brain parenchyma sonography in movement disorders: state of the art. Ultrasound Med Biol. 2007;33(1):15–25.

    Article  PubMed  Google Scholar 

  33. Haussen DC, Katsnelson M, Rodriguez A, Campo N, Campo-Bustillo I, Romano JG, et al. Moderate correlation between breath‐holding and CO2 inhalation/hyperventilation methods for transcranial doppler evaluation of cerebral vasoreactivity. J Clin Ultrasound. 2012;40 9:554–8.

    Article  Google Scholar 

  34. Bor-Seng-Shu E, Pedroso JL, Andrade DCd, Barsottini OGP, Andrade LAFd, Barbosa ER, et al. Transcranial sonography in Parkinson’s disease. Einstein (Sao Paulo). 2012;10:242–6.

    Article  PubMed  Google Scholar 

  35. Zhou HY, Huang P, Sun Q, Du JJ, Cui SS, Hu YY, et al. The role of substantia nigra sonography in the differentiation of Parkinson’s disease and multiple system atrophy. Transl Neurodegener. 2018;7:15.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Ben-Assayag E, Mijajlovic M, Shenhar-Tsarfaty S, Bova I, Shopin L, Bornstein NM. Leukoaraiosis is a chronic atherosclerotic disease. ScientificWorldJournal. 2012;2012:532141.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Demirkiran M, Bozdemir H, Sarica Y. Vascular parkinsonism: a distinct, heterogeneous clinical entity. Acta Neurol Scand. 2001;104 2:63–7.

    Article  Google Scholar 

  38. Rath S, Kumar A, Pathak A, Verma A, Singh V, Chaurasia R, et al. Vascular Parkinsonism and Parkinson&#8217;s disease: a prospective, clinico-radiological comparative study. Annals of Movement Disorders. 2021;4 2:80 − 5;

  39. Rampello L, Alvano A, Battaglia G, Raffaele R, Vecchio I, Malaguarnera M. Different clinical and evolutional patterns in late idiopathic and vascular parkinsonism. J Neurol. 2005;252 9:1045–9.

  40. Pan M, Gao H, Long L, Xu Y, Liu M, Zou J, et al. Serum uric acid in patients with Parkinson’s disease and vascular parkinsonism: a cross-sectional study. Neuroimmunomodulation. 2013;20 1:19–28.

    Article  CAS  Google Scholar 

  41. Peters SAE, Carcel C, Millett ERC, Woodward M. Sex differences in the association between major risk factors and the risk of stroke in the UK Biobank cohort study. Neurology. 2020;95 20:e2715–e26.

    Article  Google Scholar 

  42. Benitez-Rivero S, Lama MJ, Huertas-Fernandez I, Alvarez de Toledo P, Caceres-Redondo MT, Martin-Rodriguez JF, et al. Clinical features and neuropsychological profile in vascular parkinsonism. J Neurol Sci. 2014;345(1–2):193–7.

    Article  PubMed  Google Scholar 

  43. Winikates J, Jankovic J. Clinical correlates of vascular parkinsonism. Arch Neurol. 1999;56 1:98–102.

    Article  Google Scholar 

  44. Yamanouchi H, Nagura H. Neurological signs and frontal white matter lesions in vascular parkinsonism. A clinicopathologic study. Stroke. 1997;28 5:965–9.

    Article  Google Scholar 

  45. Benitez-Rivero S, Marin-Oyaga VA, Garcia-Solis D, Huertas-Fernandez I, Garcia-Gomez FJ, Jesus S, et al. Clinical features and 123I-FP-CIT SPECT imaging in vascular parkinsonism and Parkinson’s disease. J Neurol Neurosurg Psychiatry. 2013;84 2:122–9.

    Article  Google Scholar 

  46. Szirmai I. Vascular or lower body parkinsonism: rise and fall of a diagnosis. Ideggyogy Sz. 2011;64:11–2.

    Google Scholar 

  47. Factor SA. The clinical spectrum of freezing of gait in atypical parkinsonism. Mov Disord. 2008;23(Suppl 2):431–8.

    Article  Google Scholar 

  48. Glass PG, Lees AJ, Bacellar A, Zijlmans J, Katzenschlager R, Silveira-Moriyama L. The clinical features of pathologically confirmed vascular parkinsonism. J Neurol Neurosurg Psychiatry. 2012;83 10:1027–9.

    Article  Google Scholar 

  49. Santangelo G, Vitale C, Trojano L, De Gaspari D, Bilo L, Antonini A, et al. Differential neuropsychological profiles in parkinsonian patients with or without vascular lesions. Mov Disord. 2010;25. 1:50 – 6; doi.

  50. Dunet V, Deverdun J, Charroud C, Le Bars E, Molino F, Menjot de Champfleur S, et al. Cognitive impairment and basal ganglia functional connectivity in vascular parkinsonism. AJNR Am J Neuroradiol. 2016;37 12:2310–6.

    Article  Google Scholar 

  51. Raimundo R, Mendes M, Jesus R, Azoia C, Almeida A, Velon A. Motor and non-motor symptoms differences between Vascular Parkinsonism (VP) and Parkinson’s Disease (PD) patients in early stages. Movement Disorders. Wiley 111 River ST, Hoboken 07030 – 5774, NJ USA; 2019;34:658–S.

  52. Colosimo C, Morgante L, Antonini A, Barone P, Avarello TP, Bottacchi E, et al. Non-motor symptoms in atypical and secondary parkinsonism: the PRIAMO study. J Neurol. 2010;257 1:5–14.

    Article  Google Scholar 

  53. Martinez-Martin P, Chaudhuri KR, Rojo-Abuin JM, Rodriguez-Blazquez C, Alvarez-Sanchez M, Arakaki T, et al. Assessing the non-motor symptoms of Parkinson’s disease: MDS-UPDRS and NMS Scale. Eur J Neurol. 2015;22 1:37–43.

    Article  Google Scholar 

  54. Nicoletti A, Luca A, Baschi R, Cicero CE, Mostile G, Davi M, et al. Vascular risk factors, white matter lesions and cognitive impairment in Parkinson’s disease: the PACOS longitudinal study. J Neurol. 2021;268(2):549–58.

    Article  PubMed  CAS  Google Scholar 

  55. Chen YF, Tseng YL, Lan MY, Lai SL, Su CS, Liu JS, et al. The relationship of leukoaraiosis and the clinical severity of vascular parkinsonism. J Neurol Sci. 2014;346(1–2):255–9.

    Article  PubMed  Google Scholar 

  56. Venegas-Francke P. Transcranial sonography in the discrimination of Parkinson’s disease versus vascular parkinsonism. Int Rev Neurobiol. 2010;90:147–56.

    Article  PubMed  Google Scholar 

  57. Shafieesabet A, Fereshtehnejad SM, Shafieesabet A, Delbari A, Baradaran HR, Postuma RB, et al. Hyperechogenicity of substantia nigra for differential diagnosis of Parkinson’s disease: a meta-analysis. Parkinsonism Relat Disord. 2017;42:1–11.

    Article  PubMed  Google Scholar 

  58. Tsai CF, Wu RM, Huang YW, Chen LL, Yip PK, Jeng JS. Transcranial color-coded sonography helps differentiation between idiopathic Parkinson’s disease and vascular parkinsonism. J Neurol. 2007;254 4:501–7.

    Article  Google Scholar 

  59. Busse K, Heilmann R, Kleinschmidt S, Abu-Mugheisib M, Hoppner J, Wunderlich C, et al. Value of combined midbrain sonography, olfactory and motor function assessment in the differential diagnosis of early Parkinson’s disease. J Neurol Neurosurg Psychiatry. 2012;83 4:441–7.

    Article  Google Scholar 

  60. Caba LM, Ferrairo JIT, Torres IM, Costa JFV, Munoz RB, Martin AL. Increased pulsatility index supports diagnosis of vascular parkinsonism versus idiopathic Parkinson’s disease. Neurologia (Engl Ed). 2020;35 8:563–7.

    Article  Google Scholar 

  61. Basri MI, Farida I, Goysal Y, Tammasse J, Akbar M. The mean velocity of posterior cerebral artery and basilar artery in Parkinson’s disease with sleep disorders. Med Clínica Práctica. 2021;4:100207.

    Article  Google Scholar 

  62. Zhang C, Wu B, Wang X, Chen C, Zhao R, Lu H, et al. Vascular, flow and perfusion abnormalities in Parkinson’s disease. Parkinsonism Relat Disord. 2020;73:8–13.

    Article  PubMed  Google Scholar 

  63. Brisson RT, de Cassia Leite Fernandes R, Fulgencio de Lima Arruda J, Silva LD, Sales Dantas de Lima MA, Zuma Rosso AL. Ultrasonographic changes in Brain Hemodynamics in patients with Parkinson’s Disease and Risk factors for Cerebrovascular Disease: a pilot study. Parkinsons Dis. 2021;2021:1713496.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Kalra S, Grosset DG, Benamer HTS. Differentiating vascular parkinsonism from idiopathic Parkinson’s disease: A systematic review. Mov Disord. 2010;25. 2:149 – 56; doi.

  65. Espay AJ. What the VaP? The meaning of vascular parkinsonism. Parkinsonism Relat Disord. 2022;94:132–4.

    Article  PubMed  Google Scholar 

  66. Ferguson AC, Thrippleton S, Henshall D, Whittaker E, Conway B, MacLeod M, et al. Frequency and phenotype associations of Rare variants in 5 monogenic cerebral small Vessel Disease genes in 200,000 UK Biobank participants. Neurol Genet. 2022;8 5:e200015.

    Article  CAS  Google Scholar 

  67. Whittaker E, Thrippleton S, Chong LYW, Collins VG, Ferguson AC, Henshall DE, et al. Systematic review of cerebral phenotypes Associated with Monogenic Cerebral Small-Vessel Disease. J Am Heart Assoc. 2022;11 12:e025629.

    Article  Google Scholar 

  68. Sondergaard CB, Nielsen JE, Hansen CK, Christensen H. Hereditary cerebral small vessel disease and stroke. Clin Neurol Neurosurg. 2017;155:45–57.

    Article  PubMed  Google Scholar 

Download references




The authors did not receive support from any organization for the submitted work.

Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).

Author information

Authors and Affiliations



PG: organization of data, execution of the project, analysis of data, reviewing, writing of first draft, and reviewing and critique. TR: organization of data, execution of the project and statistical analysis, writing of first draft, and reviewing and critique. MF: organization of data, execution of the project and statistical analysis, writing of first draft, and reviewing and critique. EH: organization of the project, execution of the research project, designing, analysis, and execution of results, reviewing results and critique, and reviewing drafted manuscript and critique. YI: analysis of data, reviewing results and critique, and reviewing drafted manuscript and critique. MH: conceptualization of the research, supervising the research, reviewing the results, reviewing the drafted manuscript and critique. MA: conceptualization of the research, supervising the research, reviewing the results, reviewing the drafted manuscript and critique. AS: Conceptualization of the research, organization of the research project, Execution of the research, supervising the research, designing the statistical analysis, executing the statistical analysis, reviewing the results, writing the first draft manuscript, reviewing the first draft manuscript and critique.

Corresponding author

Correspondence to Ali Shalash.

Ethics declarations

Ethical approval and consent to participate

The study protocol was approved by Ain shams university, Faculty of Medicine Research Ethical Committee FWA 000017585,4 August 2019. Written informed consent was obtained from the patients participating in the study, or the first-degree relatives if the patient was unable to provide consent after information. The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. We confirm that we have read the journal’s position on issues involved in ethical publication and affirm that this work is consistent with those guidelines.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

George, P., Roushdy, T., Fathy, M. et al. The clinical and neuroimaging differences between vascular parkinsonism and Parkinson’s disease: a case-control study. BMC Neurol 24, 56 (2024).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: